Document Open Access Logo

An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts

Authors Brandon Jew, Jiajin Li, Sriram Sankararaman, Jae Hoon Sul

PDF
Thumbnail PDF

File

LIPIcs.WABI.2021.10.pdf
  • Filesize: 1.25 MB
  • 17 pages

Document Identifiers

Author Details

Brandon Jew
  • Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
Jiajin Li
  • Department of Human Genetics, University of California, Los Angeles, CA, USA
Sriram Sankararaman
  • Department of Human Genetics, University of California, Los Angeles, CA, USA
  • Department of Computer Science, University of California, Los Angeles, CA, USA
  • Department of Computational Medicine, University of California, Los Angeles, CA, USA
Jae Hoon Sul
  • Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA

Funding

  • Jew, Brandon: National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1650604.
  • Sankararaman, Sriram: NIH R35GM125055, Alfred P. Sloan Research Fellowship, NSF III-1705121, CAREER 1943497.
  • Sul, Jae Hoon: National Institute of Environmental Health Sciences (NIEHS) [K01 ES028064]; the National Science Foundation grant [#1705197]; the National Institute of Neurological Disorders and Stroke (NINDS) [R01 NS102371]; and NINDS [R03 HL150604].

Cite AsGet BibTex

Brandon Jew, Jiajin Li, Sriram Sankararaman, and Jae Hoon Sul. An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts. In 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 201, pp. 10:1-10:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.WABI.2021.10

Abstract

Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model. Whereas existing exact methods may be cubic in their time complexity with respect to the number of individuals, our approach for multiple-context LMMs (mcLMM) is linear. These improvements allow for large-scale analyses requiring computing time and memory magnitudes of order less than existing methods. As examples, we apply our approach to identify expression quantitative trait loci from large-scale gene expression data measured across multiple tissues as well as joint analyses of multiple phenotypes in genome-wide association studies at biobank scale.

Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
  • Applied computing → Computational genomics
Keywords
  • Meta-analysis
  • Linear mixed models
  • multiple-context genetic association

