Advances in DNA technologies combined with molecular markers revealing DNA polymorphisms, enabled the transition of genetics from observable phenotypes to the underlying genetic regulation. The progress can assist in the efficient selection of elite genotypes to accelerate the development of new varieties with desired phenotype.
Genetic mapping was introduced in the late 1980s to understand the genetic basis of quantitative traits (Lander and Botstein, 1989). Major advancements in molecular biology techniques led to rapid and cost-effective genotyping that is useful to identify genomic regions affecting quantitative traits in most crops of interest (Ingvarsson and Street, 2011). Genetic mapping mainly deals with the identification and localization of genomic regions (Quantitaive Trait Locus or QTL) associated with a trait of interest, and the estimation of phenotypic variance explained by the the identified QTL in specific populations.
At present, linkage mapping and association mapping are the two most commonly used genetic mapping strategies to dissect genetic bases of complex traits. Both approaches attempt to locate genomic regions underpinning trait variation by using statistical means to discover co-inheritance of genes or loci with genetic markers (Oraguzie and Wilcox, 2007).
2.6.1 Linkage mapping
2.6.1.1 Mapping populations
To develop a linkage mapping population, two parents with contrasting phenotypes are needed. The selection of two suitable genotypes of a species is the first step towards the successful development of a mapping population. The parents are considered to be appropriate when they are genetically divergent, produce fertile progenies, show adequate polymorphisms in genetic markers, and exhibit a medium level of segregation distortion (Semagn et al., 2006b). A mapping population can be doubled haploids (DHs), second filial generation (F2), backcross
(BC), near isogenic lines (NILs) and recombinant inbred lines (RILs) for self-pollinating species (He et al., 2001; Doerge, 2002). Population type and size can influence the accuracy of
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linkage maps. Insufficient numbers of individuals of a mapping population results in fragmented linkage groups and aberrant gene orders. Co-dominant markers produce more accurate linkage maps for RILs and F2 than for other types of population. Whereas, maps with
dominant markers are better for RILs, BC, DHs and NILs than for F2 mapping population. A
high resolution genetic map requires a large number of individuals and polymorphic markers (Ferreira et al., 2006).
2.6.1.2 Linkage mapping methods
Several linkage mapping methods have been reported to identify QTLs such as single marker analysis, simple interval mapping and multiple QTL mapping (Shaukeen, 2015).
The single marker analysis (SMA) is also called marker regression analysis, and it is the simplest method that allows rapid scan of QTLs (Soller et al., 1976). In this approach, offspring are grouped into various classes according to marker genotypes. The average of the marker genotype groups is estimated and compared using T-test. The marker may be linked to a QTL if significant difference is detected in phenotypic effect between marker genotype classes (Soller et al., 1976). SMA is limited because markers linked to one QTL may be linked to another QTL and this cannot be determined using this approach. QTLs distant from markers cannot be detected and QTL effects are underestimated. Thus, its accuracy is lower than that of other methods (Jansen and Stam, 1994).
Simple interval mapping (SIM) is a better method than SMA for QTL analysis. SIM uses markers flanking the putative QTLs to estimate their locations, thus, increasing the probability of identifying the QTLs. SIM uses likelihood ratio test to determine every QTL position in the interval created by flanking markers (Lander and Botstein, 1989). SIM is considered a better method than SMA because the evidence for a QTL is visualized with a curve, the position of the QTL is determined by support intervals, the estimation of QTL effect is improved, and data with missing marker genotype information can also be analyzed (Broman, 2001). Nevertheless, SIM can deal with only one QTL at a time on a linkage group, and therefore it becomes less effective when several QTLs are contained in the same linkage group (Lander and Botstein, 1989; Broman, 2001)
To overcome the multiple QTL problem with SIM, Jansen (1993) and Zeng (1993) proposed that regression analysis should be introduced to SIM. Zeng (1993) named it as composite interval mapping (CIM). In CIM, markers linked to other QTLs are taken into consideration, enhancing the power to detect multiple QTLs by reducing error variance. When QTL mapping models are extended to a multiple QTL model, they become even more powerful
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and accurate to identify QTLs. This method is called Multiple QTL mapping (MIM). MIM takes into consideration the effects of multiple QTLs on the one under investigation and simultaneously incorporate several putative QTLs into the model (Kao et al., 1999). MIM may have avoided the complicated background selection problem with CIM but the advantage still exists. For example, MIM introduces different background selection models, however, with different models, different mapping results are generated (Li et al., 2007). Inclusive composite interval mapping (ICIM) was proposed by Li et al. (2007) to improve the conventional CIM by performing background marker selection only once with stepwise regression of all marker information simultaneously. ICIM simplified the computation, increased QTL detection power and minimized false positives or biased QTL effects (Li et al., 2007). Most notably, ICIM can analyze epistatic interaction between QTLs and QTL by environment interaction (QEI) (Li et al., 2008).
2.6.2 Association mapping
Linkage mapping has been established as an extremely useful technique to identify genomic regions underlying varying phenotypic expressions of quantitative traits (Ingvarsson and Street, 2011). However, it has some limitations. To overcome the limitations of linkage mapping, association or linkage disequilibrium (LD) mapping has been developed to dissect complex traits in plants (Barhen et al., 1995). LD is the non-random associations between loci. Association study identifies the non-random associations between markers and adaptive traits in natural populations (Nordborg and Weigel, 2008). Population structure in the natural population, is a very strong confounding factor in an association mapping study (Nordborg and Weigel, 2008).
Assocaiton mapping has several advantages over linkage mapping. Linkage mapping uses highly pedigreed family lines arising from controlled crosses with two genetically distinct parental lines (Semagn et al., 2006b). Creating such populations can take five to ten years to reach the appropriate generation for linkage analysis. However, association mapping uses a natural population with a wide genetic background. The natural population usually have more than two families with a small family size (Wurschum, 2012). The diverse genetic backgrounds of natural population enable the simultaneous analysis of several alleles and traits compared to the linkage analysis where structured populations are studied for only one or a few pairs of alleles per locus at a time if they are present in the parental lines (Wurschum, 2012).
Both the mapping methods utilize the power of recombination events. However, QTLs in linkage mapping, due to limited number of crossing overs during the population
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development, are usually localized in 10 – 30 cM intervals of flanking markers that may contain 20-30 million base pairs coding for hundreds of genes resulting in low mapping resolution (Oraguzie et al., 2007; Zhu et al., 2008). Association mapping has higher mapping resolution compared to linkage mapping as natural populations are derived from common ancestors, and therefore, have undergone a large number of historical and revolutionary recombination events (Zhu et al., 2008). Therefore, instead of detecting gene loci where a quantitative trait nucleotide (QTN) is present, association study can identify causative sequences as close as possible to the QTN (Oraguzie and Wilcox, 2007). Therefore, a very high number of markers are required for an association mapping study (Oraguzie and Wilcox, 2007).
The use of bi-parental populations in linkage mapping to identify QTL restricts its use in marker assisted selection (MAS) schemes due to the specificity of the QTL in the population used in the study (Holland, 2007; Bernardo, 2008). In association mapping, the detected QTL effects have wider applicability as these have been developed from population of genetically diverse individuals. In addition, other differences between linkage and association mapping include that markers and QTLs co-segregate as expected in Mendelian laws in bi-parental mapping population. However, in natural populations of association mapping, markers could segregate from nearby markers at different frequencies. Association study can also search whole genome for specific sequences and/or candidate genes for polymorphisms (Oraguzie and Wilcox, 2007).