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51. Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants

Author

Kristin L. Young, Virginia Fisher, Xuan Deng, Jennifer A. Brody, Misa Graff, Elise Lim, Bridget M. Lin, Hanfei Xu, Najaf Amin, Ping An, Stella Aslibekyan, Alison E. Fohner, Bertha Hidalgo, Petra Lenzini, Robert Kraaij, Carolina Medina-Gomez, Ivana Prokić, Fernando Rivadeneira, Colleen Sitlani, Ran Tao, Jeroen van Rooij, Di Zhang, Jai G. Broome, Erin J. Buth, Benjamin D. Heavner, Deepti Jain, Albert V. Smith, Kathleen Barnes, Meher Preethi Boorgula, Sameer Chavan, Dawood Darbar, Mariza De Andrade, Xiuqing Guo, Jeffrey Haessler, Marguerite R. Irvin, Rita R. Kalyani, Sharon L.R. Kardia, Charles Kooperberg, Wonji Kim, Rasika A. Mathias, Merry-Lynn McDonald, Braxton D. Mitchell, Patricia A. Peyser, Elizabeth A. Regan, Susan Redline, Alexander P. Reiner, Stephen S. Rich, Jerome I. Rotter, Jennifer A. Smith, Scott Weiss, Kerri L. Wiggins, Lisa R. Yanek, Donna Arnett, Nancy L. Heard-Costa, Suzanne Leal, Danyu Lin, Barbara McKnight, Michael Province, Cornelia M. van Duijn, Kari E. North, L. Adrienne Cupples, Ching-Ti Liu, Epidemiology, and Internal Medicine

Subjects
Anthropometry, central obesity, Quantitative Trait Loci, Human Genome, Cell Cycle Proteins, body mass index, Good Health and Well Being, Clinical Research, Genetics, Molecular Medicine, Humans, Intercellular Signaling Peptides and Proteins, 2.1 Biological and endogenous factors, Exome, Generic health relevance, Aetiology, exome sequencing, Genetics (clinical), Genome-Wide Association Study, height, Biotechnology
Abstract

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

Published
2023
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52. Rare genetic variants explain missing heritability in smoking

Author

Seon-Kyeong Jang, Luke Evans, Allison Fialkowski, Donna K. Arnett, Allison E. Ashley-Koch, Kathleen C. Barnes, Diane M. Becker, Joshua C. Bis, John Blangero, Eugene R. Bleecker, Meher Preethi Boorgula, Donald W. Bowden, Jennifer A. Brody, Brian E. Cade, Brenda W. Campbell Jenkins, April P. Carson, Sameer Chavan, L. Adrienne Cupples, Brian Custer, Scott M. Damrauer, Sean P. David, Mariza de Andrade, Carla L. Dinardo, Tasha E. Fingerlin, Myriam Fornage, Barry I. Freedman, Melanie E. Garrett, Sina A. Gharib, David C. Glahn, Jeffrey Haessler, Susan R. Heckbert, John E. Hokanson, Lifang Hou, Shih-Jen Hwang, Matthew C. Hyman, Renae Judy, Anne E. Justice, Robert C. Kaplan, Sharon L. R. Kardia, Shannon Kelly, Wonji Kim, Charles Kooperberg, Daniel Levy, Donald M. Lloyd-Jones, Ruth J. F. Loos, Ani W. Manichaikul, Mark T. Gladwin, Lisa Warsinger Martin, Mehdi Nouraie, Olle Melander, Deborah A. Meyers, Courtney G. Montgomery, Kari E. North, Elizabeth C. Oelsner, Nicholette D. Palmer, Marinelle Payton, Anna L. Peljto, Patricia A. Peyser, Michael Preuss, Bruce M. Psaty, Dandi Qiao, Daniel J. Rader, Nicholas Rafaels, Susan Redline, Robert M. Reed, Alexander P. Reiner, Stephen S. Rich, Jerome I. Rotter, David A. Schwartz, Aladdin H. Shadyab, Edwin K. Silverman, Nicholas L. Smith, J. Gustav Smith, Albert V. Smith, Jennifer A. Smith, Weihong Tang, Kent D. Taylor, Marilyn J. Telen, Ramachandran S. Vasan, Victor R. Gordeuk, Zhe Wang, Kerri L. Wiggins, Lisa R. Yanek, Ivana V. Yang, Kendra A. Young, Kristin L. Young, Yingze Zhang, Dajiang J. Liu, Matthew C. Keller, and Scott Vrieze

Subjects
Social Psychology, Tobacco Smoke and Health, Smoking, Human Genome, Experimental and Cognitive Psychology, Single Nucleotide, Polymorphism, Single Nucleotide, Article, Behavioral Neuroscience, Phenotype, Gene Frequency, Tobacco, Genetics, Polymorphism, Genome-Wide Association Study, Cancer
Abstract

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

Published
2022

53. Genetic variants modify the associations of concentrations of methylmalonic acid, vitamin B-12, vitamin B-6, and folate with bone mineral density

Author

David Karasik, Ching-Ti Liu, Lisette C. P. G. M. de Groot, Annelies C. Ham, Kerry E Broe, Natasja M. van Schoor, Hanfei Xu, Jacob Selhub, Ligi Paul, L. Adrienne Cupples, Douglas P. Kiel, Katerina Trajanoska, Nathalie van der Velde, Yanhua Zhou, Robert R. McLean, Marian T. Hannan, Yi-Hsiang Hsu, Paul F. Jacques, Internal Medicine, Geriatrics, AMS - Ageing & Vitality, APH - Aging & Later Life, Epidemiology and Data Science, and APH - Personalized Medicine

Subjects
0301 basic medicine, Male, Bone density, Homocysteine, Medicine (miscellaneous), chemistry.chemical_compound, 0302 clinical medicine, Bone Density, Medicine, genetic polymorphism, Methionine synthase, Child, DXA, Nutrition and Dietetics, biology, Middle Aged, Nutritional Biology, Vitamin B 12, Original Research Communications, Child, Preschool, Vitamin B Complex, Female, Vitamin, Adult, musculoskeletal diseases, medicine.medical_specialty, Adolescent, Genotype, B vitamins, 030209 endocrinology & metabolism, 03 medical and health sciences, Young Adult, Folic Acid, Internal medicine, Humans, Vitamin B12, Aged, VLAG, business.industry, Genetic Variation, MTRR, Vitamin B 6, 030104 developmental biology, Endocrinology, chemistry, Methylenetetrahydrofolate reductase, biology.protein, business, bone mineral density, QCT, Methylmalonic Acid
Abstract

Background: Elevated plasma homocysteine has been found to be associated with an increased risk of osteoporosis, especially hip and vertebral fractures. The plasma concentration of homocysteine is dependent on the activities of several B vitamin-dependent enzymes, such as methylenetetrahydrofolate reductase (MTHFR), methionine synthase (MTR), methionine synthase reductase (MTRR), and cystathionine β-synthase (CBS). Objectives: We investigated whether genetic variants in some of the genes involved in 1 carbon metabolism modify the association of B vitamin-related measures with bone mineral density (BMD) and strength. Methods: We measured several B vitamins and biomarkers in participants of the Framingham Offspring Study, and performed analyses of methylmalonic acid (MMA) continuously and

Published
2021
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54. Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program

Author

Daniel DiCorpo, Sheila M. Gaynor, Emily M. Russell, Kenneth E. Westerman, Laura M. Raffield, Timothy D. Majarian, Peitao Wu, Chloé Sarnowski, Heather M. Highland, Anne Jackson, Natalie R. Hasbani, Paul S. de Vries, Jennifer A. Brody, Bertha Hidalgo, Xiuqing Guo, James A. Perry, Jeffrey R. O’Connell, Samantha Lent, May E. Montasser, Brian E. Cade, Deepti Jain, Heming Wang, Ricardo D’Oliveira Albanus, Arushi Varshney, Lisa R. Yanek, Leslie Lange, Nicholette D. Palmer, Marcio Almeida, Juan M. Peralta, Stella Aslibekyan, Abigail S. Baldridge, Alain G. Bertoni, Lawrence F. Bielak, Chung-Shiuan Chen, Yii-Der Ida Chen, Won Jung Choi, Mark O. Goodarzi, James S. Floyd, Marguerite R. Irvin, Rita R. Kalyani, Tanika N. Kelly, Seonwook Lee, Ching-Ti Liu, Douglas Loesch, JoAnn E. Manson, Ryan L. Minster, Take Naseri, James S. Pankow, Laura J. Rasmussen-Torvik, Alexander P. Reiner, Muagututi’a Sefuiva Reupena, Elizabeth Selvin, Jennifer A. Smith, Daniel E. Weeks, Huichun Xu, Jie Yao, Wei Zhao, Stephen Parker, Alvaro Alonso, Donna K. Arnett, John Blangero, Eric Boerwinkle, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, Ravindranath Duggirala, Jiang He, Susan R. Heckbert, Sharon L. R. Kardia, Ryan W. Kim, Charles Kooperberg, Simin Liu, Rasika A. Mathias, Stephen T. McGarvey, Braxton D. Mitchell, Alanna C. Morrison, Patricia A. Peyser, Bruce M. Psaty, Susan Redline, Alan R. Shuldiner, Kent D. Taylor, Ramachandran S. Vasan, Karine A. Viaud-Martinez, Jose C. Florez, James G. Wilson, Robert Sladek, Stephen S. Rich, Jerome I. Rotter, Xihong Lin, Josée Dupuis, James B. Meigs, Jennifer Wessel, and Alisa K. Manning

Subjects
Medicine (miscellaneous), Nerve Tissue Proteins, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, Immunologic, Receptors, and Blood Institute (U.S.), Diabetes Mellitus, Genetics, Humans, Insulin, 2.1 Biological and endogenous factors, Receptors, Immunologic, Polymorphism, Precision Medicine, Aetiology, Lung, Metabolic and endocrine, Diabetes, Human Genome, Fasting, Single Nucleotide, National Heart, United States, Glucose, Good Health and Well Being, Diabetes Mellitus, Type 2, National Heart, Lung, and Blood Institute (U.S.), General Agricultural and Biological Sciences, Type 2, Biotechnology
Abstract

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.

Published
2022
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55. Recommendations on the use and reporting of race, ethnicity, and ancestry in genetic research: Experiences from the NHLBI TOPMed program

Author

Alyna T. Khan, Stephanie M. Gogarten, Caitlin P. McHugh, Adrienne M. Stilp, Tamar Sofer, Michael L. Bowers, Quenna Wong, L. Adrienne Cupples, Bertha Hidalgo, Andrew D. Johnson, Merry-Lynn N. McDonald, Stephen T. McGarvey, Matthew R.G. Taylor, Stephanie M. Fullerton, Matthew P. Conomos, and Sarah C. Nelson

Subjects
Article
Abstract

How race, ethnicity, and ancestry are used in genomic research has wide-ranging implications for how research is translated into clinical care and incorporated into public understanding. Correlation between race and genetic ancestry contributes to unresolved complexity for the scientific community, as illustrated by heterogeneous definitions and applications of these variables. Here, we offer commentary and recommendations on the use of race, ethnicity, and ancestry across the arc of genetic research, including data harmonization, analysis, and reporting. While informed by our experiences as researchers affiliated with the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, these recommendations are applicable to basic and translational genomic research in diverse populations with genome-wide data. Moving forward, considerable collaborative effort will be required to ensure that race, ethnicity, and ancestry are described and used appropriately to generate scientific knowledge that yields broad and equitable benefit.

Published
2022

56. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Author

Nancy L. Heard-Costa, Lucas Barwick, Clary B. Clish, Celeste Eng, Joanne M. Murabito, Esteban G. Burchard, Yii-Der Ida Chen, Daniel I. Chasman, Robert C. Kaplan, James B. Meigs, Deborah A. Nickerson, Cashell E. Jaquish, Eric Boerwinkle, Jennifer A. Brody, Charles Kooperberg, Mark T. Gladwin, Sebastian Schoenherr, Keng-Han Lin, John Barnard, Ryan D. Hernandez, Andrew D. Johnson, Edwin K. Silverman, Mollie A. Minear, Michelle Daya, Barbara A. Konkle, Sharon R. Browning, Daniel E. Weeks, Wendy S. Post, Alexander P. Reiner, Kathryn L. Lunetta, Gina M. Peloso, David Van Den Berg, Dan E. Arking, Seung-been Lee, Leslie A. Lange, Cristen J. Willer, Zachary A. Szpiech, Tasha E. Fingerlin, Wayne E. Clarke, Xutong Zhao, Stephen S. Rich, Nora Franceschini, Sudha Seshadri, Chloé Sarnowski, Hyun Min Kang, Sayantan Das, Michael C. Zody, Stephanie M. Fullerton, Dean Bobo, Alanna C. Morrison, Brian Custer, Nona Sotoodehnia, Shannon Kelly, Thomas W. Blackwell, Bruce M. Psaty, Yingze Zhang, Susan R. Heckbert, Robert E. Gerszten, M. Benjamin Shoemaker, Daniel Taliun, Leslie S. Emery, André Corvelo, Michael H. Cho, Braxton D. Mitchell, Xiaoming Liu, Stella Aslibekyan, Paul L. Auer, Brandon Chalazan, Sarah C. Nelson, Seung Hoan Choi, Jeong-Sun Seo, Matthew P. Conomos, Anne-Katrin Emde, Lawrence F. Bielak, Alisa K. Manning, Allison E. Ashley-Koch, Diane Fatkin, Xiaowen Tian, Emelia J. Benjamin, D. C. Rao, Mina K. Chung, Myriam Fornage, Daniel Levy, Michael D. Kessler, Weihong Tang, Daniel J. Gottlieb, Pradeep Natarajan, Jessica Lasky-Su, Amol C. Shetty, Cathy C. Laurie, Dan M. Roden, Timothy D. O’Connor, Jedidiah Carlson, Lewis C. Becker, Achilleas N. Pitsillides, Karine A. Viaud-Martinez, Raul Torres, Adolfo Correa, Christian Fuchsberger, Deborah A. Meyers, Alvaro Alonso, Sanghamitra Mohanty, Jonathon LeFaive, Soren Germer, Julie L. Mikulla, François Aguet, Susan K. Dutcher, Sarah A Gagliano Taliun, Ani Manichaikul, Lori Garman, Xiuqing Guo, Timothy A. Thornton, David D. McManus, Albert V. Smith, Kristin G. Ardlie, Anna Köttgen, Sharon L.R. Kardia, Quenna Wong, Jill M. Johnsen, Andrea Natale, Richard A. Gibbs, Douglas P. Kiel, Ingo Ruczinski, Susan Redline, Lukas Forer, Scott I. Vrieze, May E. Montasser, Rasika A. Mathias, Jerome I. Rotter, Jacob Pleiness, Chunyu Liu, Brian L. Browning, James G. Wilson, Weiniu Gan, Christine M. Albert, Marilyn J. Telen, Courtney G. Montgomery, Steven A. Lubitz, Robert Klemmer, Ramachandran S. Vasan, Nathan Pankratz, Mariza de Andrade, Vivien A. Sheehan, Kenneth Rice, Xihong Lin, Eimear E. Kenny, Stephanie M. Gogarten, John Blangero, Donna K. Arnett, Jiang He, Pankaj Qasba, James F. Casella, Patrick T. Ellinor, Nicholette D. Palmer, R. Graham Barr, Scott T. Weiss, Joanne E. Curran, Bruce S. Weir, Kari E. North, L. Adrienne Cupples, Dawn L. DeMeo, Tanika N. Kelly, Angel C.Y. Mak, Russell P. Tracy, David A. Schwartz, Kent D. Taylor, Rebecca L. Beer, Daniel N. Harris, George J. Papanicolaou, Marguerite R. Irvin, Stephen T. McGarvey, Sebastian Zöllner, Patricia A. Peyser, Brian E. Cade, Ruth J. F. Loos, Douglas Loesch, Nicholas L. Smith, Gonçalo R. Abecasis, Jennifer A. Smith, Michael E. Hall, Lu-Chen Weng, Jeffrey R. O'Connell, Adrienne M. Stilp, Donald W. Bowden, Kathleen C. Barnes, Stacey Gabriel, Michael Boehnke, Wayne Huey-Herng Sheu, and Dawood Darbar

Subjects
Quality Control, Heterozygote, Genomics, Computational biology, Biology, Polymorphism, Single Nucleotide, Genome, Article, DNA sequencing, INDEL Mutation, Loss of Function Mutation, Genetics research, Genetic variation, Humans, Precision Medicine, Genetic association, Population Density, Multidisciplinary, Whole Genome Sequencing, Genome, Human, Genetic Variation, Rare variants, United States, Genetic architecture, Phenotype, Cytochrome P-450 CYP2D6, Haplotypes, Mutagenesis, Sample Size, Next-generation sequencing, National Heart, Lung, and Blood Institute (U.S.), Imputation (genetics), Reference genome
Abstract

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%., The goals, resources and design of the NHLBI Trans-Omics for Precision Medicine (TOPMed) programme are described, and analyses of rare variants detected in the first 53,831 samples provide insights into mutational processes and recent human evolutionary history.

Published
2021
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57. Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions

Author

Diana van Heemst, Oyomoare Osazuwa-Peters, RJ Waken, Ching-Ti Liu, Paul S de Vries, Lisa de las Fuentes, Xiaoyu Zhang, D. C. Rao, Alanna C. Morrison, Wenyi Wang, Ko Willems van Dijk, Michael R. Brown, Yun Ju Sung, Karen Schwander, Eric Boerwinkle, L. Adrienne Cupples, Hanfei Xu, and Raymond Noordam

Subjects
0303 health sciences, Framingham Risk Score, business.industry, 030305 genetics & heredity, MEDLINE, Cardiometabolic Risk Factors, Environmental exposure, Disease, Moderation, Missing data, Article, 03 medical and health sciences, Sample size determination, Meta-analysis, Hypertension, Genetics, Humans, Medicine, Gene-Environment Interaction, Healthy Lifestyle, Obesity, business, Genetics (clinical), Genome-Wide Association Study, Demography
Abstract

Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naive, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered due to lower sample size, and the Naive Approach, which utilized all available data and ignored the missingness, was slightly inflated. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach, a straightforward and non-inflated approach, to handle heterogeneity among cohorts in the LRS based genome-wide interaction meta-analyses.

Published
2021
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58. Lymphocyte activation gene-3-associated protein networks are associated with HDL-cholesterol and mortality in the Trans-omics for Precision Medicine program

Author

Ani Manichaikul, Honghuang Lin, Chansuk Kang, Chaojie Yang, Stephen S. Rich, Kent D. Taylor, Xiuqing Guo, Jerome I. Rotter, W. Craig Johnson, Elaine Cornell, Russell P. Tracy, J. Peter Durda, Yongmei Liu, Ramachandran S. Vasan, L. Adrienne Cupples, Robert E. Gerszten, Clary B. Clish, Deepti Jain, Matthew P. Conomos, Thomas Blackwell, George J. Papanicolaou, and Annabelle Rodriguez

Subjects
Aging, HDL, Cholesterol, HDL, Human Genome, Medicine (miscellaneous), Membrane Proteins, Lymphocyte Activation, Atherosclerosis, Cardiovascular, General Biochemistry, Genetics and Molecular Biology, Chromatin, Cholesterol, Heart Disease, Good Health and Well Being, Clinical Research, Genetics, Humans, 2.1 Biological and endogenous factors, Precision Medicine, Aetiology, General Agricultural and Biological Sciences, Heart Disease - Coronary Heart Disease, Biotechnology
Abstract

Deficiency of the immune checkpoint lymphocyte activation gene-3 (LAG3) protein is significantly associated with both elevated HDL-cholesterol (HDL-C) and myocardial infarction risk. We determined the association of genetic variants within ±500 kb of LAG3 with plasma LAG3 and defined LAG3-associated plasma proteins with HDL-C and clinical outcomes. Whole genome sequencing and plasma proteomics were obtained from the Multi-Ethnic Study of Atherosclerosis (MESA) and the Framingham Heart Study (FHS) cohorts as part of the Trans-Omics for Precision Medicine program. In situ Hi-C chromatin capture was performed in EBV-transformed cell lines isolated from four MESA participants. Genetic association analyses were performed in MESA using multivariate regression models, with validation in FHS. A LAG3-associated protein network was tested for association with HDL-C, coronary heart disease, and all-cause mortality. We identify an association between the LAG3 rs3782735 variant and plasma LAG3 protein. Proteomics analysis reveals 183 proteins significantly associated with LAG3 with four proteins associated with HDL-C. Four proteins discovered for association with all-cause mortality in FHS shows nominal associations in MESA. Chromatin capture analysis reveals significant cis interactions between LAG3 and C1S, LRIG3, TNFRSF1A, and trans interactions between LAG3 and B2M. A LAG3-associated protein network has significant associations with HDL-C and mortality.

