Heritability estimates and predictive ability for pig meat quality traits using identity-by-state and identity-by-descent relationships in an F2 population.

Genomic relationships can be computed with dense genome-wide genotypes through different methods, either based on identity-by-state (IBS) or identity-by-descent (IBD). The latter has been shown to increase the accuracy of both estimated relationships and predicted breeding values. However, it is not clear whether an IBD approach would achieve greater heritability ( h 2 ) and predictive ability ( r ̂ y , y ̂ ) than its IBS counterpart for data with low-depth pedigrees. Here, we compare both approaches in terms of the estimated of h 2 and r ̂ y , y ̂ , using data on meat quality and carcass traits recorded in experimental crossbred pigs, with a pedigree constrained to only three generations. Three animal models were fitted which differed on the relationship matrix: an IBS model ( G IBS ), an IBD (defined within the known pedigree) model ( G IBD ), and a pedigree model ( A 22 ). In 9 of 20 traits, the range of increase for the estimates of σ u 2 and h 2 was 1.2-2.9 times greater with G IBS and G IBD models than with A 22 . Whereas for all traits, both parameters were similar between genomic models. The r ̂ y , y ̂ of the genomic models was higher compared to A 22 . A scarce increment in r ̂ y , y ̂ was found with G IBS when compared to G IBD , most likely due to the former recovering sizeable relationships among founder F0 animals.

Altered Larval Yellow Perch Swimming Behavior Due to Methylmercury and PCB126 Detected Using Hidden Markov Chain Models.

Fish swimming behavior is a commonly measured response in aquatic ecotoxicology because behavior is considered a whole organism-level effect that integrates many sensory systems. Recent advancements in animal behavior models, such as hidden Markov chain models (HMM), suggest an improved analytical approach for toxicology. Using both new and traditional approaches, we examined the sublethal effects of PCB126 and methylmercury on yellow perch (YP) larvae (Perca flavescens) using three doses. Both approaches indicate larvae increase activity after exposure to either chemical. The middle methylmercury-dosed larvae showed multiple altered behavior patterns. First, larvae had a general increase in activity, typically performing more behavior states, more time swimming, and more swimming bouts per second. Second, when larvae were in a slow or medium swimming state, these larvae tended to switch between these states more often. Third, larvae swam slower during the swimming bouts. The upper PCB126-dosed larvae exhibited a higher proportion and a fast swimming state, but the total time spent swimming fast decreased. The middle PCB126-dosed larvae transitioned from fast to slow swimming states less often than the control larvae. These results indicate that developmental exposure to very low doses of these neurotoxicants alters YP larvae overall swimming behaviors, suggesting neurodevelopment alteration.

Phenomes: the current frontier in animal breeding.

Improvements in genomic technologies have outpaced the most optimistic predictions, allowing industry-scale application of genomic selection. However, only marginal gains in genetic prediction accuracy can now be expected by increasing marker density up to sequence, unless causative mutations are identified. We argue that some of the most scientifically disrupting and industry-relevant challenges relate to 'phenomics' instead of 'genomics'. Thanks to developments in sensor technology and artificial intelligence, there is a wide range of analytical tools that are already available and many more will be developed. We can now address some of the pressing societal demands on the industry, such as animal welfare concerns or efficiency in the use of resources. From the statistical and computational point of view, phenomics raises two important issues that require further work: penalization and dimension reduction. This will be complicated by the inherent heterogeneity and 'missingness' of the data. Overall, we can expect that precision livestock technologies will make it possible to collect hundreds of traits on a continuous basis from large numbers of animals. Perhaps the main revolution will come from redesigning animal breeding schemes to explicitly allow for high-dimensional phenomics. In the meantime, phenomics data will definitely enlighten our knowledge on the biological basis of phenotypes.

Safety and efficacy of topically administered netarsudil (Rhopressa™) in normal and glaucomatous dogs with ADAMTS10-open-angle glaucoma (ADAMTS10-OAG).

