Biology subjects

Covarrubias-Pazaran, G.

Publications and source records attributed to Covarrubias-Pazaran, G..

5 recordsLinked to original sources

bioRxiv2025

Early-stage sparse testing strategies to increase genetic gain in plant breeding programmes

Early-stage sparse testing can significantly increase genetic gain in plant breeding programmes, facilitating the development of varieties with high and stable performance across farmers fields. This is achieved through (1) increased selection accuracy, enabled by broader sampling of trial sites across the Target Population of Environments (TPE), and (2) increased selection intensity by testing more selection candidates at a reduced replication rate. Early-stage agronomic testing typically involves evaluating a large number of selection candidates at one or a few experimental sites. Although these sites may poorly represent the TPE, strong selection pressure is generally applied. However, if the genetic correlation between performance at the experimental sites and in the TPE is low, most of the genetic gain achieved through selection will not be expressed under farmers conditions. Sparse testing addresses this challenge by using farm-as-incomplete-block designs, in which different subsets of selection candidates are evaluated across sites. By leveraging a genomic relationship matrix (GRM) to connect genotypes across environments, sparse testing enables early-stage multi-environment trials with broader coverage of the TPE. Here, we used stochastic simulation to compare various partially replicated sparse testing strategies to three fully replicated conventional early-stage testing strategies, with and without GRM. All sparse testing strategies achieved substantially higher and more stable genetic gain than the conventional strategies. Our results show that sparse testing provides breeders with a powerful and flexible framework to rethink early-stage trials and design cost-effective, multi-location experiments that lay the foundation for increased genetic gains in farmers fields. Key messageEarly-stage sparse testing can significantly increase genetic gain by (1) improving selection accuracy through broader sampling of the Target Population of Environments (TPE), and (2) increasing selection intensity.

genetics↗

bioRxiv2021

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly-related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G+T, G+M and G+T+M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17 and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly-related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

genetics↗

bioRxiv2018

Software update: Moving the R package sommer to multivariate mixed models for genome-assisted prediction

In the last decade the use of mixed models has become a pivotal part in the implementation of genome-assisted prediction in plant and animal breeding programs. Exploiting the use genetic correlation among traits through multivariate predictions has been proposed in recent years as a way to boost prediction accuracy and understand pleiotropy and other genetic and ecological phenomena better. Multiple mixed model solvers able to use relationship matrices or deal with marker-based incidence matrices have been released in the last years but multivariate versions are scarse. Such solvers have become quite popular in plant and animal breeding thanks to user-friendly platforms such as R. Among such software one of the most recent and popular is the sommer package. In this short communication we discuss the update of the package that is able to run multivariate mixed models with multiple random effects and different covariance structures at the level of random effects and trait-to-trait covariance along with other functionalities for genetic analysis and field trial analysis to enhance the genome-assisted prediction capabilities of researchers.

genetics↗

bioRxiv2016

SOFIA: an R package for enhancing genetic visualization with Circos

Visualization of data from any stage of genetic and genomic research is one of the most useful approaches for detecting potential errors, ensuring accuracy and reproducibility, and presentation of the resulting data. Currently software such as Circos, ClicO FS, and RCircos, among others, provide tools for plotting a variety of genetic data types in a concise manner for data exploration and presentation. However, each of the programs have one or more disadvantages that limit their usability in data exploration or construction of publication quality figures, such as inflexibility in formatting and configuration, reduced image quality, lack of potential for automation, or requirements of high-level computational expertise. Therefore, we developed the R package SOFIA, which leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. The advantages of integrating both R and Circos into SOFIA are numerous. R is a very powerful, mid-level programming language widely used among the genetic and genomic research community, while Circos has proven to be a novel software for arranging genomic data to create aesthetical publication quality circular figures. Producing Circos figures in R with SOFIA is simple, requires minimal coding experience, even for complex figures that incorporate high-dimensional genetic information, and allows simultaneous analysis and visual exploration of genomic and genetic data in a single programming environment.

genetics↗

bioRxiv2016

Construction of a high-density American cranberry (Vaccinium macrocarpon Ait.) composite map using genotyping-by-sequencing for multi-pedigree linkage mapping

The American cranberry (Vaccinium macrocarpon Ait.) is a recently domesticated, economically important, fruit crop with limited molecular resources. New genetic resources could accelerate genetic gain in cranberry through characterization of its genomic structure and by enabling molecular-assisted breeding strategies. To increase the availability of cranberry genomic resources, genotyping-by-sequencing (GBS) was used to discover and genotype thousands of single nucleotide polymorphisms (SNPs) within three inter-related cranberry full-sib populations. Additional simple sequence repeat (SSR) loci were added to the SNP datasets and used to construct bin maps for the parents of the populations, which were then merged to create the first high-density cranberry composite map containing 6073 markers (5437 SNPs and 636 SSRs) on 12 linkage groups (LGs) spanning 1124 cM. Interestingly, higher rates of recombination were observed in maternal than paternal gametes. The large number of markers in common (mean of 57.3) and the high degree of observed collinearity (mean Pair-wise Spearman Rank Correlations > 0.99) between the LGs of the parental maps demonstrates the utility of GBS in cranberry for identifying polymorphic SNP loci that are transferable between pedigrees and populations in future trait-association studies. Furthermore, the high-density of markers anchored within the component maps allowed identification of segregation distortion regions, placement of centromeres on each of the 12 LGs, and anchoring of genomic scaffolds. Collectively, the results represent an important contribution to the current understanding of cranberry genomic structure and to the availability of molecular tools for future genetic research and breeding efforts in cranberry.

genomics↗