Published
2022

59. Mendelian randomization supports bidirectional causality between telomere length and clonal hematopoiesis of indeterminate potential

Author

Tetsushi, Nakao, Alexander G, Bick, Margaret A, Taub, Seyedeh M, Zekavat, Md M, Uddin, Abhishek, Niroula, Cara L, Carty, John, Lane, Michael C, Honigberg, Joshua S, Weinstock, Akhil, Pampana, Christopher J, Gibson, Gabriel K, Griffin, Shoa L, Clarke, Romit, Bhattacharya, Themistocles L, Assimes, Leslie S, Emery, Adrienne M, Stilp, Quenna, Wong, Jai, Broome, Cecelia A, Laurie, Alyna T, Khan, Albert V, Smith, Thomas W, Blackwell, Veryan, Codd, Christopher P, Nelson, Zachary T, Yoneda, Juan M, Peralta, Donald W, Bowden, Marguerite R, Irvin, Meher, Boorgula, Wei, Zhao, Lisa R, Yanek, Kerri L, Wiggins, James E, Hixson, C Charles, Gu, Gina M, Peloso, Dan M, Roden, Muagututi'a S, Reupena, Chii-Min, Hwu, Dawn L, DeMeo, Kari E, North, Shannon, Kelly, Solomon K, Musani, Joshua C, Bis, Donald M, Lloyd-Jones, Jill M, Johnsen, Michael, Preuss, Russell P, Tracy, Patricia A, Peyser, Dandi, Qiao, Pinkal, Desai, Joanne E, Curran, Barry I, Freedman, Hemant K, Tiwari, Sameer, Chavan, Jennifer A, Smith, Nicholas L, Smith, Tanika N, Kelly, Bertha, Hidalgo, L Adrienne, Cupples, Daniel E, Weeks, Nicola L, Hawley, Ryan L, Minster, Ranjan, Deka, Take T, Naseri, Lisa, de Las Fuentes, Laura M, Raffield, Alanna C, Morrison, Paul S, Vries, Christie M, Ballantyne, Eimear E, Kenny, Stephen S, Rich, Eric A, Whitsel, Michael H, Cho, M Benjamin, Shoemaker, Betty S, Pace, John, Blangero, Nicholette D, Palmer, Braxton D, Mitchell, Alan R, Shuldiner, Kathleen C, Barnes, Susan, Redline, Sharon L R, Kardia, Gonçalo R, Abecasis, Lewis C, Becker, Susan R, Heckbert, Jiang, He, Wendy, Post, Donna K, Arnett, Ramachandran S, Vasan, Dawood, Darbar, Scott T, Weiss, Stephen T, McGarvey, Mariza, de Andrade, Yii-Der Ida, Chen, Robert C, Kaplan, Deborah A, Meyers, Brian S, Custer, Adolfo, Correa, Bruce M, Psaty, Myriam, Fornage, JoAnn E, Manson, Eric, Boerwinkle, Barbara A, Konkle, Ruth J F, Loos, Jerome I, Rotter, Edwin K, Silverman, Charles, Kooperberg, John, Danesh, Nilesh J, Samani, Siddhartha, Jaiswal, Peter, Libby, Patrick T, Ellinor, Nathan, Pankratz, Benjamin L, Ebert, Alexander P, Reiner, Rasika A, Mathias, Ron, Do, and Pradeep, Natarajan

Subjects
Multidisciplinary
Abstract

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in the Trans-Omics for Precision Medicine (TOPMed) program ( n = 63,302) and UK Biobank ( n = 47,080). Bidirectional Mendelian randomization studies were consistent with longer genetically imputed LTL increasing propensity to develop CHIP, but CHIP then, in turn, hastens to shorten measured LTL (mLTL). We also demonstrated evidence of modest mediation between CHIP and CAD by mLTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

Published
2022
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60. Sociodemographic Patterns of Exposure to Civil Aircraft Noise in the United States

Author

Matthew C. Simon, Jaime E. Hart, Jonathan I. Levy, Trang VoPham, Andrew Malwitz, Daniel Nguyen, Matthew Bozigar, L. Adrienne Cupples, Peter James, Francine Laden, and Junenette L. Peters

Subjects
Aircraft, Airports, Noise, Transportation, Health, Toxicology and Mutagenesis, Ethnicity, Public Health, Environmental and Occupational Health, Humans, Environmental Exposure, Minority Groups, United States
Abstract

Communities with lower socioeconomic status and higher prevalence of racial/ethnic minority populations are often more exposed to environmental pollutants. Although studies have shown associations between aircraft noise and property values and various health outcomes, little is known about how aircraft noise exposures are sociodemographically patterned.Our aim was to describe characteristics of populations exposed to aviation noise by race/ethnicity, education, and income in the United States.Aircraft noise contours characterized as day-night average sound level (DNL) were developed for 90 U.S. airports in 2010 for DNLAggregated across multiple airports, block groups with a higher Hispanic population had higher odds of being exposed to aircraft noise. For example, the multinomial analysis showed that a 10-percentage point increase in a block group's Hispanic population was associated with an increased odds ratio of 39% (95% CI: 25%, 54%) of being exposed toThese results suggest that across U.S. airports, there is indication of sociodemographic disparities in noise exposures. https://doi.org/10.1289/EHP9307.

Published
2022
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61. Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed

Author

Margaret A. Taub, Matthew P. Conomos, Rebecca Keener, Kruthika R. Iyer, Joshua S. Weinstock, Lisa R. Yanek, John Lane, Tyne W. Miller-Fleming, Jennifer A. Brody, Laura M. Raffield, Caitlin P. McHugh, Deepti Jain, Stephanie M. Gogarten, Cecelia A. Laurie, Ali Keramati, Marios Arvanitis, Albert V. Smith, Benjamin Heavner, Lucas Barwick, Lewis C. Becker, Joshua C. Bis, John Blangero, Eugene R. Bleecker, Esteban G. Burchard, Juan C. Celedón, Yen Pei C. Chang, Brian Custer, Dawood Darbar, Lisa de las Fuentes, Dawn L. DeMeo, Barry I. Freedman, Melanie E. Garrett, Mark T. Gladwin, Susan R. Heckbert, Bertha A. Hidalgo, Marguerite R. Irvin, Talat Islam, W. Craig Johnson, Stefan Kaab, Lenore Launer, Jiwon Lee, Simin Liu, Arden Moscati, Kari E. North, Patricia A. Peyser, Nicholas Rafaels, Christine Seidman, Daniel E. Weeks, Fayun Wen, Marsha M. Wheeler, L. Keoki Williams, Ivana V. Yang, Wei Zhao, Stella Aslibekyan, Paul L. Auer, Donald W. Bowden, Brian E. Cade, Zhanghua Chen, Michael H. Cho, L. Adrienne Cupples, Joanne E. Curran, Michelle Daya, Ranjan Deka, Celeste Eng, Tasha E. Fingerlin, Xiuqing Guo, Lifang Hou, Shih-Jen Hwang, Jill M. Johnsen, Eimear E. Kenny, Albert M. Levin, Chunyu Liu, Ryan L. Minster, Take Naseri, Mehdi Nouraie, Muagututi‘a Sefuiva Reupena, Ester C. Sabino, Jennifer A. Smith, Nicholas L. Smith, Jessica Lasky-Su, James G. Taylor, Marilyn J. Telen, Hemant K. Tiwari, Russell P. Tracy, Marquitta J. White, Yingze Zhang, Kerri L. Wiggins, Scott T. Weiss, Ramachandran S. Vasan, Kent D. Taylor, Moritz F. Sinner, Edwin K. Silverman, M. Benjamin Shoemaker, Wayne H.-H. Sheu, Frank Sciurba, David A. Schwartz, Jerome I. Rotter, Daniel Roden, Susan Redline, Benjamin A. Raby, Bruce M. Psaty, Juan M. Peralta, Nicholette D. Palmer, Sergei Nekhai, Courtney G. Montgomery, Braxton D. Mitchell, Deborah A. Meyers, Stephen T. McGarvey, Angel C.Y. Mak, Ruth J.F. Loos, Rajesh Kumar, Charles Kooperberg, Barbara A. Konkle, Shannon Kelly, Sharon L.R. Kardia, Robert Kaplan, Jiang He, Hongsheng Gui, Frank D. Gilliland, Bruce D. Gelb, Myriam Fornage, Patrick T. Ellinor, Mariza de Andrade, Adolfo Correa, Yii-Der Ida Chen, Eric Boerwinkle, Kathleen C. Barnes, Allison E. Ashley-Koch, Donna K. Arnett, Christine Albert, Cathy C. Laurie, Goncalo Abecasis, Deborah A. Nickerson, James G. Wilson, Stephen S. Rich, Daniel Levy, Ingo Ruczinski, Abraham Aviv, Thomas W. Blackwell, Timothy Thornton, Jeff O’Connell, Nancy J. Cox, James A. Perry, Mary Armanios, Alexis Battle, Nathan Pankratz, Alexander P. Reiner, and Rasika A. Mathias

Subjects
Genetics, Biochemistry, Genetics and Molecular Biology (miscellaneous), Article
Abstract

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWAS for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally-diverse individuals (European, African, Asian and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n=109,122 individuals. We identified 59 sentinel variants (p-value

Published
2022

62. Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes

Author

George Hindy, Peter Dornbos, Mark D. Chaffin, Dajiang J. Liu, Minxian Wang, Margaret Sunitha Selvaraj, David Zhang, Joseph Park, Carlos A. Aguilar-Salinas, Lucinda Antonacci-Fulton, Diego Ardissino, Donna K. Arnett, Stella Aslibekyan, Gil Atzmon, Christie M. Ballantyne, Francisco Barajas-Olmos, Nir Barzilai, Lewis C. Becker, Lawrence F. Bielak, Joshua C. Bis, John Blangero, Eric Boerwinkle, Lori L. Bonnycastle, Erwin Bottinger, Donald W. Bowden, Matthew J. Bown, Jennifer A. Brody, Jai G. Broome, Noël P. Burtt, Brian E. Cade, Federico Centeno-Cruz, Edmund Chan, Yi-Cheng Chang, Yii-Der I. Chen, Ching-Yu Cheng, Won Jung Choi, Rajiv Chowdhury, Cecilia Contreras-Cubas, Emilio J. Córdova, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, John Danesh, Paul S. de Vries, Ralph A. DeFronzo, Harsha Doddapaneni, Ravindranath Duggirala, Susan K. Dutcher, Patrick T. Ellinor, Leslie S. Emery, Jose C. Florez, Myriam Fornage, Barry I. Freedman, Valentin Fuster, Ma. Eugenia Garay-Sevilla, Humberto García-Ortiz, Soren Germer, Richard A. Gibbs, Christian Gieger, Benjamin Glaser, Clicerio Gonzalez, Maria Elena Gonzalez-Villalpando, Mariaelisa Graff, Sarah E. Graham, Niels Grarup, Leif C. Groop, Xiuqing Guo, Namrata Gupta, Sohee Han, Craig L. Hanis, Torben Hansen, Jiang He, Nancy L. Heard-Costa, Yi-Jen Hung, Mi Yeong Hwang, Marguerite R. Irvin, Sergio Islas-Andrade, Gail P. Jarvik, Hyun Min Kang, Sharon L.R. Kardia, Tanika Kelly, Eimear E. Kenny, Alyna T. Khan, Bong-Jo Kim, Ryan W. Kim, Young Jin Kim, Heikki A. Koistinen, Charles Kooperberg, Johanna Kuusisto, Soo Heon Kwak, Markku Laakso, Leslie A. Lange, Jiwon Lee, Juyoung Lee, Seonwook Lee, Donna M. Lehman, Rozenn N. Lemaitre, Allan Linneberg, Jianjun Liu, Ruth J.F. Loos, Steven A. Lubitz, Valeriya Lyssenko, Ronald C.W. Ma, Lisa Warsinger Martin, Angélica Martínez-Hernández, Rasika A. Mathias, Stephen T. McGarvey, Ruth McPherson, James B. Meigs, Thomas Meitinger, Olle Melander, Elvia Mendoza-Caamal, Ginger A. Metcalf, Xuenan Mi, Karen L. Mohlke, May E. Montasser, Jee-Young Moon, Hortensia Moreno-Macías, Alanna C. Morrison, Donna M. Muzny, Sarah C. Nelson, Peter M. Nilsson, Jeffrey R. O’Connell, Marju Orho-Melander, Lorena Orozco, Colin N.A. Palmer, Nicholette D. Palmer, Cheol Joo Park, Kyong Soo Park, Oluf Pedersen, Juan M. Peralta, Patricia A. Peyser, Wendy S. Post, Michael Preuss, Bruce M. Psaty, Qibin Qi, D.C. Rao, Susan Redline, Alexander P. Reiner, Cristina Revilla-Monsalve, Stephen S. Rich, Nilesh Samani, Heribert Schunkert, Claudia Schurmann, Daekwan Seo, Jeong-Sun Seo, Xueling Sim, Rob Sladek, Kerrin S. Small, Wing Yee So, Adrienne M. Stilp, E. Shyong Tai, Claudia H.T. Tam, Kent D. Taylor, Yik Ying Teo, Farook Thameem, Brian Tomlinson, Michael Y. Tsai, Tiinamaija Tuomi, Jaakko Tuomilehto, Teresa Tusié-Luna, Miriam S. Udler, Rob M. van Dam, Ramachandran S. Vasan, Karine A. Viaud Martinez, Fei Fei Wang, Xuzhi Wang, Hugh Watkins, Daniel E. Weeks, James G. Wilson, Daniel R. Witte, Tien-Yin Wong, Lisa R. Yanek, Sekar Kathiresan, Daniel J. Rader, Jerome I. Rotter, Michael Boehnke, Mark I. McCarthy, Cristen J. Willer, Pradeep Natarajan, Jason A. Flannick, Amit V. Khera, Gina M. Peloso, NIH - National Heart, Lung, and Blood Institute (NHLBI) (Estados Unidos), Centre of Excellence in Complex Disease Genetics, HUS Abdominal Center, Institute for Molecular Medicine Finland, Leif Groop Research Group, Clinicum, Department of Medicine, HUS Internal Medicine and Rehabilitation, University of Helsinki, and Department of Public Health

Subjects
APOC3, Blood Glucose, Multifactorial Inheritance, gene-based association, Polymorphism, Single Nucleotide, Article, Open Reading Frames, ANGPTL3, lipid, OF-FUNCTION MUTATIONS, BINDING, Databases, Genetic, SEQUENCE VARIANTS, Genetics, Humans, Exome, Genetic Predisposition to Disease, SINUSOIDAL ENDOTHELIAL-CELLS, Genetics (clinical), Alleles, RISK, IDENTIFICATION, 1184 Genetics, developmental biology, physiology, association, cholesterol, Genetic Variation, Computational Biology, Molecular Sequence Annotation, PERILIPIN, Lipid Metabolism, Lipids, Genetics, Population, Phenotype, Diabetes Mellitus, Type 2, Liver, Case-Control Studies, exome sequencing, TRIGLYCERIDES, Genome-Wide Association Study
Abstract

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels. This work was supported by a grant from the Swedish Research Council (2016-06830) and grants from the National Heart, Lung, and Blood Institute (NHLBI): R01HL142711 and R01HL127564. Please refer to the supplemental information for the full acknowledgements. Sí

Published
2022
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63. Whole Genome Sequencing Identifies CRISPLD2 as a Lung Function Gene in Children With Asthma

Author

Priyadarshini Kachroo, Julian Hecker, Bo L. Chawes, Tarunveer S. Ahluwalia, Michael H. Cho, Dandi Qiao, Rachel S. Kelly, Su H. Chu, Yamini V. Virkud, Mengna Huang, Kathleen C. Barnes, Esteban G. Burchard, Celeste Eng, Donglei Hu, Juan C. Celedón, Michelle Daya, Albert M. Levin, Hongsheng Gui, L. Keoki Williams, Erick Forno, Angel C.Y. Mak, Lydiana Avila, Manuel E. Soto-Quiros, Michelle M. Cloutier, Edna Acosta-Pérez, Glorisa Canino, Klaus Bønnelykke, Hans Bisgaard, Benjamin A. Raby, Christoph Lange, Scott T. Weiss, Jessica A. Lasky-Su, Namiko Abe, Goncalo Abecasis, Christine Albert, Nicholette (Nichole) Palmer Allred, Laura Almasy, Alvaro Alonso, Seth Ament, Peter Anderson, Pramod Anugu, Deborah Applebaum-Bowden, Dan Arking, Donna K. Arnett, Allison Ashley-Koch, Stella Aslibekyan, Tim Assimes, Paul Auer, Dimitrios Avramopoulos, John Barnard, Kathleen Barnes, R. Graham Barr, Emily Barron-Casella, Terri Beaty, Diane Becker, Lewis Becker, Rebecca Beer, Ferdouse Begum, Amber Beitelshees, Emelia Benjamin, Marcos Bezerra, Larry Bielak, Joshua Bis, Thomas Blackwell, John Blangero, Eric Boerwinkle, Ingrid Borecki, Russell Bowler, Jennifer Brody, Ulrich Broeckel, Jai Broome, Karen Bunting, Esteban Burchard, Jonathan Cardwell, Cara Carty, Richard Casaburi, James Casella, Mark Chaffin, Christy Chang, Daniel Chasman, Sameer Chavan, Bo-Juen Chen, Wei-Min Chen, Yii-Der Ida Chen, Seung Hoan Choi, Lee-Ming Chuang, Mina Chung, Elaine Cornell, Adolfo Correa, Carolyn Crandall, James Crapo, L. Adrienne Cupples, Joanne Curran, Jeffrey Curtis, Brian Custer, Coleen Damcott, Dawood Darbar, Sayantan Das, Sean David, Colleen Davis, Mariza de Andrade, Michael DeBaun, Ranjan Deka, Dawn DeMeo, Scott Devine, Ron Do, Qing Duan, Ravi Duggirala, Peter Durda, Susan Dutcher, Charles Eaton, Lynette Ekunwe, Patrick Ellinor, Leslie Emery, Charles Farber, Leanna Farnam, Tasha Fingerlin, Matthew Flickinger, Myriam Fornage, Nora Franceschini, Mao Fu, Stephanie M. Fullerton, Lucinda Fulton, Stacey Gabriel, Weiniu Gan, Yan Gao, Margery Gass, Bruce Gelb, Xiaoqi (Priscilla) Geng, Soren Germer, Chris Gignoux, Mark Gladwin, David Glahn, Stephanie Gogarten, Da-Wei Gong, Harald Goring, C. Charles Gu, Yue Guan, Xiuqing Guo, Jeff Haessler, Michael Hall, Daniel Harris, Nicola Hawley, Jiang He, Ben Heavner, Susan Heckbert, Ryan Hernandez, David Herrington, Craig Hersh, Bertha Hidalgo, James Hixson, John Hokanson, Kramer Holly, Elliott Hong, Karin Hoth, Chao (Agnes) Hsiung, Haley Huston, Chii Min Hwu, Marguerite Ryan Irvin, Rebecca Jackson, Deepti Jain, Cashell Jaquish, Min A. Jhun, Jill Johnsen, Andrew Johnson, Craig Johnson, Rich Johnston, Kimberly Jones, Hyun Min Kang, Robert Kaplan, Sharon Kardia, Sekar Kathiresan, Laura Kaufman, Shannon Kelly, Eimear Kenny, Michael Kessler, Alyna Khan, Greg Kinney, Barbara Konkle, Charles Kooperberg, Stephanie Krauter, Ethan Lange, Leslie Lange, Cathy Laurie, Cecelia Laurie, Meryl LeBoff, Seunggeun Shawn Lee, Wen-Jane Lee, Jonathon LeFaive, David Levine, Dan Levy, Joshua Lewis, Yun Li, Honghuang Lin, Keng Han Lin, Simin Liu, Yongmei Liu, Ruth Loos, Steven Lubitz, Kathryn Lunetta, James Luo, Michael Mahaney, Barry Make, Ani Manichaikul, JoAnn Manson, Lauren Margolin, Lisa Martin, Susan Mathai, Rasika Mathias, Patrick McArdle, Merry-Lynn McDonald, Sean McFarland, Stephen McGarvey, Hao Mei, Deborah A. Meyers, Julie Mikulla, Nancy Min, Mollie Minear, Ryan L. Minster, Braxton Mitchell, May E. Montasser, Solomon Musani, Stanford Mwasongwe, Josyf C. Mychaleckyj, Girish Nadkarni, Rakhi Naik, Pradeep Natarajan, Sergei Nekhai, Deborah Nickerson, Kari North, Jeff O'Connell, Tim O'Connor, Heather Ochs-Balcom, James Pankow, George Papanicolaou, Margaret Parker, Afshin Parsa, Sara Penchev, Juan Manuel Peralta, Marco Perez, James Perry, Ulrike Peters, Patricia Peyser, Lawrence S. Phillips, Sam Phillips, Toni Pollin, Wendy Post, Julia Powers Becker, Meher Preethi Boorgula, Michael Preuss, Dmitry Prokopenko, Bruce Psaty, Pankaj Qasba, Zhaohui Qin, Nicholas Rafaels, Laura Raffield, Vasan Ramachandran, D.C. Rao, Laura Rasmussen-Torvik, Aakrosh Ratan, Susan Redline, Robert Reed, Elizabeth Regan, Alex Reiner, Ken Rice, Stephen Rich, Dan Roden, Carolina Roselli, Jerome Rotter, Ingo Ruczinski, Pamela Russell, Sarah Ruuska, Kathleen Ryan, Phuwanat Sakornsakolpat, Shabnam Salimi, Steven Salzberg, Kevin Sandow, Vijay Sankaran, Christopher Scheller, Ellen Schmidt, Karen Schwander, David Schwartz, Frank Sciurba, Christine Seidman, Jonathan Seidman, Vivien Sheehan, Amol Shetty, Aniket Shetty, Wayne Hui-Heng Sheu, M. Benjamin Shoemaker, Brian Silver, Edwin Silverman, Jennifer Smith, Josh Smith, Nicholas Smith, Tanja Smith, Sylvia Smoller, Beverly Snively, Tamar Sofer, Nona Sotoodehnia, Adrienne Stilp, Elizabeth Streeten, Yun Ju Sung, Jessica Su-Lasky, Jody Sylvia, Adam Szpiro, Carole Sztalryd, Daniel Taliun, Hua Tang, Margaret Taub, Kent Taylor, Simeon Taylor, Marilyn Telen, Timothy A. Thornton, Lesley Tinker, David Tirschwell, Hemant Tiwari, Russell Tracy, Michael Tsai, Dhananjay Vaidya, Peter VandeHaar, Scott Vrieze, Tarik Walker, Robert Wallace, Avram Walts, Emily Wan, Fei Fei Wang, Karol Watson, Daniel E. Weeks, Bruce Weir, Scott Weiss, Lu-Chen Weng, Cristen Willer, Kayleen Williams, Carla Wilson, James Wilson, Quenna Wong, Huichun Xu, Lisa Yanek, Ivana Yang, Rongze Yang, Norann Zaghloul, Maryam Zekavat, Yingze Zhang, Snow Xueyan Zhao, Wei Zhao, Xiuwen Zheng, Degui Zhi, Xiang Zhou, Michael Zody, and Sebastian Zoellner

Subjects
Adult, Costa Rica, Male, Pulmonary and Respiratory Medicine, Adolescent, Vital Capacity, Single-nucleotide polymorphism, Pedigree chart, Critical Care and Intensive Care Medicine, Young Adult, 03 medical and health sciences, FEV1/FVC ratio, 0302 clinical medicine, Polymorphism (computer science), Forced Expiratory Volume, Humans, Medicine, SNP, 030212 general & internal medicine, Child, Asthma, Whole Genome Sequencing, business.industry, Middle Aged, respiratory system, medicine.disease, respiratory tract diseases, Minor allele frequency, 030228 respiratory system, Genetic epidemiology, Child, Preschool, Interferon Regulatory Factors, Immunology, Respiratory Physiological Phenomena, Female, Cardiology and Cardiovascular Medicine, business, Cell Adhesion Molecules
Abstract

BACKGROUND: Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma. METHODS: WGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician’s diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes. RESULTS: A genome-wide significant association was identified between baseline FEV(1)/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10(−8) in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10(−6)). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV(1) (P = 3.3 × 10(−3)), postbronchodilator (PB) FEV(1) (7.3 × 10(−3)), and PB FEV(1)/FVC ratio (P = 2.7 × 10(−3)). The identified baseline FEV(1)/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR. CONCLUSIONS: These findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.

