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Infectious diseases cause tremendous financial losses in the pork industry, emphasizing the importance of disease resilience, which is the ability of an animal to maintain performance under disease. Previously, a natural polymicrobial disease challenge model was established, in which pigs were challenged in the late nursery phase by multiple pathogens to maximize expression of genetic differences in disease resilience. Genetic analysis found that performance traits in this model, including growth rate, feed and water intake, and carcass traits, as well as clinical disease phenotypes, were heritable and could be selected for to increase disease resilience of pigs. The objectives of the current study were to identify genomic regions that are associated with disease resilience in this model, using genome-wide association studies and fine-mapping methods, and to use gene set enrichment analyses to determine whether genomic regions associated with disease resilience are enriched for previously published quantitative trait loci, functional pathways, and differentially expressed genes subject to physiological states. Multiple quantitative trait loci were detected for all recorded performance and clinical disease traits. The major histocompatibility complex region was found to explain substantial genetic variance for multiple traits, including for growth rate in the late nursery (12.8%) and finisher (2.7%), for several clinical disease traits (up to 2.7%), and for several feeding and drinking traits (up to 4%). Further fine mapping identified 4 quantitative trait loci in the major histocompatibility complex region for growth rate in the late nursery that spanned the subregions for class I, II, and III, with 1 single-nucleotide polymorphism in the major histocompatibility complex class I subregion capturing the largest effects, explaining 0.8-27.1% of genetic variance for growth rate and for multiple clinical disease traits. This single-nucleotide polymorphism was located in the enhancer of TRIM39 gene, which is involved in innate immune response. The major histocompatibility complex region was pleiotropic for growth rate in the late nursery and finisher, and for treatment and mortality rates. Growth rate in the late nursery showed strong negative genetic correlations in the major histocompatibility complex region with treatment or mortality rates (-0.62 to -0.85) and a strong positive genetic correlation with growth rate in the finisher (0.79). Gene set enrichment analyses found genomic regions associated with resilience phenotypes to be enriched for previously identified disease susceptibility and immune capacity quantitative trait loci, for genes that were differentially expressed following bacterial or virus infection and immune response, and for gene ontology terms related to immune and inflammatory response. In conclusion, the major histocompatibility complex and other quantitative trait loci that harbor immune-related genes were identified to be associated with disease resilience traits in a large-scale natural polymicrobial disease challenge. The major histocompatibility complex region was pleiotropic for growth rate under challenge and for clinical disease traits. Four quantitative trait loci were identified across the class I, II, and III subregions of the major histocompatibility complex for nursery growth rate under challenge, with 1 single-nucleotide polymorphism in the major histocompatibility complex class I subregion capturing the largest effects. The major histocompatibility complex and other quantitative trait loci identified play an important role in host response to infectious diseases and can be incorporated in selection to improve disease resilience, in particular the identified single-nucleotide polymorphism in the major histocompatibility complex class I subregion.

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.

Newcastle disease (ND) has a great impact on poultry health and welfare with its most virulent (velogenic) strain. In addition, issues exacerbated by the increase in global temperatures necessitates a greater understanding of the host immune response when facing a combination of biotic and abiotic stress factors in poultry production. Previous investigations have revealed that the host immune response is tissue-specific. The goal of this study was to identify genes and/or signaling pathways associated with immune response to NDV (Newcastle disease virus) in the trachea, an essential organ where NDV replicate after the infection, by profiling the tissue specific transcriptome response in two genetically distinct inbred chicken lines when exposed to both abiotic and biotic stressors. Fayoumis appear to be able to respond more effectively (lower viral titer, higher antibody levels, immune gene up-regulation) and earlier than Leghorns. Our results suggest NDV infection in Fayoumis appears to elicit proinflammatory processes, and pathways such as the inhibition of cell viability, cell proliferation of lymphocytes, and transactivation of RNA, more rapidly than in Leghorns. These differences in immune response converge at later timepoints which may indicate that Leghorns eventually regulate its immune response to infection. The profiling of the gene expression response in the trachea adds to our understanding of the chicken host response to NDV infection and heat stress on a whole genome level and provides potential candidate genes and signaling pathways for further investigation into the characterization of the time-specific and pathway specific responses in Fayoumis and Leghorns.

