From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding

Abstract

This article reviews methods of integration of transcriptomics (and equally proteomics and metabolomics), genetics, and genomics in the form of systems genetics into existing genome analyses and their potential use in animal breeding and quantitative genomic modeling of complex traits. Genetical genomics or the expression quantitative trait loci (eQTL) mapping method and key findings in this research are reviewed. Various procedures and potential uses of eQTL mapping, global linkage clustering, and systems genetics are illustrated using actual analysis on recombinant inbred lines of mice with data on gene expression (for diabetes- and obesity-related genes), pathway, and single nucleotide polymorphism (SNP) linkage maps. Experimental and bioinformatics difficulties and possible solutions are discussed. The main uses of this systems genetics approach in quantitative genomics were shown to be in refinement of the identified QTL, candidate gene and SNP discovery, understanding gene-environment and gene-gene interactions, detection of candidate regulator genes/eQTL, discriminating multiple QTL/eQTL, and detection of pleiotropic QTL/eQTL, in addition to its use in reconstructing regulatory networks. The potential uses in animal breeding are direct selection on heritable gene expression measures, termed "expression assisted selection," and genetical genomic selection of both QTL and eQTL based on breeding values of the respective genes, termed "expression-assisted evaluation."

Fig. 1

Whole-genome scan for eQTL that influence gene expression of insulin (Ins1) on Chr 19 (at 51.83 Mb) using mouse BXD recombinant inbred line data from INIA Brain mRNA M430 PDNN database of WebQTL (trait ID: 1422447_at_A,). Effects of QTL alleles on gene expression and likelihood ratio statistics (LRS; based on 1000 permutation tests) are plotted. (The additive effect is half the difference in the mean phenotype of all cases that are homozygous for one parental allele at this marker minus the mean of all cases that are homozygous for the other parental allele at this marker. In the case of BXD strains, e.g., a positive additive effect indicates that DBA/2J alleles increase trait values. Negative additive effect indicates that C57BL/6J alleles increase trait values.)

Fig. 2

Detailed view of eQTL that affect expression of the Ins1 gene that maps to Chromosome 12 showing genetic linkage map (A) and corresponding physical map (B).

Fig. 3

Global display of linkage clusters. The x axis shows the 100 most correlated genes based on their expression phenotypes in a hierarchical cluster tree. This tree was built based on distances computed using 1 - r, where r is the Pearson correlation. On the y axis all the chromosomes from 1 to 19 and X are aligned. The colors in the heat map symbolize different values of the linkage statistics. For example, blue–green regions are those with higher phenotypic expression values associated with the parental allele C57BL/6J and the red–yellow regions are those in which the other parental allele DBA/2J is associated with high trait values. Gray and black areas in the map have insignificant linkage statistics.

Fig. 4

Whole-genome-wide pairwise scanning of epistatic genes influencing gene expression of insulin 1 (Ins1) on Chr 19 (at 51.83 Mb) using mouse BXD recombinant inbred line data from INIA Brain mRNA M430 PDNN database of WebQTL (trait ID: 1422447_at_A). The upper left half of the plot highlights any epistatic interactions (corresponding to the column labeled “LRS Interact”). In contrast, the lower right half provides a summary of LRS of the full model, representing cumulative effects of linear and nonlinear terms (column labeled “LRS Full”) based on WebQTL.

Fig. 5

Multiple (pleiotropic) mapping of eQTL that influence gene expression traits insulin 1, insulin 2, and glucose transporter gene (slc2g5). Example trans-eQTL on Chr 12 is significant and affects all three transcripts.