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2. Diagnostic Criteria for Identifying Individuals at High Risk of Progression From Mild or Moderate to Severe Alcohol Use Disorder

Author

Miller, Alex P, Kuo, Sally I-Chun, Johnson, Emma C, Tillman, Rebecca, Brislin, Sarah J, Dick, Danielle M, Kamarajan, Chella, Kinreich, Sivan, Kramer, John, McCutcheon, Vivia V, Plawecki, Martin H, Porjesz, Bernice, Schuckit, Marc A, Salvatore, Jessica E, Edenberg, Howard J, Bucholz, Kathleen K, Meyers, Jaquelyn L, Agrawal, Arpana, Hesselbrock, Victor, Foroud, Tatiana, Liu, Yunlong, Kuperman, Samuel, Pandey, Ashwini K, Bierut, Laura J, Rice, John, Tischfield, Jay A, Hart, Ronald P, Almasy, Laura, Goate, Alison, Slesinger, Paul, Scott, Denise M, Bauer, Lance O, Nurnberger, John I, Wetherill, Leah, Xuei, Xiaoling, Lai, Dongbing, O'Connor, Sean J, Chan, Grace, Chorlian, David B, Zhang, Jian, Barr, Peter B, Pandey, Gayathri, Mullins, Niamh, Anokhin, Andrey P, Hartz, Sarah, Saccone, Scott, Moore, Jennifer C, Aliev, Fazil, Pang, Zhiping, Merikangas, Alison, Chin, Hemin, and Parsian, Abbas

Subjects
Biomedical and Clinical Sciences, Clinical Research, Alcoholism, Alcohol Use and Health, Substance Misuse, Brain Disorders, Mental Health, Prevention, Aetiology, 2.1 Biological and endogenous factors, Mental health, Good Health and Well Being, Collaborative Study on the Genetics of Alcoholism, Biomedical and clinical sciences, Health sciences
Abstract

ImportanceCurrent Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) diagnoses of substance use disorders rely on criterion count-based approaches, disregarding severity grading indexed by individual criteria.ObjectiveTo examine correlates of alcohol use disorder (AUD) across count-based severity groups (ie, mild, moderate, mild-to-moderate, severe), identify specific diagnostic criteria indicative of greater severity, and evaluate whether specific criteria within mild-to-moderate AUD differentiate across relevant correlates and manifest in greater hazards of severe AUD development.Design, setting, and participantsThis cohort study involved 2 cohorts from the family-based Collaborative Study on the Genetics of Alcoholism (COGA) with 7 sites across the United States: cross-sectional (assessed 1991-2005) and longitudinal (assessed 2004-2019). Statistical analyses were conducted from December 2022 to June 2023.Main outcomes and measuresSociodemographic, alcohol-related, psychiatric comorbidity, brain electroencephalography (EEG), and AUD polygenic score measures as correlates of DSM-5 AUD levels (ie, mild, moderate, severe) and criterion severity-defined mild-to-moderate AUD diagnostic groups (ie, low-risk vs high-risk mild-to-moderate).ResultsA total of 13 110 individuals from the cross-sectional COGA cohort (mean [SD] age, 37.8 [14.2] years) and 2818 individuals from the longitudinal COGA cohort (mean baseline [SD] age, 16.1 [3.2] years) were included. Associations with alcohol-related, psychiatric, EEG, and AUD polygenic score measures reinforced the role of increasing criterion counts as indexing severity. Yet within mild-to-moderate AUD (2-5 criteria), the presence of specific high-risk criteria (eg, withdrawal) identified a group reporting heavier drinking and greater psychiatric comorbidity even after accounting for criterion count differences. In longitudinal analyses, prior mild-to-moderate AUD characterized by endorsement of at least 1 high-risk criterion was associated with more accelerated progression to severe AUD (adjusted hazard ratio [aHR], 11.62; 95% CI, 7.54-17.92) compared with prior mild-to-moderate AUD without endorsement of high-risk criteria (aHR, 5.64; 95% CI, 3.28-9.70), independent of criterion count.Conclusions and relevanceIn this cohort study of a combined 15 928 individuals, findings suggested that simple count-based AUD diagnostic approaches to estimating severe AUD vulnerability, which ignore heterogeneity among criteria, may be improved by emphasizing specific high-risk criteria. Such emphasis may allow better focus on individuals at the greatest risk and improve understanding of the development of AUD.

Published
2023

3. The Collaborative Study on the Genetics of Alcoholism: Overview

Author

Agrawal, Arpana, Brislin, Sarah J, Bucholz, Kathleen K, Dick, Danielle, Hart, Ronald P, Johnson, Emma C, Meyers, Jacquelyn, Salvatore, Jessica, Slesinger, Paul, Liu, Y, Plawecki, MH, Kamarajan, C, Pandey, A, Bierut, L, Rice, J, Schuckit, M, Scott, D, Bauer, L, Wetherill, L, Xuei, X, Lai, D, O'Connor, S, Chan, G, Chorlian, DB, Zhang, J, Barr, P, Kinreich, S, Pandey, G, Mullins, N, Anokhin, A, Hartz, S, McCutcheon, V, Saccone, S, Moore, J, Aliev, F, Pang, Z, Kuo, S, Chin, H, Parsian, A, Almasy, Laura, Foroud, Tatiana, Goate, Alison, Hesselbrock, Victor, Kramer, John, Kuperman, Samuel, Merikangas, Alison K, Nurnberger, John I, Tischfield, Jay, Edenberg, Howard J, and Porjesz, Bernice

Subjects
Biological Sciences, Genetics, Neurosciences, Clinical Research, Alcoholism, Alcohol Use and Health, Substance Misuse, Brain Disorders, Human Genome, Mental Health, Behavioral and Social Science, Mental health, Good Health and Well Being, COGA Collaborators, AUD, EEG, ERP, SSAGA, alcohol dependence, alcohol use disorder, brain, developmental, family, genomics, lifespan, longitudinal, psychiatric, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

Alcohol use disorders (AUD) are commonly occurring, heritable and polygenic disorders with etiological origins in the brain and the environment. To outline the causes and consequences of alcohol-related milestones, including AUD, and their related psychiatric comorbidities, the Collaborative Study on the Genetics of Alcoholism (COGA) was launched in 1989 with a gene-brain-behavior framework. COGA is a family based, diverse (~25% self-identified African American, ~52% female) sample, including data on 17,878 individuals, ages 7-97 years, in 2246 families of which a proportion are densely affected for AUD. All participants responded to questionnaires (e.g., personality) and the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) which gathers information on psychiatric diagnoses, conditions and related behaviors (e.g., parental monitoring). In addition, 9871 individuals have brain function data from electroencephalogram (EEG) recordings while 12,009 individuals have been genotyped on genome-wide association study (GWAS) arrays. A series of functional genomics studies examine the specific cellular and molecular mechanisms underlying AUD. This overview provides the framework for the development of COGA as a scientific resource in the past three decades, with individual reviews providing in-depth descriptions of data on and discoveries from behavioral and clinical, brain function, genetic and functional genomics data. The value of COGA also resides in its data sharing policies, its efforts to communicate scientific findings to the broader community via a project website and its potential to nurture early career investigators and to generate independent research that has broadened the impact of gene-brain-behavior research into AUD.

Published
2023

4. Subcortical Brain Alterations in Carriers of Genomic Copy Number Variants.

Author

Kumar, Kuldeep, Modenato, Claudia, Moreau, Clara, Ching, Christopher, Harvey, Annabelle, Martin-Brevet, Sandra, Huguet, Guillaume, Jean-Louis, Martineau, Douard, Elise, Martin, Charles-Olivier, Younis, Nadine, Tamer, Petra, Maillard, Anne, Rodriguez-Herreros, Borja, Pain, Aurélie, Kushan, Leila, Isaev, Dmitry, Alpert, Kathryn, Ragothaman, Anjani, Turner, Jessica, Wang, Lei, Ho, Tiffany, Schmaal, Lianne, Silva, Ana, van den Bree, Marianne, Linden, David, Owen, Michael, Hall, Jeremy, Lippé, Sarah, Dumas, Guillaume, Draganski, Bogdan, Gutman, Boris, Sønderby, Ida, Andreassen, Ole, Schultz, Laura, Almasy, Laura, Glahn, David, Bearden, Carrie, Thompson, Paul, and Jacquemont, Sébastien

Subjects
Depressive Disorders, Genetics/Genomics, Neurodevelopmental Disorders, Neuroimaging, Schizophrenia Spectrum and Other Psychotic Disorders, Male, Adult, Humans, Child, Adolescent, Young Adult, Middle Aged, Aged, Aged, 80 and over, DNA Copy Number Variations, Schizophrenia, Brain, Attention Deficit Disorder with Hyperactivity, Genomics
Abstract

OBJECTIVE: Copy number variants (CNVs) are well-known genetic pleiotropic risk factors for multiple neurodevelopmental and psychiatric disorders (NPDs), including autism (ASD) and schizophrenia. Little is known about how different CNVs conferring risk for the same condition may affect subcortical brain structures and how these alterations relate to the level of disease risk conferred by CNVs. To fill this gap, the authors investigated gross volume, vertex-level thickness, and surface maps of subcortical structures in 11 CNVs and six NPDs. METHODS: Subcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (CNVs at 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2; age range, 6-80 years; 340 males) and 782 control subjects (age range, 6-80 years; 387 males) as well as ENIGMA summary statistics for ASD, schizophrenia, attention deficit hyperactivity disorder, obsessive-compulsive disorder, bipolar disorder, and major depression. RESULTS: All CNVs showed alterations in at least one subcortical measure. Each structure was affected by at least two CNVs, and the hippocampus and amygdala were affected by five. Shape analyses detected subregional alterations that were averaged out in volume analyses. A common latent dimension was identified, characterized by opposing effects on the hippocampus/amygdala and putamen/pallidum, across CNVs and across NPDs. Effect sizes of CNVs on subcortical volume, thickness, and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and schizophrenia. CONCLUSIONS: The findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions, as well distinct effects, with some CNVs clustering with adult-onset conditions and others with ASD. These findings provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD and why a single CNV increases the risk for a diverse set of NPDs.

