Genetic and phenotypic traits in livestock

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.

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 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.

This study examines the association between mitochondrial DNA copy number and production traits in pigs, finding correlations with backfat thickness and carcase lean percentage.

This paper introduces an algorithm to approximate p-values for single-step GWAS in large populations, reducing computational costs while maintaining detection power.

This paper introduces AlphaSimR, a software tool for stochastic simulations in plant breeding, providing scripts and vignettes for various breeding strategies.

The paper evaluates a multi-trait multi-environment genomic prediction model for improving yield and protein content in field peas under stress and non-stress conditions using breeding lines from a pulse crop program.

This study compares ddRAD-seq and 20X WGS for variant calling and genotyping in laying hens, showing ddRAD-seq can retain 40% of SNPs with 98% reliability after quality control.

This paper introduces a one-stage spatio-temporal P-spline method for analyzing high-throughput phenotyping data, showing it performs similarly to a two-stage approach in genomic selection.

The paper develops a low-cost SNP panel using RAD-Seq for kinship analysis and pedigree reconstruction in cattle, achieving 90% accuracy in distinguishing F1 and F2 generations.

The paper identifies gene expression changes associated with domestication by comparing pigs, chickens, wild boars, and red junglefowl using RNA-seq data and meta-analysis.

This study characterizes allele-specific regulatory variation in hybrid pigs using multi-omic data across tissues and developmental stages, identifying genes influenced by parent-of-origin and genotype effects.

This study develops an expected cross value (ECV) criterion and integer linear programming framework to optimize mate selection in breeding programs by maximizing desirable genetic inheritance while controlling inbreeding.

The paper integrates environmental gradients into genomic evaluation using reaction norms to predict tree growth resilience under climate change.

This paper validates the linear regression method for assessing population bias and accuracy of predictions, showing it works for non-linear models when data are appropriately partitioned.

This paper compares selection strategies for crop breeding, finding that optimal haplotype selection (OHS) maintains more genetic diversity and achieves higher long-term gains than truncation selection or optimal cross selection.

This study compares methods for quantifying inbreeding depression in structured populations using simulated and empirical data, finding that weighted gamete correlation and specific GRM matrices improve estimation.

The paper develops a deep learning classifier for bovine mastitis susceptibility using whole genome sequence data, overcoming the p>>n problem through LASSO and deep learning integration.

The paper imputes diploid genotypes from low-coverage ancient cattle genomes using a modern reference panel, achieving high accuracy and SNP recovery across different downsample depths.

The paper investigates how uncertainty in honeybee mating strategies affects genetic parameter estimates and proposes methods to correct biases in genetic evaluations.

The paper introduces 11 wild-derived inbred mouse strains with expanded genomic and phenotypic diversity, enhancing the scope of biomedical research.

This paper develops an AI method using simulation and optimization to improve genetic gain in plant breeding by optimizing progeny allocation strategies for mating pairs.

The paper analyzes ancestry and selection in Bengal cats using sequencing data to identify genetic factors underlying coat color traits.

The paper assembles a haplotype-resolved genome of Vechur cattle using hybrid short- and long-read sequencing, identifies genetic variants, and links them to traits like growth and disease resistance.

This study evaluates genomic prediction strategies for carotenoid traits in maize, finding that genome-wide markers outperform gene-specific markers and that GP methods are effective across environments.