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Genomic selection is an efficient approach to get shorter breeding cycles in recurrent selection programs and greater genetic gains with selection of superior individuals. Despite advances in genotyping techniques, genetic studies for polyploid species have been limited to a rough approximation of studies in diploid species. The major challenge is to distinguish the different types of heterozygotes present in polyploid populations. In this work, we evaluated different genomic prediction models applied to a recurrent selection population of 530 genotypes of Panicum maximum, an autotetraploid forage grass. We also investigated the effect of the allele dosage in the prediction, i.e., considering tetraploid (GS-TD) or diploid (GS-DD) allele dosage. A longitudinal linear mixed model was fitted for each one of the six phenotypic traits, considering different covariance matrices for genetic and residual effects. A total of 41,424 genotyping-by-sequencing markers were obtained using 96-plex and Pst1 restriction enzyme, and quantitative genotype calling was performed. Six predictive models were generalized to tetraploid species and predictive ability was estimated by a replicated fivefold cross-validation process. GS-TD and GS-DD models were performed considering 1,223 informative markers. Overall, GS-TD data yielded higher predictive abilities than with GS-DD data. However, different predictive models had similar predictive ability performance. In this work, we provide bioinformatic and modeling guidelines to consider tetraploid dosage and observed that genomic selection may lead to additional gains in recurrent selection program of P. maximum.

Persimmon ( Diospyros kaki Thunb.) is one of the major tree crops in East Asia and is generally hexaploid. A single ASTRINGENCY ( AST) locus controls the astringency/non-astringency (A/NA) trait of persimmon fruit, one of the most important traits for consumption, on each of the six corresponding chromosomes. Although several molecular approaches are in progress to elucidate the molecular mechanisms of astringency trait in persimmon, the distinct polysomic behavior of the AST locus remains to be solved. The aim of this study was to perform fine genotyping of a highly polymorphic marker locus linked to the AST locus, detect the allele pairing in ten segregated F lines derived from hybridization of A-type × NA-type cultivars, and identify the basis of hexaploid inheritance at the AST locus in persimmon. The results showed that persimmon cultivars frequently produce aneuploid offspring bearing an extra chromosome with the AST locus, with the incidence of aneuploidy varying among the cultivars. On the examination of hexasomic behavior in persimmon cultivars, the ratios of individuals bearing each allele pair segregated from A-type parents showed a good fit to the expected ratios in an autohexaploid inheritance model, except for cvs. Luo-tian-tian-shi and Sa-gok-shi which fitted to an autoallohexaploid inheritance model. These results suggest variable hexasomic behavior among persimmon cultivars. [ABSTRACT FROM AUTHOR]

Genotyping required to track family membership in aquaculture breeding programs is reduced dramatically by estimating the contributions of different families to pooled samples of tissue. This approach is relevant to widely differing scenarios involving animals, plants, and microbes. For the family membership scenario, SNP markers are genotyped for the contributing families' parents, and quantitatively genotyped to estimate allele frequencies within the mixed-family pooled tissue. Results are used to infer proportional contributions of the different families to the pool. Different computational strategies were tested for bias and sampling error. A correlation of 99% between estimated and true genetic contributions was achieved using 20 (50) randomly chosen SNPs at a standard error of allele frequency estimates of 0.01 (0.02). Optimal grouping of families and choice of markers further increases performance markedly. Trait means and distributions of families can be quite accurately estimated by tissue sampling across the range of trait values. [ABSTRACT FROM AUTHOR]

Abstract: Monitoring antiviral resistance in influenza is critical to public health epidemiology and pandemic preparedness activities. Effective monitoring requires methods to detect low-level resistance and to monitor the change in resistance as a function of time and drug treatment. Resistance-conferring single-nucleotide mutations in influenza virus are ideal targets for such methods. In the present study, fives sets of paired TaqMan® allele-specific PCR (ASPCR) assays were developed and validated for quantitative single-nucleotide polymorphism (SNP) analysis. This novel method using ΔCt is termed allele-specific mixture analysis (ASMA) or FluASMA. The FluASMA assays target L26F, V27A, A30T, and S31N mutations in the A/Albany/1/98 (H3N2) M2 gene and H275Y mutation in the A/New Caledonia/20/99 (H1N1) NA gene and have a limit of quantification of 0.25–0.50% mutant. The error for % mutant estimation was less than 10% in all FluASMA assays, with intra-run ΔCt coefficient of variance (CoV) at ≤2% and inter-run ΔCt CoV at ≤5%. Results from the current study demonstrate that FluASMA is a highly sensitive and quantitative SNP analysis method, even for minor mutant components (<1%). [Copyright &y& Elsevier]