Biology subjects

Alvarez, A. T.

Publications and source records attributed to Alvarez, A. T..

3 recordsLinked to original sources

bioRxiv2020

Bayesian genomic models boost prediction accuracy for resistance against Streptococcus agalactiae in Nile tilapia (Oreochromus nilioticus)

Streptococcosis due to Streptococcus agalactiae is a major bacterial disease in Nile tilapia, and development of the resistant genetic strains can be a sustainable approach towards combating this problematic disease. Thus, a controlled disease trial was performed on 120 full-sib families to i) quantify and characterize the potential of genomic selection for S. agalactiae resistance in Nile tilapia and to ii) select the best genomic model and optimal SNP-chip for this trait. In total, 40 fish per family (15 fish intraperitoneally injected and 25 fish as cohabitants) were selected for the challenge test and mortalities recorded every 3 hours, until no mortalities occurred for a period of 3 consecutive days. Genotypes (50,690 SNPs) and phenotypes (0 for dead and 1 for alive) for 2472 cohabitant fish were available. The pedigree-based analysis utilized a deep pedigree, going 17 generations back in time. Genetic parameters were obtained using various genomic selection models (GBLUP, BayesB, BayesC, BayesR and BayesS) and traditional pedigree-based model (PBLUP). The genomic models were further analyzed using 10 different subsets of SNP-densities for optimum marker density selection. Prediction accuracy and bias were evaluated using 5 replicates of 10-fold cross-validation. Using an appropriate Bayesian genomic selection model and optimising it for SNP density increased prediction accuracy up to [~]71%, compared to a pedigree-based model. This result is encouraging for practical implementation of genomic selection for S. agalactiae resistance in Nile tilapia breeding programs.

genomics↗

bioRxiv2019

Genomic prediction for commercial traits using univariate and multivariate approaches in Nile tilapia (Oreochromis niloticus)

BackgroundOver the past three decades, Nile tilapia industry has grown into a significant aquaculture industry spread over 120 tropical and sub-tropical countries around the world accounting for 7.4% of global aquaculture production in 2015. Across species, genomic selection has been shown to increase predictive ability and genetic gain, also extending into aquaculture. Hence, the aim of this paper is to compare the predictive abilities of pedigree- and genomic-based models in univariate and multivariate approaches, with the aim to utilize genomic selection in a Nile tilapia breeding program. A total of 1444 fish were genotyped (48,960 SNP loci) and phenotyped for body weight at harvest (BW), fillet weight (FW) and fillet yield (FY). The pedigree-based analysis utilized a deep pedigree, including 14 generations. Estimated breeding values (EBVs and GEBVs) were obtained with traditional pedigree-based (PBLUP) and genomic (GBLUP) models, using both univariate and multivariate approaches. Prediction accuracy and bias were evaluated using 5 replicates of 10-fold cross-validation with three different cross-validation approaches. Further, impact of these models and approaches on the genetic evaluation was assessed based on the ranking of the selection candidates.\n\nResultsGBLUP univariate models were found to increase the prediction accuracy and reduce bias of prediction compared to other PBLUP and multivariate approaches. Relative to pedigree-based models, prediction accuracy increased by [~]20% for FY, >75% for FW and >43% for BW. GBLUP models caused major re-ranking of the selection candidates, with no significant difference in the ranking due to univariate or multivariate GBLUP approaches. The heritabilities using multivariate GBLUP models for BW, FW and FY were 0.19 {+/-} 0.04, 0.17 {+/-} 0.04 and 0.23 {+/-} 0.04 respectively. BW showed very high genetic correlation with FW (0.96 {+/-} 0.01) and a slightly negative genetic correlation with FY (-0.11 {+/-} 0.15).\n\nConclusionPredictive ability of genomic prediction models is substantially higher than for classical pedigree-based models. Genomic selection is therefore beneficial to the Nile tilapia breeding program, and it is recommended in routine genetic evaluations of commercial traits in the Nile tilapia breeding nucleus.

genomics↗

bioRxiv2018

Development and validation of 58K SNP-array and high-density linkage map in Nile tilapia (O. niloticus)

Despite being the second most important aquaculture species in the world accounting for 7.4% of global production in 2015, tilapia aquaculture has lacked genomic tools like SNP-arrays and high-density linkage maps to improve selection accuracy and accelerate genetic progress. In this paper we describe the development of a genotyping array containing more than 58,000 SNPs for Nile tilapia (Oreochromis niloticus). SNPs were identified from whole genome resequencing of 32 individuals from the commercial population of the Genomar strain, and selected for the SNP-array based on polymorphic information content and physical distribution across the genome using the Orenil1.1 genome assembly as reference sequence. SNP-performance was evaluated by genotyping 4991 individuals, including 689 offspring belonging to 41 full-sib families, which revealed high-quality genotype data for 43,588 of the SNPs. A preliminary genetic linkage map was constructed using Lepmap2 which in turn was integrated with information from the O_niloticus_UMD1 genome assembly to produce an integrated physical and genetic linkage map comprising 40,186 SNPs distributed across 22 linkage groups. Around one-third of the linkage groups showed a different recombination rate between sexes, with male and female map lengths differing by a factor of 1.2 (1359.6cM and 1632.9cM respectively), with most linkage groups displayed a sigmoid recombination profile. Finally, the sex-determining locus in this population was mapped to position 40.53 cM on linkage group 23, in the vicinity of the anti-Mullerian hormone (amh) gene. These new resources has the potential to greatly influence and improve the genetic gain when applying genomic selection and surpass the difficulties of efficient selection for invasive traits in tilapia.

genomics↗