Newcastle disease (ND) is a global threat to domestic poultry, especially in rural areas of Africa and Asia, where the loss of entire backyard local chicken flocks often threatens household food security and income. To investigate the genetics of Ghanaian local chicken ecotypes to Newcastle disease virus (NDV), in this study, three popular Ghanaian chicken ecotypes (regional populations) were challenged with a lentogenic NDV strain at 28 days of age. This study was conducted in parallel with a similar study that used three popular Tanzanian local chicken ecotypes and after two companion studies in the United States, using Hy-line Brown commercial laying birds. In addition to growth rate, NDV response traits were measured following infection, including anti-NDV antibody levels [pre-infection and 10 days post-infection (dpi)], and viral load (2 and 6 dpi). Genetic parameters were estimated, and two genome-wide association study analysis methods were used on data from 1,440 Ghanaian chickens that were genotyped on a chicken 600K Single Nucleotide Polymorphism (SNP) chip. Both Ghana and Tanzania NDV challenge studies revealed moderate to high (0.18 - 0.55) estimates of heritability for all traits, except viral clearance where the heritability estimate was not different from zero for the Tanzanian ecotypes. For the Ghana study, 12 quantitative trait loci (QTL) for growth and/or response to NDV from single-SNP analyses and 20 genomic regions that explained more than 1% of genetic variance using the Bayes B method were identified. Seven of these windows were also identified as having at least one significant SNP in the single SNP analyses for growth rate, anti-NDV antibody levels, and viral load at 2 and 6 dpi. An important gene for growth during stress, CHORDC1 associated with post-infection growth rate was identified as a positional candidate gene, as well as other immune related genes, including VAV2, IL12B, DUSP1, and IL17B. The QTL identified in the Ghana study did not overlap with those identified in the Tanzania study. However, both studies revealed QTL with genes vital for growth and immune response during NDV challenge. The Tanzania parallel study revealed an overlapping QTL on chromosome 24 for viral load at 6 dpi with the US NDV study in which birds were challenged with NDV under heat stress. This QTL region includes genes related to immune response, including TIRAP, ETS1, and KIRREL3. The moderate to high estimates of heritability and the identified QTL suggest that host response to NDV of local African chicken ecotypes can be improved through selective breeding to enhance increased NDV resistance and vaccine efficacy.

Disease has large negative impacts on poultry production. A more comprehensive understanding of host-pathogen interaction can lead to new and improved strategies to maintain health. In particular, host genetic factors can lead to a more effective response to pathogens, hereafter termed resistance. Fayoumi and Leghorn chicken lines have demonstrated relative resistance and susceptibility, respectively, to the Newcastle disease virus (NDV) vaccine strain and many other pathogens. This biological model was used to better understand the host response to a vaccine strain of NDV across three tissues and time points, using RNA-seq. Analyzing the Harderian gland, trachea, and lung tissues together using weighted gene co-expression network analysis (WGCNA) identified important genes that were co-expressed and associated with parameters including: genetic line, days post-infection (dpi), challenge status, sex, and tissue. Pathways and driver genes, such as EIF2AK2, MPEG1, and TNFSF13B, associated with challenge status, dpi, and genetic line were of particular interest as candidates for disease resistance. Overall, by jointly analyzing the three tissues, this study identified genes and gene networks that led to a more comprehensive understanding of the whole animal response to lentogenic NDV than that obtained by analyzing the tissues individually.

