Mapping As You Go

Abstract

The advent of high throughput molecular technologies has led to an expectation that breeding programs will use marker–trait associations to conduct marker‐assisted selection (MAS) for traits. Many challenges exist with this molecular breeding approach for so‐called complex traits. A major restriction to date has been the limited ability to detect and quantify marker–trait relationships, especially for traits influenced by the effects of gene‐by‐gene and gene‐by‐environment interactions. A further complication has been that estimates of quantitative trait loci (QTL) effects are biased by the necessity of working with a limited set of genotypes in a limited set of environments, and hence the applications of these estimates are not as effective as expected when used more broadly within a breeding program. The approach considered in this paper, referred to as the Mapping As You Go (MAYG) approach, continually revises estimates of QTL allele effects by remapping new elite germplasm generated over cycles of selection, thus ensuring that QTL estimates remain relevant to the current set of germplasm in the breeding program. Mapping As You Go is a mapping‐MAS strategy that explicitly recognizes that alleles of QTL for complex traits can have different values as the current breeding material changes with time. Simulation was used to investigate the effectiveness of the MAYG approach applied to complex traits. The results indicated that greater levels of response were achieved and these responses were less variable when estimates were revised frequently compared with situations where estimates were revised infrequently or not at all.