Genetic and phenotypic traits in livestock

The paper identifies a QTL spanning the TGF-β2 locus associated with muscle fiber hypertrophy in rainbow trout using GWAS and transcriptomic analysis.

The paper estimates recent population history of Swedish cattle breeds using linkage disequilibrium methods and finds a recent population decline following the start of systematic breeding.

This paper evaluates robust Random Forests for genomic prediction under contamination, finding data transformation as the most effective strategy for improving performance in contaminated datasets.

DeepBioGS integrates genomic selection with crop growth models using deep learning to improve prediction of genotype performance across environments.

The paper analyzes genomic data from Brazilian goat breeds to identify runs of homozygosity and assess inbreeding, linking genetic regions to traits under selective pressure.

SAFE-h2 is a tool that filters SNPs to improve heritability estimation by removing those with negative contributions, revealing phenotypic variance from both additive and non-additive allelic effects.

This study analyzes the correlation between body weight and body measurements in female Jiangyue donkeys and identifies the best growth curve model for their weight growth.

This study validates genomic selection in loblolly pine, showing higher genetic gain and accurate predictions using ssGBLUP models with scaled genomic relationships.

This study predicts single cross performance in perennial ryegrass using genomic models, finding high predictive accuracy for dry matter yield and nutritive quality traits.

The paper creates a high-resolution cattle regulatory variant atlas and identifies cross-species activity in human cells, highlighting both conservation and species-specific effects.

This study investigates inbreeding depression in wild barn owls using whole-genome sequences, showing its presence in growth and adult traits, and identifying genomic regions with significant effects.

This paper develops a GEI-based genomic prediction method for cotton traits using dimensionality reduction and Bayesian models, showing better performance than deep learning approaches.

This study uses temporal allele frequency changes to detect adaptive variants in winter wheat breeding programs, finding that selection signals identify non-neutral variants associated with grain yield and protein content.

CLCNet improves genomic prediction accuracy in plants by integrating contrastive learning and chromosome-aware feature selection, outperforming existing models across four crop species and ten traits.

This study evaluates genomic prediction and GWAS in an expanded rice NAM population, finding that prediction accuracy plateaus at moderate marker density and training size, while GWAS benefits from higher density.

This paper optimizes wheat breeding programs using an evolutionary algorithm, achieving higher genetic gain and diversity while maintaining a fixed budget.

The paper introduces a genomic prediction model that forecasts yield performance across environments and future climates and identifies adapted maize landraces by integrating genomic offset and gene diversity.

This paper uses GWAS and genomic prediction to identify genetic factors influencing lucerne traits relevant to ideotype breeding, finding high accuracy in diverse populations but lower in synthetic ones.

This study performs a GWAS on sugar kelp to identify genetic markers associated with morphometric and metabolic traits, finding major-effect loci and polygenic traits for breeding programs.

This study assembles a high-quality telomere-to-telomere genome of Wuzhishan pigs, revealing genetic diversity and structural variations compared to a previous assembly.

This study compares machine learning and conventional models for genomic prediction of fertility traits in Holstein heifers using low-coverage sequencing data, finding that KRR_GRM improves accuracy over traditional methods when using only genomic data.

This study investigates the impact of trait measurement error on quantitative genetic analysis of pig body weight, finding no genetic influence on the error and equivalent heritability between scale-based and image-based measurements.

This study improves genomic prediction of ash dieback resistance by incorporating environmental factors, showing enhanced transferability across environments.

The paper characterizes TAPBP alleles in Korean Native Chickens, identifies novel variants, and explores haplotypic associations with BF2.

The paper estimates genetic parameters and sex-specific architecture of fertility traits in Arctic charr, identifying genetic correlations and trade-offs affecting reproductive success.

This study compares genomic selection accuracy and bias between pedigree-based and marker-based models in Pekin ducks, finding higher accuracy and lower bias with genomic methods for traits with low heritability.

CanVAS harmonizes and imputes canine genotype data from multiple sources to create a unified resource for genetic studies.

This study examines body measurements of Somali dromedary camels to identify morphometric differences between local types and sex.

This study shows that ultra low-coverage whole genome sequencing can reliably estimate canine inbreeding using runs of homozygosity and inbreeding coefficients.

The paper develops a multi-kernel mixed model to analyze indirect genetic effects (IGEs) in plant genotypes, finding evidence of intergenotypic competition in aspen and apple trees.