Multiple testing correction in linear mixed models

• Published in: Genome Biology 2016-04, Vol.17 (1), p.62-62, Article 62
• Main Authors: Joo, Jong Wha J, Hormozdiari, Farhad, Han, Buhm, Eskin, Eleazar
• Format: Article
• Language: English
• Subjects: Animal models; Animals; Bioinformatics; Computational Biology - methods; Databases, Genetic; Datasets; Genetics; Genome-wide association studies; Genome-Wide Association Study - methods; Genome-Wide Association Study - standards; Genomes; Genotype & phenotype; Heritability; Humans; Hypotheses; Linear Models; Mice; Population; Population structure; Software; Yeasts - genetics
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-016-0903-6

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. 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. 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.