Genetic and phenotypic traits in livestock

This study evaluates how genetic relationship between training and validation populations affects genomic prediction accuracy in Atlantic salmon for body weight and sea lice resistance.

The paper identifies novel recessive loci affecting lactation traits in cattle using non-additive QTL mapping on 124,000 sequence-imputed cows, revealing differences in genetic architecture between milk yield and composition traits.

The paper identifies growth-related genetic variation in Australian snapper using genome-wide association and reduced genome representation data, revealing a polygenic architecture with high heritability.

This study identifies genetic loci associated with growth traits in New Zealand silver trevally using QTL mapping and GWAS, revealing 10 genetic hotspots and demonstrating the utility of combining these approaches.

This paper develops a mathematical model to quantify resilience in swine during the fattening period using growth data and creates a resilience index with low heritability estimates.

This study characterizes phenotypic traits of indigenous goats in two districts of southern Ethiopia, revealing differences in morphology and coat color patterns between populations.

The paper develops a gene-to-phenotype network for shoot branching in plants to explore how genetic canalization affects selection responses and trait prediction across different contexts.

This study examines the genetic architecture and genomic prediction accuracy of 30 apple quantitative traits across six European locations, identifying novel associations and demonstrating the impact of genotype by environment interactions.

This study evaluates across-population genomic prediction in grapevine for 15 traits, finding predictive abilities of 0.6 for cross means and 0.26 for individual values, suggesting potential for genomic selection in breeding.

The paper evaluates genomic prediction models on pea germplasm to improve selection efficiency using SNP markers and phenotypic data.

This paper introduces a framework for temporal and genomic analysis of additive genetic variance in breeding programs, showing good concordance between simulated and estimated variances.

This paper demonstrates that low-coverage Oxford Nanopore sequencing can accurately predict genomic breeding values in cattle, comparable to SNP array-based methods, with potential for on-farm or clinical use.

This study combines genetic resources and elite material populations to improve genomic prediction accuracy in apple breeding by optimizing training set composition and population integration.

The paper investigates the effects of classical mutant alleles on cattle complex traits and fitness, finding differential impacts on fertility, stature, milk production, and mastitis risk.

This study improves genomic selection in highly polyploid species by incorporating allele dosage and dominance effects, showing significant gains in predictive ability in certain conditions.

The paper identifies genomic selection signatures in Pakistani Kamori goats using whole genome sequencing and detects candidate genes associated with traits like body weight and coat color.

The paper demonstrates the effectiveness of the Practical Haplotype Graph for genotype imputation in the highly heterozygous crop cassava, achieving higher accuracy than Beagle.

This study compares 17 genomic selection models for predicting a complex trait in wheat breeding, finding that parametric models like cBLUP provide consistent moderate accuracy, making them suitable for practical implementation.

The paper identifies differentially expressed lncRNAs in swine mammary glands during colostrogenesis to lactogenesis and suggests their potential roles in parturition and colostrum formation.

The paper uses ROH detection to analyze genetic diversity and inbreeding in eight Italian beef cattle breeds, identifying breed-specific genomic regions and inbreeding patterns.

This study integrates phenomic, genomic, and transcriptomic data from yaks to identify genes associated with body weight and beef quality traits.

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 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.