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Combined linkage and association analysis in pedigrees
K D Siegmund1, H Vora, W J Gauderman
1Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA.
Genetic Epidemiology
|January 17, 2002
Summary
This study introduces a novel model to pinpoint functional genetic variants influencing quantitative traits. The method successfully identified a key polymorphism in MG1 affecting trait Q1, demonstrating its effectiveness in genetic association studies.
Area of Science:
- Genetics
- Statistical Genetics
- Population Genetics
Background:
- Identifying functional polymorphisms is crucial for understanding quantitative trait variation.
- Population stratification and admixture can confound genetic association studies.
Purpose of the Study:
- To develop and validate a combined linkage and association model for quantitative traits in pedigrees.
- To identify functional polymorphisms and assess population stratification effects.
Main Methods:
- Applied a combined linkage and association model to pedigree data.
- Defined functional polymorphisms as variants with high association chi-squared values and low lod scores.
- Utilized simulated data from a population isolate for model testing.
Main Results:
- Successfully identified a functional polymorphism in gene 6 (MG1) affecting quantitative trait 1 (Q1) in simulated data.
- The identified polymorphism showed a strong association (chi-squared = 88.1, p < 0.001) and no residual linkage (lod score = 0.003).
- Simultaneous modeling of variants at 11 loci identified multiple functional variants for Q5 and gene 2, reducing lod score from 8.7 to 0.9.
Conclusions:
- The combined model effectively identifies functional polymorphisms while accounting for population structure.
- The approach minimizes false positives arising from population stratification and admixture.
- This method offers a robust framework for genetic dissection of complex traits.