Biology subjects

Resende, M. F.

Publications and source records attributed to Resende, M. F..

2 recordsLinked to original sources

bioRxiv2018

Genomic prediction of autotetraploids; influence of relationship matrices, allele dosage, and continuous genotyping calls in phenotype prediction

Estimation of allele dosage in autopolyploids is challenging and current methods often result in the misclassification of genotypes. Here we propose and compare the use of next generation sequencing read depth as continuous parameterization for autotetraploid genomic prediction of breeding values, using blueberry (Vaccinium corybosum spp.) as a model. Additionally, we investigated the influence of different sources of information to build relationship matrices in phenotype prediction; no relationship, pedigree, and genomic information, considering either diploid or tetraploid parameterizations. A real breeding population composed of 1,847 individuals was phenotyped for eight yield and fruit quality traits over two years. Analyses were based on extensive pedigree (since 1908) and high-density marker data (86K markers). Our results show that marker-based matrices can yield significantly better prediction than pedigree for most of the traits, based on model fitting and expected genetic gain. Continuous genotypic based models performed as well as the current best models and presented a significantly better goodness-of-fit for all traits analyzed. This approach also reduces the computational time required for marker calling and avoids problems associated with misclassification of genotypic classes when assigning dosage in polyploid species. Accuracies are encouraging for application of genomic selection (GS) for blueberry breeding. Conservatively, GS could reduce the time for cultivar release by three years. GS could increase the genetic gain per cycle by 86% on average when compared to phenotypic selection, and 32% when compared with pedigree-based selection.

genomics↗

bioRxiv2018

Integration of Dominance and Marker x Environment Interactions into Maize Genomic Prediction Models

Hybrid breeding programs are driven by the potential to explore the heterosis phenomenon in traits with non-additive inheritance. Traditionally, progress has been achieved by crossing lines from different heterotic groups and measuring phenotypic performance of hybrids in multiple environment trials. With the reduction in genotyping prices, genomic selection has become a reality for phenotype prediction and a promising tool to predict hybrid performances. However, its prediction ability is directly associated with models that represent the trait and breeding scheme under investigation. Herein, we assess modelling approaches where dominance effects and multi-environment statistical are considered for genomic selection in maize hybrid. To this end, we evaluated the predictive ability of grain yield and grain moisture collected over three production cycles in different locations. Hybrid genotypes were inferred in silico based on their parental inbred lines using single-nucleotide polymorphism markers obtained via a 500k SNP chip. We considered the importance to decomposes additive and dominance marker effects into components that are constant across environments and deviations that are group-specific. Prediction within and across environments were tested. The incorporation of dominance effect increased the predictive ability for grain production by up to 30% in some scenarios. Contrastingly, additive models yielded better results for grain moisture. For multi-environment modelling, the inclusion of interaction effects increased the predictive ability overall. More generally, we demonstrate that including dominance and genotype by environment interactions resulted in gains in accuracy and hence could be considered for genomic selection implementation in maize breeding programs.

genomics↗