Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe.

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project Genomic Management Tools to Optimise Resilience and Efficiency, and the Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle.

GWAS and genetic and phenotypic correlations of plasma metabolites with complete blood count traits in healthy young pigs reveal implications for pig immune response.

Introduction: In this study estimated genetic and phenotypic correlations between fifteen complete blood count (CBC) traits and thirty-three heritable plasma metabolites in young healthy nursery pigs. In addition, it provided an opportunity to identify candidate genes associated with variation in metabolite concentration and their potential association with immune response, disease resilience, and production traits. Methods: The blood samples were collected from healthy young pigs and Nuclear Magnetic Resonance (NMR) was used to quantify plasma metabolites. CBC was determined using the ADVIA® 2120i Hematology System. Genetic correlations of metabolite with CBC traits and single step genome-wide association study (ssGWAS) were estimated using the BLUPF90 programs. Results: Results showed low phenotypic correlation estimates between plasma metabolites and CBC traits. The highest phenotypic correlation was observed between lactic acid and plasma basophil concentration (0.36 ± 0.04; p < 0.05). Several significant genetic correlations were found between metabolites and CBC traits. The plasma concentration of proline was genetically positively correlated with hemoglobin concentration (0.94 ± 0.03; p < 0.05) and L-tyrosine was negatively correlated with mean corpuscular hemoglobin (MCH; -0.92 ± 0.74; p < 0.05). The genomic regions identified in this study only explained a small percentage of the genetic variance of metabolites levels that were genetically correlated with CBC, resilience, and production traits. Discussion: The results of this systems approach suggest that several plasma metabolite phenotypes are phenotypically and genetically correlated with CBC traits, suggesting that they may be potential genetic indicators of immune response following disease challenge. Genomic analysis revealed genes and pathways that might interact to modulate CBC, resilience, and production traits.

The influence of litter birth weight phenotype on embryonic and placental development at day 30 of gestation in multiparous purebred Large White sows.

The aim of this study was to understand the intrauterine biological processes associated with the low litter birth weight phenotype in pigs. Analyses were conducted on reproductive data from a purebred Large White maternal line to identify sows (>2 parities) with repeatable high or low litter birth weight phenotype (HLBWP or LLBWP). A total of 40 sows were selected (n = 20 HLBWP and n = 20 LLBWP) and bred with semen from purebred Large White boars of proven fertility. Sows were euthanized on day 28-30 of gestation (day 29.5 ± 0.6) and samples of placenta and embryos collected. Total number of embryos (TNE), embryonic weight (EW), embryonic viability, and crown-rump (CRL) measurements were recorded, along with the ovulation rate (OR) and allantochorionic fluid volume (AFV). No significant difference was detected (P > 0.05) in OR, TNE, and number of viable embryos on day 30 of gestation between the two groups. There was no significant difference in EW (LLBWP: 0.80 ± 0.05 g; HLBWP: 0.88 ± 0.04 g, P = 0.18) or CRL (LLBWP: 21.5 ± 0.7 mm; HLBWP: 21.9 ± 0.68 mm, P = 0.46). Placental development represented by the average AFV was significantly lower in the LLBWP compared to HLBWP (LLBWP: 131 ± 9.82 mL; HLBWP: 149 ± 9.39 mL, P = 0.03). In conclusion, placental development may be the main factor causing lower BW of entire litters in LLBWP sows.

Predicting enteric methane emission in lactating Holsteins based on reference methane data collected by the GreenFeed system.

Methane emission is not included in the current breeding goals for dairy cattle mainly due to the expense and difficulty in obtaining sufficient data to generate accurate estimates of the relevant traits. While several models have been developed to predict methane emission from milk spectra using reference methane data obtained by the respiration chamber, SF6 and sniffer methods, the prediction of methane emission from milk mid-infrared (MIR) spectra using reference methane data collected by the GreenFeed system has not yet been explored. Methane emission was monitored for 151 cows using the GreenFeed system. Prediction models were developed for daily and average (for the trial period of 12 or 14 days) methane production (g/d), yield (g/kg DM intake (DMI)) and intensity (g/kg of fat- and protein-corrected milk) using partial least squares regression. The predictions were evaluated in 100 repeated validation cycles, where animals were randomly partitioned into training (80%) and testing (20%) populations for each cycle. The best performing model was observed for average methane intensity using MIR, parity and DMI with validation coefficient of determination (R2val) and RMSE of prediction of 0.66 and 4.7 g/kg of fat- and protein-corrected milk, respectively. The accuracy of the best models for average methane production and average methane yield were poor (R2val = 0.28 and 0.12, respectively). A lower accuracy of prediction was observed for methane intensity and production (R2val = 0.42 and 0.17) when daily records were used while prediction for methane yield was comparable to that for average methane yield (R2val = 0.16). Our results suggest the potential to predict methane intensity with moderate accuracy. In this case, prediction models for average methane values were generally better than for daily measures when using the GreenFeed system to obtain reference methane emission measurements.

