Using mating designs to uncover QTL and the genetic architecture of complex traits

Abstract

Analysis of quantitative trait loci (QTL) affecting complex traits is often pursued in single-cross experiments. For most purposes, including breeding, some assessment is desired of the generalizability of the QTL findings and of the overall genetic architecture of the trait. Single-cross experiments provide a poor basis for these purposes, as comparison across experiments is hampered by segregation of different allelic combinations among different parents and by contextdependent effects of QTL. To overcome this problem, we combined the benefits of QTL analysis (to identify genomic regions affecting trait variation) and classic diallel analysis (to obtain insight into the general inheritance of the trait) by analyzing multiple mapping families that are connected via shared parents. We first provide a theoretical derivation of main (general combining ability (GCA)) and interaction (specific combining ability (SCA)) effects on F 2 family means relative to variance components in a randomly mating reference population. Then, using computer simulations to generate F 2 families derived from 10 inbred parents in different partial-diallel designs, we show that QTL can be detected and that the residual among-family variance can be analyzed. Standard diallel analysis methods are applied in order to reveal the presence and mode of action (in terms of GCA and SCA) of undetected polygenes. Given a fixed experiment size (total number of individuals), we demonstrate that QTL detection and estimation of the genetic architecture of polygenic effects are competing goals, which should be explicitly accounted for in the experimental design. Our approach provides a general strategy for exploring the genetic architecture, as well as the QTL underlying variation in quantitative traits.

The relationship between parental genetic or phenotypic divergence and progeny variation in the maize nested association mapping population

“…Sampling strategies should reflect the goal of the research. Sampling fewer families with more progeny per family seems to provide higher power for QTL detection, but sampling more families with fewer progeny seems to provide more reliable estimates of the overall genetic architecture (for example, general and specific combining ability variances), allele number and QTL variance (Wu and Jannink, 2004;Verhoeven et al, 2006). Different analysis approaches have different optimal sampling strategies as well.…”

“…component of genetic variation for all traits. However, if marker effects are to be tested as nested within families, a minimum sample of at least 40 progeny per family seems necessary to maintain good power of QTL detection (Wu and Jannink, 2004;Verhoeven et al, 2006;Yu et al, 2008). Larger sample sizes could be drawn from mapping families with greater parental PD, as some traits do exhibit a moderate relationship between PD and GVC.…”

Comparison of biometrical models for joint linkage association mapping

“…Apart from the LD generated by the sampling of the parents, LD will also inevitably arise in designs with multiple segregating populations owing to the mating design (Verhoeven et al, 2006). Within segregating populations alleles at unlinked loci will be in linkage equilibrium, but they will be in LD across populations.…”

“…This can create a population structure and thus an inter-chromosomal LD that was lower in the balanced DIA design than in the SRR design (Supplementary Figure 7). Verhoeven et al (2006), therefore, concluded that it is essential to account for population structure in the biometrical model.…”

“…It is, therefore, populationspecific and must be examined empirically for the data set under consideration (Rafalski, 2002). In addition, LD in the JLAM population affects the QTL detection power and is influenced by the mating design (Verhoeven et al, 2006).…”