title: Whole-genome analysis of multienvironment or multitrait QTL in MAGIC.
abstract: Multiparent Advanced Generation Inter-Cross (MAGIC) populations are now being utilized to more accurately identify the underlying genetic basis of quantitative traits through quantitative trait loci (QTL) analyses and subsequent gene discovery. The expanded genetic diversity present in such populations and the amplified number of recombination events mean that QTL can be identified at a higher resolution. Most QTL analyses are conducted separately for each trait within a single environment. Separate analysis does not take advantage of the underlying correlation structure found in multienvironment or multitrait data. By using this information in a joint analysis-be it multienvironment or multitrait - it is possible to gain a greater understanding of genotype- or QTL-by-environment interactions or of pleiotropic effects across traits. Furthermore, this can result in improvements in accuracy for a range of traits or in a specific target environment and can influence selection decisions. Data derived from MAGIC populations allow for founder probabilities of all founder alleles to be calculated for each individual within the population. This presents an additional layer of complexity and information that can be utilized to identify QTL. A whole-genome approach is proposed for multienvironment and multitrait QTL analysis in MAGIC. The whole-genome approach simultaneously incorporates all founder probabilities at each marker for all individuals in the analysis, rather than using a genome scan. A dimension reduction technique is implemented, which allows for high-dimensional genetic data. For each QTL identified, sizes of effects for each founder allele, the percentage of genetic variance explained, and a score to reflect the strength of the QTL are found. The approach was demonstrated to perform well in a small simulation study and for two experiments, using a wheat MAGIC population.
authors.affiliation: Computational Informatics and Food Futures National Research Flagship, CSIRO, Atherton, QLD 4883, Australia School of Agriculture, Food and Wine, The University of Adelaide, Adelaide, SA 5005, Australia Ari.Verbyla@csiro.au.
authors.forename: Arūnas P
authors.initials: AP
authors.lastname: Verbyla
authors.affiliation: Plant Industry and Food Futures National Research Flagship, CSIRO, Canberra, ACT 2601, Australia.
authors.forename: Colin R
authors.initials: CR
authors.lastname: Cavanagh
authors.affiliation: Computational Informatics and Food Futures National Research Flagship, CSIRO, Canberra, ACT 2601, Australia.
authors.forename: Klara L
authors.initials: KL
authors.lastname: Verbyla
mesh_terms: D003198:Computer Simulation; D003433:Crosses, Genetic; D035264:Flowers; D018745:Genome, Plant; D005838:Genotype; D008957:Models, Genetic; D040641:Quantitative Trait Loci; D012639:Seeds / Q000033:anatomy & histology; D014908:Triticum / Q000033:anatomy & histology / Q000235:genetics* / Q000502:physiology
keywords: MPP; Multiparent Advanced Generation Inter-Cross (MAGIC); Multiparental populations; QTL; WGAIM; mixed models; multienvironment; multitrait