SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests

2022/09/22 by Wei Zhou, Wenjian Bi, Zhangchen Zhao +6 · 2 citations
Biochemistry, Genetics and Molecular Biology · #Genetic Associations and Epidemiology #Genomic variations and chromosomal abnormalities #Genomics and Rare Diseases

paper · pdf · doi:10.1038/s41588-022-01178-w

openalex publication_date 2022/09/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene-phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene-phenotype associations.

Citations

• Computationally efficient whole-genome regression for quantitative and binary traits

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