Genetic and phenotypic traits in livestock

PHARP provides a high-quality pig haplotype reference panel for genotype imputation, enabling improved GWAS and genomic prediction.

This paper develops a joint learning approach to improve genomic prediction in polyploid grasses, achieving over 50% accuracy improvements over traditional methods.

The paper develops a high-density 665 K SNP array for rainbow trout and demonstrates its utility in detecting linkage disequilibrium at shorter genomic distances.

This study evaluates genomic selection for improving growth traits in European flat oysters using low-density SNP panels and finds that similar prediction accuracies can be achieved with fewer markers.

This paper uses simulation to address two key questions in plant breeding: the number of entries needed to select the best one with a defined probability and the probability of selecting the top entries when some are chosen.

This paper predicts dog phenotypes (breed, height, weight) from genotypes using linear and non-linear methods, showing that linear methods outperform non-linear for breed classification but not for height and weight regression.

This study genotypes over 11,000 domestic cats to analyze genetic variants related to blood type, disease, and traits, revealing breed-specific distributions and diversity.

This study compares heritability estimators under different LD structures and relatedness, finding no significant performance differences between fixed and random SNP-effect methods.

The paper identifies rare and population-specific functional variants in commercial pig lines and assesses their impact on production traits and genomic prediction.

The paper identifies a genetic variant in the LEPR gene associated with body fat reserves in sheep through genome-wide association studies.

This study evaluates the use of public ambient temperature data in swine genetic evaluation for litter size traits in Japan, finding that incorporating temperature improves phenotypic variability explanation without harming genetic evaluation performance.

The paper estimates genetic parameters of oxygen consumption in mice and identifies candidate causal genes related to metabolic processes.

The paper quantifies diet-dependent genetic contributions to body weight in mice and identifies loci associated with age- and diet-dependent body weight variation.

The paper estimates genetic parameters and performs genome-wide association to identify resistance to Flavobacterium columnare in rainbow trout, finding genomic selection improves prediction accuracy.

This paper demonstrates a method to recover high-quality host genomes from gut metagenomic data using genotype imputation, showing it can improve host population analysis.

The paper develops genomic prediction methods for plant metabolites using metabolite GWAS and kernel models, improving accuracy for LC-MS metabolites but not GC-MS.

The paper identifies a genetic locus associated with tolerance to Theileria parva infection in cattle, showing a strong heritability and replication across cohorts.

This study improves genomic prediction accuracy in Eucalyptus by integrating additive and dominance genetic effects from GWAS into genomic selection models.

This study examines the effects of shearing frequency on Australian Merino sheep, identifying epigenetic markers and physiological changes associated with different shearing treatments.

The paper uses proteomics to identify differentially expressed proteins in coarse and fine cashmere goat skin, suggesting potential regulators of cashmere fineness.

The paper uses whole genome sequencing to analyze genetic diversity and selection in Guangxi indigenous chickens, identifying candidate genes under selection and population structure.

This study investigates whether using the APY algorithm with truncated pedigree and phenotypes reduces computational costs in ssGREML for variance component estimation, finding that it slightly decreases ordering and symbolic factorization time without affecting estimates.

The paper introduces a new hidden Markov model for estimating homozygous-by-descent probabilities with nested ancestor layers, improving accuracy and interpretability over previous methods.

This study simulates the long-term effects of genomic selection on genetic architecture, showing that non-additive effects significantly impact genetic variance and allele frequency changes over generations.

The paper introduces NN-LMM, a method that extends linear mixed models to multi-layer neural networks for genomic prediction using intermediate omics data, showing improved performance over existing approaches.

This study compares classification and regression models for predicting skewed phenotypes in winter wheat disease resistance, finding that transformed regression and classification models perform best.

This study characterizes the genomes of mouse lines selected for specific traits over 140 generations, revealing genetic variation and candidate genes associated with fertility, obesity, and endurance.

The paper creates a graph genome incorporating global cattle diversity, showing it includes more sequence and improves mapping and variant calling.

This study compares linear models and gradient boosting machine for predicting complex traits in outbred mice, finding linear models generally outperform GBM, except for traits with epistatic effects.

This paper benchmarks phasing software using a whole-genome sequenced cattle pedigree, finding ShapeIT4.1 and Beagle5.2 to be the most accurate for long haplotype segments.