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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.

Vitamin B is essential for maintaining normal life activities in humans and animals who have to intake the microelement from the outside, especially from cereal products. In the present study 166 Chinese and foreign wheat cultivars planted in two environments were characterized for variation in vitamin B1 and B2 contents. A genome-wide association study (GWAS) using the wheat 90 K SNP assay identified 17 loci for vitamin B1 and 7 for vitamin B2 contents. Linear regression analysis showed a significantly positive correlation of the number of favorable alleles with vitamin B1 and B2 contents. Marker-trait associations (MTAs) at IWB43809 (6AS, 0 cM) and IWB69903 (6AS, 13 cM) were new and stable, and significantly associated with vitamin B1 content across two environments. The loci identified in this study and associated SNP markers could be used for improvement of vitamin B1 and B2 contents to obtain superior quality along with grain yield in wheat. Keywords: 90 K SNP assay, GWAS, Vitamin B1, Vitamin B2, Triticum aestivum

Background Black point is a serious threat to wheat production and can be managed by host resistance. Marker-assisted selection (MAS) has the potential to accelerate genetic improvement of black point resistance in wheat breeding. We performed a genome-wide association study (GWAS) using the high-density wheat 90 K and 660 K single nucleotide polymorphism (SNP) assays to better understand the genetic basis of black point resistance and identify associated molecular markers. Results Black point reactions were evaluated in 166 elite wheat cultivars in five environments. Twenty-five unique loci were identified on chromosomes 2A, 2B, 3A, 3B (2), 3D, 4B (2), 5A (3), 5B (3), 6A, 6B, 6D, 7A (5), 7B and 7D (2), respectively, explaining phenotypic variation ranging from 7.9 to 18.0%. The highest number of loci was detected in the A genome (11), followed by the B (10) and D (4) genomes. Among these, 13 were identified in two or more environments. Seven loci coincided with known genes or quantitative trait locus (QTL), whereas the other 18 were potentially novel loci. Linear regression showed a clear dependence of black point scores on the number of favorable alleles, suggesting that QTL pyramiding will be an effective approach to increase resistance. In silico analysis of sequences of resistance-associated SNPs identified 6 genes possibly involved in oxidase, signal transduction and stress resistance as candidate genes involved in black point reaction. Conclusion SNP markers significantly associated with black point resistance and accessions with a larger number of resistance alleles can be used to further enhance black point resistance in breeding. This study provides new insights into the genetic architecture of black point reaction. Electronic supplementary material The online version of this article (10.1186/s12870-017-1167-3) contains supplementary material, which is available to authorized users.

Background Identification of loci for grain yield (GY) and related traits, and dissection of the genetic architecture are important for yield improvement through marker-assisted selection (MAS). Two genome-wide association study (GWAS) methods were used on a diverse panel of 166 elite wheat varieties from the Yellow and Huai River Valleys Wheat Zone (YHRVWD) of China to detect stable loci and analyze relationships among GY and related traits. Results A total of 326,570 single nucleotide polymorphism (SNP) markers from the wheat 90 K and 660 K SNP arrays were chosen for GWAS of GY and related traits, generating a physical distance of 14,064.8 Mb. One hundred and twenty common loci were detected using SNP-GWAS and Haplotype-GWAS, among which two were potentially functional genes underpinning kernel weight and plant height (PH), eight were at similar locations to the quantitative trait loci (QTL) identified in recombinant inbred line (RIL) populations in a previous study, and 78 were potentially new. Twelve pleiotropic loci were detected on eight chromosomes; among these the interval 714.4–725.8 Mb on chromosome 3A was significantly associated with GY, kernel number per spike (KNS), kernel width (KW), spike dry weight (SDW), PH, uppermost internode length (UIL), and flag leaf length (FLL). GY shared five loci with thousand kernel weight (TKW) and PH, indicating significantly affected by two traits. Compared with the total number of loci for each trait in the diverse panel, the average number of alleles for increasing phenotypic values of GY, TKW, kernel length (KL), KW, and flag leaf width (FLW) were higher, whereas the numbers for PH, UIL and FLL were lower. There were significant additive effects for each trait when favorable alleles were combined. UIL and FLL can be directly used for selecting high-yielding varieties, whereas FLW can be used to select spike number per unit area (SN) and KNS. Conclusions The loci and significant SNP markers identified in the present study can be used for pyramiding favorable alleles in developing high-yielding varieties. Our study proved that both GWAS methods and high-density genetic markers are reliable means of identifying loci for GY and related traits, and provided new insight to the genetic architecture of GY. Electronic supplementary material The online version of this article (10.1186/s12870-019-1781-3) contains supplementary material, which is available to authorized users.

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)

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)

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)

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)

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)