Boosting the Power of Rare Variant Association Studies by Imputation Using Large-scale Sequencing Population

Area of Science:

• Genomics
• Population Genetics
• Statistical Genetics

Background:

• Population-scale whole-genome sequencing (WGS) enables precise capture of rare variants, crucial for understanding complex trait heritability often missed by conventional genome-wide association studies (GWASs).
• The utility of imputed genetic data versus WGS for rare variant association studies remains an open question.

Purpose of the Study:

• To evaluate the consistency and performance of imputed SNP array data against WGS for rare variant detection.
• To assess the power of imputed data for association studies across various traits and sample sizes.

Main Methods:

• Utilized WGS data (n=150,119) from the UK Biobank as ground truth.
• Assessed imputation quality (TOPMed, HRC+UK10K) for rare variants using R-square and Cramer's V.
• Performed association tests on 45 traits comparing WGS and imputed data at different sample sizes.
• Conducted meta-analysis for lung and ovarian cancer using SNP array and WGS data.

Main Results:

• TOPMed imputation achieved good quality (R-square > 0.6) even for extremely rare variants (minor allele count ≤ 5).
• TOPMed-imputed data showed higher consistency with WGS (Cramer's V > 0.75) across ethnicities.
• At equal sample sizes, imputed data did not outperform WGS, but TOPMed-imputed data results were more concordant.
• Increasing sample size to 488,377 with TOPMed-imputed data boosted rare variant detection by 27.71% (quantitative) and 10-fold (binary).
• Meta-analysis identified more variants and genes compared to WGS-only results.

Conclusions:

• Imputed rare variants from large sequencing cohorts can enhance the power of association tests, especially when WGS sample sizes are limited.
• TOPMed-imputed data offers a valuable resource for rare variant association studies, approaching WGS quality and improving discovery power with scale.