Published
2019
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64. How knowledge of elevated amyloid impacts neuropsychological performance in cognitively normal older adults: Findings from the REVEAL SCAN Study

Author

Jason Karlawish, Kristin Harkins, Clara A Chen, L. Adrienne Cupples, J. Scott Roberts, Kathleen A. Welsh‐Bohmer, and Robert C. Green

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2021
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65. Abstract 10651: Identification of Allele-Specific Gene Expression in Association with Blood Pressure

Author

Amena Keshawarz, Tianxiao Huan, Roby Joehanes, Chen Yao, Ching-Ti Liu, Cumhur Demirkale, Vasan S Ramachandran, L Adrienne Cupples, and Daniel Levy

Subjects
Physiology (medical), Cardiology and Cardiovascular Medicine
Abstract

Introduction: Risk of hypertension is explained by both behavioral and genetic factors that may act on transcriptomic mechanisms to affect blood pressure variability. Methods: Whole genome sequencing and RNA sequencing data were obtained on 1,671 Framingham Heart Study (FHS) participants (mean age 54±13 years). Allelic expression of heterozygous SNPs was measured and allelic imbalance was identified using a binomial test. SNPs with significant allelic imbalance were evaluated in linear regression to test the association between the reference/total allele count ratio and 1) systolic blood pressure (SBP), and 2) diastolic blood pressure (DBP). Models were adjusted for age, sex, BMI, and family structure. Results: At a Bonferroni-corrected p-value0.01. Prior GWAS have identified >2,000 SNPs associated with SBP, DBP, or hypertension; we identified allelic expression imbalance of 60 SNPs (in 44 genes) that were reported in GWAS to be associated with SBP, and of eight SNPs (in two genes) that are associated with DBP. These genes were significantly enriched for the immune effector process pathway (p=1.4E-7). Fifteen SNPs were located in promoter or promoter-flanking regions, while the remaining SNPs were located in protein-coding regions. Seventeen SNPs were also cis -eQTLs, of which four transcripts also were associated with SBP or DBP in the FHS cohort. Conclusion: We identified ASE linked to blood pressure. Our results suggested that novel transcriptomic regulatory mechanisms involving allelic imbalance at specific SNPs may play a role in blood pressure regulation.

Published
2021
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66. A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies

Author

Adolfo Correa, Corbin Quick, Jennifer A. Brody, Daniel E. Weeks, Rounak Dey, Joanne E. Curran, Charles Kooperberg, Wei Zhao, Brian G. Kral, Lisa W. Martin, Christen J. Willer, Donald W. Bowden, Eric Boerwinkle, Theodore Arapoglou, Joshua C. Bis, Barry I. Freedman, Leslie A. Lange, Ryan Sun, James G. Wilson, Lawrence F. Bielak, May E. Montasser, Kent D. Taylor, Jerome I. Rotter, Ramachandran S. Vasan, L. Adrienne Cupples, Rita R. Kalyani, Hufeng Zhou, Ani Manichaikul, John Blangero, Han Chen, Patricia A. Peyser, Stephen S. Rich, Brian E. Cade, Sheila M. Gaynor, Paul S. de Vries, Xihong Lin, Susan Redline, Thomas W. Blackwell, Margaret Sunitha Selvaraj, Jeffrey R. O'Connell, Xihao Li, Bruce M. Psaty, Ravindranath Duggirala, Matthew P. Conomos, Kenneth Rice, Donna K. Arnett, Muagututi‘a Sefuiva Reupena, Alanna C. Morrison, Nicholette D. Palmer, Jennifer A. Smith, Harald H H Göring, Alexander P. Reiner, Lisa R. Yanek, Braxton D. Mitchell, Laura M. Raffield, Yaowu Liu, Xiuqing Guo, Gina M. Peloso, Zilin Li, Pradeep Natarajan, and Take Naseri

Subjects
Whole genome sequencing, Technology, Genome, Whole Genome Sequencing, Human Genome, Genetic Variation, Scale (descriptive set theory), Computational biology, Replicate, TOPMed Lipids Working Group, Biological Sciences, Biology, Medical and Health Sciences, Annotation, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Phenotype, Good Health and Well Being, Genetics, Humans, Generic health relevance, Genome-Wide Association Study, Biotechnology, Developmental Biology, Genetic association
Abstract

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare variants’ (RVs) associations with complex human traits. Variant set analysis is a powerful approach to study RV association, and a key component of it is constructing RV sets for analysis. However, existing methods have limited ability to define analysis units in the noncoding genome. Furthermore, there is a lack of robust pipelines for comprehensive and scalable noncoding RV association analysis. Here we propose a computationally-efficient noncoding RV association-detection framework that uses STAAR (variant-set test for association using annotation information) to group noncoding variants in gene-centric analysis based on functional categories. We also propose SCANG (scan the genome)-STAAR, which uses dynamic window sizes and incorporates multiple functional annotations, in a non-gene-centric analysis. We furthermore develop STAARpipeline to perform flexible noncoding RV association analysis, including gene-centric analysis as well as fixed-window-based and dynamic-window-based non-gene-centric analysis. We apply STAARpipeline to identify noncoding RV sets associated with four quantitative lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several noncoding RV associations in an additional 9,123 TOPMed samples.

Published
2021
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67. Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Author

Akhil Pampana, Xiuqing Guo, Tanika N. Kelly, Yii-Der Ida Chen, Charles Kooperberg, Chii-Min Hwu, Mariaelisa Graff, Jiang He, Xihao Li, Patrick T. Ellinor, Joshua C. Bis, Kari E. North, Nancy L. Heard-Costa, Joseph Park, Stacey Gabriel, Joanne E. Curran, Braxton D. Mitchell, Lee-Ming Chuang, Ravindranath Duggirala, Jerome I. Rotter, Robert C. Kaplan, Soren Germer, Pradeep Natarajan, Take Naseri, Xihong Lin, Susan K. Dutcher, Stella Aslibekyan, Ryan W. Kim, Daniel J. Rader, Richard A. Gibbs, Myriam Fornage, Eric Boerwinkle, Bertha Hidalgo, Muagututi’a S. Reupena, Deborah A. Nickerson, Zhe Wang, Donald W. Bowden, Yuxuan Wang, Alanna C. Morrison, Stephen S. Rich, David Zhang, Gina M. Peloso, Xiaohui Li, Martin Lisa, Lisa de las Fuentes, Zilin Li, Alexander P. Reiner, Jennifer A. Brody, Lisa R. Yanek, Marguerite R. Irvin, Bruce M. Psaty, Bao Wei, Preuss Michael, Leslie A. Lange, John T. Wilkins, Russell P. Tracy, Paul S. de Vries, Wei Zhao, Rasika A. Mathias, Susan Redline, Xiao Sun, Kent D. Taylor, Barry I. Freedman, Ani Manichaikul, Donna K. Arnett, Nicholette D. Palmer, Cristen J. Willer, Steven A. Lubitz, Sharon L.R. Kardia, L. Adrienne Cupples, Ramachandran S. Vasan, May E. Montasser, Ren-Hua Chung, Margaret Sunitha Selvaraj, Jeffrey R. O'Connell, Ruth J. F. Loos, Jennifer A. Smith, John Blangero, Brian G. Kral, Karine A. Viaud Martinez, Stephen T. McGarvey, Adolfo Correa, Michael Y. Tsai, Patricia A. Peyser, and Brian E. Cade

Subjects
Genetics, Whole genome sequencing, Genome Scan, Blood lipids, Biology, medicine.disease, Genome, symbols.namesake, Mendelian inheritance, symbols, medicine, lipids (amino acids, peptides, and proteins), Allele, Gene, Dyslipidemia
Abstract

Plasma lipids are heritable modifiable causal factors for coronary artery disease, the leading cause of death globally. Despite the well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing, partly due to limited sample sizes, ancestral diversity, and interpretation of potential clinical significance. Increasingly larger whole genome sequence datasets with plasma lipids coupled with methodologic advances enable us to more fully catalog the allelic spectrum for lipids. Here, among 66,329 ancestrally diverse (56% non-European ancestry) participants, we associate 428M variants from deep-coverage whole genome sequences with plasma lipids. Approximately 400M of these variants were not studied in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with plasma lipids through analysis of common and rare coding variants. We additionally discover several significantly associated rare non-coding variants largely at Mendelian lipid genes. Notably, we detect rareLDLRintronic variants associated with markedly increased LDL-C, similar to rareLDLRexonic variants. In conclusion, we conducted a systematic whole genome scan for plasma lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

Published
2021
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68. Whole genome sequence analysis of blood lipid levels in66,000 individuals

Author

Margaret Sunitha, Selvaraj, Xihao, Li, Zilin, Li, Akhil, Pampana, David Y, Zhang, Joseph, Park, Stella, Aslibekyan, Joshua C, Bis, Jennifer A, Brody, Brian E, Cade, Lee-Ming, Chuang, Ren-Hua, Chung, Joanne E, Curran, Lisa, de Las Fuentes, Paul S, de Vries, Ravindranath, Duggirala, Barry I, Freedman, Mariaelisa, Graff, Xiuqing, Guo, Nancy, Heard-Costa, Bertha, Hidalgo, Chii-Min, Hwu, Marguerite R, Irvin, Tanika N, Kelly, Brian G, Kral, Leslie, Lange, Xiaohui, Li, Martin, Lisa, Steven A, Lubitz, Ani W, Manichaikul, Preuss, Michael, May E, Montasser, Alanna C, Morrison, Take, Naseri, Jeffrey R, O'Connell, Nicholette D, Palmer, Patricia A, Peyser, Muagututia S, Reupena, Jennifer A, Smith, Xiao, Sun, Kent D, Taylor, Russell P, Tracy, Michael Y, Tsai, Zhe, Wang, Yuxuan, Wang, Wei, Bao, John T, Wilkins, Lisa R, Yanek, Wei, Zhao, Donna K, Arnett, John, Blangero, Eric, Boerwinkle, Donald W, Bowden, Yii-Der Ida, Chen, Adolfo, Correa, L Adrienne, Cupples, Susan K, Dutcher, Patrick T, Ellinor, Myriam, Fornage, Stacey, Gabriel, Soren, Germer, Richard, Gibbs, Jiang, He, Robert C, Kaplan, Sharon L R, Kardia, Ryan, Kim, Charles, Kooperberg, Ruth J F, Loos, Karine A, Viaud-Martinez, Rasika A, Mathias, Stephen T, McGarvey, Braxton D, Mitchell, Deborah, Nickerson, Kari E, North, Bruce M, Psaty, Susan, Redline, Alexander P, Reiner, Ramachandran S, Vasan, Stephen S, Rich, Cristen, Willer, Jerome I, Rotter, Daniel J, Rader, Xihong, Lin, Gina M, Peloso, and Sebastian, Zoellner

Subjects
Multidisciplinary, Whole Genome Sequencing, General Physics and Astronomy, Humans, General Chemistry, Cholesterol, LDL, Lipids, General Biochemistry, Genetics and Molecular Biology, Alleles, Genome-Wide Association Study
Abstract

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

Published
2021
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69. Multi-omics insights into the biological mechanisms underlying gene-by-lifestyle interactions with smoking and alcohol consumption detected by genome-wide trans-ancestry meta-analysis

Author

Nora Franceschini, W. James Gauderman, L. Adrienne Cupples, Alisa K. Manning, Paul S. de Vries, Karen Schwander, Michael A. Province, C. Charles Gu, Vincent Laville, Daniel Levy, Daniel I. Chasman, Alanna C. Morrison, Timothy D. Majarian, Dabeeru C. Rao, Charles N. Rotimi, Michael R. Brown, Yun Ju Sung, Hugues Aschard, Amy R. Bentley, and Mary F. Feitosa

Subjects
Concordance, Meta-analysis, Genotype, Trait, Epigenetics, Computational biology, Biology, Phenotype, Gene, Genome
Abstract

Gene-lifestyle interaction analyses have identified genetic variants whose effect on cardiovascular risk-raising traits is modified by alcohol consumption and smoking behavior. The biological mechanisms of these interactions remain largely unknown, but may involve epigenetic modification linked to perturbation of gene expression. Diverse, individual-level datasets including genotypes, methylation and gene expression conditional on lifestyle factors, are ideally suited to study this hypothesis, yet are often unavailable for large numbers of individuals. Summary-level data, such as effect sizes of genetic variants on a phenotype, present an opportunity for multi-omic study of the biological mechanisms underlying gene-lifestyle interactions. We propose a method that unifies disparate, publicly available summary datasets to build mechanistic hypotheses in models of smoking behavior and alcohol consumption with blood lipid levels and blood pressure measures. Of 897 observed genetic interactions, discovered through genome-wide analysis in diverse multi-ethnic cohorts, 48 were identified with lifestyle-related differentially methylated sites within close proximity and linked to target genes. Smoking behavior and blood lipids account for 37 and 28 of these signals respectively. Five genes also showed differential expression conditional on lifestyle factors within these loci with mechanisms supported in the literature. Our analysis demonstrates the utility of summary data in characterizing observed gene-lifestyle interactions and prioritizes genetic loci for experimental follow up related to blood lipids, blood pressure, and cigarette smoking. We show concordance between multiple trait-or exposure-related associations from diverse assays, driving hypothesis generation for better understanding gene-lifestyle interactions.

Published
2021
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70. BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion

Author

Ramachandran S. Vasan, Nathan Pankratz, Eric Boerwinkle, Charles Kooperberg, Nicholas L. Smith, Jiwon Lee, Xiuqing Guo, Elizabeth D. Schifano, Matthew P. Conomos, Man Zhang, Andrew D. Johnson, Kent D. Taylor, Jeffrey Haessler, Yao Hu, Alanna C. Morrison, Jeffrey R. O'Connell, W. T. Longstreth, Brandon J. Coombes, Jerome I. Rotter, Deepti Jain, James S. Pankow, Nuzulul Kurniansyah, Amanda A. Seyerle, Stephen S. Rich, Ben Heavner, Daniel J. Gottlieb, Sina A. Gharib, Bruce M. Psaty, Stephanie M. Gogarten, Han Chen, L. Adrienne Cupples, Kenneth Rice, Tamar Sofer, Cecelia A. Laurie, Alexander P. Reiner, and Susan Redline

Subjects
Genetics, Mixed model, mixed models, Disease, Biology, QH426-470, DNA sequencing, Article, targeted gene analysis, Test (assessment), genome sequencing, Covariate, Molecular Medicine, diverse populations, Allele, Null hypothesis, rare variant assocaition testing, Genetics (clinical), Type I and type II errors
Abstract

Summary: Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.

Published
2021

71. Exome Sequence Association Study of Levels and Longitudinal Change of Cardiovascular Risk Factor Phenotypes in European-Americans and African-Americans from the Atherosclerosis Risk in Communities Study

Author

Elise Lim, Ching-Ti Liu, Eric Boerwinkle, L. Adrienne Cupples, Elena V. Feofanova, Han Chen, and MinJae Lee

Subjects
medicine.medical_specialty, Epidemiology, Blood lipids, Article, 03 medical and health sciences, Risk Factors, Internal medicine, Hyperlipidemia, Medicine, Humans, Exome, Risk factor, Genetics (clinical), 030304 developmental biology, 0303 health sciences, business.industry, 030305 genetics & heredity, medicine.disease, Atherosclerosis, Pulse pressure, Minor allele frequency, Black or African American, Phenotype, Cardiovascular Diseases, Heart Disease Risk Factors, Cohort, business, Dyslipidemia
Abstract

Cardiovascular disease (CVD) is responsible for 31% of all deaths worldwide. Among CVD risk factors are age, race, increased systolic blood pressure (BP), and dyslipidemia. Both BP and blood lipids levels change with age, with a dose-dependent relationship between the cumulative exposure to hyperlipidemia and the risk of CVD. We performed an exome sequence association study using longitudinal data with up to 7805 European Americans (EAs) and 3171 African Americans (AAs) from the Atherosclerosis Risk in Communities (ARIC) study. We assessed associations of common (minor allele frequency > 5%) nonsynonymous and splice-site variants and gene-based sets of rare variants with levels and with longitudinal change of seven CVD risk factor phenotypes (BP traits: systolic BP, diastolic BP, pulse pressure; lipids traits: triglycerides, total cholesterol, high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C]). Furthermore, we investigated the relationship of the identified variants and genes with select CVD endpoints. We identified two novel genes: DCLK3 associated with the change of HDL-C levels in AAs and RAB7L1 associated with the change of LDL-C levels in EAs. RAB7L1 is further associated with an increased risk of heart failure in ARIC EAs. Investigation of the contribution of genetic factors to the longitudinal change of CVD risk factor phenotypes promotes our understanding of the etiology of CVD outcomes, stressing the importance of incorporating the longitudinal structure of the cohort data in future analyses.

Published
2021

72. 73-OR: Clonal Hematopoiesis of Indeterminate Potential (CHIP), Glycemic Traits, and Type 2 Diabetes (T2D) Risk

Author

DEIRDRE K. TOBIAS, ALISA MANNING, JENNIFER WESSEL, KENNETH E. WESTERMAN, SRIDHARAN RAGHAVAN, LAURA RAFFIELD, JOSEE DUPUIS, ALEXANDER BICK, PEITAO WU, DANIEL A. DICORPO, BRUCE M. PSATY, ALANNA C. MORRISON, VASAN S. RAMACHANDRAN, L. ADRIENNE CUPPLES, LESLIE LANGE, ADOLFO CORREA, CHARLES L. KOOPERBERG, LESLEY F. TINKER, BARBARA V. HOWARD, ALEX REINER, JEROME I. ROTTER, JASON M. COLLINS, JAMES B. MEIGS, and JOANN E. MANSON

Subjects
Endocrinology, Diabetes and Metabolism, Internal Medicine
Abstract

Objective: CHIP is an aging-related accumulation of somatic mutations in hematopoietic stem cells, leading to clonal expansion. CHIP presence has been implicated in elevated atherosclerotic heart disease (CHD) and all-cause mortality, but its association with incident T2D is unknown. We hypothesize CHIP is adversely associated with glycemic traits and T2D. Methods: We included N=16,054 participants in NHLBI Trans-omics for Precision Medicine (TOPMed), without prior T2D or chronic disease at blood draw. CHIP was derived from blood DNA whole genome sequencing using the GATK MuTECT2 somatic variant caller in pre-specified leukemogenic driver mutations. We estimated CHIP overall and candidate CHD-related mutations (DNMT3A, TET2, ASXL1, JAK2) in relation to baseline glycemic traits (fasting glucose, insulin, HOMA-IR) with linear regression. We evaluated CHIP with incident T2D using Cox regression to estimate hazard ratios and 95% confidence intervals (HR [CI]). Models adjusted for age, sex, body mass index, smoking, and family history of T2D. We combined cohort-specific estimates via random effects meta-analysis with inverse variance weighting. Results: CHIP was present in n=1,088 (7%) of participants. We observed 1,853 incident T2D cases over mean=10y follow-up. Baseline fasting glucose, insulin, and HOMA-IR did not differ by CHIP status. Participants with CHIP had a higher risk of incident T2D than those without CHIP (1.27 [1.08, 1.51]), with a significant dose-response per additional CHIP mutation (p-trend=0.005). Analyses of CHD-related CHIP vs. no CHIP mutations suggested associations with higher T2D risk for DNMT3A (1.21 [0.97, 1.51]) and TET2 (1.35 [0.93, 1.97]), and ASXL1 (1.92 [0.71, 5.13]), although analyses of individual mutations were underpowered. Conclusions: CHIP is associated with a higher T2D risk. This relationship may be unrelated to baseline glycemic traits. CHIP-T2D association suggests a potential role for CHD-related pathology in T2D. Disclosure D. K. Tobias: None. D. A. Dicorpo: None. B. M. Psaty: None. A. C. Morrison: None. V. S. Ramachandran: None. L. Cupples: None. L. Lange: None. A. Correa: None. C. L. Kooperberg: None. L. F. Tinker: None. B. V. Howard: None. A. Manning: Employee; Spouse/Partner; Emulate, Inc, Stock/Shareholder; Spouse/Partner; Invitae. A. Reiner: None. J. I. Rotter: None. J. M. Collins: None. J. B. Meigs: Consultant; Self; Quest Diagnostics. J. E. Manson: None. J. Wessel: Employee; Spouse/Partner; Eli Lilly and Company. K. E. Westerman: None. S. Raghavan: None. L. Raffield: None. J. Dupuis: None. A. Bick: None. P. Wu: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK078616 to J.B.M., J.D., D.A.D., P.W.), (U01DK105554 to J.D., D.A.D.)