Published
2023

9. Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions.

Author

Moreau, Clara, Kumar, Kuldeep, Harvey, Annabelle, Huguet, Guillaume, Urchs, Sebastian, Schultz, Laura, Sharmarke, Hanad, Jizi, Khadije, Martin, Charles-Olivier, Younis, Nadine, Tamer, Petra, Martineau, Jean-Louis, Orban, Pierre, Silva, Ana, Hall, Jeremy, van den Bree, Marianne, Owen, Michael, Linden, David, Lippé, Sarah, Almasy, Laura, Glahn, David, Thompson, Paul, Bourgeron, Thomas, Bellec, Pierre, Jacquemont, Sebastien, and Bearden, Carrie

Subjects
autism spectrum disorder, copy-number variant, functional connectivity, pleiotropy, psychiatry, Humans, Genetic Pleiotropy, Magnetic Resonance Imaging, Mental Disorders, Brain, Connectome
Abstract

Pleiotropy occurs when a genetic variant influences more than one trait. This is a key property of the genomic architecture of psychiatric disorders and has been observed for rare and common genomic variants. It is reasonable to hypothesize that the microscale genetic overlap (pleiotropy) across psychiatric conditions and cognitive traits may lead to similar overlaps at the macroscale brain level such as large-scale brain functional networks. We took advantage of brain connectivity, measured by resting-state functional MRI to measure the effects of pleiotropy on large-scale brain networks, a putative step from genes to behaviour. We processed nine resting-state functional MRI datasets including 32 726 individuals and computed connectome-wide profiles of seven neuropsychiatric copy-number-variants, five polygenic scores, neuroticism and fluid intelligence as well as four idiopathic psychiatric conditions. Nine out of 19 pairs of conditions and traits showed significant functional connectivity correlations (rFunctional connectivity), which could be explained by previously published levels of genomic (rGenetic) and transcriptomic (rTranscriptomic) correlations with moderate to high concordance: rGenetic-rFunctional connectivity = 0.71 [0.40-0.87] and rTranscriptomic-rFunctional connectivity = 0.83 [0.52; 0.94]. Extending this analysis to functional connectivity profiles associated with rare and common genetic risk showed that 30 out of 136 pairs of connectivity profiles were correlated above chance. These similarities between genetic risks and psychiatric disorders at the connectivity level were mainly driven by the overconnectivity of the thalamus and the somatomotor networks. Our findings suggest a substantial genetic component for shared connectivity profiles across conditions and traits, opening avenues to delineate general mechanisms-amenable to intervention-across psychiatric conditions and genetic risks.

Published
2023

10. Genetic Heterogeneity Shapes Brain Connectivity in Psychiatry.

Author

Moreau, Clara, Harvey, Annabelle, Kumar, Kuldeep, Huguet, Guillaume, Urchs, Sebastian, Douard, Elise, Schultz, Laura, Sharmarke, Hanad, Jizi, Khadije, Martin, Charles-Olivier, Younis, Nadine, Tamer, Petra, Rolland, Thomas, Martineau, Jean-Louis, Orban, Pierre, Silva, Ana, Hall, Jeremy, van den Bree, Marianne, Owen, Michael, Linden, David, Labbe, Aurelie, Lippé, Sarah, Almasy, Laura, Glahn, David, Thompson, Paul, Bourgeron, Thomas, Bellec, Pierre, Jacquemont, Sebastien, and Bearden, Carrie

Subjects
Autism spectrum disorder, Copy number variant, Functional connectivity, Genetic heterogeneity, Polygenic score, Transdiagnostic approach, Humans, Genetic Heterogeneity, Genetic Predisposition to Disease, Multifactorial Inheritance, Brain, DNA Copy Number Variations, Psychiatry, Genome-Wide Association Study
Abstract

BACKGROUND: Polygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically informed approaches have been implemented in neuroimaging studies to address this issue. However, the effects on functional connectivity of rare and common genetic risks for psychiatric disorders are largely unknown. Our objectives were to estimate and compare the effect sizes on brain connectivity of psychiatric genomic risk factors with various levels of complexity: oligogenic copy number variants (CNVs), multigenic CNVs, and polygenic risk scores (PRSs) as well as idiopathic psychiatric conditions and traits. METHODS: Resting-state functional magnetic resonance imaging data were processed using the same pipeline across 9 datasets. Twenty-nine connectome-wide association studies were performed to characterize the effects of 15 CNVs (1003 carriers), 7 PRSs, 4 idiopathic psychiatric conditions (1022 individuals with autism, schizophrenia, bipolar conditions, or attention-deficit/hyperactivity disorder), and 2 traits (31,424 unaffected control subjects). RESULTS: Effect sizes on connectivity were largest for psychiatric CNVs (estimates: 0.2-0.65 z score), followed by psychiatric conditions (0.15-0.42), neuroticism and fluid intelligence (0.02-0.03), and PRSs (0.01-0.02). Effect sizes of CNVs on connectivity were correlated to their effects on cognition and risk for disease (r = 0.9, p = 5.93 × 10-6). However, effect sizes of CNVs adjusted for the number of genes significantly decreased from small oligogenic to large multigenic CNVs (r = -0.88, p = 8.78 × 10-6). PRSs had disproportionately low effect sizes on connectivity compared with CNVs conferring similar risk for disease. CONCLUSIONS: Heterogeneity and polygenicity affect our ability to detect brain connectivity alterations underlying psychiatric manifestations.

Published
2023

11. Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features

Author

Kamarajan, Chella, Pandey, Ashwini K, Chorlian, David B, Meyers, Jacquelyn L, Kinreich, Sivan, Pandey, Gayathri, de Viteri, Stacey Subbie-Saenz, Zhang, Jian, Kuang, Weipeng, Barr, Peter B, Aliev, Fazil, Anokhin, Andrey P, Plawecki, Martin H, Kuperman, Samuel, Almasy, Laura, Merikangas, Alison, Brislin, Sarah J, Bauer, Lance, Hesselbrock, Victor, Chan, Grace, Kramer, John, Lai, Dongbing, Hartz, Sarah, Bierut, Laura J, McCutcheon, Vivia V, Bucholz, Kathleen K, Dick, Danielle M, Schuckit, Marc A, Edenberg, Howard J, and Porjesz, Bernice

Subjects
Basic Behavioral and Social Science, Brain Disorders, Behavioral and Social Science, Substance Misuse, Neurosciences, Prevention, Mental Health, Alcoholism, Alcohol Use and Health, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Neurological, Mental health, Good Health and Well Being, alcohol use disorder, EEG source functional connectivity, default mode network, alcohol-related memory problems, random forests, Psychology, Cognitive Sciences
Abstract

Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.

