Improving the robustness of the weighted pairwise correlation test for linkage analysis

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• The weighted pairwise correlation (WPC) approach offers flexible genetic linkage tests for various traits.
• However, WPC tests show inflated type-I errors in large pedigrees due to unlinked susceptibility genes.
• This limits WPC's reliability for complex family structures.

Purpose of the Study:

• To develop a corrected WPC method for accurate genetic linkage analysis in large pedigrees.
• To address inflated type-I errors caused by correlations in large pedigrees.
• To enhance the power and reliability of WPC tests for complex genetic studies.

Main Methods:

• Proposed a variance correction method for WPC statistics to account for trait correlations.
• Utilized a transformation yielding uncorrelated residuals for statistical analysis.
• Developed three statistics based on residual permutation distributions: ordinary, martingale, and rank residuals.

Main Results:

• Simulation studies demonstrated that corrected WPC statistics exhibit good type-I error rates.
• The corrected approach effectively accounts for correlations induced by unlinked genes in large pedigrees.
• The corrected WPC tests were applied to Alzheimer's disease pedigrees.

Conclusions:

• The corrected WPC approach provides reliable genetic linkage testing for large pedigrees.
• This method improves the accuracy of genetic association studies for complex diseases.
• The corrected WPC is adaptable for quantitative, qualitative, and survival data analysis.