Functional group-based linkage analysis of gene expression trait loci

We explored approaches to using multiple related traits (gene expression levels) in linkage analysis. We first grouped mRNA transcripts according to their functions annotated in biological process of gene ontology (GO). We then compared using sample average, principal-components analysis (PCA), and linear discriminant analysis (LDA) to derive a univariate composite trait. Our results showed that PCA generally yielded stronger evidence for linkage, through the LDA component had the highest heritability. We also developed an algorithm to search for clusters of linkage peaks from multiple traits in the same group and a heuristic method for calculating p-value evaluating the linkage peak clustering. Future research is needed to develop rigorous methods in mapping of genes affecting the expression of a group of transcripts.