Statistical power of expression quantitative trait loci for mapping of complex trait loci in natural populations.

摘要

A number of recent genomewide surveys have found numerous QTL for gene expression, often with intermediate to high heritability values. As a result, there is currently a great deal of interest in genetical genomics--that is, the combination of genomewide expression data and molecular marker data to elucidate the genetics of complex traits. To date, most genetical genomics studies have focused on generating candidate genes for previously known trait loci or have otherwise leveraged existing knowledge about trait-related genes. The purpose of this study is to explore the potential for genetical genomics approaches in the context of genomewide scans for complex trait loci. I explore the expected strength of association between expression-level traits and a clinical trait, as a function of the underlying genetic model in natural populations. I give calculations of statistical power for detecting differential expression between affected and unaffected individuals. I model both reactive and causative expression-level traits with both additive and multiplicative multilocus models for the relationship between phenotype and genotype and explore a variety of assumptions about dominance, number of segregating loci, and other parameters. There are two key results. If a transcript is causative for the disease (in the sense that disease risk depends directly on transcript level), then the power to detect association between transcript and disease is quite good. Sample sizes on the order of 100 are sufficient for 80% power. On the other hand, if the transcript is reactive to a disease locus, then the correlation between expression-level traits and disease is low unless the expression-level trait shares several causative loci with the disease--that is, the expression-level trait itself is a complex trait. Thus, there is a trade-off between the power to show association between a reactive expression-level trait and the clinical trait of interest and the power to map expression-level QTL (eQTL) for that expression-level trait. Gene expression-level traits that are most strongly correlated with the clinical trait will themselves be complex traits and therefore often hard to map. Likewise, the expression-level traits that are easiest to map will tend to have a low correlation with the clinical trait. These results show some fundamental principles for understanding power in eQTL-based mapping studies.