As a major infectious disease in chickens, Newcastle disease virus (NDV) causes considerable economic losses in the poultry industry, especially in developing countries where there is limited access to effective vaccination. Therefore, enhancing resistance to the virus in commercial chickens through breeding is a promising way to promote poultry production. In this study, we investigated gene expression changes at 2 and 6 days post inoculation (dpi) at day 21 with a lentogenic NDV in a commercial egg-laying chicken hybrid using RNA sequencing analysis. By comparing NDV-challenged and non-challenged groups, 526 differentially expressed genes (DEGs) (false discovery rate (FDR) < 0.05) were identified at 2 dpi, and only 36 at 6 dpi. For the DEGs at 2 dpi, Ingenuity Pathway Analysis predicted inhibition of multiple signaling pathways in response to NDV that regulate immune cell development and activity, neurogenesis, and angiogenesis. Up-regulation of interferon induced protein with tetratricopeptide repeats 5 (IFIT5) in response to NDV was consistent between the current and most previous studies. Sprouty RTK signaling antagonist 1 (SPRY1), a DEG in the current study, is in a significant quantitative trait locus associated with virus load at 6 dpi in the same population. These identified pathways and DEGs provide potential targets to further study breeding strategy to enhance NDV resistance in chickens.

Newcastle disease virus (NDV) is a highly contagious avian pathogen that poses a tremendous threat to poultry producers in endemic zones due to its epidemic potential. To investigate host genetic resistance to NDV while under the effects of heat stress, a genome-wide association study (GWAS) was performed on Hy-Line Brown layer chickens that were challenged with NDV while under high ambient temperature to identify regions associated with host viral titer, circulating anti-NDV antibody titer, and body weight change. A single nucleotide polymorphism (SNP) on chromosome 1 was associated with viral titer at two days post-infection (dpi), while 30 SNPs spanning a quantitative trait loci (QTL) on chromosome 24 were associated with viral titer at 6 dpi. Immune related genes, such as CAMK1d and CCDC3 on chromosome 1, associated with viral titer at 2 dpi, and TIRAP, ETS1, and KIRREL3, associated with viral titer at 6 dpi, were located in two QTL regions for viral titer that were identified in this study. This study identified genomic regions and candidate genes that are associated with response to NDV during heat stress in Hy-Line Brown layer chickens. Regions identified for viral titer on chromosome 1 and 24, at 2 and 6 dpi, respectively, included several genes that have key roles in regulating the immune response.

Newcastle Disease (ND) is a continuing global threat to domestic poultry, especially in developing countries, where severe outbreaks of velogenic ND virus (NDV) often cause major economic losses to households. Local chickens are of great importance to rural family livelihoods through provision of high-quality protein. To investigate the genetic basis of host response to NDV, three popular Tanzanian chicken ecotypes (regional populations) were challenged with a lentogenic (vaccine) strain of NDV at 28 days of age. Various host response phenotypes, including anti-NDV antibody levels (pre-infection and 10 days post-infection, dpi), and viral load (2 and 6 dpi) were measured, in addition to growth rate. We estimated genetic parameters and conducted genome-wide association study analyses by genotyping 1399 chickens using the Affymetrix 600K chicken SNP chip. Estimates of heritability of the evaluated traits were moderate (0.18-0.35). Five quantitative trait loci (QTL) associated with growth and/or response to NDV were identified by single-SNP analyses, with some regions explaining ≥1% of genetic variance based on the Bayes-B method. Immune related genes, such as ETS1, TIRAP, and KIRREL3, were located in regions associated with viral load at 6 dpi. The moderate estimates of heritability and identified QTL indicate that NDV response traits may be improved through selective breeding of chickens to enhance increased NDV resistance and vaccine efficacy in Tanzanian local ecotypes.

: Newcastle disease virus (NDV) is a devastating worldwide poultry pathogen with major implications for global food security. In this study, two highly inbred and genetically distinct chicken lines, Fayoumis and Leghorns, were exposed to a lentogenic strain of NDV, while under the effects of heat stress, in order to understand the genetic mechanisms of resistance during high ambient temperatures. Fayoumis, which are relatively more resistant to pathogens than Leghorns, had larger numbers of differentially expressed genes (DEGs) during the early stages of infection when compared to Leghorns and subsequently down-regulated their immune response at the latter stages to return to homeostasis. Leghorns had very few DEGs across all observed time points, with the majority of DEGs involved with metabolic and glucose-related functions. Proteomic analysis corroborates findings made within Leghorns, while also identifying interesting candidate genes missed by expression profiling. Poor correlation between changes observed in the proteomic and transcriptomic datasets highlights the potential importance of integrative approaches to understand the mechanisms of disease response. Overall, this study provides novel insights into global protein and expression profiles of these two genetic lines, and provides potential genetic targets involved with NDV resistance during heat stress in poultry.

