Genetic and phenotypic traits in livestock

This paper develops a deep learning framework to predict the regulatory impact of non-coding variants in farmed animals using chromatin data, improving genomic prediction for livestock and aquaculture.

This study evaluates genome prediction accuracy in Brassica napus using nested association mapping, finding that rrBLUP and RKHS models and SNaP markers improve prediction accuracy for agronomic traits.

This paper reviews genetic diversity estimates of rare UK livestock breeds, finding gaps in DNA-based measures and discrepancies between census and DNA-based Ne estimates.

This paper estimates direct and maternal genetic effects on piglet birth weight and its variability, finding maternal effects are crucial while direct effects are negligible.

This study improves genomic prediction of stalk lodging resistance in maize by integrating multi-environment data, showing that multi-environment models significantly enhance prediction accuracy for most traits.

The paper introduces randPedPCA, an R package for rapid PCA of large pedigrees using sparse matrices and randomised linear algebra, demonstrating its efficiency on simulated and real datasets.

The paper demonstrates genome-wide imputation using a low-density amplicon panel in Pacific oysters to reduce genotyping costs and improve selective breeding.

This study uses GWAS and RHM to identify genomic regions associated with protein efficiency and performance traits in pigs, highlighting candidate genes and noncoding RNAs.

This paper introduces GPFN, a new genomic prediction method that outperforms linear models on multiple traits and matches their performance on a structured prediction task.

This paper introduces a Python-based model to study how family-based selection affects phenotypic variability in D. melanogaster, showing that it can increase variability more effectively than individual-based selection.

This study identifies rare variants under positive selection in wild boar linked to cold adaptation, showing convergent molecular evolution with humans.

The paper develops an algorithm to accurately assign breed origin to alleles in crossbred dairy cattle, achieving high accuracy in simulated breeding programs.

GSOR and MTAO detect widespread cis-regulatory effects on bovine complex traits by linking gene expression with genetic scores and multi-trait associations.

This study shows that using cryopreserved genetic resources can help manage genetic diversity in animal populations, either by slowing its loss or guiding selection, but requires careful management to avoid issues with old genetic material.

The paper analyzes the genomes of Asiatic mouflons in Iran to explore their role in sheep domestication and genetic introgression into domestic sheep populations.

The paper tests three methods to identify causal genes for milk lipid traits in cattle using GWAS, transcriptomics, and gene ontology analysis, finding significant overlaps but limited power.

The paper evaluates regional heritability mapping methods to identify QTLs in Soay sheep, finding that some methods detect new trait-associated regions not found by GWAS.

The paper investigates the association between life-history loci and morphological traits in Atlantic salmon, finding complex relationships that may result from pleiotropy or linkage.

This study shows that reducing the generation interval in honeybee breeding by focusing on early traits can increase genetic gain, though it requires strategies to manage inbreeding.

This study evaluates factors affecting genomic prediction accuracy in switchgrass, finding that exome capture and multi-allelic variants improve predictions for complex traits.

This study improves genomic prediction of wheat traits by integrating gene expression and environmental data, showing that combining transcriptome and environment data enhances prediction accuracy.

This paper classifies and compares multi-trait genomic prediction models using an Oryza sativa diversity panel to identify strengths and limitations of existing approaches.

The paper identifies genetic variants associated with antibody responses to infectious diseases in Tanzanian dairy cattle using genome-wide association studies.

This study uses transcriptomic data to predict breeding values in loblolly pine by analyzing gene expression patterns and their relationship to genetic variation.

This paper applies environmental genomic selection to barley landraces to identify key parents for breeding adapted to specific environments.

This paper optimizes 3' mRNA sequencing for cost-effective gene expression phenotyping in cattle using whole blood samples, identifying the best library prep kit and sequencing depth.

This study identifies pleiotropic QTLs for key cheese-making traits in Penicillium roqueforti by analyzing F1 hybrids from cheese and non-cheese populations.

This study constructs a draft pangenome for Indian desi cattle using short-read sequencing, identifying novel sequences that improve genetic variant discovery and mapping accuracy.

The paper develops formulas to estimate heritabilities and genetic correlations in large genomic models using predictivity, validated with real and simulated data.

The paper identifies a structural variant upstream of KIT associated with facial depigmentation in white-headed cattle using pangenome analysis and long-read sequencing.