Genetic and phenotypic traits in livestock

This study evaluates genomic selection accuracy for growth and wood quality traits in Norway spruce using exome capture, finding that full-sib family structures and 8000 SNPs are sufficient for effective prediction.

This paper introduces phenomic selection, a low-cost, high-throughput method using near-infrared spectroscopy to predict complex traits in wheat and poplar with accuracy comparable to molecular markers.

The paper develops a 58K SNP-array and high-density linkage map for Nile tilapia to improve genomic selection and genetic progress in aquaculture.

The paper applies structural equation models to GWAS in chickens to decompose SNP effects into direct, indirect, and total effects, showing greater insight into causal relationships than traditional multi-trait methods.

A consumer genomics study of 6,000 dogs identifies a 98.6-kb duplication linked to blue eyes and heterochromia in Siberian Huskies through a large-scale GWAS.

The paper identifies additional Merle alleles in dogs and highlights mosaicism and health risks associated with Merle breeding.

The paper identifies QTL affecting muscle yield in rainbow trout using a 50K transcribed gene SNP-chip, finding larger genetic variance explained compared to previous studies.

This study compares hidden Markov model-based imputation methods in animal breeding, finding that pre-phasing with Eagle2 or HAPI-UR and haploid imputation with minimac3 or IMPUTE2 is faster and more accurate than diploid imputation.

This study compares liver proteomic profiles of two pig breeds using label-free LC-MS, identifying 25 differentially expressed proteins linked to metabolic differences.

This paper applies random regression models to predict longitudinal shoot growth traits in rice using genomic data, showing improved accuracy over single time point predictions.

DeepGS predicts phenotypes from genotypes using deep learning, achieving higher accuracy than conventional methods and RR-BLUP across multiple traits.

This paper develops a method to integrate individual-level genomic data and summary statistics across populations to improve allele substitution effect estimates and genomic predictions.

The paper identifies two mutations affecting prolificacy in two sheep breeds and estimates their allele effects on litter size.

The paper identifies bovine sQTLs associated with RNA splicing variation and shows they are shared across tissues and linked to complex traits.

The paper presents the draft genome and transcriptome maps of the Yeonsan Ogye chicken breed, highlighting structural variations and tissue-specific gene expression patterns.

This paper compares optimal cross selection with truncation selection in plant breeding programs, showing optimal cross selection achieves higher long-term genetic gain by improving efficiency and reducing genetic drift.

The paper identifies a genomic region containing REC8 and RNF212B associated with recombination rate variation in red deer, showing heritability and conservation across mammals.

This study uses genomic prediction with genotype by environment interaction to improve rice breeding for adaptation to alternate wetting and drying, showing multi-environment models outperform single-environment models.

AlphaMate optimizes selection, diversity maintenance, and mate allocation in breeding programs using multi-objective evolutionary algorithms, improving genetic gain efficiency in plants and animals.

The paper estimates genomic heritability in sweet cherry, finding non-additive genetic variance is significant for key traits, and highlights the importance of including these components in breeding models.

The paper identifies differentially expressed small RNAs in pigs with divergent backfat deposition and predicts their target genes to understand fat trait regulation.

The paper determines the minimum SNP density needed for accurate parentage verification in cattle and introduces a genomic sample QC pipeline for large cattle datasets.

This paper develops and evaluates broad-based multi-trait Bayesian regression methods for genomic prediction, showing improved accuracy over single-trait and restricted multi-trait approaches.

This study identifies a genomic region associated with resistance to Ostreid Herpesvirus in Pacific oysters using a genome-wide association study and linkage mapping.

This study uses genome-wide SNP data to assess genetic diversity and conservation status of Kerry cattle, revealing reduced effective population size and increased inbreeding.

The paper identifies a SNP associated with resistance to P. salmonis in coho salmon and shows genomic selection models perform similarly to pedigree-based methods.

This paper integrates crop growth models with whole genome prediction to improve phenotypic prediction by accounting for genotype-by-environment-by-management interactions in maize breeding.

This study identifies 126 genomic regions under selection in French sheep, revealing allelic heterogeneity and introgression at adaptive loci.

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.