Published
2023

13. A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels

Author

Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Blue, Nathan, Boerwinkle, Eric, Bowden, Donald W., Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April P., Carty, Cara, Casaburi, Richard, Casas Romero, Juan P, Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Ida Chen, Yii-Der, Cho, Michael, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Chung, Ren-Hua, Clish, Clary, Comhair, Suzy, Conomos, Matthew, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, de las Fuentes, Lisa, de Vries, Paul, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Dugan-Perez, Shannon, Duggirala, Ravi, Durda, Jon Peter, Dutcher, Susan K., Eaton, Charles, Ekunwe, Lynette, El Boueiz, Adel, Ellinor, Patrick, Emery, Leslie, Erzurum, Serpil, Farber, Charles, Farek, Jesse, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Gelb, Bruce, Geraci, Mark, Germer, Soren, Gerszten, Robert, Ghosh, Auyon, Gibbs, Richard, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Graw, Sharon, Gray, Kathryn J., Grine, Daniel, Gross, Colin, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Gupta, Namrata, Haessler, Jeff, Hall, Michael, Han, Yi, Hanly, Patrick, Harris, Daniel, Hawley, Nicola L., He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hobbs, Brian, Hokanson, John, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Kramer, Holly, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Jiwon, Lee, Sandra, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Dan, Lewis, Joshua, Li, Xiaohui, Li, Yun, Lin, Henry, Lin, Honghuang, Lin, Xihong, Liu, Simin, Liu, Yongmei, Liu, Yu, Loos, Ruth J. F., Lubitz, Steven, Lunetta, Kathryn, Luo, James, Magalang, Ulysses, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, Mathias, Rasika, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, McGoldrick, Daniel, McHugh, Caitlin, McNeil, Becky, Mei, Hao, Meigs, James, Menon, Vipin, Mestroni, Luisa, Metcalf, Ginger, Meyers, Deborah A, Mignot, Emmanuel, Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L, Mitchell, Braxton D., Moll, Matt, Momin, Zeineen, Montasser, May E., Montgomery, Courtney, Muzny, Donna, Mychaleckyj, Josyf C, Nadkarni, Girish, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nekhai, Sergei, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nickerson, Deborah, Nkechinyere, Osuji, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pack, Allan, Paik, David T., Palmer, Nicholette, Pankow, James, Papanicolaou, George, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S, Pleiness, Jacob, Pollin, Toni, Post, Wendy, Becker, Julia Powers, Boorgula, Meher Preethi, Preuss, Michael, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Rajendran, Mahitha, Ramachandran, Vasan S., Rao, D. C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Reeves, Catherine, Regan, Elizabeth, Reiner, Alex, Reupena, Muagututi‘a Sefuiva, Rice, Ken, Rich, Stephen, Robillard, Rebecca, Robine, Nicolas, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sabino, Ester Cerdeira, Saleheen, Danish, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay G., Santibanez, Jireh, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sériès, Frédéric, Sheehan, Vivien, Sherman, Stephanie L., Shetty, Amol, Shetty, Aniket, Hui-Heng Sheu, Wayne, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Skomro, Robert, Smith, Albert Vernon, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Snyder, Michael, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent D., Taylor, Matthew, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tishkoff, Sarah, Tiwari, Hemant, Tong, Catherine, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, Van Den Berg, David, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Wang, Jiongming, Watson, Karol, Watt, Jennifer, Weeks, Daniel E., Weinstock, Joshua, Weir, Bruce, Weiss, Scott T, Weng, Lu-Chen, Wessel, Jennifer, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Joseph, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yu, Ketian, Zekavat, Seyedeh Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Ziv, Elad, Zody, Michael, Zoellner, Sebastian, Lindstrom, Sara, Wang, Lu, Smith, Erin N., Gordon, William, van Hylckama Vlieg, Astrid, Brody, Jennifer A., Pattee, Jack W., Haessler, Jeffrey, Brumpton, Ben M., Chasman, Daniel I., Suchon, Pierre, Chen, Ming-Huei, Turman, Constance, Germain, Marine, Wiggins, Kerri L., MacDonald, James, Braekkan, Sigrid K., Armasu, Sebastian M., Pankratz, Nathan, Jackson, Rabecca D., Nielsen, Jonas B., Giulianini, Franco, Puurunen, Marja K., Ibrahim, Manal, Heckbert, Susan R., Bammler, Theo K., Frazer, Kelly A., McCauley, Bryan M., Taylor, Kent, Pankow, James S., Reiner, Alexander P., Gabrielsen, Maiken E., Deleuze, Jean-François, O'Donnell, Chris J., Kim, Jihye, McKnight, Barbara, Kraft, Peter, Hansen, John-Bjarne, Rosendaal, Frits R., Heit, John A., Psaty, Bruce M., Tang, Weihong, Hveem, Kristian, Ridker, Paul M., Morange, Pierre-Emmanuel, Johnson, Andrew D., Kabrhel, Christopher, AlexandreTrégouët, David, Smith, Nicholas L., de Vries, Paul S., Reventun, Paula, Brown, Michael R., Heath, Adam S., Huffman, Jennifer E., Le, Ngoc-Quynh, Bebo, Allison, Temprano-Sagrera, Gerard, Raffield, Laura M., Ozel, Ayse Bilge, Thibord, Florian, Lewis, Joshua P., Rodriguez, Benjamin A. T., Polasek, Ozren, Yanek, Lisa R., Carrasquilla, German D., Marioni, Riccardo E., Kleber, Marcus E., Trégouët, David-Alexandre, Yao, Jie, Li-Gao, Ruifang, Joshi, Peter K., Trompet, Stella, Martinez-Perez, Angel, Ghanbari, Mohsen, Howard, Tom E., Reiner, Alex P., Arvanitis, Marios, Ryan, Kathleen A., Bartz, Traci M., Rudan, Igor, Faraday, Nauder, Linneberg, Allan, Davies, Gail, Delgado, Graciela E., Klaric, Lucija, Noordam, Raymond, van Rooij, Frank, Curran, Joanne E., Wheeler, Marsha M., Osburn, William O., O'Connell, Jeffrey R., Beswick, Andrew, Kolcic, Ivana, Souto, Juan Carlos, Becker, Lewis C., Hansen, Torben, Doyle, Margaret F., Harris, Sarah E., Moissl, Angela P., Rich, Stephen S., Campbell, Harry, Stott, David J., Soria, Jose Manuel, de Maat, Moniek P. M., Brody, Lawrence C., Auer, Paul L., Ben-Shlomo, Yoav, Hayward, Caroline, Mathias, Rasika A., Kilpeläinen, Tuomas O., Lange, Leslie A., Cox, Simon R., März, Winfried, Rotter, Jerome I., Mook-Kanamori, Dennis O., Wilson, James F., van der Harst, Pim, Jukema, J. Wouter, Ikram, M. Arfan, Desch, Karl C., Sabater-Lleal, Maria, Lowenstein, Charles J., and Morrison, Alanna C.

Published
2024
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14. Genes To Mental Health (G2MH): A Framework to Map the Combined Effects of Rare and Common Variants on Dimensions of Cognition and Psychopathology

Author

Jacquemont, Sébastien, Huguet, Guillaume, Klein, Marieke, Chawner, Samuel JRA, Donald, Kirsten A, van den Bree, Marianne BM, Sebat, Jonathan, Ledbetter, David H, Constantino, John N, Earl, Rachel K, McDonald-McGinn, Donna M, van Amelsvoort, Therese, Swillen, Ann, O’Donnell-Luria, Anne H, Glahn, David C, Almasy, Laura, Eichler, Evan E, Scherer, Stephen W, Robinson, Elise, Bassett, Anne S, Martin, Christa Lese, Finucane, Brenda, Vorstman, Jacob AS, Bearden, Carrie E, and Gur, Raquel E

Subjects
Clinical and Health Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Behavioral and Social Science, Biotechnology, Human Genome, Brain Disorders, Mental Health, Genetics, Basic Behavioral and Social Science, Mental health, Good Health and Well Being, Cognition, Humans, Mental Disorders, Psychiatry, Psychopathology, Genes to Mental Health Network, Autism Spectrum Disorder, Diagnosis and Classification, Genetics/Genomics, Intellectual Disabilities, Neurodevelopmental Disorders, Schizophrenia Spectrum and Other Psychotic Disorders, Medical and Health Sciences, Psychology and Cognitive Sciences, Clinical sciences, Clinical and health psychology
Abstract

Rare genomic disorders (RGDs) confer elevated risk for neurodevelopmental psychiatric disorders. In this era of intense genomics discoveries, the landscape of RGDs is rapidly evolving. However, there has not been comparable progress to date in scalable, harmonized phenotyping methods. As a result, beyond associations with categorical diagnoses, the effects on dimensional traits remain unclear for many RGDs. The nature and specificity of RGD effects on cognitive and behavioral traits is an area of intense investigation: RGDs are frequently associated with more than one psychiatric condition, and those studied to date affect, to varying degrees, a broad range of developmental and cognitive functions. Although many RGDs have large effects, phenotypic expression is typically influenced by additional genomic and environmental factors. There is emerging evidence that using polygenic risk scores in individuals with RGDs offers opportunities to refine prediction, thus allowing for the identification of those at greatest risk of psychiatric illness. However, translation into the clinic is hindered by roadblocks, which include limited genetic testing in clinical psychiatry, and the lack of guidelines for following individuals with RGDs, who are at high risk of developing psychiatric symptoms. The Genes to Mental Health Network (G2MH) is a newly funded National Institute of Mental Health initiative that will collect, share, and analyze large-scale data sets combining genomics and dimensional measures of psychopathology spanning diverse populations and geography. The authors present here the most recent understanding of the effects of RGDs on dimensional behavioral traits and risk for psychiatric conditions and discuss strategies that will be pursued within the G2MH network, as well as how expected results can be translated into clinical practice to improve patient outcomes.