Published
2021
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73. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration

Author

Sina A. Gharib, Kenneth Rice, Donna K. Arnett, Tõnu Esko, Annemarie I. Luik, Niki Dimou, Dabeeru C. Rao, Pamela J. Schreiner, Melissa A. Richard, Reedik Mägi, Nicholette D. Palmer, Stefan Weiss, Han Chen, Solomon K. Musani, W. James Gauderman, Christian Gieger, Kari E. North, Salman M. Tajuddin, Diana van Heemst, Rainer Rauramaa, Stephen B. Kritchevsky, Henry Völzke, Minjung Kho, Michele K. Evans, Ruth J. F. Loos, Correa Adolfo, Pedro Marques-Vidal, Diane M. Becker, Claude Bouchard, José Haba-Rubio, Georg Homuth, Kurt Lohman, Nora Franceschini, Caizheng Yu, Peter Vollenweider, Ervin F. Fox, Thomas Meitinger, Karen Schwander, Heming Wang, Morris A. Swertz, Aldi T. Kraja, José Eduardo Krieger, Xiuqing Guo, Tangchun Wu, Raymond Noordam, Yongmei Liu, Michael R. Brown, Patrick C.N. Rensen, Annette Peters, Jingmin Liu, Tuomo Rankinen, Robert B. Wallance, Timo A. Lakka, Stephen S. Rich, Yun Ju Sung, Meian He, Till Roenneberg, Wanqing Wen, Dennis O. Mook-Kanamori, M. A. Province, Evangelos Evangelou, L. Adrienne Cupples, Maxime M Bos, Paul S. de Vries, Hans J. Grabe, Lisa R. Yanek, Jiwon Lee, Mario Sims, Bruce M. Psaty, Treva Rice, Tanika N. Kelly, Brenda W.J.H. Penninx, Brigitte Kühnel, Maria E. Graff, Alexandre C. Pereira, Daniel J. Gottlieb, Charles N. Rotimi, Lynne E. Wagenknecht, Ashley van der Spek, Lisa W. Martin, Jeffrey R. O'Connell, Changwei Li, James E. Hixson, Chuan Gao, Alisa K. Manning, Sharon L.R. Kardia, Stella Aslibekyan, Andres Metspalu, Sami Heikkinen, Maris Alver, Xiao-Ou Shu, Amy R. Bentley, Susan Redline, Konstantin Strauch, Tamara B. Harris, Yuri Milaneschi, L.F. Bielak, Marjan Ilkov, Myriam Fornage, Ilja M. Nolte, André G. Uitterlinden, Dina Vojinovic, Hugues Aschard, Paul Elliott, Kent D. Taylor, Najaf Amin, James M. Shikany, Yong-Bing Xiang, Jerome I. Rotter, Vilmundur Gudnason, Cornelia M. van Duijn, Ko Willems van Dijk, Andrea R. V. R. Horimoto, Jonathan Marten, Olli T. Raitakari, Vincent Laville, Pirjo Komulainen, Zhe Wang, Traci M. Bartz, Mary F. Feitosa, Mike A. Nalls, Eric Boerwinkle, Alanna C. Morrison, Melanie Waldenberger, Alexander P. Reiner, Christie M. Ballantyne, Wei Zheng, Raphael Heinzer, M. Arfan Ikram, Colleen M. Sitlani, Lisa de las Fuentes, Patricia B. Munroe, Patricia A. Peyser, Brian E. Cade, Tamar Sofer, Xiaofeng Zhu, Nienke R. Biermasz, Harold Snieder, Elise Lim, Tuomas O. Kilpeläinen, Ching-Ti Liu, Terho Lehtimäki, Thomas W. Winkler, Leo-Pekka Lyytikäinen, Gudny Eiriksdottir, Stephen Sidney, Alan B. Zonderman, Charles Kooperberg, Leiden University Medical Center (LUMC), Universiteit Leiden, Brigham & Women’s Hospital [Boston] (BWH), Harvard Medical School [Boston] (HMS), Universität Regensburg (UR), National Institutes of Health [Bethesda] (NIH), University of Copenhagen = Københavns Universitet (UCPH), Icahn School of Medicine at Mount Sinai [New York] (MSSM), The University of Texas Health Science Center at Houston (UTHealth), Washington University School of Medicine in St. Louis, Washington University in Saint Louis (WUSTL), Massachusetts General Hospital [Boston], Harvard T.H. Chan School of Public Health, Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), University of Mississippi Medical Center (UMMC), Erasmus University Medical Center [Rotterdam] (Erasmus MC), University of Alabama at Birmingham [ Birmingham] (UAB), University of Washington [Seattle], Universidade de São Paulo Medical School (FMUSP), Icelandic Heart Association [Kopavogur, Iceland] (IHA), University of Michigan [Ann Arbor], University of Michigan System, University of Georgia [USA], School of Public Health [Boston], Boston University [Boston] (BU), Wake Forest University, Pennington Biomedical Research Center, Louisiana State University (LSU), University of Edinburgh, University of Tartu, Imperial College London, University of Ioannina, Huazhong University of Science and Technology [Wuhan] (HUST), Helmholtz Zentrum München = German Research Center for Environmental Health, Fimlab Laboratories [Tampere, Finland], University of Tampere [Finland], Lausanne University Hospital, University of Groningen [Groningen], This project was supported by a grant from the US National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (R01HL118305). This research was supported in part by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health. Tuomas O. Kilpeläinen was supported by the Danish Council for Independent Research (DFF–6110-00183) and the Novo Nordisk Foundation (NNF18CC0034900, NNF17OC0026848 and NNF15CC0018486). Diana van Heemst was supported by the European Commission funded project HUMAN (Health-2013-INNOVATION-1-602757). Susan Redline was supported in part by NIH R35HL135818 and HL11338., European Project: 602757,EC:FP7:HEALTH,FP7-HEALTH-2013-INNOVATION-1,HUMAN(2013), Epidemiology, Radiology & Nuclear Medicine, Internal Medicine, Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Psychiatry, APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Digital Health, University of Copenhagen = Københavns Universitet (KU), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Helmholtz-Zentrum München (HZM), Van Duijn, CM, Wang, Heming [0000-0002-1486-7495], Bentley, Amy R [0000-0002-0827-9101], Schwander, Karen [0000-0002-0193-4134], Manning, Alisa [0000-0003-0247-902X], Aschard, Hugues [0000-0002-7554-6783], Richard, Melissa [0000-0003-0129-9860], de Las Fuentes, Lisa [0000-0002-4689-325X], Feitosa, Mary [0000-0002-0933-2410], van der Spek, Ashley [0000-0001-7136-0159], Wang, Zhe [0000-0002-8046-4969], Marten, Jonathan [0000-0001-6916-2014], Laville, Vincent [0000-0003-4946-698X], Evangelou, Evangelos [0000-0002-5488-2999], He, Meian [0000-0002-2096-921X], Lyytikäinen, Leo-Pekka [0000-0002-7200-5455], Marques-Vidal, Pedro [0000-0002-4548-8500], Nolte, Ilja M [0000-0001-5047-4077], Palmer, Nicholette D [0000-0001-8883-2511], Snieder, Harold [0000-0003-1949-2298], Weiss, Stefan [0000-0002-3553-4315], Yanek, Lisa R [0000-0001-7117-1075], Adolfo, Correa [0000-0002-9501-600X], Dimou, Niki [0000-0003-1678-9328], Heikkinen, Sami [0000-0002-6083-2402], Ikram, M Arfan [0000-0003-0372-8585], Krieger, Jose E [0000-0001-5464-1792], Lee, Jiwon [0000-0002-4079-7494], Liu, Jingmin [0000-0003-0001-6013], Martin, Lisa W [0000-0003-4352-0914], Metspalu, Andres [0000-0002-3718-796X], Rensen, Patrick CN [0000-0002-8455-4988], Rich, Stephen S [0000-0003-3872-7793], Rotter, Jerome I [0000-0001-7191-1723], Sofer, Tamar [0000-0001-8520-8860], Taylor, Kent D [0000-0002-2756-4370], Uitterlinden, André G [0000-0002-7276-3387], van Dijk, Ko Willems [0000-0002-2172-7394], Elliott, Paul [0000-0002-7511-5684], Esko, Tõnu [0000-0003-1982-6569], Grabe, Hans J [0000-0003-3684-4208], Zheng, Wei [0000-0003-1226-070X], Bouchard, Claude [0000-0002-0048-491X], Gudnason, Vilmundur [0000-0001-5696-0084], Loos, Ruth JF [0000-0002-8532-5087], Cupples, L Adrienne [0000-0003-0273-7965], Munroe, Patricia B [0000-0002-4176-2947], Liu, Ching-Ti [0000-0002-0703-0742], Apollo - University of Cambridge Repository, Læknadeild (HÍ), Faculty of Medicine (UI), Heilbrigðisvísindasvið (HÍ), School of Health Sciences (UI), Háskóli Íslands, University of Iceland, Home Office, Imperial College Healthcare NHS Trust- BRC Funding, Medical Research Council (MRC), and UK DRI Ltd

Subjects
Male, 0301 basic medicine, Epidemiology, General Physics and Astronomy, Genome-wide association study, Genome-wide association studies, Svefnrannsóknir, 0302 clinical medicine, MESH: Aged, 80 and over, Polymorphism (computer science), lcsh:Science, MESH: Phylogeny, Phylogeny, Aged, 80 and over, Genetics, MESH: Aged, [STAT.AP]Statistics [stat]/Applications [stat.AP], Multidisciplinary, MESH: Middle Aged, medicine.diagnostic_test, MESH: Polymorphism, Single Nucleotide, Chromosome Mapping, Middle Aged, Explained variation, Lipids, Sleep in non-human animals, Blóðfita, ddc, 3. Good health, MESH: Young Adult, Female, lipids (amino acids, peptides, and proteins), Erfðarannsóknir, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Adult, Adolescent, MESH: Sleep, Science, Biology, Polymorphism, Single Nucleotide, MESH: Genetic Loci, Article, General Biochemistry, Genetics and Molecular Biology, Young Adult, 03 medical and health sciences, Phylogenetics, Svefn, medicine, Humans, SNP, Dyslipidaemias, Aged, MESH: Adolescent, MESH: Humans, Faraldsfræði, PCSK9, MESH: Adult, General Chemistry, MESH: Lipids, MESH: Male, 030104 developmental biology, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Genetic Loci, lcsh:Q, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Sleep, Lipid profile, MESH: Chromosome Mapping, MESH: Female, 030217 neurology & neurosurgery
Abstract

Publisher's version (útgefin grein)., Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles., This project was supported by a grant from the US National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (R01HL118305). This research was supported in part by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health. Tuomas O. Kilpeläinen was supported by the Danish Council for Independent Research (DFF–6110-00183) and the Novo Nordisk Foundation (NNF18CC0034900, NNF17OC0026848 and NNF15CC0018486). Diana van Heemst was supported by the European Commission funded project HUMAN (Health-2013-INNOVATION-1-602757). Susan Redline was supported in part by NIH R35HL135818 and HL11338. Study-specific acknowledgements can be found in the Supplementary Notes 2 and 4. The data on coronary artery disease have been contributed by the Myocardial Infarction Genetics and CARDIoGRAM investigators, and have been downloaded from www.CARDIOGRAMPLUSC4D.ORG.

Published
2019
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74. Direct Versus Calculated LDL Cholesterol and C-Reactive Protein in Cardiovascular Disease Risk Assessment in the Framingham Offspring Study

Author

Norihiro Furusyo, Ernst J. Schaefer, Bela F. Asztalos, Ching-Ti Liu, L. Adrienne Cupples, Masumi Ai, Hiroaki Ikezaki, Elise Lim, Katsuyuki Nakajima, and Virginia Fisher

Subjects
Male, medicine.medical_specialty, Offspring, Clinical Biochemistry, 030204 cardiovascular system & hematology, Risk Assessment, Gastroenterology, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Internal medicine, Diabetes mellitus, medicine, Humans, Prospective Studies, cardiovascular diseases, 030212 general & internal medicine, Prospective cohort study, Proportional Hazards Models, Univariate analysis, Framingham Risk Score, biology, business.industry, Cholesterol, Biochemistry (medical), C-reactive protein, Cholesterol, LDL, Middle Aged, medicine.disease, C-Reactive Protein, chemistry, Cardiovascular Diseases, Multivariate Analysis, biology.protein, Female, lipids (amino acids, peptides, and proteins), Risk assessment, business, Biomarkers
Abstract

BACKGROUNDIncreases in circulating LDL cholesterol (LDL-C) and high-sensitivity C-reactive protein (hsCRP) concentrations are significant risk factors for cardiovascular disease (CVD). We assessed direct LDL-C and hsCRP concentrations compared to standard risk factors in the Framingham Offspring Study.METHODSWe used stored frozen plasma samples (−80 °C) obtained after an overnight fast from 3147 male and female participants (mean age, 58 years) free of CVD at cycle 6 of the Framingham Offspring Study. Overall, 677 participants (21.5%) had a CVD end point over a median of 16.0 years of follow-up. Total cholesterol (TC), triglyceride (TG), HDL cholesterol (HDL-C), direct LDL-C (Denka Seiken and Kyowa Medex methods), and hsCRP (Dade Behring method) concentrations were measured by automated analysis. LDL-C was also calculated by both the Friedewald and Martin methods.RESULTSConsidering all CVD outcomes on univariate analysis, significant factors included standard risk factors (age, hypertension, HDL-C, hypertension treatment, sex, diabetes, smoking, and TC concentration) and nonstandard risk factors (non-HDL-C, direct LDL-C and calculated LDL-C, TG, and hsCRP concentrations). On multivariate analysis, only the Denka Seiken direct LDL-C and the Dade Behring hsCRP were still significant on Cox regression analysis and improved the net risk reclassification index, but with modest effects. Discordance analysis confirmed the benefit of the Denka Seiken direct LDL-C method for prospective hard CVD endpoints (new-onset myocardial infarction, stroke, and/or CVD death).CONCLUSIONSOur data indicate that the Denka Seiken direct LDL-C and Dade Behring hsCRP measurements add significant, but modest, information about CVD risk, compared to standard risk factors and/or calculated LDL-C.

Published
2019
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75. A fully adjusted two-stage procedure for rank-normalization in genetic association studies

Author

Dmitry Shungin, Jeffrey R. O'Connell, Xiuwen Zheng, Kelsey Grinde, Solomon K. Musani, Ramachandran S. Vasan, Tamar Sofer, Ramon A. Durazo-Arvizo, John R. Shaffer, L. Adrienne Cupples, Kenneth Rice, Stephanie M. Gogarten, Cathy C. Laurie, Cecelia A. Laurie, Laura M. Raffield, Alex P. Reiner, and Leslie A. Lange

Subjects
Normalization (statistics), 0303 health sciences, Epidemiology, media_common.quotation_subject, 030305 genetics & heredity, food and beverages, Rate control, Statistical power, 03 medical and health sciences, Linear regression, Statistics, Trait, Genetics (clinical), Normality, 030304 developmental biology, Type I and type II errors, media_common, Mathematics, Genetic association
Abstract

When testing genotype–phenotype associations using linear regression, departure of the trait distribution from normality can impact both Type I error rate control and statistical power, with worse ...

Published
2019
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76. Association of the PHACTR1/EDN1 Genetic Locus With Spontaneous Coronary Artery Dissection

Author

David Adlam, Timothy M. Olson, Nicolas Combaret, Jason C. Kovacic, Siiri E. Iismaa, Abtehale Al-Hussaini, Megan M. O'Byrne, Sara Bouajila, Adrien Georges, Ketan Mishra, Peter S. Braund, Valentina d’Escamard, Siying Huang, Marios Margaritis, Christopher P. Nelson, Mariza de Andrade, Daniella Kadian-Dodov, Catherine A. Welch, Stephani Mazurkiewicz, Xavier Jeunemaitre, Claire Mei Yi Wong, Eleni Giannoulatou, Michael Sweeting, David Muller, Alice Wood, Lucy McGrath-Cadell, Diane Fatkin, Sally L. Dunwoodie, Richard Harvey, Cameron Holloway, Jean-Philippe Empana, Xavier Jouven, Jeffrey W. Olin, Rajiv Gulati, Marysia S. Tweet, Sharonne N. Hayes, Nilesh J. Samani, Robert M. Graham, Pascal Motreff, Nabila Bouatia-Naji, Loïc Belle, Patrick Dupouy, Pierre Barnay, Nicolas Meneveau, Martine Gilard, Gilles Rioufol, Grégoire Range, Philippe Brunel, Nicolas Delarche, Emmanuelle Filippi, Louis Le Bivic, Brahim Harbaoui, Hakim Benamer, Guillaume Cayla, Olivier Varenne, Stephane Peggy Manzo-Silberman, Johanne Silvain, Christian Spaulding, Christophe Caussin, Edouard Gerbaud, Yann Valy, René Koning, Thibault Lhermusier, Stanislas Champin, Emmanuel Salengro, Arnaud Fluttaz, Amer Zabalawi, Yves Cottin, Emmanuel Teiger, Christophe Saint-Etienne, Grégory Ducrocq, Stéphanie Marliere, Emmanuel Boiffard, Pierre Aubry, Jean Louis Georges, Didier Bresson, Fabien De Poli, Gaëtan Karrillon, Vincent Roule, Laurent Bali, Mathieu Valla, Antoine Gerbay, David Houpe, Olivier Dubreuil, Arsène Monnier, Norbert Mayaud, Aurélie Manchuelle, Philippe Commeau, Marc Bedossa, Majid Nikpay, Anuj Goel, Hong-Hee Won, Leanne M. Hall, Christina Willenborg, Stavroula Kanoni, Danish Saleheen, Theodosios Kyriakou, Jemma C. Hopewell, Thomas R. Webb, Lingyao Zeng, Abbas Dehghan, Maris Alver, Sebastian M. Armasu, Kirsi Auro, Andrew Bjonnes, Daniel I. Chasman, Shufeng Chen, Ian Ford, Nora Franceschini, Christian Gieger, Christopher Grace, Stefan Gustafsson, Jie Huang, Shih-Jen Hwang, Yun Kyoung Kim, Marcus E. Kleber, King Wai Lau, Xiangfeng Lu, Yingchang Lu, Leo P. Lyytikäinen, Evelin Mihailov, Alanna Morrison, Natalia Pervjakova, Liming Qu, Lynda M. Rose, Elias Salfati, Richa Saxena, Markus Scholz, Albert V. Smith, Emmi Tikkanen, Andre Uitterlinden, Xueli Yang, Weihua Zhang, Wei Zhao, Paul S. de Vries, Natalie R. van Zuydam, Sonia S. Anand, Lars Bertram, Frank Beutner, George Dedoussis, Philippe Frossard, Dominique Gauguier, Alison H. Goodall, Omri Gottesman, Marc Haber, Bok-Ghee Han, Jianfeng Huang, Shapour Jalilzadeh, Thorsten Kessler, Inke R. König, Lars Lannfelt, Wolfgang Lieb, Lars Lind, Cecilia M. Lindgren, Maisa Lokki, Patrik K. Magnusson, Nadeem H. Mallick, Narinder Mehra, Thomas Meitinger, Fazal-ur-Rehman Memon, Andrew P. Morris, Markku S. Nieminen, Nancy L. Pedersen, Annette Peters, Loukianos S. Rallidis, Asif Rasheed, Maria Samuel, Svati H. Shah, Juha Sinisalo, Kathleen E. Stirrups, Stella Trompet, Laiyuan Wang, Khan S. Zaman, Diego Ardissino, Eric Boerwinkle, Ingrid B. Borecki, Erwin P. Bottinger, Julie E. Buring, John C. Chambers, Rory Collins, L Adrienne Cupples, John Danesh, Ilja Demuth, Roberto Elosua, Stephen E. Epstein, Tõnu Esko, Mary F. Feitosa, Oscar H. Franco, Maria Grazia Franzosi, Christopher B. Granger, Dongfeng Gu, Vilmundur Gudnason, Alistair S. Hall, Anders Hamsten, Tamara B. Harris, Stanley L. Hazen, Christian Hengstenberg, Albert Hofman, Erik Ingelsson, Carlos Iribarren, J Wouter Jukema, Pekka J. Karhunen, Bong-Jo Kim, Jaspal S. Kooner, Iftikhar J. Kullo, Terho Lehtimäki, Ruth J. Loos, Olle Melander, Andres Metspalu, Winfried März, Colin N. Palmer, Markus Perola, Thomas Quertermous, Daniel J. Rader, Paul M. Ridker, Samuli Ripatti, Robert Roberts, Veikko Salomaa, Dharambir K. Sanghera, Stephen M. Schwartz, Udo Seedorf, Alexandre F. Stewart, David J. Stott, Joachim Thiery, Pierre A. Zalloua, Christopher J. O'Donnell, Muredach P. Reilly, Themistocles L. Assimes, John R. Thompson, Jeanette Erdmann, Robert Clarke, Hugh Watkins, Sekar Kathiresan, Ruth McPherson, Panos Deloukas, Heribert Schunkert, Martin Farrall, Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, Mayo Clinic [Rochester], Service de Cardiologie Maladies Vasculaires [CHU Clermont-Ferrand], CHU Gabriel Montpied [Clermont-Ferrand], CHU Clermont-Ferrand-CHU Clermont-Ferrand, Icahn School of Medicine at Mount Sinai [New York] (MSSM), Victor Chang Cardiac Research Institute, University of New South Wales [Sydney] (UNSW), St. Vincent’s Clinical School [Sydney, Australia], UNSW Faculty of Medicine [Sydney], University of New South Wales [Sydney] (UNSW)-University of New South Wales [Sydney] (UNSW), Department of Health Sciences Research [Mayo Clinic] (HSR), Mayo Clinic, Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Université Paris Descartes - Faculté de Médecine (UPD5 Médecine), Université Paris Descartes - Paris 5 (UPD5), Sorbonne Université - Faculté de Médecine (SU FM), Sorbonne Université (SU), Service de génétique [CHU HEGP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Department of Cardiovascular Medicine, Mayo Clinic, Hôpital Universitaire Carémeau [Nîmes] (CHU Nîmes), and Centre Hospitalier Universitaire de Nîmes (CHU Nîmes)

