Genetic evaluation of complex traits is often challenged by heterogeneous, incomplete or costly phenotyping. Many biological processes require multiple traits to be characterized. Moreover, combining several traits becomes difficult when the configuration of available traits is complex (e.g., increasing number, complex missing patterns), leading to more demanding computations. To overcome these limitations, a deregression-based pooling method was developed to pre-process multitrait information into a single pseudo-phenotype of the target trait (i.e., a pooled phenotype). This method was assessed in a terminal crossbreeding situation in pigs. The behavior of estimated breeding values and the accuracy of reliabilities systems were studied on different datasets (i.e., different data availability scenarios - a real and 3 simulated). Three modeling steps were used: (1) a multitrait model generating input for deregressions, (2) an adjusted scalar deregression, combining information into a pooled phenotype using (3) a single trait model applied to this pooled phenotype to assess how these results match the multitrait predictions from step (1). In all data structure scenarios, unadjusted deregression underestimates breeding values and distorts reliabilities. In contrast, applying expansion and reliability-alignment factors as adjustments restored the distributions and scale of estimated breeding values, yielding a near-perfect agreement with the multitrait model. Across all simulated scenarios, including the one with the complete absence of the target trait, the adjusted deregression process achieved the highest correlations between true and estimated breeding values. The deregression-based process provides an effective and flexible approach for combining heterogeneous multitrait information into a unique pooled phenotype in a single trait setting, while maintaining genetic scale and reliability of the target trait in a multiple trait setting. Unlike classical deregression, the adjusted deregression process prevents overshrinking, maintains accuracy and reproduces the behavior of a multitrait evaluation. This pre-processing would be a practical solution for integrating complex or partially recorded traits into routine genetic evaluation pipelines. This method is applicable even if the main target trait is limited or missing, provided that moderately to highly correlated traits are available. The strategy could be extended to other situations as long as any double-counting can be avoided.
Understanding the genetic regulation of metabolism is essential for improving animal health and productivity and for strengthening the use of pigs as translational models for human metabolic diseases. Metabolomics, when integrated with genomic data, provides a powerful framework for identifying genetic factors that influence intermediate molecular phenotypes closely linked to physiology. While large-scale metabolomics genome‑wide association studies (GWAS) have been extensively performed in humans, comparable studies in pigs remain limited. Among omics disciplines, metabolomics, the systematic study of metabolites central to physiology, represents the omics layer most closely linked to end-point phenotypes in mammals, such as health and production traits (e.g. growth rate). Metabolite levels are influenced by genetics, diet, and microbiome composition, with human studies showing high heritability and identifying numerous metabolite quantitative trait loci (mQTLs). While such findings have advanced our understanding of disease biology in humans, comparable studies in pigs remain limited. We performed GWAS of the serum metabolome in 213 three‑way crossbred pigs [(Landrace × Yorkshire) × Duroc], analyzing 248 metabolites and 30,628 metabolite ratios. We identified 41 significant metabolite–variant associations, which were consolidated into 18 mQTLs based on linkage disequilibrium (LD). Several regions were associated with multiple, biochemically related metabolites, while others showed highly specific effects. Cross-species comparison with the human GWAS catalog revealed candidate genes within seven mQTLs. Through an integrative prioritization strategy, putative causal genes—predominantly enzymes and transporters—were identified for 14 of 18 mQTLs. Our results expand the catalog of genetic loci influencing serum metabolite variation in pigs and highlight both conserved and pig specific components of metabolic regulation. These findings provide a foundation for incorporating metabolomics into pig breeding programs and reinforce the value of pigs as a translational model for studying metabolite-associated traits and diseases in humans.
