Search

Your search keyword '"Kaanan P. Shah"' showing total 23 results
Start Over Author "Kaanan P. Shah"
23 results on '"Kaanan P. Shah"'

1. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

Author

Alvaro N. Barbeira, Scott P. Dickinson, Rodrigo Bonazzola, Jiamao Zheng, Heather E. Wheeler, Jason M. Torres, Eric S. Torstenson, Kaanan P. Shah, Tzintzuni Garcia, Todd L. Edwards, Eli A. Stahl, Laura M. Huckins, GTEx Consortium, Dan L. Nicolae, Nancy J. Cox, and Hae Kyung Im

Subjects
Science
Abstract

Phenotypic variation and diseases are influenced by factors such as genetic variants and gene expression. Here, Barbeira et al. develop S-PrediXcan to compute PrediXcan results using summary data, and investigate the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes.

Published
2018
Full Text
View/download PDF

2. Supplementary Table 2 from Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Author

Celine M. Vachon, John L. Hopper, V. Shane Pankratz, Peter A. Fasching, Douglas F. Easton, Alison M. Dunning, Paul Pharoah, Jacques Simard, Joe Dennis, Kyriaki Michailidou, Qin Wang, Manjeet K. Bolla, Inger Torhild Gram, Anne-Lise Borresen-Dale, Vessela N. Kristensen, Grethe I. Grenaker Alnaes, Giske Ursin, Julia A. Knight, Irene L. Andrulis, Carmel Apicella, Melissa C. Southey, Kavitha Krishnan, Laura Baglietto, Graham G. Giles, Christopher Haiman, Gertraud Maskarinec, Christy Woolcott, Laurence N. Kolonel, Loic Le Marchand, Mark A. Helvie, Heang-Ping Chan, Kaanan P. Shah, Julie A. Douglas, Matthias W. Beckmann, Katharina Heusinger, Sebastian M. Jud, Nicholas J. Wareham, Paula Smith, Kay-Tee Khaw, Robert N. Luben, Ruth M.L. Warren, Jean Leyland, Judith Brown, Fergus J. Couch, Kristen Purrington, Janet E. Olson, Julie Cunningham, Matt Jensen, Per Hall, Kamila Czene, Louise Eriksson, Jingmei Li, Aditi Hazra, Peter Kraft, Sara Lindstrom, Rulla M. Tamimi, Christopher Scott, Isabel dos Santos Silva, Deborah J. Thompson, and Jennifer Stone

Abstract

Supplementary Table 2. Associations (95% Confidence Interval (CI) in brackets) between all 77 breast cancer susceptibility variants and percent dense area, dense area, and non-dense area

Published
2023
Full Text
View/download PDF

3. Supplementary Table 3 from Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Author

Celine M. Vachon, John L. Hopper, V. Shane Pankratz, Peter A. Fasching, Douglas F. Easton, Alison M. Dunning, Paul Pharoah, Jacques Simard, Joe Dennis, Kyriaki Michailidou, Qin Wang, Manjeet K. Bolla, Inger Torhild Gram, Anne-Lise Borresen-Dale, Vessela N. Kristensen, Grethe I. Grenaker Alnaes, Giske Ursin, Julia A. Knight, Irene L. Andrulis, Carmel Apicella, Melissa C. Southey, Kavitha Krishnan, Laura Baglietto, Graham G. Giles, Christopher Haiman, Gertraud Maskarinec, Christy Woolcott, Laurence N. Kolonel, Loic Le Marchand, Mark A. Helvie, Heang-Ping Chan, Kaanan P. Shah, Julie A. Douglas, Matthias W. Beckmann, Katharina Heusinger, Sebastian M. Jud, Nicholas J. Wareham, Paula Smith, Kay-Tee Khaw, Robert N. Luben, Ruth M.L. Warren, Jean Leyland, Judith Brown, Fergus J. Couch, Kristen Purrington, Janet E. Olson, Julie Cunningham, Matt Jensen, Per Hall, Kamila Czene, Louise Eriksson, Jingmei Li, Aditi Hazra, Peter Kraft, Sara Lindstrom, Rulla M. Tamimi, Christopher Scott, Isabel dos Santos Silva, Deborah J. Thompson, and Jennifer Stone

Abstract

Supplementary Table 3. Associations (95% Confidence Interval (CI) in brackets) between breast cancer susceptibility variants and the mammographic measures with adjustment for case-control status

Published
2023
Full Text
View/download PDF

4. Supplementary Table 1 from Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Author

Celine M. Vachon, John L. Hopper, V. Shane Pankratz, Peter A. Fasching, Douglas F. Easton, Alison M. Dunning, Paul Pharoah, Jacques Simard, Joe Dennis, Kyriaki Michailidou, Qin Wang, Manjeet K. Bolla, Inger Torhild Gram, Anne-Lise Borresen-Dale, Vessela N. Kristensen, Grethe I. Grenaker Alnaes, Giske Ursin, Julia A. Knight, Irene L. Andrulis, Carmel Apicella, Melissa C. Southey, Kavitha Krishnan, Laura Baglietto, Graham G. Giles, Christopher Haiman, Gertraud Maskarinec, Christy Woolcott, Laurence N. Kolonel, Loic Le Marchand, Mark A. Helvie, Heang-Ping Chan, Kaanan P. Shah, Julie A. Douglas, Matthias W. Beckmann, Katharina Heusinger, Sebastian M. Jud, Nicholas J. Wareham, Paula Smith, Kay-Tee Khaw, Robert N. Luben, Ruth M.L. Warren, Jean Leyland, Judith Brown, Fergus J. Couch, Kristen Purrington, Janet E. Olson, Julie Cunningham, Matt Jensen, Per Hall, Kamila Czene, Louise Eriksson, Jingmei Li, Aditi Hazra, Peter Kraft, Sara Lindstrom, Rulla M. Tamimi, Christopher Scott, Isabel dos Santos Silva, Deborah J. Thompson, and Jennifer Stone

Abstract

Supplementary Table 1. Mean (95% Confidence Interval (CI)) mammographic density measurements by covariate distribution

Published
2023
Full Text
View/download PDF

5. Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues.

