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4. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Author

Zhang, Martin Jinye, Hou, Kangcheng, Dey, Kushal K, Sakaue, Saori, Jagadeesh, Karthik A, Weinand, Kathryn, Taychameekiatchai, Aris, Rao, Poorvi, Pisco, Angela Oliveira, Zou, James, Wang, Bruce, Gandal, Michael, Raychaudhuri, Soumya, Pasaniuc, Bogdan, and Price, Alkes L

Subjects
Human Genome, Genetics, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Gene Expression Profiling, Genome-Wide Association Study, Multifactorial Inheritance, RNA-Seq, Single-Cell Analysis, Triglycerides, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Single-cell RNA sequencing (scRNA-seq) provides unique insights into the pathology and cellular origin of disease. We introduce single-cell disease relevance score (scDRS), an approach that links scRNA-seq with polygenic disease risk at single-cell resolution, independent of annotated cell types. scDRS identifies cells exhibiting excess expression across disease-associated genes implicated by genome-wide association studies (GWASs). We applied scDRS to 74 diseases/traits and 1.3 million single-cell gene-expression profiles across 31 tissues/organs. Cell-type-level results broadly recapitulated known cell-type-disease associations. Individual-cell-level results identified subpopulations of disease-associated cells not captured by existing cell-type labels, including T cell subpopulations associated with inflammatory bowel disease, partially characterized by their effector-like states; neuron subpopulations associated with schizophrenia, partially characterized by their spatial locations; and hepatocyte subpopulations associated with triglyceride levels, partially characterized by their higher ploidy levels. Genes whose expression was correlated with the scDRS score across cells (reflecting coexpression with GWAS disease-associated genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

Published
2022

7. Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity

Author

Gazal, Steven, Weissbrod, Omer, Hormozdiari, Farhad, Dey, Kushal K, Nasser, Joseph, Jagadeesh, Karthik A, Weiner, Daniel J, Shi, Huwenbo, Fulco, Charles P, O’Connor, Luke J, Pasaniuc, Bogdan, Engreitz, Jesse M, and Price, Alkes L

Subjects
Biological Sciences, Genetics, Human Genome, Good Health and Well Being, Genome-Wide Association Study, Phenotype, Polymorphism, Single Nucleotide, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Disease-associated single-nucleotide polymorphisms (SNPs) generally do not implicate target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis. Here, we developed a heritability-based framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk. Our optimal combined S2G strategy (cS2G) included seven constituent S2G strategies and achieved a precision of 0.75 and a recall of 0.33, more than doubling the recall of any individual strategy. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 5,095 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. We further applied cS2G to provide an empirical assessment of disease omnigenicity; we determined that the top 1% of genes explained roughly half of the SNP heritability linked to all genes and that gene-level architectures vary with variant allele frequency.

Published
2022

8. GBAT: a gene-based association test for robust detection of trans-gene regulation

Author

Liu, Xuanyao, Mefford, Joel A, Dahl, Andrew, He, Yuan, Subramaniam, Meena, Battle, Alexis, Price, Alkes L, and Zaitlen, Noah

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Biotechnology, Human Genome, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genetic Testing, Genome-Wide Association Study, Genotype, Humans, Polymorphism, Single Nucleotide, RNA, Messenger, Gene expression, eQTLs, trans-eQTLs, transgene regulation, trans gene regulation, Environmental Sciences, Information and Computing Sciences, Bioinformatics
Abstract

The observation that disease-associated genetic variants typically reside outside of exons has inspired widespread investigation into the genetic basis of transcriptional regulation. While associations between the mRNA abundance of a gene and its proximal SNPs (cis-eQTLs) are now readily identified, identification of high-quality distal associations (trans-eQTLs) has been limited by a heavy multiple testing burden and the proneness to false-positive signals. To address these issues, we develop GBAT, a powerful gene-based pipeline that allows robust detection of high-quality trans-gene regulation signal.

