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4. Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Author

Zhou, Wei, Kanai, Masahiro, Wu, Kuan-Han H, Rasheed, Humaira, Tsuo, Kristin, Hirbo, Jibril B, Wang, Ying, Bhattacharya, Arjun, Zhao, Huiling, Namba, Shinichi, Surakka, Ida, Wolford, Brooke N, Faro, Valeria Lo, Lopera-Maya, Esteban A, Läll, Kristi, Favé, Marie-Julie, Partanen, Juulia J, Chapman, Sinéad B, Karjalainen, Juha, Kurki, Mitja, Maasha, Mutaamba, Brumpton, Ben M, Chavan, Sameer, Chen, Tzu-Ting, Daya, Michelle, Ding, Yi, Feng, Yen-Chen A, Guare, Lindsay A, Gignoux, Christopher R, Graham, Sarah E, Hornsby, Whitney E, Ingold, Nathan, Ismail, Said I, Johnson, Ruth, Laisk, Triin, Lin, Kuang, Lv, Jun, Millwood, Iona Y, Moreno-Grau, Sonia, Nam, Kisung, Palta, Priit, Pandit, Anita, Preuss, Michael H, Saad, Chadi, Setia-Verma, Shefali, Thorsteinsdottir, Unnur, Uzunovic, Jasmina, Verma, Anurag, Zawistowski, Matthew, Zhong, Xue, Afifi, Nahla, Al-Dabhani, Kawthar M, Thani, Asma Al, Bradford, Yuki, Campbell, Archie, Crooks, Kristy, de Bock, Geertruida H, Damrauer, Scott M, Douville, Nicholas J, Finer, Sarah, Fritsche, Lars G, Fthenou, Eleni, Gonzalez-Arroyo, Gilberto, Griffiths, Christopher J, Guo, Yu, Hunt, Karen A, Ioannidis, Alexander, Jansonius, Nomdo M, Konuma, Takahiro, Lee, Ming Ta Michael, Lopez-Pineda, Arturo, Matsuda, Yuta, Marioni, Riccardo E, Moatamed, Babak, Nava-Aguilar, Marco A, Numakura, Kensuke, Patil, Snehal, Rafaels, Nicholas, Richmond, Anne, Rojas-Muñoz, Agustin, Shortt, Jonathan A, Straub, Peter, Tao, Ran, Vanderwerff, Brett, Vernekar, Manvi, Veturi, Yogasudha, Barnes, Kathleen C, Boezen, Marike, Chen, Zhengming, Chen, Chia-Yen, Cho, Judy, Smith, George Davey, Finucane, Hilary K, Franke, Lude, Gamazon, Eric R, Ganna, Andrea, Gaunt, Tom R, Ge, Tian, Huang, Hailiang, and Huffman, Jennifer

Subjects
Human Genome, Genetics, Biotechnology, Generic health relevance, Good Health and Well Being, Biobank of the Americas, Biobank Japan Project, BioMe, BioVU, CanPath - Ontario Health Study, China Kadoorie Biobank Collaborative Group, Colorado Center for Personalized Medicine, deCODE Genetics, Estonian Biobank, FinnGen, Generation Scotland, Genes & Health Research Team, LifeLines, Mass General Brigham Biobank, Michigan Genomics Initiative, National Biobank of Korea, Penn Medicine BioBank, Qatar Biobank, QSkin Sun and Health Study, Taiwan Biobank, HUNT Study, UCLA ATLAS Community Health Initiative, Uganda Genome Resource, UK Biobank, GWAS, ancestry diversity, biobank, genetic association studies, meta-analysis, phenotype harmonization
Abstract

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Published
2022

5. Neural-Network-Directed Genetic Programmer for Discovery of Governing Equations

Author

Razavi, Shahab and Gamazon, Eric R.

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning
Abstract

We develop a symbolic regression framework for extracting the governing mathematical expressions from observed data. The evolutionary approach, faiGP, is designed to leverage the properties of a function algebra that have been encoded into a grammar, providing a theoretical guarantee of universal approximation and a way to minimize bloat. In this framework, the choice of operators of the grammar may be informed by a physical theory or symmetry considerations. Since there is currently no theory that can derive the 'constants of nature', an empirical investigation on extracting these coefficients from an evolutionary process is of methodological interest. We quantify the impact of different types of regularizers, including a diversity metric adapted from studies of the transcriptome and a complexity measure, on the performance of the framework. Our implementation, which leverages neural networks and a genetic programmer, generates non-trivial symbolically equivalent expressions ("Ramanujan expressions") or approximations with potentially interesting numerical applications. To illustrate the framework, a model of ligand-receptor binding kinetics, including an account of gene regulation by transcription factors, and a model of the regulatory range of the cistrome from omics data are presented. This study has important implications on the development of data-driven methodologies for the discovery of governing equations in experimental data derived from new sensing systems and high-throughput screening technologies.

Published
2022

7. Transcriptome-wide association study and Mendelian randomization in pancreatic cancer identifies susceptibility genes and causal relationships with type 2 diabetes and venous thromboembolism

Author

Lindstrom, Sara, Wang, Lu, Smith, Erin, Gordon, William, Van Hylckama Vlieg, Astrid, De Andrade, Mariza, Brody, Jennifer, Pattee, Jack, Haessler, Jeffrey, Brumpton, Ben, Chasman, Daniel, Suchon, Pierre, Chen, Ming-Huei, Turman, Constance, Germain, Marine, Wiggins, Kerri, MacDonald, James, Braekkan, Sigrid, Armasu, Sebastian, Pankratz, Nathan, Jackson, Rabecca, Nielsen, Jonas, Giulianini, Franco, Puurunen, Marja, Ibrahim, Manal, Heckbert, Susan, Bammler, Theo, Frazer, Kelly, McCauley, Bryan, Taylor, Kent, Pankow, James, Reiner, Alexander, Gabrielsen, Maiken, Deleuze, Jean-François, O'Donnell, Chris, Kim, Jihye, McKnight, Barbara, Kraft, Peter, Hansen, John-Bjarne, Rosendaal, Frits, Heit, John, Psaty, Bruce, Tang, Weihong, Kooperberg, Charles, Hveem, Kristian, Ridker, Paul, Morange, Pierre-Emmanuel, Johnson, Andrew, Kabrhel, Christopher, Trégouët, David-Alexandre, Smith, Nicholas, Tan, Marcus C.B., Isom, Chelsea A., Liu, Yangzi, Wu, Lang, Zhou, Dan, and Gamazon, Eric R.

