Increased genomic prediction accuracy in wheat breeding using a large Australian panel

Abstract

Key message Genomic prediction accuracy within a large panel was found to be substantially higher than that previously observed in smaller populations, and also higher than QTL-based prediction. AbstractIn recent years, genomic selection for wheat breeding has been widely studied, but this has typically been restricted to population sizes under 1000 individuals. To assess its efficacy in germplasm representative of commercial breeding programmes, we used a panel of 10,375 Australian wheat breeding lines to investigate the accuracy of genomic prediction for grain yield, physical grain quality and other physiological traits. To achieve this, the complete panel was phenotyped in a dedicated field trial and genotyped using a custom AxiomTM Affymetrix SNP array. A high-quality consensus map was also constructed, allowing the linkage disequilibrium present in the germplasm to be investigated. Using the complete SNP array, genomic prediction accuracies were found to be substantially higher than those previously observed in smaller populations and also more accurate compared to prediction approaches using a finite number of selected quantitative trait loci. Multi-trait genetic correlations were also assessed at an additive and residual genetic level, identifying a negative genetic correlation between grain yield and protein as well as a positive genetic correlation between grain size and test weight.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-017-2975-4) contains supplementary material, which is available to authorized users.

The effects of training population design on genomic prediction accuracy in wheat

“…It is important for genomic prediction of a complex trait that it displays a reasonable 260 heritability. Our estimate of broad sense heritability for yield (0.65) is well within range of 261 similar studies in wheat (Poland et al, 2012;Combs and Bernardo, 2013;Michel et al, 2016;262 Schopp et al, 2017;Norman et al, 2017). We note that the heritability values within individual 263 families ( Figure ) cover the whole range of heritability for this trait reported in the literature.…”

Optimising Genomic Selection in Wheat: Effect of Marker Density, Population Size and Population Structure on Prediction Accuracy

“…A training set model was fitted using an adaptation of the linear mixed model defined in (1) with non-genetic parameters fixed at their estimates from the full model. Marker effects were then predicted using the methods described in Norman et al (2017), namely${\stackrel{true\sim }{\mathrm{boldq}}}_t={\mathrm{boldM}}_t^{\text{T}}{\mathrm{boldK}}_t^{-1}{\stackrel{true\sim }{\mathrm{bolda}}}_t$where ${\mathrm{boldM}}_t$ and ${\mathrm{boldK}}_t$ were the genetic marker data and additive relationship matrix respectively for the training set of lines and ${\stackrel{true\sim }{\mathrm{bolda}}}_t$ were GBLUPs for training lines calculated using (3). Genomic predictions for lines in the validation set were then determined using${\stackrel{true\sim }{\mathrm{bolda}}}_v={\mathrm{boldM}}_v{\stackrel{true\sim }{\mathrm{boldq}}}_t$where ${\mathrm{boldM}}_v$ is the genetic marker data for the validation set and ${\stackrel{true\sim }{\mathrm{boldq}}}_t$ is defined in (4).…”

Genomic selection in multi‐environment plant breeding trials using a factor analytic linear mixed model

“…Marker (genotypic) data are available for 2,845 (of the 2,869) varieties, genotyped according to a high confidence set of 16,124 single‐nucleotide polymorphisms (SNPs) (see Norman et al., , for further details). For each marker, individuals were coded as either ‐1 (homozygous minor allele), 0 (heterozygous) or 1 (homozygous major allele).…”