Population Structure and Genome-Wide Association Analysis for Salinity Tolerance in Bread Wheat Using Snp, SSR and Scot Marker Assays

Abstract

Wheat is an essential staple food in the developing world, where demand is projected to grow exponentially in the future; simultaneously, climate changes are projected to reduce supply in the near future. One of the main consequences of climate change is salinity, which negatively impacts the world's cultivated area and therefore affects the global wheat production. Our objectives are to study the population structure of several Egyptian and international wheat accessions and to identify the genetic factors controlling the salinity stress response of bread wheat. In addition, we have attempt to identify genes that control some important agronomic parameters of wheat under salinity stress were identified. The wheat germplasm panel consisted of 70 accessions obtained from Egypt, Syria and Iran. The assessment of salinity tolerance was conducted over the years of 2018 and 2019 in the field and in the greenhouse. The genome association analysis (GWAS) and population structure analysis was conducted using six SCoT, five SSR and 93 SNP markers. Analysis of the population structure using allele frequency and phylogenetic analysis indicated that the studied wheat accessions were belong to four population groups. Where, for the most portion, Egyptian, Syrian and Iranian accessions are clustered depending on their country of origin. The GWAS analysis revealed 13 SNP markers that were significantly associated with morpho-agronomic wheat traits during salinity stress. These markers were closely related to genes that are known to have a direct link to wheat response to salinity stress such as CYP709B2, MDIS2, STAY-GREEN, PIP5K9, and MSSP2 genes. This study revealed the genetic structure of adapted and imported wheat accessions, which could be used to select potential wheat accessions for local breeding programs. In addition, the SNP genotyping assay is a very potential technology that could be efficiently applied to detect genes that control bread wheat response to salinity stress.