Abstract
In this study, we anchored genotyping-by-sequencing data to the International Wheat Genome Sequencing Consortium Reference Sequence v1.0 assembly to generate over 40,000 high quality single nucleotide polymorphism markers on a panel of 376 elite European winter wheat varieties released between 1946 and 2007. We compared association mapping and genomic prediction accuracy for a range of productivity traits with previous results based on lower density dominant DArT markers. The results demonstrate that the availability of RefSeq v1.0 supports higher precision trait mapping and provides the density of markers required to obtain accurate predictions of traits controlled by multiple small effect loci, including grain yield.
Highlights
Wheat breeding has focused on phenotypic selection for final yield potential combined with morphological and disease resistance traits (Cavanagh et al, 2013)
Marker-trait association for Flowering time (FT) and grain yield (GY) was calculated on best linear unbiased estimates (BLUEs) for each site per year and overall values from all sites
The proportion of SNP markers across the three wheat genomes was highest on the B genome (52%) followed by the A genome (32%) and the D genome (10%), which was lowest, as expected
Summary
Wheat breeding has focused on phenotypic selection for final yield potential combined with morphological and disease resistance traits (Cavanagh et al, 2013). There is additional potential for the introgression of favorable genetic regions controlling variation in agronomically significant quantitative trait loci (QTL) through the routine application of genomic selection (GS) schemes that are based on the combined merit of genome-wide markers (Meuwissen et al, 2001; Stamp and Visser, 2012). For crop geneticists and plant breeders, the adoption and applicability of genotyping-by-sequencing (GBS) has been recently demonstrated for a wide range of crops. This includes the detection of QTL controlling agronomic traits in rice and soybean (Begum et al, 2015; Sonah et al, 2015) and the detection of introgressions in cotton, Brassica, and sorghum (Kim et al, 2016)
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