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Supplementary Materials

References

  1. François Aguet, Andrew A. Brown, Stephane E. Castel, Joe R. Davis, Yuan He, Brian Jo, Pejman Mohammadi, YoSon Park, Princy Parsana, Ayellet V. Segrè, Benjamin J. Strober, Zachary Zappala, Beryl B. Cummings, Ellen T. Gelfand, Kane Hadley, Katherine H. Huang, Monkol Lek, Xiao Li, Jared L. Nedzel, Duyen Y. Nguyen, Michael S. Noble, Timothy J. Sullivan, Taru Tukiainen, Daniel G. MacArthur, Gad Getz, Anjene Addington, Ping Guan, Susan Koester, A. Roger Little, Nicole C. Lockhart, Helen M. Moore, Abhi Rao, Jeffery P. Struewing, Simona Volpi, Lori E. Brigham, Richard Hasz, Marcus Hunter, Christopher Johns, Mark Johnson, Gene Kopen, William F. Leinweber, John T. Lonsdale, Alisa McDonald, Bernadette Mestichelli, Kevin Myer, Bryan Roe, Michael Salvatore, Saboor Shad, Jeffrey A. Thomas, Gary Walters, Michael Washington, Joseph Wheeler, Jason Bridge, Barbara A. Foster, Bryan M. Gillard, Ellen Karasik, Rachna Kumar, Mark Miklos, Michael T. Moser, Scott D. Jewell, Robert G. Montroy, Daniel C. Rohrer, Dana Valley, Deborah C. Mash, David A. Davis, Leslie Sobin, Mary E. Barcus, Philip A. Branton, Nathan S. Abell, Brunilda Balliu, Olivier Delaneau, Laure Frésard, Eric R. Gamazon, Diego Garrido-Martín, Ariel D. H. Gewirtz, Genna Gliner, Michael J. Gloudemans, Buhm Han, Amy Z. He, Farhad Hormozdiari, Xin Li, Boxiang Liu, Eun Yong Kang, Ian C. McDowell, Halit Ongen, John J. Palowitch, Christine B. Peterson, Gerald Quon, Stephan Ripke, Ashis Saha, Andrey A. Shabalin, Tyler C. Shimko, Jae Hoon Sul, Nicole A. Teran, Emily K. Tsang, Hailei Zhang, Yi-Hui Zhou, Carlos D. Bustamante, Nancy J. Cox, Roderic Guigó, Manolis Kellis, Mark I. McCarthy, Donald F. Conrad, Eleazar Eskin, Gen Li, Andrew B. Nobel, Chiara Sabatti, Barbara E. Stranger, Xiaoquan Wen, Fred A. Wright, Kristin G. Ardlie, Emmanouil T. Dermitzakis, Tuuli Lappalainen, Robert E. Handsaker, Seva Kashin, Konrad J. Karczewski, Duyen T. Nguyen, Casandra A. Trowbridge, Ruth Barshir, Omer Basha, Alexis Battle, Gireesh K. Bogu, Andrew Brown, Christopher D. Brown, Lin S. Chen, Colby Chiang, Farhan N. Damani, Barbara E. Engelhardt, Pedro G. Ferreira, Ariel D.H. Gewirtz, Roderic Guigo, Ira M. Hall, Cedric Howald, Hae Kyung Im, Eun Yong Kang, Yungil Kim, Sarah Kim-Hellmuth, Serghei Mangul, Jean Monlong, Stephen B. Montgomery, Manuel Muñoz-Aguirre, Anne W. Ndungu, Dan L. Nicolae, Meritxell Oliva, Nikolaos Panousis, Panagiotis Papasaikas, Anthony J. Payne, Jie Quan, Ferran Reverter, Michael Sammeth, Alexandra J. Scott, Reza Sodaei, Matthew Stephens, Sarah Urbut, Martijn van de Bunt, Gao Wang, Hualin S. Xi, Esti Yeger-Lotem, Judith B. Zaugg, Joshua M. Akey, Daniel Bates, Joanne Chan, Melina Claussnitzer, Kathryn Demanelis, Morgan Diegel, Jennifer A. Doherty, Andrew P. Feinberg, Marian S. Fernando, Jessica Halow, Kasper D. Hansen, Eric Haugen, Peter F. Hickey, Lei Hou, Farzana Jasmine, Ruiqi Jian, Lihua Jiang, Audra Johnson, Rajinder Kaul, Muhammad G. Kibriya, Kristen Lee, Jin Billy Li, Qin Li, Jessica Lin, Shin Lin, Sandra Linder, Caroline Linke, Yaping Liu, Matthew T. Maurano, Benoit Molinie, Jemma Nelson, Fidencio J. Neri, Yongjin Park, Brandon L. Pierce, Nicola J. Rinaldi, Lindsay F. Rizzardi, Richard Sandstrom, Andrew Skol, Kevin S. Smith, Michael P. Snyder, John Stamatoyannopoulos, Hua Tang, Li Wang, Meng Wang, Nicholas Van Wittenberghe, Fan Wu, Rui Zhang, Concepcion R. Nierras, Latarsha J. Carithers, Jimmie B. Vaught, Sarah E. Gould, Nicole C. Lockart, Casey Martin, Anjene M. Addington, Susan E. Koester, GTEx Consortium, Lead analysts:, Data Analysis & Coordinating Center (LDACC): Laboratory, NIH program management:, Biospecimen collection:, Pathology:, eQTL manuscript working group:, Data Analysis & Coordinating Center (LDACC)-Analysis Working Group Laboratory, Statistical Methods groups-Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, and Biospecimen Collection Source Site-NDRI. Genetic effects on gene expression across human tissues. Nature, 550(7675):204-213, October 2017. URL: https://doi.org/10.1038/nature24277.