Published
2022

15. Whole genome sequence analysis of platelet traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) initiative

Author

Little, Amarise, Hu, Yao, Sun, Quan, Jain, Deepti, Broome, Jai, Chen, Ming-Huei, Thibord, Florian, McHugh, Caitlin, Surendran, Praveen, Blackwell, Thomas W, Brody, Jennifer A, Bhan, Arunoday, Chami, Nathalie, de Vries, Paul S, Ekunwe, Lynette, Heard-Costa, Nancy, Hobbs, Brian D, Manichaikul, Ani, Moon, Jee-Young, Preuss, Michael H, Ryan, Kathleen, Wang, Zhe, Wheeler, Marsha, Yanek, Lisa R, Abecasis, Goncalo R, Almasy, Laura, Beaty, Terri H, Becker, Lewis C, Blangero, John, Boerwinkle, Eric, Butterworth, Adam S, Choquet, Hélène, Correa, Adolfo, Curran, Joanne E, Faraday, Nauder, Fornage, Myriam, Glahn, David C, Hou, Lifang, Jorgenson, Eric, Kooperberg, Charles, Lewis, Joshua P, Lloyd-Jones, Donald M, Loos, Ruth JF, Min, Yuan-I, Mitchell, Braxton D, Morrison, Alanna C, Nickerson, Deborah A, North, Kari E, O'Connell, Jeffrey R, Pankratz, Nathan, Psaty, Bruce M, Vasan, Ramachandran S, Rich, Stephen S, Rotter, Jerome I, Smith, Albert V, Smith, Nicholas L, Tang, Hua, Tracy, Russell P, Conomos, Matthew P, Laurie, Cecelia A, Mathias, Rasika A, Li, Yun, Auer, Paul L, Consortium, NHLBI Trans-Omics for Precision Medicine, Thornton, Timothy, Reiner, Alexander P, Johnson, Andrew D, and Raffield, Laura M

Subjects
Biological Sciences, Genetics, Hematology, Biotechnology, Clinical Research, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Blood Platelets, Genome-Wide Association Study, Humans, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Polymorphism, Single Nucleotide, Precision Medicine, United States, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Medical and Health Sciences, Genetics & Heredity
Abstract

Platelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing (WGS) from NHLBI's Trans-Omics for Precision Medicine initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet-related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several genome-wide association study identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of WGS in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.

Published
2022

16. Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease

Author

Katz, Daniel H, Tahir, Usman A, Bick, Alexander G, Pampana, Akhil, Ngo, Debby, Benson, Mark D, Yu, Zhi, Robbins, Jeremy M, Chen, Zsu-Zsu, Cruz, Daniel E, Deng, Shuliang, Farrell, Laurie, Sinha, Sumita, Schmaier, Alec A, Shen, Dongxiao, Gao, Yan, Hall, Michael E, Correa, Adolfo, Tracy, Russell P, Durda, Peter, Taylor, Kent D, Liu, Yongmei, Johnson, W Craig, Guo, Xiuqing, Yao, Jie, Ida Chen, Yii-Der, Manichaikul, Ani W, Jain, Deepti, Bouchard, Claude, Sarzynski, Mark A, Rich, Stephen S, Rotter, Jerome I, Wang, Thomas J, Wilson, James G, Natarajan, Pradeep, Gerszten, Robert E, Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April, Carty, Cara, Casaburi, Richard, Casas Romero, Juan P, Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, and Chavan, Sameer

Subjects
Epidemiology, Health Sciences, Aging, Heart Disease - Coronary Heart Disease, Human Genome, Genetics, Prevention, Cardiovascular, Heart Disease, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Adult, Black People, Cardiovascular Diseases, Female, Genome-Wide Association Study, Humans, Male, Proteome, cardiovascular disease, genetics, proteomics, race and ethnicity, National Heart, Lung, and Blood Institute TOPMed (Trans-Omics for Precision Medicine) Consortium†, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences, Sports science and exercise
Abstract

BackgroundPlasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants.MethodsProteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics).ResultsWe identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10-11. These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P=3.27×10-30) and MMP-3 (β=-0.60±0.05, P=1.67×10-32), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P=1.34×10-17) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure.ConclusionsTaken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.

Published
2022

17. Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program

Author

Wheeler, Marsha M, Stilp, Adrienne M, Rao, Shuquan, Halldórsson, Bjarni V, Beyter, Doruk, Wen, Jia, Mihkaylova, Anna V, McHugh, Caitlin P, Lane, John, Jiang, Min-Zhi, Raffield, Laura M, Jun, Goo, Sedlazeck, Fritz J, Metcalf, Ginger, Yao, Yao, Bis, Joshua B, Chami, Nathalie, de Vries, Paul S, Desai, Pinkal, Floyd, James S, Gao, Yan, Kammers, Kai, Kim, Wonji, Moon, Jee-Young, Ratan, Aakrosh, Yanek, Lisa R, Almasy, Laura, Becker, Lewis C, Blangero, John, Cho, Michael H, Curran, Joanne E, Fornage, Myriam, Kaplan, Robert C, Lewis, Joshua P, Loos, Ruth JF, Mitchell, Braxton D, Morrison, Alanna C, Preuss, Michael, Psaty, Bruce M, Rich, Stephen S, Rotter, Jerome I, Tang, Hua, Tracy, Russell P, Boerwinkle, Eric, Abecasis, Goncalo R, Blackwell, Thomas W, Smith, Albert V, Johnson, Andrew D, Mathias, Rasika A, Nickerson, Deborah A, Conomos, Matthew P, Li, Yun, Þorsteinsdóttir, Unnur, Magnússon, Magnús K, Stefansson, Kari, Pankratz, Nathan D, Bauer, Daniel E, Auer, Paul L, and Reiner, Alex P

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Hematology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Cardiovascular, Good Health and Well Being, Humans, Genome-Wide Association Study, Whole Genome Sequencing, Blood Cells
Abstract

Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

Published
2022

18. Whole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program

Author

Mikhaylova, Anna V, McHugh, Caitlin P, Polfus, Linda M, Raffield, Laura M, Boorgula, Meher Preethi, Blackwell, Thomas W, Brody, Jennifer A, Broome, Jai, Chami, Nathalie, Chen, Ming-Huei, Conomos, Matthew P, Cox, Corey, Curran, Joanne E, Daya, Michelle, Ekunwe, Lynette, Glahn, David C, Heard-Costa, Nancy, Highland, Heather M, Hobbs, Brian D, Ilboudo, Yann, Jain, Deepti, Lange, Leslie A, Miller-Fleming, Tyne W, Min, Nancy, Moon, Jee-Young, Preuss, Michael H, Rosen, Jonathon, Ryan, Kathleen, Smith, Albert V, Sun, Quan, Surendran, Praveen, de Vries, Paul S, Walter, Klaudia, Wang, Zhe, Wheeler, Marsha, Yanek, Lisa R, Zhong, Xue, Abecasis, Goncalo R, Almasy, Laura, Barnes, Kathleen C, Beaty, Terri H, Becker, Lewis C, Blangero, John, Boerwinkle, Eric, Butterworth, Adam S, Chavan, Sameer, Cho, Michael H, Choquet, Hélène, Correa, Adolfo, Cox, Nancy, DeMeo, Dawn L, Faraday, Nauder, Fornage, Myriam, Gerszten, Robert E, Hou, Lifang, Johnson, Andrew D, Jorgenson, Eric, Kaplan, Robert, Kooperberg, Charles, Kundu, Kousik, Laurie, Cecelia A, Lettre, Guillaume, Lewis, Joshua P, Li, Bingshan, Li, Yun, Lloyd-Jones, Donald M, Loos, Ruth JF, Manichaikul, Ani, Meyers, Deborah A, Mitchell, Braxton D, Morrison, Alanna C, Ngo, Debby, Nickerson, Deborah A, Nongmaithem, Suraj, North, Kari E, O’Connell, Jeffrey R, Ortega, Victor E, Pankratz, Nathan, Perry, James A, Psaty, Bruce M, Rich, Stephen S, Soranzo, Nicole, Rotter, Jerome I, Silverman, Edwin K, Smith, Nicholas L, Tang, Hua, Tracy, Russell P, Thornton, Timothy A, Vasan, Ramachandran S, Zein, Joe, Mathias, Rasika A, Consortium, NHLBI Trans-Omics for Precision Medicine, Reiner, Alexander P, and Auer, Paul L

Subjects
Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Good Health and Well Being, Asthma, Biomarkers, Dermatitis, Atopic, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Leukocytes, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Polymorphism, Single Nucleotide, Prognosis, Proteome, Pulmonary Disease, Chronic Obstructive, Quantitative Trait Loci, United Kingdom, United States, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, blood-cell counts, whole-genome sequencing, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Many common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.