Published
2024
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15. Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation

Author

Lo Faro, Valeria, Bhattacharya, Arjun, Zhou, Wei, Zhou, Dan, Wang, Ying, Läll, Kristi, Kanai, Masahiro, Lopera-Maya, Esteban, Straub, Peter, Pawar, Priyanka, Tao, Ran, Zhong, Xue, Namba, Shinichi, Sanna, Serena, Nolte, Ilja M., Okada, Yukinori, Ingold, Nathan, MacGregor, Stuart, Snieder, Harold, Surakka, Ida, Shortt, Jonathan, Gignoux, Chris, Rafaels, Nicholas, Crooks, Kristy, Verma, Anurag, Verma, Shefali S., Guare, Lindsay, Rader, Daniel J., Willer, Cristen, Martin, Alicia R., Brantley, Milam A., Jr., Gamazon, Eric R., Jansonius, Nomdo M., Joos, Karen, Cox, Nancy J., and Hirbo, Jibril

Published
2024
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17. Rare and common genetic determinants of metabolic individuality and their effects on human health

Author

Surendran, Praveen, Stewart, Isobel D., Au Yeung, Victoria P. W., Pietzner, Maik, Raffler, Johannes, Wörheide, Maria A., Li, Chen, Smith, Rebecca F., Wittemans, Laura B. L., Bomba, Lorenzo, Menni, Cristina, Zierer, Jonas, Rossi, Niccolò, Sheridan, Patricia A., Watkins, Nicholas A., Mangino, Massimo, Hysi, Pirro G., Di Angelantonio, Emanuele, Falchi, Mario, Spector, Tim D., Soranzo, Nicole, Michelotti, Gregory A., Arlt, Wiebke, Lotta, Luca A., Denaxas, Spiros, Hemingway, Harry, Gamazon, Eric R., Howson, Joanna M. M., Wood, Angela M., Danesh, John, Wareham, Nicholas J., Kastenmüller, Gabi, Fauman, Eric B., Suhre, Karsten, Butterworth, Adam S., and Langenberg, Claudia

Published
2022
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19. Post-GWAS analysis of six substance use traits improves the identification and functional interpretation of genetic risk loci

Author

Marees, Andries T, Gamazon, Eric R, Gerring, Zachary, Vorspan, Florence, Fingal, Josh, van den Brink, Wim, Smit, Dirk JA, Verweij, Karin JH, Kranzler, Henry R, Sherva, Richard, Farrer, Lindsay, Consortium, International Cannabis, Gelernter, Joel, and Derks, Eske M

Subjects
Biological Sciences, Genetics, Drug Abuse (NIDA only), Substance Misuse, Biotechnology, Brain Disorders, Mental health, Cardiovascular, Good Health and Well Being, Blood, Brain, Drug Users, Gene Expression Profiling, Gene Expression Regulation, Genetic Predisposition to Disease, Humans, Meta-Analysis as Topic, Phenotype, Quantitative Trait Loci, Substance-Related Disorders, Transcriptome, Addiction, eQTLs, Functional annotation, GTEx, Substance use, S-PrediXcan, International Cannabis Consortium, Medical and Health Sciences, Psychology and Cognitive Sciences, Substance Abuse, Biochemistry and cell biology, Pharmacology and pharmaceutical sciences, Epidemiology
Abstract

BackgroundLittle is known about the functional mechanisms through which genetic loci associated with substance use traits ascertain their effect. This study aims to identify and functionally annotate loci associated with substance use traits based on their role in genetic regulation of gene expression.MethodsWe evaluated expression Quantitative Trait Loci (eQTLs) from 13 brain regions and whole blood of the Genotype-Tissue Expression (GTEx) database, and from whole blood of the Depression Genes and Networks (DGN) database. The role of single eQTLs was examined for six substance use traits: alcohol consumption (N = 537,349), cigarettes per day (CPD; N = 263,954), former vs. current smoker (N = 312,821), age of smoking initiation (N = 262,990), ever smoker (N = 632,802), and cocaine dependence (N = 4,769). Subsequently, we conducted a gene level analysis of gene expression on these substance use traits using S-PrediXcan.ResultsUsing an FDR-adjusted p-value

