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. 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. 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.

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. 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. 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.

Detection of senescence’s dynamics in crop breeding is time consuming and needs considerable details regarding its rate of progression and intensity. Normalized difference red-edge index (NDREI) along with four other spectral vegetative indices (SVIs) derived from unmanned aerial vehicle (UAV) based spatial imagery, were evaluated for rapid and accurate prediction of senescence. For this, 32 selected winter wheat genotypes were planted under full and limited irrigation treatments. Significant variations for all five SVIs: green normalize difference vegetation index (GNDVI), simple ratio (SR), green chlorophyll index (GCI), red-edge chlorophyll index (RECI), and normalized difference red-edge index (NDREI) among genotypes and between treatments, were observed from heading to late grain filling stages. The SVIs showed strong relationship (R² = 0.69 to 0.78) with handheld measurements of chlorophyll and leaf area index (LAI), while negatively correlated (R² = 0.75 to 0.77) with canopy temperature (CT) across the treatments. NDREI as a new SVI showed higher correlations with ground data under both treatments, similarly as exhibited by other four SVIs. There were medium to strong correlations (r = 0.23–0.63) among SVIs, thousand grain weight (TGW) and grain yield (GY) under both treatments. Senescence rate was calculated by decreasing values of SVIs from their peak values at heading stage, while variance for senescence rate among genotypes and between treatments could be explained by SVIs variations. Under limited irrigation, 10% to 15% higher senescence rate was detected as compared with full irrigation. Principle component analysis corroborated the negative association of high senescence rate with TGW and GY. Some genotypes, such as Beijing 0045, Nongda 5181, and Zhongmai 175, were selected with low senescence rate, stable TGW and GY in both full and limited irrigation treatments, nearly in accordance with the actual performance of these cultivars in field. Thus, SVIs derived from UAV appeared as a promising tool for rapid and precise estimation of senescence rate at maturation stages.