Efficient variance components analysis across millions of genomes

Ali Pazokitoroudi, Yue Wu, Kathryn S. Burch, Kangcheng Hou, Aaron Zhou, Bogdan Pasaniuc and Sriram Sankararaman ()
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Ali Pazokitoroudi: UCLA
Yue Wu: UCLA
Kathryn S. Burch: UCLA
Kangcheng Hou: UCLA
Aaron Zhou: UCLA
Bogdan Pasaniuc: UCLA
Sriram Sankararaman: UCLA

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract While variance components analysis has emerged as a powerful tool in complex trait genetics, existing methods for fitting variance components do not scale well to large-scale datasets of genetic variation. Here, we present a method for variance components analysis that is accurate and efficient: capable of estimating one hundred variance components on a million individuals genotyped at a million SNPs in a few hours. We illustrate the utility of our method in estimating and partitioning variation in a trait explained by genotyped SNPs (SNP-heritability). Analyzing 22 traits with genotypes from 300,000 individuals across about 8 million common and low frequency SNPs, we observe that per-allele squared effect size increases with decreasing minor allele frequency (MAF) and linkage disequilibrium (LD) consistent with the action of negative selection. Partitioning heritability across 28 functional annotations, we observe enrichment of heritability in FANTOM5 enhancers in asthma, eczema, thyroid and autoimmune disorders.

Date: 2020
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DOI: 10.1038/s41467-020-17576-9

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