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We developed and validated 56 gene-specific semi-thermal asymmetric reverse PCR (STARP) markers for 46 genes of important wheat quality, biotic and abiotic stress resistance, grain yield, and adaptation-related traits for marker-assisted selection in wheat breeding. Development of high-throughput, low-cost, gene-specific molecular markers is important for marker-assisted selection in wheat breeding. In this study, we developed 56 gene-specific semi-thermal asymmetric reverse PCR (STARP) markers for wheat quality, tolerance to biotic and abiotic stresses, grain yield, and adaptation-related traits. The STARP assays were validated by (1) comparison of the assays with corresponding diagnostic STS/CAPS markers on 40 diverse wheat cultivars and (2) characterization of allelic effects based on the phenotypic and genotypic data of three segregating populations and 305 diverse wheat accessions from China and 13 other countries. The STARP assays showed the advantages of high-throughput, accuracy, flexibility, simple assay design, low operational costs, and platform compatibility. The state-of-the-art assays of this study provide a robust and reliable molecular marker toolkit for wheat breeding programs.

We identified 21 new and stable QTL, and 11 QTL clusters for yield-related traits in three bread wheat populations using the wheat 90 K SNP assay. Identification of quantitative trait loci (QTL) for yield-related traits and closely linked molecular markers is important in order to identify gene/QTL for marker-assisted selection (MAS) in wheat breeding. The objectives of the present study were to identify QTL for yield-related traits and dissect the relationships among different traits in three wheat recombinant inbred line (RIL) populations derived from crosses Doumai × Shi 4185 (D × S), Gaocheng 8901 × Zhoumai 16 (G × Z) and Linmai 2 × Zhong 892 (L × Z). Using the available high-density linkage maps previously constructed with the wheat 90 K iSelect single nucleotide polymorphism (SNP) array, 65, 46 and 53 QTL for 12 traits were identified in the three RIL populations, respectively. Among them, 34, 23 and 27 were likely to be new QTL. Eighteen common QTL were detected across two or three populations. Eleven QTL clusters harboring multiple QTL were detected in different populations, and the interval 15.5-32.3 cM around the Rht-B1 locus on chromosome 4BS harboring 20 QTL is an important region determining grain yield (GY). Thousand-kernel weight (TKW) is significantly affected by kernel width and plant height (PH), whereas flag leaf width can be used to select lines with large kernel number per spike. Eleven candidate genes were identified, including eight cloned genes for kernel, heading date (HD) and PH-related traits as well as predicted genes for TKW, spike length and HD. The closest SNP markers of stable QTL or QTL clusters can be used for MAS in wheat breeding using kompetitive allele-specific PCR or semi-thermal asymmetric reverse PCR assays for improvement of GY.

Figure S3. Manhattan plots for grain yield and related traits in each environment and BLUE value in the diverse panel based on SNP-GWAS. a, grain yield; b, spike number per square meter; c, kernel number per spike; d, thousand-kernel weight; e, kernel length; f, kernel width; g, spike length; h, spike dry weight; i, heading date; j, plant height; k, uppermost internode length; l, flag leaf length; m, flag leaf width; 1, 2012–2013 Anyang; 2, 2012–2013 Suixi; 3, 2013–2014 Anyang; 4, 2013–2014 Suixi; 5, 2014–2015 Anyang; 6, 2014–2015 Shijiazhuang. (DOCX 11525 kb)

Table S1. Analysis of phenotypic data for grain yield and related traits in the diverse panel. (DOCX 16 kb)

Figure S3. Manhattan plots for grain yield and related traits in each environment and BLUE value in the diverse panel based on SNP-GWAS. a, grain yield; b, spike number per square meter; c, kernel number per spike; d, thousand-kernel weight; e, kernel length; f, kernel width; g, spike length; h, spike dry weight; i, heading date; j, plant height; k, uppermost internode length; l, flag leaf length; m, flag leaf width; 1, 2012–2013 Anyang; 2, 2012–2013 Suixi; 3, 2013–2014 Anyang; 4, 2013–2014 Suixi; 5, 2014–2015 Anyang; 6, 2014–2015 Shijiazhuang. (DOCX 11525 kb)

Table S4. Genome coverage, physical distance and marker polymorphism. (DOCX 16 kb)

Table S3. Correlation coefficients among grain yield and related traits in the diverse panel. (DOCX 13 kb)

Figure S4. Manhattan plots for grain yield and related traits in each environment and BLUE value in the diverse panel based on Haplotype-GWAS. See footnote to Fig. S3 for traits and experimental sites. (DOCX 13093 kb)

