Genotypic and phenotypic characterisation of three local chicken ecotypes of Ghana based on principal component analysis and body measurements.

This study aimed to characterise three Ghanaian local chicken ecotypes, namely, Interior Savannah, Forest, and Coastal Savannah, based on morphological data and single nucleotide polymorphism (SNP) genotypes. Morphological data including body weight, shank length, body girth, back length, thigh length, beak length, comb length, and wattle length were collected from 250 local chickens. DNA isolated from blood of 1,440 local chickens was used for SNP genotyping with the Affymetrix chicken 600k SNP chip. Principal component analysis showed that Forest and Coastal Savannah birds were closely related. Generally, all three ecotypes exhibited high genetic diversity, especially birds from the Interior Savannah zone. Morphological characterisation showed that ecotype (p = 0.016) and sex (p = 0.000) had significant effects on body weight. Birds of the Interior Savannah ecotype were the heaviest (p = 0.004), with mean weights of 1.23 kg for females and 1.40 kg for males. Sex also had a strong significant effect on most of the morphological measurements, but the sex * ecotype interaction effect was not significant. Very few of the feather phenotypes previously reported to be associated with heat resistance-frizzle (2%) and naked neck (1.6%)-were found in the studied populations. It is concluded that the three local ecotypes are genetically diverse but with similar morphological features and the information provided would be useful for future selection decisions.

Use of the linear regression method to evaluate population accuracy of predictions from non-linear models.

Background: To address the limitations of commonly used cross-validation methods, the linear regression method (LR) was proposed to estimate population accuracy of predictions based on the implicit assumption that the fitted model is correct. This method also provides two statistics to determine the adequacy of the fitted model. The validity and behavior of the LR method have been provided and studied for linear predictions but not for nonlinear predictions. The objectives of this study were to 1) provide a mathematical proof for the validity of the LR method when predictions are based on conditional means, regardless of whether the predictions are linear or non-linear 2) investigate the ability of the LR method to detect whether the fitted model is adequate or inadequate, and 3) provide guidelines on how to appropriately partition the data into training and validation such that the LR method can identify an inadequate model. Results: We present a mathematical proof for the validity of the LR method to estimate population accuracy and to determine whether the fitted model is adequate or inadequate when the predictor is the conditional mean, which may be a non-linear function of the phenotype. Using three partitioning scenarios of simulated data, we show that the one of the LR statistics can detect an inadequate model only when the data are partitioned such that the values of relevant predictor variables differ between the training and validation sets. In contrast, we observed that the other LR statistic was able to detect an inadequate model for all three scenarios. Conclusion: The LR method has been proposed to address some limitations of the traditional approach of cross-validation in genetic evaluation. In this paper, we showed that the LR method is valid when the model is adequate and the conditional mean is the predictor, even when it is a non-linear function of the phenotype. We found one of the two LR statistics is superior because it was able to detect an inadequate model for all three partitioning scenarios (i.e., between animals, by age within animals, and between animals and by age) that were studied.

Genomic Regions and Candidate Genes Affecting Response to Heat Stress with Newcastle Virus Infection in Commercial Layer Chicks Using Chicken 600K Single Nucleotide Polymorphism Array.

Heat stress results in significant economic losses to the poultry industry. Genetics plays an important role in chickens adapting to the warm environment. Physiological parameters such as hematochemical parameters change in response to heat stress in chickens. To explore the genetics of heat stress resilience in chickens, a genome-wide association study (GWAS) was conducted using Hy-Line Brown layer chicks subjected to either high ambient temperature or combined high temperature and Newcastle disease virus infection. Hematochemical parameters were measured during three treatment phases: acute heat stress, chronic heat stress, and chronic heat stress combined with NDV infection. Significant changes in blood parameters were recorded for 11 parameters (sodium (Na+, potassium (K+), ionized calcium (iCa2+), glucose (Glu), pH, carbon dioxide partial pressure (PCO2), oxygen partial pressure (PO2), total carbon dioxide (TCO2), bicarbonate (HCO3), base excess (BE), and oxygen saturation (sO2)) across the three treatments. The GWAS revealed 39 significant SNPs (p < 0.05) for seven parameters, located on Gallus gallus chromosomes (GGA) 1, 3, 4, 6, 11, and 12. The significant genomic regions were further investigated to examine if the genes within the regions were associated with the corresponding traits under heat stress. A candidate gene list including genes in the identified genomic regions that were also differentially expressed in chicken tissues under heat stress was generated. Understanding the correlation between genetic variants and resilience to heat stress is an important step towards improving heat tolerance in poultry.

