Mixed linear model approach adapted for genome-wide association studies

Zhiwu Zhang; Elhan Ersoz; Chao-Qiang Lai; Rory J Todhunter; Hemant K Tiwari; Michael A Gore; Peter J Bradbury; Jianming Yu; Donna K Arnett; Jose M Ordovas; Edward S Buckler

doi:10.1038/ng.546

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Mixed linear model approach adapted for genome-wide association studies

Journal article

Open access

Peer reviewed

Mixed linear model approach adapted for genome-wide association studies

Zhiwu Zhang, Elhan Ersoz, Chao-Qiang Lai, Rory J Todhunter, Hemant K Tiwari, Michael A Gore, Peter J Bradbury, Jianming Yu, Donna K Arnett, Jose M Ordovas, …

Nature genetics, Vol.42(4), pp.355-360

04/2010

DOI: https://doi.org/10.1038/ng.546

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https://hdl.handle.net/2376/108835

PMCID: PMC2931336

PMID: 20208535

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Abstract

Family

Linear Models

Population Groups

Software

Genome-Wide Association Study - methods

Humans

Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups. We also present a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components. We applied these two methods both independently and combined in selected genetic association datasets from human, dog and maize. The joint implementation of these two methods markedly reduced computing time and either maintained or improved statistical power. We used simulations to demonstrate the usefulness in controlling for substructure in genetic association datasets for a range of species and genetic architectures. We have made these methods available within an implementation of the software program TASSEL.

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Title: Mixed linear model approach adapted for genome-wide association studies
Creators: Zhiwu Zhang - Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. zz19@cornell.edu
Elhan Ersoz
Chao-Qiang Lai
Rory J Todhunter
Hemant K Tiwari
Michael A Gore
Peter J Bradbury
Jianming Yu
Donna K Arnett
Jose M Ordovas
Edward S Buckler
Publication Details: Nature genetics, Vol.42(4), pp.355-360
Academic Unit: Crop and Soil Sciences, Department of
Publisher: United States
Grant note: 5U01HL072524-06 / NHLBI NIH HHS HL54776 / NHLBI NIH HHS R01 HL054776 / NHLBI NIH HHS R01 HL054776-07 / NHLBI NIH HHS 1R21AR055228-01A1 / NIAMS NIH HHS R01 HL054776-11 / NHLBI NIH HHS R21 AR055228 / NIAMS NIH HHS R01 HL054776-06 / NHLBI NIH HHS R01 HL054776-09A1 / NHLBI NIH HHS R01 HL054776-12 / NHLBI NIH HHS R01 HL054776-04 / NHLBI NIH HHS U01 HL072524 / NHLBI NIH HHS R01 HL054776-05 / NHLBI NIH HHS R01 HL054776-13 / NHLBI NIH HHS U 01 HL72524 / NHLBI NIH HHS R01 HL054776-10 / NHLBI NIH HHS R01 HL054776-08 / NHLBI NIH HHS
Identifiers: 99900547017601842
Language: English
Resource Type: Journal article