Author

Heather E Wheeler, Kaanan P Shah, Jonathon Brenner, Tzintzuni Garcia, Keston Aquino-Michaels, GTEx Consortium, Nancy J Cox, Dan L Nicolae, and Hae Kyung Im

Subjects
Genetics, QH426-470
Abstract

Understanding the genetic architecture of gene expression traits is key to elucidating the underlying mechanisms of complex traits. Here, for the first time, we perform a systematic survey of the heritability and the distribution of effect sizes across all representative tissues in the human body. We find that local h2 can be relatively well characterized with 59% of expressed genes showing significant h2 (FDR < 0.1) in the DGN whole blood cohort. However, current sample sizes (n ≤ 922) do not allow us to compute distal h2. Bayesian Sparse Linear Mixed Model (BSLMM) analysis provides strong evidence that the genetic contribution to local expression traits is dominated by a handful of genetic variants rather than by the collective contribution of a large number of variants each of modest size. In other words, the local architecture of gene expression traits is sparse rather than polygenic across all 40 tissues (from DGN and GTEx) examined. This result is confirmed by the sparsity of optimal performing gene expression predictors via elastic net modeling. To further explore the tissue context specificity, we decompose the expression traits into cross-tissue and tissue-specific components using a novel Orthogonal Tissue Decomposition (OTD) approach. Through a series of simulations we show that the cross-tissue and tissue-specific components are identifiable via OTD. Heritability and sparsity estimates of these derived expression phenotypes show similar characteristics to the original traits. Consistent properties relative to prior GTEx multi-tissue analysis results suggest that these traits reflect the expected biology. Finally, we apply this knowledge to develop prediction models of gene expression traits for all tissues. The prediction models, heritability, and prediction performance R2 for original and decomposed expression phenotypes are made publicly available (https://github.com/hakyimlab/PrediXcan).

Published
2016
Full Text
View/download PDF

7. Integrated genomic profiling expands clinical options for patients with cancer

Author

Benjamin D. Leibowitz, Nike Beaubier, Jason Perera, Ariane Lozac’hmeur, Ameen A. Salahudeen, Aly A. Khan, Timothy Taxter, Kaanan P. Shah, Derick Hoskinson, Emily Kudalkar, Jackson Michuda, Jerod Parsons, Alexandria M. Bobe, Denise Lau, Stephen Bush, Robert Huether, Alan L. Chang, Wei Zhu, Robert Tell, Kevin P. White, Martin Bontrager, and Catherine Igartua

Subjects
Male, Oncology, medicine.medical_specialty, Genomic profiling, medicine.medical_treatment, Biomedical Engineering, Bioengineering, Applied Microbiology and Biotechnology, Germline, Fusion gene, 03 medical and health sciences, 0302 clinical medicine, Neoplasms, Internal medicine, Biomarkers, Tumor, Humans, Medicine, natural sciences, Molecular Targeted Therapy, Precision Medicine, 030304 developmental biology, 0303 health sciences, Sequence Analysis, RNA, business.industry, Gene Expression Profiling, Genomics, Sequence Analysis, DNA, Evidence-based medicine, Immunotherapy, Transcriptome Sequencing, Gene Expression Regulation, Neoplastic, Clinical trial, Molecular Medicine, Biomarker (medicine), Female, business, 030217 neurology & neurosurgery, Biotechnology
Abstract

Genomic analysis of paired tumor–normal samples and clinical data can be used to match patients to cancer therapies or clinical trials. We analyzed 500 patient samples across diverse tumor types using the Tempus xT platform by DNA-seq, RNA-seq and immunological biomarkers. The use of a tumor and germline dataset led to substantial improvements in mutation identification and a reduction in false-positive rates. RNA-seq enhanced gene fusion detection and cancer type classifications. With DNA-seq alone, 29.6% of patients matched to precision therapies supported by high levels of evidence or by well-powered studies. This proportion increased to 43.4% with the addition of RNA-seq and immunotherapy biomarker results. Combining these data with clinical criteria, 76.8% of patients were matched to at least one relevant clinical trial on the basis of biomarkers measured by the xT assay. These results indicate that extensive molecular profiling combined with clinical data identifies personalized therapies and clinical trials for a large proportion of patients with cancer and that paired tumor–normal plus transcriptome sequencing outperforms tumor-only DNA panel testing. Genomic profiling of cancer samples yields more therapeutic options by including germline, RNA-seq and immunotherapy biomarkers.

