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| Item Type: | Article |
|---|---|
| Title: | Further improvements to linear mixed models for genome-wide association studies |
| Creators Name: | Widmer, C., Lippert, C., Weissbrod, O., Fusi, N., Kadie, C., Davidson, R., Listgarten, J. and Heckerman, D. |
| Abstract: | We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals in a cohort. These similarities are estimated from single nucleotide polymorphisms (SNPs) or other genetic variants. Traditionally, all available SNPs are used to estimate the GSM. In empirical studies across a wide range of synthetic and real data, we find that modifications to this approach improve GWAS performance as measured by type I error control and power. Specifically, when only population structure is present, a GSM constructed from SNPs that well predict the phenotype in combination with principal components as covariates controls type I error and yields more power than the traditional LMM. In any setting, with or without population structure or family relatedness, a GSM consisting of a mixture of two component GSMs, one constructed from all SNPs and another constructed from SNPs that well predict the phenotype again controls type I error and yields more power than the traditional LMM. Software implementing these improvements and the experimental comparisons are available at http://microsoft.com/science. |
| Keywords: | Algorithms, Genetic Models, Genome-Wide Association Study, Genotype, Linear Models, Phenotype, Single Nucleotide Polymorphism, Software, Animals, Mice |
| Source: | Scientific Reports |
| ISSN: | 2045-2322 |
| Publisher: | Nature Publishing Group |
| Volume: | 4 |
| Page Range: | 6874 |
| Date: | 12 November 2014 |
| Official Publication: | https://doi.org/10.1038/srep06874 |
| PubMed: | View item in PubMed |
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