Subjects
Adult, Male, medicine.medical_specialty, Myocardial infarction, Coronary Vessel Anomalies, Fibromuscular dysplasia, 030204 cardiovascular system & hematology, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Internal medicine, medicine, Prevalence, Fibromuscular Dysplasia, Humans, 030212 general & internal medicine, Vascular Diseases, Artery dissection, MESH: Australia, United Kingdom, USA, Coronary Vessel Anomalies / epidemiology, Endothelin-1 / genetics, Microfilament proteins / genetics, Genetic association, Aged, Endothelin-1, business.industry, Microfilament Proteins, Australia, Cardiovascular disease in women, Middle Aged, medicine.disease, R1, United States, 3. Good health, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Genetic Loci, Case-Control Studies, Cardiology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Female, France, Cardiology and Cardiovascular Medicine, Scad, business
Abstract

Background: \ud Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of acute coronary syndromes (ACS) afflicting predominantly younger to middle-aged women. Observational studies have reported a high prevalence of extracoronary vascular anomalies, especially fibromuscular dysplasia (FMD) and a low prevalence of coincidental cases of atherosclerosis. PHACTR1/EDN1 is a genetic risk locus for several vascular diseases, including FMD and coronary artery disease, with the putative causal noncoding variant at the rs9349379 locus acting as a potential enhancer for the endothelin-1 (EDN1) gene.\ud \ud Objectives: \ud This study sought to test the association between the rs9349379 genotype and SCAD.\ud \ud Methods: \ud Results from case control studies from France, United Kingdom, United States, and Australia were analyzed to test the association with SCAD risk, including age at first event, pregnancy-associated SCAD (P-SCAD), and recurrent SCAD.\ud \ud Results: \ud The previously reported risk allele for FMD (rs9349379-A) was associated with a higher risk of SCAD in all studies. In a meta-analysis of 1,055 SCAD patients and 7,190 controls, the odds ratio (OR) was 1.67 (95% confidence interval [CI]: 1.50 to 1.86) per copy of rs9349379-A. In a subset of 491 SCAD patients, the OR estimate was found to be higher for the association with SCAD in patients without FMD (OR: 1.89; 95% CI: 1.53 to 2.33) than in SCAD cases with FMD (OR: 1.60; 95% CI: 1.28 to 1.99). There was no effect of genotype on age at first event, P-SCAD, or recurrence.\ud \ud Conclusions: \ud The first genetic risk factor for SCAD was identified in the largest study conducted to date for this condition. This genetic link may contribute to the clinical overlap between SCAD and FMD.

Published
2019
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77. Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium

Author

Panos Deloukas, Christina-Alexandra Schulz, Caren E. Smith, Lu Qi, Mary F. Feitosa, Melissa E. Garcia, Natalia Pervjakova, Denise K. Houston, Michael A. Province, Stavroula Kanoni, Torben Hansen, Jean Shin, Lynda M. Rose, Oscar H. Franco, Yongmei Liu, Trudy Voortman, Marie-Claude Vohl, Luigi Ferrucci, Chloé Sarnowski, Paul S. de Vries, Jose C. Florez, John C. Lieske, Louis Pérusse, Misa Graff, Jian'an Luan, Nicola M. McKeown, Felix R. Day, L. Adrienne Cupples, Qibin Qi, Frank J. A. van Rooij, Robert A. Scott, Josée Dupuis, Stephen S. Rich, Jorma Viikari, Tarunveer S. Ahluwalia, Leo-Pekka Lyytikäinen, Paul M. Ridker, Tomáš Paus, Stephen Turner, Harri Rissanen, Audrey Y. Chu, Albert Hofman, Olli T. Raitakari, Paul Knekt, Daniel I. Chasman, Nita G. Forouhi, Vera Mikkilä, Toshiko Tanaka, Mika Kähönen, George Dedoussis, Minjung Kho, Yun J. Sung, Anne E. Justice, Julius S. Ngwa, Ani Manichaikul, M. Carola Zillikens, Jordi Merino, Dena G. Hernandez, Rozenn N. Lemaitre, Tao Huang, Sharon L.R. Kardia, Jing Hua Zhao, Stefania Bandinelli, Veikko Salomaa, Arne Astrup, Wei Zhao, David S. Siscovick, John Blangero, Zdenka Pausova, Dabeeru C. Rao, Oluf Pedersen, Craig E. Pennell, Wendy H. Oddy, Jose M. Ordovas, Jessica C. Kiefte-de Jong, Marju Orho-Melander, Hassan S. Dashti, Satu Männistö, Tuomo Rankinen, Constantina Papoutsakis, Sherly X. Li, Antti Jula, Kari E. North, Thorkild I. A. Sørensen, André G. Uitterlinden, Alexis C. Frazier-Wood, Nicholas J. Wareham, Ulrika Ericson, Markus Perola, Ioanna P. Kalafati, Renée de Mutsert, Jinyan Huang, Ruifang Li-Gao, Claudia Langenberg, Mike A. Nalls, Olivia Li, Joanne E. Curran, Dennis O. Mook-Kanamori, Niina Eklund, Jerome I. Rotter, Angelo Tremblay, Claude Bouchard, Jennifer A. Smith, Terho Lehtimäki, Mary K. Wojczynski, Carol A. Wang, Epidemiology, Internal Medicine, Zhao, Jing Hua [0000-0003-4930-3582], Luan, Jian'an [0000-0003-3137-6337], Day, Felix [0000-0003-3789-7651], Langenberg, Claudia [0000-0002-5017-7344], Wareham, Nicholas [0000-0003-1422-2993], Forouhi, Nita [0000-0002-5041-248X], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, Male, medicine.medical_specialty, Aging, Calorie, Genotype, Heart Diseases, Receptors, Retinoic Acid, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Locus (genetics), Genome-wide association study, Biology, Polymorphism, Single Nucleotide, Article, White People, Cohort Studies, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Genetic variation, medicine, Humans, Genetic Predisposition to Disease, Obesity, Molecular Biology, Aged, 2. Zero hunger, Genetics, Family aggregation, Membrane Proteins, Genomics, Nutrients, Middle Aged, medicine.disease, 3. Good health, Fibroblast Growth Factors, Psychiatry and Mental health, 030104 developmental biology, Genetic Loci, Meta-analysis, Medical genetics, Female, Energy Intake, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10(−6)) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

Published
2019
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78. Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions

Author

Alexandre C. Pereira, Jie Yao, Makoto Hirata, Ozren Polasek, Dragana Vuckovic, Mariaelisa Graff, Jing Hua Zhao, Michael R. Brown, Xuan Deng, Barbara V. Howard, Vilmundur Gudnason, Tuomo Rankinen, Yuan Shi, Timo A. Lakka, Ulrich Broeckel, Anuradhani Kasturiratne, Eric Boerwinkle, Charles Kooperberg, Hugues Aschard, Stephen S. Rich, Pamela J. Schreiner, Dan E. Arking, Hans J. Grabe, Ilaria Gandin, Mario Sims, Carl D. Langefeld, Yii-Der Ida Chen, Meian He, W. James Gauderman, Bruce M. Psaty, Kent D. Taylor, Tõnu Esko, Olli T. Raitakari, Saima Afaq, Maik Pietzner, Federica Laguzzi, Dennis O. Mook-Kanamori, Gregory P. Wilson, Xiuqing Guo, Jaspal S. Kooner, David R. Jacobs, Yajuan Wang, Raha Pazoki, Wei Zhao, Andres Metspalu, Mary F. Feitosa, Jost B. Jonas, Tin Aung, Tamara B. Harris, Karin Leander, Nancy L. Pedersen, Charles B. Eaton, Sharon L.R. Kardia, Bernardo L. Horta, Candace M. Kammerer, Terrence Forrester, Alexander P. Reiner, Paul W. Franks, Tanika N. Kelly, Igor Rudan, Ruben N. Eppinga, Melissa A. Richard, Colin A. McKenzie, Christine Williams, Christian Gieger, Joseph H. Lee, Brigitte Kühnel, Christopher P. Nelson, Wen Bin Wei, Antonietta Robino, Anne U. Jackson, E. Shyong Tai, Alisa K. Manning, Lisa R. Yanek, Paul Elliott, Lynne E. Wagenknecht, Ananda R. Wickremasinghe, José Eduardo Krieger, Peter S. Sever, Nora Franceschini, André G. Uitterlinden, Paul M. Ridker, Yun J. Sung, Karen L. Mohlke, Thomas H. Mosley, Nilesh J. Samani, Patricia B. Munroe, Jennifer A. Smith, John M. C. Connell, H. Janaka de Silva, Nicholas Y. Q. Tan, Jian'an Luan, Jennifer E. Huffman, Anna F. Dominiczak, Carsten Oliver Schmidt, Christie M. Ballantyne, Peter J. van der Most, Anuj Goel, Jerome I. Rotter, Amy R. Bentley, Lihua Wang, Rajkumar Dorajoo, Lenore J. Launer, Yechiel Friedlander, Mickaël Canouil, Traci M. Bartz, Thomas Meitinger, Robert A. Scott, Mike A. Nalls, Xueling Sim, Sarah E. Harris, Michael A. Province, Chew-Kiat Heng, Zhe Wang, Norihiro Kato, Evangelos Evangelou, Philippe Froguel, Virginia Fisher, Leslie J. Raffel, Renée de Mutsert, Michele K. Evans, Brenda W.J.H. Penninx, Yih Chung Tham, Tin Louie, Nana Matoba, Lawrence F. Bielak, Andrea R. V. R. Horimoto, Jingmin Liu, Lynda M. Rose, Nona Sotoodehnia, Daniel I. Chasman, Jasmin Divers, John M. Starr, Patricia A. Peyser, Ervin R. Fox, Barry I. Freedman, Megan L. Grove, Jeffrey R. O'Connell, Rob M. van Dam, Michiaki Kubo, Jessica D. Faul, Fang-Chi Hsu, Yize Li, Ilja M. Nolte, Caroline Hayward, Paolo Gasparini, Chiea Chuen Khor, Changwei Li, Xu Chen, James E. Hixson, Patrik K. E. Magnusson, John C. Chambers, Yoichiro Kamatani, Sami Heikkinen, M. Abdullah Said, Martin Farrall, Alena Stančáková, Konstantin Strauch, Albert V. Smith, Yik Ying Teo, Bamidele O. Tayo, Tamar Sofer, Caizheng Yu, Maris Alver, Oscar H. Franco, Yongmei Liu, Ioanna Ntalla, Myriam Fornage, W. Craig Johnson, Dina Vojinovic, William R. Scott, David R. Weir, Yanick Hagemeijer, Raymond Noordam, Mathilde Boissel, Cornelia M. van Duijn, Karen Schwander, Jingzhong Ding, James Scott, Diana van Heemst, Ching-Yu Cheng, Woon-Puay Koh, Jianjun Liu, Kenneth J. Mukamal, Blair H. Smith, Claude Bouchard, Claudia Langenberg, Jingjing Liang, Wei Zheng, Najaf Amin, Ulf de Faire, Bing Yu, Archie Campbell, Kari E. North, Salman M. Tajuddin, Fernando Pires Hartwig, Steve B. Kritchevsky, Tibor V. Varga, Pirjo Komulainen, Hugh Watkins, Johanna Kuusisto, Kurt Lohman, Rozenn N. Lemaitre, Ani Manichaikul, Dabeeru C. Rao, Kenneth Rice, Weihua Zhang, Franco Giulianini, Reedik Mägi, Solomon K. Musani, Wanqing Wen, Nicholas J. Wareham, Charles N. Rotimi, Marzyeh Amini, Rainer Rauramaa, Nicholette D. Palmer, Annette Peters, Neil R Poulter, Benjamin Lehne, Matthias Nauck, Harold Snieder, Donald W. Bowden, M. Arfan Ikram, Lisa de las Fuentes, C. Charles Gu, Xiaofeng Zhu, Markku Laakso, Tuomas O. Kilpeläinen, Ching-Ti Liu, Michael Boehnke, Jin-Fang Chai, Lili Milani, L. Adrienne Cupples, Yuri Milaneschi, Kiang Liu, Stephen Sidney, Alan B. Zonderman, Thomas W. Winkler, Ian J. Deary, Fumihiko Takeuchi, Mary K. Wojczynski, Pim van der Harst, Leo-Pekka Lyytikäinen, Jennifer G. Robinson, Chuan Gao, Xiao-Ou Shu, Diane M. Becker, Mohsen Ghanbari, Alanna C. Morrison, Jian-Min Yuan, Tomohiro Katsuya, Ivana Kolcic, Melanie Waldenberger, Terho Lehtimäki, Heikki A. Koistinen, Erin B. Ware, Ya Xing Wang, Marie Loh, Treva Rice, Qing Duan, Aldi T. Kraja, Sabanayagam Charumathi, Tien Yin Wong, Paul S. de Vries, Yujie Wang, Muhammad Riaz, Home Office, Action on Hearing Loss, Imperial College Healthcare NHS Trust- BRC Funding, British Heart Foundation, Medical Research Council (MRC), Wellcome Trust, National Institute for Health Research, Luan, Jian'an [0000-0003-3137-6337], Pietzner, Maik [0000-0003-3437-9963], Zhao, Jing Hua [0000-0003-4930-3582], Langenberg, Claudia [0000-0002-5017-7344], Wareham, Nicholas [0000-0003-1422-2993], Apollo - University of Cambridge Repository, APH - Mental Health, Psychiatry, APH - Digital Health, The University of Texas Health Science Center at Houston (UTHealth), National Institutes of Health [Bethesda] (NIH), Washington University in Saint Louis (WUSTL), University of Regensburg, Queen Mary University of London (QMUL), Washington University School of Medicine in St. Louis, Harbor UCLA Medical Center [Torrance, Ca.], University of North Carolina at Chapel Hill (UNC), Singapore National Eye Centre and Duke-NUS [Singapore], Singapore Eye Research Institute [Singapore] (SERI), National University of Singapore (NUS), Erasmus University Medical Center [Rotterdam] (Erasmus MC), Département de Biologie Computationnelle - Department of Computational Biology, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), University Management, Department of Medicine, University of Helsinki, Endokrinologian yksikkö, HUS Abdominal Center, Lifelines Cohort, Groningen, The Netherlands (Lifelines Cohort Study), Epidemiology, Neurology, Radiology & Nuclear Medicine, Internal Medicine, Life Course Epidemiology (LCE), Cardiovascular Centre (CVC), de Vries, Paul S, Brown, Michael R, Bentley, Amy R, Sung, Yun J, Winkler, Thomas W, Ntalla, Ioanna, Schwander, Karen, Kraja, Aldi T, Guo, Xiuqing, Franceschini, Nora, Cheng, Ching-Yu, Sim, Xueling, Vojinovic, Dina, Huffman, Jennifer E, Musani, Solomon K, Li, Changwei, Feitosa, Mary F, Richard, Melissa A, Noordam, Raymond, Aschard, Hugue, Bartz, Traci M, Bielak, Lawrence F, Deng, Xuan, Dorajoo, Rajkumar, Lohman, Kurt K, Manning, Alisa K, Rankinen, Tuomo, Smith, Albert V, Tajuddin, Salman M, Evangelou, Evangelo, Graff, Mariaelisa, Alver, Mari, Boissel, Mathilde, Chai, Jin Fang, Chen, Xu, Divers, Jasmin, Gandin, Ilaria, Gao, Chuan, Goel, Anuj, Hagemeijer, Yanick, Harris, Sarah E, Hartwig, Fernando P, He, Meian, Horimoto, Andrea R V R, Hsu, Fang-Chi, Jackson, Anne U, Kasturiratne, Anuradhani, Komulainen, Pirjo, Kühnel, Brigitte, Laguzzi, Federica, Lee, Joseph H, Luan, Jian'An, Lyytikäinen, Leo-Pekka, Matoba, Nana, Nolte, Ilja M, Pietzner, Maik, Riaz, Muhammad, Said, M Abdullah, Scott, Robert A, Sofer, Tamar, Stancáková, Alena, Takeuchi, Fumihiko, Tayo, Bamidele O, van der Most, Peter J, Varga, Tibor V, Wang, Yajuan, Ware, Erin B, Wen, Wanqing, Yanek, Lisa R, Zhang, Weihua, Zhao, Jing Hua, Afaq, Saima, Amin, Najaf, Amini, Marzyeh, Arking, Dan E, Aung, Tin, Ballantyne, Christie, Boerwinkle, Eric, Broeckel, Ulrich, Campbell, Archie, Canouil, Mickaël, Charumathi, Sabanayagam, Chen, Yii-Der Ida, Connell, John M, de Faire, Ulf, de Las Fuentes, Lisa, de Mutsert, Renée, de Silva, H Janaka, Ding, Jingzhong, Dominiczak, Anna F, Duan, Qing, Eaton, Charles B, Eppinga, Ruben N, Faul, Jessica D, Fisher, Virginia, Forrester, Terrence, Franco, Oscar H, Friedlander, Yechiel, Ghanbari, Mohsen, Giulianini, Franco, Grabe, Hans J, Grove, Megan L, Gu, C Charle, Harris, Tamara B, Heikkinen, Sami, Heng, Chew-Kiat, Hirata, Makoto, Hixson, James E, Howard, Barbara V, Ikram, M Arfan, Jacobs, David R, Johnson, Craig, Jonas, Jost Bruno, Kammerer, Candace M, Katsuya, Tomohiro, Khor, Chiea Chuen, Kilpeläinen, Tuomas O, Koh, Woon-Puay, Koistinen, Heikki A, Kolcic, Ivana, Kooperberg, Charle, Krieger, Jose E, Kritchevsky, Steve B, Kubo, Michiaki, Kuusisto, Johanna, Lakka, Timo A, Langefeld, Carl D, Langenberg, Claudia, Launer, Lenore J, Lehne, Benjamin, Lemaitre, Rozenn N, Li, Yize, Liang, Jingjing, Liu, Jianjun, Liu, Kiang, Loh, Marie, Louie, Tin, Mägi, Reedik, Manichaikul, Ani W, Mckenzie, Colin A, Meitinger, Thoma, Metspalu, Andre, Milaneschi, Yuri, Milani, Lili, Mohlke, Karen L, Mosley, Thomas H, Mukamal, Kenneth J, Nalls, Mike A, Nauck, Matthia, Nelson, Christopher P, Sotoodehnia, Nona, O'Connell, Jeff R, Palmer, Nicholette D, Pazoki, Raha, Pedersen, Nancy L, Peters, Annette, Peyser, Patricia A, Polasek, Ozren, Poulter, Neil, Raffel, Leslie J, Raitakari, Olli T, Reiner, Alex P, Rice, Treva K, Rich, Stephen S, Robino, Antonietta, Robinson, Jennifer G, Rose, Lynda M, Rudan, Igor, Schmidt, Carsten O, Schreiner, Pamela J, Scott, William R, Sever, Peter, Shi, Yuan, Sidney, Stephen, Sims, Mario, Smith, Blair H, Smith, Jennifer A, Snieder, Harold, Starr, John M, Strauch, Konstantin, Tan, Nichola, Taylor, Kent D, Teo, Yik Ying, Tham, Yih Chung, Uitterlinden, André G, van Heemst, Diana, Vuckovic, Dragana, Waldenberger, Melanie, Wang, Lihua, Wang, Yujie, Wang, Zhe, Wei, Wen Bin, Williams, Christine, Wilson, Gregory, Wojczynski, Mary K, Yao, Jie, Yu, Bing, Yu, Caizheng, Yuan, Jian-Min, Zhao, Wei, Zonderman, Alan B, Becker, Diane M, Boehnke, Michael, Bowden, Donald W, Chambers, John C, Deary, Ian J, Esko, Tõnu, Farrall, Martin, Franks, Paul W, Freedman, Barry I, Froguel, Philippe, Gasparini, Paolo, Gieger, Christian, Horta, Bernardo L, Kamatani, Yoichiro, Kato, Norihiro, Kooner, Jaspal S, Laakso, Markku, Leander, Karin, Lehtimäki, Terho, Magnusson, Patrik K E, Penninx, Brenda, Pereira, Alexandre C, Rauramaa, Rainer, Samani, Nilesh J, Scott, Jame, Shu, Xiao-Ou, van der Harst, Pim, Wagenknecht, Lynne E, Wang, Ya Xing, Wareham, Nicholas J, Watkins, Hugh, Weir, David R, Wickremasinghe, Ananda R, Zheng, Wei, Elliott, Paul, North, Kari E, Bouchard, Claude, Evans, Michele K, Gudnason, Vilmundur, Liu, Ching-Ti, Liu, Yongmei, Psaty, Bruce M, Ridker, Paul M, van Dam, Rob M, Kardia, Sharon L R, Zhu, Xiaofeng, Rotimi, Charles N, Mook-Kanamori, Dennis O, Fornage, Myriam, Kelly, Tanika N, Fox, Ervin R, Hayward, Caroline, van Duijn, Cornelia M, Tai, E Shyong, Wong, Tien Yin, Liu, Jingmin, Rotter, Jerome I, Gauderman, W Jame, Province, Michael A, Munroe, Patricia B, Rice, Kenneth, Chasman, Daniel I, Cupples, L Adrienne, Rao, Dabeeru C, Morrison, Alanna C, and Academic Medical Center

