Genetic and phenotypic traits in livestock

The paper evaluates low-pass whole genome sequencing and imputation in dogs, optimizing variant filtering to reduce imputation error and provides guidelines for GWAS using imputed genotypes.

This study uses multi-trait machine and deep learning models with spectral data to improve genomic selection predictions for grain yield and protein content in wheat.

The paper develops genomic mate selection criteria for outbred species, demonstrating accurate prediction of genetic variances and covariances in cassava to improve breeding outcomes.

This paper develops a quantitative genetic theory for infectious disease prevalence, showing indirect genetic effects dominate heritable variation and response to selection.

The paper develops a method to model substitution effect correlations across populations and generations considering non-additive gene action, using variance components and genetic distances.

This paper identifies a bias in statistical methods for estimating marker heritability and proposes a bias-corrected approach using average semivariance.

This study identifies the effective start of genomic selection in pigs, beef cattle, and broilers by analyzing divergence between genetic and RMS trends from BLUP and ssGBLUP.

This paper evaluates the utility of combining microbiome and genome data for complex trait prediction using simulation and variance-component analysis, finding that microbiome data improve prediction accuracy but causal effects are hard to identify.

GAPIT Version 3 introduces multiple loci test methods and improved GP/GS methods to enhance statistical power and prediction accuracy for genomic association and prediction.

This study identifies indirect genetic effects (IGE) from peers in mice using igeGWAS and confirms IGE genes through knockout experiments.

The paper identifies SNPs in leptin and IGF-1 genes in Karan Fries cows and finds a positive correlation between rs29004509 (C>T) and milk yield.

MegaLMM improves genetic value prediction accuracy by analyzing thousands of traits simultaneously in genomic studies.

The paper identifies genomic loci associated with birth date and environmental adaptation in US beef cattle using large-scale genomic data and linear mixed models.

The paper identifies a 12 kb multi-allelic CNV associated with mastitis resistance in dairy cattle by linking it to increased GC expression and a cis-regulatory enhancer.

This study uses machine and deep learning models to predict end-use quality traits in wheat breeding, achieving higher accuracy than traditional methods.

The paper uses Bayesian models to analyze cattle traits by incorporating functionally significant variants, showing improved genomic prediction accuracy when combining these variants with HD SNPs.

This study evaluates genomic prediction in family bulks for loblolly pine, finding that GWFP outperforms individual GEBV predictions and is suitable for breeding programs using family-level selection.

This paper introduces NN-Bayes, a Bayesian neural network method for genome-wide prediction and association studies, showing improved performance over linear models in simulations and real data.

This study estimates heritability of antibiotic usage in pigs and finds moderate to high heritability, suggesting potential for breeding programs to reduce antibiotic use.

This study evaluates BayesR's predictive performance and interpretability in genomic prediction using simulated data with varying genetic architectures and heritabilities.

The paper identifies genomic regions under positive selection in Changthangi sheep, linked to high-altitude adaptation, by comparing with other breeds.

This study estimates recombination rate variation in pigs using multiocus iterative peeling across 150,000 pigs, identifies heritability, and finds three genomic regions associated with recombination.

The paper identifies genotype-by-environment interactions affecting growth traits in cattle using genome-wide association analyses and finds these interactions contribute to phenotypic variance.

This study identifies candidate genes for sea lice resistance in Atlantic salmon and rainbow trout through genome-wide association studies.

This paper summarizes genomic prediction strategies for autotetraploid blueberry, showing the importance of model parametrization, sequence depth, and genotype calling for improving breeding efficiency.

The paper identifies genomic regions affecting feed efficiency and production traits in pigs using GWAS across divergently selected lines.

The paper compares genomic prediction models for maize traits, finding that selecting top SNP interactions improves prediction accuracy compared to additive models.

This study uses SNP data to analyze the ancestry and divergent selection signatures in Ghurrah pigs, revealing introgression from Asian and European breeds and selection in traits like meat quality and olfaction.

This study analyzes genetic diversity, population structure, and maternal origin of South Indian draught cattle breeds, identifying risks of endangerment and taurine admixture.