marker-assisted selection, the genetic basis of the traits is identified and genetic markers are used to select for the desired genes underlying phenotypes. After the discovery and validation of the association between markers and traits, subsequent testing can be done on individuals alone and they can be ranked based on the genetic outcome. MAS provides the possibility of performing selection in early life, lessening generation intervals, and accelerating the breeding process (Bao, 2011). Therefore the use of molecular approach to detect marker-trait relationships is superior to traditional phenotypic-based selection (Ragavendran and Muir, 2011).
Furthermore, traditional breeding in ...view middle of the document...
This model has been remarkably invaluable for animal breeding as the foundation for the theory of breeding value estimation (Henderson, 1984). Second, the finite model or major gene model is variation for a quantitative trait that is affected by numerous genes, some of which have a large effect on the phenotype (Hayes and Goddard, 2001). The identification of genes with major effects is performed by single locus genetic marker analysis whereby genetic markers are used as landmarks on a genetic linkage map. This allows identification of regions of the genome that contain major genes. A genetic linkage map is a linear order of genetic markers which are grouped in linkage groups along the chromosome of a species (Korol et al., 2007; Beaumont et al., 2010). Once the genetic map is constructed, the association between molecular markers and traits is examined using QTL analysis. Quantitative trait loci (QTL) is the localised regions of genome that contain genes underlying quantitative traits (Gjedrem and Baranski, 2009). Other study that can be done to identify genes of major effect controlling phenotypes is Genome-Wide Association Studies (GWAS).
1.1.1. QTL mapping
The basis of QTL mapping is linkage between genetic marker alleles and QTL alleles. QTL mapping identifies genes of large effect (Rothschild and Ruvinsky, 2007). QTLs are detected when allelic variations at marker loci are signiﬁcantly linked to the variations of traits. Those loci give information on the breeding value of individuals bearing these alleles (Beaumont et al., 2010). The power of finding a QTL is affected by the number and type of markers, the genome coverage of the markers, the number of individuals per family, and the number of families (Beaumont et al., 2010).
QTL mapping in several aquaculture species for several traits shows the potential for MAS in aquaculture (Beaumont et al., 2010). Most of the QTL studies focused on growth-related and disease resistance traits. QTL studies on growth have been conducted in the Kuruma prawn (Li et al, 2006), the Pacific white shrimp (Andriantahina et al., 2013), Asian seabass (Xia et al., 2013) and Atlantic salmon (Baranski et al. 2010; Gutierrez et al., 2012). Other studies included thermal tolerance in rainbow trout (Oncorhynchus mykiss) (Perry et al, 2005), infectious pancreatic necrosis (IPN) resistance in salmon (Houston et al., 2012), and resistance to infectious salmon anaemia (ISA) reported by Moen et al. (2007) as summarised in Table 1.
Recent developments and success of genetic markers, genotyping, and genetic linkage and QTL mapping use for MAS have been published in a book, Aquaculture Genome Technologies (Liu, 2007). However, appropriate scale of QTL mapping is required to the success of MAS. Only a few of studies resulted in detailed QTL mapping because most linkage maps had a low or medium density of genetic marker, and thus the accuracy of QTL mapping is low. As a consequence, there are few applications of QTLs in...