Subjects
Male, Epidemiology, Genome-wide association study, Cardiovascular, MESH: Genotype, Substance Misuse, Alcohol Use and Health, 0302 clinical medicine, Aetiology, triglycerides, 11 Medical and Health Sciences, Public, Environmental & Occupational Health, [STAT.AP]Statistics [stat]/Applications [stat.AP], MESH: Middle Aged, MESH: Life Style, genome wide, gene-alcohol interaction, lipid levels, Lipid Measurement, Lifelines Cohort, DENSITY-LIPOPROTEIN CHOLESTEROL, MESH: Young Adult, 030220 oncology & carcinogenesis, Kexin, LOW-FREQUENCY, MESH: Cholesterol, HDL, MESH: Cholesterol, LDL, MESH: Triglycerides, HDL, Alcohol Drinking, Genotype, alcohol consumption, MESH: Phenotype, LDL, lipids, 03 medical and health sciences, Genetics, Humans, Life Style, METAANALYSIS, 01 Mathematical Sciences, Aged, MESH: Adolescent, HDL CHOLESTEROL, MESH: Humans, Science & Technology, Cholesterol, APOBEC1, Lifelines Cohort, Groningen, The Netherlands (Lifelines Cohort Study), MESH: Vascular Endothelial Growth Factor B, Racial Groups, cholesterol, The Netherlands, MESH: Adult, CONSUMPTION, chemistry, MESH: Genome-Wide Association Study, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Lipid profile, MESH: Female, MESH: Alcohol Drinking, MESH: Continental Population Groups, Vascular Endothelial Growth Factor B, Original Contributions, Blood lipids, VEGF-B, Medical and Health Sciences, Mathematical Sciences, BLOOD-LIPIDS, chemistry.chemical_compound, 2.1 Biological and endogenous factors, 030212 general & internal medicine, MESH: Aged, medicine.diagnostic_test, PLASMA, gene-environment interactions, Middle Aged, 3142 Public health care science, environmental and occupational health, Alcoholism, Phenotype, Female, gene-lifestyle interactions, genome-wide association studies, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Life Sciences & Biomedicine, Groningen, Adult, Adolescent, Biology, Young Adult, medicine, CORONARY-HEART-DISEASE, Human Genome, Cholesterol, HDL, Lipid metabolism, Cholesterol, LDL, InterAct Consortium, MESH: Lipids, MESH: Male, INDIVIDUALS, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Genome-Wide Association Study
Abstract

© The Author(s) 2019. A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-Alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2- degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10?6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10?8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models. National Heart, Lung, and Blood Institute; American Heart Association Grant

Published
2019
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79. Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data

Author

Pierrick, Wainschtein, Deepti, Jain, Zhili, Zheng, L Adrienne, Cupples, Aladdin H, Shadyab, Barbara, McKnight, Benjamin M, Shoemaker, Braxton D, Mitchell, Bruce M, Psaty, Charles, Kooperberg, Ching-Ti, Liu, Christine M, Albert, Dan, Roden, Daniel I, Chasman, Dawood, Darbar, Donald M, Lloyd-Jones, Donna K, Arnett, Elizabeth A, Regan, Eric, Boerwinkle, Jerome I, Rotter, Jeffrey R, O'Connell, Lisa R, Yanek, Mariza, de Andrade, Matthew A, Allison, Merry-Lynn N, McDonald, Mina K, Chung, Myriam, Fornage, Nathalie, Chami, Nicholas L, Smith, Patrick T, Ellinor, Ramachandran S, Vasan, Rasika A, Mathias, Ruth J F, Loos, Stephen S, Rich, Steven A, Lubitz, Susan R, Heckbert, Susan, Redline, Xiuqing, Guo, Y -D Ida, Chen, Cecelia A, Laurie, Ryan D, Hernandez, Stephen T, McGarvey, Michael E, Goddard, Cathy C, Laurie, Kari E, North, Leslie A, Lange, Bruce S, Weir, Loic, Yengo, Jian, Yang, and Michael, Zody

Subjects
Multifactorial Inheritance, Humans, Polymorphism, Single Nucleotide, Alleles, Linkage Disequilibrium, Genome-Wide Association Study
Abstract

Analyses of data from genome-wide association studies on unrelated individuals have shown that, for human traits and diseases, approximately one-third to two-thirds of heritability is captured by common SNPs. However, it is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular whether the causal variants are rare, or whether it is overestimated due to bias in inference from pedigree data. Here we estimated heritability for height and body mass index (BMI) from whole-genome sequence data on 25,465 unrelated individuals of European ancestry. The estimated heritability was 0.68 (standard error 0.10) for height and 0.30 (standard error 0.10) for body mass index. Low minor allele frequency variants in low linkage disequilibrium (LD) with neighboring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection. Our results imply that rare variants, in particular those in regions of low linkage disequilibrium, are a major source of the still missing heritability of complex traits and disease.

Published
2021

80. Genetic architecture of smoking: Evaluating rare variant contribution from deep whole-genome sequencing of up to 26,000 individuals

Author

Anne E. Justice, Ruth J. F. Loos, Robert C. Kaplan, Seon-Kyeong Jang, Matthew C. Keller, Daniel J. Rader, Nicholas L. Smith, Scott M. Damrauer, Joshua C. Bis, Dajiang J. Liu, Charles Kooperberg, Yingze Zhang, Elizabeth C. Oelsner, Kari E. North, Kerri L. Wiggins, David A. Schwartz, Renae Judy, Jeffrey Haessler, Olle Melander, Scott I. Vrieze, Kent D. Taylor, Daniel Levy, Victor Gordeuk, Kendra A. Young, Weihong Tang, Deborah A. Meyers, Mariza de Andrade, John E. Hokanson, Donna K. Arnett, Luke M. Evans, Jerome I. Rotter, Anna L. Peljto, Kristin L. Young, Dandi Qiao, Sean P. David, Allison Fialkowski, Susan R. Heckbert, Shih-Jen Hwang, Zhe Wang, Lisa W. Martin, Lisa R. Yanek, Wonji Kim, Matthew Hyman, Ramachandran S. Vasan, Mark T. Gladwin, Stephen S. Rich, Albert V. Smith, Mehdi Nouraie, Edwin K. Silverman, John Blangero, Eugene R. Bleecker, Tasha E. Fingerlin, Jennifer A. Brody, L. Adrienne Cupples, Ani Manichaikul, Diane M. Becker, Ivana V. Yang, Bruce M. Psaty, Aladdin Shadyab, David C. Glahn, Sina A. Gharib, Michael Preuss, Gustav Smith, Alexander P. Reiner, and Robert M. Reed

Subjects
Whole genome sequencing, Computational biology, Biology, Genetic architecture
Abstract

Background Across complex traits, common variants explain only a modest amount of variance, with SNP-heritability consistently below heritability estimates from close relatives. Here, we examined the contribution of rare variant to tobacco use risk in up to 26,000 individuals of European ancestry in the Trans-Omics for Precision Medicine (TOPMed) program with whole genome sequence (WGS;~30X coverage).Method We grouped about 35 million genetic variants by their minor allele frequencies (MAF) and linkage disequilibrium (LD) and estimated SNP-heritability for age of smoking initiation (N = 14,747), cigarettes smoked per day (N = 15,425), smoking cessation (N = 17,871) and initiation (N = 26,340) using linear mixed model. Rare variant population structure is detected and adjusted for by permutation procedure. We estimated an upper bound for narrow-sense heritability for tobacco use using available pedigrees consisting of close relatives in TOPMed.Results Rare variants with MAF 0.1–0.01%, mostly from non-protein altering region, accounted for 26% of variation in age of initiation and 15% for cessation. Follow-up analysis indicated that about one-third of these rare variants contribtion is potentially confounded with rare variants structure even after adjusting for principal components. After further conservative adjustment of population structure, we estimated SNP-based heritability to be 0.21 (SE = 0.08) for age of initiation, 0.15 (0.06) for cigarettes per day, 0.21 (0.09) for cessation, and 0.24 (0.07) for initiation, 1.8–4.5 times higher than previous SNP-based estimates. Our pedigree-based upper-bound for SNP-based heritability ranged from 0.18–0.35.Conclusion The substantial contribution of rare variants for several smoking phenotypes sheds light on the missing heritability and genetic etiology of tobacco use. It also informs fine-mapping strategies since the majority of the rare variant contribution was located in non-coding regulatory regions.

Published
2021
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81. Robust, flexible, and scalable tests for Hardy–Weinberg equilibrium across diverse ancestries

Author

Nicholas L. Smith, Alan M. Kwong, Gonçalo R. Abecasis, Jennifer A. Smith, Celeste Eng, Charles Kooperberg, John Barnard, L. Adrienne Cupples, Ani Manichaikul, Wonji Kim, Dan M. Roden, Nicholette D. Palmer, M. Benjamin Shoemaker, Eric Boerwinkle, Michael Boehnke, John Blangero, Marguerite R. Irvin, Hemant K. Tiwari, Albert V. Smith, Alexander P. Reiner, Daniel E. Weeks, Tanika N. Kelly, Mariza de Andrade, Steven A. Lubitz, Laura J. Scott, Kathleen C. Barnes, Esteban G. Burchard, Han Chen, Scott T. Weiss, May E. Montasser, Yan Gao, Angel C.Y. Mak, Brian E. Cade, Thomas W. Blackwell, Courtney G. Montgomery, Solomon K. Musani, Xiuqing Guo, Daniel I. Chasman, Gina M. Peloso, Jessica Su, Hyun Min Kang, Dandi Qiao, Matthew P. Conomos, Patrick T. Ellinor, Jonathon LeFaive, and Rasika A. Mathias

Subjects
Genotype, Population structure, Biology, Linkage Disequilibrium, Statistical power, 03 medical and health sciences, 0302 clinical medicine, Gene Frequency, Statistics, Genetics, False positive paradox, Humans, Alleles, 030304 developmental biology, Investigation, 0303 health sciences, Models, Statistical, Models, Genetic, Hardy–Weinberg principle, Exact test, Genetics, Population, Phenotype, Data quality, Metric (mathematics), Scalability, Software, 030217 neurology & neurosurgery
Abstract

Traditional Hardy–Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

Published
2021
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82. Bidirectional Mendelian randomization supports bidirectional causality between telomere length and clonal hematopoiesis of intermediate potential

Author

Dan M. Roden, John Blangero, Myriam Fornage, Kerri L. Wiggins, Benjamin L. Ebert, Gina M. Peloso, Tetsushi Nakao, John Lane, Russell P. Tracy, Lisa de las Fuentes, Ryan L. Minster, Donna K. Arnett, Seyedeh M. Zekavat, Laura M. Raffield, Akhil Pampana, Stephen S. Rich, Kathleen C. Barnes, R. Mathias, Alyna T. Khan, Lewis C. Becker, James E. Hixson, Gabriel K. Griffin, Nicholas L. Smith, JoAnn E. Manson, Robert C. Kaplan, Gonçalo R. Abecasis, Nathan Pankratz, Alexander P. Reiner, Donald M. Lloyd-Jones, Sharon L.R. Kardia, C. Charles Gu, Wendy Post, Lisa R. Yanek, Tanika N. Kelly, Hemant K. Tiwari, Jennifer A. Smith, Shoa L. Clarke, Ramachandran S. Vasan, Themistocles L. Assimes, Betty S. Pace, Jill M. Johnsen, Cara L. Carty, Pinkal Desai, Barry I. Freedman, Pradeep Natarajan, Margaret A. Taub, S Redline, Adrienne M. Stilp, Ranjan Deka, Alexander G. Bick, Donald W. Bowden, Mariza de Andrade, Abhishek Niroula, Joanne E. Curran, Quenna Wong, Siddhartha Jaiswal, Chii-Min Hwu, Michael Preuss, Christie M. Ballantyne, Shannon Kelly, Patrick T. Ellinor, Sameer Chavan, Dandi Qiao, Nicola L. Hawley, Charles Kooperberg, Juan M. Peralta, Braxton D. Mitchell, Solomon K. Musani, Jerome I. Rotter, Ruth J.F. Loos, Zachary T. Yoneda, Bruce M. Psaty, Christopher J. Gibson, Ron Do, Barbara A. Konkle, Marguerite R. Irvin, Jai G. Broome, Take Naseri, Alanna C. Morrison, L. Adrienne Cupples, Bertha A. Hildalgo, Jiang He, Mesbah Uddin, Dawood Darbar, Cecelia A. Laurie, Eric A. Whitsel, Patricia A. Peyser, Brian Custer, Michael H. Cho, Scott T. Weiss, Peter Libby, Susan R. Heckbert, Albert V. Smith, Joshua S. Weinstock, Meher Preethi Boorgula, M. Benjamin Shoemaker, Muagututi’a S. Reupena, Michael C. Honigberg, Nicholette D. Palmer, Wei Zhao, Paul S. Vries, Edwin K. Silverman, Daniel E. Weeks, Romit Bhattacharya, Joshua C. Bis, Kari E. North, Thomas W. Blackwell, Dawn L. DeMeo, Stephen T. McGarvey, Leslie S. Emery, A. R. Shuldiner, Yii-Der Ida Chen, Eric Boerwinkle, Adolfo Correa, Deborah A. Meyers, and Eimear E. Kenny

Subjects
Mendelian randomization, Locus (genetics), Telomerase reverse transcriptase, CAD, Computational biology, Biology, Causality, Germline, Telomere, Genetic association
Abstract

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in Trans-Omics for Precision Medicine (TOPMed) program (N=63,302) and UK Biobank (N=48,658). Bidirectional Mendelian randomization studies were consistent with LTL lengthening increasing propensity to develop CHIP, but CHIP then in turn hastening LTL shortening. We also demonstrated evidence of modest mediation between CHIP and CAD by LTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

Published
2021
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83. Revisiting methods for modeling longitudinal and survival data: Framingham Heart Study

Author

Julius S. Ngwa, Michael P. LaValley, David R. Gagnon, Debbie M. Cheng, L. Adrienne Cupples, and Howard Cabral

Subjects
Mixed model, Epidemiology, Computer science, Bayesian probability, Health Informatics, Residual, Framingham Heart Study, Bias, Time dependent covariate models, Statistics, Covariate, Humans, Computer Simulation, Longitudinal Studies, Survival analysis, lcsh:R5-920, Residual variance, Models, Statistical, Proportional hazards model, Bayes Theorem, Random effects model, Survival Analysis, Two-step approach, Cox model, Joint longitudinal and survival model, Weibull distribution, lcsh:Medicine (General), Research Article, Mixed effect modeling
Abstract

Background Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years. Conclusions Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Published
2021
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84. Whole Genome Sequence Association Analysis of Fasting Glucose and Fasting Insulin Levels in Diverse Cohorts from the NHLBI TOPMed Program

Author

Charles Kooperberg, Stephen T. McGarvey, Robert Sladek, Tanika N. Kelly, Juan M. Peralta, Ching-Ti Liu, Yii-Der Ida Chen, Alain G. Bertoni, Alanna C. Morrison, Ravindranath Duggirala, James A. Perry, Jose C. Florez, JoAnn E. Manson, James G. Wilson, Seonwook Lee, Rita R. Kalyani, Elizabeth Selvin, Emily M. Russell, Lawrence F. Bielak, Timothy D. Majarian, Patricia A. Peyser, Daniel DiCorpo, Brian E. Cade, Josée Dupuis, L. Adrienne Cupples, Kent D. Taylor, Eric Boerwinkle, Heming Wang, Xihong Lin, Laura M. Raffield, Sharon L.R. Kardia, Alvaro Alonso, Peitao Wu, Jerome I. Rotter, Douglas Loesch, Ricardo D’Oliveira Albanus, Samantha Lent, Jennifer A. Smith, Abigail S. Baldridge, Wei Zhao, Ramachandran S. Vasan, Bertha Hidalgo, Mark O. Goodarzi, Daniel E. Weeks, Arushi Varshney, Anne U. Jackson, Marguerite R. Irvin, Xiuqing Guo, Leslie A. Lange, Marcio Almeida, Kenneth Westerman, Jeffrey R. O'Connell, Braxton D. Mitchell, Alisa K. Manning, Susan R. Heckbert, Jiang He, Jie Yao, Donna K. Arnett, Stephen S. Rich, Paul S. de Vries, Heather M. Highland, Huichun Xu, Joanne E. Curran, Nicholette D. Palmer, Bruce M. Psaty, James S. Floyd, Won Jung Choi, Adolfo Correa, John Blangero, James B. Meigs, May E. Montasser, Ryan W. Kim, Jennifer A. Brody, Deepti Jain, Stephen C. J. Parker, Simin Liu, Laura J. Rasmussen-Torvik, Sheila M. Gaynor, Susan Redline, Alexander P. Reiner, Chloé Sarnowski, Alan R. Shuldiner, James S. Pankow, Jennifer Wessel, Lisa R. Yanek, Stella Aslibekyan, Karine A. Viaud-Martinez, Natalie R Hasbani, Rasika A. Mathias, and Chung-Shiuan Chen

Subjects
Genetics, Fasting glucose, Minor allele frequency, Whole genome sequencing, Biology, Exome, Gene, Genome, Fasting insulin, Genetic association
Abstract

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome and exome arrays, resulting in over 100 associated variants. We extended this work with a high-coverage whole genome sequencing (WGS) analysis from fifteen cohorts in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. More than 23,000 non-diabetic individuals from five self-reported race/ethnicities (African, Asian, European, Hispanic and Samoan) were included for each trait. We analyzed 60M variants in race/ethnicity-specific and pooled single variant and rare variant aggregate tests. Twenty-two variants across sixteen gene regions were found significantly associated with FG or FI, eight of which were rare (Minor Allele Frequency, MAFG6PC2locus we identified a distinct FG signal at rare variant rs2232326 (MAF=0.01) after conditioning on known common variants. Functional annotations show rs2232326 to be disruptive and likely damaging while being weakly transcribed in islets. A pair of FG-associated variants were identified near theSLC30A8locus. These variants, one of which was rare (MAF=0.001) and Asian race/ethnicity-specific, were shown to be in islet-specific active enhancer regions. Other associated regions include rare variants nearROBO1andPTPRT, and common variants nearMTNR1B, GCK, GCKR, FOXA2, APOB, TCF7L2, andADCY5. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions down to a minor allele count of 20, creating a foundation for future sequencing-based investigation of glycemic traits.