Published
2020

9. Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions

Author

Marshall, Christian R., Merico, Daniele, Thiruvahindrapuram, Bhooma, Wang, Zhouzhi, Scherer, Stephen W., Howrigan, Daniel P, Ripke, Stephan, Bulik-Sullivan, Brendan, Farh, Kai-How, Fromer, Menachem, Goldstein, Jacqueline I., Huang, Hailiang, Lee, Phil, Daly, Mark J., Neale, Benjamin M., Belliveau, Richard A., Jr., Bergen, Sarah E., Bevilacqua, Elizabeth, Chambert, Kimberley D., O'Dushlaine, Colm, Scolnick, Edward M., Smoller, Jordan W., Moran, Jennifer L., Palotie, Aarno, Petryshen, Tracey L., Wu, Wenting, Greer, Douglas S., Antaki, Danny, Shetty, Aniket, Gujral, Madhusudan, Brandler, William M., Malhotra, Dheeraj, Fuentes Fajarado, Karin V., Maile, Michelle S., Holmans, Peter A., Carrera, Noa, Craddock, Nick, Escott-Price, Valentina, Georgieva, Lyudmila, Hamshere, Marian L., Kavanagh, David, Legge, Sophie E., Pocklington, Andrew J., Richards, Alexander L., Ruderfer, Douglas M., Williams, Nigel M., Kirov, George, Owen, Michael J., Pinto, Dalila, Cai, Guiqing, Davis, Kenneth L., Drapeau, Elodie, Friedman, Joseph I, Haroutunian, Vahram, Parkhomenko, Elena, Reichenberg, Abraham, Silverman, Jeremy M., Buxbaum, Joseph D., Domenici, Enrico, Agartz, Ingrid, Djurovic, Srdjan, Mattingsdal, Morten, Melle, Ingrid, Andreassen, Ole A., Jönsson, Erik G., Söderman, Erik, Albus, Margot, Alexander, Madeline, Laurent, Claudine, Levinson, Douglas F., Amin, Farooq, Atkins, Joshua, Cairns, Murray J., Scott, Rodney J., Tooney, Paul A., Wu, Jing Qin, Bacanu, Silviu A., Bigdeli, Tim B., Reimers, Mark A., Webb, Bradley T., Wolen, Aaron R., Wormley, Brandon K., Kendler, Kenneth S., Riley, Brien P., Kähler, Anna K., Magnusson, Patrik K.E., Hultman, Christina M., Bertalan, Marcelo, Hansen, Thomas, Olsen, Line, Rasmussen, Henrik B., Werge, Thomas, Mattheisen, Manuel, Black, Donald W., Bruggeman, Richard, Buccola, Nancy G., Buckner, Randy L., Roffman, Joshua L., Byerley, William, Cahn, Wiepke, Kahn, René S, Strengman, Eric, Ophoff, Roel A., Carr, Vaughan J., Catts, Stanley V., Henskens, Frans A., Loughland, Carmel M., Michie, Patricia T., Pantelis, Christos, Schall, Ulrich, Jablensky, Assen V., Kelly, Brian J., Campion, Dominique, Cantor, Rita M., Cheng, Wei, Cloninger, C. Robert, Svrakic, Dragan M, Cohen, David, Cormican, Paul, Donohoe, Gary, Morris, Derek W., Corvin, Aiden, Gill, Michael, Crespo-Facorro, Benedicto, Crowley, James J., Farrell, Martilias S., Giusti-Rodríguez, Paola, Kim, Yunjung, Szatkiewicz, Jin P., Williams, Stephanie, Curtis, David, Pimm, Jonathan, Gurling, Hugh, McQuillin, Andrew, Davidson, Michael, Weiser, Mark, Degenhardt, Franziska, Forstner, Andreas J., Herms, Stefan, Hoffmann, Per, Hofman, Andrea, Cichon, Sven, Nöthen, Markus M., Del Favero, Jurgen, DeLisi, Lynn E., McCarley, Robert W., Levy, Deborah L., Mesholam-Gately, Raquelle I., Seidman, Larry J., Dikeos, Dimitris, Papadimitriou, George N., Dinan, Timothy, Duan, Jubao, Sanders, Alan R., Gejman, Pablo V., Gershon, Elliot S., Dudbridge, Frank, Eichhammer, Peter, Eriksson, Johan, Salomaa, Veikko, Essioux, Laurent, Fanous, Ayman H., Knowles, James A., Pato, Michele T., Pato, Carlos N., Frank, Josef, Meier, Sandra, Schulze, Thomas G., Strohmaier, Jana, Witt, Stephanie H., Rietschel, Marcella, Franke, Lude, Karjalainen, Juha, Freedman, Robert, Olincy, Ann, Freimer, Nelson B., Purcell, Shaun M., Roussos, Panos, Stahl, Eli A., Sklar, Pamela, Giegling, Ina, Hartmann, Annette M., Konte, Bettina, Rujescu, Dan, Godard, Stephanie, Hirschhorn, Joel N., Pers, Tune H., Price, Alkes, Esko, Tõnu, Gratten, Jacob, Lee, S. Hong, Visscher, Peter M., Wray, Naomi R., Mowry, Bryan J., de Haan, Lieuwe, Meijer, Carin J., Hansen, Mark, Ikeda, Masashi, Iwata, Nakao, Joa, Inge, Kalaydjieva, Luba, Keller, Matthew C., Kennedy, James L., Zai, Clement C., Knight, Jo, Lerer, Bernard, Liang, Kung-Yee, Lieberman, Jeffrey, Stroup, T. Scott, Lönnqvist, Jouko, Suvisaari, Jaana, Maher, Brion S., Maier, Wolfgang, Mallet, Jacques, McDonald, Colm, McIntosh, Andrew M., Blackwood, Douglas H.R., Metspalu, Andres, Milani, Lili, Milanova, Vihra, Mokrab, Younes, Collier, David A., Müller-Myhsok, Bertram, Murphy, Kieran C., Murray, Robin M., Powell, John, Myin-Germeys, Inez, Van Os, Jim, Nenadic, Igor, Nertney, Deborah A., Nestadt, Gerald, Pulver, Ann E., Nicodemus, Kristin K., Nisenbaum, Laura, Nordin, Annelie, Adolfsson, Rolf, O'Callaghan, Eadbhard, Oh, Sang-Yun, O'Neill, F. Anthony, Paunio, Tiina, Pietiläinen, Olli, Perkins, Diana O., Quested, Digby, Savitz, Adam, Li, Qingqin S., Schwab, Sibylle G., Shi, Jianxin, Spencer, Chris C.A., Thirumalai, Srinivas, Veijola, Juha, Waddington, John, Walsh, Dermot, Wildenauer, Dieter B., Bramon, Elvira, Darvasi, Ariel, Posthuma, Danielle, St. Clair, David, Shanta, Omar, Klein, Marieke, Park, Peter J., Weinberger, Daniel, Moran, John V., Gage, Fred H., Vaccarino, Flora M., Gleeson, Joseph, Mathern, Gary, Courchesne, Eric, Roy, Subhojit, Bizzotto, Sara, Coulter, Michael, Dias, Caroline, D'Gama, Alissa, Ganz, Javier, Hill, Robert, Huang, August Yue, Khoshkhoo, Sattar, Kim, Sonia, Lodato, Michael, Miller, Michael, Borges-Monroy, Rebeca, Rodin, Rachel, Zhou, Zinan, Bohrson, Craig, Chu, Chong, Cortes-Ciriano, Isidro, Dou, Yanmei, Galor, Alon, Gulhan, Doga, Kwon, Minseok, Luquette, Joe, Viswanadham, Vinay, Jones, Attila, Rosenbluh, Chaggai, Cho, Sean, Langmead, Ben, Thorpe, Jeremy, Erwin, Jennifer, Jaffe, Andrew, McConnell, Michael, Narurkar, Rujuta, Paquola, Apua, Shin, Jooheon, Straub, Richard, Abyzov, Alexej, Bae, Taejeong, Jang, Yeongjun, Wang, Yifan, Gage, Fred, Linker, Sara, Reed, Patrick, Wang, Meiyan, Urban, Alexander, Zhou, Bo, Zhu, Xiaowei, Pattni, Reenal, Amero, Aitor Serres, Juan, David, Lobon, Irene, Marques-Bonet, Tomas, Moruno, Manuel Solis, Perez, Raquel Garcia, Povolotskaya, Inna, Soriano, Eduardo, Averbuj, Dan, Ball, Laurel, Breuss, Martin, Yang, Xiaoxu, Chung, Changuk, Emery, Sarah B., Flasch, Diane A., Kidd, Jeffrey M., Kopera, Huira C., Kwan, Kenneth Y., Mills, Ryan E., Moldovan, John B., Sun, Chen, Zhao, Xuefang, Zhou, Weichen, Frisbie, Trenton J., Cherskov, Adriana, Fasching, Liana, Jourdon, Alexandre, Pochareddy, Sirisha, Scuderi, Soraya, Sestan, Nenad, Maury, Eduardo A., Sherman, Maxwell A., Genovese, Giulio, Gilgenast, Thomas G., Kamath, Tushar, Burris, S.J., Rajarajan, Prashanth, Flaherty, Erin, Akbarian, Schahram, Chess, Andrew, McCarroll, Steven A., Loh, Po-Ru, Phillips-Cremins, Jennifer E., Brennand, Kristen J., Macosko, Evan Z., Walters, James T.R., O’Donovan, Michael, Sullivan, Patrick, Sebat, Jonathan, Lee, Eunjung A., and Walsh, Christopher A.

Published
2023
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10. Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia

Author

Ripke, Stephan, Neale, Benjamin M., Corvin, Aiden, Walters, James T.R., Farh, Kai-How, Holmans, Peter A., Lee, Phil, Bulik-Sullivan, Brendan, Collier, David A., Huang, Hailiang, Pers, Tune H., Agartz, Ingrid, Agerbo, Esben, Albus, Margot, Alexander, Madeline, Amin, Farooq, Bacanu, Silviu A., Begemann, Martin, Belliveau, Richard A., Jr., Bene, Judit, Bergen, Sarah E., Bevilacqua, Elizabeth, Bigdeli, Tim B., Black, Donald W., Bruggeman, Richard, Buccola, Nancy G., Buckner, Randy L., Byerley, William, Cahn, Wiepke, Cai, Guiqing, Campion, Dominique, Cantor, Rita M., Carr, Vaughan J., Carrera, Noa, Catts, Stanley V., Chambert, Kimberley D., Chan, Raymond C.K., Chan, Ronald Y.L., Chen, Eric Y.H., Cheng, Wei, Cheung, Eric FC., Chong, Siow Ann, Cloninger, C. Robert, Cohen, David, Cohen, Nadine, Cormican, Paul, Craddock, Nick, Crowley, James J., Curtis, David, Davidson, Michael, Davis, Kenneth L., Degenhardt, Franziska, Del Favero, Jurgen, Demontis, Ditte, Dikeos, Dimitris, Dinan, Timothy, Djurovic, Srdjan, Donohoe, Gary, Drapeau, Elodie, Duan, Jubao, Dudbridge, Frank, Durmishi, Naser, Eichhammer, Peter, Eriksson, Johan, Escott-Price, Valentina, Essioux, Laurent, Fanous, Ayman H., Farrell, Martilias S., Frank, Josef, Franke, Lude, Freedman, Robert, Freimer, Nelson B., Friedl, Marion, Friedman, Joseph I., Fromer, Menachem, Genovese, Giulio, Georgieva, Lyudmila, Giegling, Ina, Giusti-Rodríguez, Paola, Godard, Stephanie, Goldstein, Jacqueline I., Golimbet, Vera, Gopal, Srihari, Gratten, Jacob, de Haan, Lieuwe, Hammer, Christian, Hamshere, Marian L., Hansen, Mark, Hansen, Thomas, Haroutunian, Vahram, Hartmann, Annette M., Henskens, Frans A., Herms, Stefan, Hirschhorn, Joel N., Hoffmann, Per, Hofman, Andrea, Hollegaard, Mads V., Hougaard, David M., Ikeda, Masashi, Joa, Inge, Julià, Antonio, Kahn, René S., Kalaydjieva, Luba, Karachanak-Yankova, Sena, Karjalainen, Juha, Kavanagh, David, Keller, Matthew C., Kennedy, James L., Khrunin, Andrey, Kim, Yunjung, Klovins, Janis, Knowles, James A., Konte, Bettina, Kucinskas, Vaidutis, Kucinskiene, Zita Ausrele, Kuzelova-Ptackova, Hana, Kähler, Anna K., Laurent, Claudine, Lee, Jimmy, Lee, S. Hong, Legge, Sophie E., Lerer, Bernard, Li, Miaoxin, Li, Tao, Liang, Kung-Yee, Lieberman, Jeffrey, Limborska, Svetlana, Loughland, Carmel M., Lubinski, Jan, Lönnqvist, Jouko, Macek, Milan, Magnusson, Patrik K.E., Maher, Brion S., Maier, Wolfgang, Mallet, Jacques, Marsal, Sara, Mattheisen, Manuel, Mattingsdal, Morten, McCarley, Robert W., McDonald, Colm, McIntosh, Andrew M., Meier, Sandra, Meijer, Carin J., Melegh, Bela, Melle, Ingrid, Mesholam-Gately, Raquelle I., Metspalu, Andres, Michie, Patricia T., Milani, Lili, Milanova, Vihra, Mokrab, Younes, Morris, Derek W., Mors, Ole, Murphy, Kieran C., Murray, Robin M., Myin-Germeys, Inez, Müller-Myhsok, Bertram, Nelis, Mari, Nenadic, Igor, Nertney, Deborah A., Nestadt, Gerald, Nicodemus, Kristin K., Nikitina-Zake, Liene, Nisenbaum, Laura, Nordin, Annelie, O'Callaghan, Eadbhard, O'Dushlaine, Colm, O'Neill, F. Anthony, Oh, Sang-Yun, Olincy, Ann, Olsen, Line, Van Os, Jim, Pantelis, Christos, Papadimitriou, George N., Papiol, Sergi, Parkhomenko, Elena, Pato, Michele T., Paunio, Tiina, Pejovic-Milovancevic, Milica, Perkins, Diana O., Pietiläinen, Olli, Pimm, Jonathan, Pocklington, Andrew J., Powell, John, Price, Alkes, Pulver, Ann E., Purcell, Shaun M., Quested, Digby, Rasmussen, Henrik B., Reichenberg, Abraham, Reimers, Mark A., Richards, Alexander L., Roffman, Joshua L., Roussos, Panos, Ruderfer, Douglas M., Salomaa, Veikko, Sanders, Alan R., Schall, Ulrich, Schubert, Christian R., Schulze, Thomas G., Schwab, Sibylle G., Scolnick, Edward M., Scott, Rodney J., Seidman, Larry J., Shi, Jianxin, Sigurdsson, Engilbert, Silagadze, Teimuraz, Silverman, Jeremy M., Sim, Kang, Slominsky, Petr, Smoller, Jordan W., So, Hon-Cheong, Spencer, Chris C.A., Stahl, Eli A., Stefansson, Hreinn, Steinberg, Stacy, Stogmann, Elisabeth, Straub, Richard E., Strengman, Eric, Strohmaier, Jana, Stroup, T Scott, Subramaniam, Mythily, Suvisaari, Jaana, Svrakic, Dragan M., Szatkiewicz, Jin P., Söderman, Erik, Thirumalai, Srinivas, Toncheva, Draga, Tosato, Sarah, Veijola, Juha, Waddington, John, Walsh, Dermot, Wang, Dai, Wang, Qiang, Webb, Bradley T., Weiser, Mark, Wildenauer, Dieter B., Williams, Nigel M., Williams, Stephanie, Witt, Stephanie H., Wolen, Aaron R., Wong, Emily H.M., Wormley, Brandon K., Xi, Hualin Simon, Zai, Clement C., Zheng, Xuebin, Zimprich, Fritz, Wray, Naomi R., Stefansson, Kari, Visscher, Peter M., Adolfsson, Rolf, Andreassen, Ole A., Blackwood, Douglas H.R., Bramon, Elvira, Buxbaum, Joseph D., Børglum, Anders D., Cichon, Sven, Darvasi, Ariel, Domenici, Enrico, Ehrenreich, Hannelore, Esko, Tõnu, Gejman, Pablo V., Gill, Michael, Gurling, Hugh, Hultman, Christina M., Iwata, Nakao, Jablensky, Assen V., Jönsson, Erik G., Kendler, Kenneth S., Kirov, George, Knight, Jo, Lencz, Todd, Levinson, Douglas F., Li, Qingqin S., Liu, Jianjun, Malhotra, Anil K., McCarroll, Steven A., McQuillin, Andrew, Moran, Jennifer L., Mortensen, Preben B., Mowry, Bryan J., Nöthen, Markus M., Ophoff, Roel A., Owen, Michael J., Palotie, Aarno, Pato, Carlos N., Petryshen, Tracey L., Posthuma, Danielle, Rietschel, Marcella, Riley, Brien P., Rujescu, Dan, Sham, Pak C., Sklar, Pamela, St Clair, David, Weinberger, Daniel R., Wendland, Jens R., Werge, Thomas, Daly, Mark J., Sullivan, Patrick F., O'Donovan, Michael C., Qin, Shengying, Sawa, Akira, Kahn, Rene, Hong, Kyung Sue, Shi, Wenzhao, Tsuang, Ming, Itokawa, Masanari, Feng, Gang, Glatt, Stephen J., Ma, Xiancang, Tang, Jinsong, Ruan, Yunfeng, Liu, Ruize, Zhu, Feng, Horiuchi, Yasue, Lee, Byung Dae, Joo, Eun-Jeong, Myung, Woojae, Ha, Kyooseob, Won, Hong-Hee, Baek, Ji Hyung, Chung, Young Chul, Kim, Sung-Wan, Kusumawardhani, Agung, Chen, Wei J., Hwu, Hai-Gwo, Hishimoto, Akitoyo, Otsuka, Ikuo, Sora, Ichiro, Toyota, Tomoko, Yoshikawa, Takeo, Kunugi, Hiroshi, Hattori, Kotaro, Ishiwata, Sayuri, Numata, Shusuke, Ohmori, Tetsuro, Arai, Makoto, Ozeki, Yuji, Fujii, Kumiko, Kim, Se Joo, Lee, Heon-Jeong, Ahn, Yong Min, Kim, Se Hyun, Akiyama, Kazufumi, Shimoda, Kazutaka, Kinoshita, Makoto, Hsu, Yu-Han H., Pintacuda, Greta, Nacu, Eugeniu, Kim, April, Tsafou, Kalliopi, Petrossian, Natalie, Crotty, William, Suh, Jung Min, Riseman, Jackson, Martin, Jacqueline M., Biagini, Julia C., Mena, Daya, Ching, Joshua K.T., Malolepsza, Edyta, Li, Taibo, Singh, Tarjinder, Ge, Tian, Egri, Shawn B., Tanenbaum, Benjamin, Stanclift, Caroline R., Apffel, Annie M., Carr, Steven A., Schenone, Monica, Jaffe, Jake, Fornelos, Nadine, Eggan, Kevin C., and Lage, Kasper

Published
2023
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13. Distinguishing correlation from causation using genome-wide association studies

Author

O'Connor, Luke J. and Price, Alkes L.

