marker-assisted selection in wheat - ictsd

340Marker-assisted selection – Current status and future perspectives in crops, livestock, forestry and fish100%Figure 2Identification of selfed individuals within families of scallopsusing different types of marker data 190%80%70%60%50%40%30%20%10%0%MICRO-5 SNPs-5 AFLP-5 AFLP-15 AFLP-30Vertical bars represent the proportion of the 100 replicates in which the mean posterior probabilities of being selfed(for true selfed individuals) were greater than 0.95.1MICRO-5: five microsatellites with six equally frequent alleles each. SNPs-5: five SNP markers with equal allelefrequencies. AFLP-5, -15 or -30: 5, 15 or 30 AFLP markers. The design of the simulations of self-fertilization in scallops:the amount of self-fertilization was modelled using a truncated normal distribution which best fitted the empiricaldistribution of self-fertilization (Martinez and di Giovanni, 2006). A Bayesian model was used to infer mutuallyexclusive posterior probabilities of being either selfed or outbred (Anderson and Thompson, 2002). It was assumedthat parental information was lacking, with unlinked markers and vague priors. Selfed individuals were regarded ashaving been detected correctly when the posterior probabilities of being selfed were greater than 0.95 (this criterionwas determined empirically for operational reasons).Source: V. Martinez, in preparation.than 0.95 in about 90 percent of the familiessimulated (100 in total). Similar results wereobtained with 30 AFLP markers, but thesepercentages were considerably reduced forsmaller numbers of AFLPs or SNPs.The information from these markerscan be used to cull individuals, to constructa relationship matrix in which all unusualrelationships are incorporated in analysesused for obtaining unbiased estimates ofheritability and genetic correlations, andfor estimating breeding values from realdata sets (Martinez, 2006a).Identifying QTL and majorgenes influencing complexquantitative traitsMolecular biology can greatly help the discoveryof factors influencing the expressionof quantitative traits. There are a number ofways in which this information can be used,the difference between them being the levelof resolution with which these factors canbe mapped. For example, loci with majoreffects on quantitative traits (QTL) aremapped by using markers to track inheritanceof chromosomal regions in familiesor in inbred line crosses using the extentof linkage disequilibrium generated in thepopulation. This approach gives a limitedamount of mapping resolution. Fine mappingrequires information from additionalmarkers and individuals sampled across theoutbred population and, while helping tonarrow the confidence interval of the positionof the QTL, this is only the startingpoint for identifying the polymorphisms inthe performance-determining genes themselves.In practice, identification of genesinfluencing specific traits is achieved using