Heritability and genetic correlations of plasma metabolites of pigs with production, resilience and carcass traits under natural polymicrobial disease challenge.

Metabolites in plasma of healthy nursery pigs were quantified using nuclear magnetic resonance. Heritabilities of metabolite concentration were estimated along with their phenotypic and genetic correlations with performance, resilience, and carcass traits in growing pigs exposed to a natural polymicrobial disease challenge. Variance components were estimated by GBLUP. Heritability estimates were low to moderate (0.11 ± 0.08 to 0.19 ± 0.08) for 14 metabolites, moderate to high (0.22 ± 0.09 to 0.39 ± 0.08) for 17 metabolites, and highest for L-glutamic acid (0.41 ± 0.09) and hypoxanthine (0.42 ± 0.08). Phenotypic correlation estimates of plasma metabolites with performance and carcass traits were generally very low. Significant genetic correlation estimates with performance and carcass traits were found for several measures of growth and feed intake. Interestingly the plasma concentration of oxoglutarate was genetically negatively correlated with treatments received across the challenge nursery and finisher (- 0.49 ± 0.28; P < 0.05) and creatinine was positively correlated with mortality in the challenge nursery (0.85 ± 0.76; P < 0.05). These results suggest that some plasma metabolite phenotypes collected from healthy nursery pigs are moderately heritable and genetic correlations with measures of performance and resilience after disease challenge suggest they may be potential genetic indicators of disease resilience.

Economic comparison of an ear tag automated activity monitor for estrus detection with timed-AI in Holstein heifers.

The objective of this study was to compare the economic performance of an ear tag automated activity monitor system (AAM) versus a timed-AI (TAI) protocol in Holstein heifers. In total, 340 heifers were enrolled onto the study at 13.5 mo of age and randomly assigned to receive either an AAM (n = 170) or TAI (n = 170) protocol before breeding eligibility (D 0). Heifers in the AAM group were fitted with an ear tag AAM and bred based on high activity alert from the system. Heifers in the TAI group received a progesterone releasing intravaginal device on D -8, followed by device removal and prostaglandin on D -3 and gonadotropin-releasing hormone with TAI on D 0. In both treatments, the majority of heifers received sex-sorted semen for the first AI and conventional semen for subsequent AIs, with three opportunities to become pregnant. All heifers were diagnosed for pregnancy approximately 25 d post AI using transrectal ultrasonography, with confirmation at 30 and 45 d. Non-pregnant heifers in the TAI group, were resynchronized using the same TAI protocol. A partial budget was used to compare the costs and benefits of switching from a TAI to an AAM protocol in heifers, including protocol, labour, and rearing costs for each treatment, as well as estimated calf and milk value. Sensitivity analyses were also conducted to determine the effect of pregnancy per AI (P/AI), outsourcing AI, AAM tag cost and herd size on the net outcome. There was no difference in overall P/AI or days to pregnancy between treatments. However, number of AI was greater in the TAI than the AAM group. For the first AI, the P/AI was less in the TAI compared to the AAM group; however, the interval to first AI was less in TAI. There was minimal difference in performance for the second and third AI. There was a net gain of $11.97 per heifer when switching from a TAI to AAM protocol, due to the increased P/AI to the first AI and reduced cost of hormones. Several variables in the sensitivity analyses affected the net outcome. Considering only the first AI, switching to an AAM collar and a larger herd size all increased the net gain. Considering a greater P/AI to the first AI in the TAI group, outsourcing AI, using more AAM ear tags, and smaller herd sizes resulted in a net loss when switching from TAI to AAM. The AAM system resulted in exceptional P/AI and may be an economically viable alternative to improve heifer reproductive efficiency in herds with suboptimal P/AI to TAI.

Performance and optimization of an ear tag automated activity monitor for estrus prediction in dairy heifers.