Published
2020

20. Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Author

Zhou, Wei, Kanai, Masahiro, Wu, Kuan-Han H., Rasheed, Humaira, Tsuo, Kristin, Hirbo, Jibril B., Wang, Ying, Bhattacharya, Arjun, Zhao, Huiling, Namba, Shinichi, Surakka, Ida, Wolford, Brooke N., Lo Faro, Valeria, Lopera-Maya, Esteban A., Läll, Kristi, Favé, Marie-Julie, Chapman, Sinéad B., Karjalainen, Juha, Kurki, Mitja, Mutaamba, Maasha, Partanen, Juulia J., Brumpton, Ben M., Chavan, Sameer, Chen, Tzu-Ting, Daya, Michelle, Ding, Yi, Feng, Yen-Chen A., Gignoux, Christopher R., Graham, Sarah E., Hornsby, Whitney E., Ingold, Nathan, Johnson, Ruth, Laisk, Triin, Lin, Kuang, Lv, Jun, Millwood, Iona Y., Palta, Priit, Pandit, Anita, Preuss, Michael H., Thorsteinsdottir, Unnur, Uzunovic, Jasmina, Zawistowski, Matthew, Zhong, Xue, Campbell, Archie, Crooks, Kristy, de Bock, Geertruida H., Douville, Nicholas J., Finer, Sarah, Fritsche, Lars G., Griffiths, Christopher J., Guo, Yu, Hunt, Karen A., Konuma, Takahiro, Marioni, Riccardo E., Nomdo, Jansonius, Patil, Snehal, Rafaels, Nicholas, Richmond, Anne, Shortt, Jonathan A., Straub, Peter, Tao, Ran, Vanderwerff, Brett, Barnes, Kathleen C., Boezen, Marike, Chen, Zhengming, Chen, Chia-Yen, Cho, Judy, Smith, George Davey, Finucane, Hilary K., Franke, Lude, Gamazon, Eric R., Ganna, Andrea, Gaunt, Tom R., Ge, Tian, Huang, Hailiang, Huffman, Jennifer, Koskela, Jukka T., Lajonchere, Clara, Law, Matthew H., Li, Liming, Lindgren, Cecilia M., Loos, Ruth J.F., MacGregor, Stuart, Matsuda, Koichi, Olsen, Catherine M., Porteous, David J., Shavit, Jordan A., Snieder, Harold, Trembath, Richard C., Vonk, Judith M., Whiteman, David, Wicks, Stephen J., Wijmenga, Cisca, Wright, John, Zheng, Jie, Zhou, Xiang, Awadalla, Philip, Boehnke, Michael, Cox, Nancy J., Geschwind, Daniel H., Hayward, Caroline, Hveem, Kristian, Kenny, Eimear E., Lin, Yen-Feng, Mägi, Reedik, Martin, Hilary C., Medland, Sarah E., Okada, Yukinori, Palotie, Aarno V., Pasaniuc, Bogdan, Sanna, Serena, Smoller, Jordan W., Stefansson, Kari, van Heel, David A., Walters, Robin G., Zöllner, Sebastian, Martin, Alicia R., Willer, Cristen J., Daly, Mark J., Neale, Benjamin M., Lopera, Esteban, Kerminen, Sini, Wu, Kuan-Han, Bhatta, Laxmi, Brumpton, Ben, Deelen, Patrick, Murakami, Yoshinori, Willer, Cristen, and Hirbo, Jibril

Published
2023
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22. Molecular and clinical characterization of a founder mutation causing G6PC3 deficiency

Author

Zhen, Xin, primary, Betti, Michael J, additional, Kars, Meltem Ece, additional, Patterson, Andrew, additional, Medina Torres, Edgar Alejandro, additional, Scheffler Mendoza, Selma Cecilia, additional, Herrera Sanchez, Diana Andrea, additional, Lopez-Herrera, Gabriela, additional, Svyryd, Yevgeniya, additional, Mutchinick, Osvaldo M., additional, Gamazon, Eric, additional, Rathmell, Jeffrey C, additional, Itan, Yuval, additional, Markle, Janet, additional, O'Farrill Romanillos, Patricia, additional, Lugo-Reyes, Saul Oswaldo, additional, and Martinez-Barricarte, Ruben, additional

Published
2024
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24. Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

