Key message The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Abstract Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat ( Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici) . Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes. Electronic supplementary material The online version of this article (10.1007/s00122-019-03276-6) contains supplementary material, which is available to authorized users.

‘TAM 112’ (Reg. No. CV‐1101, PI 643143), a hard red winter wheat (Triticum aestivum L.) cultivar with experimental designation TX98V9628, was developed and released by Texas A&M AgriLife Research in 2005. TAM 112 is an F4–derived line from the cross U1254‐7‐9‐2‐1/TXGH10440 made at Vernon, TX, in 1992. U1254‐7‐9‐2 is a USDA–ARS germplasm line from the Plant Science and Entomology Research unit, Manhattan, KS, and TXGH10440 is a sibling selection of the cultivar TAM 110. TAM 112 is an awned, medium‐early maturing, semidwarf wheat with red glumes. It was released primarily for its excellent grain yield potential particularly in dryland environments of the southern Great Plains; resistance to stem rust (caused by Puccinia graminis Pers.:Pers. f. sp. tritici Eriks. & E. Henn.), powdery mildew [caused by Blumeria graminis (DC.) E.O. Speer f. sp. tritici Em. Marchal], and greenbug [Schizaphis graminum (Rondani)]; and good milling and bread‐baking characteristics. Compared with existing hard red winter wheat cultivars at the time of release, TAM 112 is most similar to TAM 110 with respect to area of adaptation and disease and insect resistance, but it has significantly higher yield and better bread‐baking characteristics than TAM 110. Licensed to Watley Seed Company for marketing, TAM 112 is currently one of the most popular hard red winter wheat cultivars adapted to the dryland production system in the Texas High Plains and similar areas in the southern Great Plains.

The aim of this study was to identify quantitative trait locus (QTL) associated with grain yield (GY) in a recombinant inbred line (RIL) population from a cross between two elite soft red winter wheat (SRWW) cultivars ('Pioneer 26R61' and 'AGS2000'). The RIL population was grown from 2011 to 2014 in 12 site-year combinations throughout the southeastern US. Overall, AGS2000 was the higher yielding parental line, out-performing 26R61 in seven of the 12 environments. Mean GY for the RILs ranged from 3.39 to 7.16 t ha -1 with significant genotype, environment and genotype by environmental interaction effects. Nine stable QTL were detected for yield, explaining up to 53 % of the phenotypic variation when fit into a multiple-QTL model. The QTL with the largest effect was detected at the Vrn-B1 locus with the short vernalization winter allele from AGS2000 favorable for yield. In addition, vrn-B1 acted additively with a region on chromosome 2B near the Ppd-B1 locus, indicating that a shorter vernalization requirement combined with the Ppd-B1b allele for photoperiod sensitivity may play a key role in adaptation of SRWW to the southern US. Single nucleotide polymorphism markers linked to additional QTL on chromosomes 3A and 3B were in agreement with a previous genome-wide association study in spring wheat, confirming the importance of these regions for yield across environments and germplasm pools. Overall the stable QTL were more predictive of GY compared to individual site-year QTL, indicating that a targeted QTL approach can be utilized by breeding programs to enrich for favorable loci.Electronic supplementary material The online version of this article (

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Quantitative trait loci (QTL) analysis could help to identify suitable molecular markers for marker-assisted breeding (MAB). A mapping population of 124 F5:7recombinant inbred lines derived from the cross ‘TAM 112’/‘TAM 111’ was grown under 28 diverse environments and evaluated for grain yield, test weight, heading date, and plant height. The objective of this study was to detect QTL conferring grain yield and agronomic traits from multiple mega-environments. Through a linkage map with 5,948 single nucleotide polymorphisms (SNPs), 51 QTL were consistently identified in two or more environments or analyses. Ten QTL linked to two or more traits were also identified on chromosomes 1A, 1D, 4B, 4D, 6A, 7B, and 7D. Those QTL explained up to 13.3% of additive phenotypic variations with the additive logarithm of odds (LOD(A)) scores up to 11.2. The additive effect increased yield up to 8.16 and 6.57 g m−2 and increased test weight by 2.14 and 3.47 kg m−3 with favorable alleles from TAM 111 and TAM 112, respectively. Seven major QTL for yield and six for TW with one in common were of our interest on MAB as they explained 5% or more phenotypic variations through additive effects. This study confirmed previously identified loci and identified new QTL and the favorable alleles for improving grain yield and agronomic traits.