The objectives of this study were to evaluate the performance of the SCR eSense ear tag automated activity monitor (AAM) to detect estrus behavior in Holstein heifers and to determine the optimal time from estrus alert to artificial insemination (AI) using sex-sorted or conventional semen. In total, 281 heifers were fitted with the AAM once eligible for breeding (>13.5 m of age). For the first AI, estrus was synchronized using 500 µg of cloprostenol (PGF), given 14 d apart, and heifers were given estrus detection patches (Estrotect™) after the second PGF. Heifers were inseminated at randomly attributed times after high activity alert from the AAM system or if the estrus patch had ≥ 50% colour change. Most heifers received sex-sorted semen for the first AI and conventional semen for subsequent inseminations. Pregnancy diagnosis was performed at 30 d post AI and heifers had four opportunities to become pregnant. In a subset of heifers (n = 149), ovaries were scanned every 12 h from the time of AI until ovulation (OV). The system recorded a heat index (measure of estrus strength), maximum activity change, maximum rumination change and duration of high activity. The sensitivity was 91.0%, with a false positive and false negative rate of 8.0%, and the positive predictive value to detect true estrus events was 83.5%. Pregnancy per AI to first AI was 67.6% and 97.9% of heifers become pregnant after four inseminations. Most false positive estrus events had a heat index < 45 and a rumination change < -20, while false negative events had a rumination change ≥ -20. Odds of pregnancy was not associated with any estrus characteristics measured by the system. However, pre-ovulatory follicle diameter had a weak correlation (r < 0.25) with all estrus characteristics. The average (range) interval of onset of high activity, peak activity and end of high activity to OV was 28 h (16-46 h), 22 h (10-40 h) and 16 h (0-36 h), respectively. For conventional semen, each hour increase in interval from activity onset or peak activity to AI reduced the predicted probability of pregnancy by 3.8 and 4.2%, respectively. For sex-sorted semen, the relationship between activity onset or peak activity to AI and predicted probability of pregnancy was quadratic, but not significant. Overall, the SCR eSense ear tag AAM performed well and strategies to identify false positive and false negative estrus events, along with optimization of timing of AI, should further improve performance in Holstein heifers.

Invited review: Advances and challenges in application of feedomics to improve dairy cow production and health.

Dairy cattle science has evolved greatly over the past century, contributing significantly to the improvement in milk production achieved today. However, a new approach is needed to meet the increasing demand for milk production and address the increased concerns about animal health and welfare. It is now easy to collect and access large and complex data sets consisting of molecular, physiological, and metabolic data as well as animal-level data (such as behavior). This provides new opportunities to better understand the mechanisms regulating cow performance. The recently proposed concept of feedomics could help achieve this goal by increasing our understanding of interactions between the different components or levels and their impact on animal production. Feedomics is an emerging field that integrates a range of omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, and metatranscriptomics) to provide these insights. In this way, we can identify the best strategies to improve overall animal productivity, product quality, welfare, and health. This approach can help research communities elucidate the complex interactions among nutrition, environment, management, animal genetics, metabolism, physiology, and the symbiotic microbiota. In this review, we summarize the outcomes of the most recent research on omics in dairy cows and highlight how an integrated feedomics approach could be applied in the future to improve dairy cow production and health. Specifically, we focus on 2 topics: (1) improving milk yield and milk quality, and (2) understanding metabolic physiology in transition dairy cows, which are 2 important challenges faced by the dairy industry worldwide.

Integrating RNA-seq and GWAS reveals novel genetic mutations for buffalo reproductive traits.

Genome-wide association study (GWAS) has been applied in buffalo breeding programs and been used to identify a number of candidate genes associated with buffalo reproductive traits. The genetic code of specific genes underlying buffalo reproductive traits remains unclear. Association study that measures both genetic and transcriptional variation has been applied for the investigation of complex traits. To investigate genes involved in buffalo reproductive traits, integrated RNA-seq results were investigated of buffalo granulosa cells and candidate genes which were reported to be associated with buffalo reproductive traits in a previous GWAS. A large number of variants were detected by RNA-seq, and 214 variants were located within the buffalo reproductive candidate genes identified by GWAS. A further association study in 462 Italian Mediterranean buffalo indicated that 25 SNPs distributed in 13 genes were associated with reproductive traits. Of the 13 genes, 11 were expressed in granulosa cells of all antral follicle development stages, and significant difference was found in the expression of NDUFS2 between follicles of diameter <8 mm and > 8 mm. These findings extend the results of GWAS by expanding the knowledge about new and potentially functional single-nucleotide polymorphisms and provide useful information about regulatory genes affecting buffalo reproductive traits.