OBJECTIVE: To evaluate safety and efficacy of topically administered 0.02% netarsudil ophthalmic solution (Rhopressa™; Aerie Pharmaceutical) in normal and glaucomatous dogs with ADAMTS10-open-angle glaucoma (ADAMTS10-OAG). ANIMALS STUDIED: Five normal and 5 glaucomatous Beagle dogs with ADAMTS10-OAG. PROCEDURES: In each dog, left or right eye was randomly selected for netarsudil treatment. Contralateral eyes were sham-treated with balanced salt solution (BSS). Following a 1-week baseline period, dogs were treated once daily (q24h) during week 2, and twice daily (q12h) during week 3; week 4 served as washout period. Efficacy was measured by diurnal intraocular pressure (IOP) and pupil diameter. Safety was assessed by routine ophthalmic examination, gonioscopy, and pachymetry. Differences in least square means of quantitative outcome measures were compared between netarsudil and BSS sham-treated eyes by linear Gaussian model. RESULTS: Baseline IOPs were 18.5 ± 0.5 mm Hg (mean ± SEM) in normal and 27.8 ± 1.0 mm Hg in OAG dogs. Even though mean IOPs were lower in netarsudil- vs sham-treated eyes, the overall differences were neither significant nor clinically relevant, regardless of treatment frequency (q24h-normal: sham 16.4 ± 1.1 mm Hg vs treatment 15.6 ± 1.0 mm Hg; q24hr-OAG: sham 25.8 ± 2.3 mm Hg vs. treatment 25.7 ± 2.4 mm Hg; q12hr-normal: sham 15.4 ± 0.8 mm Hg vs. treatment 14.4 ± 0.8 mm Hg; q12hr-OAG: sham 26.3 ± 1.7 mm Hg vs. treatment 25.4 ± 1.8 mm Hg). Netarsudil administration was well tolerated but resulted in significant, moderate-to-severe conjunctival hyperemia (P < .001). CONCLUSIONS: Once or twice daily administration of netarsudil resulted in marginal and clinically irrelevant IOP decreases in normal and OAG-affected dogs. Except for conjunctival hyperemia, the drug was well tolerated.

Safety and efficacy of topically administered netarsudil-latanoprost fixed dose combination (FDC; Rocklatan™) in normal and glaucomatous dogs with ADAMTS10-open-angle glaucoma (ADAMTS10-OAG).

OBJECTIVE: The aim of the study was to evaluate safety and efficacy of topically administered 0.02% netarsudil-0.005% latanoprost fixed-dose combination (FDC) (Rocklatan™; Aerie Pharmaceutical) in normal and glaucomatous dogs with ADAMTS10-open-angle glaucoma (ADAMTS10-OAG). ANIMALS STUDIED: Five normal and five glaucomatous beagle dogs with ADAMTS10-OAG were the study animals. PROCEDURES: In each dog, left (OS) or right eye (OD) was randomly selected for netarsudil-latanoprost FDC treatment. Contralateral eyes served as latanoprost-treated controls. The study was divided into four consecutive study periods: following a 4-day baseline period, two sequential 8-day study periods followed with once daily (q24h) and twice daily (q12h) treatments and ending with a washout period. Efficacy was measured by diurnal intraocular pressure (IOP) and pupil diameter. Safety was assessed by routine ophthalmic examination, gonioscopy, and pachymetry. Differences in least square means of quantitative outcome measures were compared between FDC and latanoprost treatments by using the linear Gaussian model. RESULTS: Baseline IOPs were 13.6 ± 0.7 mmHg (mean ± SEM) in normal and 28.3 ± 1.4 mmHg in OAG-affected dogs. There was a significant decrease in mean diurnal IOP following FDC administration in both normal (q24h: -2.1 mmHg; q12h: -4.1 mmHg) and glaucomatous dogs (q24h: -14.2 mmHg; q12h: -17.7 mmHg; p < .0001). There was no significant difference in the treatment effect when comparing FDC to latanoprost. Both FDC and latanoprost administration resulted in similarly significant pupil constriction (p < .0001). The FDC administration was well-tolerated but resulted in conjunctival hyperemia. CONCLUSIONS: Once or twice daily administration of netarsudil-latanoprost FDC (Rocklatan™) and latanoprost was equally effective in lowering IOP in normal and OAG-affected dogs. There was no netarsudil-related added treatment effect.

