{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:18:07Z","timestamp":1777522687859,"version":"3.51.4"},"reference-count":43,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Biology"],"published-print":{"date-parts":[[2022,3,1]]},"abstract":"<jats:p>Motivated by empirical arguments that are well known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) applied to GWAS. First, we study the sensitivity of LMMs to the inclusion of a candidate single nucleotide polymorphism (SNP) in the kinship matrix, which is often done in practice to speed up computations. Our results shed light on the size of the error incurred by including a candidate SNP, providing a justification to this technique to trade off velocity against veracity. Second, we investigate how mixed models can correct confounders in GWAS, which is widely accepted as an advantage of LMMs over traditional methods. We consider two sources of confounding factors\u2014population stratification and environmental confounding factors\u2014and study how different methods that are commonly used in practice trade off these two confounding factors differently.<\/jats:p>","DOI":"10.1089\/cmb.2021.0157","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T10:10:33Z","timestamp":1646129433000},"page":"233-242","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["Trade-offs of Linear Mixed Models in Genome-Wide Association Studies"],"prefix":"10.1177","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1826-4069","authenticated-orcid":false,"given":"Haohan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA."}]},{"given":"Bryon","family":"Aragam","sequence":"additional","affiliation":[{"name":"Booth School of Business, University of Chicago, Chicago, Illinois, USA."}]},{"given":"Eric P.","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA."}]}],"member":"179","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"e_1_3_6_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/gepi.1045"},{"key":"e_1_3_6_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2008.10.008"},{"key":"e_1_3_6_4_1","author":"Bonnet A.","year":"2018","unstructured":"Bonnet A. 2018. Heritability estimation in case-control studies. Electron. J. Statist. 12, 1662\u20131716. [Epub ahead of print]; DOI: 10.1214\/18-EJS1424.","journal-title":"Electron. J. Statist"},{"key":"e_1_3_6_5_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1005048"},{"key":"e_1_3_6_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.1999.00997.x"},{"key":"e_1_3_6_7_1","doi-asserted-by":"publisher","DOI":"10.1006\/tpbi.2001.1542"},{"key":"e_1_3_6_8_1","volume-title":"Proceedings of the 19th International Conference on Artificial Intelligence and Statistics","author":"Dicker L.H.","year":"2016","unstructured":"Dicker L.H., , and Erdogdu M.A. 2016. Maximum likelihood for variance estimation in high-dimensional linear models. In Gretton, A., and Robert, C.C., eds. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:159\u2013167. Cadiz, Spain."},{"key":"e_1_3_6_9_1","first-page":"388","article-title":"Assessing the impact of population stratification on genetic association studies. Nat","volume":"36","author":"Freedman M.L.","year":"2004","unstructured":"Freedman M.L., , Reich D., , Penney K.L., et al. 2004. Assessing the impact of population stratification on genetic association studies. Nat. Genet. 36, 388.","journal-title":"Genet"},{"key":"e_1_3_6_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-1809.2012.00708.x"},{"key":"e_1_3_6_11_1","doi-asserted-by":"crossref","unstructured":"Heckerman D. 2018. Accounting for hidden common causes when inferring cause and effect from observational data. arXiv preprint arXiv:1801.00727.","DOI":"10.1145\/3309720"},{"key":"e_1_3_6_12_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0075707"},{"key":"e_1_3_6_13_1","doi-asserted-by":"publisher","DOI":"10.7205\/MILMED.170.8.688"},{"key":"e_1_3_6_14_1","first-page":"2127","article-title":"On high-dimensional misspecified mixed model analysis in genome-wide association study","volume":"44","author":"Li C.","year":"2016","unstructured":"Jiang. J., Li C., , Paul D., , Yang C., et al. 2016. On high-dimensional misspecified mixed model analysis in genome-wide association study. Ann. Stat. 44, 