ROADTRIPS: case-control association testing with partially or completely unknown population and pedigree structure

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) are crucial for identifying genetic variants linked to complex disorders.
• Ignoring population structure or pedigree relationships in GWAS can lead to inaccurate findings (spurious associations) and decreased statistical power.

Purpose of the Study:

• To introduce ROADTRIPS, a new statistical method for case-control association testing.
• To address challenges posed by partially or completely unknown population and pedigree structures in genetic studies.

Main Methods:

• ROADTRIPS utilizes a covariance matrix derived from genome-screen data to adjust for unknown population and pedigree structures.
• It leverages known pedigree information to maximize statistical power.
• The method accommodates diverse sample compositions, including related and unrelated individuals, and is computationally efficient for large datasets.

Main Results:

• Simulations involving related individuals and population structure (including admixture) show ROADTRIPS significantly outperforms existing methods in both power and control of Type 1 error rates.
• Application to rheumatoid arthritis and alcohol dependence datasets identified genome-wide significant associations after Bonferroni correction.

Conclusions:

• ROADTRIPS offers a robust and powerful solution for association testing in complex genetic studies with intricate population and pedigree structures.
• The method is versatile, applicable to various study designs from small pedigrees to isolated populations with unknown relatedness.