Dysregulated FXR-FGF19 signaling and choline metabolism are associated with gut dysbiosis and hyperplasia in a novel pig model of pediatric NASH.

To investigate the role of bile acids (BAs) in the pathogenesis of diet-induced nonalcoholic steatohepatitis (NASH), we fed a "Western-style diet" [high fructose, high fat (HFF)] enriched with fructose, cholesterol, and saturated fat for 10 wk to juvenile Iberian pigs. We also supplemented probiotics with in vitro BA deconjugating activity to evaluate their potential therapeutic effect in NASH. Liver lipid and function, cytokines, and hormones were analyzed using commercially available kits. Metabolites, BAs, and fatty acids were measured by liquid chromatography-mass spectrometry. Histology and gene and protein expression analyses were performed using standard protocols. HFF-fed pigs developed NASH, cholestasis, and impaired enterohepatic Farnesoid-X receptor (FXR)-fibroblast growth factor 19 (FGF19) signaling in the absence of obesity and insulin resistance. Choline depletion in HFF livers was associated with decreased lipoprotein and cholesterol in serum and an increase of choline-containing phospholipids in colon contents and trimethylamine-N-oxide in the liver. Additionally, gut dysbiosis and hyperplasia increased with the severity of NASH, and were correlated with increased colonic levels of choline metabolites and secondary BAs. Supplementation of probiotics in the HFF diet enhanced NASH, inhibited hepatic autophagy, increased excretion of taurine and choline, and decreased gut microbial diversity. In conclusion, dysregulation of BA homeostasis was associated with injury and choline depletion in the liver, as well as increased biliary secretion, gut metabolism and excretion of choline-based phospholipids. Choline depletion limited lipoprotein synthesis, resulting in hepatic steatosis, whereas secondary BAs and choline-containing phospholipids in colon may have promoted dysbiosis, hyperplasia, and trimethylamine synthesis, causing further damage to the liver.NEW & NOTEWORTHY Impaired Farnesoid-X receptor (FXR)-fibroblast growth factor 19 (FGF19) signaling and cholestasis has been described in nonalcoholic fatty liver disease (NAFLD) patients. However, therapeutic interventions with FXR agonists have produced contradictory results. In a swine model of pediatric nonalcoholic steatohepatitis (NASH), we show that the uncoupling of intestinal FXR-FGF19 signaling and a decrease in FGF19 levels are associated with a choline-deficient phenotype of NASH and increased choline excretion in the gut, with the subsequent dysbiosis, colonic hyperplasia, and accumulation of trimethylamine-N-oxide in the liver.

Genetic control of longissimus dorsi muscle gene expression variation and joint analysis with phenotypic quantitative trait loci in pigs.

