Christian Maltecca

In common wheat ( Triticum aestivum L.), variation at the Vrn1 and Ppd1 loci changes plant phenology, resulting in differential adaptation useful to breeders. Because the effects of phenology on grain yield are conditional on environmental factors, the incorporation of markers for Vrn1 and Ppd1 alleles into breeding approaches has proven difficult. Historic phenotypic, genotypic, and environmental data can provide insight into the relationship between major phenology alleles and grain yield as mediated by environmental variables. Analyses of eight years of breeding trials (1,038 lines at 219 site-years) determined that weak vrn1 alleles and Ppd1 insensitivity alleles have variable effects. These effects change depending on an environment’s winter temperature and latitude, respectively, but the environmentally-driven changes in effect of vrn1 alleles are much more variable than effects of Ppd1 alleles. Mediation analyses showed phenologically-dependent environmental variables are one mechanism through which phenological changes created by vrn1 and Ppd1 variants alter yield across multiple developmental stages. Environmentally-driven relationships between flowering time and yield were modeled to estimate yield allele effects as a function of effects on phenology and environmental variables. Weak winter alleles at vrn1 may play a stabilizing role on yield, due to the correlation of winter conditions that drive larger effects with later-season warm temperatures that penalize later maturity. A better mechanistic understanding of how these loci drive environment-specific yield effects may facilitate utilization of markers for these alleles and improve predictive modeling of yield.

Managing inbreeding while maintaining genetic gain is a critical challenge in dairy cattle breeding programs. Current North American genetic evaluations adjust breeding values for expected future inbreeding using genomic relationship matrix (GRM) approaches, but runs of homozygosity (ROH)-based methods may offer advantages for detecting recent autozygosity. This study compared ROH-based versus GRM-based inbreeding penalties across 20 generations of simulated genomic selection. Using AlphaSimR, we simulated dairy cattle populations under 3 genetic scenarios: additive only, low non-additive effects (σ²D/σ²A = 0.10, inbreeding depression = 0.30σG per 10% ΔF), and high non-additive effects (σ²D/σ²A = 0.50, inbreeding depression = 1.20σG per 10% ΔF). Each penalty method was tested at 3 intensities (0.5 ×, 1 ×, 2 × the inbreeding depression parameter) with 10 replicates per scenario-treatment combination. GRM-based penalties achieved lower inbreeding accumulation than ROH-based penalties, with differences of 0.13 to 0.22 in genomic F at matched penalty intensities. However, this enhanced inbreeding control was associated with reduced genetic gain, with GRM-based selection achieving 0.6 to 1.7 genetic standard deviations less gain than ROH-based selection. GRM-based selection retained more additive variance (66-107%) compared with ROH-based selection (63-74%), with values exceeding 100% indicating partial recovery of variance depleted during burn-in. Dominance variance retention was also greater for GRM-based selection (105-114% vs. 93-98%), reflecting maintained heterozygosity. Interestingly, GRM-based selection accumulated more inbreeding depression than ROH-based approaches despite lower inbreeding levels, potentially reflecting more effective purging under the stronger directional selection maintained by ROH-based methods. Results illustrate trade-offs between inbreeding control and genetic gain, with optimal strategy depending on breeding program objectives and trait genetic architecture.