Published
2019
Full Text
View/download PDF

8. Clinical validation of the tempus xT next-generation targeted oncology sequencing assay

Author

Kaanan P. Shah, Jason Perera, Eric Lefkofsky, Jerod Parsons, Aly Khan, Denise Lau, Catherine Iguartua, Robert Tell, Timothy Taxter, Martin Bontrager, Shelly Sorrells, Kimberly Yeatts, Stephen Bush, Jackson Michuda, Nike Beaubier, Shelley M. MacNeil, Kevin P. White, Alan Chang, Timothy Baker, Brett Mahon, Robert Huether, and Philip Ellis

Subjects
0301 basic medicine, Oncology, medicine.medical_specialty, Somatic cell, Microsatellite instability, Biology, medicine.disease, next-generation sequencing assay validation, DNA sequencing, tumor profiling, Fusion gene, Transcriptome, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, 030220 oncology & carcinogenesis, Internal medicine, medicine, Copy-number variation, Indel, Gene, Research Paper
Abstract

We developed and clinically validated a hybrid capture next generation sequencing assay to detect somatic alterations and microsatellite instability in solid tumors and hematologic malignancies. This targeted oncology assay utilizes tumor-normal matched samples for highly accurate somatic alteration calling and whole transcriptome RNA sequencing for unbiased identification of gene fusion events. The assay was validated with a combination of clinical specimens and cell lines, and recorded a sensitivity of 99.1% for single nucleotide variants, 98.1% for indels, 99.9% for gene rearrangements, 98.4% for copy number variations, and 99.9% for microsatellite instability detection. This assay presents a wide array of data for clinical management and clinical trial enrollment while conserving limited tissue.

Published
2019

9. A method to prioritize quantitative traits and individuals for sequencing in family-based studies.

Author

Kaanan P Shah and Julie A Douglas

Subjects
Medicine, Science
Abstract

Owing to recent advances in DNA sequencing, it is now technically feasible to evaluate the contribution of rare variation to complex traits and diseases. However, it is still cost prohibitive to sequence the whole genome (or exome) of all individuals in each study. For quantitative traits, one strategy to reduce cost is to sequence individuals in the tails of the trait distribution. However, the next challenge becomes how to prioritize traits and individuals for sequencing since individuals are often characterized for dozens of medically relevant traits. In this article, we describe a new method, the Rare Variant Kinship Test (RVKT), which leverages relationship information in family-based studies to identify quantitative traits that are likely influenced by rare variants. Conditional on nuclear families and extended pedigrees, we evaluate the power of the RVKT via simulation. Not unexpectedly, the power of our method depends strongly on effect size, and to a lesser extent, on the frequency of the rare variant and the number and type of relationships in the sample. As an illustration, we also apply our method to data from two genetic studies in the Old Order Amish, a founder population with extensive genealogical records. Remarkably, we implicate the presence of a rare variant that lowers fasting triglyceride levels in the Heredity and Phenotype Intervention (HAPI) Heart study (p = 0.044), consistent with the presence of a previously identified null mutation in the APOC3 gene that lowers fasting triglyceride levels in HAPI Heart study participants.

Published
2013
Full Text
View/download PDF

10. Polygenic risk scores can predict AD‐related pathologies

Author

Julie Collens, Lon S. Schneider, Carlos Cruchaga, Thomas G. Beach, Jared Cara, and Kaanan P. Shah

Subjects
Genetics, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, business.industry, Health Policy, Medicine, Polygenic risk score, Neurology (clinical), Disease, Geriatrics and Gerontology, business
Published
2020
Full Text
View/download PDF

11. Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control

Author

Mark I. McCarthy, Weihua Meng, Brian L. Yaspan, M Imamura, Mark W. Christiansen, Niina Sandholm, Yii-Der Ida Chen, Sharon G. Adler, Lucia Sobrin, Craig L. Hanis, Valeriya Lyssenko, Shiro Maeda, Yang Hai, Paul Mitchell, Roberta McKean-Cowdin, Xiuqing Guo, John R. Sedor, David S. Siscovick, Sudha K. Iyengar, Heather Hancock, Jane Z. Kuo, Barbara E.K. Klein, David-Alexandre Tregouet, Elisabet Agardh, Kent D. Taylor, Andrew D. Morris, S. Mohsen Hosseini, Andrew D. Paterson, I-Te Lee, Wayne Huey-Herng Sheu, Emma Ahlqvist, Kathryn P. Burdon, Leif Groop, Ayellet V. Segrè, Samy Hadjadj, Samaneh Davoudi, Lynn K. Stanwyck, Emily Y. Chew, Xiaohui Li, Michael A. Grassi, Jie Jin Wang, Samuela Pollack, Albert V. Smith, Kyu Hyung Park, Michiaki Kubo, Mary Frances Cotch, Yucheng Jia, Ching J. Chen, Colin N. A. Palmer, Helen M. Colhoun, Alan D. Penman, R. Varma, Per-Henrik Groop, Tien Yin Wong, Barry I. Freedman, Eli Ipp, Alex S. F. Doney, Gavin Tan, Ronald Klein, Kaanan P. Shah, Jamie E Craig, Donald W. Bowden, Jerome I. Rotter, Robert P. Igo, Darryl Nousome, Ching-Yu Cheng, Michel Marre, Maggie C.Y. Ng, Latchezar Dimitrov, Jeeyun Ahn, Atsushi Takahashi, Richard A. Jensen, Aaron Leong, Jihye Kim, Iiro Toppila, Elizabeth J. Rossin, Alkes L. Price, Diabetes and Obesity Research Program, University of Helsinki, Department of Medicine, Nefrologian yksikkö, Research Programs Unit, Clinicum, HUS Abdominal Center, and Per Henrik Groop / Principal Investigator