Subjects
Statistics - Methodology, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Genome-wide association studies (GWAS) have emerged as a rich source of genetic clues into disease biology, and they have revealed strong genetic correlations among many diseases and traits. Some of these genetic correlations may reflect causal relationships. We developed a method to quantify causal relationships between genetically correlated traits using GWAS summary association statistics. In particular, our method quantifies what part of the genetic component of trait 1 is also causal for trait 2 using mixed fourth moments $E(\alpha_1^2\alpha_1\alpha_2)$ and $E(\alpha_2^2\alpha_1\alpha_2)$ of the bivariate effect size distribution. If trait 1 is causal for trait 2, then SNPs affecting trait 1 (large $\alpha_1^2$) will have correlated effects on trait 2 (large $\alpha_1\alpha_2$), but not vice versa. We validated this approach in extensive simulations. Across 52 traits (average $N=331$k), we identified 30 putative genetically causal relationships, many novel, including an effect of LDL cholesterol on decreased bone mineral density. More broadly, we demonstrate that it is possible to distinguish between genetic correlation and causation using genetic association data., Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216

Published
2018

14. Multiethnic Genome-wide Association Study of Diabetic Retinopathy using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control

Author

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

Subjects
Biomedical and Clinical Sciences, Genetics, Eye Disease and Disorders of Vision, Diabetes, Human Genome, Prevention, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Good Health and Well Being, Blood Glucose, Diabetes Mellitus, Type 2, Diabetic Retinopathy, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Glycated Hemoglobin, Humans, Meta-Analysis as Topic, Polymorphism, Single Nucleotide, Protein Binding, Family Investigation of Nephropathy and Diabetes-Eye Research Group, DCCT/EDIC Research Group, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

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

Published
2019

15. Leveraging Polygenic Functional Enrichment to Improve GWAS Power.

Author

Kichaev, Gleb, Bhatia, Gaurav, Loh, Po-Ru, Gazal, Steven, Burch, Kathryn, Freund, Malika, Schoech, Armin, Pasaniuc, Bogdan, and Price, Alkes

Subjects
GWAS, complex traits, functional genomics, Calibration, Databases, Genetic, Datasets as Topic, False Positive Reactions, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Probability, Reproducibility of Results, United Kingdom
Abstract

Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, functionally informed novel discovery of risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9%-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N = 130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to unweighted raw p values that do not use functional data. We replicated the additional loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66-0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N = 416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.

Published
2019

16. Functional disease architectures reveal unique biological role of transposable elements

Author

Hormozdiari, Farhad, van de Geijn, Bryce, Nasser, Joseph, Weissbrod, Omer, Gazal, Steven, Ju, Chelsea J-T, Connor, Luke O’, Hujoel, Margaux LA, Engreitz, Jesse, Hormozdiari, Fereydoun, and Price, Alkes L

Subjects
Biological Sciences, Genetics, Human Genome, Generic health relevance, Algorithms, Autoimmune Diseases, Brain Diseases, DNA Transposable Elements, Disease, Evolution, Molecular, Gene Expression Regulation, Genome, Human, Humans, Inheritance Patterns, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Retroelements, Short Interspersed Nucleotide Elements
Abstract

Transposable elements (TE) comprise roughly half of the human genome. Though initially derided as junk DNA, they have been widely hypothesized to contribute to the evolution of gene regulation. However, the contribution of TE to the genetic architecture of diseases remains unknown. Here, we analyze data from 41 independent diseases and complex traits to draw three conclusions. First, TE are uniquely informative for disease heritability. Despite overall depletion for heritability (54% of SNPs, 39 ± 2% of heritability), TE explain substantially more heritability than expected based on their depletion for known functional annotations. This implies that TE acquire function in ways that differ from known functional annotations. Second, older TE contribute more to disease heritability, consistent with acquiring biological function. Third, Short Interspersed Nuclear Elements (SINE) are far more enriched for blood traits than for other traits. Our results can help elucidate the biological roles that TE play in the genetic architecture of diseases.

Published
2019

20. A genome‐wide association study suggests new evidence for an association of the NADPH Oxidase 4 (NOX4) gene with severe diabetic retinopathy in type 2 diabetes

Author

Meng, Weihua, Shah, Kaanan P, Pollack, Samuela, Toppila, Iiro, Hebert, Harry L, McCarthy, Mark I, Groop, Leif, Ahlqvist, Emma, Lyssenko, Valeriya, Agardh, Elisabet, Daniell, Mark, Kaidonis, Georgia, Craig, Jamie E, Mitchell, Paul, Liew, Gerald, Kifley, Annette, Wang, Jie Jin, Christiansen, Mark W, Jensen, Richard A, Penman, Alan, Hancock, Heather A, Chen, Ching J, Correa, Adolfo, Kuo, Jane Z, Li, Xiaohui, Chen, Yii‐der I, Rotter, Jerome I, Klein, Ronald, Klein, Barbara, Wong, Tien Y, Morris, Andrew D, Doney, Alexander SF, Colhoun, Helen M, Price, Alkes L, Burdon, Kathryn P, Groop, Per‐Henrik, Sandholm, Niina, Grassi, Michael A, Sobrin, Lucia, Palmer, Colin NA, and Consortium, Surrogate markers for Micro‐and Macro‐vascular hard endpoints for Innovative diabetes Tools study group Wellcome Trust Case Control

Subjects
Biomedical and Clinical Sciences, Ophthalmology and Optometry, Eye Disease and Disorders of Vision, Genetics, Diabetes, Eye, Metabolic and endocrine, Adult, Diabetes Mellitus, Type 2, Diabetic Retinopathy, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotyping Techniques, Humans, Laser Coagulation, Male, Middle Aged, NADPH Oxidase 4, Polymorphism, Single Nucleotide, Scotland, White People, diabetes, diabetic complications, diabetic retinopathy, genome-wide association study, NOX4, Wellcome Trust Case Control Consortium 2 (WTCCC2), Surrogate markers for Micro- and Macro-vascular hard endpoints for Innovative diabetes Tools (SUMMIT) study group, Clinical Sciences, Neurosciences, Opthalmology and Optometry, Ophthalmology & Optometry, Ophthalmology and optometry
Abstract

PurposeDiabetic retinopathy is the most common eye complication in patients with diabetes. The purpose of this study is to identify genetic factors contributing to severe diabetic retinopathy.MethodsA genome-wide association approach was applied. In the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) datasets, cases of severe diabetic retinopathy were defined as type 2 diabetic patients who were ever graded as having severe background retinopathy (Level R3) or proliferative retinopathy (Level R4) in at least one eye according to the Scottish Diabetic Retinopathy Grading Scheme or who were once treated by laser photocoagulation. Controls were diabetic individuals whose longitudinal retinopathy screening records were either normal (Level R0) or only with mild background retinopathy (Level R1) in both eyes. Significant Single Nucleotide Polymorphisms (SNPs) were taken forward for meta-analysis using multiple Caucasian cohorts.ResultsFive hundred and sixty cases of type 2 diabetes with severe diabetic retinopathy and 4,106 controls were identified in the GoDARTS cohort. We revealed that rs3913535 in the NADPH Oxidase 4 (NOX4) gene reached a p value of 4.05 × 10-9 . Two nearby SNPs, rs10765219 and rs11018670 also showed promising p values (p values = 7.41 × 10-8 and 1.23 × 10-8 , respectively). In the meta-analysis using multiple Caucasian cohorts (excluding GoDARTS), rs10765219 and rs11018670 showed associations for diabetic retinopathy (p = 0.003 and 0.007, respectively), while the p value of rs3913535 was not significant (p = 0.429).ConclusionThis genome-wide association study of severe diabetic retinopathy suggests new evidence for the involvement of the NOX4 gene.

Published
2018

21. Quantitative analysis of population-scale family trees with millions of relatives

Author

Kaplanis, Joanna, Gordon, Assaf, Shor, Tal, Weissbrod, Omer, Geiger, Dan, Wahl, Mary, Gershovits, Michael, Markus, Barak, Sheikh, Mona, Gymrek, Melissa, Bhatia, Gaurav, MacArthur, Daniel G, Price, Alkes L, and Erlich, Yaniv

Subjects
Genetics, Generic health relevance, Datasets as Topic, Family, Genealogy and Heraldry, Humans, Longevity, Models, Genetic, Pedigree, Population, General Science & Technology
Abstract

Family trees have vast applications in fields as diverse as genetics, anthropology, and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. We collected 86 million profiles from publicly available online data shared by genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of human longevity and to provide insights into the geographical dispersion of families. We also report a simple digital procedure to overlay other data sets with our resource.

