Genetic and phenotypic traits in livestock

This study evaluates the accuracy and bias of single-step genomic prediction under selection by simulating cattle populations and comparing models with and without fitting microg as a fixed effect.

This paper compares different prior distributions and genomic window strategies for genome-wide association analysis, finding that heavy-tailed and variable selection priors with adaptive windows perform best.

biMM efficiently estimates genetic variances and covariances for cohorts with high-dimensional phenotype measurements using a bivariate linear mixed model.

This study evaluates methods for updating training data in genomewide selection over breeding cycles, finding that updating with best predicted lines or both best and worst predicted lines yields high accuracy and genetic gain in the short-term, while smaller, more recent training populations provide a slight advantage long-term.

This study simulates how gene editing impacts dairy cattle breeding programs by reducing harmful allele frequencies and inbreeding.

This paper introduces metafounders as pseudo-individuals to reduce bias in genomic evaluations by incorporating ancestral relationships estimated from marker genotypes and pedigrees.

This study evaluates imputation accuracy in cattle and identifies QTL for milk fat percentage using imputed variants.

The paper compares mutation calling from whole exome and RNA sequencing data in lactating Holstein cows, finding RNAseq can serve as a proxy for exome sequencing with some limitations.

The paper investigates the genetic architecture of long-term selection responses in Virginia chicken lines, identifying multiple loci and genetic mechanisms contributing to body weight variation.

This paper reviews bioinformatics resources and omics technologies used in boar taint studies in pigs, highlighting their role in identifying QTLs and biomarkers.