Genetic and phenotypic traits in livestock

This study uses machine and deep learning models to predict end-use quality traits in wheat breeding, achieving higher accuracy than traditional methods.

The paper uses Bayesian models to analyze cattle traits by incorporating functionally significant variants, showing improved genomic prediction accuracy when combining these variants with HD SNPs.

This study evaluates genomic prediction in family bulks for loblolly pine, finding that GWFP outperforms individual GEBV predictions and is suitable for breeding programs using family-level selection.

This paper introduces NN-Bayes, a Bayesian neural network method for genome-wide prediction and association studies, showing improved performance over linear models in simulations and real data.

This study estimates heritability of antibiotic usage in pigs and finds moderate to high heritability, suggesting potential for breeding programs to reduce antibiotic use.

This study evaluates BayesR's predictive performance and interpretability in genomic prediction using simulated data with varying genetic architectures and heritabilities.

The paper identifies a frameshift mutation in IFT80 causing embryonic lethality in Holstein cattle and validates it using CRISPR-Cas9 knockout embryos.

The paper identifies genomic regions under positive selection in Changthangi sheep, linked to high-altitude adaptation, by comparing with other breeds.

This study estimates recombination rate variation in pigs using multiocus iterative peeling across 150,000 pigs, identifies heritability, and finds three genomic regions associated with recombination.

The paper identifies genotype-by-environment interactions affecting growth traits in cattle using genome-wide association analyses and finds these interactions contribute to phenotypic variance.

This study identifies candidate genes for sea lice resistance in Atlantic salmon and rainbow trout through genome-wide association studies.

This study evaluates the impact of genomic preselection on accuracy and bias in ssGBLUP genetic evaluations using real pig data, finding minimal effects on accuracy and negligible bias.

This paper summarizes genomic prediction strategies for autotetraploid blueberry, showing the importance of model parametrization, sequence depth, and genotype calling for improving breeding efficiency.

The paper identifies genomic regions affecting feed efficiency and production traits in pigs using GWAS across divergently selected lines.

The paper compares genomic prediction models for maize traits, finding that selecting top SNP interactions improves prediction accuracy compared to additive models.

The paper optimizes genomic prediction training sets using enviromic data and genetic algorithms, increasing genetic gains by 142% per dollar invested.

The paper assesses how SNP calling quality affects TRD detection in goats, finding that low-quality SNPs can produce false TRD signals.

This study uses SNP data to analyze the ancestry and divergent selection signatures in Ghurrah pigs, revealing introgression from Asian and European breeds and selection in traits like meat quality and olfaction.

The paper studies long-range linkage disequilibrium (LRLD) and its relationship with Marek's Disease resistance QTL in chicken populations, finding complex LD structures that suggest causative mutations may lie farther from markers than previously thought.

This study analyzes genetic diversity, population structure, and maternal origin of South Indian draught cattle breeds, identifying risks of endangerment and taurine admixture.

The paper identifies environmental drivers of adaptation in Ethiopian indigenous chickens and finds genomic regions associated with altitude, water scarcity, and feeding behavior.

This study conducts a meta-analysis of genetic parameters for quantitative traits in aquatic animals, finding moderate heritability for growth and disease resistance, but low heritability for carcass and reproductive traits.

The paper identifies a gene associated with growth in golden pompano using genomic and genetic analyses of a natural population.

This study compares the impact of two cattle reference genomes on genomic analyses, finding negligible differences in most tasks but significant effects on selection signature detection.

The paper examines the distribution of gene-environment product interactions in quantitative traits and their impact on heritability and phenotypic variance.

The paper identifies a SNP in the Wnt4 gene regulatory region that affects NFAT5 binding and is associated with chicken keel and comb length traits.

The paper compares index selection and independent culling in genomic selection for multi-trait breeding programs, showing index selection is more efficient when economic weights are known.

The paper introduces CV-α, a validation method for genomic prediction that improves precision and enables statistical tests by reducing residual dependence compared to traditional methods.

This study uses simulations to show that constraining genetic space and reducing population size in hybrid breeding improves heritable gains, explaining the success of traditional breeding strategies.