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

• Published in: Heredity Vol. 96; no. 2; pp. 139 - 149
• Main Authors: Verhoeven, K J F, Jannink, J-L, McIntyre, L M
• Format: Journal Article
• Language: English
• Published: England Springer Nature B.V 01.02.2006
• Subjects: Animals; Breeding; Computer Simulation; Experimental design; Female; Gene mapping; Genetic Variation; Inbreeding; Male; Mode of action; Models, Genetic; Pedigree; Quantitative Trait Loci; Quantitative Trait, Heritable
• ISSN: 0018-067X; 1365-2540
• DOI: 10.1038/sj.hdy.6800763

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 context-dependent 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.