A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Abstract

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer ofNIPSNAP3Aand an intergenic region on chromosome 1.

Non-coding rare variant associations with blood traits on 166 740 UK Biobank genomes

“…Previous studies on non-coding rare variants and lipid traits found that ∼32% signals are independent of common variants, although a more lenient association cutoff was used (P<1.2e -3 , Bonferroni multiple test correction over 43 tests) 48 . A more recent approach on the same traits indicated that only ∼8% non-coding associations were independent, more in line with our results 51 . Regarding CDS regions, we observed that ∼40% CDS gene-trait associations are independent from known GWAS common variants, a large increase compared to non-coding regions.…”

“…For instance, we took a conservative approach by only considering a subset of 42 traits for which we had successfully obtained independent GWAS hits, whereas other studies might deem a rare variant association independent if no nearby GWAS hits are present 10 . Moreover, we performed conditional analysis across all possible gene-trait pairs and performed multiple test correction accordingly, whereas other studies only performed conditional analysis and multiple test correction on a subset of previously significant gene-trait associations 47,48,51 . Regarding traits, a study on 9 red blood phenotypes, which encompassed both coding and non-coding variants, determined that only 13% single variant-trait associations are independent from common variants 49 , suggesting estimates of independence may be lower for some blood cell traits.…”

“…Regarding CDS regions, we observed that ~40% CDS gene-trait associations are independent from known GWAS common variants, a large increase compared to non-coding regions. Analysis in lipid traits found between 66% and 75% coding rare variant associations being independent 48,51 . Moreover, previous studies on UK Biobank exome-seq conditioning on GWAS variants indicated that >90% of the CDS associations are independent 10 .…”

“…CC-BY-ND 4.0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 4, 2023. ; https://doi.org/10.1101/2023.12.01.569422 doi: bioRxiv preprint analysis across all possible gene-trait pairs and performed multiple test correction accordingly, whereas other studies only performed conditional analysis and multiple test correction on a subset of previously significant gene-trait associations 47,48,51 . Regarding traits, a study on 9 red blood phenotypes, which encompassed both coding and non-coding variants, determined that only 13% single variant-trait associations are independent from common variants 49 , suggesting estimates of independence may be lower for some blood cell traits.…”