Gene

Biology

Statistics

Set (abstract data type)

Computer science

Computational biology

Single-nucleotide polymorphism

Genetics

Exome sequencing

Minor allele frequency

Genome-wide association study

Mathematics

Biobank

Genetic association

Allele frequency

Genotype

Exome

Type I and type II errors

Programming language

Allele

Phenotype

Nature Genetics

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.

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