Genetic and phenotypic traits in livestock

The paper evaluates genome-enabled prediction models for black tea quality and drought tolerance traits using genomic data from segregating populations and validation cultivars.

This study identifies heritable genetic variation in immune traits of Soay sheep and finds large-effect genes outside the MHC region associated with antibody levels.

This study shows genomic data can improve genetic evaluation accuracy in smallholder dairy farms with small herds and weak genetic connections.

This study compares genomic and pedigree-based models for predicting commercial traits in Nile tilapia, finding genomic models significantly improve prediction accuracy and reduce bias.

This study identifies heterosis-related genes in mules through transcriptome analysis, finding that alternative splicing plays a significant role in traits like muscular endurance and cognition.

This study improves genetic gain in breeding programs by incorporating within family variance into optimal cross selection, enhancing both short and long term gains.

This study analyzes genetic data from 11,790 dogs to reveal hidden allele variation in coat color and physical traits, showing how genetic diversity affects breed characteristics and potential.

This study compares genomic selection models for rubber trees, finding that multi-environment models with nonlinear and linear kernels improve prediction accuracy and genetic gain compared to single-environment models.

The paper identifies and analyzes deleterious SNPs in a barley breeding population, showing they decrease in frequency over breeding cycles and are less common in high-yielding lines.

This paper evaluates machine learning methods for phenotype prediction in yeast, rice, and wheat, finding that standard ML methods often outperform classical statistical genetics approaches.

The paper analyzes genetic diversity and population structure of Brazilian sheep breeds using SNP data to identify relationships and admixture events.

This study models how increasing recombination in two plant species improves genetic gain in genomic selection programs over generations.

The paper introduces BGVD, a database for bovine genomic variations and selection signatures, enabling data retrieval and visualization for cattle genomics research.

This study uses population genomics to analyze the Galway sheep breed, finding gene flow from British Leicester and Suffolk sheep, intermediate genetic diversity, and genomic signatures of selection related to meat and wool production.

The paper identifies selection signatures in Atlantic salmon populations adapted to different environments using haplotype-based statistics, highlighting candidate genes related to growth traits.

The paper improves genomic selection in Norway spruce by incorporating non-additive effects and marker-by-environment interactions, leading to higher predictive ability and genetic gain.

This study develops an unbiased pipeline for allele-specific expression analysis in sheep, revealing pervasive and tissue-specific allelic imbalance across genes and SNVs.

The paper presents a high-quality phased chromosome-scale genome assembly of a Chinese indigenous pig and identifies genetic differences between this breed and Duroc pigs.

This study analyzes inbreeding trends in Thoroughbred horses, showing a significant decline in genetic diversity linked to popular sire lines and selection for athletic traits.

The paper improves genome-wide association studies in dogs by imputing variants from 365 whole-genome sequences, increasing marker count 130-fold and enhancing power for complex trait mapping.

The paper identifies issues with cross-validation in multi-trait genomic prediction and proposes three solutions to improve model selection.

The paper identifies sequence variants with regulatory and evolutionary significance in cattle and ranks them using a Functional-And-Evolutionary Trait Heritability score to improve genomic prediction.

The paper uses low-coverage whole-genome sequencing in Duroc pigs to identify SNPs and perform GWAS, finding QTLs for teat number and back fat thickness.

CVRMS selects optimal biomarkers for genome-wide prediction using ridge regression, improving phenotype predictability across diverse datasets.

This study analyzes population structure, linkage disequilibrium, and effective population size in three farmed Nile tilapia populations using SNP data from whole-genome sequencing.

The paper identifies 25 QTL for economically important traits in Brown Swiss cattle using multi-trait meta-analyses of GWAS data from 4,578 bulls.

This paper simulates genomic selection strategies in a small dairy population to evaluate their impact on genetic gain, variation, and sustainability.

This paper proposes a genomic selection model that incorporates SNP-specific variance predictors, showing improved accuracy compared to traditional methods.

CRUMBLER predicts cattle ancestry using SNP data and reference populations to estimate breed composition in admixed animals.

This study applies random regression models with genomic Legendre polynomials and B-splines to predict rice shoot area under control and water-limited conditions, showing improved prediction accuracy compared to a baseline model.