Efficient variance components analysis across millions of genomes.
Ali Pazokitoroudi1, Yue Wu1, Kathryn S Burch2
1Department of Computer Science, UCLA, Los Angeles, CA, 90095, USA.
Nature Communications
|August 13, 2020
Summary
We developed a fast and accurate method for variance components analysis in large genetic datasets. This approach reveals insights into trait heritability and the impact of genetic variation, including evidence of negative selection.
Area of Science:
- Genetics
- Bioinformatics
- Statistical Genetics
Background:
- Variance components analysis is crucial for understanding complex traits.
- Current methods struggle with large genetic datasets.
- Efficient analysis is needed for large-scale genomic studies.
Purpose of the Study:
- To present an accurate and efficient method for variance components analysis.
- To enable analysis of large-scale genetic variation data.
- To estimate and partition SNP-heritability for complex traits.
Main Methods:
- Developed a scalable method for variance components analysis.
- Applied the method to analyze 22 traits in 300,000 individuals.
- Utilized genotypes from approximately 8 million common and low-frequency SNPs.
- Partitioned heritability across 28 functional annotations.
Main Results:
- The method efficiently estimates numerous variance components on large datasets.
- Observed increasing per-allele squared effect sizes with decreasing minor allele frequency (MAF) and linkage disequilibrium (LD).
- Found heritability enrichment in FANTOM5 enhancers for several immune and autoimmune disorders.
Conclusions:
- The new method significantly improves the scalability of variance components analysis.
- Findings suggest negative selection acting on common and low-frequency variants.
- Heritability is enriched in specific functional elements like enhancers for certain traits.
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