SURFBAT: a surrogate family based association test building on large imputation reference panels

Area of Science:

• Genetics
• Statistical Genetics
• Population Genetics

Background:

• Standard genotype-phenotype association tests use genome-wide principal components for population stratification adjustment.
• This approach lacks resolution for fine-scale population structure and admixture, impacting statistical power and precision.
• Geographic recruitment of control individuals, common in France, exacerbates issues with population stratification.

Purpose of the Study:

• To develop a novel association testing method robust to fine-scale population stratification.
• To enable efficient use of large genotype imputation panels as control groups.
• To improve power and precision in genetic association studies of admixed or geographically structured populations.

Main Methods:

• Introduced SURFBAT (surrogate family based association test), approximating the transmission-disequilibrium test.
• Utilized genotype imputation to match haplotypes between case and control groups.
• Approximated local ancestry informed posterior probabilities of un-transmitted parental alleles using imputation panel haplotypes.

Main Results:

• SURFBAT demonstrates robustness to fine-scale population stratification without explicit local ancestry estimation.
• The method effectively uses imputation reference panels as ancestry-matched controls.
• Validated on simulated data and a real-world Brugada syndrome dataset.

Conclusions:

• SURFBAT offers a powerful and precise alternative for genotype-phenotype association testing in structured populations.
• It overcomes limitations of traditional principal component-based methods.
• Facilitates the use of large reference panels for association studies, enhancing genetic discovery.