Genomic Selection and Genome-Wide Association Studies for Grain Protein Content Stability in a Nested Association Mapping Population of Wheat

Abstract

Grain protein content (GPC) is controlled by complex genetic systems and their interactions and is an important quality determinant for hard spring wheat as it has a positive effect on bread and pasta quality. GPC is variable among genotypes and strongly influenced by the environment. Thus, understanding the genetic control of wheat GPC and identifying genotypes with improved stability is an important breeding goal. The objectives of this research were to identify genetic backgrounds with less variation for GPC across environments and identify quantitative trait loci (QTLs) controlling the stability of GPC. A spring wheat nested association mapping (NAM) population of 650 recombinant inbred lines (RIL) derived from 26 diverse founder parents crossed to one common parent, ‘Berkut’, was phenotyped over three years of field trials (2014–2016). Genomic selection models were developed and compared based on predictions of GPC and GPC stability. After observing variable genetic control of GPC within the NAM population, seven RIL families displaying reduced marker-by-environment interaction were selected based on a stability index derived from a Finlay–Wilkinson regression. A genome-wide association study identified eighteen significant QTLs for GPC stability with a Bonferroni-adjusted p-value < 0.05 using four different models and out of these eighteen QTLs eight were identified by two or more GWAS models simultaneously. This study also demonstrated that genome-wide prediction of GPC with ridge regression best linear unbiased estimates reached up to r = 0.69. Genomic selection can be used to apply selection pressure for GPC and improve genetic gain for GPC.

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

“…As the dataset was from a breeding program, some lines were continuously removed each year with new genotypes in the subsequent year, resulting in an unbalanced dataset. More information about the dataset is referred to Aoun et al, 2021a andSandhu et al, 2021b. End-use quality data were collected separately at both locations for all the genotypes.…”

“…CODI is one of the baking parameters and is estimated by following the AACC Approved Method 10-52.02. More information about the phenotyping is referred to Aoun et al, 2021a andSandhu et al, 2021b. Genotyping: Genotyping by sequencing (GBS) was used for genotyping the complete population using the facilities from Genomics Sciences Laboratory, Raleigh, NC (Poland et al, 2012). The complete details about the genotyping and SNP calling was reported in Aoun et al (2021a) and Sandhu et al (2021a).…”

“…Phenotypic data analysis: To account for the unbalanced dataset in this study, adjusted means were extracted using residuals obtained from the unreplicated genotypes in individual environments using the augmented complete block design model implemented in the R statistical program. Adjusted means were obtained according to the method implemented in Sandhu et al (2021b), and the model equation is given as follows:…”

“…In previous studies, we have shown the advantages of multitrait GS models (Sandhu et al, 2021e) and machine and deep learning models for predicting complex traits in wheat (Sandhu et al, 2021b;Sandhu et al, 2021c). Building upon the findings of previous studies, this study's objectives were to 1) optimize the uni-and multi-trait GS models for seven end-use quality traits, 2) compare the performances of four uni-and multi-trait GS models using cross-validation and independent predictions, and 3) assess the potential of across-location prediction using multi-trait models and with the inclusion of genotype by environment interaction component.…”

Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs

“…These rare variants could be identified by linkage mapping with segregation of alleles in the mapping population, which will provide higher power to detect rare QTL. Furthermore, several nested association mapping (NAM) and multi-parent advanced generation intercross (MAGIC) populations have been developed in most of the crop species discussed in this review for marker trait associations (MTAs) with high power and resolution during mapping studies (Yu et al, 2008;Diaz et al, 2021;Sandhu et al, 2021d).…”