Set-based rare variant association tests for biobank scale sequencing data sets

Abstract

UK Biobank has released the whole-exome sequencing (WES) data for 200,000 participants, but the best practices remain unclear for rare variant tests, and an existing approach, SAIGE-GENE, can have inflated type I error rates with high computation cost. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency compared to SAIGE-GENE. In the analysis of UKBB WES data of 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene-phenotype associations. In addition, we showed that incorporating multiple MAF cutoffs and functional annotations can help identify novel gene-phenotype associations and SAIGE-GENE+ can facilitate this.

Investigation of Genetic Variants and Causal Biomarkers Associated with Brain Aging

“…Second, genome-wide association tests were conducted on different test levels to uncover novel loci associated with brain aging. We applied a single-variant test, SAIGE (Scalable and Accurate Implementation of GEneralized mixed model), to the array-genotyped and imputed markers, and a gene-based test, SAIGE-GENE+, to the WES datasets [13, 14]. Lastly, we examined linear and nonlinear causal relationships between the delta age and the phenotypes (metabolomics and blood) with Mendelian randomization methods.…”

“…SAIGE uses a mixed effect model to account for the relatedness among the individuals. SAIGE-GENE+ is a gene-based rare variant association test and it performs BURDEN, SKAT, and SKAT-O tests [14]. Since the number of tests decreases in the gene-based test, multiple testing correction is less stringent.…”

Whole Exome Sequencing Analyses Support a Role of Vitamin D Metabolism in Ischemic Stroke

“…Further, ultra-rare variants with MAF>1×10 −5 were removed to mitigate the potential inflation due to low minor allele counts. 25 The same set of covariates as the single variant analysis was adjusted in the gene-based test. Exome-level significance was set as 0.05 over the total number of genes tested.…”

The HUNT study: A population-based cohort for genetic research

“…These methods, which are computationally efficient in biobank-scale data, allowed us to perform association testing in HUNT for both single variants (using SAIGE) and gene-based burden tests (using SAIGE-GENE) while accounting for sample relatedness with a sparse identical by state sharing matrix. 14,[16][17][18] These methods account for case-control imbalance of binary phenotypes, typical in a population-based sample, by using the saddlepoint approximation to calibrate unbalanced case-control ratios in score tests based on logistic mixed models. 14 We demonstrated a vast improvement in reducing type I error rates when analyzing unbalanced case-control ratios with SAIGE in HUNT.…”