Genetic and phenotypic traits in livestock

The paper introduces StageWise, an R package for genomic selection in multi-environment, multi-trait plant breeding, showing improved model fit and selection indices.

This study simulates Atlantic salmon breeding programs to show that mixing between parallel year groups improves genomic prediction accuracy by reducing genetic distance and increasing genetic variation.

This study compares single- and multi-environment genomic prediction models for agronomic traits in sesame, finding that multi-environment analysis improves prediction accuracy by 15%-58%.

The paper constructs a graph-based sheep pan-genome to identify structural variations and their association with tail phenotypes in domestic and wild sheep.

The paper analyzes the prevalence and clinical impact of Mendelian disease variants in over a million dogs using genotype and electronic medical record data.

The paper discusses the importance of the MET-TPE relationship in genomic prediction for improving breeding outcomes in target populations of environments.

This study uses mitochondrial DNA analysis to investigate the genetic diversity and evolutionary origins of Philippine native pigs, revealing their ancestry from three Sus scrofa lineages and no domestication from endemic wild pigs.

This paper develops a deep learning model to predict phenotypes using structural variations and transposable elements, showing higher accuracy than SNPs and conventional models.

The paper uses multi-trait genomic prediction models and simulations to show that complex trait genetic architecture improves long-term genetic gains in wheat breeding.

The paper estimates the functional effects of KEGG pathways on bovine stature using SNP summary statistics from GWAS and mixed linear models.

This paper develops a genomic selection framework using weighted kernels to improve multi-environment prediction by incorporating genetic associations and population characteristics, achieving up to 31% higher predictability in winter wheat.

This paper enhances multi-year genomic prediction of wheat breeding data by integrating environmental associations (EPA) to improve genotype-by-environment interaction predictions.

This study examines how SNP density affects heritability estimates and QTL detection in Soay sheep body size traits using two SNP datasets.

This study examines bone properties in laying hens under different housing systems, finding environmental effects on bone strength but limited genetic associations.

Ghat identifies selection on polygenic traits by analyzing allele frequency changes and effect sizes, demonstrating its power on wheat traits and simulated populations.

This study shows genomic selection (GS) is as effective as phenotypic selection (PS) for increasing soybean seed yield, with similar gains and reduced breeding costs.

The paper optimizes the core subset selection in the APY approximation to improve stability and repeatability of genomic predictions.

This study uses low-coverage sequencing and imputation to identify QTLs for wool traits in rabbits, finding six QTLs with phenotypic contributions up to 7.50%.

This study evaluates prediction models for tocochromanol content in maize grain using genomic and transcriptomic data, finding that incorporating biological knowledge improves predictive ability.

This study compares genetic gain in recurrent selection with overlapping vs. discrete generations across different selection methods and heritability levels using simulations.

This paper develops a machine learning approach for predicting rubber tree stem circumference using molecular markers, achieving higher accuracy than traditional models in a single-environment scenario.

This study estimates the genetic parameters of antibody levels in colostrum and calf serum in Swedish dairy cattle and finds positive genetic correlations with IgG and negative correlations with somatic cell score.

The paper develops multifactorial methods to estimate and predict quantitative traits by integrating haplotype and epistasis effects up to third-order, improving genomic prediction accuracy.

This study uses random regression models to analyze spatio-temporal genomic data in ryegrass, finding improved prediction accuracy for nutritive quality traits but not dry matter yield.

This study uses low-coverage sequencing across multiple generations of a chicken intercross to identify novel minor-effect QTL contributing to body weight, increasing power and explaining more phenotypic variance.

This study evaluates genomic prediction models incorporating gene transcripts and finds that transcript-based models improve accuracy when phenotypes are measured close to transcript sampling, but combining transcript and SNP data does not consistently outperform single-omics models.

This study evaluates the benefits of using whole-genome sequence data for genomic prediction in pigs, finding small improvements over marker arrays in some scenarios.

This study identifies human functional variants with conserved effects in livestock species, showing their utility for understanding disease mechanisms.

The paper identifies HOXB13 as a candidate gene for tail length variation in Merinolandschaf sheep through haplotype mapping and candidate variant analysis.

The paper develops optimal experimental designs and a software tool to estimate SNP effects on infectious disease transmission traits like susceptibility, infectivity, and recoverability.