Published
2021
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85. Identification of novel and rare variants associated with handgrip strength using whole genome sequence data from the NHLBI Trans-Omics in Precision Medicine (TOPMed) Program

Author

Han Chen, Braxton D. Mitchell, Charles Kooperberg, TOPMed Longevity, Kenneth Rice, Kathleen A. Ryan, David W. Fardo, Hyun Min Kang, Alanna C. Morrison, Arch G. Mainous, Jeffrey R. O'Connell, Albert V. Smith, Stephanie M. Gogarten, Ramachandran S. Vasan, Jan Bressler, Donna K. Arnett, Sylvia Wassertheil-Smoller, Joanne M. Murabito, Deepti Jain, B. Gwen Windham, Kathryn L. Lunetta, Benjamin D. Heavner, L. Adrienne Cupples, Mary L. Biggs, David Karasik, Bruce M. Psaty, Marguerite R. Irvin, Chloé Sarnowski, and Douglas P. Kiel

Subjects
0301 basic medicine, Male, Muscle Physiology, Muscle Functions, Physiology, Genome-wide association study, Cohort Studies, 0302 clinical medicine, Cell Signaling, Biomechanics, Precision Medicine, Aged, 80 and over, Multidisciplinary, Hand Strength, Genomics, Middle Aged, Biobank, Medicine, Female, Genomic Signal Processing, Research Article, Signal Transduction, Adult, Science, Computational biology, Biology, Polymorphism, Single Nucleotide, 03 medical and health sciences, Hand strength, Genetic variation, Genome-Wide Association Studies, Genetics, Humans, Genetic association, Aged, Whole genome sequencing, Whole Genome Sequencing, Racial Groups, Biology and Life Sciences, Computational Biology, Human Genetics, Cell Biology, Omics, Genome Analysis, 030104 developmental biology, Genetic Loci, human activities, 030217 neurology & neurosurgery
Abstract

Handgrip strength is a widely used measure of muscle strength and a predictor of a range of morbidities including cardiovascular diseases and all-cause mortality. Previous genome-wide association studies of handgrip strength have focused on common variants primarily in persons of European descent. We aimed to identify rare and ancestry-specific genetic variants associated with handgrip strength by conducting whole-genome sequence association analyses using 13,552 participants from six studies representing diverse population groups from the Trans-Omics in Precision Medicine (TOPMed) Program. By leveraging multiple handgrip strength measures performed in study participants over time, we increased our effective sample size by 7–12%. Single-variant analyses identified ten handgrip strength loci among African-Americans: four rare variants, five low-frequency variants, and one common variant. One significant and four suggestive genes were identified associated with handgrip strength when aggregating rare and functional variants; all associations were ancestry-specific. We additionally leveraged the different ancestries available in the UK Biobank to further explore the ancestry-specific association signals from the single-variant association analyses. In conclusion, our study identified 11 new loci associated with handgrip strength with rare and/or ancestry-specific genetic variations, highlighting the added value of whole-genome sequencing in diverse samples. Several of the associations identified using single-variant or aggregate analyses lie in genes with a function relevant to the brain or muscle or were reported to be associated with muscle or age-related traits. Further studies in samples with sequence data and diverse ancestries are needed to confirm these findings.

Published
2021

86. A system for phenotype harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) program

Author

Tanika N. Kelly, May E Montasser, Alyna T. Khan, Laura M. Raffield, Carla Wilson, Elizabeth C. Oelsner, Kerri L. Wiggins, Ming-Huei Chen, Gina M. Peloso, Adolfo Correa, Andrew D. Johnson, Donna K. Arnett, Xiuqing Guo, Jai G. Broome, Daniel E. Weeks, Rebecca D. Jackson, Lucia Juarez, Stephen T. McGarvey, Pradeep Natarajan, Braxton D. Mitchell, Kent D. Taylor, Bruce M. Psaty, Santhi K Ganesh, Cathy C. Laurie, Nicola L. Hawley, Leslie S. Emery, Adrienne M. Stilp, Alanna C. Morrison, Jennifer A Smith, Charles Kooperberg, Catherine M. D’Augustine, Jan Graffelman, Paul S. de Vries, Chancellor Hohensee, Sharon L R Kardia, Patricia A Peyser, Wan-Ling Hsu, Erin J Buth, Kathleen C. Barnes, Susan R. Heckbert, Ramachandran S. Vasan, Nathan Pankratz, Karen M. Mutalik, Quenna Wong, Brian E. Cade, Jingmin Liu, Joshua C. Bis, Cecelia A. Laurie, Kari E. North, Fei Fei Wang, Mariza de Andrade, Nancy L. Heard-Costa, William Craig Johnson, L. Adrienne Cupples, Scott T. Weiss, Seyed Mehdi Nouraie, Patrick T. Ellinor, Jerome I. Rotter, Weiniu Gan, Shannon Kelly, Stephen S. Rich, Cashell E. Jaquish, Dongquan Chen, Nora Franceschini, Lisa R. Yanek, Jiwon Lee, Alexander P. Reiner, Megan L. Grove, Stella Aslibekyan, Myriam Fornage, Lawrence F Bielak, Rasika A. Mathias, Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, and Universitat Politècnica de Catalunya. COSDA-UPC - COmpositional and Spatial Data Analysis

Subjects
0301 basic medicine, Program evaluation, Computer science, Epidemiology, common data elements, hematologic disease, Matemàtiques i estadística::Matemàtica aplicada a les ciències [Àrees temàtiques de la UPC], Medical and Health Sciences, Mathematical Sciences, 0302 clinical medicine, Documentation, cardiovascular disease, and Blood Institute (U.S.), 030212 general & internal medicine, Phenomics, Precision Medicine, Lung, lung diseases, Sleep-wake disorders, phenotypes, 92 Biology and other natural sciences::92B Mathematical biology in general [Classificació AMS], Common data elements, Cardiovascular disease, Phenotype, Phenotypes, Biomatemàtica, Information Dissemination, Harmonization, 62 Statistics::62D05 Sampling theory, sample surveys [Classificació AMS], Hematologic disease, 03 medical and health sciences, Data Aggregation, Clinical Research, Controlled vocabulary, Genetics, Humans, AcademicSubjects/MED00860, sleep-wake disorders, Sampling (Statistics), Genetic Association Studies, Lung diseases, Biomathematics, Data collection, Study Design, Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària [Àrees temàtiques de la UPC], Information dissemination, Human Genome, National Heart, Precision medicine, Data science, United States, 030104 developmental biology, Good Health and Well Being, National Heart, Lung, and Blood Institute (U.S.), Mostreig (Estadística), Program Evaluation
Abstract

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute’s Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948–2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.

Published
2021

87. Revisiting Methods For Modeling Longitudinal and Survival Data: The Framingham Heart Study

Author

Julius S Ngwa, Howard J Cabral, Debbie M Cheng, David R Gagnon, Michael P LaValley, and L. Adrienne Cupples

Abstract

Background: Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods: In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results: Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.Conclusions: Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Published
2020
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88. Rare coding variants in 35 genes associate with circulating lipid levels – a multi-ancestry analysis of 170,000 exomes

Author

Marju Orho-Melander, Humberto García-Ortiz, Xueling Sim, Amp-T D-Genes, Cheol Joo Park, Gina M. Peloso, Jason Flannick, Brian Tomlinson, Hyun Min Kang, Emilio J. Cordova, Stephen S. Rich, Richard A. Gibbs, Angélica Martínez-Hernández, Lorena Orozco, Harsha Doddapaneni, Lisa R. Yanek, Jiwon Lee, Namrata Gupta, Valeriya Lyssenko, Sohee Han, James B. Meigs, Bong-Jo Kim, Bruce M. Psaty, Leslie S. Emery, Kerrin S. Small, Pradeep Natarajan, May E. Montasser, Christian Gieger, Sharon L.R. Kardia, Sarah C. Nelson, Craig L. Hanis, Heikki A. Koistinen, María Elena González-Villalpando, Edmund Chan, Michael Y. Tsai, Benjamin Glaser, Thomas Meitinger, Matthew J. Bown, Mariaelisa Graff, John Danesh, Sekar Kathiresan, Tiinamaija Tuomi, Ramachandran S. Vasan, Gil Atzmon, Alyna T. Khan, Diego Ardissino, Yii-Der Ida Chen, David Zhang, Rob M. van Dam, Wendy S. Post, Barry I. Freedman, D. C. Rao, Michael Preuss, Donna M. Lehman, L. Adrienne Cupples, Colin N. A. Palmer, Claudia H. T. Tam, Hortensia Moreno-Macías, Markku Laakso, Peter Dornbos, Teresa Tusié-Luna, Stella Aslibekyan, Marguerite R. Irvin, Daniel J. Rader, Jee-Young Moon, Eimear E. Kenny, Lisa W. Martin, Jennifer A. Brody, Amit Khera, Erwin P. Bottinger, Sarah E. Graham, Myriam Fornage, Ruth McPherson, Nancy L. Heard-Costa, Michael Boehnke, Clicerio Gonzalez, Ryan W. Kim, Yi-Cheng Chang, Peter M. Nilsson, Yik Ying Teo, Robert Sladek, Cristen J. Willer, Fei Fei Wang, Donna K. Arnett, Mark Chaffin, Karine A. Viaud Martinez, Alanna C. Morrison, Leslie A. Lange, Ravindranath Duggirala, Donna M. Muzny, Kent D. Taylor, Niels Grarup, Soren Germer, Patricia A. Peyser, Brian E. Cade, Lewis C. Becker, Steven A. Lubitz, Nicholette D. Palmer, Susan K. Dutcher, Ronald C.W. Ma, Xuenan Mi, Xiuqing Guo, Hugh Watkins, Eric Boerwinkle, Qibin Qi, Johanna Kuusisto, Christie M. Ballantyne, Tanika N. Kelly, Rajiv Chowdhury, Elvia Mendoza-Caamal, Wing-Yee So, Tien Yin Wong, Torben Hansen, Cecilia Contreras-Cubas, Jeong-Sun Seo, Mi Yeong Hwang, Daekwan Seo, Dajiang J. Liu, Cristina Revilla-Monsalve, Paul S. de Vries, Daniel R. Witte, Yi-Jen Hung, Olle Melander, Karen L. Mohlke, Lucinda Antonacci-Fulton, Francisco Barajas-Olmos, Soo Heon Kwak, Daniel E. Weeks, Claudia Schurmann, Ginger A. Metcalf, Young-Jin Kim, Adrienne M. Stilp, Lori L. Bonnycastle, John Blangero, Ralph A. DeFronzo, Donald W. Bowden, Rasika A. Mathias, Oluf Pedersen, Rozenn N. Lemaitre, Stephen T. McGarvey, Heribert Schunkert, Jaakko Tuomilehto, Farook Thameem, Valentin Fuster, Joshua C. Bis, George Hindy, Allan Linneberg, James G. Wilson, Kyong Soo Park, Sergio A. Islas-Andrade, Ching-Yu Cheng, Won Jung Choi, Minxian X. Wang, Xuzhi Wang, Adolfo Correa, Jai G. Broome, Gail P. Jarvik, Alexander P. Reiner, E. Shyong Tai, Juyoung Lee, Mark I. McCarthy, Nilesh J. Samani, Susan Redline, Carlos A. Aguilar-Salinas, Jerome I. Rotter, Ma. Eugenia Garay-Sevilla, Jiang He, Patrick T. Ellinor, Joseph Park, Joanne E. Curran, Nir Barzilai, Federico Centeno-Cruz, Seonwook Lee, Lawrence F. Bielak, Jianjun Liu, Charles Kooperberg, Juan M. Peralta, Jose C. Florez, Leif Groop, Noël P. Burtt, Margaret Sunitha Selvaraj, Jeffrey R. O'Connell, and Ruth J. F. Loos

Subjects
Genetics, 0303 health sciences, education.field_of_study, Population, Single-nucleotide polymorphism, Genome-wide association study, Biology, DNA sequencing, Minor allele frequency, 03 medical and health sciences, 0302 clinical medicine, SNP, education, Gene, 030217 neurology & neurosurgery, Exome sequencing, 030304 developmental biology
Abstract

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels. Ten of these: ALB, SRSF2, JAK2, CREB3L3, TMEM136, VARS, NR1H3, PLA2G12A, PPARG and STAB1 have not been implicated for lipid levels using rare coding variation in population-based samples. We prioritize 32 genes identified in array-based genome-wide association study (GWAS) loci based on gene-based associations, of which three: EVI5, SH2B3, and PLIN1, had no prior evidence of rare coding variant associations. Most of the associated genes showed evidence of association in multiple ancestries. Also, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes, and for genes closest to GWAS index single nucleotide polymorphisms (SNP). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

Published
2020
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89. Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease

Author

Whitney E. Hornsby, Patricia A. Peyser, Albert V. Smith, Anita Pandit, Allison E. Ashley-Koch, Brooke N. Wolford, Morten S. Olesen, Michael Boehnke, Steven A. Lubitz, Lars G. Fritsche, Charles Kooperberg, Wayne Huey-Herng Sheu, Rasika A. Mathias, Sarah E. Graham, Ketian Yu, Yii Der Chen, Scott T. Weiss, Juan M. Peralta, David Schlessinger, Austen Grooms, Seyed Mehdi Nouraie, Xingnan Li, Ying Zhao, Russell P. Tracy, Chad M. Brummett, Sachin Kheterpal, Kent D. Taylor, Jill M. Johnsen, Jerome I. Rotter, Kristian Hveem, Pia R. Lundegaard, Vivek Rai, Victor R. Gordeuk, Anne Heidi Skogholt, Stella Aslibekyan, Alexander P. Reiner, Sebastian Schoenherr, Adolfo Correa, Matthew Zawistowski, Lewis C. Becker, Lukas Forer, Michelle Daya, Jonathon LeFaive, Mari Løset, Ben Michael Brumpton, Bertha Hidalgo, Marguerite R. Irvin, Seunggeun Lee, Ren-Hua Chung, Maiken Elvestad Gabrielsen, Francesco Cucca, William Overton, Gregory J.M. Zajac, Luis Villacorta, Stephen S. Rich, Sayantan Das, Karen Schwander, Yingze Zhang, Thomas W. Blackwell, Ida Surakka, Pradeep Natarajan, Solomon K. Musani, Tanmoy Roychowdhury, Kathleen C. Barnes, Donna K. Arnett, Jonas B. Nielsen, Vivien A. Sheehan, Akua Acheampong, Oddgeir L. Holmen, He Zhang, Carlo Sidore, Lisa R. Yanek, Oren Rom, Jessica Lasky-Su, Nicholette D. Palmer, Seung Hoan Choi, Amanda Schaefer, Yuhao Liu, Sarah A Gagliano Taliun, John Blangero, Daniel Taliun, Y. Eugene Chen, Deborah A. Meyers, L. Adrienne Cupples, Courtney G. Montgomery, Sekar Kathiresan, James G. Wilson, Marilyn J. Telen, Ramachandran S. Vasan, Jin Chen, Nicholas Rafaels, Bjørn Olav Åsvold, Lawrence F. Bielak, Hyun Min Kang, Christian Fuchsberger, Barbara A. Konkle, Sebastian Zöllner, Wei Zhou, Rebecca D. Jackson, Patrick T. Ellinor, Gonçalo R. Abecasis, Jifeng Zhang, Eugene R. Bleecker, Xiuqing Guo, Jennifer A. Smith, Cristen J. Willer, and Joanne E. Curran

Subjects
0301 basic medicine, LOCI, EXOME, General Physics and Astronomy, Genome-wide association study, Disease, 030204 cardiovascular system & hematology, Bioinformatics, Genome-wide association studies, chemistry.chemical_compound, THALASSEMIA, 0302 clinical medicine, Loss of Function Mutation, WIDE ASSOCIATION, Medicine, Molecular Targeted Therapy, Phenomics, Biological Specimen Banks, Multidisciplinary, Fatty liver, Lipids, Cardiovascular diseases, Liver, Gene Targeting, Science, METABOLISM, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Humans, Gene Silencing, Adverse effect, Genome, Human, business.industry, Cholesterol, PATHWAYS, General Chemistry, medicine.disease, United Kingdom, Computational biology and bioinformatics, MODEL, 030104 developmental biology, Receptors, LDL, chemistry, LDL receptor, Liver function, business, Dyslipidemia, GENERATION, Genome-Wide Association Study
Abstract

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10−8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD., Drugs targeting cardiovascular disease (CVD) can have negative consequences for liver function. Here, the authors combine genome wide analyses on 69,479 individuals to identify loss-of-function variants with beneficial effects on CVD-related traits without negative impacts on liver function.

Published
2020
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90. Presence and transmission of mitochondrial heteroplasmic mutations in human populations of European and African ancestry

Author

Jun Ding, Xianbang Sun, Brian E. Cade, Gonçalo R. Abecasis, Leslie A. Lange, Achilleas N. Pitsillides, Jessica L. Fetterman, Susan Redline, Laura M. Raffield, L. Adrienne Cupples, Heming Wang, Daniel Levy, Chunyu Liu, Thomas W. Blackwell, James G. Wilson, Ramachandran S. Vasan, Adolfo Correa, Yong Qian, and Pramod Anugu

Subjects
Nonsynonymous substitution, Genetics, Mitochondrial DNA, Whole Genome Sequencing, Concordance, Black People, Cell Biology, Biology, Heteroplasmy, DNA, Mitochondrial, White People, Article, Mitochondria, Germline mutation, Mutation, Molecular Medicine, Coding region, Humans, Allele, Molecular Biology, Genome-Wide Association Study
Abstract

We investigated the concordance of mitochondrial DNA heteroplasmic mutations (heteroplasmies) in 6,745 maternal pairs of European (EA, n=4,718 pairs) and African (AA, n=2,027 pairs) Americans in whole blood. Mother-offspring pairs displayed the highest concordance rate, followed by sibling-sibling and more distantly-related maternal pairs. The allele fractions of concordant heteroplasmies exhibited high correlation (R(2)=0.8) between paired individuals. Discordant heteroplasmies were more likely to be in coding regions, be nonsynonymous or nonsynonymous-deleterious (p

Published
2020

91. GENERATING SURVIVAL TIMES WITH TIME-VARYING COVARIATES USING THE LAMBERT W FUNCTION

Author

Julius S. Ngwa, David R. Gagnon, Michael P. LaValley, Debbie M. Cheng, L. Adrienne Cupples, and Howard Cabral

Subjects
Statistics and Probability, Hazard (logic), Time-varying covariate, 021103 operations research, Proportional hazards model, 0211 other engineering and technologies, Statistical model, 02 engineering and technology, 01 natural sciences, Article, 010104 statistics & probability, symbols.namesake, Modeling and Simulation, Lambert W function, Statistics, Covariate, symbols, Statistics::Methodology, 0101 mathematics, Mathematics, Weibull distribution, Event (probability theory)
Abstract

Simulation studies provide an important statistical tool in evaluating survival methods, requiring an appropriate data-generating process to simulate data for an underlying statistical model. Many studies with time-to-event outcomes use the Cox proportional hazard model. While methods for simulating such data with time-invariant predictors have been described, methods for simulating data with time-varying covariates are sorely needed. Here, we describe an approach for generating data for the Cox proportional hazard model with time-varying covariates when event times follow an Exponential or Weibull distribution. For each distribution, we derive a closed-form expression to generate survival times and link the time-varying covariates with the hazard function. We consider a continuous time-varying covariate measured at regular intervals over time, as well as time-invariant covariates, in generating time-to-event data under a number of scenarios. Our results suggest this method can lead to simulation studies with reliable and robust estimation of the association parameter in Cox-Weibull and Cox-Exponential models.

Published
2020

92. Abstract 13902: Gender Differences in Lipoprotein Parameters in Cardiovascular Disease Risk; The Framingham Offspring Study

Author

Ernst J. Schaefer, Bela F. Asztalos, Masayuki Murata, Ching-Ti Liu, Hiroaki Ikezaki, L. Adrienne Cupples, Elise Lim, and Norihiro Furusyo

Subjects
Framingham Risk Score, Offspring, business.industry, Physiology (medical), Disease risk, Physiology, Medicine, lipids (amino acids, peptides, and proteins), Cardiology and Cardiovascular Medicine, business, Lipoprotein
Abstract

Introduction: Elevated serum lipoprotein cholesterol levels have been considered as atherosclerosis cardiovascular disease (ASCVD) risk factors. However, gender differences among these lipoproteins have not become clear. Hypothesis: Our hypothesis was that gender differences exist with regard to serum lipoprotein cholesterol levels and the risk of ASCVD risk in the prospective Framingham Offspring Study. Methods: A total of 1300 men and 1676 women, none of whom had ASCVD at baseline, were followed for a median time of 16 years for new onset fatal or non fatal myocardial infarction or stroke, coronary angioplasty or bypass, or carotid or peripheral vascular surgery (302 of 1300 or 23.2% of men and 226 of 1676 or 13.5% of women developed). At baseline, standard risk factors including fasting total and high density lipoprotein (HDL) cholesterol, and triglycerides were assessed, as were direct, small dense, and large low density lipoprotein (LDL) cholesterol and LDL triglyceride (LDL-TG), remnant lipoprotein cholesterol and lipoprotein(a) were measured. Univariate and multivariate analyses were carried out to assess relationships to disease risk. Results: All parameters were associated with ASCVD risk on univariate analysis. On multivariate analysis after controlling for all standard risk factors (age, hypertension, use of anti-hypertensive medication, diabetes, smoking, total cholesterol, HDL cholesterol, and use of cholesterol lowering medication), only lipoprotein(a) was significant in men with a 1.26 hazard ratio (P = 0.009), while small dense LDL cholesterol and LDL-TG, but not lipoprotein(a), were significant risk factors in women (hazard ratios 1.65 and 1.35, P = 0.007 and 0.015, respectively). Using the pooled cohort equation, only lipoprotein(a) added significant risk information in men and only small dense LDL cholesterol and LDL-TG added significant risk information in women. Conclusions: Our data indicate that men are at significantly higher disease risk then women, and that elevated lipoprotein(a) is a significant independent ASCVD risk factor in men, while small dense LDL cholesterol and LDL-TG are significant independent ASCVD risk factors in women after controlling for all other standard cardiovascular disease risk factors.

