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Family-based association tests for qualitative and quantitative traits using single-nucleotide polymorphism and
J B Wilk1, J S Volcjak, R H Myers
1Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA.
Genetic Epidemiology
|January 17, 2002
Summary
Single-nucleotide polymorphism (SNP) data effectively identified disease genes using family-based association tests. However, SNP and microsatellite data showed limitations in distinguishing functional variants and identifying associations with complex traits.
Area of Science:
- Genetics
- Statistical Genetics
- Genetic Epidemiology
Background:
- Evaluating genetic association methods is crucial for identifying disease-related genes.
- Simulated data allows for controlled comparison of different genetic markers and analysis techniques.
- Understanding the performance of single-nucleotide polymorphisms (SNPs) versus microsatellites in various population structures is important.
Purpose of the Study:
- To compare the effectiveness of different family-based association tests (TDT, SDT, FBAT) using SNP data.
- To assess the impact of trait definitions (dichotomous vs. quantitative) and population types (outbred vs. isolate) on association results.
- To contrast the performance of SNP data with microsatellite data in identifying genetic associations.
Main Methods:
- Utilized Genetic Analysis Workshop 12 simulated data, including nuclear families and extended pedigrees.
- Applied Transmission Disequilibrium Testing (TDT), Sibship Disequilibrium Testing (SDT), and Family-Based Association Testing (FBAT) with SNP data.
- Analyzed both dichotomous and quantitative trait definitions, and compared SNP data with microsatellite data in isolate and outbred populations.
Main Results:
- SNP data, analyzed with TDT, SDT, and FBAT, showed comparable results in identifying disease genes.
- Family-based association tests using SNP data successfully identified a gene directly influencing liability (MG6) but struggled with a gene influencing a quantitative trait (MG1) unless the trait was defined quantitatively.
- Microsatellite data were less successful than SNP data in identifying associations, and association magnitudes were similar across outbred and isolate populations for SNPs.
Conclusions:
- SNP data are effective for identifying disease-associated genes using family-based methods, particularly when the gene directly affects liability.
- Quantitative trait definitions improve the detection of associations for genes influencing traits indirectly.
- SNP data generally outperform microsatellite data for association studies, and family-based tests show consistent performance across different population types.