Review: Biological determinants of between-animal variation in feed efficiency of growing beef cattle.

Animal's feed efficiency in growing cattle (i.e. the animal ability to reach a market or adult BW with the least amount of feed intake), is a key factor in the beef cattle industry. Feeding systems have made huge progress to understand dietary factors influencing the average animal feed efficiency. However, there exists a considerable amount of animal-to-animal variation around the average feed efficiency observed in beef cattle reared in similar conditions, which is still far from being understood. This review aims to identify biological determinants and molecular pathways involved in the between-animal variation in feed efficiency with particular reference to growing beef cattle phenotyped for residual feed intake (RFI). Moreover, the review attempts to distinguish true potential determinants from those revealed through simple associations or indirectly linked to RFI through their association with feed intake. Most representative and studied biological processes which seem to be connected to feed efficiency were reviewed, such as feeding behaviour, digestion and methane production, rumen microbiome structure and functioning, energy metabolism at the whole body and cellular levels, protein turnover, hormone regulation and body composition. In addition, an overall molecular network analysis was conducted for unravelling networks and their linked functions involved in between-animal variation in feed efficiency. The results from this review suggest that feeding and digestive-related mechanisms could be associated with RFI mainly because they co-vary with feed intake. Although much more research is warranted, especially with high-forage diets, the role of feeding and digestive related mechanisms as true determinants of animal variability in feed efficiency could be minor. Concerning the metabolic-related mechanisms, despite the scarcity of studies using reference methods it seems that feed efficient animals have a significantly lower energy metabolic rate independent of the associated intake reduction. This lower heat production in feed efficient animals may result from a decreased protein turnover and a higher efficiency of ATP production in mitochondria, both mechanisms also identified in the molecular network analysis. In contrast, hormones and body composition could not be conclusively related to animal-to-animal variation in feed efficiency. The analysis of potential biological networks underlying RFI variations highlighted other significant pathways such as lipid metabolism and immunity and stress response. Finally, emerging knowledge suggests that metabolic functions underlying genetic variation in feed efficiency could be associated with other important traits in animal production. This emphasizes the relevance of understanding the biological basis of relevant animal traits to better define future balanced breeding programmes.

Development and validation of a small SNP panel for feed efficiency in beef cattle.

The objective of this study was to develop and validate a customized cost-effective single nucleotide polymorphism (SNP) panel for genetic improvement of feed efficiency in beef cattle. The SNPs identified in previous association studies and through extensive analysis of candidate genomic regions and genes, were screened for their functional impact and allele frequency in Angus and Hereford breeds used as validation candidates for the panel. Association analyses were performed on genotypes of 159 SNPs from new samples of Angus (n = 160), Hereford (n = 329), and Angus-Hereford crossbred (n = 382) cattle using allele substitution and genotypic models in ASReml. Genomic heritabilities were estimated for feed efficiency traits using the full set of SNPs, SNPs associated with at least one of the traits (at P ≤ 0.05 and P < 0.10), as well as the Illumina bovine 50K representing a widely used commercial genotyping panel. A total of 63 SNPs within 43 genes showed association (P ≤ 0.05) with at least one trait. The minor alleles of SNPs located in the GHR and CAST genes were associated with decreasing effects on residual feed intake (RFI) and/or RFI adjusted for backfat (RFIf), whereas minor alleles of SNPs within MKI67 gene were associated with increasing effects on RFI and RFIf. Additionally, the minor allele of rs137400016 SNP within CNTFR was associated with increasing average daily gain (ADG). The SNPs genotypes within UMPS, SMARCAL, CCSER1, and LMCD1 genes showed significant over-dominance effects whereas other SNPs located in SMARCAL1, ANXA2, CACNA1G, and PHYHIPL genes showed additive effects on RFI and RFIf. Gene enrichment analysis indicated that gland development, as well as ion and cation transport are important physiological mechanisms contributing to variation in feed efficiency traits. The study revealed the effect of the Jak-STAT signaling pathway on feed efficiency through the CNTFR, OSMR, and GHR genes. Genomic heritability using the 63 significant (P ≤ 0.05) SNPs was 0.09, 0.09, 0.13, 0.05, 0.05, and 0.07 for ADG, dry matter intake, midpoint metabolic weight, RFI, RFIf, and backfat, respectively. These SNPs contributed to genetic variation in the studied traits and thus can potentially be used or tested to generate cost-effective molecular breeding values for feed efficiency in beef cattle.