Genetic architecture of body weight, carcass, and internal organs traits of Ghanaian local chickens.

Information on the genetic architecture of the production traits of indigenous African chicken is limited. We performed a genome-wide association study using imputed Affymetrix Axiom® 600K SNP-chip genotypes on 1,113 chickens from three agroecological zones of Ghana. After quality control, a total of 382,240 SNPs remained. Variance components and heritabilities for some growth, carcass and internal organ traits were estimated. The genetic and phenotypic correlations among these traits were also estimated. The estimated heritabilities of body weight at week 22 (BW22), average daily gain (ADG), dressed weight, breast weight, thigh weight, wing weight, drumstick weight, and neck weight were high and ranged from 0.50 to 0.69. Estimates of heritabilities for head weight, shank weight, and gizzard weight were moderate (0.31-0.35) while those of liver weight, back weight, dressing percentage, and heart weight were low (0.13-0.21). The estimated heritabilities of dressed weight, breast weight, wing weight, drumstick weight, neck weight, shank weight, and gizzard weight, corrected for BW22, were moderate (0.29-0.38), while the remaining traits had low heritability estimates (0.13-0.21). A total of 58 1-Mb SNP windows on chromosomes 1, 2, 4, 5, 6, 7, 8, 9, 13, 18, and 33 each explained more than 1% of the genetic variance for at least one of these traits. These genomic regions contained many genes previously reported to have effects on growth, carcass, and internal organ traits of chickens, including EMX2, CALCUL1, ACVR1B, CACNB1, RB1, MLNR, FOXO1, NCARPG, LCORL, LAP3, LDB2, KPNA3, and CAB39L. The moderate to high heritability estimates and high positive genetic correlations suggest that BW22, ADG, dressed weight, breast weight, thigh weight, wing weight, drumstick weight, and neck weight could be improved through selective breeding.

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.

Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality.

To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.

Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience.

BACKGROUND: Disease resilience is the ability of an animal to maintain productive performance under disease conditions and is an important selection target. In pig breeding programs, disease resilience must be evaluated on selection candidates without exposing them to disease. To identify potential genetic indicators for disease resilience that can be measured on selection candidates, we focused on the blood transcriptome of 1594 young healthy pigs with subsequent records on disease resilience. Transcriptome data were obtained by 3'mRNA sequencing and genotype data were from a 650 K genotyping array. RESULTS: Heritabilities of the expression of 16,545 genes were estimated, of which 5665 genes showed significant estimates of heritability (p < 0.05), ranging from 0.05 to 0.90, with or without accounting for white blood cell composition. Genes with heritable expression levels were spread across chromosomes, but were enriched in the swine leukocyte antigen region (average estimate > 0.2). The correlation of heritability estimates with the corresponding estimates obtained for genes expressed in human blood was weak but a sizable number of genes with heritable expression levels overlapped. Genes with heritable expression levels were significantly enriched for biological processes such as cell activation, immune system process, stress response, and leukocyte activation, and were involved in various disease annotations such as RNA virus infection, including SARS-Cov2, as well as liver disease, and inflammation. To estimate genetic correlations with disease resilience, 3205 genotyped pigs, including the 1594 pigs with transcriptome data, were evaluated for disease resilience following their exposure to a natural polymicrobial disease challenge. Significant genetic correlations (p < 0.05) were observed with all resilience phenotypes, although few exceeded expected false discovery rates. Enrichment analysis of genes ranked by estimates of genetic correlations with resilience phenotypes revealed significance for biological processes such as regulation of cytokines, including interleukins and interferons, and chaperone mediated protein folding. CONCLUSIONS: These results suggest that expression levels in the blood of young healthy pigs for genes in biological pathways related to immunity and endoplasmic reticulum stress have potential to be used as genetic indicator traits to select for disease resilience.

The WUR0000125 PRRS resilience SNP had no apparent effect on pigs' infectivity and susceptibility in a novel transmission trial.

BACKGROUND: Porcine reproductive and respiratory syndrome (PRRS) remains one of the most important infectious diseases for the pig industry. A novel small-scale transmission experiment was designed to assess whether the WUR0000125 (WUR for Wageningen University and Research) PRRS resilience single nucleotide polymorphism (SNP) confers lower susceptibility and infectivity to pigs under natural porcine reproductive and respiratory syndrome virus (PRRSV-2) transmission. METHODS: Commercial full- and half-sib piglets (n = 164) were assigned as either Inoculation, Shedder, or Contact pigs. Pigs were grouped according to their relatedness structure and WUR genotype, with R- and R+ referring to pigs with zero and one copy of the dominant WUR resilience allele, respectively. Barcoding of the PRRSV-2 strain (SD09-200) was applied to track pig genotype-specific transmission. Blood and nasal swab samples were collected and concentrations of PRRSV-2 were determined by quantitative (q)-PCR and cell culture and expressed in units of median tissue culture infectious dose (TCID50). The Log10TCID50 at each sampling event, derived infection status, and area under the curve (AUC) were response variables in linear and generalized linear mixed models to infer WUR genotype differences in Contact pig susceptibility and Shedder pig infectivity. RESULTS: All Shedder and Contact pigs, except one, became infected through natural transmission. There was no significant (p > 0.05) effect of Contact pig genotype on any virus measures that would indicate WUR genotype differences in susceptibility. Contact pigs tended to have higher serum AUC (p = 0.017) and log10TCID50 (p = 0.034) when infected by an R+ shedder, potentially due to more infectious R+ shedders at the early stages of the transmission trial. However, no significant Shedder genotype effect was found in serum (p = 0.274) or nasal secretion (p = 0.951) that would indicate genotype differences in infectivity. CONCLUSIONS: The novel design demonstrated that it is possible to estimate genotype effects on Shedder pig infectivity and Contact pig susceptibility that are not confounded by family effects. The study, however, provided no supportive evidence that genetic selection on WUR genotype would affect PRRSV-2 transmission. The results of this study need to be independently validated in a larger trial using different PRRSV strains before dismissing the effects of the WUR marker or the previously detected GBP5 gene on PRRSV transmission.

Identification of the genetic basis of sow pelvic organ prolapse.

Introduction: Pelvic organ prolapse (POP) is one contributor to recent increases in sow mortality that have been observed in some populations and environments, leading to financial losses and welfare concerns. Methods: With inconsistent previous reports, the objective here was to investigate the role of genetics on susceptibility to POP, using data on 30,429 purebred sows, of which 14,186 were genotyped (25K), collected from 2012 to 2022 in two US multiplier farms with a high POP incidence of 7.1% among culled and dead sows and ranging from 2% to 4% of all sows present by parity. Given the low incidence of POP for parities 1 and >6, only data from parities 2 to 6 were retained for analyses. Genetic analyses were conducted both across parities, using cull data (culled for POP versus another reason), and by parity, using farrowing data. (culled for POP versus culled for another reason or not culled). Results and Discussion: Estimates of heritability from univariate logit models on the underlying scale were 0.35 ± 0.02 for the across-parity analysis and ranged from 0.41 ± 0.03 in parity 2 to 0.15 ± 0.07 in parity 6 for the by-parity analyses. Estimates of genetic correlations of POP between parities based on bivariate linear models indicated a similar genetic basis of POP across parities but less similar with increasing distance between parities. Genome wide association analyses revealed six 1 Mb windows that explained more than 1% of the genetic variance in the across-parity data. Most regions were confirmed in several by-parity analyses. Functional analyses of the identified genomic regions showed a potential role of several genes on chromosomes 1, 3, 7, 10, 12, and 14 in susceptibility to POP, including the Estrogen Receptor gene. Gene set enrichment analyses showed that genomic regions that explained more variation for POP were enriched for several terms from custom transcriptome and gene ontology libraries. Conclusion: The influence of genetics on susceptibility to POP in this population and environment was confirmed and several candidate genes and biological processes were identified that can be targeted to better understand and mitigate the incidence of POP.

Genome wide association study of thyroid hormone levels following challenge with porcine reproductive and respiratory syndrome virus.

Introduction: Porcine reproductive and respiratory syndrome virus (PRRSV) causes respiratory disease in piglets and reproductive disease in sows. Piglet and fetal serum thyroid hormone (i.e., T3 and T4) levels decrease rapidly in response to Porcine reproductive and respiratory syndrome virus infection. However, the genetic control of T3 and T4 levels during infection is not completely understood. Our objective was to estimate genetic parameters and identify quantitative trait loci (QTL) for absolute T3 and/or T4 levels of piglets and fetuses challenged with Porcine reproductive and respiratory syndrome virus. Methods: Sera from 5-week-old pigs (N = 1792) at 11 days post inoculation (DPI) with Porcine reproductive and respiratory syndrome virus were assayed for T3 levels (piglet_T3). Sera from fetuses (N = 1,267) at 12 or 21 days post maternal inoculation (DPMI) with Porcine reproductive and respiratory syndrome virus of sows (N = 145) in late gestation were assayed for T3 (fetal_T3) and T4 (fetal_T4) levels. Animals were genotyped using 60 K Illumina or 650 K Affymetrix single nucleotide polymorphism (SNP) panels. Heritabilities, phenotypic correlations, and genetic correlations were estimated using ASREML; genome wide association studies were performed for each trait separately using Julia for Whole-genome Analysis Software (JWAS). Results: All three traits were low to moderately heritable (10%-16%). Phenotypic and genetic correlations of piglet_T3 levels with weight gain (0-42 DPI) were 0.26 ± 0.03 and 0.67 ± 0.14, respectively. Nine significant quantitative trait loci were identified for piglet_T3, on Sus scrofa chromosomes (SSC) 3, 4, 5, 6, 7, 14, 15, and 17, and collectively explaining 30% of the genetic variation (GV), with the largest quantitative trait loci identified on SSC5, explaining 15% of the genetic variation. Three significant quantitative trait loci were identified for fetal_T3 on SSC1 and SSC4, which collectively explained 10% of the genetic variation. Five significant quantitative trait loci were identified for fetal_T4 on SSC1, 6, 10, 13, and 15, which collectively explained 14% of the genetic variation. Several putative immune-related candidate genes were identified, including CD247, IRF8, and MAPK8. Discussion: Thyroid hormone levels following Porcine reproductive and respiratory syndrome virus infection were heritable and had positive genetic correlations with growth rate. Multiple quantitative trait loci with moderate effects were identified for T3 and T4 levels during challenge with Porcine reproductive and respiratory syndrome virus and candidate genes were identified, including several immune-related genes. These results advance our understanding of growth effects of both piglet and fetal response to Porcine reproductive and respiratory syndrome virus infection, revealing factors associated with genomic control of host resilience.

Plasma protein levels of young healthy pigs as indicators of disease resilience.

Selection for disease resilience, which refers to the ability of an animal to maintain performance when exposed to disease, can reduce the impact of infectious diseases. However, direct selection for disease resilience is challenging because nucleus herds must maintain a high health status. A possible solution is indirect selection of indicators of disease resilience. To search for such indicators, we conducted phenotypic and genetic quantitative analyses of the abundances of 377 proteins in plasma samples from 912 young and visually healthy pigs and their relationships with performance and subsequent disease resilience after natural exposure to a polymicrobial disease challenge. Abundances of 100 proteins were significantly heritable (false discovery rate (FDR) <0.10). The abundance of some proteins was or tended to be genetically correlated (rg) with disease resilience, including complement system proteins (rg = -0.24, FDR = 0.001) and IgG heavy chain proteins (rg = -0.68, FDR = 0.22). Gene set enrichment analyses (FDR < 0.2) based on phenotypic and genetic associations of protein abundances with subsequent disease resilience revealed many pathways related to the immune system that were unfavorably associated with subsequent disease resilience, especially the innate immune system. It was not possible to determine whether the observed levels of these proteins reflected baseline levels in these young and visually healthy pigs or were the result of a response to environmental disturbances that the pigs were exposed to before sample collection. Nevertheless, results show that, under these conditions, the abundance of proteins in some immune-related pathways can be used as phenotypic and genetic predictors of disease resilience and have the potential for use in pig breeding and management.

A challenge of selection for disease resilience is that it is difficult to directly select pigs that have greater resilience to multiple diseases in the healthy nucleus herd environment which is essential for breeding programs. A possible alternative is to select an indicator trait or marker that can be measured in a healthy setting, is heritable, and is associated with the genetics of disease resilience. In this study, we investigated plasma protein levels measured on young healthy pigs as indicator traits to select for disease resilience. For this purpose, we used plasma proteome data collected prior to the natural exposure of nursery pigs to multiple diseases, performed phenotypic and genetic quantitative analyses, and investigated their relationships with disease resilience. Our results suggest that plasma protein levels of young healthy pigs have the potential as biomarkers to select for disease resistance.

Exploration of plasma metabolite levels in healthy nursery pigs in response to environmental enrichment and disease resilience.

The purpose of this study was to explore plasma metabolite levels in young healthy pigs and their potential association with disease resilience and estimate genetic and phenotypic correlation with the change in lymphocyte concentration following disease challenge. Plasma samples were collected from 968 healthy nursery pigs over 15 batches at an average of 28 ± 3.23 d of age. Forty-four metabolites were identified and quantified by nuclear magnetic resonance. Pigs were then introduced into a natural disease challenge barn, and were classified into four groups based on the growth rate of each animal in the grow-to-finish phase (GFGR) and treatment rate (TR): resilient (RES), average (MID), susceptible (SUS), and dead (pigs that died before harvest). Blood samples were collected from all pigs before and 2 wk after disease challenge and complete blood count was determined. Environmental enrichment (inedible point source objects) was provided for half of the pigs in seven batches (N = 205) to evaluate its impact on resilience and metabolite concentrations. Concentration of all metabolites was affected by batch, while entry age affected the concentration of 16 metabolites. The concentration of creatinine was significantly lower for pigs classified as "dead" and "susceptible" when compared to "average" (P < 0.05). Pigs that received enrichment had significantly lower concentrations of six metabolites compared with pigs that did not receive enrichment (P ≤ 0.05). Both, group classification and enrichment affected metabolites that are involved in the same pathways of valine, leucine, and isoleucine biosynthesis and degradation. Resilient pigs had higher increase in lymphocyte concentration after disease challenge. The concentration of plasma l-α-aminobutyric acid was significantly negatively genetically correlated with the change in lymphocyte concentration following challenge. In conclusion, creatinine concentration in healthy nursery pigs was lower in pigs classified as susceptible or dead after disease challenge, whilst l-α-aminobutyric may be a genetic biomarker of lymphocyte response after pathogen exposure, and both deserve further investigation. Batch, entry age, and environmental enrichment were important factors affecting the concentration of metabolites and should be taken into consideration in future studies.

The focus of this study was to explore plasma metabolite levels in young healthy pigs and their potential association with health outcome classification following the exposure to a polymicrobial disease challenge. In addition, we explored the effect of the environmental enrichment on metabolite concentrations. Finally, we estimated genetic and phenotypic correlations between metabolites and the magnitude of change in lymphocytes levels following exposure to a polymicrobial disease challenge. We found that concentration of creatinine was lower in pigs that died before marketing, classified as "dead" and susceptible when compared to average group. This indicates that creatinine can be used as an early indicator of death and/or susceptibility of disease in pigs. Providing environmental enrichment affected the concentration of six metabolites and branched chain amino acids index. Such results would be very useful to design environmental enrichment strategies when pigs are challenged by disease in commercial farms. The magnitude of change in lymphocytes level was negatively genetically correlated with l-α-aminobutyric acid. This result indicates that l-αs-aminobutyric acid can be an early indicator of the magnitude of change in lymphocytes level. Such indicator can be collected from nucleus breeding herds in healthy animals and could provide an early biomarker of resilience.