BACKGROUND: Economically important growth and meat quality traits in pigs are controlled by cascading molecular events occurring during development and continuing throughout the conversion of muscle to meat. However, little is known about the genes and molecular mechanisms involved in this process. Evaluating transcriptomic profiles of skeletal muscle during the initial steps leading to the conversion of muscle to meat can identify key regulators of polygenic phenotypes. In addition, mapping transcript abundance through genome-wide association analysis using high-density marker genotypes allows identification of genomic regions that control gene expression, referred to as expression quantitative trait loci (eQTL). In this study, we perform eQTL analyses to identify potential candidate genes and molecular markers regulating growth and meat quality traits in pigs. RESULTS: Messenger RNA transcripts obtained with RNA-seq of longissimus dorsi muscle from 168 F2 animals from a Duroc x Pietrain pig resource population were used to estimate gene expression variation subject to genetic control by mapping eQTL. A total of 339 eQTL were mapped (FDR ≤ 0.01) with 191 exhibiting local-acting regulation. Joint analysis of eQTL with phenotypic QTL (pQTL) segregating in our population revealed 16 genes significantly associated with 21 pQTL for meat quality, carcass composition and growth traits. Ten of these pQTL were for meat quality phenotypes that co-localized with one eQTL on SSC2 (8.8-Mb region) and 11 eQTL on SSC15 (121-Mb region). Biological processes identified for co-localized eQTL genes include calcium signaling (FERM, MRLN, PKP2 and CHRNA9), energy metabolism (SUCLG2 and PFKFB3) and redox hemostasis (NQO1 and CEP128), and results support an important role for activation of the PI3K-Akt-mTOR signaling pathway during the initial conversion of muscle to meat. CONCLUSION: Co-localization of eQTL with pQTL identified molecular markers significantly associated with both economically important phenotypes and gene transcript abundance. This study reveals candidate genes contributing to variation in pig production traits, and provides new knowledge regarding the genetic architecture of meat quality phenotypes.

Evidence for transcriptome-wide RNA editing among Sus scrofa PRE-1 SINE elements.

BACKGROUND: RNA editing by ADAR (adenosine deaminase acting on RNA) proteins is a form of transcriptional regulation that is widespread among humans and other primates. Based on high-throughput scans used to identify putative RNA editing sites, ADAR appears to catalyze a substantial number of adenosine to inosine transitions within repetitive regions of the primate transcriptome, thereby dramatically enhancing genetic variation beyond what is encoded in the genome. RESULTS: Here, we demonstrate the editing potential of the pig transcriptome by utilizing DNA and RNA sequence data from the same pig. We identified a total of 8550 mismatches between DNA and RNA sequences across three tissues, with 75% of these exhibiting an A-to-G (DNA to RNA) discrepancy, indicative of a canonical ADAR-catalyzed RNA editing event. When we consider only mismatches within repetitive regions of the genome, the A-to-G percentage increases to 94%, with the majority of these located within the swine specific SINE retrotransposon PRE-1. We also observe evidence of A-to-G editing within coding regions that were previously verified in primates. CONCLUSIONS: Thus, our high-throughput evidence suggests that pervasive RNA editing by ADAR can exist outside of the primate lineage to dramatically enhance genetic variation in pigs.

Molecular advances in QTL discovery and application in pig breeding.

Thousands of quantitative trait loci (QTL) have been identified for a wide range of economically important phenotypes in pigs. Recently, QTL analyses have begun to use high-density single nucleotide polymorphism (SNP) panels and applications have extended beyond experimental intercrosses to outbred populations by exploiting long-range linkage disequilibrium that results in higher resolution QTL mapping. Relevant phenotypes generally fall under categories of growth and body composition, carcass and meat quality, reproduction, and disease resistance. A few expression QTL (eQTL) studies have been performed that integrate transcriptional profiles with genotype data by considering expression levels as response variables in QTL analyses for identifying genes controlling important trait phenotypes. Rapidly evolving genomics technologies, including RNAseq, provide tremendous opportunities for QTL and eQTL discovery. In this review, we discuss recent progress in pig QTL and eQTL discovery, including approaches for allele-specific expression, and implications of these discoveries for pig breeding and genetics.

Linkage disequilibrium, persistence of phase and effective population size estimates in Hereford and Braford cattle.