Published
2021

19. Whole-genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles

Author

Huffman, Jennifer E., Nicholas, Jayna, Hahn, Julie, Heath, Adam S., Raffield, Laura M., Yanek, Lisa R., Brody, Jennifer A., Thibord, Florian, Almasy, Laura, Bartz, Traci M., Bielak, Lawrence F., Bowler, Russell P., Carrasquilla, Germán D., Chasman, Daniel I., Chen, Ming-Huei, Emmert, David B., Ghanbari, Mohsen, Haessler, Jeffrey, Hottenga, Jouke-Jan, Kleber, Marcus E., Le, Ngoc-Quynh, Lee, Jiwon, Lewis, Joshua P., Li-Gao, Ruifang, Luan, Jian'an, Malmberg, Anni, Mangino, Massimo, Marioni, Riccardo E., Martinez-Perez, Angel, Pankratz, Nathan, Polasek, Ozren, Richmond, Anne, Rodriguez, Benjamin A. T., Rotter, Jerome I., Steri, Maristella, Suchon, Pierre, Trompet, Stella, Weiss, Stefan, Zare, Marjan, Auer, Paul, Cho, Michael H., Christofidou, Paraskevi, Davies, Gail, de Geus, Eco, Deleuze, Jean-François, Delgado, Graciela E., Ekunwe, Lynette, Faraday, Nauder, Gögele, Martin, Greinacher, Andreas, Gao, He, Howard, Tom, Joshi, Peter K., Kilpeläinen, Tuomas O., Lahti, Jari, Linneberg, Allan, Naitza, Silvia, Noordam, Raymond, Paüls-Vergés, Ferran, Rich, Stephen S., Rosendaal, Frits R., Rudan, Igor, Ryan, Kathleen A., Souto, Juan Carlos, van Rooij, Frank J. A., Wang, Heming, Zhao, Wei, Becker, Lewis C., Beswick, Andrew, Brown, Michael R., Cade, Brian E., Campbell, Harry, Cho, Kelly, Crapo, James D., Curran, Joanne E., de Maat, Moniek P. M., Doyle, Margaret, Elliott, Paul, Floyd, James S., Fuchsberger, Christian, Grarup, Niels, Guo, Xiuqing, Harris, Sarah E., Hou, Lifang, Kolcic, Ivana, Kooperberg, Charles, Menni, Cristina, Nauck, Matthias, O'Connell, Jeffrey R., Orrù, Valeria, Psaty, Bruce M., Räikkönen, Katri, Smith, Jennifer A., Soria, Jose Manuel, Stott, David J., van Hylckama Vlieg, Astrid, Watkins, Hugh, Willemsen, Gonneke, Wilson, Peter W. F., Ben-Shlomo, Yoav, Blangero, John, Boomsma, Dorret, Cox, Simon R., Dehghan, Abbas, Eriksson, Johan G., Fiorillo, Edoardo, Fornage, Myriam, Hansen, Torben, Hayward, Caroline, Ikram, M. Arfan, Jukema, J. Wouter, Kardia, Sharon L. R., Lange, Leslie A., März, Winfried, Mathias, Rasika A., Mitchell, Braxton D., Mook-Kanamori, Dennis O., Morange, Pierre-Emmanuel, Pedersen, Oluf, Pramstaller, Peter P., Redline, Susan, Reiner, Alexander, Ridker, Paul M., Silverman, Edwin K., Spector, Tim D., Völker, Uwe, Wareham, Nicholas J., Wilson, James F., Yao, Jie, Trégouët, David-Alexandre, Johnson, Andrew D., Wolberg, Alisa S., de Vries, Paul S., Sabater-Lleal, Maria, Morrison, Alanna C., and Smith, Nicholas L.

Published
2024
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21. Whole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program

Author

Hu, Yao, Stilp, Adrienne M, McHugh, Caitlin P, Rao, Shuquan, Jain, Deepti, Zheng, Xiuwen, Lane, John, de Bellefon, Sébastian Méric, Raffield, Laura M, Chen, Ming-Huei, Yanek, Lisa R, Wheeler, Marsha, Yao, Yao, Ren, Chunyan, Broome, Jai, Moon, Jee-Young, de Vries, Paul S, Hobbs, Brian D, Sun, Quan, Surendran, Praveen, Brody, Jennifer A, Blackwell, Thomas W, Choquet, Hélène, Ryan, Kathleen, Duggirala, Ravindranath, Heard-Costa, Nancy, Wang, Zhe, Chami, Nathalie, Preuss, Michael H, Min, Nancy, Ekunwe, Lynette, Lange, Leslie A, Cushman, Mary, Faraday, Nauder, Curran, Joanne E, Almasy, Laura, Kundu, Kousik, Smith, Albert V, Gabriel, Stacey, Rotter, Jerome I, Fornage, Myriam, Lloyd-Jones, Donald M, Vasan, Ramachandran S, Smith, Nicholas L, North, Kari E, Boerwinkle, Eric, Becker, Lewis C, Lewis, Joshua P, Abecasis, Goncalo R, Hou, Lifang, O’Connell, Jeffrey R, Morrison, Alanna C, Beaty, Terri H, Kaplan, Robert, Correa, Adolfo, Blangero, John, Jorgenson, Eric, Psaty, Bruce M, Kooperberg, Charles, Walton, Russell T, Kleinstiver, Benjamin P, Tang, Hua, Loos, Ruth JF, Soranzo, Nicole, Butterworth, Adam S, Nickerson, Debbie, Rich, Stephen S, Mitchell, Braxton D, Johnson, Andrew D, Auer, Paul L, Li, Yun, Mathias, Rasika A, Lettre, Guillaume, Pankratz, Nathan, Laurie, Cathy C, Laurie, Cecelia A, Bauer, Daniel E, Conomos, Matthew P, Reiner, Alexander P, and Consortium, NHLBI Trans-Omics for Precision Medicine

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Biotechnology, Clinical Research, Hematology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Adult, Aged, Chromosomes, Human, Pair 16, Datasets as Topic, Erythrocytes, Female, Gene Editing, Genetic Variation, Genome-Wide Association Study, HEK293 Cells, Humans, Male, Middle Aged, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Quality Control, Reproducibility of Results, United States, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, base editing, red blood cell traits, whole-genome sequencing, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Whole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.

Published
2021

22. Genome‐wide admixture mapping of DSM‐IV alcohol dependence, criterion count, and the self‐rating of the effects of ethanol in African American populations

Author

Lai, Dongbing, Kapoor, Manav, Wetherill, Leah, Schwandt, Melanie, Ramchandani, Vijay A, Goldman, David, Chao, Michael, Almasy, Laura, Bucholz, Kathleen, Hart, Ronald P, Kamarajan, Chella, Meyers, Jacquelyn L, Nurnberger, John I, Tischfield, Jay, Edenberg, Howard J, Schuckit, Marc, Goate, Alison, Scott, Denise M, Porjesz, Bernice, Agrawal, Arpana, and Foroud, Tatiana

Subjects
Pharmacology and Pharmaceutical Sciences, Biological Sciences, Biomedical and Clinical Sciences, Genetics, Brain Disorders, Human Genome, Substance Misuse, Alcoholism, Alcohol Use and Health, Good Health and Well Being, Black or African American, Alcoholism, Case-Control Studies, Diagnostic and Statistical Manual of Mental Disorders, Ethanol, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Retrospective Studies, Self Report, White People, admixture mapping, African American, criterion count, DSM-IV alcohol dependence, response to ethanol, Clinical Sciences, Neurosciences, Clinical sciences
Abstract

African Americans (AA) have lower prevalence of alcohol dependence and higher subjective response to alcohol than European Americans. Genome-wide association studies (GWAS) have identified genes/variants associated with alcohol dependence specifically in AA; however, the sample sizes are still not large enough to detect variants with small effects. Admixture mapping is an alternative way to identify alcohol dependence genes/variants that may be unique to AA. In this study, we performed the first admixture mapping of DSM-IV alcohol dependence diagnosis, DSM-IV alcohol dependence criterion count, and two scores from the self-rating of effects of ethanol (SRE) as measures of response to alcohol: the first five times of using alcohol (SRE-5) and average of SRE across three times (SRE-T). Findings revealed a region on chromosome 4 that was genome-wide significant for SRE-5 (p value = 4.18E-05). Fine mapping did not identify a single causal variant to be associated with SRE-5; instead, conditional analysis concluded that multiple variants collectively explained the admixture mapping signal. PPARGC1A, a gene that has been linked to alcohol consumption in previous studies, is located in this region. Our finding suggests that admixture mapping is a useful tool to identify genes/variants that may have been missed by current GWAS approaches in admixed populations.

Published
2021

24. Epigenome-wide DNA methylation association study of circulating IgE levels identifies novel targets for asthma

Author

Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K., Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Blue, Nathan, Boerwinkle, Eric, Bowden, Donald W., Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April P., Carty, Cara, Casaburi, Richard, Casas Romero, Juan P., Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Ida Chen, Yii-Der, Cho, Michael, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Chung, Ren-Hua, Clish, Clary, Comhair, Suzy, Conomos, Matthew, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, de las Fuentes, Lisa, de Vries, Paul, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Dugan-Perez, Shannon, Duggirala, Ravi, Durda, Jon Peter, Dutcher, Susan K., Eaton, Charles, Ekunwe, Lynette, El Boueiz, Adel, Ellinor, Patrick, Emery, Leslie, Erzurum, Serpil, Farber, Charles, Farek, Jesse, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Gelb, Bruce, Geraci, Mark, Germer, Soren, Gerszten, Robert, Ghosh, Auyon, Gibbs, Richard, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Graw, Sharon, Gray, Kathryn J., Grine, Daniel, Gross, Colin, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Gupta, Namrata, Haessler, Jeff, Hall, Michael, Han, Yi, Hanly, Patrick, Harris, Daniel, Hawley, Nicola L., He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hobbs, Brian, Hokanson, John, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Kramer, Holly, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Jiwon, Lee, Sandra, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Daniel, Lewis, Joshua, Li, Xiaohui, Li, Yun, Lin, Henry, Lin, Honghuang, Lin, Xihong, Liu, Simin, Liu, Yongmei, Liu, Yu, Loos, Ruth J.F., Lubitz, Steven, Lunetta, Kathryn, Luo, James, Magalang, Ulysses, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, Mathias, Rasika, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, McGoldrick, Daniel, McHugh, Caitlin, McNeil, Becky, Mei, Hao, Meigs, James, Menon, Vipin, Mestroni, Luisa, Metcalf, Ginger, Meyers, Deborah A., Mignot, Emmanuel, Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L., Mitchell, Braxton D., Moll, Matt, Momin, Zeineen, Montasser, May E., Montgomery, Courtney, Muzny, Donna, Mychaleckyj, Josyf C., Nadkarni, Girish, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nekhai, Sergei, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nickerson, Deborah, Nkechinyere, Osuji, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pack, Allan, Paik, David T., Palmer, Nicholette, Pankow, James, Papanicolaou, George, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S., Pleiness, Jacob, Pollin, Toni, Post, Wendy, Powers Becker, Julia, Preethi Boorgula, Meher, Preuss, Michael, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Rajendran, Mahitha, Ramachandran, Vasan S., Rao, D.C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Reeves, Catherine, Regan, Elizabeth, Reiner, Alex, Reupena, Muagututi‘a Sefuiva, Rice, Ken, Rich, Stephen, Robillard, Rebecca, Robine, Nicolas, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sabino, Ester Cerdeira, Saleheen, Danish, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay G., Santibanez, Jireh, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sériès, Frédéric, Sheehan, Vivien, Sherman, Stephanie L., Shetty, Amol, Shetty, Aniket, Sheu, Wayne Hui-Heng, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Skomro, Robert, Smith, Albert Vernon, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Snyder, Michael, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent, Taylor, Matthew, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tishkoff, Sarah, Tiwari, Hemant, Tong, Catherine, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, Van Den Berg, David, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Wang, Jiongming, Watson, Karol, Watt, Jennifer, Weeks, Daniel E., Weinstock, Joshua, Weir, Bruce, Weiss, Scott T., Weng, Lu-Chen, Wessel, Jennifer, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Williams, Scott, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Baojun, Wu, Joseph, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yu, Ketian, Zekavat, Seyedeh Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Ziv, Elad, Zody, Michael, Zoellner, Sebastian, Recto, Kathryn, Kachroo, Priyadarshini, Huan, Tianxiao, Lee, Gha Young, Bui, Helena, Lee, Dong Heon, Gereige, Jessica, Yao, Chen, Hwang, Shih-Jen, Joehanes, Roby, O’Connor, George T., and DeMeo, Dawn L.

Published
2023
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26. De novo mutations across 1,465 diverse genomes reveal mutational insights and reductions in the Amish founder population

Author

Kessler, Michael D, Loesch, Douglas P, Perry, James A, Heard-Costa, Nancy L, Taliun, Daniel, Cade, Brian E, Wang, Heming, Daya, Michelle, Ziniti, John, Datta, Soma, Celedón, Juan C, Soto-Quiros, Manuel E, Avila, Lydiana, Weiss, Scott T, Barnes, Kathleen, Redline, Susan S, Vasan, Ramachandran S, Johnson, Andrew D, Mathias, Rasika A, Hernandez, Ryan, Wilson, James G, Nickerson, Deborah A, Abecasis, Goncalo, Browning, Sharon R, Zöllner, Sebastian, O’Connell, Jeffrey R, Mitchell, Braxton D, Lung, and Blood Institute Trans-Omics for Precision Medicine Consortium National Heart, Group, TOPMed Population Genetics Working, O’Connor, Timothy D, Aalbers, Sanne, Abdalla, Moustafa, Abdul-Rahman, Omar, Abecasis, Gonçalo, Abhyankar, Avinash, Adrianto, Indra, Aguet, Francois, Akers, Rachel, Al-Tobasei, Rafet, Albert, Christine, Aldred, Micheala, Almasy, Laura, Almeida, Marcio, Alonso, Alvaro, Ament, Seth, Ampleford, Elizabeth, An, Ping, Anderson, Christopher D, Andersson, Charlotte, Anugu, Pramod, Appelbaum, Elizabeth, Ardlie, Kristin, Arking, Dan, Armasu, Sebastian M, Arnett, Donna K, Arruda, Heather, Arvanitis, Marios, Ashley-Koch, Allison, Ashrani, Aneel, Aslibekyan, Stella, Assimes, Tim, Atkinson, Elizabeth, Auer, Paul, Austin, Thomas R, Avery, Christy, Avila-Pacheco, Julian, Avillach, Paul, Aviv, Abraham, Avramopoulos, Dimitrios, Ballantyne, Christie, Balte, Pallavi, Bamshad, Michael, Bancks, Mike, Barnard, John, Barr, R Graham, Barron-Casella, Emily, Bartz, Traci, Barwick, Lucas, Basu, Saonli, Battle, Alexis, Baumann, Michael, Beame, David, Beaty, Terri, Beck, Gerald, Becker, Lewis, Becker, Diane, Beer, Rebecca, Begum, Ferdouse, Beiser, Alexa, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Berk-Rauch, Hanna, Besich, Zachary M, Bezerra, Marcos, Bhatt, Surya, Bi, Wenjian, Bick, Alexander, and Bielak, Larry

Subjects
Biological Sciences, Genetics, Human Genome, Good Health and Well Being, Adult, Amish, Cohort Studies, DNA Mutational Analysis, Female, Genetics, Population, Genome, Human, Heterozygote, Humans, Male, Mutation, Pedigree, Whole Genome Sequencing, Young Adult, National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Population Genetics Working Group, de novo mutations, diversity, mutation rate, recombination
Abstract

De novo mutations (DNMs), or mutations that appear in an individual despite not being seen in their parents, are an important source of genetic variation whose impact is relevant to studies of human evolution, genetics, and disease. Utilizing high-coverage whole-genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) Program, we called 93,325 single-nucleotide DNMs across 1,465 trios from an array of diverse human populations, and used them to directly estimate and analyze DNM counts, rates, and spectra. We find a significant positive correlation between local recombination rate and local DNM rate, and that DNM rate explains a substantial portion (8.98 to 34.92%, depending on the model) of the genome-wide variation in population-level genetic variation from 41K unrelated TOPMed samples. Genome-wide heterozygosity does correlate with DNM rate, but only explains

Published
2020

27. Sibling comparisons elucidate the associations between educational attainment polygenic scores and alcohol, nicotine and cannabis.

Author

Salvatore, Jessica, Barr, Peter, Stephenson, Mallory, Aliev, Fazil, Kuo, Sally, Su, Jinni, Agrawal, Arpana, Almasy, Laura, Bierut, Laura, Bucholz, Kathleen, Chan, Grace, Edenberg, Howard, Johnson, Emma, McCutcheon, Vivia, Meyers, Jacquelyn, Tischfield, Jay, Wetherill, Leah, Dick, Danielle, and Schuckit, Marc

Subjects
Alcohol, Collaborative Study on the Genetics of Alcoholism, cannabis, nicotine, polygenic risk score, sibling comparisons, Adult, Aged, Aged, 80 and over, Alcoholism, Diagnostic and Statistical Manual of Mental Disorders, Educational Status, Female, Genome-Wide Association Study, Humans, Male, Marijuana Abuse, Middle Aged, Multifactorial Inheritance, Siblings, Substance-Related Disorders, Tobacco Use Disorder, United States, White People
Abstract

BACKGROUND AND AIMS: The associations between low educational attainment and substance use disorders (SUDs) may be related to a common genetic vulnerability. We aimed to elucidate the associations between polygenic scores for educational attainment and clinical criterion counts for three SUDs (alcohol, nicotine and cannabis). DESIGN: Polygenic association and sibling comparison methods. The latter strengthens inferences in observational research by controlling for confounding factors that differ between families. SETTING: Six sites in the United States. PARTICIPANTS: European ancestry participants aged 25 years and older from the Collaborative Study on the Genetics of Alcoholism (COGA). Polygenic association analyses included 5582 (54% female) participants. Sibling comparisons included 3098 (52% female) participants from 1226 sibling groups nested within the overall sample. MEASUREMENTS: Outcomes included criterion counts for DSM-5 alcohol use disorder (AUDSX), Fagerström nicotine dependence (NDSX) and DSM-5 cannabis use disorder (CUDSX). We derived polygenic scores for educational attainment (EduYears-GPS) using summary statistics from a large (> 1 million) genome-wide association study of educational attainment. FINDINGS: In polygenic association analyses, higher EduYears-GPS predicted lower AUDSX, NDSX and CUDSX [P

Published
2020

28. Whole Genome Sequencing Identifies CRISPLD2 as a Lung Function Gene in Children With Asthma

Author

Kachroo, Priyadarshini, Hecker, Julian, Chawes, Bo L, Ahluwalia, Tarunveer S, Cho, Michael H, Qiao, Dandi, Kelly, Rachel S, Chu, Su H, Virkud, Yamini V, Huang, Mengna, Barnes, Kathleen C, Burchard, Esteban G, Eng, Celeste, Hu, Donglei, Celedón, Juan C, Daya, Michelle, Levin, Albert M, Gui, Hongsheng, Williams, L Keoki, Forno, Erick, Mak, Angel CY, Avila, Lydiana, Soto-Quiros, Manuel E, Cloutier, Michelle M, Acosta-Pérez, Edna, Canino, Glorisa, Bønnelykke, Klaus, Bisgaard, Hans, Raby, Benjamin A, Lange, Christoph, Weiss, Scott T, Lasky-Su, Jessica A, National Heart, Lung, Abe, Namiko, Abecasis, Goncalo, Albert, Christine, Allred, Nicholette Palmer, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Barnard, John, Barnes, Kathleen, Barr, R Graham, Barron-Casella, Emily, Beaty, Terri, Becker, Diane, Becker, Lewis, Beer, Rebecca, Begum, Ferdouse, Beitelshees, Amber, Benjamin, Emelia, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Boerwinkle, Eric, Borecki, Ingrid, Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Bunting, Karen, Burchard, Esteban, Cardwell, Jonathan, Carty, Cara, Casaburi, Richard, Casella, James, Chaffin, Mark, Chang, Christy, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Chen, Yii-Der Ida, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, and Das, Sayantan

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Clinical Research, Asthma, Genetics, Pediatric, Biotechnology, Human Genome, Lung, 2.1 Biological and endogenous factors, Respiratory, Adolescent, Adult, Cell Adhesion Molecules, Child, Child, Preschool, Costa Rica, Female, Forced Expiratory Volume, Humans, Interferon Regulatory Factors, Male, Middle Aged, Respiratory Physiological Phenomena, Vital Capacity, Whole Genome Sequencing, Young Adult, airway hyperresponsiveness, asthma, lung function, whole genome sequencing, National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Consortium, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundAsthma 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.MethodsWGS 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.ResultsA genome-wide significant association was identified between baseline FEV1/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 FEV1 (P = 3.3 × 10-3), postbronchodilator (PB) FEV1 (7.3 × 10-3), and PB FEV1/FVC ratio (P = 2.7 × 10-3). The identified baseline FEV1/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.ConclusionsThese 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

30. 295. Rare Variant Genetic Architecture of the Human Cortical MRI Phenotypes in General Population

Author

Kumar, Kuldeep, primary, Kazem, Sayeh, additional, Liao, Zhijie, additional, Kopal, Jakub, additional, Huguet, Guillaume, additional, Renne, Thomas, additional, Jean-Louis, Martineau, additional, Xie, Zhe, additional, Saci, Zohra, additional, Almasy, Laura, additional, Glahn, David, additional, Paus, Tomas, additional, Dumas, Guillaume, additional, Bearden, Carrie, additional, Thompson, Paul, additional, Bethlehem, Richard, additional, Warrier, Varun, additional, and Jacquemont, Sebastien, additional

Published
2024
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37. Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes

Author

Huguet, Guillaume, primary, Renne, Thomas, additional, Poulain, Cecile, additional, Dubuc, Alma, additional, Kumar, Kuldeep, additional, Kazem, Sayeh, additional, Engchuan, Worrawat Bank, additional, Shanta, Omar, additional, Douard, Elise, additional, Proulx, Catherine, additional, Jean-Louis, Martineau, additional, Saci, Zohra, additional, Mollon, Josephine, additional, Schultz, Laura M, additional, Knowles, Emma EM, additional, Cox, Simon R, additional, Porteous, David, additional, Davies, Gail, additional, Redmond, Paul, additional, Harris, Sarah E, additional, Schumann, Gunter, additional, Dumas, Guillaume, additional, Labbe, Aurelie, additional, Pausova, Zdenka, additional, Paus, Tomas, additional, Scherer, Stephen W., additional, Sebat, Jonathan, additional, Almasy, Laura, additional, Glahn, David C, additional, and Jacquemont, Sebastien, additional

Published
2024
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39. A genetic association study of circulating coagulation Factor VIII and von Willebrand Factor levels

Author

de Vries, Paul S., primary, Reventun, Paula, additional, Brown, Michael R, additional, Heath, Adam S, additional, Huffman, Jennifer E., additional, Le, Ngoc-Quynh, additional, Bebo, Allison, additional, Brody, Jennifer A, additional, Temprano-Sagrera, Gerard, additional, Raffield, Laura M, additional, Ozel, Ayse Bilge, additional, Thibord, Florian, additional, Jain, Deepti, additional, Lewis, Joshua P, additional, Rodriguez, Benjamin A.T., additional, Pankratz, Nathan, additional, Taylor, Kent D, additional, Polasek, Ozren, additional, Chen, Ming-Huei, additional, Yanek, Lisa R, additional, Carrasquilla, German, additional, Marioni, Riccardo, additional, Kleber, Marcus E, additional, Trégouët, David-Alexandre, additional, Yao, Jie, additional, Li-Gao, Ruifang, additional, Joshi, Peter K, additional, Trompet, Stella, additional, Martinez-Perez, Angel, additional, Ghanbari, Mohsen, additional, Howard, Tom E., additional, Reiner, Alex P, additional, Arvanitis, Marios, additional, Ryan, Kathleen A, additional, Bartz, Traci M, additional, Rudan, Igor, additional, Faraday, Nauder, additional, Linneberg, Allan, additional, Ekunwe, Lynette, additional, Davies, Gail, additional, Delgado, Graciela E, additional, Suchon, Pierre, additional, Guo, Xiuqing, additional, Rosendaal, Frits R., additional, Klaric, Lucija, additional, Noordam, Raymond, additional, van Rooij, Frank J.A., additional, Curran, Joanne, additional, Wheeler, Marsha Maria, additional, Osburn, William O, additional, O'Connell, Jeffrey R, additional, Boerwinkle, Eric, additional, Beswick, Andrew, additional, Psaty, Bruce M., additional, Kolcic, Ivana, additional, Souto, Juan Carlos Carlos, additional, Becker, Lewis, additional, Hansen, Torben, additional, Doyle, Margaret F., additional, Harris, Sarah, additional, Moissl, Angela Patricia, additional, Deleuze, Jean-François, additional, Rich, Stephen S, additional, van Hylckama Vlieg, Astrid, additional, Campbell, Harry, additional, Stott, David, additional, Soria, Jose Manuel, additional, de Maat, Moniek P.M., additional, Almasy, Laura, additional, Brody, Lawrence C., additional, Auer, Paul, additional, Mitchell, Braxton D, additional, Ben-Shlomo, Yoav, additional, Fornage, Myriam, additional, Hayward, Caroline, additional, Mathias, Rasika, additional, Kilpeläinen, Tuomas O, additional, Lange, Leslie, additional, Cox, Simon R, additional, Maerz, Winfried, additional, Morange, Pierre-Emmanuel, additional, Rotter, Jerome I, additional, Mook-Kanamori, Dennis O, additional, Wilson, James, additional, van der Harst, Pim, additional, Jukema, Johan Wouter W., additional, Ikram, Mohammad Arfan, additional, Blangero, John, additional, Kooperberg, Charles, additional, Desch, Karl, additional, Johnson, Andrew D., additional, Sabater-Lleal, Maria, additional, Lowenstein, Charles, additional, Smith, Nicholas L., additional, and Morrison, Alanna, additional

Published
2024
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40. Imaging local genetic influences on cortical folding

Author

Alexander-Bloch, Aaron F., Raznahan, Armin, Vandekar, Simon N., Seidlitz, Jakob, Lu, Zhixin, Mathias, Samuel R., Knowles, Emma, Mollon, Josephine, Rodrigue, Amanda, Curran, Joanne E., Görring, Harald H. H., Satterthwaite, Theodore D., Gur, Raquel E., Bassett, Danielle S., Hoftman, Gil D., Pearlson, Godfrey, Shinohara, Russell T., Liu, Siyuan, Fox, Peter T., Almasy, Laura, Blangero, John, and Glahn, David C.

Published
2020

41. Genome-wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability

Author

Huguet, Guillaume, Schramm, Catherine, Douard, Elise, Tamer, Petra, Main, Antoine, Monin, Pauline, England, Jade, Jizi, Khadije, Renne, Thomas, Poirier, Myriam, Nowak, Sabrina, Martin, Charles-Olivier, Younis, Nadine, Knoth, Inga Sophia, Jean-Louis, Martineau, Saci, Zohra, Auger, Maude, Tihy, Frédérique, Mathonnet, Géraldine, Maftei, Catalina, Léveillé, France, Porteous, David, Davies, Gail, Redmond, Paul, Harris, Sarah E., Hill, W. David, Lemyre, Emmanuelle, Schumann, Gunter, Bourgeron, Thomas, Pausova, Zdenka, Paus, Tomas, Karama, Sherif, Lippe, Sarah, Deary, Ian J., Almasy, Laura, Labbe, Aurélie, Glahn, David, Greenwood, Celia M. T., and Jacquemont, Sébastien

Published
2021
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44. Automated Cmr-based Measurements Are Predictive of Pulmonic Valve Replacement in Repaired Tetralogy of Fallot

Author

Thompson, Elizabeth, primary, Kim, Jin-seo, additional, Bhattaru, Abhijit, additional, Vu, Phuong, additional, Swago, Sophia, additional, Donnelly, Elizabeth, additional, zhang, Xuemei, additional, Vuthuri, Lipika, additional, Lanzilotta, Kristen, additional, Loth, Annefleur, additional, Whitehead, Kevin, additional, Duda, Jeffrey, additional, Gee, James, additional, Almasy, Laura, additional, Goldmuntz, Elizabeth, additional, Fogel, Mark, additional, and Witschey, Walter, additional

Published
2024
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45. Prosaposin is a regulator of progranulin levels and oligomerization.

