Joint modeling of linkage and association using affected sib-pair data

Area of Science:

• Genetics
• Statistical genetics
• Computational biology

Background:

• Identifying single-nucleotide polymorphisms (SNPs) that explain linkage signals is crucial for understanding complex diseases.
• Joint modeling of linkage and association provides a powerful framework for genetic analysis.

Purpose of the Study:

• To compare existing methods for identifying SNPs that explain linkage signals.
• To propose and evaluate a new method, the homozygote sharing transmission-disequilibrium test (HSTDT).
• To detect linkage and association or identify SNPs explaining linkage signals for rheumatoid arthritis.

Main Methods:

• Comparison of existing methods: FBAT, transmission-disequilibrium test, conditional logistic regression, LAMP, and homozygote sharing test (HST).
• Development and application of the homozygote sharing transmission-disequilibrium test (HSTDT).
• Utilized Genetic Analysis Workshop (GAW) 15 simulated affected sib-pair data for rheumatoid arthritis on chromosome 6.

Main Results:

• All tested methods showed similar power for testing association in the presence of linkage, except for HST which had lower power.
• For testing linkage adjusting for association, LAMP and HST demonstrated similar power, outperforming conditional logistic regression.
• Conditional logistic regression was more powerful than FBAT when testing linkage or association.

Conclusions:

• The HSTDT is a promising method for identifying SNPs that explain linkage signals in genetic association studies.
• Different statistical approaches offer varying power depending on the specific genetic model and hypothesis being tested.
• Accurate identification of disease-associated SNPs is essential for advancing our understanding of complex genetic disorders like rheumatoid arthritis.