Many complex human diseases such as alcoholism and cancer are rated on ordinal scales. Well-developed statistical methods for the genetic mapping of quantitative traits may not be appropriate for ordinal traits.
We propose a class of variance-component models for the joint linkage and association analysis of ordinal traits. The proposed models accommodate arbitrary pedigrees and allow covariates and gene-environment interactions.
We develop efficient likelihood-based inference procedures under the proposed models. The maximum likelihood estimators are approximately unbiased, normally distributed, and statistically efficient. Extensive simulation studies demonstrate that the proposed methods perform well in practical situations.
An application to data from the Collaborative Study on the Genetics of Alcoholism is provided.
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