Author

Zhou, Wei, Kanai, Masahiro, Wu, Kuan-Han H., Rasheed, Humaira, Tsuo, Kristin, Hirbo, Jibril B., Wang, Ying, Bhattacharya, Arjun, Zhao, Huiling, Namba, Shinichi, Surakka, Ida, Wolford, Brooke N., Lo Faro, Valeria, Lopera-Maya, Esteban A., Läll, Kristi, Favé, Marie-Julie, Partanen, Juulia J., Chapman, Sinéad B., Karjalainen, Juha, Kurki, Mitja, Maasha, Mutaamba, Brumpton, Ben M., Chavan, Sameer, Chen, Tzu-Ting, Daya, Michelle, Ding, Yi, Feng, Yen-Chen A., Guare, Lindsay A., Gignoux, Christopher R., Graham, Sarah E., Hornsby, Whitney E., Ingold, Nathan, Ismail, Said I., Johnson, Ruth, Laisk, Triin, Lin, Kuang, Lv, Jun, Millwood, Iona Y., Moreno-Grau, Sonia, Nam, Kisung, Palta, Priit, Pandit, Anita, Preuss, Michael H., Saad, Chadi, Setia-Verma, Shefali, Thorsteinsdottir, Unnur, Uzunovic, Jasmina, Verma, Anurag, Zawistowski, Matthew, Zhong, Xue, Afifi, Nahla, Al-Dabhani, Kawthar M., Al Thani, Asma, Bradford, Yuki, Campbell, Archie, Crooks, Kristy, de Bock, Geertruida H., Damrauer, Scott M., Douville, Nicholas J., Finer, Sarah, Fritsche, Lars G., Fthenou, Eleni, Gonzalez-Arroyo, Gilberto, Griffiths, Christopher J., Guo, Yu, Hunt, Karen A., Ioannidis, Alexander, Jansonius, Nomdo M., Konuma, Takahiro, Michael Lee, Ming Ta, Lopez-Pineda, Arturo, Matsuda, Yuta, Marioni, Riccardo E., Moatamed, Babak, Nava-Aguilar, Marco A., Numakura, Kensuke, Patil, Snehal, Rafaels, Nicholas, Richmond, Anne, Rojas-Muñoz, Agustin, Shortt, Jonathan A., Straub, Peter, Tao, Ran, Vanderwerff, Brett, Vernekar, Manvi, Veturi, Yogasudha, Barnes, Kathleen C., Boezen, Marike, Chen, Zhengming, Chen, Chia-Yen, Cho, Judy, Smith, George Davey, Finucane, Hilary K., Franke, Lude, Gamazon, Eric R., Ganna, Andrea, Gaunt, Tom R., Ge, Tian, Huang, Hailiang, Huffman, Jennifer, Katsanis, Nicholas, Koskela, Jukka T., Lajonchere, Clara, Law, Matthew H., Li, Liming, Lindgren, Cecilia M., Loos, Ruth J.F., MacGregor, Stuart, Matsuda, Koichi, Olsen, Catherine M., Porteous, David J., Shavit, Jordan A., Snieder, Harold, Takano, Tomohiro, Trembath, Richard C., Vonk, Judith M., Whiteman, David C., Wicks, Stephen J., Wijmenga, Cisca, Wright, John, Zheng, Jie, Zhou, Xiang, Awadalla, Philip, Boehnke, Michael, Bustamante, Carlos D., Cox, Nancy J., Fatumo, Segun, Geschwind, Daniel H., Hayward, Caroline, Hveem, Kristian, Kenny, Eimear E., Lee, Seunggeun, Lin, Yen-Feng, Mbarek, Hamdi, Mägi, Reedik, Martin, Hilary C., Medland, Sarah E., Okada, Yukinori, Palotie, Aarno V., Pasaniuc, Bogdan, Rader, Daniel J., Ritchie, Marylyn D., Sanna, Serena, Smoller, Jordan W., Stefansson, Kari, van Heel, David A., Walters, Robin G., Zöllner, Sebastian, Biobank of the Americas, Biobank Japan Project, BioMe, BioVU, CanPath - Ontario Health Study, China Kadoorie Biobank Collaborative Group, Colorado Center for Personalized Medicine, deCODE Genetics, Estonian Biobank, FinnGen, Generation Scotland, Genes & Health Research Team, LifeLines, Mass General Brigham Biobank, Michigan Genomics Initiative, National Biobank of Korea, Penn Medicine BioBank, Qatar Biobank, The Qskin Sun and Health Study, Taiwan Biobank, The Hunt Study, Ucla Atlas Community Health Initiative, Uganda Genome Resource, Uk Biobank, Martin, Alicia R., Willer, Cristen J., Daly, Mark J., Neale, Benjamin M., and Elzur, Roy

Published
2022
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26. Publisher Correction: Gene expression imputation across multiple brain regions provides insights into schizophrenia risk

Author

Huckins, Laura M, Dobbyn, Amanda, Ruderfer, Douglas M, Hoffman, Gabriel, Wang, Weiqing, Pardiñas, Antonio F, Rajagopal, Veera M, Als, Thomas D, T. Nguyen, Hoang, Girdhar, Kiran, Boocock, James, Roussos, Panos, Fromer, Menachem, Kramer, Robin, Domenici, Enrico, Gamazon, Eric R, Purcell, Shaun, Demontis, Ditte, Børglum, Anders D, Walters, James TR, O’Donovan, Michael C, Sullivan, Patrick, Owen, Michael J, Devlin, Bernie, Sieberts, Solveig K, Cox, Nancy J, Im, Hae Kyung, Sklar, Pamela, and Stahl, Eli A

Subjects
Genetics, Neurosciences, Human Genome, Serious Mental Illness, Schizophrenia, Brain Disorders, Mental Health, Biotechnology, Mental health, CommonMind Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, iPSYCH-GEMS Schizophrenia Working Group, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

In the HTML version of the article originally published, the author group 'The Schizophrenia Working Group of the Psychiatric Genomics Consortium' was displayed incorrectly. The error has been corrected in the HTML version of the article.

Published
2019

27. Genome-wide association and transcriptome studies identify target genes and risk loci for breast cancer.

Author

Ferreira, Manuel A, Gamazon, Eric R, Al-Ejeh, Fares, Aittomäki, Kristiina, Andrulis, Irene L, Anton-Culver, Hoda, Arason, Adalgeir, Arndt, Volker, Aronson, Kristan J, Arun, Banu K, Asseryanis, Ella, Azzollini, Jacopo, Balmaña, Judith, Barnes, Daniel R, Barrowdale, Daniel, Beckmann, Matthias W, Behrens, Sabine, Benitez, Javier, Bermisheva, Marina, Białkowska, Katarzyna, Blomqvist, Carl, Bogdanova, Natalia V, Bojesen, Stig E, Bolla, Manjeet K, Borg, Ake, Brauch, Hiltrud, Brenner, Hermann, Broeks, Annegien, Burwinkel, Barbara, Caldés, Trinidad, Caligo, Maria A, Campa, Daniele, Campbell, Ian, Canzian, Federico, Carter, Jonathan, Carter, Brian D, Castelao, Jose E, Chang-Claude, Jenny, Chanock, Stephen J, Christiansen, Hans, Chung, Wendy K, Claes, Kathleen BM, Clarke, Christine L, EMBRACE Collaborators, GC-HBOC Study Collaborators, GEMO Study Collaborators, Couch, Fergus J, Cox, Angela, Cross, Simon S, Czene, Kamila, Daly, Mary B, de la Hoya, Miguel, Dennis, Joe, Devilee, Peter, Diez, Orland, Dörk, Thilo, Dunning, Alison M, Dwek, Miriam, Eccles, Diana M, Ejlertsen, Bent, Ellberg, Carolina, Engel, Christoph, Eriksson, Mikael, Fasching, Peter A, Fletcher, Olivia, Flyger, Henrik, Friedman, Eitan, Frost, Debra, Gabrielson, Marike, Gago-Dominguez, Manuela, Ganz, Patricia A, Gapstur, Susan M, Garber, Judy, García-Closas, Montserrat, García-Sáenz, José A, Gaudet, Mia M, Giles, Graham G, Glendon, Gord, Godwin, Andrew K, Goldberg, Mark S, Goldgar, David E, González-Neira, Anna, Greene, Mark H, Gronwald, Jacek, Guénel, Pascal, Haiman, Christopher A, Hall, Per, Hamann, Ute, He, Wei, Heyworth, Jane, Hogervorst, Frans BL, Hollestelle, Antoinette, Hoover, Robert N, Hopper, John L, Hulick, Peter J, Humphreys, Keith, Imyanitov, Evgeny N, ABCTB Investigators, HEBON Investigators, and BCFR Investigators