2127\u20132160.","journal-title":"Ann. Stat"},{"key":"e_1_3_6_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.548"},{"key":"e_1_3_6_16_1","doi-asserted-by":"publisher","DOI":"10.1534\/genetics.107.080101"},{"key":"e_1_3_6_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2376"},{"key":"e_1_3_6_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.1681"},{"key":"e_1_3_6_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2620"},{"key":"e_1_3_6_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2037"},{"key":"e_1_3_6_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3190"},{"key":"e_1_3_6_22_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-LNMS5713"},{"key":"e_1_3_6_23_1","first-page":"607","article-title":"Risky substance use environments and addiction: A new frontier for environmental justice research. Int. J. Environ. Res","volume":"13","author":"Mennis J.","year":"2016","unstructured":"Mennis J., , Stahler G., , and Mason M. 2016. Risky substance use environments and addiction: A new frontier for environmental justice research. Int. J. Environ. Res. Public Health, 13, 607.","journal-title":"Public Health"},{"key":"e_1_3_6_24_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.0020190"},{"key":"e_1_3_6_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17576-9"},{"key":"e_1_3_6_26_1","volume-title":"RECOMB","author":"Sankararaman S.","year":"2019","unstructured":"Sankararaman S. 2019. Fast estimation of genetic correlation for biobank-scale data, 322. In Cowen, L.J., ed. RECOMB, Springer, Washington, DC, USA."},{"key":"e_1_3_6_27_1","doi-asserted-by":"publisher","DOI":"10.1214\/19-AOAS1291"},{"key":"e_1_3_6_28_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2314"},{"key":"e_1_3_6_29_1","doi-asserted-by":"publisher","DOI":"10.1038\/nbt0308-256b"},{"key":"e_1_3_6_30_1","doi-asserted-by":"publisher","DOI":"10.1214\/17-EJS1386"},{"key":"e_1_3_6_31_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty644"},{"key":"e_1_3_6_32_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2410"},{"key":"e_1_3_6_33_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177704731"},{"key":"e_1_3_6_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2015.10.002"},{"key":"e_1_3_6_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3382"},{"key":"e_1_3_6_36_1","first-page":"513","article-title":"Counterpoint: Bias from population stratification is not a major threat to the validity of conclusions from epidemiological studies of common polymorphisms and cancer","volume":"11","author":"Wacholder S.","year":"2002","unstructured":"Wacholder S., , Rothman N., , and Caporaso N. 2002. Counterpoint: Bias from population stratification is not a major threat to the validity of conclusions from epidemiological studies of common polymorphisms and cancer. Cancer Epidemiol. Biomarkers Prev. 11, 513\u2013520.","journal-title":"Cancer Epidemiol. Biomarkers Prev"},{"key":"e_1_3_6_37_1","first-page":"431","article-title":"Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies","volume":"2017","author":"Wang H.","year":"2017","unstructured":"Wang H., , Aragam B., , and Xing E.P. 2017. Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies. IEEE BIBM, 2017, 431\u2013438.","journal-title":"IEEE BIBM"},{"key":"e_1_3_6_38_1","doi-asserted-by":"publisher","DOI":"10.1002\/gepi.20003"},{"key":"e_1_3_6_39_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty253"},{"key":"e_1_3_6_40_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.608"},{"key":"e_1_3_6_41_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2876"},{"key":"e_1_3_6_42_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng1702"},{"key":"e_1_3_6_43_1","first-page":"2027","article-title":"A unified framework for variance component estimation with summary statistics in genome-wide association studies. Ann. Appl","volume":"11","author":"Zhou X.","year":"2017","unstructured":"Zhou X. 2017. A unified framework for variance component estimation with summary statistics in genome-wide association studies. Ann. Appl. Stat. 11, 2027.","journal-title":"Stat"},{"key":"e_1_3_6_44_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2310"}],"container-title":["Journal of Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1089\/cmb.2021.0157","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/cmb.2021.0157","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/cmb.2021.0157","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T16:18:24Z","timestamp":1777393104000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1089\/cmb.2021.0157"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,1]]},"references-count":43,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,3,1]]}},"alternative-id":["10.1089\/cmb.2021.0157"],"URL":"https:\/\/doi.org\/10.1089\/cmb.2021.0157","relation":{},"ISSN":["1066-5277","1557-8666"],"issn-type":[{"value":"1066-5277","type":"print"},{"value":"1557-8666","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,1]]}}}