Genome-wide association studies to identify quantitative trait loci affecting milk production traits in water buffalo.

Water buffalo is the second largest resource of milk supply around the world, and it is well known for its distinctive milk quality in terms of fat, protein, lactose, vitamin, and mineral contents. Understanding the genetic architecture of milk production traits is important for future improvement by the buffalo breeding industry. The advance of genome-wide association studies (GWAS) provides an opportunity to identify potential genetic variants affecting important economical traits. In the present study, GWAS was performed for 489 buffaloes with 1,424 lactation records using the 90K Affymetrix Buffalo SNP Array (Affymetrix/Thermo Fisher Scientific, Santa Clara, CA). Collectively, 4 candidate single nucleotide polymorphisms (SNP) in 2 genomic regions were found to associate with buffalo milk production traits. One region affecting milk fat and protein percentage was located on the equivalent of Bos taurus autosome (BTA)3, spanning 43.3 to 43.8 Mb, which harbored the most likely candidate genes MFSD14A, SLC35A3, and PALMD. The other region on the equivalent of BTA14 at 66.5 to 67.0 Mb contained candidate genes RGS22 and VPS13B and influenced buffalo total milk yield, fat yield, and protein yield. Interestingly, both of the regions were reported to have quantitative trait loci affecting milk performance in dairy cattle. Furthermore, we suggest that buffaloes with the C allele at AX-85148558 and AX-85073877 loci and the G allele at AX-85106096 locus can be selected to improve milk fat yield in this buffalo-breeding program. Meanwhile, the G allele at AX-85063131 locus can be used as the favorable allele for improving milk protein percentage. Genomic prediction showed that the reliability of genomic estimated breeding values (GEBV) of 6 milk production traits ranged from 0.06 to 0.22, and the correlation between estimated breeding values and GEBV ranged from 0.23 to 0.35. These findings provide useful information to understand the genetic basis of buffalo milk properties and may play a role in accelerating buffalo breeding programs using genomic approaches.

Coadministration of the Campylobacter jejuni N-Glycan-Based Vaccine with Probiotics Improves Vaccine Performance in Broiler Chickens.

Source attribution studies report that the consumption of contaminated poultry is the primary source for acquiring human campylobacteriosis. Oral administration of an engineered Escherichia coli strain expressing the Campylobacter jejuni N-glycan reduces bacterial colonization in specific-pathogen-free leghorn chickens, but only a fraction of birds respond to vaccination. Optimization of the vaccine for commercial broiler chickens has great potential to prevent the entry of the pathogen into the food chain. Here, we tested the same vaccination approach in broiler chickens and observed similar efficacies in pathogen load reduction, stimulation of the host IgY response, the lack of C. jejuni resistance development, uniformity in microbial gut composition, and the bimodal response to treatment. Gut microbiota analysis of leghorn and broiler vaccine responders identified one member of Clostridiales cluster XIVa, Anaerosporobacter mobilis, that was significantly more abundant in responder birds. In broiler chickens, coadministration of the live vaccine with A. mobilis or Lactobacillus reuteri, a commonly used probiotic, resulted in increased vaccine efficacy, antibody responses, and weight gain. To investigate whether the responder-nonresponder effect was due to the selection of a C. jejuni "supercolonizer mutant" with altered phase-variable genes, we analyzed all poly(G)-containing loci of the input strain compared to nonresponder colony isolates and found no evidence of phase state selection. However, untargeted nuclear magnetic resonance (NMR)-based metabolomics identified a potential biomarker negatively correlated with C. jejuni colonization levels that is possibly linked to increased microbial diversity in this subgroup. The comprehensive methods used to examine the bimodality of the vaccine response provide several opportunities to improve the C. jejuni vaccine and the efficacy of any vaccination strategy.IMPORTANCECampylobacter jejuni is a common cause of human diarrheal disease worldwide and is listed by the World Health Organization as a high-priority pathogen. C. jejuni infection typically occurs through the ingestion of contaminated chicken meat, so many efforts are targeted at reducing C. jejuni levels at the source. We previously developed a vaccine that reduces C. jejuni levels in egg-laying chickens. In this study, we improved vaccine performance in meat birds by supplementing the vaccine with probiotics. In addition, we demonstrated that C. jejuni colonization levels in chickens are negatively correlated with the abundance of clostridia, another group of common gut microbes. We describe new methods for vaccine optimization that will assist in improving the C. jejuni vaccine and other vaccines under development.

