Genetic and phenotypic traits in livestock

The paper evaluates methods for predicting inbreeding levels using genomic data in small populations, finding that model-based and rule-based approaches perform well under various conditions.

This study constructs a water buffalo pangenome using graph genomics, revealing extensive structural variation and population-specific selection signatures in 711 global samples.

hibayes provides an R package for fitting Bayesian regression models in genomic prediction and GWAS, supporting individual-level, summary-level, and single-step approaches.

GP-ML-DC improves genome prediction by combining machine learning and deep learning with a two-phase dimensionality reduction approach, outperforming existing methods on cattle milk traits.

This study identifies a structural variation in the CDKN2A gene that regulates shank color in chickens by inhibiting dermal melanin production.

The paper evaluates the usefulness criterion for mate selection in breeding programs and finds it provides little benefit over selecting parents based on predicted family means.

This paper evaluates phenomic prediction (PP) accuracy in rice breeding, finding that multi-environment data improve PP performance, making it comparable to genomic prediction (GP) when all genotypes have NIRS measurements.

The paper identifies genetic variants linked to fertility and health in dairy heifers, finding overlaps with genes associated with similar traits in mice and humans.

This study identifies genomic variations in Indian cattle breeds that contribute to immune traits and disease resistance through whole-genome sequencing and comparative analysis.

The paper identifies MSTN gene mutations and molecular pathways associated with muscle hypertrophy in the Kuri guinea pig breed compared to native breeds.

This paper introduces MVBLUP, a method that integrates multi-view data for phenotype prediction, showing improved accuracy over single-view approaches.

SimpleMating predicts and optimizes breeding crosses using genomic selection to maximize genetic gain while minimizing inbreeding in simulated maize breeding programs.

This study analyzes the CASP14 gene's tissue-specific expression and functional loci in sheep horns using RNA-Seq, WGS, and KASP assays, identifying three potential functional sites linked to horn development.

The paper introduces ConvCGP, a deep learning framework combining CNNs and autoencoders to predict rice trait genotypic values from compressed genome-wide polymorphism data, outperforming existing methods.

This study applies statistical and deep learning models to multi-environmental genomic prediction in apple, showing improved predictive ability for certain traits.

The paper presents a telomere-to-telomere goat genome assembly, identifies structural variants and genes associated with domestication and cashmere traits, and expands the reference genome with previously unresolved regions.

The paper develops a convex optimization framework for mate allocation in polyploid populations to control inbreeding and predict heterosis, showing improved genetic gain with pedigree kinship.

The paper constructs a sheep developmental gene expression atlas and identifies key genes associated with economically important traits by analyzing gene expression across developmental stages.

This study constructs a pig pangenome and identifies structural variants (SVs) that contribute to pig characteristics, showing their role in heritability and gene expression.

The paper uses local ancestry inference tools to analyze admixture in African and European cattle populations, identifying functional consequences of ancestry on disease resistance and physiological traits.

MegaLMM improves genomic predictions in new environments by incorporating environmental covariates into a statistical model, showing higher accuracy than traditional methods in maize breeding.

The paper introduces SMARTER-database, a tool to standardize and integrate SNP array datasets for sheep and goat breeds to enhance genetic diversity analysis.

The paper proposes using principal component analysis to improve computational efficiency in multi-trait genetic evaluations, achieving faster results with comparable accuracy.

The paper introduces cldla, a bioinformatics workflow for performing combined linkage disequilibrium and linkage analysis (cLDLA) to identify genetic basis of complex traits, with features for heritability estimation and visualization.

This study assesses the impact of a conservation program on genetic diversity and inbreeding in two French chicken breeds using whole-genome sequencing data.

This paper presents a near-complete pig genome assembly, improving upon the existing reference by closing gaps and enhancing gene prediction, which supports better genetic research and applications.

This study uses a genotype-first approach to identify new mutations related to craniofacial morphology and stature in dogs, highlighting the role of allelic heterogeneity in breed trait selection.

This study identifies extensive β-defensin gene copy number variation in cattle, particularly DEFB103, and links it to fertility-related traits.

This study validates genomic prediction of cross progeny variance using simulations and experimental data in winter bread wheat, showing it depends on trait architecture and requires sufficient progeny numbers.

The paper analyzes genomic data from Beefalo cattle and finds most lack detectable bison ancestry, challenging the breed association's 3/8 bison requirement.