Subjects
EMBRACE Collaborators, GC-HBOC Study Collaborators, GEMO Study Collaborators, ABCTB Investigators, HEBON Investigators, BCFR Investigators, Humans, Breast Neoplasms, Genetic Predisposition to Disease, Gene Expression Profiling, Quantitative Trait Loci, Female, Genome-Wide Association Study, Prevention, Cancer, Human Genome, Aging, Breast Cancer, Genetics, Biotechnology, 2.1 Biological and endogenous factors
Abstract

Genome-wide association studies (GWAS) have identified more than 170 breast cancer susceptibility loci. Here we hypothesize that some risk-associated variants might act in non-breast tissues, specifically adipose tissue and immune cells from blood and spleen. Using expression quantitative trait loci (eQTL) reported in these tissues, we identify 26 previously unreported, likely target genes of overall breast cancer risk variants, and 17 for estrogen receptor (ER)-negative breast cancer, several with a known immune function. We determine the directional effect of gene expression on disease risk measured based on single and multiple eQTL. In addition, using a gene-based test of association that considers eQTL from multiple tissues, we identify seven (and four) regions with variants associated with overall (and ER-negative) breast cancer risk, which were not reported in previous GWAS. Further investigation of the function of the implicated genes in breast and immune cells may provide insights into the etiology of breast cancer.

Published
2019

28. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk

Author

Huckins, Laura M, Dobbyn, Amanda, Ruderfer, Douglas M, Hoffman, Gabriel, Wang, Weiqing, Pardiñas, Antonio F, Rajagopal, Veera M, Als, Thomas D, T. Nguyen, Hoang, Girdhar, Kiran, Boocock, James, Roussos, Panos, Fromer, Menachem, Kramer, Robin, Domenici, Enrico, Gamazon, Eric R, Purcell, Shaun, Demontis, Ditte, Børglum, Anders D, Walters, James TR, O’Donovan, Michael C, Sullivan, Patrick, Owen, Michael J, Devlin, Bernie, Sieberts, Solveig K, Cox, Nancy J, Im, Hae Kyung, Sklar, Pamela, and Stahl, Eli A

Subjects
Biological Sciences, Genetics, Mental Health, Brain Disorders, Human Genome, Schizophrenia, 2.1 Biological and endogenous factors, Aetiology, Mental health, Brain, Case-Control Studies, Gene Expression, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Risk, Transcriptome, CommonMind Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, iPSYCH-GEMS Schizophrenia Working Group, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

Published
2019

29. Bid maintains mitochondrial cristae structure and function and protects against cardiac disease in an integrative genomics study.

Author

Salisbury-Ruf, Christi, Bertram, Clinton, Vergeade, Aurelia, Lark, Daniel, Shi, Qiong, Heberling, Marlene, Fortune, Niki, Okoye, G, Jerome, W, Wells, Quinn, Fessel, Josh, Moslehi, Javid, Chen, Heidi, Roberts, L, Boutaud, Olivier, Gamazon, Eric, and Zinkel, Sandra

Subjects
Bcl-2 family, cell biology, cristae, electronic health record, human, human genetics & genomics, maize, mitochondria, mouse, myocardial infarction, Animals, Apoptosis, BH3 Interacting Domain Death Agonist Protein, Beclin-1, Cell Respiration, Fibrosis, Gene Expression Regulation, Genome-Wide Association Study, Genomics, Heart Diseases, Heart Ventricles, Humans, Mice, Inbred C57BL, Mitochondria, Mitochondrial Proton-Translocating ATPases, Mutation, Myeloid Progenitor Cells, Myocardial Infarction, Myocytes, Cardiac, Polymorphism, Single Nucleotide, Protein Multimerization, Protein Structure, Secondary, Protein Subunits, Reactive Oxygen Species, Reproducibility of Results, Up-Regulation
Abstract

Bcl-2 family proteins reorganize mitochondrial membranes during apoptosis, to form pores and rearrange cristae. In vitro and in vivo analysis integrated with human genetics reveals a novel homeostatic mitochondrial function for Bcl-2 family protein Bid. Loss of full-length Bid results in apoptosis-independent, irregular cristae with decreased respiration. Bid-/- mice display stress-induced myocardial dysfunction and damage. A gene-based approach applied to a biobank, validated in two independent GWAS studies, reveals that decreased genetically determined BID expression associates with myocardial infarction (MI) susceptibility. Patients in the bottom 5% of the expression distribution exhibit >4 fold increased MI risk. Carrier status with nonsynonymous variation in Bids membrane binding domain, BidM148T, associates with MI predisposition. Furthermore, Bid but not BidM148T associates with Mcl-1Matrix, previously implicated in cristae stability; decreased MCL-1 expression associates with MI. Our results identify a role for Bid in homeostatic mitochondrial cristae reorganization, that we link to human cardiac disease.