BACKGROUND: The existence of moderate to high levels of linkage disequilibrium (LD) between genetic markers and quantitative trait loci (QTL) affecting traits of interest is fundamental for the success of genome-wide association (GWAS) and genomic selection (GS) studies. Knowledge about the extent and the pattern of LD in livestock populations is essential to determine the density of single nucleotide polymorphisms (SNP) required for accurate GWAS and GS. Moreover, observed LD is related to historical effective population sizes (Ne), and can provide insights into the genetic diversity history of populations. Estimates of the consistency of linkage phase across breeds (R H,B ) can be used to determine if there is sufficient relationship to use pooled reference populations in multi-breed GS programs. The objective of this study was to estimate LD levels, persistence of phase and effective population size in Hereford and Braford cattle populations sampled in Brazil. RESULTS: Mean LD estimates, measured using the squared correlation of alleles at two loci (r (2)), obtained between adjacent SNP across all chromosomes were 0.21 ± 0.27 for Herefords (391 samples with 41,241 SNP) and 0.16 ± 0.22 for Brafords (2044 samples and 41,207 SNP). Estimated r(2) was > 0.2 and 0.3, respectively, for 34 and 25% of adjacent markers in Herefords, and 26 and 17% in Brafords. Estimated N e for Brafords and Herefords at the current generation was 220 and 153 individuals, respectively. The two breeds demonstrated moderate to strong persistence of phase at all distances (R H,B = 0.53 to 0.97). The largest phase correlations were found in the 0 to 50 Kb bins (R H,B = 0.92 to 0.97). Estimated LD decreased rapidly with increasing distance between SNP, however, useful linkage for GWAS and GS (r(2) > 0.2) was found spanning to ~50 Kb. CONCLUSIONS: Panels containing about 50,000 and 150,000 SNP markers are necessary to detect minimal levels of LD between adjacent markers that would be useful for GWAS and GS studies to Hereford and Braford breeds, respectively. Markers are expected to be linked to the same QTL alleles in distances < 50 Kb in both populations due to observed high persistence of phase levels.

A hidden Markov approach for ascertaining cSNP genotypes from RNA sequence data in the presence of allelic imbalance by exploiting linkage disequilibrium.

BACKGROUND: Allelic specific expression (ASE) increases our understanding of the genetic control of gene expression and its links to phenotypic variation. ASE testing is implemented through binomial or beta-binomial tests of sequence read counts of alternative alleles at a cSNP of interest in heterozygous individuals. This requires prior ascertainment of the cSNP genotypes for all individuals. To meet the needs, we propose hidden Markov methods to call SNPs from next generation RNA sequence data when ASE possibly exists. RESULTS: We propose two hidden Markov models (HMMs), HMM-ASE and HMM-NASE that consider or do not consider ASE, respectively, in order to improve genotyping accuracy. Both HMMs have the advantages of calling the genotypes of several SNPs simultaneously and allow mapping error which, respectively, utilize the dependence among SNPs and correct the bias due to mapping error. In addition, HMM-ASE exploits ASE information to further improve genotype accuracy when the ASE is likely to be present. Simulation results indicate that the HMMs proposed demonstrate a very good prediction accuracy in terms of controlling both the false discovery rate (FDR) and the false negative rate (FNR). When ASE is present, the HMM-ASE had a lower FNR than HMM-NASE, while both can control the false discovery rate (FDR) at a similar level. By exploiting linkage disequilibrium (LD), a real data application demonstrate that the proposed methods have better sensitivity and similar FDR in calling heterozygous SNPs than the VarScan method. Sensitivity and FDR are similar to that of the BCFtools and Beagle methods. The resulting genotypes show good properties for the estimation of the genetic parameters and ASE ratios. CONCLUSIONS: We introduce HMMs, which are able to exploit LD and account for the ASE and mapping errors, to simultaneously call SNPs from the next generation RNA sequence data. The method introduced can reliably call for cSNP genotypes even in the presence of ASE and under low sequencing coverage. As a byproduct, the proposed method is able to provide predictions of ASE ratios for the heterozygous genotypes, which can then be used for ASE testing.

Whole blood microarray analysis of pigs showing extreme phenotypes after a porcine reproductive and respiratory syndrome virus infection.