In livestock populations, genome-wide association studies (GWAS) can produce strong, apparently localized associations even when no truly discrete nearby causal effect exists. This occurs because small effective population sizes, strong family structure, long-range linkage disequilibrium (LD), and diffuse polygenic architecture can cause the effects of many variants to accumulate and be captured jointly across broad genomic intervals, making variant-level associations difficult to interpret biologically. Using real pig genotypes, we constructed a benchmark in which phenotypes were simulated under diffuse polygenic architecture across a genome partitioned into alternating effect and null windows, with central-null regions (at least 1 Mb away from effect-containing regions) positioned to detect long-range LD-driven signal propagation. We evaluated nine configurations of six GWAS methods (BOLT-LMM, REGENIE, fastGWA, FarmCPU, BLINK, and SLEMM) under this architecture. The central finding is that strong associations, of the kind normally read as evidence of nearby moderate- or large-effect variants, are produced by many of these methods even though the simulated signal is distributed across many tiny effects and cannot be localized to any single variant. The methods differed sharply in the extent of locus-level spillover: several produced large numbers of genome-wide significant loci within central-null regions, whereas the full-GRM mixed-model benchmark (SLEMM) produced no genome-wide significant loci in central-null regions. These results show that, under a highly polygenic architecture with livestock-like LD, GWAS tool choice has major consequences for biological interpretation. When the goal is to localize biologically meaningful signals rather than to flag association peaks that may merely reflect tiny effects accumulated through LD across a broad block, methods that control long-range LD spillover should be prioritized.

Background: The objective of this study was to test whether the negative effects of inbreeding on production traits varied according to the level of environmental load. Traits analyzed were milk, fat, and protein yields, and somatic cell score (SCS). Environmental conditions were described using temperature (TEMP), relative humidity (RH), and the temperature–humidity index (THI), each divided into five equally sized classes. For each trait, the environmental variable with the largest impact was used to evaluate inbreeding effects across its classes. Inbreeding was measured using pedigree (F _PED ), the diagonal of the genomic relationship matrix (F _GRM ), and runs of homozygosity (F _ROH ). Results: Genomic-based inbreeding measures resulted in larger estimated inbreeding effects, compared to pedigree measures, with F _GRM and F _ROH showing similar results. RH most affected milk yield, with losses of ~ 600 g/day in the highest RH class. Inbreeding led to losses of ~ 100 g/day per 1% increase, with genomic measures scaled to match F _PED . For milk yield, losses were ~ 20% greater under higher environmental stress. THI was the most impactful variable for fat yield, with losses of ~ 90 g/day in the highest THI category. Inbreeding-related losses increased in more stressful environments, with average reductions of 3 g/day per 1% inbreeding, reaching 4 g/day in the highest THI class. Protein yield and SCS were mainly influenced by TEMP, with losses of ~ 70 g/day of protein and increases of 0.06 SCS units. Inbreeding caused losses of ~ 3.5 g/day in protein yield and increases of ~ 0.006 SCS units per 1% inbreeding. For these two traits, the relationship between inbreeding effects and environmental stress was less clear. For milk and fat yields, there was a significant interaction between environmental and inbreeding effects, suggesting that inbreeding depression influences heat tolerance in dairy cows. Conclusions: For milk and fat yields, there was a significant interaction between environmental and inbreeding effects, indicating that environmental conditions modulate the expression of inbreeding depression. These findings suggest that inbreeding depression influences heat tolerance in dairy cows.