Comparative transcriptomic analysis of porcine peripheral blood reveals differentially expressed genes from the cytokine-cytokine receptor interaction pathway related to health status.

While some research has looked into the host genetic response in pigs challenged with specific viruses or bacteria, few studies have explored the expression changes of transcripts in the peripheral blood of sick pigs that may be infected with multiple pathogens on farms. In this study, the architecture of the peripheral blood transcriptome of 64 Duroc sired commercial pigs, including 18 healthy animals at entry to a growing facility (set as a control) and 23 pairs of samples from healthy and sick pen mates, was generated using RNA-Seq technology. In total, 246 differentially expressed genes were identified to be specific to the sick animals. Functional enrichment analysis for those genes revealed that the over-represented gene ontology terms for the biological processes category were exclusively immune activity related. The cytokine-cytokine receptor interaction pathway was significantly enriched. Nine functional genes from this pathway encoding members (as well as their receptors) of the interleukins, chemokines, tumor necrosis factors, colony stimulating factors, activins, and interferons exhibited significant transcriptional alteration in sick animals. Our results suggest a subset of novel marker genes that may be useful candidate genes in the evaluation and prediction of health status in pigs under commercial production conditions.

Genomewide association of piglet responses to infection with one of two porcine reproductive and respiratory syndrome virus isolates.

Porcine reproductive and respiratory syndrome (PRRS) is a devastating disease in the swine industry. Identification of host genetic factors that enable selection for improved performance during PRRS virus (PRRSV) infection would reduce the impact of this disease on animal welfare and production efficiency. We conducted genomewide association study (GWAS) analyses of data from 13 trials of approximately 200 commercial crossbred nursery-age piglets that were experimentally infected with 1 of 2 type 2 isolates of PRRSV (NVSL 97-7985 [NVSL] and KS2006-72109 [KS06]). Phenotypes analyzed were viral load (VL) in blood during the first 21 d after infection (dpi) and weight gain (WG) from 0 to 42 dpi. We accounted for the previously identified QTL in the region on SSC4 in our models to increase power to identify additional regions. Many regions identified by single-SNP analyses were not identified using Bayes-B, but both analyses identified the same regions on SSC3 and SSC5 to be associated with VL in the KS06 trials and on SSC6 in the NVSL trials ( < 5 × 10); for WG, regions on SSC5 and SSC17 were associated in the NVSL trials ( < 3 × 10). No regions were identified with either method for WG in the KS06 trials. Except for the region on SSC4, which was associated with VL for both isolates (but only with WG for NVSL), identified regions did not overlap between the 2 PRRSV isolate data sets, despite high estimates of the genetic correlation between isolates for traits based on these data. We also identified genomic regions whose associations with VL or WG interacted with either PRRSV isolate or with genotype at the SSC4 QTL. Gene ontology (GO) annotation terms for genes located near moderately associated SNP ( < 0.003) were enriched for multiple immunologically (VL) and metabolism- (WG) related GO terms. The biological relevance of these regions suggests that, although it may increase the number of false positives, the use of single-SNP analyses and a relaxed threshold also increased the identification of true positives. In conclusion, although only the SSC4 QTL was associated with response to both PRRSV isolates, genes near associated SNP were enriched for the same GO terms across PRRSV isolates, suggesting that host responses to these 2 isolates are affected by the actions of many genes that function together in similar biological processes.

Genomic investigation of piglet resilience following porcine epidemic diarrhea outbreaks.

Porcine epidemic diarrhea virus (PEDV) belongs to the Coronaviridae family and causes malabsorptive watery diarrhea, vomiting, dehydration and imbalanced blood electrolytes in pigs. Since the 1970s, PED outbreaks have become a source of problems in pig producing countries all over the world, causing large economic losses for pig producers. Although the infection in adults is not fatal, in naïve suckling piglets mortality is close to 100%. In this study, we investigated genome-wide differences between dead and recovered suckling piglets from commercial farms after PED outbreaks. Samples from 262 animals (156 dead and 106 recovered) belonging to several commercial lines were collected from five different farms in three different countries (USA, Canada and Germany) and genotyped with the porcine 80K SNP chip. Mean Fst value was calculated in 1-Mb non-overlapping windows between dead and recovered individuals, and the results were normalized to find differences within the comparison. Seven windows with high divergence between dead and recovered were detected-five on chromosome 2, one on chromosome 4 and one on chromosome 15-in total encompassing 152 genes. Several of these genes are either under- or overexpressed in many virus infections, including Coronaviridae (such as SARS-CoV). A total of 32 genes are included in one or more Gene Ontology terms that can be related to PED development, such as Golgi apparatus, as well as mechanisms generally linked to resilience or diarrhea development (cell proliferation, ion transport, ATPase activity). Taken together this information provides a first genomic picture of PEDV resilience in suckling piglets.

