Genome-wide association study of yield components in spring wheat collection harvested under two water regimes in Northern Kazakhstan

Abstract

Background Bread wheat is the most important cereal in Kazakhstan, where it is grown on over 12 million hectares. One of the major constraints affecting wheat grain yield is drought due to the limited water supply. Hence, the development of drought-resistant cultivars is critical for ensuring food security in this country. Therefore, identifying quantitative trait loci (QTLs) associated with drought tolerance as an essential step in modern breeding activities, which rely on a marker-assisted selection approach. Methods A collection of 179 spring wheat accessions was tested under irrigated and rainfed conditions in Northern Kazakhstan over three years (2018, 2019, and 2020), during which data was collected on nine traits: heading date (HD), seed maturity date (SMD), plant height (PH), peduncle length (PL), number of productive spikes (NPS), spike length (SL), number of kernels per spike (NKS), thousand kernel weight (TKW), and kernels yield per m2 (YM2). The collection was genotyped using a 20,000 (20K) Illumina iSelect SNP array, and 8,662 polymorphic SNP markers were selected for a genome-wide association study (GWAS) to identify QTLs for targeted agronomic traits. Results Out of the total of 237 discovered QTLs, 50 were identified as being stable QTLs for irrigated and rainfed conditions in the Akmola region, Northern Kazakhstan; the identified QTLs were associated with all the studied traits except PH. The results indicate that nine QTLs for HD and 11 QTLs for SMD are presumably novel genetic factors identified in the irrigated and rainfed conditions of Northern Kazakhstan. The identified SNP markers of the QTLs for targeted traits in rainfed conditions can be applied to develop new competitive spring wheat cultivars in arid zones using a marker-assisted selection approach.

Identification of Quantitative Trait Loci for Leaf Rust and Stem Rust Seedling Resistance in Bread Wheat Using a Genome-Wide Association Study

“…The QTLs identified in this study were analyzed and compared with the QTLs previously reported for APR resistance to LR and SR that were identified using GWAS data from RIBSP 2018–2019 [ 33 ] and the QTLs previously reported to be associated with yield-related traits identified using GWAS data from northern Kazakhstan 2018–2020 [ 34 ] ( Table 4 ). In addition, the location of each identified QTL was compared to the genetic positions of known Lr and Sr genes ( Table 4 ).…”

“…Notably, all nine QTLs in this comparative assessment were characterized by similar directions of QTL effects, i.e., toward either resistance or susceptibility. In addition, five out of these nine QTLs were earlier found to be associated with yield components [ 34 ] ( Table 4 ), suggesting that these QTLs might directly influence the field performance of bread wheat. The comparative evaluation of all eleven identified QTLs suggests that two QTLs ( QLr.ipbb-1A.3 and QLr.ipbb-1B.5 ) were presumably novel, as they were not reported in previously published LR and SR studies ( Table 4 ).…”

Identification of quantitative trait loci of agronomic traits in bread wheat using a Pamyati Azieva × Paragon mapping population harvested in three regions of Kazakhstan

“…In this study, the MP developed by using two genetically distinct parents bred in the UK and Russian Federation was genotyped based on 4,595 SNPs in all three subgenomes. The SNP genotyping of this MP provided higher resolution than in our latest QTL mapping effort using Avalon x Cadenza MP, which relied on 3,647 polymorphic SNPs ( Amalova et al, 2021a ). Moreover, whereas the Avalon x Cadenza doubled haploid MP was constructed using two British cultivars, the PAxP MP was developed using varietal representatives of two genetically distant pools that were tested in three wheat-growing regions of the country.…”

“…The QTL analysis was performed using data from every year and location and the QTL identification was conducted as previously described in Amalova et al (2021a) and Amalova et al (2021a) . Specifically, the composite interval mapping (CIM) method of Windows-based QTL Cartographer v2.5 software ( Wang, Basten & Zeng, 2012 ), with the logarithm of the odds ratio (LOD) threshold of 3.0 was applied.…”

“…Earlier, QTL mapping for yield and its components, grain quality, and drought and disease resistance in Kazakhstan has been successfully performed using the DH mapping populations (MPs) Chinese Spring × SQ1 ( Quarrie et al, 2005 ; Abugalieva, 2007 ; Abugalieva et al., 2010 ; Abugalieva et al, 2014 ) and Avalon × Cadenza ( Amalova et al, 2021a ). Still, the further development of local MPs is an important part of the local wheat breeding programs.…”