Published
2018

22. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights.

Author

Gusev, Alexander, Mancuso, Nicholas, Won, Hyejung, Kousi, Maria, Finucane, Hilary K, Reshef, Yakir, Song, Lingyun, Safi, Alexias, Schizophrenia Working Group of the Psychiatric Genomics Consortium, McCarroll, Steven, Neale, Benjamin M, Ophoff, Roel A, O'Donovan, Michael C, Crawford, Gregory E, Geschwind, Daniel H, Katsanis, Nicholas, Sullivan, Patrick F, Pasaniuc, Bogdan, and Price, Alkes L

Subjects
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Brain, Chromatin, Animals, Zebrafish, Humans, Genetic Predisposition to Disease, Mitogen-Activated Protein Kinase 3, Microtubule-Associated Proteins, Zebrafish Proteins, Gene Expression Profiling, Schizophrenia, Gene Dosage, Multifactorial Inheritance, Quantitative Trait Loci, Protein Phosphatase 2, Genome-Wide Association Study, Human Genome, Genetics, Mental Health, Neurosciences, Brain Disorders, Serious Mental Illness, 2.1 Biological and endogenous factors, Mental health, Developmental Biology, Biological Sciences, Medical and Health Sciences
Abstract

Genome-wide association studies (GWAS) have identified over 100 risk loci for schizophrenia, but the causal mechanisms remain largely unknown. We performed a transcriptome-wide association study (TWAS) integrating a schizophrenia GWAS of 79,845 individuals from the Psychiatric Genomics Consortium with expression data from brain, blood, and adipose tissues across 3,693 primarily control individuals. We identified 157 TWAS-significant genes, of which 35 did not overlap a known GWAS locus. Of these 157 genes, 42 were associated with specific chromatin features measured in independent samples, thus highlighting potential regulatory targets for follow-up. Suppression of one identified susceptibility gene, mapk3, in zebrafish showed a significant effect on neurodevelopmental phenotypes. Expression and splicing from the brain captured most of the TWAS effect across all genes. This large-scale connection of associations to target genes, tissues, and regulatory features is an essential step in moving toward a mechanistic understanding of GWAS.

Published
2018

23. Genetically Determined Plasma Lipid Levels and Risk of Diabetic Retinopathy: A Mendelian Randomization Study

Author

Sobrin, Lucia, Chong, Yong He, Fan, Qiao, Gan, Alfred, Stanwyck, Lynn K, Kaidonis, Georgia, Craig, Jamie E, Kim, Jihye, Liao, Wen-Ling, Huang, Yu-Chuen, Lee, Wen-Jane, Hung, Yi-Jen, Guo, Xiuqing, Hai, Yang, Ipp, Eli, Pollack, Samuela, Hancock, Heather, Price, Alkes, Penman, Alan, Mitchell, Paul, Liew, Gerald, Smith, Albert V, Gudnason, Vilmundur, Tan, Gavin, Klein, Barbara EK, Kuo, Jane, Li, Xiaohui, Christiansen, Mark W, Psaty, Bruce M, Sandow, Kevin, Jensen, Richard A, Klein, Ronald, Cotch, Mary Frances, Wang, Jie Jin, Jia, Yucheng, Chen, Ching J, Chen, Yii-Der Ida, Rotter, Jerome I, Tsai, Fuu-Jen, Hanis, Craig L, Burdon, Kathryn P, Wong, Tien Yin, Cheng, Ching-Yu, Spracklen, Cassandra N, Chen, Peng, Kim, Young Jin, Wang, Xu, Cai, Hui, Li, Shengxu, Long, Jirong, Wu, Ying, Wang, Ya-Xing, Takeuchi, Fumihiko, Wu, Jer-Yuarn, Jung, Keum-Ji, Hu, Cheng, Akiyama, Koichi, Zhang, Yonghong, Moon, Sanghoon, Johnson, Todd A, Li, Huaixing, Dorajoo, Rajkumar, He, Meian, Cannon, Maren E, Roman, Tamara S, Salfati, Elias, Lin, Keng-Hung, Sheu, Wayne HH, Absher, Devin, Adair, Linda S, Assimes, Themistocles L, Aung, Tin, Cai, Qiuyin, Chang, Li-Ching, Chen, Chien-Hsiun, Chien, Li-Hsin, Chuang, Lee-Ming, Chuang, Shu-Chun, Du, Shufa, Fann, Cathy SJ, Feranil, Alan B, Friedlander, Yechiel, Gordon-Larsen, Penny, Gu, Dongfeng, Gui, Lixuan, Guo, Zhirong, Heng, Chew-Kiat, Hixson, James, Hou, Xuhong, Hsiung, Chao Agnes, Hu, Yao, Hwang, Mi Yeong, Hwu, Chii-Min, Isono, Masato, Juang, Jyh-Ming Jimmy, Khor, Chiea-Chuen, Kim, Yun Kyoung, Koh, Woon-Puay, Kubo, Michiaki, and Lee, I-Te

Subjects
Biomedical and Clinical Sciences, Cancer, Prevention, Eye Disease and Disorders of Vision, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Aged, Diabetic Retinopathy, Female, Genome-Wide Association Study, Humans, Lipids, Male, Mendelian Randomization Analysis, Middle Aged, Polymorphism, Single Nucleotide, Risk, Asian Genetic Epidemiology Network Consortium, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Results from observational studies examining dyslipidemia as a risk factor for diabetic retinopathy (DR) have been inconsistent. We evaluated the causal relationship between plasma lipids and DR using a Mendelian randomization approach. We pooled genome-wide association studies summary statistics from 18 studies for two DR phenotypes: any DR (N = 2,969 case and 4,096 control subjects) and severe DR (N = 1,277 case and 3,980 control subjects). Previously identified lipid-associated single nucleotide polymorphisms served as instrumental variables. Meta-analysis to combine the Mendelian randomization estimates from different cohorts was conducted. There was no statistically significant change in odds ratios of having any DR or severe DR for any of the lipid fractions in the primary analysis that used single nucleotide polymorphisms that did not have a pleiotropic effect on another lipid fraction. Similarly, there was no significant association in the Caucasian and Chinese subgroup analyses. This study did not show evidence of a causal role of the four lipid fractions on DR. However, the study had limited power to detect odds ratios less than 1.23 per SD in genetically induced increase in plasma lipid levels, thus we cannot exclude that causal relationships with more modest effect sizes exist.

Published
2017

26. Dissecting the genetics of complex traits using summary association statistics

Author

Pasaniuc, Bogdan and Price, Alkes L

Subjects
Biological Sciences, Genetics, Human Genome, Biotechnology, 2.1 Biological and endogenous factors, 2.5 Research design and methodologies (aetiology), Aetiology, Generic health relevance, Computer Simulation, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Models, Statistical, Phenotype, Quantitative Trait Loci, Quantitative Trait, Heritable, Biochemistry and Cell Biology, Plant Biology, Developmental Biology, Biochemistry and cell biology
Abstract