Newcastle disease is considered the number one disease constraint to poultry production in low and middle-income countries, however poultry that is raised in resource-poor areas often experience multiple environmental challenges. Heat stress has a negative impact on production, and immune response to pathogens can be negatively modulated by heat stress. Candidate genes and regions chosen for this study were based on previously reported associations with response to immune stimulants, pathogens, or heat, including: TLR3, TLR7, MX, MHC-B (major histocompatibility complex, gene complex), IFI27L2, SLC5A1, HSPB1, HSPA2, HSPA8, IFRD1, IL18R1, IL1R1, AP2A2, and TOLLIP. Chickens of a commercial egg-laying line were infected with a lentogenic strain of NDV (Newcastle disease virus); half the birds were maintained at thermoneutral temperature and the other half were exposed to high ambient temperature before the NDV challenge and throughout the remainder of the study. Phenotypic responses to heat, to NDV, or to heat + NDV were measured. Selected SNPs (single nucleotide polymorphisms) within 14 target genes or regions were genotyped; and genotype effects on phenotypic responses to NDV or heat + NDV were tested in each individual treatment group and the combined groups. Seventeen significant haplotype effects, among seven genes and seven phenotypes, were detected for response to NDV or heat or NDV + heat. These findings identify specific genetic variants that are associated with response to heat and/or NDV which may be useful in the genetic improvement of chickens to perform favorably when faced with pathogens and heat stress.

Enhancing genetic resistance of chickens to Newcastle Disease Virus (NDV) provides a promising way to improve poultry health, and to alleviate poverty and food insecurity in developing countries. In this study, two inbred chicken lines with different responses to NDV, Fayoumi and Leghorn, were challenged with LaSota NDV strain at 21 days of age. Through transcriptome analysis, gene expression in spleen at 2 and 6 days post-inoculation was compared between NDV-infected and control groups, as well as between chicken lines. At a false discovery rate

In low income countries, chickens play a vital role in daily life. They provide a critical source of protein through egg production and meat. Newcastle disease, caused by avian paramyxovirus type 1, has been ranked as the most devastating disease for scavenging chickens in Africa and Asia. High mortality among flocks infected with velogenic strains leads to a devastating loss of dietary protein and buying power for rural households. Improving the genetic resistance of chickens to Newcastle Disease virus (NDV), in addition to vaccination, is a practical target for improvement of poultry production in low income countries. Because response to NDV has a component of genetic control, it can be influenced through selective breeding. Adding genomic information to a breeding program can increase the amount of genetic progress per generation. In this study, we challenged a commercial egg-laying line with a lentogenic strain of NDV, measured phenotypic responses, collected genotypes, and associated genotypes with phenotypes. Collected phenotypes included viral load at 2 and 6 days post-infection (dpi), antibody levels pre-challenge and 10 dpi, and growth rates pre- and post-challenge. Six suggestive QTL associated with response to NDV and/or growth were identified, including novel and known QTL confirming previously reported associations with related traits. Additionally, previous RNA-seq analysis provided support for several of the genes located in or near the identified QTL. Considering the trend of negative genetic correlation between antibody and Newcastle Disease tolerance (growth under disease) and estimates of moderate to high heritability, we provide evidence that these NDV response traits can be influenced through selective breeding. Producing chickens that perform favorably in challenging environments will ultimately increase the supply of quality protein for human consumption.

Behind each eye of the chicken resides a unique lymph tissue, the Harderian gland, for which RNA sequencing (RNA-seq) analysis is novel. We characterized the response of this tissue to Newcastle disease virus (NDV) in two inbred lines with different susceptibility to NDV across three time points. Three-week-old relatively resistant (Fayoumi) and relatively susceptible (Leghorn) birds were inoculated with a high-titered (107EID50) La Sota strain of NDV via an oculonasal route. At 2, 6, and 10 days post infection (dpi) Harderian glands were collected and analyzed via RNA-seq. The Fayoumi had significantly more detectable viral transcripts in the Harderian gland at 2 dpi than the Leghorn, but cleared the virus by 6 dpi. At all three time points, few genes were declared differentially expressed (DE) between the challenged and nonchallenged birds, except for the Leghorns at 6 dpi, and these DE genes were predicted to activate an adaptive immune response. Relative to the Leghorn, the Fayoumi was predicted to activate more immune pathways in both challenged and nonchallenged birds suggesting a more elevated immune system in the Fayoumis under homeostatic conditions. Overall, this study helped characterize the function of this important tissue and its response to NDV.