BACKGROUND: The presence of variability in the response of pigs to Porcine Reproductive and Respiratory Syndrome virus (PRRSv) infection, and recent demonstration of significant genetic control of such responses, leads us to believe that selection towards more disease resistant pigs could be a valid strategy to reduce its economic impact on the swine industry. To find underlying molecular differences in PRRS susceptible versus more resistant pigs, 100 animals with extremely different growth rates and viremia levels after PRRSv infection were selected from a total of 600 infected pigs. A microarray experiment was conducted on whole blood RNA samples taken at 0, 4 and 7 days post infection (dpi) from these pigs. From these data, we examined associations of gene expression with weight gain and viral load phenotypes. The single nucleotide polymorphism (SNP) marker WUR10000125 (WUR) on the porcine 60 K SNP chip was shown to be associated with viral load and weight gain after PRRSv infection, and so the effect of the WUR10000125 (WUR) genotype on expression in whole blood was also examined. RESULTS: Limited information was obtained through linear modeling of blood gene differential expression (DE) that contrasted pigs with extreme phenotypes, for growth or viral load or between animals with different WUR genotype. However, using network-based approaches, molecular pathway differences between extreme phenotypic classes could be identified. Several gene clusters of interest were found when Weighted Gene Co-expression Network Analysis (WGCNA) was applied to 4 dpi contrasted with 0 dpi data. The expression pattern of one such cluster of genes correlated with weight gain and WUR genotype, contained numerous immune response genes such as cytokines, chemokines, interferon type I stimulated genes, apoptotic genes and genes regulating complement activation. In addition, Partial Correlation and Information Theory (PCIT) identified differentially hubbed (DH) genes between the phenotypically divergent groups. GO enrichment revealed that the target genes of these DH genes are enriched in adaptive immune pathways. CONCLUSION: There are molecular differences in blood RNA patterns between pigs with extreme phenotypes or with a different WUR genotype in early responses to PRRSv infection, though they can be quite subtle and more difficult to discover with conventional DE expression analyses. Co-expression analyses such as WGCNA and PCIT can be used to reveal network differences between such extreme response groups.

Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations.

BACKGROUND: Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects. RESULTS: The standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements.The following steps are used to locate genome segments displaying strong association. The marker with the highest - log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance. CONCLUSIONS: The standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.

Current challenges and future of agricultural genomes to phenomes in the USA.

Dramatic improvements in measuring genetic variation across agriculturally relevant populations (genomics) must be matched by improvements in identifying and measuring relevant trait variation in such populations across many environments (phenomics). Identifying the most critical opportunities and challenges in genome to phenome (G2P) research is the focus of this paper. Previously (Genome Biol, 23(1):1-11, 2022), we laid out how Agricultural Genome to Phenome Initiative (AG2PI) will coordinate activities with USA federal government agencies expand public-private partnerships, and engage with external stakeholders to achieve a shared vision of future the AG2PI. Acting on this latter step, AG2PI organized the "Thinking Big: Visualizing the Future of AG2PI" two-day workshop held September 9-10, 2022, in Ames, Iowa, co-hosted with the United State Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA). During the meeting, attendees were asked to use their experience and curiosity to review the current status of agricultural genome to phenome (AG2P) work and envision the future of the AG2P field. The topic summaries composing this paper are distilled from two 1.5-h small group discussions. Challenges and solutions identified across multiple topics at the workshop were explored. We end our discussion with a vision for the future of agricultural progress, identifying two areas of innovation needed: (1) innovate in genetic improvement methods development and evaluation and (2) innovate in agricultural research processes to solve societal problems. To address these needs, we then provide six specific goals that we recommend be implemented immediately in support of advancing AG2P research.

Methods of tagSNP selection and other variables affecting imputation accuracy in swine.

