Genetic and phenotypic traits in livestock

The paper identifies miRNA SNPs in pigs and associates them with lipid traits and gene expression, finding a significant SNP in the apical loop of ssc-miR-326 linked to PPP1CC mRNA levels.

This study evaluates genomic prediction models for arsenic tolerance and grain yield in rice using multi-environment and multi-trait approaches, finding improved predictive accuracy with advanced models.

The paper develops and validates a combined-species SNP array for European seabass and gilthead seabream to enable high-throughput genotyping for genomic selection and population studies.

The paper characterizes a haplotype-reference panel for low-pass sequencing genotyping in Swiss Large White pigs, showing high concordance with microarray genotypes and significant genetic differentiation between lines.

The paper evaluates Bayesian genomic models for predicting resistance to Streptococcus agalactiae in Nile tilapia, finding improved prediction accuracy compared to pedigree-based models.

This study compares linear mixed models to detect QTL and QTL-by-environment interactions in tomato phenotype data across cropping seasons.

The paper examines the relationship between genetic diversity in domesticated chicken populations and their genetic distance to wild populations, finding a strong inverse correlation.

This study analyzes the genetic structure and diversity of seven Indian buffalo breeds using SNP markers, identifying markers associated with traits but highlighting the need for an indigenous SNP chip.

This study evaluates the impact of heterozygous SNPs on ROH identification in cattle and shows that consecutive runs provide more accurate results than sliding runs.

The paper identifies four novel recessive QTL for male fertility in dairy cattle and prioritizes a missense variant in SPATA16 as a candidate causal variant.

This study identifies SNPs in BMP15 and GDF9 genes associated with litter size in Bangladeshi Black Bengal goats and finds significant associations at specific nucleotide positions.

AlphaPart partitions genetic trends to identify sources of genetic gain in breeding programs using a simulated pig example.

This study uses morphometric parameters to predict weaning weight classes in piglets, finding that medium piglets with diverse morphometry are more likely to change weight classes than light or heavy piglets.

The paper investigates the evolutionary history and adaptation of two northern Russian cattle breeds, identifying a convergent mutation in NRAP associated with cold adaptation.

The paper introduces Hi-GLMM, a hierarchical generalized linear mixed model for GWAS of binary traits, showing improved statistical power and computational efficiency in analyzing maize and human disease data.

Optim-GRAMMAR improves statistical power of mixed model associations by efficiently controlling for population structure with reduced markers.

The paper introduces a generalized gametic relationship matrix to reduce computational complexity in parent-of-origin analyses by combining average and single gametic effects.

This study shows that reducing genotyping marker density in cross-pollinated crops decreases predictive accuracy over generations, suggesting that higher marker density is better for long-term breeding.

This study identifies genetic mechanisms underlying the rumpless trait in Piao chickens through transcriptomic and genomic analyses, revealing candidate genes and selection pressures.

The paper introduces the GCA R package for genetic connectedness analysis using pedigree and genomic data, providing tools to calculate connectedness statistics and assess data comparability.

This paper detects LEPR gene SNPs in chicken populations using T-ARMS PCR and assesses their correlation with body weight and egg productivity.

This study shows that dairy breeding programs can maximize genetic gain by reallocating resources from phenotyping to genotyping, without increasing overall investment.

This paper shows genomic selection in intercrop breeding programs leads to greater genetic gain than phenotypic selection, regardless of genetic correlation with monocrop yield.

This study resequences Shenxian pigs to identify genetic variations and selected regions, revealing 19 candidate genes potentially linked to slow growth and lactation traits.

This paper optimizes deep learning hyperparameters for genomic prediction using differential evolution, showing improved performance and reduced overfitting across livestock datasets.

EnvRtype provides a software toolkit for integrating enviromics data into quantitative genomics to improve whole-genome prediction accuracy across different environments.

MoBPSweb simulates and compares breeding programs by integrating a unified language with simulation software, evaluating scenarios based on genomic gains, inbreeding rates, and accuracy of breeding value estimation.

This study uses whole genome resequencing to analyze genetic diversity and differentiation between two Shenxian pig subpopulations, finding significant genetic stratification and distance.

This paper introduces a Bayesian method that improves genomic modeling by incorporating expert knowledge through prior distributions, enhancing robustness and selection accuracy in plant breeding.

This study simulates sugar kelp breeding to identify obstacles to genetic gain and suggests strategies to improve breeding efficiency.