Whole‑Genome QTL Analysis For Nested Association Mapping Populations

Genetic dissection of quantitative traits in plants has become an important tool in breeding of improved varieties. The most commonly used methods to map QTL are linkage analysis in bi-parental populations and association mapping in diversity panels. However, bi-parental populations are restricted in terms of allelic diversity and recombination events. Despite the fact that association mapping overcomes these limitations, it has low power to detect rare alleles associated with a trait of interest. Multi-parent populations such as multi-parent advanced generation inter-cross (MAGIC) and nested association mapping (NAM) populations have been developed to combine strengths of both mapping approaches, capturing more recombination events and allelic diversity than bi-parental populations and in a greater frequency than a diversity panel. Nested association mapping uses multiple RIL families connected by a single common parent. Such a population structure presents some additional challenges compared to traditional mapping, in particular the population design and the large number of molecular markers that need to be integrated simultaneously into the analysis. We present a method for QTL mapping for NAM populations adapted from multi-parent whole genome average interval mapping (MPWGAIM) where the NAM design is incorporated through the probability of inheriting founder alleles for every marker across the genome. This method is based on a mixed linear model in a one-stage analysis of raw phenotypes together with markers. It simultaneously scans the whole-genome through an iterative process leading to a multi-locus model. The approach was applied to a wheat NAM population in order to perform QTL mapping for plant height. The method was developed in R, with main dependencies being the R packages MPWGAIM and asreml. This approach establishes the basis for further studies and extensions such as the combination of multiple NAM populations.