Subjects
Blood Glucose, 0301 basic medicine, Oncology, medicine.medical_specialty, Genotype, Endocrinology, Diabetes and Metabolism, LOCI, 030209 endocrinology & metabolism, Genome-wide association study, VARIANTS, Polymorphism, Single Nucleotide, 03 medical and health sciences, 0302 clinical medicine, Meta-Analysis as Topic, RESOURCE, Internal medicine, Diabetes mellitus, REVEALS, Genetic variation, Internal Medicine, medicine, Humans, Genetic Predisposition to Disease, CHINESE PATIENTS, Allele, METAANALYSIS, POLYMORPHISMS, Glycemic, Glycated Hemoglobin, Diabetic Retinopathy, business.industry, Diabetic retinopathy, medicine.disease, PREVALENCE, 3. Good health, SEVERITY, 030104 developmental biology, Diabetes Mellitus, Type 2, 3121 General medicine, internal medicine and other clinical medicine, Multiple comparisons problem, RISK-FACTORS, Medical genetics, Erratum, business, Genome-Wide Association Study, Protein Binding
Abstract

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value

Published
2018
Full Text
View/download PDF

12. Clinical validation of the Tempus xO assay

Author

Christina Lee, Mathew J. Barber, Nike Beaubier, Dan R. Robinson, Kevin P. White, Tim Taxter, Timothy Baker, Gary A. Palmer, Kaanan P. Shah, Stephen Bush, Eric Lefkofsky, Robert Huether, Robert J. Lonigro, Denise Lau, Arul M. Chinnaiyan, Sam Bettis, Yi-Mi Wu, Amy Franzen, Robert Tell, Marcin Cieslik, Xuhong Cao, Jerod Parsons, Amber Thomas, Casey Frankenberger, Ali Weiner, Martin Bontrager, Gene Selkov, and Aly Khan

Subjects
0301 basic medicine, business.industry, RNA, Gene rearrangement, genomic test, Tempus, Predictive value, Molecular biology, Germline, Transcriptome, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, gene panel, Oncology, 030220 oncology & carcinogenesis, Gene panel, cancer, Medicine, business, transcriptome, Research Paper
Abstract

We have developed a clinically validated NGS assay that includes tumor, germline and RNA sequencing. We apply this assay to clinical specimens and cell lines, and we demonstrate a clinical sensitivity of 98.4% and positive predictive value of 100% for the clinically actionable variants measured by the assay. We also demonstrate highly accurate copy number measurements and gene rearrangement identification.

Published
2018
Full Text
View/download PDF

13. A novel MERTK mutation causing retinitis pigmentosa

Author

Segun Jung, Kaanan P. Shah, Michael A. Grassi, Ravi Madduri, Hasenin Al-khersan, and Alex Rodriguez

Subjects
Male, 0301 basic medicine, Proband, DNA Mutational Analysis, Nonsense mutation, Biology, Retina, Article, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Locus heterogeneity, medicine, Humans, Exome, Exome sequencing, Genetic testing, Genetics, c-Mer Tyrosine Kinase, medicine.diagnostic_test, Genetic heterogeneity, DNA, MERTK, medicine.disease, Sensory Systems, Pedigree, Ophthalmoscopy, Ophthalmology, 030104 developmental biology, Mutation, 030221 ophthalmology & optometry, Female, Allelic heterogeneity, Retinitis Pigmentosa
Abstract

Retinitis pigmentosa (RP) is a genetically heterogeneous inherited retinal dystrophy. To date, over 80 genes have been implicated in RP. However, the disease demonstrates significant locus and allelic heterogeneity not entirely captured by current testing platforms. The purpose of the present study was to characterize the underlying mutation in a patient with RP without a molecular diagnosis after initial genetic testing. Whole-exome sequencing of the affected proband was performed. Candidate gene mutations were selected based on adherence to expected genetic inheritance pattern and predicted pathogenicity. Sanger sequencing of MERTK was completed on the patient’s unaffected mother, affected brother, and unaffected sister to determine genetic phase. Eight sequence variants were identified in the proband in known RP-associated genes. Sequence analysis revealed that the proband was a compound heterozygote with two independent mutations in MERTK, a novel nonsense mutation (c.2179C > T) and a previously reported missense variant (c.2530C > T). The proband’s affected brother also had both mutations. Predicted phase was confirmed in unaffected family members. Our study identifies a novel nonsense mutation in MERTK in a family with RP and no prior molecular diagnosis. The present study also demonstrates the clinical value of exome sequencing in determining the genetic basis of Mendelian diseases when standard genetic testing is unsuccessful.

Published
2017
Full Text
View/download PDF

14. Erratum. Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control. Diabetes 2019;68:441—456

Author

Aaron Leong, Yii-Der Ida Chen, Iiro Toppila, Elizabeth J. Rossin, Alan D. Penman, I-Te Lee, Kathryn P. Burdon, Yucheng Jia, Sudha K. Iyengar, Helen M. Colhoun, Michael A. Grassi, Jie Jin Wang, Tien Yin Wong, Barry I. Freedman, Kaanan P. Shah, Ching J. Chen, Weihua Meng, Brian L. Yaspan, Samaneh Davoudi, David S. Siscovick, Per-Henrik Groop, Samy Hadjadj, Michel Marre, Ching-Yu Cheng, John R. Sedor, Xiuqing Guo, Albert V. Smith, Robert P. Igo, Kyu Hyung Park, Jerome I. Rotter, Andrew D. Paterson, Samuela Pollack, Sharon G. Adler, Alkes L. Price, Emily Y. Chew, Emma Ahlqvist, Eli Ipp, Jamie E Craig, Richard A. Jensen, Barbara E.K. Klein, Maggie C.Y. Ng, Leif Groop, Craig L. Hanis, Xiaohui Li, Atsushi Takahashi, Roberta McKean-Cowdin, Mark P. Christiansen, Lynn K. Stanwyck, Paul Mitchell, Shiro Maeda, Alex S. F. Doney, Darryl Nousome, Ronald Klein, Yang Hai, Mark I. McCarthy, Niina Sandholm, Kent D. Taylor, Andrew D. Morris, Rohit Varma, David-Alexandre Trégouët, Jihye Kim, Valeriya Lyssenko, Colin N. A. Palmer, Mary Frances Cotch, Jane Z. Kuo, Elisabet Agardh, S. Mohsen Hosseini, Michiaki Kubo, Jeeyun Ahn, Gavin Tan, Minako Imamura, Lucia Sobrin, Heather Hancock, Wayne Huey-Herng Sheu, Donald W. Bowden, Ayellet V. Segrè, and Latchezar Dimitrov