During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

Published
2017

27. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets

Author

Delorey, Toni M., Ziegler, Carly G. K., Heimberg, Graham, Normand, Rachelly, Yang, Yiming, Segerstolpe, Åsa, Abbondanza, Domenic, Fleming, Stephen J., Subramanian, Ayshwarya, Montoro, Daniel T., Jagadeesh, Karthik A., Dey, Kushal K., Sen, Pritha, Slyper, Michal, Pita-Juárez, Yered H., Phillips, Devan, Biermann, Jana, Bloom-Ackermann, Zohar, Barkas, Nikolaos, Ganna, Andrea, Gomez, James, Melms, Johannes C., Katsyv, Igor, Normandin, Erica, Naderi, Pourya, Popov, Yury V., Raju, Siddharth S., Niezen, Sebastian, Tsai, Linus T.-Y., Siddle, Katherine J., Sud, Malika, Tran, Victoria M., Vellarikkal, Shamsudheen K., Wang, Yiping, Amir-Zilberstein, Liat, Atri, Deepak S., Beechem, Joseph, Brook, Olga R., Chen, Jonathan, Divakar, Prajan, Dorceus, Phylicia, Engreitz, Jesse M., Essene, Adam, Fitzgerald, Donna M., Fropf, Robin, Gazal, Steven, Gould, Joshua, Grzyb, John, Harvey, Tyler, Hecht, Jonathan, Hether, Tyler, Jané-Valbuena, Judit, Leney-Greene, Michael, Ma, Hui, McCabe, Cristin, McLoughlin, Daniel E., Miller, Eric M., Muus, Christoph, Niemi, Mari, Padera, Robert, Pan, Liuliu, Pant, Deepti, Pe’er, Carmel, Pfiffner-Borges, Jenna, Pinto, Christopher J., Plaisted, Jacob, Reeves, Jason, Ross, Marty, Rudy, Melissa, Rueckert, Erroll H., Siciliano, Michelle, Sturm, Alexander, Todres, Ellen, Waghray, Avinash, Warren, Sarah, Zhang, Shuting, Zollinger, Daniel R., Cosimi, Lisa, Gupta, Rajat M., Hacohen, Nir, Hibshoosh, Hanina, Hide, Winston, Price, Alkes L., Rajagopal, Jayaraj, Tata, Purushothama Rao, Riedel, Stefan, Szabo, Gyongyi, Tickle, Timothy L., Ellinor, Patrick T., Hung, Deborah, Sabeti, Pardis C., Novak, Richard, Rogers, Robert, Ingber, Donald E., Jiang, Z. Gordon, Juric, Dejan, Babadi, Mehrtash, Farhi, Samouil L., Izar, Benjamin, Stone, James R., Vlachos, Ioannis S., Solomon, Isaac H., Ashenberg, Orr, Porter, Caroline B. M., Li, Bo, Shalek, Alex K., Villani, Alexandra-Chloé, Rozenblatt-Rosen, Orit, and Regev, Aviv

Published
2021
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28. Genome-wide enhancer maps link risk variants to disease genes

Author

Nasser, Joseph, Bergman, Drew T., Fulco, Charles P., Guckelberger, Philine, Doughty, Benjamin R., Patwardhan, Tejal A., Jones, Thouis R., Nguyen, Tung H., Ulirsch, Jacob C., Lekschas, Fritz, Mualim, Kristy, Natri, Heini M., Weeks, Elle M., Munson, Glen, Kane, Michael, Kang, Helen Y., Cui, Ang, Ray, John P., Eisenhaure, Thomas M., Collins, Ryan L., Dey, Kushal, Pfister, Hanspeter, Price, Alkes L., Epstein, Charles B., Kundaje, Anshul, Xavier, Ramnik J., Daly, Mark J., Huang, Hailiang, Finucane, Hilary K., Hacohen, Nir, Lander, Eric S., and Engreitz, Jesse M.

Published
2021
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30. Transethnic Genetic-Correlation Estimates from Summary Statistics

Author

Brown, Brielin C, Consortium, Asian Genetic Epidemiology Network Type 2 Diabetes, Ye, Chun Jimmie, Price, Alkes L, and Zaitlen, Noah

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Autoimmune Disease, Human Genome, Bioengineering, Arthritis, Diabetes, Generic health relevance, Arthritis, Rheumatoid, Body Height, Body Mass Index, Diabetes Mellitus, Type 2, Ethnicity, Genome-Wide Association Study, Genotype, Humans, Likelihood Functions, Models, Genetic, Phenotype, Polymorphism, Single Nucleotide, Sample Size, Software, Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

The increasing number of genetic association studies conducted in multiple populations provides an unprecedented opportunity to study how the genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population genetics. Here, we have developed a method for estimating the transethnic genetic correlation: the correlation of causal-variant effect sizes at SNPs common in populations. This methods takes advantage of the entire spectrum of SNP associations and uses only summary-level data from genome-wide association studies. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We applied our method to data on gene expression, rheumatoid arthritis, and type 2 diabetes and overwhelmingly found that the genetic correlation was significantly less than 1. Our method is implemented in a Python package called Popcorn.

Published
2016

31. Integrative approaches for large-scale transcriptome-wide association studies

Author

Gusev, Alexander, Ko, Arthur, Shi, Huwenbo, Bhatia, Gaurav, Chung, Wonil, Penninx, Brenda WJH, Jansen, Rick, de Geus, Eco JC, Boomsma, Dorret I, Wright, Fred A, Sullivan, Patrick F, Nikkola, Elina, Alvarez, Marcus, Civelek, Mete, Lusis, Aldons J, Lehtimäki, Terho, Raitoharju, Emma, Kähönen, Mika, Seppälä, Ilkka, Raitakari, Olli T, Kuusisto, Johanna, Laakso, Markku, Price, Alkes L, Pajukanta, Päivi, and Pasaniuc, Bogdan

Subjects
Biological Sciences, Genetics, Obesity, Human Genome, Cancer, Generic health relevance, Animals, Gene Expression Regulation, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Mice, Phenotype, Quantitative Trait Loci, Transcriptome, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼ 3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

Published
2016

32. Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection

Author

Zhang, Martin, primary, Price, Alkes L., additional, Durvasula, Arun, additional, Chiang, Colby, additional, Koch, Evan, additional, Schoech, Armin, additional, Strober, Benjamin, additional, Shi, Huwenbo, additional, Barton, Alison, additional, Kim, Samuel, additional, Weissbrod, Omer, additional, Loh, Po-Ru, additional, Gazal, Steven, additional, and Sunyaev, Shamil, additional

Published
2023
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33. Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection

Author

Zhang, Martin Jinye, primary, Durvasula, Arun, additional, Chiang, Colby, additional, Koch, Evan M., additional, Strober, Benjamin J., additional, Shi, Huwenbo, additional, Barton, Alison R., additional, Kim, Samuel S., additional, Weissbrod, Omer, additional, Loh, Po-Ru, additional, Gazal, Steven, additional, Sunyaev, Shamil, additional, and Price, Alkes L., additional

Published
2023
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36. Leveraging Distant Relatedness to Quantify Human Mutation and Gene-Conversion Rates

Author

Palamara, Pier Francesco, Francioli, Laurent C, Wilton, Peter R, Genovese, Giulio, Gusev, Alexander, Finucane, Hilary K, Sankararaman, Sriram, Consortium, Genome of the Netherlands, Sunyaev, Shamil R, de Bakker, Paul IW, Wakeley, John, Pe’er, Itsik, and Price, Alkes L

Subjects
Biological Sciences, Genetics, Biotechnology, Human Genome, Alleles, Gene Frequency, Genome, Human, Germ-Line Mutation, Haplotypes, Humans, INDEL Mutation, Linear Models, Models, Genetic, Mutation Rate, Recombination, Genetic, Genome of the Netherlands Consortium, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

The rate at which human genomes mutate is a central biological parameter that has many implications for our ability to understand demographic and evolutionary phenomena. We present a method for inferring mutation and gene-conversion rates by using the number of sequence differences observed in identical-by-descent (IBD) segments together with a reconstructed model of recent population-size history. This approach is robust to, and can quantify, the presence of substantial genotyping error, as validated in coalescent simulations. We applied the method to 498 trio-phased sequenced Dutch individuals and inferred a point mutation rate of 1.66 × 10(-8) per base per generation and a rate of 1.26 × 10(-9) for

Published
2015

37. Genome-wide scan of 29,141 African Americans finds no evidence of selection since admixture

Author

Bhatia, Gaurav, Tandon, Arti, Aldrich, Melinda C., Ambrosone, Christine B., Amos, Christopher, Bandera, Elisa V., Berndt, Sonja I., Bernstein, Leslie, Blot, William J., Bock, Cathryn H., Caporaso, Neil, Casey, Graham, Deming, Sandra L., Diver, W. Ryan, Gapstur, Susan M., Gillanders, Elizabeth M., Harris, Curtis C., Henderson, Brian E., Ingles, Sue A., Isaacs, William, John, Esther M., Kittles, Rick A., Larkin, Emma, McNeill, Lorna H., Millikan, Robert C., Murphy, Adam, Neslund-Dudas, Christine, Nyante, Sarah, Press, Michael F., Rodriguez-Gil, Jorge L., Rybicki, Benjamin A., Schwartz, Ann G., Signorello, Lisa B., Spitz, Margaret, Strom, Sara S., Tucker, Margaret A., Wiencke, John K., Witte, John S., Wu, Xifeng, Yamamura, Yuko, Zanetti, Krista A., Zheng, Wei, Ziegler, Regina G., Chanock, Stephen J., Haiman, Christopher A., Reich, David, and Price, Alkes L.

