Genetic and phenotypic traits in livestock

The paper constructs a pangenome for Indian Bos indicus cattle by identifying non-reference sequences, revealing novel genomic content and common insertions with functional implications.

This paper reviews the domestication of penaeid shrimp, discusses breeding traits, and highlights the role of genomic selection and AI in improving shrimp farming sustainability.

The paper investigates genetic factors influencing meiotic crossover count, interference, and positioning in domestic pigs, finding distinct genetic architectures for these traits.

The paper identifies a deep intronic splicing mutation in the GNPAT gene in cattle causing rhizomelic chondrodysplasia punctata, and demonstrates its phenotypic effects through genetic and functional analyses.

This study compares DNA array and Genotyping-by-Sequencing (GBS) for genomic prediction of soybean oil composition traits using BayesB and BRR models.

The paper constructs a fine-scale recombination map in domestic chickens to study rapid evolutionary changes in recombination rates during domestication.

The paper uses multi-omics data from controlled conditions to predict platform and field traits in maize hybrids, showing higher predictive abilities than GBLUP for platform traits but only slight improvements for field traits.

The paper introduces lme4breeding, a fast linear mixed model for multi-trait/multi-environment experiments that handles complex covariance structures efficiently.

The paper shows RNA sequencing variants in cattle tissues are enriched for eQTL, with 75% of eGenes detected and a 2-fold enrichment compared to DNA sequencing variants.

This study performs genome-wide association analysis for haemorrhagic bowel syndrome in Swiss Large White pigs but finds no significant genetic variants associated with the disease.

This paper introduces Cross Potential Selection (CPS), a strategy for selecting crossing pairs in plant breeding to accelerate novel variety production, showing higher genetic improvements in early cycles of simulated breeding programs.

The paper derives formulas to predict background noise in trait introgression programs and uses them to optimize breeding schemes for five generations.

This study uses whole-genome resequencing to trace the origin and domestication of Chinese domestic rabbits, identifying genetic adaptations and multiple potential origins.

This paper introduces a hybrid DLGBLUP model that integrates deep learning with GBLUP to improve prediction accuracy by capturing nonlinear genetic relationships between traits.

This paper introduces a median-based algorithm to assess breed conformity using genetic markers, showing moderate correlation with SNP-based evaluations and potential for use without reference populations.

This study genotypes canine MHC-I alleles using RNA-seq data from 1,025 dogs, identifying 97 known and 52 putative new alleles and analyzing their expression and diversity patterns.

This paper integrates environmental data with genetic data using machine learning to improve genomic prediction models for maize grain yield, showing increased predictive ability and computational efficiency.

The paper annotates and categorizes functional variants in farm animals using epigenomic data, showing improved genomic prediction accuracy and identifying trait-related tissues and cell types.

This study evaluates deep learning for predicting rice traits using structural and single-nucleotide genomic variants, finding that deep learning models outperform Bayesian models in 75% of cases.

This study compares adipose gene expression profiles across four cattle breeds to identify environmental adaptations and differential gene expression related to metabolism, immunity, and feed efficiency.

BulkLMM accelerates genome-wide scans for multiple quantitative traits using LMMs by employing vectorized operations and regularization, achieving fast results on large datasets.

The paper introduces a large chicken reference panel with 13,187 genomes, showing improved imputation accuracy and statistical power for genome-wide association studies.

This study performs sequence-based GWAS on 180,000 German Holstein cattle to identify new candidate genes for milk production traits, finding 30 previously unreported genes associated with milk, fat, and protein yield.

This study demonstrates that genomic selection can effectively predict herbage yield in forage oats with high accuracy, offering advantages over traditional marker-assisted selection.

This study identifies genomic variations and candidate genes affecting growth in large yellow croaker using whole genome resequencing and GWAS.

This study simulates dairy cattle breeding programs to assess how accounting for mitochondrial DNA variation affects the accuracy of breeding values and genetic gain.

The paper assesses runs of homozygosity (ROH) in Sahiwal cattle to evaluate autozygosity, inbreeding, and candidate genes related to milk production.

This paper develops genomic-inferred cross-selection metrics to improve multi-trait genetic gain in a breeding program while maintaining additive genetic variance.

The paper presents a highly contiguous genome assembly of the inbred Babraham pig, confirming homozygosity and characterizing immunogenetic variation.

This study identifies and corrects cross-validation bias in genomic prediction methods using CVc, showing effectiveness for most methods except Lasso and ENET.