We recently reported three earliness per se quantitative trait loci (QTL) associated with flowering and maturity in a recombinant inbred lines (RILs) population derived from a cross between the spring wheat (Triticum aestivum L.) cultivars ‘Cutler’ and ‘AC Barrie’ using 488 microsatellite and diversity arrays technology (DArT) markers. Here, we present QTLs associated with flowering time, maturity, plant height, and grain yield using high density single nucleotide polymorphic (SNP) markers in the same population. A mapping population of 158 RILs and the two parents were evaluated at five environments for flowering, maturity, plant height and grain yield under field conditions, at two greenhouse environments for flowering, and genotyped with a subset of 1809 SNPs out of the 90K SNP array and 2 functional markers (Ppd-D1 and Rht-D1). Using composite interval mapping on the combined phenotype data across all environments, we identified a total of 19 QTLs associated with flowering time in greenhouse (5), and field (6) conditions, maturity (5), grain yield (2) and plant height (1). We mapped these QTLs on 8 chromosomes and they individually explained between 6.3 and 37.8% of the phenotypic variation. Four of the 19 QTLs were associated with multiple traits, including a QTL on 2D associated with flowering, maturity and grain yield; two QTLs on 4A and 7A associated with flowering and maturity, and another QTL on 4D associated with maturity and plant height. However, only the QTLs on both 2D and 4D had major effects, and they mapped adjacent to well-known photoperiod response Ppd-D1 and height reducing Rht-D1 genes, respectively. The QTL on 2D reduced flowering and maturity time up to 5 days with a yield penalty of 436 kg ha-1, while the QTL on 4D reduced plant height by 13 cm, but increased maturity by 2 days. The high density SNPs allowed us to map eight moderate effect, two major effect, and nine minor effect QTLs that were not identified in our previous study using microsatellite and DArT markers. Results from this study provide additional information to wheat researchers developing early maturing and short stature spring wheat cultivars.

Our group previously reported five quantitative trait loci (QTL) associated with plant height, test weight, thousand‐kernel weight, and grain protein content in a recombinant inbred line population derived from spring wheat (Triticum aestivum L.) cultivars ‘Attila’ and ‘CDC Go’, evaluated across three environments (2008–2010) under organic management and genotyped with 579 diversity arrays technology and Rht‐B1 markers. No QTL was identified for flowering time, maturity, grain yield, and number of tillers across all three environments. In the present study, we reanalyzed the same phenotypic data with a subset of 1200 informative single‐nucleotide polymorphic (SNP) markers out of the 90K SNP array and three gene‐specific markers (Ppd‐D1, Vrn‐A1, and Rht‐B1) to investigate if high marker density improves QTL detection. Here, five moderate‐ and eleven minor‐effect QTLs were detected across all three organic environments using the new genotypic data, including 13 QTLs that were not previously detected. Up to five QTLs were detected for each trait, except grain protein content, which individually accounted for 5.5 to 18.8% of phenotypic variance. For each trait, the total phenotypic and genetic variance explained by all detected QTLs varied from 9.3 to 39.4 and from 24.6 to 96.8%, respectively, which was much greater than in our previous study. One of the moderate‐effect QTLs on 5A was coincidental for flowering time and maturity and mapped close to the Vrn‐A1 gene, while the second moderate‐effect coincidental QTL on 4B was associated with both plant height and maturity but was 27 cM from the Rht‐B1 gene. Results from this study provide additional information for wheat researchers and organic wheat breeders.

Recently, we investigated the effect of the wheat 90K single nucleotide polymorphic (SNP) array and three gene-specific (Ppd-D1, Vrn-A1 and Rht-B1) markers on quantitative trait loci (QTL) detection in a recombinant inbred lines (RILs) population derived from a cross between two spring wheat (Triticum aestivum L.) cultivars, ‘Attila’ and ‘CDC Go’, and evaluated for eight agronomic traits at three environments under organic management. The objectives of the present study were to investigate the effect of conventional management on QTL detection in the same mapping population using the same set of markers as the organic management and compare the results with organic management. Here, we evaluated 167 RILs for number of tillers (tillering), flowering time, maturity, plant height, test weight (grain volume weight), 1000 kernel weight, grain yield, and grain protein content at seven conventionally managed environments from 2008 to 2014. Using inclusive composite interval mapping (ICIM) on phenotypic data averaged across seven environments and a subset of 1203 informative markers (1200 SNPs and 3 gene specific markers), we identified a total of 14 QTLs associated with flowering time (1), maturity (2), plant height (1), grain yield (1), test weight (2), kernel weight (4), tillering (1) and grain protein content (2). Each QTL individually explained from 6.1 to 18.4% of the phenotypic variance. Overall, the QTLs associated with each trait explained from 9.7 to 35.4% of the phenotypic and from 22.1 to 90.8% of the genetic variance. Three chromosomal regions on chromosomes 2D (61–66 cM), 4B (80–82 cM) and 5A (296–297 cM) harbored clusters of QTLs associated with two to three traits. The coincidental region on chromosome 5A harbored QTL clusters for both flowering and maturity time, and mapped about 2 cM proximal to the Vrn-A1 gene, which was in high linkage disequilibrium (0.70 ≤ r2 ≤ 0.75) with SNP markers that mapped within the QTL confidence interval. Six of the 14 QTLs (one for flowering time and plant height each, and two for maturity and kernel weight each) were common between the conventional and organic management systems, which suggests issues in directly utilizing gene discovery results based on conventional management to make in detail selection (decision) for organic management.

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