Subjects
Quantitative Biology - Populations and Evolution
Abstract

We scanned through the genomes of 29,141 African Americans, searching for loci where the average proportion of African ancestry deviates significantly from the genome-wide average. We failed to find any genome-wide significant deviations, and conclude that any selection in African Americans since admixture is sufficiently weak that it falls below the threshold of our power to detect it using a large sample size. These results stand in contrast to the findings of a recent study of selection in African Americans. That study, which had 15 times fewer samples, reported six loci with significant deviations. We show that the discrepancy is likely due to insufficient correction for multiple hypothesis testing in the previous study. The same study reported 14 loci that showed greater population differentiation between African Americans and Nigerian Yoruba than would be expected in the absence of natural selection. Four such loci were previously shown to be genome-wide significant and likely to be affected by selection, but we show that most of the 10 additional loci are likely to be false positives. Additionally, the most parsimonious explanation for the loci that have significant evidence of unusual differentiation in frequency between Nigerians and Africans Americans is selection in Africa prior to their forced migration to the Americas.

Published
2013

42. New data and an old puzzle: the negative association between schizophrenia and rheumatoid arthritis

Author

Lee, S Hong, Byrne, Enda M, Hultman, Christina M, Kähler, Anna, Vinkhuyzen, Anna AE, Ripke, Stephan, Andreassen, Ole A, Frisell, Thomas, Gusev, Alexander, Hu, Xinli, Karlsson, Robert, Mantzioris, Vasilis X, McGrath, John J, Mehta, Divya, Stahl, Eli A, Zhao, Qiongyi, Kendler, Kenneth S, Sullivan, Patrick F, Price, Alkes L, O’Donovan, Michael, Okada, Yukinori, Mowry, Bryan J, Raychaudhuri, Soumya, Wray, Naomi R, Byerley, William, Cahn, Wiepke, Cantor, Rita M, Cichon, Sven, Cormican, Paul, Curtis, David, Djurovic, Srdjan, Escott-Price, Valentina, Gejman, Pablo V, Georgieva, Lyudmila, Giegling, Ina, Hansen, Thomas F, Ingason, Andrés, Kim, Yunjung, Konte, Bettina, Lee, Phil H, McIntosh, Andrew, McQuillin, Andrew, Morris, Derek W, Nöthen, Markus M, O’Dushlaine, Colm, Olincy, Ann, Olsen, Line, Pato, Carlos N, Pato, Michele T, Pickard, Benjamin S, Posthuma, Danielle, Rasmussen, Henrik B, Rietschel, Marcella, Rujescu, Dan, Schulze, Thomas G, Silverman, Jeremy M, Thirumalai, Srinivasa, Werge, Thomas, Agartz, Ingrid, Amin, Farooq, Azevedo, Maria H, Bass, Nicholas, Black, Donald W, Blackwood, Douglas HR, Bruggeman, Richard, Buccola, Nancy G, Choudhury, Khalid, Cloninger, Robert C, Corvin, Aiden, Craddock, Nicholas, Daly, Mark J, Datta, Susmita, Donohoe, Gary J, Duan, Jubao, Dudbridge, Frank, Fanous, Ayman, Freedman, Robert, Freimer, Nelson B, Friedl, Marion, Gill, Michael, Gurling, Hugh, De Haan, Lieuwe, Hamshere, Marian L, Hartmann, Annette M, Holmans, Peter A, Kahn, René S, Keller, Matthew C, Kenny, Elaine, Kirov, George K, Krabbendam, Lydia, Krasucki, Robert, Lawrence, Jacob, Lencz, Todd, Levinson, Douglas F, Lieberman, Jeffrey A, Lin, Dan-Yu, Linszen, Don H, Magnusson, Patrik KE, Maier, Wolfgang, and Malhotra, Anil K

Subjects
Epidemiology, Health Sciences, Human Genome, Mental Health, Brain Disorders, Autoimmune Disease, Schizophrenia, Clinical Research, Arthritis, Serious Mental Illness, Genetics, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Mental health, Adolescent, Adult, Arthritis, Rheumatoid, Cohort Studies, Cross-Sectional Studies, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Young Adult, Schizophrenia Working Group of the Psychiatric Genomics Consortium and Rheumatoid Arthritis Consortium International, Schizophrenia Working Group of the Psychiatric Genomics Consortium Authors, Schizophrenia Working Group of the Psychiatric Genomics Consortium Collaborators, Rheumatoid Arthritis Consortium International Authors, Rheumatoid Arthritis Consortium International Collaborators, Statistics, Public Health and Health Services, Public health
Abstract

BackgroundA long-standing epidemiological puzzle is the reduced rate of rheumatoid arthritis (RA) in those with schizophrenia (SZ) and vice versa. Traditional epidemiological approaches to determine if this negative association is underpinned by genetic factors would test for reduced rates of one disorder in relatives of the other, but sufficiently powered data sets are difficult to achieve. The genomics era presents an alternative paradigm for investigating the genetic relationship between two uncommon disorders.MethodsWe use genome-wide common single nucleotide polymorphism (SNP) data from independently collected SZ and RA case-control cohorts to estimate the SNP correlation between the disorders. We test a genotype X environment (GxE) hypothesis for SZ with environment defined as winter- vs summer-born.ResultsWe estimate a small but significant negative SNP-genetic correlation between SZ and RA (-0.046, s.e. 0.026, P = 0.036). The negative correlation was stronger for the SNP set attributed to coding or regulatory regions (-0.174, s.e. 0.071, P = 0.0075). Our analyses led us to hypothesize a gene-environment interaction for SZ in the form of immune challenge. We used month of birth as a proxy for environmental immune challenge and estimated the genetic correlation between winter-born and non-winter born SZ to be significantly less than 1 for coding/regulatory region SNPs (0.56, s.e. 0.14, P = 0.00090).ConclusionsOur results are consistent with epidemiological observations of a negative relationship between SZ and RA reflecting, at least in part, genetic factors. Results of the month of birth analysis are consistent with pleiotropic effects of genetic variants dependent on environmental context.

Published
2015

43. Efficient Bayesian mixed-model analysis increases association power in large cohorts

Author

Loh, Po-Ru, Tucker, George, Bulik-Sullivan, Brendan K, Vilhjálmsson, Bjarni J, Finucane, Hilary K, Salem, Rany M, Chasman, Daniel I, Ridker, Paul M, Neale, Benjamin M, Berger, Bonnie, Patterson, Nick, and Price, Alkes L

Subjects
Algorithms, Bayes Theorem, Female, Genetic Association Studies, Genome, Human, Genotyping Techniques, Humans, Linear Models, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding. However, existing methods are computationally intractable in large cohorts and may not optimize power. All existing methods require time cost O(MN(2)) (where N is the number of samples and M is the number of SNPs) and implicitly assume an infinitesimal genetic architecture in which effect sizes are normally distributed, which can limit power. Here we present a far more efficient mixed-model association method, BOLT-LMM, which requires only a small number of O(MN) time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes. We applied BOLT-LMM to 9 quantitative traits in 23,294 samples from the Women's Genome Health Study (WGHS) and observed significant increases in power, consistent with simulations. Theory and simulations show that the boost in power increases with cohort size, making BOLT-LMM appealing for genome-wide association studies in large cohorts.

