ArticleScientific reports2014
Further improvements to linear mixed models for genome-wide association studies.
Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
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- Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast.Molecular systems biology · 2024Article
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- Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast.bioRxiv : the preprint server for biology · 2023Article
- Characterization of novel loci controlling seed oil content in Brassica napus by marker metabolite-based multi-omics analysis.Genome biology · 2023Article
- Transcriptome-wide gene-gene interaction associations elucidate pathways and functional enrichment of complex traits.PLoS genetics · 2023Article
- The pathway of melatonin biosynthesis in common wheat (Triticum aestivum).Journal of pineal research · 2023Article
- Genomic prediction with whole-genome sequence data in intensely selected pig lines.Genetics, selection, evolution : GSE · 2022Article
- Incorporating family disease history and controlling case-control imbalance for population-based genetic association studies.Bioinformatics (Oxford, England) · 2022Article
- Rare and population-specific functional variation across pig lines.Genetics, selection, evolution : GSE · 2022Article
- The interplay of additivity, dominance, and epistasis on fitness in a diploid yeast cross.Nature communications · 2022Article
- Review
- Genome-wide association study and its applications in the non-model crop Sesamum indicum.BMC plant biology · 2021Review
- Genome-Wide Association Mapping Unravels the Genetic Control of Seed Vigor under Low-Temperature Conditions in Rapeseed (Plants (Basel, Switzerland) · 2021Article
- Unique genetic signatures of local adaptation over space and time for diapause, an ecologically relevant complex trait, in Drosophila melanogaster.PLoS genetics · 2020Article
- Metabolite-based genome-wide association study enables dissection of the flavonoid decoration pathway of wheat kernels.Plant biotechnology journal · 2020Article
- An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat.Machine learning · 2020Article
- Extensive impact of low-frequency variants on the phenotypic landscape at population-scale.eLife · 2019Article
- Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection.Journal of experimental botany · 2019Article
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8 authors.
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No grant is acknowledged in the PubMed record.
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.
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