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Genome Biol. 2016 Apr 1;17:62. doi: 10.1186/s13059-016-0903-6.

Multiple testing correction in linear mixed models.

Author information

1
Bioinformatics IDP, University of California, Los Angeles, CA, USA.
2
Computer Science Department, University of California, Los Angeles, CA, USA.
3
Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, 138-736, Republic of Korea. buhm.han@amc.seoul.kr.
4
Computer Science Department, University of California, Los Angeles, CA, USA. eeskin@cs.ucla.edu.
5
Department of Human Genetics, University of California, Los Angeles, CA, USA. eeskin@cs.ucla.edu.

Abstract

BACKGROUND:

Multiple hypothesis testing is a major issue in genome-wide association studies (GWAS), which often analyze millions of markers. The permutation test is considered to be the gold standard in multiple testing correction as it accurately takes into account the correlation structure of the genome. Recently, the linear mixed model (LMM) has become the standard practice in GWAS, addressing issues of population structure and insufficient power. However, none of the current multiple testing approaches are applicable to LMM.

RESULTS:

We were able to estimate per-marker thresholds as accurately as the gold standard approach in real and simulated datasets, while reducing the time required from months to hours. We applied our approach to mouse, yeast, and human datasets to demonstrate the accuracy and efficiency of our approach.

CONCLUSIONS:

We provide an efficient and accurate multiple testing correction approach for linear mixed models. We further provide an intuition about the relationships between per-marker threshold, genetic relatedness, and heritability, based on our observations in real data.

PMID:
27039378
PMCID:
PMC4818520
DOI:
10.1186/s13059-016-0903-6
[Indexed for MEDLINE]
Free PMC Article

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