Estimation of genetic parameters for traits associated with reproduction, lactation, and efficiency in sows.

Increased milk production due to high litter size, coupled with low feed intake, results in excessive mobilization of sow body reserves during lactation, which can have detrimental effects on future reproductive performance. A possibility to prevent this is to improve sow lactation performance genetically, along with other traits of interest. The aim of this study was to estimate breed-specific genetic parameters (by parity, between parities, and across parities) for traits associated with lactation and reproduction in Yorkshire and Landrace sows. Performance data were available for 2,107 sows with 1 to 3 parities (3,424 farrowings total). Sow back fat, loin depth and BW at farrowing, sow feed intake (SFI), and body weight loss (BWL) during lactation showed moderate heritabilities (0.21 to 0.37) in both breeds, whereas back fat loss (BFL), loin depth loss (LDL), and litter weight gain (LWG) showed low heritabilities (0.12 to 0.18). Among the efficiency traits, sow lactation efficiency showed extremely low heritability (near zero) in Yorkshire sows but a slightly higher (0.05) estimate in Landrace sows, whereas sow residual feed intake (SRFI) and energy balance traits showed moderate heritabilities in both breeds. Genetic correlations indicated that SFI during lactation had strong negative genetic correlations with body resource mobilization traits (BWL, BFL, and LDL; -0.35 to -0.70), and tissue mobilization traits in turn had strong positive genetic correlations with LWG (+0.24 to +0.54; < 0.05). However, SFI did not have a significant genetic correlation with LWG. These genetic correlations suggest that SFI during lactation is predominantly used for reducing sow body tissue losses, rather than for milk production. Estimates of genetic correlations for the same trait measured in parities 1 and 2 ranged from 0.64 to 0.98, which suggests that first and later parities should be treated as genetically different for some traits. Genetic correlations estimated between traits in parities 1 and 2 indicated that BWF and BWL measured in parity 1 can be used as indicator traits for SFI and SRFI measured in parities 1 and 2. In conclusion, traits associated with lactation in sows have a sizable genetic component and show potential for genetic improvement.

Accuracy of genomic predictions for feed efficiency traits of beef cattle using 50K and imputed HD genotypes.

The accuracy of genomic predictions can be used to assess the utility of dense marker genotypes for genetic improvement of beef efficiency traits. This study was designed to test the impact of genomic distance between training and validation populations, training population size, statistical methods, and density of genetic markers on prediction accuracy for feed efficiency traits in multibreed and crossbred beef cattle. A total of 6,794 beef cattle data collated from various projects and research herds across Canada were used. Illumina BovineSNP50 (50K) and imputed Axiom Genome-Wide BOS 1 Array (HD) genotypes were available for all animals. The traits studied were DMI, ADG, and residual feed intake (RFI). Four validation groups of 150 animals each, including Angus (AN), Charolais (CH), Angus-Hereford crosses (ANHH), and a Charolais-based composite (TX) were created by considering the genomic distance between pairs of individuals in the validation groups. Each validation group had 7 corresponding training groups of increasing sizes ( = 1,000, 1,999, 2,999, 3,999, 4,999, 5,998, and 6,644), which also represent increasing average genomic distance between pairs of individuals in the training and validations groups. Prediction of genomic estimated breeding values (GEBV) was performed using genomic best linear unbiased prediction (GBLUP) and Bayesian method C (BayesC). The accuracy of genomic predictions was defined as the Pearson's correlation between adjusted phenotype and GEBV (), unless otherwise stated. Using 50K genotypes, the highest average achieved in purebreds (AN, CH) was 0.41 for DMI, 0.34 for ADG, and 0.35 for RFI, whereas in crossbreds (ANHH, TX) it was 0.38 for DMI, 0.21 for ADG, and 0.25 for RFI. Similarly, when imputed HD genotypes were applied in purebreds (AN, CH), the highest average was 0.14 for DMI, 0.15 for ADG, and 0.14 for RFI, whereas in crossbreds (ANHH, TX) it was 0.38 for DMI, 0.22 for ADG, and 0.24 for RFI. The of GBLUP predictions were greatly reduced with increasing genomic average distance compared to those from BayesC predictions. The results indicate that 50K genotypes, used with BayesC, are more effective for predicting GEBV in purebred cattle. Imputed HD genotypes found utility when dealing with composites and crossbreds. Formulation of a fairly large training set for genomic predictions in beef cattle should consider the genomic distance between the training and target populations.