Published
2018

33. An ancestry‐based approach for detecting interactions

Author

Park, Danny S, Eskin, Itamar, Kang, Eun Yong, Gamazon, Eric R, Eng, Celeste, Gignoux, Christopher R, Galanter, Joshua M, Burchard, Esteban, Ye, Chun J, Aschard, Hugues, Eskin, Eleazar, Halperin, Eran, and Zaitlen, Noah

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, 2.5 Research design and methodologies (aetiology), Aetiology, Black or African American, Black People, DNA Methylation, Epistasis, Genetic, Gene-Environment Interaction, Hispanic or Latino, Humans, Models, Genetic, Phenotype, White People, admixture, gene-environment interaction, gene-gene interactions, Public Health and Health Services
Abstract

BackgroundEpistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.ResultsIn this work, we develop a new statistical approach to address these issues that leverages genetic ancestry, defined as the proportion of ancestry derived from each ancestral population (e.g., the fraction of European/African ancestry in African Americans), in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, respectively, identifying nine interactions that were significant at P

Published
2018

34. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, and Lam, Vincent KL

Abstract

This corrects the article DOI: 10.1038/sdata.2017.179.

Published
2018

35. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, and Lam, Vincent KL

Subjects
Humans, Diabetes Mellitus, Type 2, European Continental Ancestry Group, Genetic Variation, Diabetes, Human Genome, Genetics, 2.1 Biological and endogenous factors, Metabolic and endocrine
Abstract

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

Published
2017

36. Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis

Author

Yang, Fan, Wang, Jiebiao, Consortium, The GTEx, Pierce, Brandon L, Chen, Lin S, Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Im, Hae Kyung, Jo, Brian, Kang, Eun Yong, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, and Wang, Gao

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Underpinning research, Aetiology, 1.1 Normal biological development and functioning, Generic health relevance, Good Health and Well Being, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Selection, Genetic, Tissue Distribution, GTEx Consortium, Medical and Health Sciences, Bioinformatics
Abstract

The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is "mediation" by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are "cis-mediators" of trans-eQTLs, including those "cis-hubs" involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.

Published
2017

37. Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Author

Saha, Ashis, Kim, Yungil, Gewirtz, Ariel DH, Jo, Brian, Gao, Chuan, McDowell, Ian C, Consortium, The GTEx, Engelhardt, Barbara E, Battle, Alexis, Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Im, Hae Kyung, Kang, Eun Yong, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, and Sul, Jae Hoon

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Human Genome, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Generic health relevance, Bayes Theorem, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genotyping Techniques, Humans, Organ Specificity, Polymorphism, Single Nucleotide, RNA Splicing, Sequence Analysis, RNA, GTEx Consortium, Medical and Health Sciences, Bioinformatics
Abstract

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

Published
2017

38. Dynamic landscape and regulation of RNA editing in mammals

Author

Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A, Xi, Hualin S, Yeger-Lotem, Esti, and Zappala, Zachary

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Adenosine Deaminase, Animals, Female, Genotype, HEK293 Cells, Humans, Male, Mice, Muscles, Nuclear Proteins, Organ Specificity, Primates, Proteolysis, RNA Editing, RNA-Binding Proteins, Spatio-Temporal Analysis, Species Specificity, Transcriptome, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, General Science & Technology
Abstract

Adenosine-to-inosine (A-to-I) RNA editing is a conserved post-transcriptional mechanism mediated by ADAR enzymes that diversifies the transcriptome by altering selected nucleotides in RNA molecules. Although many editing sites have recently been discovered, the extent to which most sites are edited and how the editing is regulated in different biological contexts are not fully understood. Here we report dynamic spatiotemporal patterns and new regulators of RNA editing, discovered through an extensive profiling of A-to-I RNA editing in 8,551 human samples (representing 53 body sites from 552 individuals) from the Genotype-Tissue Expression (GTEx) project and in hundreds of other primate and mouse samples. We show that editing levels in non-repetitive coding regions vary more between tissues than editing levels in repetitive regions. Globally, ADAR1 is the primary editor of repetitive sites and ADAR2 is the primary editor of non-repetitive coding sites, whereas the catalytically inactive ADAR3 predominantly acts as an inhibitor of editing. Cross-species analysis of RNA editing in several tissues revealed that species, rather than tissue type, is the primary determinant of editing levels, suggesting stronger cis-directed regulation of RNA editing for most sites, although the small set of conserved coding sites is under stronger trans-regulation. In addition, we curated an extensive set of ADAR1 and ADAR2 targets and showed that many editing sites display distinct tissue-specific regulation by the ADAR enzymes in vivo. Further analysis of the GTEx data revealed several potential regulators of editing, such as AIMP2, which reduces editing in muscles by enhancing the degradation of the ADAR proteins. Collectively, our work provides insights into the complex cis- and trans-regulation of A-to-I editing.

Published
2017

39. Landscape of X chromosome inactivation across human tissues

Author

Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A, Xi, Hualin S, Yeger-Lotem, Esti, and Zappala, Zachary

Subjects
Genetics, Clinical Research, Human Genome, Generic health relevance, Good Health and Well Being, Chromosomes, Human, X, Female, Genes, X-Linked, Genome, Human, Genomics, Humans, Male, Organ Specificity, Phenotype, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome, X Chromosome Inactivation, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, General Science & Technology
Abstract

X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals. The extent to which XCI is shared between cells and tissues remains poorly characterized, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression and phenotypic traits. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.

