Two-phase designs for joint quantitative-trait-dependent and genotype-dependent sampling in post-GWAS regional...

Area of Science:

• Genetics
• Statistical genomics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) identify genetic variants associated with traits but require costly follow-up.
• Regional sequencing is often prohibitive for entire cohorts post-GWAS.
• Efficient strategies are needed to prioritize sequencing for variants influencing quantitative traits (QT).

Purpose of the Study:

• To develop and evaluate a two-phase study design for post-GWAS follow-up.
• To improve the efficiency and power of identifying sequence variants associated with QTs.
• To address the cost limitations of comprehensive cohort sequencing.

Main Methods:

• Developed novel expectation-maximization-based inference within a semiparametric maximum likelihood framework.
• Utilized GWAS single nucleotide polymorphism (SNP) as a surrogate covariate for sequence variant association.
• Simulated joint quantitative trait (QT) and SNP-dependent sampling strategies.
• Assessed test validity, efficiency, and power under various sample allocations.

Main Results:

• Joint allocation balancing SNP genotype and extreme-QT strata significantly improved power.
• This joint strategy outperformed marginal QT- or SNP-based allocations.
• The method demonstrated sensitivity to sampling variation in a real-world sequencing study.

Conclusions:

• Two-phase designs balancing QT and SNP information offer a powerful and cost-effective approach for post-GWAS genetic discovery.
• The proposed inference method enhances the ability to detect sequence variant associations with QTs.
• This strategy is particularly valuable when full cohort sequencing is infeasible.