Abstract
Performing linkage and association analyses on a large set of correlated data presents an interesting set of problems. In the current setting, we have 3554 expression levels from lymphoblastoid cell lines in 194 individuals from 14 three-generation Utah CEPH (Centre d'Etude du Polymorphisme Humain) pedigrees. We formed multivariate expression phenotypes from six sets of genes. These consisted of a set of genes identified by the data providers as showing common linkage to a region of chromosome 14, as well as five other sets suggested by ontological evidence. Using principal-component analyses, we generated seven quantitative phenotypes for expression levels from these six sets of genes. We performed quantitative genome linkage screens on these traits using the expression traits from the third generation of each pedigree. As expected, the strongest linkage signal was achieved when the trait under analysis was the composite of the expressions of genes previously showing linkage to chromosome 14. In particular, this trait produced a LOD score of 5.2 on chromosome 14. The trait also produced LOD scores over 3.5 on chromosomes 1, 7, 9, and 11; this suggests that these genes may be controlled by additional genetic factors on the genome. Subsequent association analyses on the first two generations of these pedigrees identified two polymorphisms on chromosome 11 as significant after correcting for multiple tests. These results suggest that principal-component analyses are useful for the analysis of pleiotropic loci. Furthermore, we have identified two single-nucleotide polymorphisms that may influence the expression of multiple genes linked to chromosome 14.
Highlights
The Genetic Analysis Workshop 15 data set consists of 3554 expression levels from lymphoblastoid cell lines in 194 individuals from 14 three-generation Utah CEPH (Centre d'Etude du Polymorphisme Humain) pedigrees
Association analyses For the association analysis, we focused on the five regions with linkage signals over 3.5 for the traits linked to chromosome 14, including the linked region on chromosome 14
We found the first principal component of these traits was strongly associated with age (p = 9.8 × 106), so we included age in all single-nucleotide polymorphisms (SNPs) analyses
Summary
The Genetic Analysis Workshop 15 data set consists of 3554 expression levels from lymphoblastoid cell lines in 194 individuals from 14 three-generation Utah CEPH (Centre d'Etude du Polymorphisme Humain) pedigrees. In earlier work [1], the data providers performed separate linkage analyses for each expression phenotype and found evidence of pleiotropy; multiple phenotypes showed linkage to the same region on chromosome 14. Principal-components analysis (PCA) is a technique for reducing multi-dimensional data sets into lower dimensions for analysis. The goal is to capture as much variation as possible of the higher dimensional data set in a lower dimensional set. Because microarray expression data typically produces thousands of correlated observations for each array, PCA is a natural analytic technique. Several other groups have shown the utility of PCA for the decomposition of expression data [2,3,4]; here we apply the method in the context of linkage and association analyses
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