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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
Multivariate genetic analysis of an oligogenic disease
1Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA.
Joint segregation and linkage analysis successfully mapped disease susceptibility loci for complex traits. This powerful method identified specific genetic regions influencing multiple phenotypes, outperforming analyses of affection status alone.
Area of Science:
- Genetics
- Biostatistics
- Genomic analysis
Background:
- Joint multivariate segregation and linkage analysis integrates affection status, phenotypic traits, and covariates.
- This approach is powerful for mapping disease susceptibility loci with small effects (oligogenes).
Purpose of the Study:
- To evaluate the power of joint segregation and linkage analysis for mapping oligogenes.
- To analyze the GAW9 Problem 2 dataset using bivariate phenotypes.
Main Methods:
- Utilized the REGRESS program assuming a pleiotropy model (one locus influencing affection status and a quantitative trait).
- Conducted genome-wide search with markers ~10 cM apart, analyzing affection status (AF) with quantitative traits (Q2, Q3, Q4).
- Incorporated covariates including other quantitative traits, age, sex, and environmental effects.
Main Results:
- Identified linkage regions on chromosomes 1, 2, and 5 for AF/Q2, AF/Q3, and AF/Q4 phenotypes, respectively.
- Achieved significant chi-squared values (38.4, 65.4, 22.0) and lod scores (8.3, 14.2, 4.8), rejecting the null hypothesis.
- Linkage was detected up to 20 cM away, and these loci were undetectable when analyzing affection status alone.
Conclusions:
- Joint multivariate analysis effectively maps disease susceptibility loci, particularly those with small effects.
- This method demonstrates superior power in detecting genetic influences on complex traits compared to single-trait analysis.
- The identified loci on chromosomes 1, 2, and 5 are significant contributors to the analyzed phenotypes.
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