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QTL analysis provides information on how many genes control a trait, what are their effects and how they interact. The basic principle of QTL analysis relies on the association between genotypic information and phenotype (expression of trait). There can be several QTLs influencing the same trait and they can be on the same or different chromosomes. Statistical tests are applied to test if there are any significant phenotypic differences between groups of genotypes for markers spanning the genome. For example if a heterozygous AB genotype for a marker has a higher mean phenotypic value than for the AA genotype, then that marker is linked to a QTL and the “B” allele may have an additive positive effect on the phenotype (Figure 1.6). Once we know where the QTL is located on the genetic map, it is possible to make use of the whole genome sequence to investigate whether this region of the genome codes for functional genes. This can be helpful to identify candidate genes that are involved in the expression of traits.

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Figure 1.6: Graphical representation of quantitative trait locus (QTL). This QTL shows 2 groups of mapping population according to marker genotype “AA” and “AB” with significant difference between mean values of trait under study.

1.4.3.1 Methods to detect QTLs

1.4.3.1.1 Single-marker analysis (SMA)

The most simple method to determine whether an association exists between marker and phenotypic trait is known as single marker analysis or SMA (Sax, 1923; Ahn and Tanksley, 1993; Sofi and Rather, 2007). SMA can be conducted by a variety of statistical methods including t-tests, analysis of variance (ANOVA), linear regression and likelihood ratio and maximum likelihood estimation (Collard et al., 2005). However QTL detection by this method is not very accurate. The advantage of this method is that it does not require a complete linkage map and advanced software to run the analysis. However, failure to detect QTLs linked to distant markers is its

major disadvantage. High density genetic markers covering whole genome could be a used to minimise the bias posed by this approach. Results from single marker analysis are presented in tabular form as shown in Table 1.4 (all values presented here are hypothetical). A QTL is detected for the marker ‘A’ from linkage group (LG) 7 and the variation explained by this QTL is 80%. Another marker 'B' is also linked to QTL for the observed trait on LG7 which explained the 40% phenotypic variation. Whereas markers C and D from LG 5 and 2 respectively are also associated with QTL.

Table 1.4: Results from single marker analysis.

Marker Linkage group P value R2

A 7 <0.0001 80

B 7 0.001 40

C 5 0.025 27

D 2 0.5621 2

1.4.3.1.2 Simple interval mapping

To overcome the disadvantages of the single marker approach, (Lander and Botstein, 1989) introduced the interval mapping approach based on association between QTL and flanking markers along the chromosome. This approach is statistically more powerful with use of maximum likelihood and or regression or combination of both. Both models have a similar approach; first detect the QTL by testing the hypothesis of presence (H1) or absence (H0) of a QTL at a certain position (cM) within in the interval between adjacent markers on the whole chromosome repeatedly and then plot the curve against the trait and map distance (Figure 1.7). The LOD position with highest score is regarded as the position of QTL on the map (Van Ooijen, 2004). The LOD score threshold used to detect and estimate the position of QTL is normally fixed arbitrarily in a range from 2-3. However it is recommended to calculate LOD threshold value for each experiment separately and this largely depends upon recombination frequency of population under study (Van Ooijen, 1999).

In addition, the problem of the recombination event between the markers and the QTL have been compensated by using linked markers (Lander and Botstein, 1989; Liu, 1998). Commonly used software packages to conduct SIM are MapMaker/QTL (Lincoln et al., 1993) and QGene (Nelson, 1997).

Figure 1.7: Graphical representation of an output of simple interval mapping for a typical chromosome (x axis) showing a LOD profile for trait (y axis). The dotted line in red indicates the threshold of significance and output peak indicates the location of QTL at marker 5. Best flanking markers are showing highest peak.

1.4.3.1.3 Composite interval mapping (CIM)

Simple Interval Mapping is used to detect single QTL so there is possibility of declaring a single QTL when in fact multiple QTLs are located in proximity. Multiple QTLs can be resolved using the composite interval mapping approach (or multiple interval mapping) as introduced by Zeng (1994). In this approach interval mapping is combined with multiple regression using significant markers for other QTLs on the same genome as cofactors. This is known as Cockerhams model (Gupta, 2007b; Sofi and Rather, 2007). This method can identify more than one QTL on the same chromosome at different locations and also provides the opportunity to find interactions between these QTLs. CIM gives benefits over

0 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 10 L OD s co re Genetic Map (cM)

interval mapping due to more precise mapping with high power detection and reduced residual errors of effects and location of QTL (Jansen, 1993; Zeng, 1993; Jansen and Stam, 1994; Zhao-Bang, 1994). A number of software tools are used to perform CIM including MapQTL (Van Ooijen, 2004) Cartographer (Basten et al., 2004), MapManager QTX (Manly et al., 2001) and PLABQTL (Utz and Melchinger, 1996).