Background: Heat stress (HS) can disrupt the gut microbiome, yet most livestock studies rely on taxonomic summaries that overlook the ecological structure of microbial communities. Enterosignatures (ES) as latent, co-occurring microbial assemblages learned from metagenomic data, offer a framework to capture these dynamics but have scarcely been applied in livestock HS research. Methods: Shotgun metagenomes were obtained from 25 lactating sows, belonging to two genetic lines (TOL, n = 13; SEN, n = 12), which were divergently selected based on genomic breeding values (GEBVs) for heat tolerance, and exposed to HS conditions. Results were decomposed using non-negative matrix factorization (NMF), yielding 8 taxonomic (T-ES) and 5 functional (F-ES) subcommunities. Functional profiles (based on KEGG Orthology, KOs) were mapped to metagenome-assembled genomes (MAGs) to integrate metabolic attributes within each ES. Results: Temporal shifts dominated T-ES variation, with limited genetic-line effects. T-ES 1 ( p = 5.42 × 10 ⁻⁴ , Cohen’s d = 0.723) and T-ES 7 ( p = 0.007, Cohen’s d = 0.303) showed increases from day 4 to day 14. Despite modest overall genetic line effects, TOL animals progressively transitioned toward phylogenetically diverse and balanced communities, whereas SEN animals shifted toward imbalanced states characterized by enrichment of taxa with pathobiont potential or single-taxon dominance. Other T-ES displayed small to moderate effects, and T-ES 8 showed a potentially noteworthy genetic line-specific effect size at late lactation (Cohen’s d = 0.960; 95% CI: −1.80 to −0.10), though omnibus tests were non-significant ( p = 0.757), and the wide confidence interval underscores substantial uncertainty at this sample size. No F-ES reached statistical significance ( p > 0.05); moderate effect sizes (up to d = 0.638) suggest possible functional restructuring warranting investigation in larger cohorts. Conclusion: This work presents the first use of ES to track microbiome responses to HS in lactating sows. ES revealed latent taxonomic and functional subcommunities with clear temporal reorganization, offering insights not detectable with standard clustering or diversity metrics. Although genetic-line effects were modest, several ES showed biologically relevant shifts, supporting ES as a hypothesis-generating exploratory framework for linking microbial ecology to physiological adaptation under HS conditions, while warranting validation in larger, controlled trials.

Animal resilience and the gut microbiota have emerged as two major focus areas in animal science, particularly in swine production. The gut microbiota, as a dynamic and responsive ecosystem, offers unique potential to describe and interpret complex host phenotypes, reflecting genetic and environmental influences and their interplay. In parallel, enhancing animal resilience represents a key opportunity for swine production systems facing increasing pressures from climate change, sustainability requirements, and efficiency constraints, compounded by narrow profit margins and reduced labor availability per pig. Together, these factors necessitate pigs that can robustly cope with environmental and management challenges with minimal human intervention. These pressures threaten not only economic sustainability but also raise serious animal welfare concerns. However, improving resilience remains challenging because it (i) lacks a directly measurable trait and (ii) is difficult to enhance through environmental management, due to its inherent definition, and also through genetic selection, owing to its low to moderate heritability. In this context, the microbiota may capture a component of resilience-related variability that is not explained by host genetics or environmental factors alone. Accordingly, microbiota data, with their dual capacity to integrate host genetic background and environmental exposures, emerge as a valuable tool not only for monitoring resilience but also as a potential target for its improvement. In this review, we first define resilience in swine, outlining how it can be monitored and measured. We then discuss the importance of the microbiota, especially gut microbiota, in swine production. Finally, we explore the biological connections between resilience and the host microbiota, highlighting opportunities to leverage this relationship to address current and future challenges in swine systems.

Heat stress (HS) reduces milk yield in sows independent of lactation feed intake, leading to poorer piglet growth and survivability. Therefore, the study objectives were to evaluate the impacts of early to mid-gestation HS versus thermoneutral (TN) conditions on biomarkers of mammary development during late gestation and milk quality during lactation and to determine whether genomic selection for HS tolerance (TOL) or HS sensitivity (SEN) would influence these measures. We hypothesized that exposure to HS during early to mid-gestation would adversely affect biomarkers of mammary development in late gestation and milk quality during lactation, and that these adverse effects would be attenuated in individuals with higher genetic merit for TOL compared with SEN. Gilts, divergently selected for TOL (n = 15) or SEN (n = 13), were balanced by weight, bred to TOL or SEN boars, respectively, and then exposed to either TN (21.46 ± 2.92 °C; n = 7 SEN and n = 8 TOL) or cyclical HS conditions (26 to 36 °C; n = 6 SEN and n = 7 TOL) from gestation d 6.3 ± 0.9 to 65.3 ± 0.9. All animals were then maintained under TN conditions (21.40 ± 3.13 °C) until farrowing. On gestation d 105.3 ± 0.9, a mammary biopsy was collected from all gilts, fixed in 10% buffered formalin, and stained using Ki67 immunohistochemistry and hematoxylin and eosin to identify proliferating mammary epithelial cells (MEC) and estimate alveolar and lumen sizes. Both proliferating and non-proliferating MEC counts, as well as lobuloalveolar size estimations, were performed using ImageJ software. Milk samples collected on d 0, 3, 10, and 14 of lactation were assessed for milk components. Data were analyzed using PROC GLIMMIX in the SAS 9.4 software, with individual gilt as the experimental unit. Regardless of the genetic line, HS exposure in early to mid-gestation reduced (P < 0.01) late gestation MEC proliferation by 37.5% and increased (P = 0.02) mammary epithelial lumen to alveolar ratio (L:A) by 15.0%. Within the TOL line, L:A was greater in TOL+HS gilts (P < 0.01; 28.2%) when compared to TOL+TN gilts. Milk sodium-to-potassium ratio, a marker of tight junction integrity, was reduced (P = 0.04) in TOL (0.30 ± 0.06) versus SEN gilts (0.44 ± 0.06), regardless of gestation environment. In conclusion, early to mid-gestation HS negatively impacted biomarkers of mammary development, and genomic selection for TOL improved some measures of MEC integrity relative to SEN F1 replacement gilts.

