Abstract
The International HapMap project has made publicly available extensive genotypic data on a number of lymphoblastoid cell lines (LCLs). Building on this resource, many research groups have generated a large amount of phenotypic data on these cell lines to facilitate genetic studies of disease risk or drug response. However, one problem that may reduce the usefulness of these resources is the biological noise inherent to cellular phenotypes. We developed a novel method, termed Mixed Effects Model Averaging (MEM), which pools data from multiple sources and generates an intrinsic cellular growth rate phenotype. This intrinsic growth rate was estimated for each of over 500 HapMap cell lines. We then examined the association of this intrinsic growth rate with gene expression levels and found that almost 30% (2,967 out of 10,748) of the genes tested were significant with FDR less than 10%. We probed further to demonstrate evidence of a genetic effect on intrinsic growth rate by determining a significant enrichment in growth-associated genes among genes targeted by top growth-associated SNPs (as eQTLs). The estimated intrinsic growth rate as well as the strength of the association with genetic variants and gene expression traits are made publicly available through a cell-based pharmacogenomics database, PACdb. This resource should enable researchers to explore the mediating effects of proliferation rate on other phenotypes.
Highlights
The International HapMap project [1,2] has made available a vast amount of genetic variation data from a large number of individuals with diverse ethnic background
We propose a method (MEM) to address this issue by statistically combining data from various sources, and we apply it to the proliferation rates of cell lines collected as part of the International HapMap project
We show that the proliferation rate computed using our method is a better measure of the true proliferation rate of the cells and produces a much stronger association with gene expression phenotypes on the same cell lines: more than 30% of the genes tested were significantly associated with proliferation rate
Summary
The International HapMap project [1,2] has made available a vast amount of genetic variation data from a large number of individuals with diverse ethnic background. A recent population based whole-genome sequencing initiative (1000 Genomes Project [3]) sought to expand on this effort by providing a more comprehensive catalog of human genome sequence variation, including rare variants in these samples These data can be used to study the effect of genetic variants on disease processes, pharmacologic traits, and environmental responses. The commercial availability of these cell lines and the rich genetic information publicly available have enabled a large number of researchers to adopt them as in vitro models for the study of genotype-phenotype relationships in human cells [4] Consistent with this trend, a vast amount of phenotypic data such as gene expression levels, drug response, and radiation response have been made publicly available [5,6,7,8]. Our group has constructed a database, PACdb [6], a public central repository of pharmacology-related phenotypes, to host these integrative results obtained in HapMap LCLs
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