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ARS Home » Pacific West Area » Pullman, Washington » WHGQ » Research » Publications at this Location » Publication #358319
Research Project: Wheat Quality, Functionality and Marketablility in the Western U.S.

Location: Wheat Health, Genetics, and Quality Research

Title: Genetic analysis of a unique 'super soft' kernel texture phenotype in soft white spring wheat

Author
item Kumar, Neeraj
item ORENDAY-ORTIZ, JOSE - Washington State University
item Kiszonas, Alecia
item BOEHM, JEFFREY - Washington State University
item Morris, Craig

Submitted to: Journal of Cereal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/7/2018
Publication Date: 12/17/2018
Publication URL: https://handle.nal.usda.gov/10113/6259903
Citation: Kumar, N.N., Orenday-Ortiz, J., Kiszonas, A., Boehm, J.D., Morris, C.F. 2018. Genetic analysis of a unique 'super soft' kernel texture phenotype in soft white spring wheat. Journal of Cereal Science. 85:162-167. https://doi.org/10.1016/j.jcs.2018.12.003.
DOI: https://doi.org/10.1016/j.jcs.2018.12.003

Interpretive Summary: Kernel texture is a key trait that influences milling behavior, flour functionality, and the end-use quality of wheat. Kernel texture is categorized into two distinct classes, soft and hard, by use of the single kernel characterization system (SKCS), which produces a unit-less hardness index (HI). Flours produced from soft textured kernels possess lower levels of starch damage; have lower water absorption, and smaller particle size distributions than hard wheat flours. Hence their preferential use in different products. Hard wheats are primarily utilized in bread production, whereas soft wheats are used to make cakes, cookies, pastries, and some Asian-style noodles. The present study examined the unique super soft kernel trait in a soft white spring wheat population from selected germplasm. The ‘super soft’ trait was transmissible and segregating in a population of 125 recombinant inbred lines (RILs). The present study reports the identification of significant single nucleotide polymorphism (SNP) markers associated with super soft trait using single marker trait association and composite interval mapping (CIM) to determine sources of genetic variation for kernel texture conferring the super soft trait. Efforts are in progress to narrow down the genomic regions harboring major QTLs controlling super soft kernel texture to identify closely associated markers with the trait using fine mapping approach.

Technical Abstract: The preferential application of wheat flour in various products is primarily driven by kernel texture (kernel hardness). Although soft and hard wheats dominate the market classification of wheat, further variations in texture exist within each class. Herein, a population of 125 recombinant inbred lines (RILs) was developed from a cross between Alpowa soft white spring wheat and a closely related line, BC2SS163. The population was segregating for a trait, termed ‘super soft’, that manifested itself in unusually low levels of single kernel characterization system (SKCS) hardness index (HI) values (< 10). To study the genetic variation for SKCS HI, the RIL population along with their parental lines was genotyped using a high-density single nucleotide polymorphism (SNP) array. The 90K iSelect assay identified 1425 SNP markers, which were polymorphic between the two parents. A genetic linkage map was constructed with 1425 markers, which assembled to 21 linkage groups. Of the 1425 markers, 372 were mapped on 21 linkage groups involving 14 chromosomes with 3.0 LOD scores. Composite interval mapping (CIM) was conducted and ten QTLs were identified including four major QTLs controlling SKCS HI harboring three genomic regions located on 1B, 4B and 5A chromosomes. Single marker-trait association analysis was also carried out using whole set of polymorphic markers (1425) and 339 markers showed significant association with kernel texture. Out of 339 markers, ten best markers were identified with high range of PVE (15.0-19.3%) including three commonly identified regions by CIM. In addition, three QTLs for kernel size (KS) and two for kernel weight (KW) were detected independently than kernel texture. Of the three, two QTLs were co-located for KS and KW. The efforts are in progress to narrow down the genomic regions harboring major QTLs controlling super soft kernel texture to identify closely associated markers with the trait.