Advances in selection and reproduction methods, particularly the implementation of genomic evaluations alongside assisted reproductive technologies, have substantially enhanced productivity and efficiency, notably within specialized dairy cattle populations. Nevertheless, the widespread use of a limited number of elite sires and dams has also led to increased levels of inbreeding, posing a risk to animal performance in production, reproduction, and functional longevity. The objective of this study was to evaluate the effect of inbreeding on stayability in the Italian Holstein population. Stayability, defined as the ability of cows to remain productive in the herd from one parity to another, was assessed across 5 periods: STAY12, from parity 1 to 2; STAY23, from parity 2 to 3; STAY34, from parity 3 to 4; STAY13, from parity 1 to 3; and STAY14, from parity 1 to 4. Individual inbreeding coefficients were estimated using 3 methods: pedigree information, diagonal elements of the genomic relationship matrix, and the proportion of the total length of runs of homozygosity (ROH) segments relative to the total autosomal genome covered by SNPs. Furthermore, different ROH length classes were evaluated separately to distinguish between the effects of recent and ancient inbreeding. Two datasets were extracted from the breeders' association database with no interference in the herds' usual management: one including both genotyped and nongenotyped individuals and another with only genotyped animals. The first, comprising 828,056 cows, was used to assess pedigree-measured inbreeding depression in the Italian Holstein population. The second, including 48,833 genotyped cows, was used to compare inbreeding measures. The effect of inbreeding was estimated as best linear unbiased estimates on the liability scale within a Bayesian framework. In the stayability analysis, milk yield level relative to contemporary group was included as an additional fixed effect to account for its influence. Estimates were converted to the probability scale, using a cumulative distribution function, and then used to compare models and assess survival probabilities according to varying levels of inbreeding. Increased inbreeding consistently resulted in decreased stayability. The magnitude of inbreeding depression was greater for traits involving cumulative periods, such as STAY13 and STAY14. The expected variation in stayability resulting from a 1-unit increase in the pedigree inbreeding coefficients (F_PED), ranged from -0.06% to -0.44%, depending on the model and trait. Models using genomic inbreeding captured larger effects of inbreeding depression. In these cases, the expected variation in stayability ranged from -0.22% to -1.60% per 1-unit increase in F_PED. Inbreeding estimated from the sum of ROH segments longer than 2 Mb were associated with reduced stayability, whereas shorter segments were not. This suggests that recent inbreeding contributes to inbreeding depression on this trait, whereas ancient inbreeding does not. It should be noted that part of the observed inbreeding depression may be attributable to voluntary culling related to the production level of cows within their herds. Thus, in a selection index that includes milk yield and penalizes future inbreeding, stayability should preferably be analyzed using a model that incorporates production level as a fixed effect.