Genetic and phenotypic traits in livestock

This paper introduces a parent-progeny imputation method using pooled DNA samples and a Hidden Markov Model to reduce genotyping costs in plant breeding.

The paper develops a theoretical framework to estimate genomic prediction accuracy using reference populations with varying relationships to the target, showing that close relatives significantly improve accuracy.

ShinyGPAS simulates genomic prediction accuracy using deterministic formulas and provides interactive visualizations of prediction accuracy versus genetic factors.

This study compares univariate and multivariate genomic prediction models for cassava traits, finding that multivariate models improve prediction accuracy in both single and multi-environment evaluations.

The paper shows that dominance and epistasis effects contribute significantly to growth traits in hybrid Eucalyptus, improving genetic variance partitioning using marker-based relationship matrices.

The paper evaluates genomic prediction efficiency for untested single crosses using simulated data, finding high accuracy and coincidence across varying heritability and genetic models.

This study identifies shared and unique QTL for BCWD resistance in two rainbow trout populations using GWAS with different genotyping platforms.

The paper identifies lethal haplotypes in Angus cattle by analyzing SNP data and resequencing, suggesting strategies to manage inbreeding and improve genetic gains.

This study evaluates genomic selection accuracy in cassava breeding across different prediction models and scenarios, finding that training population size significantly impacts accuracy.

The paper introduces a heuristic method to identify runs of homozygosity (ROH) linked to reduced performance in livestock and shows it outperforms genome-wide inbreeding metrics.

The paper investigates the role of deleterious alleles in trait variation and heterosis in maize by integrating sequence conservation and genomic prediction models.

This paper presents a high-resolution gene expression atlas for sheep, identifying transcriptional signatures and functional annotations across tissues and developmental stages.

This paper introduces tGBS(R), a genome reduction method that improves SNP calling accuracy and reduces missing data for heterozygous loci compared to conventional GBS.

The paper analyzes mitochondrial diversity in Italian pig breeds and wild boars, finding low diversity in pigs consistent with population bottlenecks and suggesting the E2 haplogroup was rare during domestication.

This paper introduces a non-parametric polygenic method, DPR, for genetic prediction of complex traits and evaluates its performance across multiple applications.

This paper defines a multi-population genomic relationship matrix to estimate current genetic variances within populations and genetic correlations between populations, and validates its properties through simulations.

The paper analyzes recombination patterns in sheep using multiple datasets, identifies genetic loci affecting recombination rate variation, and compares results across breeds.

This study evaluates how genomic information improves genetic connectedness across management units compared to pedigree-based methods using simulation and real data from mice and cattle.

The paper develops two new SNP arrays for horses, MNEc2M and MNEc670k, and demonstrates their imputation capabilities.

The paper investigates the genetic regulation of 63 lipid metabolism genes in pig skeletal muscle using eQTL analysis and identifies 12 cis- and 18 trans-eQTLs affecting 19 loci.

This study evaluates genomic prediction accuracy for growth and wood traits in Eucalyptus species and their hybrids using SNP data and different prediction methods.

The paper demonstrates that integrating transcriptomic data with genomic data improves prediction of hybrid maize combinations, particularly for complex traits like dry matter yield.

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 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.