BACKGROUND: Genotype imputation is a cost efficient alternative to use of high density genotypes for implementing genomic selection. The objective of this study was to investigate variables affecting imputation accuracy from low density tagSNP (average distance between tagSNP from 100kb to 1Mb) sets in swine, selected using LD information, physical location, or accuracy for genotype imputation. We compared results of imputation accuracy based on several sets of low density tagSNP of varying densities and selected using three different methods. In addition, we assessed the effect of varying size and composition of the reference panel of haplotypes used for imputation. RESULTS: TagSNP density of at least 1 tagSNP per 340kb (~7000 tagSNP) selected using pairwise LD information was necessary to achieve average imputation accuracy higher than 0.95. A commercial low density (9K) tagSNP set for swine was developed concurrent to this study and an average accuracy of imputation of 0.951 based on these tagSNP was estimated. Construction of a haplotype reference panel was most efficient when these haplotypes were obtained from randomly sampled individuals. Increasing the size of the original reference haplotype panel (128 haplotypes sampled from 32 sire/dam/offspring trios phased in a previous study) led to an overall increase in imputation accuracy (IA = 0.97 with 512 haplotypes), but was especially useful in increasing imputation accuracy of SNP with MAF below 0.1 and for SNP located in the chromosomal extremes (within 5% of chromosome end). CONCLUSION: The new commercially available 9K tagSNP set can be used to obtain imputed genotypes with high accuracy, even when imputation is based on a comparably small panel of reference haplotypes (128 haplotypes). Average imputation accuracy can be further increased by adding haplotypes to the reference panel. In addition, our results show that randomly sampling individuals to genotype for the construction of a reference haplotype panel is more cost efficient than specifically sampling older animals or trios with no observed loss in imputation accuracy. We expect that the use of imputed genotypes in swine breeding will yield highly accurate predictions of GEBV, based on the observed accuracy and reported results in dairy cattle, where genomic evaluation of some individuals is based on genotypes imputed with the same accuracy as our Yorkshire population.

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels.

BACKGROUND: F(2) resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F(2) populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F(2) individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F(2) cross to estimate imputation accuracy under several genotyping scenarios. RESULTS: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F(2), IA reaches 0.99. In order to attain such high imputation accuracy the F(0) and F(1) generations should be genotyped at high density. Alternatively, when only the F(0) is genotyped at HD, while F(1) and F(2) are genotyped with a 9K panel, IA drops to 0.90. CONCLUSIONS: Combining 60K and 9K panels with imputation in F(2) populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.

Estimation of linkage disequilibrium in four US pig breeds.

BACKGROUND: The success of marker assisted selection depends on the amount of linkage disequilibrium (LD) across the genome. To implement marker assisted selection in the swine breeding industry, information about extent and degree of LD is essential. The objective of this study is to estimate LD in four US breeds of pigs (Duroc, Hampshire, Landrace, and Yorkshire) and subsequently calculate persistence of phase among them using a 60 k SNP panel. In addition, we report LD when using only a fraction of the available markers, to estimate persistence of LD over distance. RESULTS: Average r2 between adjacent SNP across all chromosomes was 0.36 for Landrace, 0.39 for Yorkshire, 0.44 for Hampshire and 0.46 for Duroc. For markers 1 Mb apart, r2 ranged from 0.15 for Landrace to 0.20 for Hampshire. Reducing the marker panel to 10% of its original density, average r2 ranged between 0.20 for Landrace to 0.25 for Duroc. We also estimated persistence of phase as a measure of prediction reliability of markers in one breed by those in another and found that markers less than 10 kb apart could be predicted with a maximal accuracy of 0.92 for Landrace with Yorkshire. CONCLUSIONS: Our estimates of LD, although in good agreement with previous reports, are more comprehensive and based on a larger panel of markers. Our estimates also confirmed earlier findings reporting higher LD in pigs than in American Holstein cattle, especially at increasing marker distances (> 1 Mb). High average LD (r2 > 0.4) between adjacent SNP found in this study is an important precursor for the implementation of marker assisted selection within a livestock species.Results of this study are relevant to the US purebred pig industry and critical for the design of programs of whole genome marker assisted evaluation and selection. In addition, results indicate that a more cost efficient implementation of marker assisted selection using low density panels with genotype imputation, would be feasible for these breeds.

Hierarchical Bayesian modeling of heterogeneous cluster- and subject-level associations between continuous and binary outcomes in dairy production.