Subjects
Pediatrics, medicine.medical_specialty, Complications, business.industry, Endocrinology, Diabetes and Metabolism, Genome-wide association study, Diabetic retinopathy, medicine.disease, Diabetes mellitus, Internal Medicine, medicine, Duration (project management), business, Glycemic
Abstract

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value

Published
2020
Full Text
View/download PDF

15. Integrative cross tissue analysis of gene expression identifies novel type 2 diabetes genes

Author

Rodrigo Bonazzola, Alvaro N. Barbeira, Graeme I. Bell, Andrew P. Morris, Jason M. Torres, Hae Kyung Im, Heather E. Wheeler, Nancy J. Cox, and Kaanan P. Shah

Subjects
Genetics, 0303 health sciences, Adipose tissue, Genome-wide association study, Type 2 diabetes, Biology, medicine.disease, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, Diabetes mellitus, Gene expression, Cohort, medicine, Pancreas, Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

To understand the mechanistic underpinnings of type 2 diabetes (T2D) loci mapped through GWAS, we performed a tissue-specific gene association study in a cohort of over 100K individuals (ncases≈ 26K,ncontrols≈ 84K) across 44 human tissues using MetaXcan, a summary statistics extension of PrediXcan. We found that 90 genes significantly (FDR < 0.05) associated with T2D, of which 24 are previously reported T2D genes, 29 are novel in established T2D loci, and 37 are novel genes in novel loci. Of these, 13 reported genes, 15 novel genes in known loci, and 6 genes in novel loci replicated (FDRrep< 0.05) in an independent study (ncases≈ 10K,ncontrols≈ 62K). We also found enrichment of significant associations in expected tissues such as liver, pancreas, adipose, and muscle but also in tibial nerve, fibroblasts, and breast. Finally, we found that monogenic diabetes genes are enriched in T2D genes from our analysis suggesting that moderate alterations in monogenic (severe) diabetes genes may promote milder and later onset type 2 diabetes.

Published
2017
Full Text
View/download PDF

16. Genetic predictors of gene expression associated with risk of bipolar disorder

Author

Kaanan P. Shah, Dan L. Nicolae, Eric R. Gamazon, Heather E. Wheeler, Hae Kyung Im, and Nancy J. Cox

Subjects
Genetics, 0303 health sciences, education.field_of_study, Population, Genome-wide association study, Disease, Biology, medicine.disease, 3. Good health, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Gene expression, medicine, Bipolar disorder, education, Gene, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association
Abstract

Bipolar disorder (BD) affects the quality of life of approximately 1% of the population and represents a major public health concern. It is known to be highly heritable but large-scale genome-wide association studies (GWAS) have discovered only a handful of markers associated with the disease. Furthermore, the biological mechanisms underlying these markers need to be elucidated. We recently published a gene-level association test, PrediXcan that integrates transcriptome regulation data to characterize the function of these markers in a tissue specific manner. In this study, we developed prediction models for mRNA levels in 10 brain regions using data from the GTEx project and performed PrediXcan analysis in WTCCC as well as in an independent cohort, GAIN. We replicate the association between predicted expression of PTPRE and BD risk in whole blood and recapitulate the association in brain tissues. PTPRE encodes the protein tyrosine phosphatase, receptor type E, that is known to be involved in RAS signaling and activation of voltage-gated K+ channels. We also found a new genome-wide significant association between lower predicted expression of BBX (bobby sox homolog) in the anterior cingulate cortex region of the brain and increased risk of BD (pWTCCC = 7.02 x 10e-6, pGAIN = 4.68 x 10e-3, pmeta = 1.11 x 10e-7). In sum, we used our mechanistically informed approach, PrediXcan, to identify and replicate two novel genome-wide significant genes using existing GWAS studies.

Published
2016
Full Text
View/download PDF

17. Safety of Two Cluster Schedules for Subcutaneous Immunotherapy in Allergic Rhinitis or Asthma Patients Sensitized to Inhalant Allergens

Author

Ralph Mösges, Kaanan P. Shah, Oliver Pfaar, Ludger Klimek, C. Moreno Aguilar, Jochen Sieber, S. Amoroso, and I. Fischer

Subjects
Adult, Male, Allergy, Adolescent, medicine.medical_treatment, Immunology, medicine.disease_cause, Young Adult, Allergen, Immunopathology, medicine, Animals, Humans, Immunology and Allergy, Adverse effect, Desensitization (medicine), Asthma, business.industry, Pyroglyphidae, fungi, Rhinitis, Allergic, Seasonal, food and beverages, General Medicine, Immunotherapy, Allergens, Middle Aged, medicine.disease, Clinical trial, Desensitization, Immunologic, Pollen, Female, business
Abstract