Published
2020
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93. Rare Non-coding Variation Identified by Large Scale Whole Genome Sequencing Reveals Unexplained Heritability of Type 2 Diabetes

Author

Heming Wang, Lu-Chen Weng, Ryan W. Kim, May E. Montasser, Stephen T. McGarvey, Anubha Mahajan, Robert Sladek, Marcio Almeida, Dan M. Roden, Deepti Jain, Barry I. Freedman, Jeffrey R. O'Connell, Donna K. Arnett, Alanna C. Morrison, Susan R. Heckbert, Nicholette D. Palmer, Jie Yao, Jorge Ferrer, Timothy D. Majarian, Wei Zhao, JoAnn E. Manson, Mark I. McCarthy, Sharon L.R. Kardia, James A. Perry, Nicholas L. Smith, Alain G. Bertoni, James G. Wilson, Mark O. Goodarzi, Leslie A. Lange, Donald W. Bowden, L. Adrienne Cupples, Laura J. Rasmussen-Torvik, Yii-Der Ida Chen, Jennifer A. Smith, James S. Floyd, Sílvia Bonàs-Guarch, Zachary T. Yoneda, Rita R. Kalyani, Won Jung Choi, Ramachandran S. Vasan, Eric Boerwinkle, Ching-Ti Liu, Stephen C. J. Parker, Susan Redline, Paul S. de Vries, Huichun Xu, Daniel DiCorpo, Adolfo Correa, James S. Pankow, Stephen S. Rich, Heather M. Highland, Ravindranath Duggirala, Elizabeth Selvin, Kent D. Taylor, Dawood Darbar, Tanika N. Kelly, Bruce M. Psaty, Simin Liu, Xianyong Yin, Michael H. Cho, Abigail S. Baldridge, Alexander P. Reiner, Patricia A. Peyser, Seung Hoan Choi, Brian E. Cade, Chloé Sarnowski, Aladdin H. Shadyab, Gregory L Kinney, Daniel E. Weeks, Braxton D. Mitchell, Alisa K. Manning, Douglas Loesch, Steven A. Lubitz, Josée Dupuis, Ryan Irvin, Samantha Lent, Sridharan Raghavan, Bertha Hidalgo, Arushi Varshney, Ricardo D’Oliveira Albanus, Xiuqing Guo, Jennifer Wessel, Rasika A. Mathias, Jennifer A. Brody, Aaron Leong, John Blangero, James B. Meigs, Chung-Shiuan Chen, Lawrence F. Bielak, Lisa R. Yanek, Stella Aslibekyan, Rami Nassir, Karine A. Viaud-Martinez, Natalie R Hasbani, Irene Miguel-Escalada, Alvaro Alonso, Charles Kooperberg, Juan M. Peralta, Jose C. Florez, M. Benjamin Shoemaker, Seonwook Lee, Peitao Wu, Jerome I. Rotter, Jiang He, Patrick T. Ellinor, Mindy D. Szeto, and Joanne E. Curran

Subjects
Whole genome sequencing, Genetics, Minor allele frequency, medicine, Type 2 diabetes, Biology, Heritability, medicine.disease, Scale (music)
Abstract

Type 2 diabetes is increasing in all ancestry groups1. Part of its genetic basis may reside among the rare (minor allele frequency 2. We analyzed high-coverage (mean depth 38.2x) whole genome sequencing from 9,639 individuals with T2D and 34,994 controls in the NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program2 to show that rare, non-coding variants that are poorly captured by genotyping arrays or imputation panels contribute h2=53% (P=4.2×10−5) to the genetic component of risk in the largest (European) ancestry subset. We coupled sequence variation with islet epigenomic signatures3 to annotate and group rare variants with respect to gene expression4, chromatin state5 and three-dimensional chromatin architecture6, and show that pancreatic islet regulatory elements contribute to T2D genetic risk (h2=8%, P=2.4×10−3). We used islet annotation to create a non-coding framework for rare variant aggregation testing. This approach identified five loci containing rare alleles in islet regulatory elements that suggest novel biological mechanisms readily linked to hypotheses about variant-to-function. Large scale whole genome sequence analysis reveals the substantial contribution of rare, non-coding variation to the genetic architecture of T2D and highlights the value of tissue-specific regulatory annotation for variant-to-function discovery.

Published
2020
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94. Presence and Transmission of Mitochondrial Heteroplasmic Mutations in Human Populations of European and African Ancestry

Author

Brian E. Cade, Adolfo Correa, Leslie A. Lange, Thomas W. Blackwell, Chunyu Liu, Jessica L. Fetterman, James F. Wilson, Yong Qian, Kaiyu Yan, Pramod Anugu, Achilleas N. Pitsillides, Gonçalo R. Abecasis, Daniel Levy, Xianbang Sun, Jun Ding, Laura M. Raffield, Heming Wang, Ramachandran S. Vasan, Susan Redline, and L. Adrienne Cupples

Subjects
Genetics, Nonsynonymous substitution, Mitochondrial DNA, Nuclear gene, Coding region, Single-nucleotide polymorphism, Allele, Biology, Genome, Heteroplasmy
Abstract

We investigated the concordance of mitochondrial DNA heteroplasmic mutations (heteroplasmies) in different types of maternal pairs (n=6,745 pairs) of European (EA, n=4,718 pairs) and African (AA, n=2,027 pairs) Americans with whole genome sequences (WGSs). The average concordance rate of heteroplasmies was highest between mother-offspring pairs, followed by sibling-sibling pairs and more distantly related maternal pairs in both EA and AA participants. The allele fractions of concordant heteroplasmies exhibited high correlation (R2=0.8) between paired individuals. Compared to concordant heteroplasmies, discordant ones were more likely to locate in coding regions, be nonsynonymous or nonsynonymous-deleterious (p0.05) at 11p11.12. Many of the top SNPs act as strong long-range cis regulators of protein tyrosine phosphatase receptor type J. This study provides further evidence that mtDNA heteroplasmies may be inherited or somatic. Somatic heteroplasmic variants increase with advancing age and are more likely to have an adverse impact on mitochondrial function. Further studies are warranted for functional characterization of the deleterious heteroplasmies occurring with advancing age and the association of the 11p11.12 region of the nuclear genome with mtDNA heteroplasmy.

Published
2020
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95. Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices

Author

Pradeep Natarajan, Akhil Pampana, Sarah E. Graham, Sanni E. Ruotsalainen, James A. Perry, Paul S. de Vries, Jai G. Broome, James P. Pirruccello, Michael C. Honigberg, Krishna Aragam, Brooke Wolford, Jennifer A. Brody, Lucinda Antonacci-Fulton, Moscati Arden, Stella Aslibekyan, Themistocles L. Assimes, Christie M. Ballantyne, Lawrence F. Bielak, Joshua C. Bis, Brian E. Cade, Ron Do, Harsha Doddapaneni, Leslie S. Emery, Yi-Jen Hung, Marguerite R. Irvin, Alyna T. Khan, Leslie Lange, Jiwon Lee, Rozenn N. Lemaitre, Lisa W. Martin, Ginger Metcalf, May E. Montasser, Jee-Young Moon, Donna Muzny, Jeffrey R. O’Connell, Nicholette D. Palmer, Juan M. Peralta, Patricia A. Peyser, Adrienne M. Stilp, Michael Tsai, Fei Fei Wang, Daniel E. Weeks, Lisa R. Yanek, James G. Wilson, Goncalo Abecasis, Donna K. Arnett, Lewis C. Becker, John Blangero, Eric Boerwinkle, Donald W. Bowden, Yi-Cheng Chang, Yii-Der I. Chen, Won Jung Choi, Adolfo Correa, Joanne E. Curran, Mark J. Daly, Susan K. Dutcher, Patrick T. Ellinor, Myriam Fornage, Barry I. Freedman, Stacey Gabriel, Soren Germer, Richard A. Gibbs, Jiang He, Kristian Hveem, Gail P. Jarvik, Robert C. Kaplan, Sharon L. R. Kardia, Eimear Kenny, Ryan W. Kim, Charles Kooperberg, Cathy C. Laurie, Seonwook Lee, Don M. Lloyd-Jones, Ruth J. F. Loos, Steven A. Lubitz, Rasika A. Mathias, Karine A. Viaud Martinez, Stephen T. McGarvey, Braxton D. Mitchell, Deborah A. Nickerson, Kari E. North, Aarno Palotie, Cheol Joo Park, Bruce M. Psaty, D. C. Rao, Susan Redline, Alexander P. Reiner, Daekwan Seo, Jeong-Sun Seo, Albert V. Smith, Russell P. Tracy, Ramachandran S. Vasan, Sekar Kathiresan, L. Adrienne Cupples, Jerome I. Rotter, Alanna C. Morrison, Stephen S. Rich, Samuli Ripatti, Cristen Willer, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, FinnGen, Gina M. Peloso, Institute for Molecular Medicine Finland, Samuli Olli Ripatti / Principal Investigator, Complex Disease Genetics, Centre of Excellence in Complex Disease Genetics, Research Programs Unit, Aarno Palotie / Principal Investigator, Genomics of Neurological and Neuropsychiatric Disorders, Department of Public Health, Biostatistics Helsinki, Department of Clinical Chemistry and Hematology, Doctoral Programme in Social Sciences, HYKS erva, Päijät-Häme Welfare Consortium, Department of Medicine, HUS Inflammation Center, Clinicum, HUS Heart and Lung Center, Marja-Riitta Taskinen Research Group, Tiinamaija Tuomi Research Group, HUS Abdominal Center, Department of Oncology, Heikki Joensuu / Principal Investigator, HUS Comprehensive Cancer Center, Department of Medical and Clinical Genetics, Lauri Antti Aaltonen / Principal Investigator, HUS Head and Neck Center, Silmäklinikka, Department of Dermatology, Allergology and Venereology, Department of Food and Nutrition, Medicum, Data Science Genetic Epidemiology Lab, HUSLAB, Department of Mathematics and Statistics, University Management, Biosciences, Pregnancy and Genes, and HUS Gynecology and Obstetrics

Subjects
0301 basic medicine, Male, Apolipoprotein B, General Physics and Astronomy, Blood lipids, Adipose tissue, Genome-wide association study, 030204 cardiovascular system & hematology, Genome-wide association studies, PCSK9, 0302 clinical medicine, Subcutaneous Tissue, GENETIC-VARIANTS, Phenomics, X chromosome, RISK, Multidisciplinary, biology, 1184 Genetics, developmental biology, physiology, Middle Aged, Lipids, X-CHROMOSOME, CORONARY-ARTERY-DISEASE, Female, medicine.medical_specialty, CHOLESTEROL/APOLIPOPROTEIN-B RATIO, APOLIPOPROTEIN-B, Genotype, Science, Nerve Tissue Proteins, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, Diabetes mellitus, Internal medicine, medicine, Humans, Genetic Predisposition to Disease, GENOME-WIDE ASSOCIATION, Allele, Eye Proteins, Genetic Association Studies, Chromosomes, Human, X, DENSITY-LIPOPROTEIN-CHOLESTEROL, Whole Genome Sequencing, business.industry, Cardiometabolic Risk Factors, FAT DISTRIBUTION, General Chemistry, Cardiovascular genetics, medicine.disease, 030104 developmental biology, Endocrinology, Gene Expression Regulation, Genetic Loci, biology.protein, business
Abstract

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10−72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10−4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10−5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids., The influence of X chromosome genetic variation on blood lipids and coronary heart disease (CHD) is not well understood. Here, the authors analyse X chromosome sequencing data across 65,322 multi-ancestry individuals, identifying associations of the Xq23 locus with lipid changes and reduced risk of CHD and diabetes mellitus.

Published
2020

96. Robust, flexible, and scalable tests for Hardy-Weinberg Equilibrium across diverse ancestries

Author

Daniel E. Weeks, Matthew P. Conomos, Tanika N. Kelly, John Barnard, Celeste Eng, Daniel I. Chasman, Gina M. Peloso, L. Adrienne Cupples, Hyun Min Kang, Dan M. Roden, Angel C.Y. Mak, Yan Gao, Xiuqing Guo, Nicholette D. Palmer, Dandi Qiao, Eric Boerwinkle, Nicholas L. Smith, Marguerite R. Irvin, May E. Montasser, Patrick T. Ellinor, Alan Kwong, Gonçalo R. Abecasis, Kathleen C. Barnes, Courtney G. Montgomery, Jennifer A. Smith, Solomon K. Musani, Brian E. Cade, Michael Boehnke, John Blangero, Jessica Lasky-Su, Thomas W. Blackwell, Laura J. Scott, Wonji Kim, Esteban G. Burchard, Jonathon LeFaive, Hemant K. Tiwari, Rasika A. Mathias, Alexander P. Reiner, Charles Kooperberg, M. Benjamin Shoemaker, Albert V. Smith, Ani Manichaikul, Steven A. Lubitz, Mariza de Andrade, Han Chen, and Scott T. Weiss

Subjects
Exact test, Computer science, Metric (mathematics), Scalability, Statistics, False positive paradox, Hardy–Weinberg principle, Genotyping, Statistical power
Abstract

Traditional Hardy-Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in datasets comprised of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence datasets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently amongst the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

Published
2020
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97. A system for phenotype harmonization in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

Author

Xiuqing Guo, Karen M. Mutalik, Nora Franceschini, Carla Wilson, Elizabeth C. Oelsner, Kerri L. Wiggins, Ming-Huei Chen, Cashell E. Jaquish, Charles Kooperberg, Nicola L. Hawley, Adrienne M. Stilp, Kathleen C. Barnes, Chancellor Hohensee, Pradeep Natarajan, Alyna T. Khan, Jingmin Liu, Gina M. Peloso, Fei Fei Wang, Adolfo Correa, Laura M. Raffield, Mariza de Andrade, Jai G. Broome, Patricia A. Peyser, Andrew D. Johnson, Brian E. Cade, Rebecca D. Jackson, Lucia Juarez, Braxton D. Mitchell, Dongquan Chen, Nancy L. Heard-Costa, Shannon Kelly, Kent D. Taylor, Susan R. Heckbert, Stephen T. McGarvey, Tanika N. Kelly, Sharon L.R. Kardia, Jerome I. Rotter, Weiniu Gan, Paul S. de Vries, Lawrence F. Bielak, May E. Montasser, Erin J Buth, Quenna Wong, Jennifer A. Smith, Patrick T. Ellinor, Alanna C. Morrison, Ramachandran S. Vasan, Nathan Pankratz, Wan-Ling Hsu, Cecelia A. Laurie, Jan Graffelman, Daniel E. Weeks, Scott T. Weiss, Seyed Mehdi Nouraie, Cathy C. Laurie, Joshua C. Bis, Kari E. North, William Craig Johnson, L. Adrienne Cupples, Catherine M. D’Augustine, Donna K. Arnett, Stephen S. Rich, Bruce M. Psaty, Leslie S. Emery, Lisa R. Yanek, Jiwon Lee, Stella Aslibekyan, Myriam Fornage, Rasika A. Mathias, Megan L. Grove, Santhi K. Ganesh, and Alexander P. Reiner

Subjects
Data sharing, Documentation, Data collection, Computer science, Controlled vocabulary, Harmonization, Precision medicine, Omics, Data science, Phenotype, Domain (software engineering), Genetic association
Abstract

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data sharing mechanisms. This system was developed for the National Heart, Lung and Blood Institute’s Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other omics data for >80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants from up to 17 TOPMed studies per phenotype. We discuss the challenges faced in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled-access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include (1) the code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify or extend these harmonizations to additional studies; and (2) results of labeling thousands of phenotype variables with controlled vocabulary terms.

Published
2020
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98. Lifestyle Risk Score for aggregating multiple lifestyle factors: Handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions

Author

Eric Boerwinkle, Alanna C. Morrison, Wenyi Wang, Paul S de Vries, Karen Schwander, Ko Willems van Dijk, Diana van Heemst, Oyomoare Osazuwa-Peters, D. C. Rao, Lisa de las Fuentes, Xiaoyu Zhang, Hanfei Xu, Raymond Noordam, Ching-Ti Liu, L. Adrienne Cupples, RJ Waken, Michael R. Brown, and Yun Ju Sung

Subjects
Lifestyle factors, Framingham Risk Score, business.industry, Meta-analysis, Medicine, Disease, Environmental exposure, Missing data, Moderation, business, Demography
Abstract

Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naïve, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to “benchmark” results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered, and the Naïve Approach, which utilized all available data and ignored the missingness, was slightly liberal. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p-values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to “benchmark” results. In conclusion, we generally recommend the Safe Approach to handle heterogeneity in the LRS based genome-wide interaction meta-analyses.

Published
2020
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99. Association of mitochondrial DNA copy number with cardiometabolic diseases in a large cross-sectional study of multiple ancestries

Author

Patricia A. Peyser, Tamar Sofer, Joshua C. Bis, Ryan J. Longchamps, Susan R. Heckbert, Daniel Levy, Bharat Thyagarajan, Gonçalo R. Abecasis, Xiuqing Guo, Xue Liu, Sudha Seshadri, Laura M. Raffield, Jennifer A. Smith, Kent D. Taylor, Myriam Fornage, Eric Boerwinkle, Stephen S. Rich, Achilleas N. Pitsillides, Lawrence F. Bielak, Adolfo Correa, Dan E. Arking, Thomas W. Blackwell, Bruce M. Psaty, James G. Wilson, Ramachandran S. Vasan, Nathan Pankratz, Claudia L. Satizabal, Wei Zhao, Jerome I. Rotter, Chunyu Liu, Jun Ding, Kerri L. Wiggins, L. Adrienne Cupples, Annette L. Fitzpatrick, Megan L. Grove, Nicholas B. Larson, and Nuzulul Kurniansyah

Subjects
0303 health sciences, Mitochondrial DNA, medicine.medical_specialty, Cross-sectional study, business.industry, 030204 cardiovascular system & hematology, medicine.disease, Obesity, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, White blood cell, Internal medicine, Cohort, Hyperlipidemia, medicine, business, 030304 developmental biology, Genetic association, Whole blood
Abstract

AimsWe tested the hypothesis that mitochondrial DNA copy number (CN) is associated with cardiometabolic disease (CMD) traits.Methods and resultsWe determined the cross-sectional association of mtDNA CN measured in whole blood with several CMD traits in 65,996 individuals (mean age 60, 54% women, and 79% European descent). Cohort- and ancestry/ethnicity-specific association analysis was performed adjusting for trait- and cohort-specific covariates. Age was slightly positively associated with age (0.03 s.d. / 10 years (95% CI=0.01, 0.05)) before 65 years, while every 10 years older age was associated with 0.14 s.d. lower level of mtDNA CN after 65 years (95% CI= -0.18, -0.10). In meta-analysis without adjustment for white blood cell (WBC) and differential count in participants of European descent (N=52,491), low mtDNA CN was associated with increased odds of obesity (OR with 95% CI=1.13 (1.11, 1.16), P=3.3e-30) and hypertension (OR=1.05 (1.03, 1.08), P=4.0e-07). Further adjusting for WBC and differential count in the same participants of European descent (N=44,035), associations became non-significant (P>0.05) for hypertension, attenuated for obesity (ORwithout cell count=1.15 (1.12, 1.18) versus ORcell count=1.06 (1.03, 1.08)) but strengthened for hyperlipidemia (ORwithout cell counts=1.03 (1.00, 1.06) versus ORcell counts=1.06 (1.03, 1.09)). The magnitude and directionality of most associations were consistent between participants of European descent and other ethnicity/ancestry origins.ConclusionLow levels of mtDNA CN in peripheral blood were associated with an increased risk of CMD diseases.

Published
2020
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100. Publisher Correction: Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries

Author

Adolfo Correa, Sekar Kathiresan, James G. Wilson, Jonathan M. Bloom, Ramachandran S. Vasan, Steven A. McCarroll, Mark Chaffin, Benjamin M. Neale, Jason Ernst, Braxton D. Mitchell, Mao Fu, Mark J. Daly, Timothy Poterba, Stephen S. Rich, Jerome I. Rotter, Jesse M. Engreitz, Ida Surakka, Veikko Salomaa, Pradeep Natarajan, Samuli Ripatti, Robert E. Handsaker, Matthew J. Budoff, Andrea Ganna, Tõnu Esko, Wendy S. Post, Andres Metspalu, Gina M. Peloso, Sanni Ruotsalainen, L. Adrienne Cupples, Chaojie Yang, Kathleen A. Ryan, Maris Alver, W. Craig Johnson, Seyedeh M. Zekavat, Michael Y. Tsai, Manolis Kellis, Ani Manichaikul, and Cotton Seed

Subjects
Plasma lipoprotein, DNA Copy Number Variations, Population genetics, Science, Quantitative Trait Loci, General Physics and Astronomy, Black People, Gene Expression, Computational biology, 01 natural sciences, Genome, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, White People, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Humans, 030212 general & internal medicine, 0101 mathematics, lcsh:Science, 030304 developmental biology, Genetic association study, 0303 health sciences, Multidisciplinary, Whole Genome Sequencing, Genome, Human, Published Erratum, 010102 general mathematics, General Chemistry, Cholesterol, LDL, Atherosclerosis, Publisher Correction, NHLBI TOPMed Lipids Working Group, Adaptor Proteins, Vesicular Transport, Geography, Cardiovascular Diseases, 030220 oncology & carcinogenesis, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, lcsh:Q, Fat metabolism, Ramachandran plot, Genome-Wide Association Study, Lipoprotein(a)
Abstract

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle that contains apolipoprotein(a), encoded by LPA, and is a highly heritable, causal risk factor for cardiovascular diseases that varies in concentrations across ancestries. Here, we use deep-coverage whole genome sequencing in 8392 individuals of European and African ancestry to discover and interpret both single-nucleotide variants and copy number (CN) variation associated with Lp(a). We observe that genetic determinants between Europeans and Africans have several unique determinants. The common variant rs12740374 associated with Lp(a) cholesterol is an eQTL for SORT1 and independent of LDL cholesterol. Observed associations of aggregates of rare non-coding variants are largely explained by LPA structural variation, namely the LPA kringle IV 2 (KIV2)-CN. Finally, we find that LPA risk genotypes confer greater relative risk for incident atherosclerotic cardiovascular diseases compared to directly measured Lp(a), and are significantly associated with measures of subclinical atherosclerosis in African Americans.

Published
2020

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