Genetic Analyses of Response of Local Ghanaian Tanzanian Chicken Ecotypes to a Natural Challenge with Velogenic Newcastle Disease Virus.

Newcastle disease is a devastating poultry disease that often causes significant economic losses in poultry in the developing countries of Africa, Asia, as well as South and Central America. Velogenic Newcastle disease virus (NDV) outbreaks are associated with high mortalities, which can threaten household livelihoods, especially in the rural areas, and lead to loss of high-quality proteins in the form of meat and eggs, as well as household purchasing power. In this study, we exposed unvaccinated Ghanaian and Tanzanian chickens of six local ecotypes to velogenic NDV strains, measured NDV response traits, sequenced their DNA on a genotyping-by-sequencing platform, and performed variance component analyses. The collected phenotypes included: growth rates (pre- and post-exposure); lesion scores (gross lesion severity) in the trachea, proventriculus, intestine, and cecal tonsils; natural antibody levels; anti-NDV antibody levels at 7 days post exposure (dpe); tear and cloacal viral load at 2, 4, and 6 dpe; and survival time. Heritability estimates were low to moderate, ranging from 0.11 for average lesion scores to 0.36 for pre-exposure growth rate. Heritability estimates for survival time were 0.23 and 0.27 for the Tanzanian and Ghanaian ecotypes, respectively. Similar heritability estimates were observed when data were analyzed either separately or combined for the two countries. Survival time was genetically negatively correlated with lesion scores and with viral load. Results suggested that response to mesogenic or velogenic NDV of these local chicken ecotypes could be improved by selective breeding. Chickens that are more resilient to velogenic NDV can improve household livelihoods in developing countries.

Identification of circulating metabolites associated with wooden breast and white striping.

Current diagnostic methods for wooden breast and white striping, common breast muscle myopathies of modern commercial broiler chickens, rely on subjective examinations of the pectoralis major muscle, time-consuming microscopy, or expensive imaging technologies. Further research on these disorders would benefit from more quantitative and objective measures of disease severity that can be used in live birds. To this end, we utilized untargeted metabolomics alongside two statistical approaches to evaluate plasma metabolites associated with wooden breast and white striping in 250 male commercial broiler chickens. First, mixed linear modeling was employed to identify metabolites with a significant association with these muscle disorders and found 98 metabolites associated with wooden breast and 44 metabolites associated with white striping (q-value < 0.05). Second, a support vector machine was constructed using stepwise feature selection to determine the smallest subset of metabolites with the highest categorization accuracy for wooden breast. The final support vector machine achieved 94% accuracy using only 6 metabolites. The metabolite 3-methylhistidine, which is often used as an index of myofibrillar breakdown in skeletal muscle, was the top metabolite for both wooden breast and white striping in our mixed linear model and was also the metabolite with highest marginal prediction accuracy (82%) for wooden breast in our support vector machine. Overall, this study identified a candidate set of metabolites for an objective measure of wooden breast or white striping severity in live birds and expanded our understanding of these muscle disorders.

Effect of the host genotype at a Porcine Reproductive and Respiratory Syndrome (PRRS) resistance marker on evolution of the modified-live PRRS vaccine virus in pigs.

Porcine reproductive and respiratory syndrome; Porcine reproductive and respiratory syndrome virus; PRRS; Whole genome sequencing; Quasispecies; WUR allele.

Quality of breeding value predictions from longitudinal analyses, with application to residual feed intake in pigs.