BackgroundNewcastle disease virus (NDV) is a threat to poultry production worldwide. A better understanding of mechanisms of resistance and susceptibility to this virus will improve measures for NDV prevention and control. Males and females from resistant Fayoumi and susceptible Leghorn lines were either challenged with a lentogenic strain of the virus or given a mock infection at 3 weeks of age. The lung transcriptomes generated by RNA-seq were studied using contrasts across the challenged and nonchallenged birds, the two lines, and three time points post-infection, and by using Weighted Gene Co-expression Network Analysis (WGNCA).ResultsGenetic line and sex had a large impact on the lung transcriptome. When contrasting the challenged and nonchallenged birds, few differentially expressed genes (DEG) were identified within each line at 2, 6, and 10 days post infection (dpi), except for the more resistant Fayoumi line at 10 dpi, for which several pathways were activated and inhibited at this time. The interaction of challenge and line at 10 dpi significantly impacted 131 genes (False Discovery Rate (FDR)

Newcastle disease virus (NDV) has a devastating impact on poultry production in developing countries. This study examined the transcriptome of tracheal epithelial cells from two inbred chicken lines that differ in NDV susceptibility after challenge with a high-titer inoculum of lentogenic NDV. The Fayoumi line had a significantly lower NDV load postchallenge than the Leghorn line, demonstrating the Fayoumi line's classification as a relatively NDV-resistant breed. Examination of the trachea transcriptome showed a large increase in immune cell infiltration in the trachea in both lines at all times postinfection. The pathways conserved across lines and at all three time points postinfection included iCOS-iCOSL signaling in T helper cells, NF-κB signaling, the role of nuclear factor of activated T cells in the regulation of the immune response, calcium-induced T lymphocyte apoptosis, phospholipase C signaling, and CD28 signaling in T helper cells. Although shared pathways were seen in the Fayoumi and Leghorn lines, each line showed unique responses as well. The downregulation of collagen and the activation of eukaryotic translation initiation factor 2 signaling in the Fayoumis relative to the Leghorns at 2 days postinfection may contribute to the resistance phenotype seen in the Fayoumis. This study provides a further understanding of host-pathogen interactions which could improve vaccine efficacy and, in combination with genome-wide association studies, has the potential to advance strategies for breeding chickens with enhanced resistance to NDV.

Abstract Selective DNA pooling is an efficient method to identify chromosomal regions that harbor quantitative trait loci (QTL) by comparing marker allele frequencies in pooled DNA from phenotypically extreme individuals. Currently used single marker analysis methods can detect linkage of markers to a QTL but do not provide separate estimates of QTL position and effect, nor do they utilize the joint information from multiple markers. In this study, two interval mapping methods for analysis of selective DNA pooling data were developed and evaluated. One was based on least squares regression (LS-pool) and the other on approximate maximum likelihood (ML-pool). Both methods simultaneously utilize information from multiple markers and multiple families and can be applied to different family structures (half-sib, F2 cross and backcross). The results from these two interval mapping methods were compared with results from single marker analysis by simulation. The results indicate that both LS-pool and ML-pool provided greater power to detect the QTL than single marker analysis. They also provide separate estimates of QTL location and effect. With large family sizes, both LS-pool and ML-pool provided similar power and estimates of QTL location and effect as selective genotyping. With small family sizes, however, the LS-pool method resulted in severely biased estimates of QTL location for distal QTL but this bias was reduced with the ML-pool.

Abstract Myostatin is a negative regulator of skeletal muscle growth. We evaluated effects of myostatin polymorphisms in three elite commercial broiler chicken lines on mortality, growth, feed conversion efficiency, ultrasound breast depth, breast percentage, eviscerated carcass weight, leg defects, blood oxygen level, and hen antibody titer to infectious bursal disease virus vaccine. Progeny mean data adjusted for fixed and mate effects and DNA from 100 sires per line were used. Single nucleotide polymorphisms (SNPs) of the myostatin gene segregating in these lines were identified by designing specific primers, amplifying individual DNA in each line by polymerase chain reaction, cloning, sequencing and aligning the corresponding products. Individual sires were genotyped for five identified SNPs which contributed to eight haplotypes. Frequencies of SNP alleles and haplotypes differed between lines. Using the allele substitution effect model, the myostatin SNPs were found to have significant (P < 0.031) associations with growth, mortality, blood oxygen and hen antibody titer to infectious bursal disease virus vaccine, although the associations were not often consistent across lines. These results suggest that the myostatin gene has pleiotropic effects on broiler performance.

Abstract Survival traits and selective genotyping datasets are typically not normally distributed, thus common models used to identify QTL may not be statistically appropriate for their analysis. The objective of the present study was to compare models for identification of QTL associated with survival traits, in particular when combined with selective genotyping. Data were simulated to model the survival distribution of a population of chickens challenged with Marek disease virus. Cox proportional hazards (CPH), linear regression (LR), and Weibull models were compared for their appropriateness to analyze the data, ability to identify associations of marker alleles with survival, and estimation of effects when all individuals were genotyped (full genotyping) and when selective genotyping was used. Little difference in power was found between the CPH and the LR model for low censoring cases for both full and selective genotyping. The simulated data were not transformed to follow a Weibull distribution and, as a result, the Weibull model generally resulted in less power than the other two models and overestimated effects. Effect estimates from LR and CPH were unbiased when all individuals were genotyped, but overestimated when selective genotyping was used. Thus, LR is preferred for analyzing survival data when the amount of censoring is low because of ease of implementation and interpretation. Including phenotypic data of non-genotyped individuals in selective genotyping analysis increased power, but resulted in LR having an inflated false positive rate, and therefore the CPH model is preferred for this scenario, although transformation of the data may also make the Weibull model appropriate for this case. The results from the research presented herein are directly applicable to interval mapping analyses.

Abstract For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP.

Abstract A method was developed to optimize simultaneous selection for a quantitative trait with a known QTL within a male and a female line to maximize crossbred performance from a two-way cross. Strategies to maximize cumulative discounted response in crossbred performance over ten generations were derived by optimizing weights in an index of a QTL and phenotype. Strategies were compared to selection on purebred phenotype. Extra responses were limited for QTL with additive and partial dominance effects, but substantial for QTL with over-dominance, for which optimal QTL selection resulted in differential selection in male and female lines to increase the frequency of heterozygotes and polygenic responses. For over-dominant QTL, maximization of crossbred performance one generation at a time resulted in similar responses as optimization across all generations and simultaneous optimal selection in a male and female line resulted in greater response than optimal selection within a single line without crossbreeding. Results show that strategic use of information on over-dominant QTL can enhance crossbred performance without crossbred testing.

Abstract Under additive inheritance, the Henderson mixed model equations (HMME) provide an efficient approach to obtaining genetic evaluations by marker assisted best linear unbiased prediction (MABLUP) given pedigree relationships, trait and marker data. For large pedigrees with many missing markers, however, it is not feasible to calculate the exact gametic variance covariance matrix required to construct HMME. The objective of this study was to investigate the consequences of using approximate gametic variance covariance matrices on response to selection by MABLUP. Two methods were used to generate approximate variance covariance matrices. The first method (Method A) completely discards the marker information for individuals with an unknown linkage phase between two flanking markers. The second method (Method B) makes use of the marker information at only the most polymorphic marker locus for individuals with an unknown linkage phase. Data sets were simulated with and without missing marker data for flanking markers with 2, 4, 6, 8 or 12 alleles. Several missing marker data patterns were considered. The genetic variability explained by marked quantitative trait loci (MQTL) was modeled with one or two MQTL of equal effect. Response to selection by MABLUP using Method A or Method B were compared with that obtained by MABLUP using the exact genetic variance covariance matrix, which was estimated using 15 000 samples from the conditional distribution of genotypic values given the observed marker data. For the simulated conditions, the superiority of MABLUP over BLUP based only on pedigree relationships and trait data varied between 0.1% and 13.5% for Method A, between 1.7% and 23.8% for Method B, and between 7.6% and 28.9% for the exact method. The relative performance of the methods under investigation was not affected by the number of MQTL in the model.

