Biology subjects

Carver, B. F.

Publications and source records attributed to Carver, B. F..

2 recordsLinked to original sources

bioRxiv2024

Identification of leaf rust resistance loci in hard winter wheat using genome-wide association mapping

Leaf rust, caused by Puccinia triticina (Pt), is a serious constraint to wheat production. Developing resistant varieties is the best approach to managing this disease. Wheat leaf rust resistance (Lr) genes have been classified into either all-stage resistance (ASR) or adult-plant resistance (APR). The objectives of this study were to identify sources of leaf rust resistance in contemporary U.S. hard winter wheat (HWW) and to dissect the genetic basis underlying leaf rust resistance in HWW. A panel of 732 elite HWW genotypes was evaluated for response to U.S. Pt races at the seedling stage and at the adult plant stage in leaf rust nurseries in Oklahoma, Texas, and Kansas. Further, the panel was genotyped using Multiplex Restriction Amplicon Sequencing (MRA-Seq) and DNA markers linked to the known ASR genes Lr18, Lr19, Lr21, Lr24, Lr37, and Lr42 and APR genes Lr34, Lr46, Lr67, Lr68, Lr77, and Lr78. Single nucleotide polymorphism (SNP) markers derived from MRA-Seq, DNA markers linked to the known Lr genes, and the phenotypic data were used for genome-wide association study (GWAS) to identify markers associated with leaf rust response. Gene postulation based on leaf rust reactions, DNA markers, and GWAS suggested the presence of Lr1, Lr2a, Lr10, Lr14a, Lr16, Lr18, Lr19, Lr21, Lr24, Lr26, Lr34, Lr37, Lr39, Lr42, Lr46, Lr68, Lr77, and Lr78 in the HWW panel. The GWAS identified 59 SNPs significantly associated with leaf rust response, of which 20 were likely associated with novel resistance loci and can be used to enhance wheat leaf rust resistance.

genomics↗

bioRxiv2022

Weighted Kernels Improve Multi-Environment Genomic Prediction

Crucial to variety improvement programs is the reliable and accurate prediction of genotypes performance across environments. However, due to the impactful presence of genotype by environment (GxE) interaction that dictates how changes in expression and function of genes influence target traits, prediction performance of genomic selection (GS) using single-environment models often falls short. Furthermore, despite the successes of genome-wide association studies (GWAS), the genetic insights derived from genome-to-phenome mapping have not yet been incorporated in predictive analytics, making GS models that use Gaussian kernel primarily an estimator of genomic similarity, instead of the underlying genetics characteristics of the populations. Here, we developed a GS framework that, in addition to capturing the overall genomic relationship, can capitalize on the signal of genetic associations of the phenotypic variation as well as the genetic characteristics of the populations. The capacity of predicting the performance of populations across environments was demonstrated by an overall gain in predictability up to 31% for the winter wheat DH population. Compared to Gaussian kernels, we showed that our multienvironment weighted kernels could better leverage the significance of genetic associations and yielded a marked improvement of 4-33% in prediction accuracy for half-sib families. Furthermore, the flexibility incorporated in our Bayesian implementation provides the generalizable capacity required for predicting multiple highly genetic heterogeneous populations across environments, allowing reliable GS for genetic improvement programs that have no access to genetically uniform material.

bioinformatics↗