Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system.

BACKGROUND: Genomic selection is widely applied for genetic improvement in livestock crossbreeding systems to select excellent nucleus purebred (PB) animals and to improve the performance of commercial crossbred (CB) animals. Most current predictions are based solely on PB performance. Our objective was to explore the potential application of genomic selection of PB animals using genotypes of CB animals with extreme phenotypes in a three-way crossbreeding system as the reference population. Using real genotyped PB as ancestors, we simulated the production of 100,000 pigs for a Duroc x (Landrace x Yorkshire) DLY crossbreeding system. The predictive performance of breeding values of PB animals for CB performance using genotypes and phenotypes of (1) PB animals, (2) DLY animals with extreme phenotypes, and (3) random DLY animals for traits of different heritabilities ([Formula: see text] = 0.1, 0.3, and 0.5) was compared across different reference population sizes (500 to 6500) and prediction models (genomic best linear unbiased prediction (GBLUP) and Bayesian sparse linear mixed model (BSLMM)). RESULTS: Using a reference population consisting of CB animals with extreme phenotypes showed a definite predictive advantage for medium- and low-heritability traits and, in combination with the BSLMM model, significantly improved selection response for CB performance. For high-heritability traits, the predictive performance of a reference population of extreme CB phenotypes was comparable to that of PB phenotypes when the effect of the genetic correlation between PB and CB performance ([Formula: see text]) on the accuracy obtained with a PB reference population was considered, and the former could exceed the latter if the reference size was large enough. For the selection of the first and terminal sires in a three-way crossbreeding system, prediction using extreme CB phenotypes outperformed the use of PB phenotypes, while the optimal design of the reference group for the first dam depended on the percentage of individuals from the corresponding breed that the PB reference data comprised and on the heritability of the target trait. CONCLUSIONS: A commercial crossbred population is promising for the design of the reference population for genomic prediction, and selective genotyping of CB animals with extreme phenotypes has the potential for maximizing genetic improvement for CB performance in the pig industry.

Genetic dissection of 26 meat cut, meat quality and carcass traits in four pig populations.

BACKGROUND: Currently, meat cut traits are integrated in pig breeding objectives to gain extra profit. However, little is known about the heritability of meat cut proportions (MCP) and their correlations with other traits. The aims of this study were to assess the heritability and genetic correlation of MCP with carcass and meat quality traits using single nucleotide polymorphism chips and conduct a genome-wide association study (GWAS) to identify candidate genes for MCP. RESULTS: Seventeen MCP, 12 carcass, and seven meat quality traits were measured in 2012 pigs from four populations (Landrace; Yorkshire; Landrace and Yorkshire hybrid pigs; Duroc, and Landrace and Yorkshire hybrid pigs). Estimates of the heritability for MCP ranged from 0.10 to 0.55, with most estimates being moderate to high and highly consistent across populations. In the combined population, the heritability estimates for the proportions of scapula bone, loin, back fat, leg bones, and boneless picnic shoulder were 0.44 ± 0.04, 0.36 ± 0.04, 0.44 ± 0.04, 0.38 ± 0.04, and 0.39 ± 0.04, respectively. Proportion of middle cuts was genetically significantly positively correlated with intramuscular fat content and backfat depth. Proportion of ribs was genetically positively correlated with carcass oblique length and straight length (0.35 ± 0.08 to 0.45 ± 0.07) and negatively correlated with backfat depth (- 0.26 ± 0.10 to - 0.45 ± 0.10). However, weak or nonsignificant genetic correlations were observed between most MCP, indicating their independence. Twenty-eight quantitative trait loci (QTL) for MCP were detected by GWAS, and 24 new candidate genes related to MCP were identified, which are involved with growth, height, and skeletal development. Most importantly, we found that the development of the bones in different parts of the body may be regulated by different genes, among which HMGA1 may be the strongest candidate gene affecting forelimb bone development. Moreover, as previously shown, VRTN is a causal gene affecting vertebra number, and BMP2 may be the strongest candidate gene affecting hindlimb bone development. CONCLUSIONS: Our results indicate that breeding programs for MCP have the potential to enhance carcass composition by increasing the proportion of expensive cuts and decreasing the proportion of inexpensive cuts. Since MCP are post-slaughter traits, the QTL and candidate genes related to these traits can be used for marker-assisted and genomic selection.

Single-cell profiling of the pig cecum at various developmental stages.