Genetic analysis of reproductive traits and antibody response in a PRRS outbreak herd.

Porcine reproductive and respiratory syndrome (PRRS) is the most economically significant disease impacting pig production in North America, Europe, and Asia, causing reproductive losses such as increased rates of stillbirth and mummified piglets. The objective of this study was to explore the genetic basis of host response to the PRRS virus (PRRSV) in a commercial multiplier sow herd before and after a PRRS outbreak, using antibody response and reproductive traits. Reproductive data comprising number born alive (NBA), number alive at 24 h (NA24), number stillborn (NSB), number born mummified (NBM), proportion born dead (PBD), number born dead (NBD), number weaned (NW), and number of mortalities through weaning (MW) of 5,227 litters from 1,967 purebred Landrace sows were used along with a pedigree comprising 2,995 pigs. The PRRS outbreak date was estimated from rolling averages of farrowing traits and was used to split the data into a pre-PRRS phase and a PRRS phase. All 641 sows in the herd during the outbreak were blood sampled 46 d after the estimated outbreak date and were tested for anti-PRRSV IgG using ELISA (sample-to-positive [S/P] ratio). Genetic parameters of traits were estimated separately for the pre-PRRS and PRRS phase data sets. Sows were genotyped using the PorcineSNP60 BeadChip, and genome-wide association studies (GWAS) were performed using method Bayes B. Heritability estimates for reproductive traits ranged from 0.01 (NBM) to 0.12 (NSB) and from 0.01 (MW) to 0.12 (NBD) for the pre-PRRS and PRRS phases, respectively. S/P ratio had heritability (0.45) and strong genetic correlations with most traits, ranging from -0.72 (NBM) to 0.73 (NBA). In the pre-PRRS phase, regions associated with NSB and PBD explained 1.6% and 3% of the genetic variance, respectively. In the PRRS phase, regions associated with NBD, NSB, and S/P ratio explained 0.8%, 11%, and 50.6% of the genetic variance, respectively. For S/P ratio, 2 regions on SSC 7 (SSC7) separated by 100 Mb explained 40% of the genetic variation, including a region encompassing the major histocompatibility complex, which explained 25% of the genetic variance. These results indicate a significant genomic component associated with PRRSV antibody response and NSB in this data set. Also, the high heritability and genetic correlation estimates for S/P ratio during the PRRS phase suggest that S/P ratio could be used as an indicator of the impact of PRRS on reproductive traits.

Reliability of molecular breeding values for Warner-Bratzler shear force and carcass traits of beef cattle - an independent validation study.

Interest in genetic improvement of carcass and tenderness traits of beef cattle using genome-based selection (GS) and marker-assisted management programs is increasing. The success of such a program depends on the presence of linkage disequilibrium between the observed markers and the underlying QTL as well as on the relationship between the discovery, validation, and target populations. For molecular breeding values (MBV) predicted for a target population using SNP markers, reliabilities of these MBV can be obtained from validation analyses conducted in an independent population distinct from the discovery set. The objective of this study was to test MBV predicted for carcass and tenderness traits of beef cattle in a Canadian-based validation population that is largely independent of a United States-based discovery set. The discovery data set comprised of genotypes and phenotypes from >2,900 multibreed beef cattle while the validation population consisted of 802 crossbred feeder heifers and steers. A bivariate animal model that fitted actual phenotype and MBV was used for validation analyses. The reliability of MBV was defined as square of the genetic correlation (R(2) g) that represents the proportion of the additive genetic variance explained by the SNP markers. Several scenarios involving different starting marker panels (384, 3K, 7K, and 50K) and different sets of SNP selected to compute MBV (50, 100, 200, 375, 400, 600, and 800) were investigated. Validation results showed that the most reliable MBV (R(2) g) were 0.34 for HCW, 0.36 for back fat thickness, 0.28 for rib eye area, 0.30 for marbling score, 0.25 for yield grade, and 0.38 for Warner-Bratzler shear force across the different scenarios explored. The results indicate that smaller SNP panels can be developed for use in genetic improvement of beef carcass and tenderness traits to exploit GS benefits.