Using Incomplete Trios to Boost Confidence in Family Based Association Studies

Area of Science:

• Genetics
• Bioinformatics
• Statistical Genetics

Background:

• Traditional family-based association tests require complete genotype data, excluding many families from large-scale sequencing studies.
• Missing genotypes in family cohorts reduce statistical power and limit the benefits of increasing whole genome sequencing data availability.

Purpose of the Study:

• To introduce the CIFBAT method for incorporating incomplete families into family-based association tests (FBAT).
• To evaluate the robustness of FBAT against missing genotype data and improve the precision of identifying genomic markers.

Main Methods:

• CIFBAT utilizes quantile intervals of the FBAT statistic by randomly completing genotypes based on Mendelian inheritance rules.
• The method computes quantile intervals over multiple randomized iterations, avoiding assumptions about population structure or missingness patterns.

Main Results:

• CIFBAT's quantile intervals effectively validate the robustness of the FBAT statistic against missing data.
• The method demonstrated higher precision in identifying significant genomic markers, including novel candidates for uterine abnormalities and Type 1 diabetes.

Conclusions:

• CIFBAT enables the inclusion of incomplete families in association studies, maximizing the value of large-scale sequencing data.
• The developed software package offers an efficient data format for genome-wide association studies and includes TDT, robustTDT, FBAT, and CIFBAT.