Project Number: 2090-21000-036-00-D
Project Type: In-House Appropriated
Start Date: Mar 4, 2019
End Date: Mar 3, 2024
Objective 1: Identify DNA markers associated with resistance to soil borne diseases in alfalfa to clearly define the genetic basis of resistance to disease and accelerate breeding programs. (NP215 2A) Objective 2: Identify alfalfa DNA markers and germplasm associated with drought and salt tolerance to clearly define the genetic basis of resistance to these stressors and accelerate breeding programs. (NP215 2A).
Approach 1: Marker-assisted selection for disease resistance will increase selection accuracy and reduce selection cycles in alfalfa breeding programs. First, genome-wide association mapping will be used to identify loci associated with VW resistance. Then, genetic regions responsible for VW resistance will be sequenced and compared among different genotypes using haplotyping and comparative genomics approaches. Significant SNP markers linked to VW resistance loci will be validated in various breeding populations provided by collaborators. High throughput platforms such as Kompetitive Allele Specific PCR (KASP) (www.lgcgenomics.com) or Taqman (www.thermofisher.com) assays will be used to test the cosegregation of marker loci and disease resistance scores. Flanking sequences for the significant SNP markers will be used for designing specific primers for array-based genotyping platforms (KASP or Taqman). Multiplex primer combinations will be used for evaluating the resistance locus or candidate gene, and all markers will be scored in a given genotype. Single markers with two character states will be tested for significant phenotypic differences between genotype groups by the t test for each trait, and Mann–Whitney U test for chip quality. Marker combinations will be analyzed using analysis of variance (ANOVA) for each trait, and Kruskal–Wallis test for chip quality. Statistical analyses will use SAS software (SAS Institute Inc. 2011, SAS OnlineDoc 9.3, Cary, NC, USA). Approach 2: Breeding for abiotic stress tolerance is challenged by genotype x environment interactions (G x E). Genomic selection provides greater gain and increased selection accuracy than conventional breeding. To develop a genome-wide marker platform and statistical models for genomic selection of drought tolerant alfalfa. BC1 populations have been developed and will be screened for drought tolerance. Selected plants will be randomly intermated in the greenhouse in order to generate an elite base population. The population will used for associated mapping and genomic selection for alleles that affect drought tolerance, salt tolerance, forage quality and other economical traits. We will test statistic models by using the majority of the training population to create a prediction model, which is then used to predict a Genomic Estimated Breeding Value (GEBV) for each of the remaining individuals in the training population based only on their genotype data. Once validated, the model can then be applied to a breeding population to calculate GEBVs of each individual based only on a plant’s genotype information. Such GEBVs represent the overall predicted value of an individual as a potential parent for crossing.