Table S5. Composition and lengths of blocks and haplotypes. (DOCX 14 kb)

Figure S2. Coverage of SNPs (a) and haplotypes (b) on all 21 bread wheat chromosomes. (DOCX 1094 kb)

Figure S1. Distribution of phenotypic values for grain yield and related traits in the diverse panel. GY, grain yield; SN, spike number per square meter; KNS, kernel number per spike; TKW, thousand-kernel weight; KL, kernel length; KW, kernel width; SL, spike length; SDW, spike dry weight; HD, heading date; PH, plant height; UIL, uppermost internode length; FLL, flag leaf length; FLW, flag leaf width. (DOCX 54 kb)

Table S2. Analysis of variance and broad-sense heritabilities (h2) for grain yield and related traits. (DOCX 13 kb)

Flour color-related traits, including brightness (L*), redness (a*), yellowness (b*) and yellow pigment content (YPC), are very important for end-use quality of wheat. Uncovering the genetic architecture of these traits is necessary for improving wheat quality by marker-assisted selection (MAS). In the present study, a genome-wide association study (GWAS) was performed on a collection of 166 bread wheat cultivars to better understand the genetic architecture of flour color-related traits using the wheat 90 and 660 K SNP arrays, and 10 allele-specific markers for known genes influencing these traits. Fifteen, 28, 25, and 32 marker–trait associations (MTAs) for L*, a*, b*, and YPC, respectively, were detected, explaining 6.5–20.9% phenotypic variation. Seventy-eight loci were consistent across all four environments. Compared with previous studies, Psy-A1, Psy-B1, Pinb-D1, and the 1B•1R translocation controlling flour color-related traits were confirmed, and four loci were novel. Two and 11 loci explained much more phenotypic variation of a* and YPC than phytoene synthase 1 gene (Psy1), respectively. Sixteen candidate genes were predicted based on biochemical information and bioinformatics analyses, mainly related to carotenoid biosynthesis and degradation, terpenoid backbone biosynthesis and glycolysis/gluconeogenesis. The results largely enrich our knowledge of the genetic basis of flour color-related traits in bread wheat and provide valuable markers for wheat quality improvement. The study also indicated that GWAS was a powerful strategy for dissecting flour color-related traits and identifying candidate genes based on diverse genotypes and high-throughput SNP arrays.

Black point scores evaluated for 166 wheat accessions. Reported values are the best linear unbiased predictions (BLUP) value for black point scores across five environments. (DOCX 430Â kb)

Black point scores evaluated for 166 wheat accessions. Reported values are the best linear unbiased predictions (BLUP) value for black point scores across five environments. (DOCX 430Â kb)

Frequency distributions for black point scores for 166 wheat accessions in five environments. (a) Anyang 2013; (b) Anyang 2014; (c) Anyang 2015; (d) Suixi 2013; (e) Suixi 2014; (f) Best linear unbiased predictions (BLUP) value for black point scores across five environments. (DOCX 292Â kb)

The 166 wheat accessions used in the genome-wide association study (GWAS) for black point reaction and their origins. (DOCX 27Â kb)

Basic statistical analysis of SNP markers used for genome-wide association study (GWAS) of 166 wheat accessions. (DOCX 19Â kb)

Basic statistical analysis of SNP markers used for genome-wide association study (GWAS) of 166 wheat accessions. (DOCX 19Â kb)

LD heatmap of all wheat chromosomes showing extent of pairwise linkage disequilibrium between SNP markers significantly associated with black point reactions. (DOCX 4561Â kb)

LD decay estimated in 166 wheat accessions based on 12,324 markers from the 90 K and 660 K SNP arrays. (DOCX 97 kb)

Accessions with high black point resistance. (DOCX 17Â kb)

Quantile-quantile (Q-Q) plot for black point scores in 166 wheat accessions by the mixed linear model (MLM) in Tassel v5.0. (a) Anyang 2013; (b) Anyang 2014; (c) Anyang 2015; (d) Suixi 2013; (e) Suixi 2014; (f) Best linear unbiased predictions (BLUP) value for black point scores across five environments. (DOCX 682Â kb)

LD decay estimated in 166 wheat accessions based on 12,324 markers from the 90 K and 660 K SNP arrays. (DOCX 97 kb)

Quantile-quantile (Q-Q) plot for black point scores in 166 wheat accessions by the FarmCPU. (a) Anyang 2013; (b) Anyang 2014; (c) Anyang 2015; (d) Suixi 2013; (e) Suixi 2014; (f) Best linear unbiased predictions (BLUP) value for black point scores across five environments. (DOCX 379Â kb)