Abstract An increased availability of genotypes at marker loci has prompted the development of models that include the effect of individual genes. Selection based on these models is known as marker-assisted selection (MAS). MAS is known to be efficient especially for traits that have low heritability and non-additive gene action. BLUP methodology under non-additive gene action is not feasible for large inbred or crossbred pedigrees. It is easy to incorporate non-additive gene action in a finite locus model. Under such a model, the unobservable genotypic values can be predicted using the conditional mean of the genotypic values given the data. To compute this conditional mean, conditional genotype probabilities must be computed. In this study these probabilities were computed using iterative peeling, and three Markov chain Monte Carlo (MCMC) methods – scalar Gibbs, blocking Gibbs, and a sampler that combines the Elston Stewart algorithm with iterative peeling (ESIP). The performance of these four methods was assessed using simulated data. For pedigrees with loops, iterative peeling fails to provide accurate genotype probability estimates for some pedigree members. Also, computing time is exponentially related to the number of loci in the model. For MCMC methods, a linear relationship can be maintained by sampling genotypes one locus at a time. Out of the three MCMC methods considered, ESIP, performed the best while scalar Gibbs performed the worst.

Abstract A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multiple alleles and arbitrary genetic effects, including dominance, epistasis, and gametic imprinting. Gametic phase disequilibrium between the QTL and between the QTL and polygenes is modeled but polygenic variance is assumed constant. Breeding programs with discrete generations, differential selection of males and females and random mating of selected parents are modeled. Polygenic EBV obtained from best linear unbiased prediction models can be accommodated. The problem was formulated as a multiple-stage optimal control problem and an iterative approach was developed for its solution. The method can be used to develop and evaluate optimal strategies for selection on multiple QTL for a wide range of situations and genetic models.

Abstract A mathematical approach to optimize selection on multiple quantitative trait loci (QTL) and an estimate of residual polygenic effects was applied to selection on two linked or unlinked additive QTL. Strategies to maximize total or cumulative discounted response over ten generations were compared to standard QTL selection on the sum of breeding values for the QTL and an estimated breeding value for polygenes, and to phenotypic selection. Optimal selection resulted in greater response to selection than standard QTL or phenotypic selection. Tight linkage between the QTL (recombination rate 0.05) resulted in a slightly lower response for standard QTL and phenotypic selection but in a greater response for optimal selection. Optimal selection capitalized on linkage by emphasizing selection on favorable haplotypes. When the objective was to maximize total response after ten generations and QTL were unlinked, optimal selection increased QTL frequencies to fixation in a near linear manner. When starting frequencies were equal for the two QTL, equal emphasis was given to each QTL, regardless of the difference in effects of the QTL and regardless of the linkage, but the emphasis given to each of the two QTL was not additive. These results demonstrate the ability of optimal selection to capitalize on information on the complex genetic basis of quantitative traits that is forthcoming.