Background: Subcutaneous immunotherapy (SCIT) usually requires a long titration phase, which can be associated with various adverse events (AEs). Objectives: It was the aim of this study to determine the safety of 2 cluster regimens for SCIT in patients with allergic rhinitis, with or without mild or moderate allergic asthma, who were sensitized to grass and/or tree pollen, or house dust mites (HDM). Patients and Methods: Adult patients were included in a European, open-label, prospective trial. Pollen-allergic patients received grass pollen, grass and olive pollen, or hazel, alder and birch pollen according to a 3-week titration cluster. HDM-allergic patients received HDM extract according to a 2-week titration cluster. The safety of the titration phase was assessed in terms of local and systemic AEs. Results: The safety analysis included 157 patients: 110 received pollen and 47 HDM extract. During the cluster titration, 248 AE episodes were reported in the pollen group and 113 in the HDM group; these were mainly local reactions. Around one third of patients (30.9% pollen and 38.3% HDM) did not experience any AE. In most cases (67.1% of pollen and 71.1% of HDM patients), AEs did not lead to a change in titration schedule. No anaphylactic reaction or other serious life-threatening systemic AEs were reported. Only 2 patients in the HDM group discontinued treatment because of AEs. Conclusions: Rapid cluster titration was well tolerated in adults with allergic rhinitis, with or without mild to moderate allergic asthma, due to pollen or HDM. This short-titration, high-dose cluster regime may allow better patient compliance and cost savings.

Published
2009
Full Text
View/download PDF

18. Correction: Corrigendum: Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Author

Ellen L. Goode, Mariza deAndrade, Julie M. Cunningham, David J. Hunter, Gretchen L. Gierach, Jennifer Stone, Andrew D. Paterson, Paula Smith, Aaron D. Norman, Robert Luben, Aditi Hazra, Kamila Czene, Heang Ping Chan, Nicholas W. Knoblauch, Peter A. Fasching, Mark A. Helvie, Pablo Fernández-Navarro, John L. Hopper, Norman F. Boyd, Nicholas J. Wareham, Judith Brown, Deborah J. Thompson, Jingmei Li, Andrew H. Beck, Per Hall, Ruth J. F. Loos, V. Shane Pankratz, Isabel dos-Santos-Silva, Jajini Verghase, Douglas F. Easton, Christopher J. Scott, Rulla M. Tamimi, Jonine D. Figueroa, Fergus J. Couch, Celine M. Vachon, Laura Baglietto, John A. Heit, Sara Lindström, Julie A. Douglas, Graham G. Giles, Peter Kraft, Kaanan P. Shah, Marina Pollán, Robert A. Vierkant, and Melissa C. Southey

Subjects
Multidisciplinary, Breast cancer, medicine, MAMMOGRAPHIC DENSITY, General Physics and Astronomy, Genome-wide association study, General Chemistry, Computational biology, Biology, Bioinformatics, medicine.disease, General Biochemistry, Genetics and Molecular Biology
Published
2015
Full Text
View/download PDF

19. PrediXcan: Trait Mapping Using Human Transcriptome Regulation

Author

Dan L. Nicolae, Nancy J. Cox, Sahar V. Mozaffari, Hae Kyung Im, Joshua C. Denny, Robert J. Carroll, Eric R. Gamazon, Keston Aquino-Michaels, Heather E. Wheeler, Anne E. Eyler, and Kaanan P. Shah

Subjects
Genetics, 0303 health sciences, Candidate gene, Mechanism (biology), In silico, Genomics, Genome-wide association study, Biology, Phenotype, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates the “imputed” gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene- based approaches such as reduced multiple testing burden, more comprehensive annotation of gene function compared to that derived from single variants, and a principled approach to the design of follow-up experiments while also integrating knowledge of regulatory function. Since no actual expression data are used in the analysis of GWAS data - only in silico expression - reverse causality problems are largely avoided. PrediXcan harnesses reference transcriptome data for disease mapping studies. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

Published
2015
Full Text
View/download PDF

20. Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Author

Christopher A. Haiman, Robert Luben, Laurence N. Kolonel, Aditi Hazra, Loic Le Marchand, Peter Kraft, Douglas F. Easton, Christy G. Woolcott, Jennifer Stone, Kavitha Krishnan, Sara Lindström, Katharina Heusinger, Julie A. Douglas, Vessela N. Kristensen, Alison M. Dunning, Christopher G. Scott, Qin Wang, Fergus J. Couch, Giske Ursin, V. Shane Pankratz, Matthew B. Jensen, Laura Baglietto, Heang Ping Chan, Matthias W. Beckmann, Rulla M. Tamimi, Celine M. Vachon, Jacques Simard, Peter A. Fasching, Manjeet K. Bolla, Kamila Czene, Kay-Tee Khaw, John L. Hopper, Judith E. Brown, Grethe I. Grenaker Alnæs, Gertraud Maskarinec, Joe Dennis, Graham G. Giles, Jean Leyland, Jingmei Li, Carmel Apicella, Kaanan P. Shah, Louise Eriksson, Irene L. Andrulis, Melissa C. Southey, Paul D.P. Pharoah, Sebastian M. Jud, Per Hall, Inger T. Gram, Isabel dos Santos Silva, Janet E. Olson, Julia A. Knight, Anne Lise Børresen-Dale, Mark A. Helvie, Kristen S. Purrington, Deborah J. Thompson, Ruth Warren, Kyriaki Michailidou, Nicholas J. Wareham, Paula Smith, Julie M. Cunningham, Thompson, Deborah [0000-0003-1465-5799], Luben, Robert [0000-0002-5088-6343], Khaw, Kay-Tee [0000-0002-8802-2903], Wareham, Nicholas [0000-0003-1422-2993], Wang, Jean [0000-0002-9139-0627], Dennis, Joe [0000-0003-4591-1214], Pharoah, Paul [0000-0001-8494-732X], Dunning, Alison [0000-0001-6651-7166], Easton, Douglas [0000-0003-2444-3247], and Apollo - University of Cambridge Repository

