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Data from: Genetic Architecture of Resistance to Stripe Rust in a Global Winter Wheat Germplasm Collection

    Virulence shifts in populations of *Puccinia striiformis* f. sp. *tritici* (*Pst*), the causal pathogen of wheat stripe rust, are a major challenge to resistance breeding. The majority of known resistance genes are already ineffective against current races of *Pst*, necessitating the identification and introgression of new sources of resistance. Germplasm core collections that reflect the range of genetic and phenotypic diversity of crop species are ideal platforms for examining the genetic architecture of complex traits such as resistance to stripe rust. We report the results of genetic characterization and genome-wide association analysis (GWAS) for resistance to stripe rust in a core subset of 1175 accessions in the National Small Grains Collection (NSGC) winter wheat germplasm collection, based on genotyping with the wheat 9K single nucleotide polymorphism (SNP) iSelect assay and phenotyping of seedling and adult plants under natural disease epidemics in four environments.

    Data from: Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design

      The type 2 modified augmented design (MAD2) is an efficient unreplicated experimental design used for evaluating large numbers of lines in plant breeding and for assessing genetic variation in a population. Statistical methods and data adjustment for soil heterogeneity have been previously described for this design. In the absence of replicated test genotypes in MAD2, their total variance cannot be partitioned into genetic and error components as required to estimate heritability and genetic correlation of quantitative traits, the two conventional genetic parameters used for breeding selection. We propose a method of estimating the error variance of unreplicated genotypes that uses replicated controls, and then of estimating the genetic parameters. Using the Delta method, we also derived formulas for estimating the sampling variances of the genetic parameters. Computer simulations indicated that the proposed method for estimating genetic parameters and their sampling variances was feasible and the reliability of the estimates was positively associated with the level of heritability of the trait. A case study of estimating the genetic parameters of three quantitative traits, iodine value, oil content, and linolenic acid content, in a biparental recombinant inbred line population of flax with 243 individuals, was conducted using our statistical models. A joint analysis of data over multiple years and sites was suggested for genetic parameter estimation. A pipeline module using SAS and Perl was developed to facilitate data analysis and appended to the previously developed MAD data analysis pipeline (http://probes.pw.usda.gov/bioinformatics_tools/MADPipeline/index.html).

      Animal Trait Correlation Database (CorrDB)

        A genetic correlation is the proportion of shared variance between two traits that is due to genetic causes; a phenotypic correlation is the degree to which two traits co-vary among individuals in a population. In the genomics era, while gene expression, genetic association, and network analysis provide unprecedented means to decode the genetic basis of complex phenotypes, it is important to recognize the possible effects genetic progress in one trait can have on other traits. This database is designed to collect all published livestock genetic/phenotypic trait correlation data, aimed at facilitating genetic network analysis or systems biology studies.