Meta-analysis across six global biobanks identifies recessive coding associations with complex traits and diseases

Area of Science:

• Genomics
• Human Genetics
• Population Studies

Background:

• Rare bi-allelic variation significantly contributes to human disease risk.
• Studying these variations is challenging due to limited sample sizes and phasing difficulties.

Purpose of the Study:

• To conduct large-scale, gene-based recessive analyses of rare and low-frequency variants.
• To identify novel associations between bi-allelic genotypes and human traits/diseases.

Main Methods:

• Utilized federated analysis of exome/genome sequencing data from over 948,000 individuals across six biobanks.
• Employed statistical phasing to infer damaging compound-heterozygous genotypes.
• Performed gene-based recessive association testing for variants with minor allele frequency <5%.

Main Results:

• Increased the number of identified bi-allelic damaging genotypes by 19% and putative gene knockouts by 19.8%.
• Identified 58 significant gene-based recessive associations (FDR ≤1% or p_rec ≤7.5 × 10^-7).
• Discovered 17 instances where recessive effects were more pronounced than additive effects, including novel associations like HBB with heart failure and LECT2 with height.

Conclusions:

• Federated approaches are powerful for studying rare bi-allelic variation effects.
• This study expands the understanding of genetic architecture underlying human diseases and traits.