Genome-wide association test of multiple continuous traits using imputed SNPs

Area of Science:

• Genetics
• Statistical genetics
• Computational biology

Background:

• Genome-wide association studies (GWAS) are crucial for identifying genetic factors in complex diseases.
• Large cohort studies collect correlated phenotypic data, enabling joint analysis for increased statistical power.
• Existing multi-trait association tests often use "best-guess" genotypes, ignoring imputation uncertainty and potentially losing power.

Purpose of the Study:

• To develop a novel statistical framework for multi-trait association tests using inverted regression.
• To incorporate imputation uncertainty into inverted regression models for genome-wide association studies (GWAS).
• To enhance the power of association tests for imputed single nucleotide polymorphisms (SNPs).

Main Methods:

• Proposed a general and efficient framework for inverted regression models.
• Accounted for imputation uncertainty in statistical analyses.
• Utilized imputed SNP dosages within the inverted regression framework.

Main Results:

• The proposed method demonstrates competitive performance in extensive numerical studies.
• The framework effectively incorporates imputation uncertainty, improving association test power.
• The approach was successfully applied to analyze diabetes-related glycemic traits in the ARIC Study.

Conclusions:

• The developed framework offers an efficient way to improve association test power for imputed SNPs in GWAS.
• Accounting for imputation uncertainty in inverted regression models is beneficial for genetic studies.
• This method provides a valuable tool for analyzing complex diseases using large cohort genetic data.