Integrating genetic and gene expression data: application to cardiovascular and metabolic traits in mice

Abstract

The millions of common DNA variations that occur in the human population, or among inbred strains of mice and rats, perturb the expression (transcript levels) of a large fraction of the genes expressed in a particular tissue. The hundreds or thousands of common cis-acting variations that occur in the population may in turn affect the expression of thousands of other genes by affecting transcription factors, signaling molecules, RNA processing, and other processes that act in trans. The levels of transcripts are conveniently quantitated using expression arrays, and the cis- and trans-acting loci can be mapped using quantitative trait locus (QTL) analysis, in the same manner as loci for physiologic or clinical traits. Thousands of such expression QTL (eQTL) have been mapped in various crosses in mice, as well as other experimental organisms, and less detailed maps have been produced in studies of cells from human pedigrees. Such an integrative genetics approach (sometimes referred to as "genetical genomics") is proving useful for identifying genes and pathways that contribute to complex clinical traits. The coincidence of clinical trait QTL and eQTL can help in the prioritization of positional candidate genes. More importantly, mathematical modeling of correlations between levels of transcripts and clinical traits in genetic crosses can allow prediction of causal interactions and the identification of "key driver" genes. An important objective of such studies will be to model biological networks in physiologic processes. When combined with high-density single nucleotide polymorphism (SNP) mapping, it should be feasible to identify genes that contribute to transcript levels using association analysis in outbred populations. In this review we discuss the basic concepts and applications of this integrative genomic approach to cardiovascular and metabolic diseases.

Fig. 1

Cascade of interactions in metabolic and cardiovascular disorders. This cartoon depicts some of the interactions thought to contribute to the development of diabetes, atherosclerosis, and their complications. Most of the interactions are likely to involve genetic factors (genes A–Z). Some genes may be markers of clinical disease (gene Z′).

Fig. 2

Cis- and trans-regulation of transcript abundance. (A) In the case of cis-regulation, the eQTL for a transcript would map over the gene encoding the transcript. In the case of trans-regulation, DNA change influencing transcript abundance occurs in a gene different from that encoding the transcript, so that the eQTL would ordinarily not be expected to map over the gene encoding the transcript. (B) The classis cis-trans test examines the amount of product derived from each allele in an F₁ heterozygote. This can be done by distinguishing the transcripts using a SNP present in the transcript. In the example shown here, the B6 allele is twice as active and the DBA, and for a cis-regulated gene, the F₁ would have a 2:1 ratio of the B6 transcript to the DBA transcript.

Fig. 3

Examples of cis- and trans-eQTL. For two representative genes, LOD curve plots are given showing the relationship of eQTL with anatomic gene position. The linkage map is shown for all autosomes, beginning at the top of Chromosome 1 and ending at the bottom of Chromosome 19. The Mogat1 gene (red bar) is located at about 50 cM on Chromosome 1 and the only major LOD score peak for Mogat1 transcript abundance is coincident with the gene, suggesting cis-regulation. The Mpo gene (red bar) is not coincident with the LOD score peaks for Mpo transcript abundance (located at about 80, 400, and 620 cM), indicating trans-regulation.

Fig. 4

Applications of integrative genomics. See text for discussion.

Fig. 5

Molecularly distinct subtypes within a population of F₂ mice for the trait of body fat. These data were produced by microarray analyses for about 23,000 transcripts using an Agilent platform. The F₂ cross was between strains DBA/2J and C57BL/6J and liver RNA was profiled. The color matrix display for hierarchically clustered genes (x axis) and extreme fat pad mass (FPM) (y axis). Dark/light blue bars indicate mice in the upper/lower of the high FPM group, and dark/light orange indicate mice in the lower/upper half of the low FPM group. Subdivision of mice in this manner defined groups in which FPM was influenced by distinct genetic loci (from Schadt et al. 2003, with permission)

Fig. 6

Causal and reactive gene interactions in a complex trait. In this example, four loci (QTL_1–4) contribute to a complex trait. The DNA variations of the QTL directly influence the functions of genes G_1–4, which in turn perturb downstream genes that are causal for obesity (G_6,7,8) or reactive for the trait (G₉, G₁₀, G₁₁). Gene G₅ is independent of the trait but will nevertheless be correlated because its levels are controlled by a causal gene (G₁).