Subjects
Oncology, Cancer Research, medicine.medical_specialty, Genotype, Genome-wide association study, Breast Neoplasms, Polymorphism, Single Nucleotide, Article, Breast cancer, Polymorphism (computer science), Risk Factors, Internal medicine, Genetic variation, Medicine, Humans, Genetic Predisposition to Disease, Polymorphism, Mammary Glands, Human, Aged, Breast Density, Gynecology, business.industry, MAMMOGRAPHIC DENSITY, Single Nucleotide, Heritability, Middle Aged, medicine.disease, Mammary Glands, Disease Susceptibility, Female, business, Body mass index, Human
Abstract

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10−5). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk. Cancer Res; 75(12); 2457–67. ©2015 AACR.

Published
2015

21. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Author

Graham G. Giles, Paula Smith, Rulla M. Tamimi, Celine M. Vachon, John A. Heit, Jennifer Stone, Heang Ping Chan, Ruth J. F. Loos, Melissa C. Southey, Julie M. Cunningham, Peter Kraft, Ellen L. Goode, Christopher G. Scott, Andrew H. Beck, Fergus J. Couch, Andrew D. Paterson, Jajini Verghase, Robert Luben, Kaanan P. Shah, Kamila Czene, Aditi Hazra, John L. Hopper, Peter A. Fasching, Robert A. Vierkant, Sara Lindström, Julie A. Douglas, V. Shane Pankratz, Laura Baglietto, Jingmei Li, Marina Pollán, Judith E. Brown, Isabel dos-Santos-Silva, Douglas F. Easton, Aaron D. Norman, Norman F. Boyd, Mark A. Helvie, Deborah J. Thompson, Per Hall, Jonine D. Figueroa, Gretchen L. Gierach, David J. Hunter, Nicholas W. Knoblauch, Pablo Fernández-Navarro, Mariza deAndrade, Nicholas J. Wareham, Instituto de Salud Carlos III, and Unión Europea

Subjects
Oncology, Genetics and Molecular Biology (all), medicine.medical_specialty, General Physics and Astronomy, Genome-wide association study, Breast Neoplasms, Biology, Bioinformatics, Biochemistry, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, Physics and Astronomy (all), 0302 clinical medicine, Breast cancer, Polymorphism (computer science), Internal medicine, medicine, Humans, Genetic Predisposition to Disease, Risk factor, Stage (cooking), Polymorphism, Mammary Glands, Human, 030304 developmental biology, Genetic association, Breast Density, 0303 health sciences, Multidisciplinary, Chemistry (all), Case-control study, Case-Control Studies, Female, Genetic Loci, Genome-Wide Association Study, Biochemistry, Genetics and Molecular Biology (all), General Chemistry, Single Nucleotide, Heritability, medicine.disease, Mammary Glands, 3. Good health, Radiography, 030220 oncology & carcinogenesis, Human
Abstract