BACKGROUND: An important goal in animal breeding is to improve longitudinal traits. The objective of this study was to explore for longitudinal residual feed intake (RFI) data, which estimated breeding value (EBV), or combination of EBV, to use in a breeding program. Linear combinations of EBV (summarized breeding values, SBV) or phenotypes (summarized phenotypes) derived from the eigenvectors of the genetic covariance matrix over time were considered, and the linear regression method (LR method) was used to facilitate the evaluation of their prediction accuracy. RESULTS: Weekly feed intake, average daily gain, metabolic body weight, and backfat thickness measured on 2435 growing French Large White pigs over a 10-week period were analysed using a random regression model. In this population, the 544 dams of the phenotyped animals were genotyped. These dams did not have own phenotypes. The quality of the predictions of SBV and breeding values from summarized phenotypes of these females was evaluated. On average, predictions of SBV at the time of selection were unbiased, slightly over-dispersed and less accurate than those obtained with additional phenotypic information. The use of genomic information did not improve the quality of predictions. The use of summarized instead of longitudinal phenotypes resulted in predictions of breeding values of similar quality. CONCLUSIONS: For practical selection on longitudinal data, the results obtained with this specific design suggest that the use of summarized phenotypes could facilitate routine genetic evaluation of longitudinal traits.

Application of Bayesian genomic prediction methods to genome-wide association analyses.

BACKGROUND: Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses. RESULTS: By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance. CONCLUSIONS: Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.

Response of three local chicken ecotypes of Ghana to lentogenic and velogenic Newcastle disease virus challenge.

This study was carried out to assess the response of three Ghanaian local chicken ecotypes to LaSota (lentogenic) and virulent field strains of Newcastle disease virus (NDV). Local chickens sampled from the Interior Savannah (IS), Forest (FO) and Coastal Savannah (CS) agro-ecological zones were bred and their offspring were challenged with LaSota NDV at 4 weeks of age. The LaSota challenge was replicated four times with different chicken groups. A total of 1438 chicks comprising 509 Coastal Savannah, 518 Forest and 411 Interior Savannah ecotypes were used. Pre- and post-challenge anti-NDV antibody titre levels were determined via ELISA assays. A second trial was conducted by introducing sick birds infected with virulent NDV to a flock of immunologically naïve chickens at 4 weeks old. Body weights were measured pre- and post-infection. Sex of the chickens was determined using a molecular method. In both trials, there was no significant difference among ecotypes in body weight and growth rate. In the LaSota trial, anti-NDV antibody titre did not differ by ecotype or sex. However, there was a positive linear relationship between body weight and antibody titre. In the velogenic NDV trial, survivability and lesion scores were similar among the three ecotypes. This study confirms that a relatively high dose of LaSota (NDV) challenge has no undesirable effect on Ghanaian local chicken ecotypes. All three Ghanaian local chicken ecotypes were susceptible to velogenic NDV challenge. Resistance to NDV by Ghanaian local chickens appears to be determined more by the individual's genetic makeup than by their ecotype.

Predictions of the accuracy of genomic prediction: connecting R2, selection index theory, and Fisher information.

BACKGROUND: Deterministic predictions of the accuracy of genomic estimated breeding values (GEBV) when combining information sources have been developed based on selection index theory (SIT) and on Fisher information (FI). These two approaches have resulted in slightly different results when considering the combination of pedigree and genomic information. Here, we clarify this apparent contradiction, both for the combination of pedigree and genomic information and for the combination of subpopulations into a joint reference population. RESULTS: First, we show that existing expressions for the squared accuracy of GEBV can be understood as a proportion of the variance explained. Next, we show that the apparent discrepancy that has been observed between accuracies based on SIT vs. FI originated from two sources. First, the FI referred to the genetic component that is captured by the marker genotypes, rather than the full genetic component. Second, the common SIT-based derivations did not account for the increase in the accuracy of GEBV due to a reduction of the residual variance when combining information sources. The SIT and FI approaches are equivalent when these sources are accounted for. CONCLUSIONS: The squared accuracy of GEBV can be understood as a proportion of the variance explained. The SIT and FI approaches for combining information for GEBV are equivalent and provide identical accuracies when the underlying assumptions are equivalent.