The genetic dissection of immune response using gene-expression studies and genome mapping

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Abstract

Functional genomics has been applied to the genetic dissection of immune response in different ways: (1) experimental crosses between lines that differ in their (non-) specific immune response have been used to detect quantitative trait loci (QTL) underlying these differences. (2) The measurement of gene expression levels for thousands of genes using microarrays or oligonucleotide chips to identify differential expression with regard to antigen challenge: (a) before and after infection, (b) resistant versus susceptible lines, or (c) combinations of both. Interpretation of QTL results is hampered by the fact that confidence regions of the QTL are large and can contain hundreds of potential candidate genes for the QTL. At the same time, the microarray experiments tend to show large numbers of differentially expressed genes without identifying the relationships between these genes. In the recently proposed ‘genetical genomics’ framework, members of a segregating population are characterised for genome-wide molecular markers and for gene expression levels. This facilitates the mapping of expression-QTL (eQTL): loci in the genome that control the expression of genes. Initial applications of this approach are critically reviewed and potential applications of this approach with regard to immune response are presented.

Introduction

The genetics underlying production traits has been thoroughly studied and exploited for genetic improvement of livestock through selective breeding for decades. For many traits, regions of the genome that affect these traits (quantitative trait loci: QTL) have been identified and in some cases even the molecular polymorphism underlying the QTL has been identified (for reviews on QTL mapping in livestock see Andersson, 2001, Andersson and Georges, 2004). There is very little doubt about the economic and welfare implications of infectious diseases or about the existence of genetic variation in disease susceptibility in livestock populations (Stear et al., 2001). However, the availability of sufficient phenotypic observations on structured populations limits research that is aimed at identifying genetic factors conferring relative susceptibility or resistance against an infectious disease. Research into the genetics underlying immune response can now benefit from expertise and infrastructure that has been generated by studying traditional traits (e.g. Sonstegard and Gasbarre, 2001). A combination of proven approaches in QTL detection and emerging technologies in gene transcription analysis can provide a fast track for unravelling the genetic networks underlying differences in immune response. We will briefly review some existing approaches and subsequently elaborate on how they can be merged into a powerful genetical genomics approach.

Section snippets

QTL approaches

For most infectious diseases, experimental populations have to be custom bred and challenged to study genetic differences in immune response and map genetic loci underlying these differences (Fig. 1A). However, some disease traits are routinely recorded because of their economic importance and the available infrastructure. As a result, QTL have been detected for mastitis (Klungland et al., 2001, Schulman et al., 2004) and somatic cell count (reviewed by Khatkar et al., 2004) using existing

Exploiting gene expression technology

The recent development of high throughput gene-expression technologies, such as microarrays, has given rise to a plethora of new research hypotheses and possibilities. Extensive reviews are available about the application (e.g. Butte, 2002), design (e.g. Churchill, 2002), and analysis (e.g. Quackenbush, 2002) of microarray studies. With regard to infectious diseases, microarrays have been proposed to study gene-expression in the parasite (Malaria: Rathod et al., 2002; Trypanosomosis: El Sayed

Genetical genomics: mapping genetic loci underlying variation in gene expression

Jansen and Nap (2001) described a strategy to combine genome-wide linkage analysis with expression studies. They propose the use of a segregating population where each member of the population is characterised for molecular markers and gene expression levels. The gene expression values are treated as a quantitative phenotype and the marker genotypes are used to map loci affecting gene expression levels (expression QTL ⇒ eQTL). Expression differences for a given gene can be caused by variation

Potential for eQTL mapping in disease resistance

The applications of eQTL mapping to date have mostly focussed on the genetic basis of gene expression differences between lines without linking this information with other phenotypic information. The exception is Schadt et al. (2003) who included obesity related phenotypes and obtained gene expression patterns and eQTL that were specific for one of two obesity types. Fig. 2 outlines a possible strategy to apply eQTL mapping to identify genomic loci that affect gene expression following disease

Concluding remarks

While the studies to date have been useful indicators of the potential of genetical genomics, their size is too small to assess the full merit of eQTL mapping. High costs, especially for the expression studies, will be associated with sizing-up these experiments to obtain the same statistical power as conventional QTL experiments we feel this is necessary to allow meaningful analyses. The high cost also poses further emphasis on careful experimental design and efficient use of animal resources.

Acknowledgements

DJK and CSH are supported by the BBSRC while ÖC acknowledges support from the Knut and Alice Wallenberg foundation.

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      Increasingly, the connection between genes and phenotypes is being established by combining genomic information with QTL studies. These studies focus on deciphering regulatory networks of polymorphic genes utilizing a combination of QTL analysis and microarray expression profiles (eQTLs) (Bing and Hoeschele, 2005; Carlborg et al., 2005; de Koning et al., 2005). The combination of these two methods, referred to as genetical genomics (de Koning et al., 2005; Jansen and Nap, 2001) is a powerful approach to inferring gene transcriptional relationships (Li et al., 2005) and has been utilized to demonstrate that regulation of many genes has a heritable basis (Cheung and Spielman, 2002; Hubner et al., 2005; Morley et al., 2004).

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    Present address: Linnaeus Centre for Bioinformatics, Uppsala University, BMC Box 598, SE-751 24 Uppsala, Sweden.

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