Genetic and phenotypic traits in livestock

This study compares AI algorithms with traditional methods for genomic prediction of disease resistance in striped catfish, finding AI methods improve prediction accuracy.

The paper develops LPChoose, a linear programming approach to optimize sequencing resource allocation in genotyped livestock populations for efficient haplotype coverage.

The paper introduces REHE and reREHE methods for fast and robust variance components estimation in linear mixed models, providing non-negative estimates comparable to REML.

This study evaluates multi-omics prediction models for oat traits across environments and populations, showing improved accuracy over genomic prediction with SNP data.

This study identifies SNPs in MLPH and PMEL17 genes associated with melanin content in Xuefeng black-bone chickens, suggesting potential molecular markers for breeding.

This paper introduces E-GP, an enviromic assembly method that improves genomic prediction accuracy for yield plasticity by integrating environmental data and optimizing field trials.

The paper proposes a Bayesian optimization method to optimize multivariate genomic prediction models using secondary traits, achieving better accuracy while reducing phenotyping costs.

This paper shows deep neural networks for genomic prediction do not estimate marker effects, instead relying on genetic relatedness.

PATRIOT improves genomic prediction accuracy in self-pollinating crops by tracing identical-by-descent segments, increasing accuracy by 16.6% over traditional methods.

The paper evaluates breeding strategies for soybean to balance short-term genetic gains with long-term diversity preservation using genomic and phenotypic selection methods.

The paper compares breeding strategies for creating a synthetic pig line, finding that combining genomic diversity management with selection starting at G4 and moderate diversity control preserves diversity and achieves genetic progress.

The paper introduces a new genomic relationship matrix that improves genomic heritability estimation and breeding value prediction across populations.

blupADC provides an R package and Shiny toolkit for comprehensive genetic data analysis in animal and plant breeding, including data processing, quality control, imputation, and genomic evaluation.

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.

The paper derives exact formulas for breeding value distributions and their moments after selection, and analyzes the impact of recombination and GXE covariance changes.

This study compares SNP and STR markers for equine parentage verification, showing SNPs can achieve comparable or better accuracy than STRs in partial parentage scenarios.

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 improves the caribou genome assembly and identifies copy number variations (CNVs) associated with adaptation in different ecotypes.

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.