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Adjusting for covariates in variance components QTL linkage analysis.
Maurice Zeegers1, Fruhling Rijsdijk, Pak Sham
1Department of Epidemiology, Faculty of Health Science, PO Box 616, Maastricht University, 6200 MD Maastricht, The Netherlands. mpa.zeegers@epid.unimaas.nl
Behavior Genetics
|February 3, 2004
Summary
Adjusting for environmental factors in quantitative trait loci (QTL) linkage analysis can increase the power to detect genes. However, this benefit diminishes as the correlation between covariates and QTL effects increases, with no adjustment sometimes being more powerful.
Area of Science:
- Genetics
- Biostatistics
Background:
- Variance components modeling is a key method for quantitative trait loci (QTL) linkage analysis.
- Detecting genes with minor effects using this method presents challenges due to low statistical power.
Purpose of the Study:
- To investigate the power of different covariate adjustment strategies in variance components linkage analysis.
- To compare the effectiveness of incorporating covariates into the means model, covariance matrix, or using residual statistics versus no adjustment.
Main Methods:
- Variance components linkage analysis.
- Three covariate adjustment methods: means model, covariance matrix, and residual statistics.
- Comparison against analysis without covariate adjustment.
Main Results:
- Covariate adjustment generally increases power to detect QTL when there is no correlation between the covariate and the QTL effect.
- Increasing correlation between covariates and QTL effects reduces the power gained from adjustment.
- When a causal association exists between QTL and covariates, not adjusting for covariates can be more powerful.
Conclusions:
- The three covariate adjustment methods (residual statistics, means model, covariance model) demonstrated equal power in detecting QTL.
- The effectiveness of covariate adjustment is dependent on the correlation between the covariate and the QTL effect, and the presence of causal associations.
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