  2. Annalisa Buniello, Jacqueline A L MacArthur, Maria Cerezo, Laura W Harris, James Hayhurst, Cinzia Malangone, Aoife McMahon, Joannella Morales, Edward Mountjoy, Elliot Sollis, Daniel Suveges, Olga Vrousgou, Patricia L Whetzel, Ridwan Amode, Jose A Guillen, Harpreet S Riat, Stephen J Trevanion, Peggy Hall, Heather Junkins, Paul Flicek, Tony Burdett, Lucia A Hindorff, Fiona Cunningham, and Helen Parkinson. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Research, 47(D1):D1005-D1012, November 2018. URL: https://doi.org/10.1093/nar/gky1120.
  3. Clare Bycroft, Colin Freeman, Desislava Petkova, Gavin Band, Lloyd T. Elliott, Kevin Sharp, Allan Motyer, Damjan Vukcevic, Olivier Delaneau, Jared O'Connell, Adrian Cortes, Samantha Welsh, Alan Young, Mark Effingham, Gil McVean, Stephen Leslie, Naomi Allen, Peter Donnelly, and Jonathan Marchini. The uk biobank resource with deep phenotyping and genomic data. Nature, 562(7726):203-209, October 2018. URL: https://doi.org/10.1038/s41586-018-0579-z.
  4. Christopher C Chang, Carson C Chow, Laurent CAM Tellier, Shashaank Vattikuti, Shaun M Purcell, and James J Lee. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4(1), February 2015. s13742-015-0047-8. URL: https://doi.org/10.1186/s13742-015-0047-8.
  5. GTEx Consortium. The GTEx consortium atlas of genetic regulatory effects across human tissues. Science, 369(6509):1318-1330, 2020. Google Scholar
  6. Buhm Han and Eleazar Eskin. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. The American Journal of Human Genetics, 88(5):586-598, May 2011. URL: https://doi.org/10.1016/j.ajhg.2011.04.014.
  7. Jong Wha J Joo, Eun Yong Kang, Elin Org, Nick Furlotte, Brian Parks, Farhad Hormozdiari, Aldons J Lusis, and Eleazar Eskin. Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure. Genetics, 204(4):1379-1390, December 2016. URL: https://doi.org/10.1534/genetics.116.189712.
  8. Hyun Min Kang, Noah A. Zaitlen, Claire M. Wade, Andrew Kirby, David Heckerman, Mark J. Daly, and Eleazar Eskin. Efficient control of population structure in model organism association mapping. Genetics, 178(3):1709-1723, 2008. URL: https://doi.org/10.1534/genetics.107.080101.
  9. Arthur Korte, Bjarni J. Vilhjálmsson, Vincent Segura, Alexander Platt, Quan Long, and Magnus Nordborg. A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nature Genetics, 44(9):1066-1071, September 2012. URL: https://doi.org/10.1038/ng.2376.
  10. Christoph Lippert, Jennifer Listgarten, Ying Liu, Carl M. Kadie, Robert I. Davidson, and David Heckerman. Fast linear mixed models for genome-wide association studies. Nature Methods, 8(10):833-835, October 2011. URL: https://doi.org/10.1038/nmeth.1681.
  11. Ani Manichaikul, Josyf C. Mychaleckyj, Stephen S. Rich, Kathy Daly, Michèle Sale, and Wei-Min Chen. Robust relationship inference in genome-wide association studies. Bioinformatics, 26(22):2867-2873, October 2010. URL: https://doi.org/10.1093/bioinformatics/btq559.
  12. Florian Privé, Hugues Aschard, Andrey Ziyatdinov, and Michael G B Blum. Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr. Bioinformatics, 34(16):2781-2787, March 2018. URL: https://doi.org/10.1093/bioinformatics/bty185.
  13. Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, and Oliver Stegle. It is all in the noise: Efficient multi-task gaussian process inference with structured residuals. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013. URL: https://proceedings.neurips.cc/paper/2013/file/59c33016884a62116be975a9bb8257e3-Paper.pdf.
  14. Vardhman K. Rakyan, Thomas A. Down, David J. Balding, and Stephan Beck. Epigenome-wide association studies for common human diseases. Nature reviews. Genetics, 12(8):529-541, July 2011. 21747404[pmid]. URL: https://doi.org/10.1038/nrg3000.