Published
2017

40. The impact of rare variation on gene expression across tissues

Author

Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A, Xi, Hualin S, Yeger-Lotem, Esti, and Zappala, Zachary

Subjects
Bayes Theorem, Female, Gene Expression Profiling, Genetic Variation, Genome, Human, Genomics, Genotype, Humans, Male, Models, Genetic, Organ Specificity, Sequence Analysis, RNA, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, General Science & Technology
Abstract

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

Published
2017

41. Genetic effects on gene expression across human tissues

Author

Aguet, François, Brown, Andrew A, Castel, Stephane E, Davis, Joe R, He, Yuan, Jo, Brian, Mohammadi, Pejman, Park, YoSon, Parsana, Princy, Segrè, Ayellet V, Strober, Benjamin J, Zappala, Zachary, Cummings, Beryl B, Gelfand, Ellen T, Hadley, Kane, Huang, Katherine H, Lek, Monkol, Li, Xiao, Nedzel, Jared L, Nguyen, Duyen Y, Noble, Michael S, Sullivan, Timothy J, Tukiainen, Taru, MacArthur, Daniel G, Getz, Gad, Addington, Anjene, Guan, Ping, Koester, Susan, Little, A Roger, Lockhart, Nicole C, Moore, Helen M, Rao, Abhi, Struewing, Jeffery P, Volpi, Simona, Brigham, Lori E, Hasz, Richard, Hunter, Marcus, Johns, Christopher, Johnson, Mark, Kopen, Gene, Leinweber, William F, Lonsdale, John T, McDonald, Alisa, Mestichelli, Bernadette, Myer, Kevin, Roe, Bryan, Salvatore, Michael, Shad, Saboor, Thomas, Jeffrey A, Walters, Gary, Washington, Michael, Wheeler, Joseph, Bridge, Jason, Foster, Barbara A, Gillard, Bryan M, Karasik, Ellen, Kumar, Rachna, Miklos, Mark, Moser, Michael T, Jewell, Scott D, Montroy, Robert G, Rohrer, Daniel C, Valley, Dana, Mash, Deborah C, Davis, David A, Sobin, Leslie, Barcus, Mary E, Branton, Philip A, Abell, Nathan S, Balliu, Brunilda, Delaneau, Olivier, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Han, Buhm, He, Amy Z, Hormozdiari, Farhad, Li, Xin, Liu, Boxiang, Kang, Eun Yong, McDowell, Ian C, Ongen, Halit, Palowitch, John J, Peterson, Christine B, Quon, Gerald, Ripke, Stephan, Saha, Ashis, Shabalin, Andrey A, Shimko, Tyler C, Sul, Jae Hoon, Teran, Nicole A, Tsang, Emily K, Zhang, Hailei, Zhou, Yi-Hui, Bustamante, Carlos D, Cox, Nancy J, and Guigó, Roderic

Subjects
Human Genome, Genetics, Biotechnology, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Aetiology, Underpinning research, Generic health relevance, Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Lead analysts:, Laboratory, Data Analysis &Coordinating Center (LDACC):, NIH program management:, Biospecimen collection:, Pathology:, eQTL manuscript working group:, General Science & Technology
Abstract

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

Published
2017

43. Brain eQTLs of European, African American, and Asian ancestry improve interpretation of schizophrenia GWAS

Author

Chen, Yu, primary, Liu, Sihan, additional, Ren, Zongyao, additional, Wang, Feiran, additional, Jiang, Yi, additional, Dai, Rujia, additional, Duan, Fangyuan, additional, Han, Cong, additional, Ning, Zhilin, additional, Xia, Yan, additional, Li, Miao, additional, Yuan, Kai, additional, Qiu, Wenying, additional, Yan, Xiao-Xin, additional, Dai, Jiapei, additional, Kopp, Richard F., additional, Huang, Jufang, additional, Xu, Shuhua, additional, Tang, Beisha, additional, Gamazon, Eric R., additional, Bigdeli, Tim, additional, Gershon, Elliot, additional, Huang, Hailiang, additional, Ma, Chao, additional, Liu, Chunyu, additional, and Chen, Chao, additional

Published
2024
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47. A Low-Frequency Inactivating Akt2 Variant Enriched in the Finnish Population is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk.

Author

Manning, Alisa, Highland, Heather M, Gasser, Jessica, Sim, Xueling, Tukiainen, Taru, Fontanillas, Pierre, Grarup, Niels, Rivas, Manuel A, Mahajan, Anubha, Locke, Adam E, Cingolani, Pablo, Pers, Tune H, Viñuela, Ana, Brown, Andrew A, Wu, Ying, Flannick, Jason, Fuchsberger, Christian, Gamazon, Eric R, Gaulton, Kyle J, Im, Hae Kyung, Teslovich, Tanya M, Blackwell, Thomas W, Bork-Jensen, Jette, Burtt, Noël P, Chen, Yuhui, Green, Todd, Hartl, Christopher, Kang, Hyun Min, Kumar, Ashish, Ladenvall, Claes, Ma, Clement, Moutsianas, Loukas, Pearson, Richard D, Perry, John RB, Rayner, N William, Robertson, Neil R, Scott, Laura J, van de Bunt, Martijn, Eriksson, Johan G, Jula, Antti, Koskinen, Seppo, Lehtimäki, Terho, Palotie, Aarno, Raitakari, Olli T, Jacobs, Suzanne BR, Wessel, Jennifer, Chu, Audrey Y, Scott, Robert A, Goodarzi, Mark O, Blancher, Christine, Buck, Gemma, Buck, David, Chines, Peter S, Gabriel, Stacey, Gjesing, Anette P, Groves, Christopher J, Hollensted, Mette, Huyghe, Jeroen R, Jackson, Anne U, Jun, Goo, Justesen, Johanne Marie, Mangino, Massimo, Murphy, Jacquelyn, Neville, Matt, Onofrio, Robert, Small, Kerrin S, Stringham, Heather M, Trakalo, Joseph, Banks, Eric, Carey, Jason, Carneiro, Mauricio O, DePristo, Mark, Farjoun, Yossi, Fennell, Timothy, Goldstein, Jacqueline I, Grant, George, Hrabé de Angelis, Martin, Maguire, Jared, Neale, Benjamin M, Poplin, Ryan, Purcell, Shaun, Schwarzmayr, Thomas, Shakir, Khalid, Smith, Joshua D, Strom, Tim M, Wieland, Thomas, Lindstrom, Jaana, Brandslund, Ivan, Christensen, Cramer, Surdulescu, Gabriela L, Lakka, Timo A, Doney, Alex SF, Nilsson, Peter, Wareham, Nicholas J, Langenberg, Claudia, Varga, Tibor V, Franks, Paul W, Rolandsson, Olov, Rosengren, Anders H, and Farook, Vidya S

Subjects
Genetics, Diabetes, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Black or African American, Alleles, Asian People, Case-Control Studies, Diabetes Mellitus, Type 2, Fasting, Finland, Gene Frequency, Genetic Predisposition to Disease, Genotype, Hispanic or Latino, Humans, Insulin, Insulin Resistance, Odds Ratio, Proto-Oncogene Proteins c-akt, White People, Medical and Health Sciences, Endocrinology & Metabolism
Abstract

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.

Published
2017

49. A variant at 9p21.3 functionally implicates CDKN2B in paediatric B-cell precursor acute lymphoblastic leukaemia aetiology.

Author

Hungate, Eric A, Vora, Sapana R, Gamazon, Eric R, Moriyama, Takaya, Best, Timothy, Hulur, Imge, Lee, Younghee, Evans, Tiffany-Jane, Ellinghaus, Eva, Stanulla, Martin, Rudant, Jéremie, Orsi, Laurent, Clavel, Jacqueline, Milne, Elizabeth, Scott, Rodney J, Pui, Ching-Hon, Cox, Nancy J, Loh, Mignon L, Yang, Jun J, Skol, Andrew D, and Onel, Kenan

Subjects
Chromosomes, Human, Pair 9, Humans, Genetic Predisposition to Disease, Case-Control Studies, Chromosome Mapping, Polymorphism, Single Nucleotide, Child, Child, Preschool, Infant, African Americans, European Continental Ancestry Group, Hispanic Americans, Female, Male, Cyclin-Dependent Kinase Inhibitor p15, Precursor B-Cell Lymphoblastic Leukemia-Lymphoma, Genetic Variation, Genome-Wide Association Study, Preschool, Chromosomes, Human, Pair 9, Polymorphism, Single Nucleotide
Abstract

Paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) is the most common cancer of childhood, yet little is known about BCP-ALL predisposition. In this study, in 2,187 cases of European ancestry and 5,543 controls, we discover and replicate a locus indexed by rs77728904 at 9p21.3 associated with BCP-ALL susceptibility (Pcombined=3.32 × 10(-15), OR=1.72) and independent from rs3731217, the previously reported ALL-associated variant in this region. Of correlated SNPs tagged by this locus, only rs662463 is significant in African Americans, suggesting it is a plausible causative variant. Functional analysis shows that rs662463 is a cis-eQTL for CDKN2B, with the risk allele associated with lower expression, and suggests that rs662463 influences BCP-ALL risk by regulating CDKN2B expression through CEBPB signalling. Functional analysis of rs3731217 suggests it is associated with BCP-ALL by acting within a splicing regulatory element determining CDKN2A exon 3 usage (P=0.01). These findings provide new insights into the critical role of the CDKN2 locus in BCP-ALL aetiology.

Published
2016

50. Meta-analysis of lipid-traits in Hispanics identifies novel loci, population-specific effects and tissue-specific enrichment of eQTLs

Author

Below, Jennifer E, Parra, Esteban J, Gamazon, Eric R, Torres, Jason, Krithika, S, Candille, Sophie, Lu, Yingchang, Manichakul, Ani, Peralta-Romero, Jesus, Duan, Qing, Li, Yun, Morris, Andrew P, Gottesman, Omri, Bottinger, Erwin, Wang, Xin-Qun, Taylor, Kent D, Ida Chen, Y-D, Rotter, Jerome I, Rich, Stephen S, Loos, Ruth JF, Tang, Hua, Cox, Nancy J, Cruz, Miguel, Hanis, Craig L, and Valladares-Salgado, Adan

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Atherosclerosis, Genetic Association Studies, Genetics, Population, Genome-Wide Association Study, Genotype, Hispanic or Latino, Humans, Linkage Disequilibrium, Lipid Metabolism, Lipids, Mexico, Organ Specificity, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable, White People
Abstract

We performed genome-wide meta-analysis of lipid traits on three samples of Mexican and Mexican American ancestry comprising 4,383 individuals, and followed up significant and highly suggestive associations in three additional Hispanic samples comprising 7,876 individuals. Genome-wide significant signals were observed in or near CELSR2, ZNF259/APOA5, KANK2/DOCK6 and NCAN/MAU2 for total cholesterol, LPL, ABCA1, ZNF259/APOA5, LIPC and CETP for HDL cholesterol, CELSR2, APOB and NCAN/MAU2 for LDL cholesterol, and GCKR, TRIB1, ZNF259/APOA5 and NCAN/MAU2 for triglycerides. Linkage disequilibrium and conditional analyses indicate that signals observed at ABCA1 and LIPC for HDL cholesterol and NCAN/MAU2 for triglycerides are independent of previously reported lead SNP associations. Analyses of lead SNPs from the European Global Lipids Genetics Consortium (GLGC) dataset in our Hispanic samples show remarkable concordance of direction of effects as well as strong correlation in effect sizes. A meta-analysis of the European GLGC and our Hispanic datasets identified five novel regions reaching genome-wide significance: two for total cholesterol (FN1 and SAMM50), two for HDL cholesterol (LOC100996634 and COPB1) and one for LDL cholesterol (LINC00324/CTC1/PFAS). The top meta-analysis signals were found to be enriched for SNPs associated with gene expression in a tissue-specific fashion, suggesting an enrichment of tissue-specific function in lipid-associated loci.

Published
2016

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