The augmentation of categorical outcomes with underlying Gaussian variables in bivariate generalized mixed effects models has facilitated the joint modeling of continuous and binary response variables. These models typically assume that random effects and residual effects (co)variances are homogeneous across all clusters and subjects, respectively. Motivated by conflicting evidence about the association between performance outcomes in dairy production systems, we consider the situation where these (co)variance parameters may themselves be functions of systematic and/or random effects. We present a hierarchical Bayesian extension of bivariate generalized linear models whereby functions of the (co)variance matrices are specified as linear combinations of fixed and random effects following a square-root-free Cholesky reparameterization that ensures necessary positive semidefinite constraints. We test the proposed model by simulation and apply it to the analysis of a dairy cattle data set in which the random herd-level and residual cow-level effects (co)variances between a continuous production trait and binary reproduction trait are modeled as functions of fixed management effects and random cluster effects.

The Social Life of Pigs: Changes in Affiliative and Agonistic Behaviors following Mixing.

This study investigated potentially affiliative behaviors in grow-finish pigs, how these behaviors changed over time and their relationship to agonistic behaviors. A total of 257 Yorkshire barrows were observed for agonistic (reciprocal fights, attacks) and affiliative (nosing, play, non-agonistic contact) behaviors after mixing (at 10 weeks of age), and weeks 3, 6, and 9 after mix. The least square means of affiliative behaviors were compared across time points. Relationships among affiliative and agonistic behaviors were assessed using generalized linear mixed models. Non-agonistic contact with conspecifics increased until week 6 then remained stable between weeks 6 and 9. Nosing was highest at mix, then decreased in the following weeks. Play was lowest at mix and highest at week 3. Affiliative behaviors were negatively related with aggression at mix (p < 0.001). Pigs who engaged in play and nosing behaviors were more likely to be involved in agonistic interactions in the weeks after mixing (p < 0.05), while pigs engaging in non-agonistic contact were less likely to be involved in agonistic interactions (p < 0.001). There appear to be relationships between affiliative and agonistic behaviors in pigs, with contact being the most predictive of less aggression. Future studies could focus on promoting positive non-agonistic contact in unfamiliar pigs as a way to mitigate aggressive interactions.

Energy balance modulates colon tumor growth: Interactive roles of insulin and estrogen.

Obesity increases colorectal cancer (CRC) risk and progression. However, the impact of obesity on CRC in women is dependent on ovarian hormone status. The purpose of this study was to determine the interactive roles of obesity and ovarian hormones on serum markers of inflammation, cell signaling, and transplanted colon tumor growth. Female C57BL/6 mice (6 wk) were either ovariectomized (OVX) or ovaries left intact (nonovariectomized, NOVX) and randomized to receive a (1) control, (2) 30% calorie-restricted (CR), or (3) diet-induced obese (DIO) diet regimen for 20 wk to induce differing levels of adiposity. Serum was collected and inflammatory and metabolic markers were measured using an antibody array (62 proteins) and ELISAs. Mice were subcutaneously injected with syngeneic MC38 colon cancer cells after 20 wk and sacrificed 4 wk later. CR mice had the smallest tumors irrespective of hormone status, whereas the largest tumors were observed in DIO-OVX mice. Glucose tolerance was impaired in OVX mice, being most severe in the DIO-OVX group. Cytokine arrays suggested that in CR animals, inhibition of tumor growth paralleled insulin sensitivity and associated changes in leptin, adiponectin, and IGF-BPs. Conversely, in DIO-OVX animals, tumor growth was associated with insulin and leptin resistance as well as higher levels of pro-inflammatory proteins. In vitro, leptin and adiponectin had no effect, whereas insulin induced MC38 cell proliferation and MAPK activation. Co-treatment with estrogen blocked the stimulatory effects of insulin. Thus, our in vitro and in vivo data indicate female reproductive hormones have a modulating effect on obesity-induced insulin resistance and inflammation, which may directly or indirectly influence CRC progression.