  15. Xavier Robin, Natacha Turck, Alexandre Hainard, Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez, and Markus Müller. pROC: an open-source package for R and s+ to analyze and compare ROC curves. BMC Bioinformatics, 12:77, 2011. Google Scholar
  16. Jack Sherman and Winifred J. Morrison. Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix. The Annals of Mathematical Statistics, 21(1):124-127, 1950. URL: https://doi.org/10.1214/aoms/1177729893.
  17. Oliver Stegle, Leopold Parts, Matias Piipari, John Winn, and Richard Durbin. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc., 7(3):500-507, 2012. Google Scholar
  18. Matthew Stephens. False discovery rates: a new deal. Biostatistics, 18(2):275-294, 2017. Google Scholar
  19. John D. Storey. A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3):479-498, 2002. URL: https://doi.org/10.1111/1467-9868.00346.
  20. Jae Hoon Sul, Buhm Han, Chun Ye, Ted Choi, and Eleazar Eskin. Effectively identifying eqtls from multiple tissues by combining mixed model and meta-analytic approaches. PLOS Genetics, 9(6):1-13, June 2013. URL: https://doi.org/10.1371/journal.pgen.1003491.
  21. The All of Us Research Program Investigators. The “all of us” research program. New England Journal of Medicine, 381(7):668-676, 2019. PMID: 31412182. URL: https://doi.org/10.1056/NEJMsr1809937.
  22. Sarah M. Urbut, Gao Wang, Peter Carbonetto, and Matthew Stephens. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nature Genetics, 51(1):187-195, January 2019. URL: https://doi.org/10.1038/s41588-018-0268-8.
  23. Peter M. Visscher, Matthew A. Brown, Mark I. McCarthy, and Jian Yang. Five years of gwas discovery. American journal of human genetics, 90(1):7-24, January 2012. 22243964[pmid]. URL: https://doi.org/10.1016/j.ajhg.2011.11.029.
  24. S. J. Welham and R. Thompson. Likelihood ratio tests for fixed model terms using residual maximum likelihood. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 59(3):701-714, 1997. URL: https://doi.org/10.1111/1467-9868.00092.
  25. Genevieve L. Wojcik, Mariaelisa Graff, Katherine K. Nishimura, Ran Tao, Jeffrey Haessler, Christopher R. Gignoux, Heather M. Highland, Yesha M. Patel, Elena P. Sorokin, Christy L. Avery, Gillian M. Belbin, Stephanie A. Bien, Iona Cheng, Sinead Cullina, Chani J. Hodonsky, Yao Hu, Laura M. Huckins, Janina Jeff, Anne E. Justice, Jonathan M. Kocarnik, Unhee Lim, Bridget M. Lin, Yingchang Lu, Sarah C. Nelson, Sung-Shim L. Park, Hannah Poisner, Michael H. Preuss, Melissa A. Richard, Claudia Schurmann, Veronica W. Setiawan, Alexandra Sockell, Karan Vahi, Marie Verbanck, Abhishek Vishnu, Ryan W. Walker, Kristin L. Young, Niha Zubair, Victor Acuña-Alonso, Jose Luis Ambite, Kathleen C. Barnes, Eric Boerwinkle, Erwin P. Bottinger, Carlos D. Bustamante, Christian Caberto, Samuel Canizales-Quinteros, Matthew P. Conomos, Ewa Deelman, Ron Do, Kimberly Doheny, Lindsay Fernández-Rhodes, Myriam Fornage, Benyam Hailu, Gerardo Heiss, Brenna M. Henn, Lucia A. Hindorff, Rebecca D. Jackson, Cecelia A. Laurie, Cathy C. Laurie, Yuqing Li, Dan-Yu Lin, Andres Moreno-Estrada, Girish Nadkarni, Paul J. Norman, Loreall C. Pooler, Alexander P. Reiner, Jane Romm, Chiara Sabatti, Karla Sandoval, Xin Sheng, Eli A. Stahl, Daniel O. Stram, Timothy A. Thornton, Christina L. Wassel, Lynne R. Wilkens, Cheryl A. Winkler, Sachi Yoneyama, Steven Buyske, Christopher A. Haiman, Charles Kooperberg, Loic Le Marchand, Ruth J. F. Loos, Tara C. Matise, Kari E. North, Ulrike Peters, Eimear E. Kenny, and Christopher S. Carlson. Genetic analyses of diverse populations improves discovery for complex traits. Nature, 570(7762):514-518, June 2019. URL: https://doi.org/10.1038/s41586-019-1310-4.
  26. Xiang Zhou and Matthew Stephens. Genome-wide efficient mixed-model analysis for association studies. Nature Genetics, 44(7):821-4, 2012. URL: https://doi.org/10.1038/ng.2310.
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact