marker-assisted selection in wheat - ictsd

Chapter 12 – Marker-assisted selection in dairy cattle 209Methods to estimate QTL effectsand location in dairy cattleIf a significant effect on a quantitative traitis associated with a genetic marker, thedifference between the means of markergenotype classes will be a biased estimateof the QTL effect due to recombinationbetween the QTL and the genetic marker.Weller (1986) first demonstrated that maximumlikelihood (ML) methodology couldbe used to obtain estimates of QTL locationand effect unbiased by recombination,while Lander and Botstein (1989) proposedinterval mapping, based on ML for a QTLbracketed between two markers. Haley andKnott (1992) and Martinez and Curnow(1992) proposed an interval mappingmethod based on non-linear regression,which was easier to apply than ML. Theirmethods are not directly applicable to halfsibdesigns because, as noted previously,linkage relationships between the QTL andthe genetic markers will be different acrossfamilies, and in some families the commonancestor will be homozygous for the QTL.Furthermore, if multiple QTL alleles aresegregating in the population, or if theobserved effect is due to several tightlylinked QTL, the magnitude of the effectwill also differ across families.A method suitable for interval mappingthat accounts for these problems has beendeveloped by Knott, Elsen and Haley (1996)and has been applied to nearly all daughterand granddaughter design analyses. Theirmethod is a modification of the non-linearregression method, and assumes a singleQTL location for all families, but estimatesa separate QTL effect for each family. Thismethod has the advantage that, unlike ML,it can readily deal with missing and uninformativegenotypes for some markers.Mackinnon and Weller (1995) proposed anML method to estimate both QTL locationand effect for half-sib designs under theassumption that only two QTL alleles aresegregating in the population. Using thismethod it is also possible to estimate QTLgenotype of the common parent of eachfamily. However, these determinations areaccurate only for relatively large QTL. Themethod of Mackinnon and Weller (1995) ismore difficult to apply than the method ofKnott, Elsen and Haley (1996), and has notcome into general usage.Lander and Botstein (1989) proposedthe LOD-score (logarithm of the odds tothe base 10) drop-off method to estimateconfidence intervals for QTL location, butseveral studies have shown that this seriouslyunderestimate the actual value (e.g.Darvasi et al., 1993). The non-parametricbootstrap method (Visscher, Thompsonand Haley, 1996) was found to be moreaccurate, but tends to overestimate confidenceintervals. Bennewitz, Reinsch andKalm (2003) proposed improvements to thebootstrap method that result in shorter butstill unbiased confidence intervals.Most studies to detect QTL in dairycattle have considered many markers andmultiple traits. In some studies nearly theentire genome was analysed, which raises aserious problem with respect to the appropriatethreshold to declare significance. Ifnormal point-wise significance levels of 5 or1 percent are used, many marker-trait combinationswill show “significance” by chance.While this is a problem for all QTL genomescans, it is even more severe for dairy cattlein which multiple half-sib families are analysed,in addition to multiple markers andtraits. Several solutions to this problem havebeen proposed, none of which is completelysatisfactory. The only solution to deal adequatelywith both multiple traits and familiesin addition to multiple markers is the falsediscovery rate (Weller et al., 1998).