The gastrointestinal tract is essential for food digestion, nutrient absorption, waste elimination, and microbial defense. Single-cell transcriptome profiling of the intestinal tract has greatly enriched our understanding of cellular diversity, functional heterogeneity, and their importance in intestinal tract development and disease. Although such profiling has been extensively conducted in humans and mice, the single-cell gene expression landscape of the pig cecum remains unexplored. Here, single-cell RNA sequencing was performed on 45 572 cells obtained from seven cecal samples in pigs at four different developmental stages (days (D) 30, 42, 150, and 730). Analysis revealed 12 major cell types and 38 subtypes, as well as their distinctive genes, transcription factors, and regulons, many of which were conserved in humans. An increase in the relative proportions of CD8 + T and Granzyme A (low expression) natural killer T cells (GZMA low NKT) cells and a decrease in the relative proportions of epithelial stem cells, Tregs, RHEX + T cells, and plasmacytoid dendritic cells (pDCs) were noted across the developmental stages. Moreover, the post-weaning period exhibited an up-regulation in mitochondrial genes, COX2 and ND2, as well as genes involved in immune activation in multiple cell types. Cell-cell crosstalk analysis indicated that IBP6 + fibroblasts were the main signal senders at D30, whereas IBP6 - fibroblasts assumed this role at the other stages. NKT cells established interactions with epithelial cells and IBP6 + fibroblasts in the D730 cecum through mediation of GZMA-F2RL1/F2RL2 pairs. This study provides valuable insights into cellular heterogeneity and function in the pig cecum at different development stages.

Developmental dynamics of the single nucleus regulatory landscape of pig hippocampus.

The hippocampus is a brain region associated with memory, learning and spatial navigation, its aging-related dysfunction is a common sign of Alzheimer's disease. Pig is a good model for human neurodegenerative disease, but our understanding of the regulatory program of the pig hippocampus and its cross-species conservation in humans remains limited. Here, we profiled chromatin accessibility in 33,409 high-quality nuclei and gene expression in 8,122 high-quality nuclei of the pig hippocampus at four postnatal stages. We identified 510,908 accessible chromatin regions (ACRs) in 12 major cell types, among which progenitor cells such as neuroblasts and oligodendrocyte progenitor cells showed a dynamic decrease from early to later developmental stages. We revealed significant enrichment of transposable elements in cell type-specific ACRs, particularly in neuroblasts. We identified oligodendrocytes as the most prominent cell type with the greatest number of genes that showed significant changes during the development. We identified ACRs and key transcription factors underlying the trajectory of neurogenesis (such as POU3F3 and EGR1) and oligodendrocyte differentiation (RXRA and FOXO6). We examined 27 Alzheimer's disease-related genes in our data and found that 15 showed cell type-specific activity (TREM2, RIN3 and CLU), and 15 genes displayed age-associated dynamic activity (BIN1, RABEP1 and APOE). We intersected our data with human genome-wide association study results to detect neurological disease-associated cell types. The present study provides a single nucleus-accessible chromatin landscape of the pig hippocampus at different developmental stages and is helpful for the exploration of pigs as a biomedical model in human neurodegenerative diseases.

FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm.

Genomic selection is an approach to select elite breeding stock based on the use of dense genetic markers and that has led to the development of various models to derive a predictive equation. However, the current genomic selection software faces several issues such as low prediction accuracy, low computational efficiency, or an inability to handle large-scale sample data. We report the development of a genomic prediction model named FMixFN with four zero-mean normal distributions as the prior distributions to optimize the predictive ability and computing efficiency. The variance of the prior distributions in our model is precisely determined based on an F2 population, and genomic estimated breeding values (GEBV) can be obtained accurately and quickly in combination with an iterative conditional expectation algorithm. We demonstrated that FMixFN improves computational efficiency and predictive ability compared to other methods, such as GBLUP, SSgblup, MIX, BayesR, BayesA, and BayesB. Most importantly, FMixFN may handle large-scale sample data, and thus should be able to meet the needs of large breeding companies or combined breeding schedules. Our study developed a Bayes genomic selection model called FMixFN, which combines stable predictive ability and high computational efficiency, and is a big data-oriented genomic selection model that has potential in the future. The FMixFN method can be freely accessed at https://zenodo.org/record/5560913 (DOI: 10.5281/zenodo.5560913).