Author

Nicholson, Alexandra M, Finch, NiCole A, Almeida, Marcio, Perkerson, Ralph B, van Blitterswijk, Marka, Wojtas, Aleksandra, Cenik, Basar, Rotondo, Sergio, Inskeep, Venette, Almasy, Laura, Dyer, Thomas, Peralta, Juan, Jun, Goo, Wood, Andrew R, Frayling, Timothy M, Fuchsberger, Christian, Fowler, Sharon, Teslovich, Tanya M, Manning, Alisa K, Kumar, Satish, Curran, Joanne, Lehman, Donna, Abecasis, Goncalo, Duggirala, Ravindranath, Pottier, Cyril, Zahir, Haaris A, Crook, Julia E, Karydas, Anna, Mitic, Laura, Sun, Ying, Dickson, Dennis W, Bu, Guojun, Herz, Joachim, Yu, Gang, Miller, Bruce L, Ferguson, Shawn, Petersen, Ronald C, Graff-Radford, Neill, Blangero, John, and Rademakers, Rosa

Subjects
Hela Cells, Animals, Mice, Knockout, Humans, Mice, Parkinson Disease, Alzheimer Disease, Saposins, Intercellular Signaling Peptides and Proteins, Polymorphism, Single Nucleotide, Gene Knockdown Techniques, Frontotemporal Dementia, Haploinsufficiency, Protein Interaction Maps, Progranulins, HeLa Cells, Biotechnology, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurosciences, Neurodegenerative, Brain Disorders, Acquired Cognitive Impairment, Dementia, 2.1 Biological and endogenous factors, Neurological, Knockout, Polymorphism, Single Nucleotide
Abstract

Progranulin (GRN) loss-of-function mutations leading to progranulin protein (PGRN) haploinsufficiency are prevalent genetic causes of frontotemporal dementia. Reports also indicated PGRN-mediated neuroprotection in models of Alzheimer's and Parkinson's disease; thus, increasing PGRN levels is a promising therapeutic for multiple disorders. To uncover novel PGRN regulators, we linked whole-genome sequence data from 920 individuals with plasma PGRN levels and identified the prosaposin (PSAP) locus as a new locus significantly associated with plasma PGRN levels. Here we show that both PSAP reduction and overexpression lead to significantly elevated extracellular PGRN levels. Intriguingly, PSAP knockdown increases PGRN monomers, whereas PSAP overexpression increases PGRN oligomers, partly through a protein-protein interaction. PSAP-induced changes in PGRN levels and oligomerization replicate in human-derived fibroblasts obtained from a GRN mutation carrier, further supporting PSAP as a potential PGRN-related therapeutic target. Future studies should focus on addressing the relevance and cellular mechanism by which PGRN oligomeric species provide neuroprotection.

Published
2016

46. Interactions Between Alcohol Metabolism Genes and Religious Involvement in Association With Maximum Drinks and Alcohol Dependence Symptoms.

Author

Chartier, Karen G, Dick, Danielle M, Almasy, Laura, Chan, Grace, Aliev, Fazil, Schuckit, Marc A, Scott, Denise M, Kramer, John, Bucholz, Kathleen K, Bierut, Laura J, Nurnberger, John, Porjesz, Bernice, and Hesselbrock, Victor M

Subjects
Substance Misuse, Brain Disorders, Alcoholism, Alcohol Use and Health, Genetics, Mental health, Good Health and Well Being, Adolescent, Adult, African Americans, Alcohol Dehydrogenase, Alcohol Drinking, Alcoholism, Female, Hispanic or Latino, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Religion, Whites, White People, Black or African American, Public Health and Health Services, Psychology, Substance Abuse
Abstract

ObjectiveVariations in the genes encoding alcohol dehydrogenase (ADH) enzymes are associated with both alcohol consumption and dependence in multiple populations. Additionally, some environmental factors have been recognized as modifiers of these relationships. This study examined the modifying effect of religious involvement on relationships between ADH gene variants and alcohol consumption-related phenotypes.MethodSubjects were African American, European American, and Hispanic American adults with lifetime exposure to alcohol (N = 7,716; 53% female) from the Collaborative Study on the Genetics of Alcoholism. Genetic markers included ADH1Brs1229984, ADH1B-rs2066702, ADH1C-rs698, ADH4-rs1042364, and ADH4-rs1800759. Phenotypes were maximum drinks consumed in a 24-hour period and total number of alcohol dependence symptoms according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Religious involvement was defined by self-reported religious services attendance.ResultsBoth religious involvement and ADH1B-rs1229984 were negatively associated with the number of maximum drinks consumed and the number of lifetime alcohol dependence symptoms endorsed. The interactions of religious involvement with ADH1B-rs2066702, ADH1C-rs698, and ADH4-rs1042364 were significantly associated with maximum drinks and alcohol dependence symptoms. Risk variants had weaker associations with maximum drinks and alcohol dependence symptoms as a function of increasing religious involvement.ConclusionsThis study provided initial evidence of a modifying effect for religious involvement on relationships between ADH variants and maximum drinks and alcohol dependence symptoms.

Published
2016

47. Effects of Alzheimer’s disease genetic risk on brain morphometric development in three multiple‐ancestry pediatric datasets

Author

Vogel, Jacob W, primary, Schultz, Laura M, additional, Larsen, Bart, additional, Seidlitz, Jakob, additional, Al‐Sharif, Noor B, additional, Barzilay, Ran, additional, Cieslak, Matthew, additional, Covitz, Sydney, additional, Gur, Raquel E, additional, Gur, Ruben C, additional, Huguet, Guillaume, additional, La Joie, Renaud, additional, McMillan, Corey T, additional, Nilsson, Nathalie I.V., additional, Poline, Jean‐Baptiste, additional, Ruparel, Kosha, additional, Shinohara, Russell T., additional, Wisse, Laura EM, additional, Wolf, Daniel, additional, Wolk, David A., additional, Alexander‐Bloch, Aaron, additional, Almasy, Laura, additional, and Satterthwaite, Theodore, additional

Published
2023
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48. Whole Genome Sequencing Identifies CRISPLD2 as a Lung Function Gene in Children With Asthma

Author

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

Published
2019
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50. Common genetic variants influence human subcortical brain structures

Author

Hibar, Derrek P, Stein, Jason L, Renteria, Miguel E, Arias-Vasquez, Alejandro, Desrivières, Sylvane, Jahanshad, Neda, Toro, Roberto, Wittfeld, Katharina, Abramovic, Lucija, Andersson, Micael, Aribisala, Benjamin S, Armstrong, Nicola J, Bernard, Manon, Bohlken, Marc M, Boks, Marco P, Bralten, Janita, Brown, Andrew A, Mallar Chakravarty, M, Chen, Qiang, Ching, Christopher RK, Cuellar-Partida, Gabriel, den Braber, Anouk, Giddaluru, Sudheer, Goldman, Aaron L, Grimm, Oliver, Guadalupe, Tulio, Hass, Johanna, Woldehawariat, Girma, Holmes, Avram J, Hoogman, Martine, Janowitz, Deborah, Jia, Tianye, Kim, Sungeun, Klein, Marieke, Kraemer, Bernd, Lee, Phil H, Olde Loohuis, Loes M, Luciano, Michelle, Macare, Christine, Mather, Karen A, Mattheisen, Manuel, Milaneschi, Yuri, Nho, Kwangsik, Papmeyer, Martina, Ramasamy, Adaikalavan, Risacher, Shannon L, Roiz-Santiañez, Roberto, Rose, Emma J, Salami, Alireza, Sämann, Philipp G, Schmaal, Lianne, Schork, Andrew J, Shin, Jean, Strike, Lachlan T, Teumer, Alexander, van Donkelaar, Marjolein MJ, van Eijk, Kristel R, Walters, Raymond K, Westlye, Lars T, Whelan, Christopher D, Winkler, Anderson M, Zwiers, Marcel P, Alhusaini, Saud, Athanasiu, Lavinia, Ehrlich, Stefan, Hakobjan, Marina MH, Hartberg, Cecilie B, Haukvik, Unn K, Heister, Angelien JGAM, Hoehn, David, Kasperaviciute, Dalia, Liewald, David CM, Lopez, Lorna M, Makkinje, Remco RR, Matarin, Mar, Naber, Marlies AM, Reese McKay, D, Needham, Margaret, Nugent, Allison C, Pütz, Benno, Royle, Natalie A, Shen, Li, Sprooten, Emma, Trabzuni, Daniah, van der Marel, Saskia SL, van Hulzen, Kimm JE, Walton, Esther, Wolf, Christiane, Almasy, Laura, Ames, David, Arepalli, Sampath, Assareh, Amelia A, Bastin, Mark E, Brodaty, Henry, Bulayeva, Kazima B, Carless, Melanie A, Cichon, Sven, Corvin, Aiden, Curran, Joanne E, and Czisch, Michael

Subjects
Biological Psychology, Biological Sciences, Genetics, Psychology, Brain Disorders, Human Genome, Mental Health, Neurosciences, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Neurological, Mental health, Adolescent, Adult, Aged, Aged, 80 and over, Aging, Apoptosis, Brain, Caudate Nucleus, Child, Female, Gene Expression Regulation, Developmental, Genetic Loci, Genetic Variation, Genome-Wide Association Study, Hippocampus, Humans, Magnetic Resonance Imaging, Male, Membrane Proteins, Middle Aged, Organ Size, Putamen, Sex Characteristics, Skull, Young Adult, Alzheimer’s Disease Neuroimaging Initiative, CHARGE Consortium, EPIGEN, IMAGEN, SYS, General Science & Technology
Abstract

The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

Published
2015

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