Composite cattle breeds are designed to combine complementary attributes from different founder breeds while retaining part of the heterosis generated by crossbreeding. Brangus cattle are nominally defined as 5/8 Angus and 3/8 Brahman, but the same global breed composition can be produced by different genomic architectures. This distinction may be important because animals with the same Brahman ancestry proportion may differ in ancestry heterozygosity, homozygous ancestry composition and segregation behaviour when used as parents. The objective of this study was to use controlled simulations to evaluate whether genomic architecture affects the risk of producing offspring outside the nominal Brangus ancestry range. We simulated two alternative pathways to the same expected Brahman ancestry proportion of 0.375. Although both pathways produced similar mean Brahman ancestry, the direct pathway generated a broader ancestry distribution than the indirect pathway, with a higher proportion of individuals outside the nominal interval. After filtering candidate sires to the nominal interval, formation-pathway-dependent genomic architectures remained evident. In a mixed inter se mating analysis extended to 100 generations and three population-size scenarios, pathway-specific ancestry-state differences were progressively eroded and the standard deviation of Brahman ancestry proportion declined after an early maximum, but non-nominal frequencies remained non-negligible over the simulated time horizon. Family-level simulations showed that matings between nominal parents differed substantially in their probability of producing non-nominal daughters. This risk depended on both the distance between the expected family mean and the target ancestry and the block-weighted segregation potential of the parental genomes. An exploratory quasibinomial model supported positive associations between non-nominal risk and displacement from the target and high-variance parental block structures. Finally, a heuristic-assisted simulation-informed sire allocation strategy increased the proportion of nominal daughters relative to random sire allocation, with the greatest gains obtained from broader sire catalogues. Nominal breed composition does not define a unique genomic state. In simulated Brangus cattle, animals that were equivalent in global ancestry could differ in genomic architecture and in the offspring ancestry distributions generated by specific matings. These results suggest that composite-breed management should consider not only the ancestry proportion of individual animals, but also the expected outcome of sire–dam pairs.
Accurate prediction of phenotypes is essential for assessing the actual productive performance of individual pigs, particularly for traits that are costly or difficult to measure directly. This study investigated the potential of integrating individual-level blood serum metabolite profiles sampled at two developmental stages (10-week and 20-week) into phenotype prediction models for five economically important traits: average daily feed intake (DFI), feed conversion ratio (FCR), backfat thickness (BF), test daily gain (TDG), and loin depth (LD). Using a BayesC modeling framework, we analyzed 1,637 pigs from a single purebred population with complete phenotype, genotype, and metabolite-profile data. We evaluated seven models, including a genotype-only model (G), metabolite-only models using metabolite-profile data from 10-week (M1), 20-week (M2), or both timepoints (M1+M2), and combined models integrating genotypes with metabolite-profile data (G+M1, G+M2, G+M1+M2). Heritability estimates were generally low to moderate, ranging from 0.08 to 0.23 for the 10-week metabolite profiles and from 0.07 to 0.26 for the 20-week metabolite profiles. Prediction accuracy of phenotypes consistently improved when metabolite-profile data were integrated into prediction models, although the magnitude of improvement varied depending on the trait and the timepoint of metabolite sampling. Prediction accuracy increased from 0.33 (G) to 0.42 (G+M2; G+M1+M2) for DFI, and from 0.27 (G) to 0.32 (G+M2; G+M1+M2) for FCR. The latter two models also delivered the largest gains over G for BF (from 0.39 to 0.46) and TDG (from 0.30 to 0.32). However, LD benefited the most when both 10-week and 20-week metabolite profiles were combined (G+M1+M2: 0.44 compared to G: 0.42). Across all traits, models combining genotype data with metabolite profiles from one or multiple timepoints achieved the highest or equally high phenotype prediction accuracies compared to the genotype-only model, reflecting complementary biological insights captured by metabolite profiles. These findings highlight the potential value of metabolite-profile data as an intermediate omics layer to enhance phenotypic prediction, particularly when integration strategies are tailored to trait-specific biology and sampling timepoints.
Investigating key genes regulating year-round estrus can improve sheep reproductive performance and industry efficiency. Although several candidate genes have been proposed, the key regulators underlying estrus seasonality remain unclear. Therefore, this study aimed to identify critical genetic variants associated with year-round estrus using whole-genome resequencing data from multiple sheep breeds. In this study, we analyzed whole-genome resequencing data from 392 sheep across six year-round and ten seasonal estrus breeds, and identified the Melanocortin 4 receptor (MC4R) gene as a key regulator of estrus seasonality. A missense mutation in the MC4R gene, I173M (g.59480440G > C; p.Ile173Met), was significantly enriched in year-round estrus breeds. Transcriptome analysis following MC4R I173M transfection revealed differential expression of genes enriched in reproductive pathways. Subsequent functional experiments demonstrated that the I173M mutation increased MC4R constitutive activity, activated cAMP and MAPK/ERK signaling, and promoted both granulosa cell proliferation and the expression of steroidogenic regulatory genes. Cells harboring the I173M mutation exhibited substantially amplified transcription levels of gonadotropin signaling components. Specifically, follicle-stimulating hormone receptor (FSHR) and luteinizing hormone receptor (LHR) were expressed at much higher levels relative to wild-type (WT) controls. Our study provides evidence that MC4R functions as a molecular modulator of estrous cyclicity, with the I173M missense mutation identified as a critical functional variant driving constitutive estrous activity in ovine species.