Published
2015

44. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

Author

Bulik-Sullivan, Brendan K, Loh, Po-Ru, Finucane, Hilary K, Ripke, Stephan, Yang, Jian, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Patterson, Nick, Daly, Mark J, Price, Alkes L, and Neale, Benjamin M

Subjects
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Humans, Regression Analysis, Sample Size, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Genome, Human, Computer Simulation, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Genome, Human, Developmental Biology, Medical and Health Sciences, Biological Sciences
Abstract

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

Published
2015

45. Fast and accurate imputation of summary statistics enhances evidence of functional enrichment

Author

Pasaniuc, Bogdan, Zaitlen, Noah, Shi, Huwenbo, Bhatia, Gaurav, Gusev, Alexander, Pickrell, Joseph, Hirschhorn, Joel, Strachan, David P, Patterson, Nick, and Price, Alkes L.

Subjects
Quantitative Biology - Quantitative Methods, Quantitative Biology - Genomics, Quantitative Biology - Populations and Evolution, Statistics - Applications
Abstract

Imputation using external reference panels is a widely used approach for increasing power in GWAS and meta-analysis. Existing HMM-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available. In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1-5%) variants (increasing to 87% (60%) when summary LD information is available from target samples) versus 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and is computationally very fast. As an empirical demonstration, we apply our method to 7 case-control phenotypes from the WTCCC data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of $\chi^2$ association statistics) compared to HMM-based imputation from individual-level genotypes at the 227 (176) published SNPs in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of 4 lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic vs. non-genic loci for these traits, as compared to an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses., Comment: 32 pages, 4 figures

Published
2013
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46. Leveraging population admixture to characterize the heritability of complex traits.

Author

Zaitlen, Noah, Pasaniuc, Bogdan, Sankararaman, Sriram, Bhatia, Gaurav, Zhang, Jianqi, Gusev, Alexander, Young, Taylor, Tandon, Arti, Pollack, Samuela, Vilhjálmsson, Bjarni J, Assimes, Themistocles L, Berndt, Sonja I, Blot, William J, Chanock, Stephen, Franceschini, Nora, Goodman, Phyllis G, He, Jing, Hennis, Anselm JM, Hsing, Ann, Ingles, Sue A, Isaacs, William, Kittles, Rick A, Klein, Eric A, Lange, Leslie A, Nemesure, Barbara, Patterson, Nick, Reich, David, Rybicki, Benjamin A, Stanford, Janet L, Stevens, Victoria L, Strom, Sara S, Whitsel, Eric A, Witte, John S, Xu, Jianfeng, Haiman, Christopher, Wilson, James G, Kooperberg, Charles, Stram, Daniel, Reiner, Alex P, Tang, Hua, and Price, Alkes L

Subjects
Humans, Prostatic Neoplasms, Cardiovascular Diseases, Body Mass Index, Models, Statistical, Case-Control Studies, Cohort Studies, Reproducibility of Results, Chromosome Mapping, Genetics, Population, Epistasis, Genetic, Genotype, Multifactorial Inheritance, Quantitative Trait, Heritable, Phenotype, Polymorphism, Single Nucleotide, Models, Genetic, Computer Simulation, Aged, Middle Aged, African Continental Ancestry Group, African Americans, United States, Female, Male, Genome-Wide Association Study, Models, Statistical, Genetics, Population, Epistasis, Genetic, Quantitative Trait, Heritable, Polymorphism, Single Nucleotide, Developmental Biology, Biological Sciences, Medical and Health Sciences
Abstract

Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)γ). We show that h(2)γ = 2FSTCθ(1 - θ)h(2), where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2).

Published
2014

48. Partitioning Heritability of Regulatory and Cell-Type-Specific Variants across 11 Common Diseases

Author

Gusev, Alexander, Lee, S Hong, Trynka, Gosia, Finucane, Hilary, Vilhjálmsson, Bjarni J, Xu, Han, Zang, Chongzhi, Ripke, Stephan, Bulik-Sullivan, Brendan, Stahl, Eli, Kähler, Anna K, Hultman, Christina M, Purcell, Shaun M, McCarroll, Steven A, Daly, Mark J, Pasaniuc, Bogdan, Sullivan, Patrick F, Neale, Benjamin M, Wray, Naomi R, Raychaudhuri, Soumya, Price, Alkes, Corvin, Aiden, Walters, James TR, Farh, Kai-How, Holmans, Peter A, Lee, Phil, Collier, David A, Huang, Hailiang, Pers, Tune H, Agartz, Ingrid, Agerbo, Esben, Albus, Margot, Alexander, Madeline, Amin, Farooq, Bacanu, Silviu A, Begemann, Martin, Belliveau, Richard A, Bene, Judit, Bergen, Sarah E, Bevilacqua, Elizabeth, Bigdeli, Tim B, Black, Donald W, Børglum, Anders D, Bruggeman, Richard, Buccola, Nancy G, Buckner, Randy L, Byerley, William, Cahn, Wiepke, Cai, Guiqing, Campion, Dominique, Cantor, Rita M, Carr, Vaughan J, Carrera, Noa, Catts, Stanley V, Chambert, Kimberly D, Chan, Raymond CK, Chen, Ronald YL, Chen, Eric YH, Cheng, Wei, Cheung, Eric FC, Chong, Siow Ann, Cloninger, C Robert, Cohen, David, Cohen, Nadine, Cormican, Paul, Craddock, Nick, Crowley, James J, Curtis, David, Davidson, Michael, Davis, Kenneth L, Degenhardt, Franziska, Del Favero, Jurgen, DeLisi, Lynn E, Demontis, Ditte, Dikeos, Dimitris, Dinan, Timothy, Djurovic, Srdjan, Donohoe, Gary, Drapeau, Elodie, Duan, Jubao, Dudbridge, Frank, Durmishi, Naser, Eichhammer, Peter, Eriksson, Johan, Escott-Price, Valentina, Essioux, Laurent, Fanous, Ayman H, Farrell, Martilias S, Frank, Josef, Franke, Lude, Freedman, Robert, Freimer, Nelson B, Friedl, Marion, Friedman, Joseph I, Fromer, Menachem, Genovese, Giulio, and Georgieva, Lyudmila

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Prevention, Computer Simulation, Genetic Diseases, Inborn, Genetic Variation, Genome-Wide Association Study, Humans, Inheritance Patterns, Models, Genetic, Open Reading Frames, Regulatory Elements, Transcriptional, Schizophrenia Working Group of the Psychiatric Genomics Consortium, SWE-SCZ Consortium, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1× enrichment; p = 3.7 × 10(-17)) and 38% (SE = 4%) of hg(2) from genotyped SNPs (1.6× enrichment, p = 1.0 × 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained

Published
2014

49. Integrating functional data to prioritize causal variants in statistical fine-mapping studies.

Author

Kichaev, Gleb, Yang, Wen-Yun, Lindstrom, Sara, Hormozdiari, Farhad, Eskin, Eleazar, Price, Alkes L, Kraft, Peter, and Pasaniuc, Bogdan

Subjects
Humans, Chromosome Mapping, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Algorithms, Models, Theoretical, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Models, Theoretical, Genetics, Developmental Biology
Abstract

Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data.

Published
2014

50. Fast and accurate imputation of summary statistics enhances evidence of functional enrichment.

Author

Shi, Huwenbo, Bhatia, Gaurav, Gusev, Alexander, Pickrell, Joseph, Hirschhorn, Joel, Strachan, David, Patterson, Nick, Price, Alkes, Pasaniuc, Bogdan, and Zaitlen, Noah

Subjects
Algorithms, Biostatistics, Case-Control Studies, Cohort Studies, Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Phenotype, Polymorphism, Single Nucleotide, Software, Time Factors
Abstract

MOTIVATION: Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available. RESULTS: In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1-5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case-control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of [Formula: see text] association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses. AVAILABILITY AND IMPLEMENTATION: Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/. CONTACT: bpasaniuc@mednet.ucla.edu or aprice@hsph.harvard.edu SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Published
2014

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