Genome-wide association study reveals new loci for yield-related traits in Sichuan wheat germplasm under stripe rust stress

Abstract

caused by fungus Puccinia striiformis Westend. f. sp. tritici Erikss. (Pst) is a serious threat to wheat production [2]. Therefore, improving wheat yield under stripe rust stress is extremely urgent. Thus, identifying loci associated with yield-related traits under stripe rust stress may provide favourable alleles and their useful markers for breeding wheat cultivars with high yield in combination with stripe rust resistance. The productive spike number per unit area, kernel number per spike (KPS) and thousand-kernel weight (TKW) are key components of wheat yield. The productive spike number per unit area mainly depends on the fertile tiller number (FTN). Most spike-related traits, such as spike length (SL), spikelet number per spike (SlPS), kernel number per spikelet (KPSl) and the spikelet compactness (SlC), affect the KPS and thus also affect the yield [3-5]. Many studies have showed that the SL has a positive correlation with KPS and SlPS [3, 4, 6, 7]. Moreover, Mohsin et al. [8] reported that the SL and the KPS had a positive effect on grain yield. Würschum et al. [7] demonstrated that KPSl was positively correlated with the KPS. The SlC is positively correlated with SlPS, but negatively related to SL [3,6,9]. The TKW, which depends on the kernel weight, is associated with the accumulation of starch produced by photosynthesis [10,11]. Therefore, wheat yield is a complex quantitative trait contributed by many morphological, physiological and biochemical components, all of which can be improved to increase the yield directly or indirectly.Genome-wide association study (GWAS) is a powerful tool to identify loci associated to target traits based on linkage disequilibrium (LD) using natural populations. It is a rapid and cost-effectiveness way to detect target markers for marker-assisted breeding. GWAS was first used in human research and has made great contributions to identify genes associated to human diseases [12][13][14][15][16]. The GWAS approach has been widely used in plant and animal research [17][18][19][20][21]. For wheat, GWAS has been successfully used for identifying quantitative trait loci (QTLs) for disease resistance and yield [22][23][24][25].The release of the high-quality genome reference IWGSC RefSeq v1.0 [26] has provided great assistance to detect linked markers and candidate genes for target traits. The availability of marker arrays for high throughput genotyping is a key for GWAS. There are many SNP arrays have been developed for wheat, such as 9K, 35K, 55K, 90K, 660K and 820K. These arrays are able to provide