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P

Published
2014

22. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

Author

Barbara E. Engelhardt, Sarah Kim-Hellmuth, Sarah Urbut, Ashis Saha, Gen Li, Jessica Halow, Panagiotis Papasaikas, Simona Volpi, Alvaro N. Barbeira, Jared L. Nedzel, Meng Wang, Nikolaos I Panousis, Benedict Paten, Lori E. Brigham, Gao Wang, Mary Barcus, Anthony Payne, Xiaoquan Wen, Nicola J. Rinaldi, Hua Tang, Seva Kashin, Casey Martin, Steven Hunter, Yi-Hui Zhou, Kristen Lee, Ian C. McDowell, Jason M. Torres, Alexandra J. Scott, Kane Hadley, Duyen T. Nguyen, Leslie H. Sobin, Jiamao Zheng, Paul Flicek, Casandra A. Trowbridge, Anjené M. Addington, Omer Basha, Brandon L. Pierce, Pejman Mohammadi, Xiao Li, Halit Ongen, Ki Sung Um, John T. Lonsdale, Alexis Battle, Takunda Matose, Kathryn Demanelis, Yuan He, Bernadette Mestichelli, Kasper D. Hansen, Marcus Hunter, Shin Lin, Richard Hasz, Thomas Juettemann, Ellen Karasik, Taru Tukiainen, Lin Chen, Fred A. Wright, Martijn van de Bunt, Meritxell Oliva, Fidencio J. Neri, François Aguet, Todd L. Edwards, Judith B. Zaugg, Michael Snyder, Gary Walters, Yongjin Park, Mary Goldman, Michael Sammeth, Kristin G. Ardlie, A. Roger Little, Reza Sodaei, Stephen J. Trevanion, Kevin S. Smith, Stephen B. Montgomery, Katherine H. Huang, John A. Stamatoyannopoulos, David E. Tabor, Gene Kopen, Melina Claussnitzer, Nancy Roche, Rodrigo Bonazzola, Deborah C. Mash, Helen M. Moore, Anne Ndungu, Lihua Jiang, Konrad J. Karczewski, Marian S. Fernando, Liqun Qi, Princy Parsana, Ping Guan, Eleazar Eskin, Kate R. Rosenbloom, Jason Bridge, Maria M. Tomaszewski, David A. Davis, Joseph Wheeler, Sarah E. Gould, Morgan Diegel, Eric S. Torstenson, Nancy J. Cox, Daniel R. Zerbino, Joshua M. Akey, Kimberly M. Valentino, Brunilda Balliu, Jingchun Zhu, Joe R. Davis, Nicholas Van Wittenberghe, Michael F. Salvatore, Susan E. Koester, Barbara A. Foster, Andrey A. Shabalin, Ira M. Hall, John Vivian, Eun Yong Kang, Nathan S. Abell, Laure Fresard, Lindsay F. Rizzardi, Ayellet V. Segrè, Dan Sheppard, Rajinder Kaul, Jeffrey McLean, Michael Washington, Joanne Chan, Mark Miklos, Benoit Molinie, Christopher Johns, Laura M. Huckins, Monkol Lek, Christopher D. Brown, Laura A. Siminoff, Alisa McDonald, Abhi Rao, Jean Monlong, Jie Quan, Diego Garrido-Martín, Barbara E. Stranger, Donald F. Conrad, Farhan N. Damani, Cédric Howald, Buhm Han, Michael S. Noble, Caroline Linke, Daniel C. Rohrer, Jimmie B. Vaught, Hae Kyung Im, Audra K. Johnson, Rui Zhang, Muhammad G. Kibriya, Matthew Stephens, Chiara Sabatti, Brian Roe, Latarsha J. Carithers, Robert E. Handsaker, Pushpa Hariharan, Boxiang Liu, Manolis Kellis, YoSon Park, Manuel Muñoz-Aguirre, Lei Hou, Matthew T. Maurano, Ariel D. H. Gewirtz, Ruth Barshir, Michael J. Gloudemans, Li Wang, Gireesh K. Bogu, Christopher Lee, Anita H. Undale, Genna Gliner, Sandra Linder, Serghei Mangul, Andrew A. Brown, Tzintzuni Garcia, Eric Haugen, Michael T. Moser, Jessica Lin, Andrew B. Nobel, Richard Sandstrom, Emmanouil T. Dermitzakis, Kieron Taylor, Brian Jo, Rachna Kumar, Laura Barker, Magali Ruffier, Ferran Reverter, Saboor Shad, Jin Billy Li, Eli A. Stahl, Mark I. McCarthy, William F. Leinweber, Gad Getz, Karna Robinson, Xin Li, Esti Yeger-Lotem, Jemma Nelson, Negin Vatanian, Scott P. Dickinson, Colby Chiang, Jeffrey A. Thomas, Kaanan P. Shah, Maximilian Haeussler, Heather M. Traino, Concepcion R. Nierras, Fan Wu, Hualin S. Xi, Yaping Liu, Daniel Bates, Peter Hickey, Eric R. Gamazon, Jennifer A. Doherty, Ellen Gelfand, Jae Hoon Sul, Jeffery P. Struewing, Beryl B. Cummings, W. James Kent, John Palowitch, Brian Craft, Anna M. Smith, Pedro G. Ferreira, Emily K. Tsang, Andrew P. Feinberg, Nicole C. Lockart, Ruiqi Jian, Robert G. Montroy, Dan L. Nicolae, Yungil Kim, Kevin Myer, Christine B. Peterson, Benjamin J. Strober, Andrew D. Skol, Qin Li, Bryan Gillard, Dana R. Valley, Zachary Zappala, Philip A. Branton, Stephane E. Castel, Daniel G. MacArthur, Mark H. Johnson, Olivier Delaneau, Maghboeba Mosavel, Roderic Guigó, Farzana Jasmine, Scott D. Jewell, Shilpi Singh, Tuuli Lappalainen, Farhad Hormozdiari, and Heather E. Wheeler

Subjects
0301 basic medicine, Science, Quantitative Trait Loci, Gene Expression, General Physics and Astronomy, Genome-wide association study, Quantitative trait locus, Biology, Polymorphism, Single Nucleotide, Genome-wide association studies, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Meta-Analysis as Topic, Genetic variation, Humans, Computer Simulation, lcsh:Science, Gene, Genetics, Multidisciplinary, Models, Genetic, Chromosome Mapping, Genetic Variation, Tissue-Specific Gene Expression, Robustness (evolution), General Chemistry, Phenotype, 030104 developmental biology, Organ Specificity, Trait, lcsh:Q, Data integration, Genome-Wide Association Study
Abstract

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes., Phenotypic variation and diseases are influenced by factors such as genetic variants and gene expression. Here, Barbeira et al. develop S-PrediXcan to compute PrediXcan results using summary data, and investigate the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes.

23. Spontaneous Tissue Expander Migration in an Irradiated Field: A Case Report

Author

Lucie Bandelac, BA, Kaanan D. Shah, MBA, MS, BA, and Susan Chung, MD, PhD

Subjects
Surgery, RD1-811
Abstract

Summary:. A 59-year-old woman with a history of bilateral breast cancer, bilateral mastectomy, and bilateral latissimus dorsi flap reconstruction with tissue expanders, before expansion, developed spontaneous unilateral tissue expander migration on the side that had been irradiated. During the operation to return the migrated tissue expander to the chest, the expander was found at the back with a seroma. The chest pocket had collapsed, and a subcutaneous tunnel inferior to the flap inset was encountered, indicating the path of migration. To our knowledge, this is the first case reported of spontaneous tissue expander dislodgement to the donor site. This case is unique in that the patient had bilateral procedures but developed tissue expander migration only on the irradiated side. This highlights the need during pocket creation to account for the fibrosis caused by radiation that can create a constricted pocket promoting migration.

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results