marker-assisted selection in wheat - ictsd

Chapter 17 – Marker-assisted selection in fish and shellfish breeding schemes 347variation underlying complex (polygenic)traits. This is because although the biologyof the trait and the genes most likelyinvolved in the expression of the phenotypemay be known, in complex traitsmany other genes may be involved in themetabolic pathway that are not obviouscandidates. For example, in aquaculture species,candidate genes have been studied forgrowth-related traits using ten conservedgene sequences known to be related to thegrowth hormone axis (Tao and Boulding,2003). In this study of Arctic charr, only asingle SNP (of ten) from five of ten geneswas found to be associated with growthrate.Another example for disease resistancetraits is the major histocompatibility complex(MHC). The genes of this complexencode highly polymorphic cell surfaceglycoproteins involved in specific immuneresponses and either specific alleles orheterozygotes at this complex were associatedwith resistance and susceptibility toA. salmonicida or infectious haematopoieticnecrosis (IHN) virus (Langefors, Lohm andGrahn, 2001; Lohm et al., 2002; Arkush etal., 2002; Grimholt et al., 2003; Bernatchezand Landry, 2003). Nevertheless, thebackground genome was quite importantfor explaining the difference in resistancebetween individuals within a family(Kjøglum, Grimholt and Larsen, 2005).Microarrays, gene expression andidentification of candidate genes forQTL analysisMicroarray technology (Knudsen, 2002)enables the expression of thousands ofgenes to be studied simultaneously. Untilnow, this information has been used primarilyfor following gene expression intreatment and control experiments in manyfields such as disease exposure and stressresponse. This information can be used todiscover new sets of candidate genes, possiblywith or without functional assignmentthat may be related to the quantitative traitof interest (Walsh and Henderson, 2004).Genes whose expression differs betweentreatments are likely to be trans-actinggenes, i.e. their expression is regulatedby other genes. Therefore, it seems likelythat seeking polymorphisms within thesegenes may not yield information aboutfactors that explain the phenotype, andthere might be problems assigning the correctsignificance threshold (Pérez-Encisoet al., 2003). Further, because many genesare part of metabolic pathways and do notact individually, the expression of a singlegene may be insufficient to explain phenotypicdifferences between individuals. Onlythose genes that directly affect phenotypicexpression (i.e. cis-acting genes) can betreated as candidate genes for subsequentuse in MAS after studying polymorphismsin their sequences. In salmonids, a microarraymade available from the Consortiumfor Genomics Research on all SalmonidsProject (cGRASP) has been used to studygene expression in fish exposed or notexposed to Pisciricketsia salmonis (Rise etal., 2004b), and microarrays in other fishand shellfish species are currently underdevelopment.A gene expression pattern can itself beregarded as a quantitative trait. Here, theinterest is in finding associations betweendifferent patterns of gene expression andmarker loci. This analysis was coined as“genetical genomics” by Jansen and Nap(2001). As is usual in QTL mapping, theanalysis attempted to dissect the transcriptionalregulation of the entire transcriptomeand to identify the effects of individualQTL affecting gene expression (the socalledeQTL; e.g. Hubner et al., 2005). To