Abstract Background Insect bite hypersensitivity is a common allergic disease in horse populations worldwide. Insect bite hypersensitivity is affected by both environmental and genetic factors. However, little is known about genes contributing to the genetic variance associated with insect bite hypersensitivity. Therefore, the aim of our study was to identify and quantify genomic associations with insect bite hypersensitivity in Shetland pony mares and Icelandic horses in the Netherlands. Methods Data on 200 Shetland pony mares and 146 Icelandic horses were collected according to a matched case–control design. Cases and controls were matched on various factors (e.g. region, sire) to minimize effects of population stratification. Breed-specific genome-wide association studies were performed using 70 k single nucleotide polymorphisms genotypes. Bayesian variable selection method Bayes-C with a threshold model implemented in GenSel software was applied. A 1 Mb non-overlapping window approach that accumulated contributions of adjacent single nucleotide polymorphisms was used to identify associated genomic regions. Results The percentage of variance explained by all single nucleotide polymorphisms was 13% in Shetland pony mares and 28% in Icelandic horses. The 20 non-overlapping windows explaining the largest percentages of genetic variance were found on nine chromosomes in Shetland pony mares and on 14 chromosomes in Icelandic horses. Overlap in identified associated genomic regions between breeds would suggest interesting candidate regions to follow-up on. Such regions common to both breeds (within 15 Mb) were found on chromosomes 3, 7, 11, 20 and 23. Positional candidate genes within 2 Mb from the associated windows were identified on chromosome 20 in both breeds. Candidate genes are within the equine lymphocyte antigen class II region, which evokes an immune response by recognizing many foreign molecules. Conclusions The genome-wide association study identified several genomic regions associated with insect bite hypersensitivity in Shetland pony mares and Icelandic horses. On chromosome 20, associated genomic regions in both breeds were within 2 Mb from the equine lymphocyte antigen class II region. Increased knowledge on insect bite hypersensitivity associated genes will contribute to our understanding of its biology, enabling more efficient selection, therapy and prevention to decrease insect bite hypersensitivity prevalence.

Abstract Background Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction. Methods Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values. Results Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied. Conclusions These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.

Abstract Background The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information. Methods The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability. Results Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.

Abstract Background Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line. Methods The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records). The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV. Results Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.

Abstract Background Information for mapping of quantitative trait loci (QTL) comes from two sources: linkage disequilibrium (non-random association of allele states) and cosegregation (non-random association of allele origin). Information from LD can be captured by modeling conditional means and variances at the QTL given marker information. Similarly, information from cosegregation can be captured by modeling conditional covariances. Here, we consider a Bayesian model based on gene frequency (BGF) where both conditional means and variances are modeled as a function of the conditional gene frequencies at the QTL. The parameters in this model include these gene frequencies, additive effect of the QTL, its location, and the residual variance. Bayesian methodology was used to estimate these parameters. The priors used were: logit-normal for gene frequencies, normal for the additive effect, uniform for location, and inverse chi-square for the residual variance. Computer simulation was used to compare the power to detect and accuracy to map QTL by this method with those from least squares analysis using a regression model (LSR). Results To simplify the analysis, data from unrelated individuals in a purebred population were simulated, where only LD information contributes to map the QTL. LD was simulated in a chromosomal segment of 1 cM with one QTL by random mating in a population of size 500 for 1000 generations and in a population of size 100 for 50 generations. The comparison was studied under a range of conditions, which included SNP density of 0.1, 0.05 or 0.02 cM, sample size of 500 or 1000, and phenotypic variance explained by QTL of 2 or 5%. Both 1 and 2-SNP models were considered. Power to detect the QTL for the BGF, ranged from 0.4 to 0.99, and close or equal to the power of the regression using least squares (LSR). Precision to map QTL position of BGF, quantified by the mean absolute error, ranged from 0.11 to 0.21 cM for BGF, and was better than the precision of LSR, which ranged from 0.12 to 0.25 cM. Conclusions In conclusion given a high SNP density, the gene frequency model can be used to map QTL with considerable accuracy even within a 1 cM region.

Abstract Background Current methods for haplotype inference without pedigree information assume random mating populations. In animal and plant breeding, however, mating is often not random. A particular form of nonrandom mating occurs when parental individuals of opposite sex originate from distinct populations. In animal breeding this is called crossbreeding and hybridization in plant breeding. In these situations, association between marker and putative gene alleles might differ between the founding populations and origin of alleles should be accounted for in studies which estimate breeding values with marker data. The sequence of alleles from one parent constitutes one haplotype of an individual. Haplotypes thus reveal allele origin in data of crossbred individuals. Results We introduce a new method for haplotype inference without pedigree that allows nonrandom mating and that can use genotype data of the parental populations and of a crossbred population. The aim of the method is to estimate line origin of alleles. The method has a Bayesian set up with a Dirichlet Process as prior for the haplotypes in the two parental populations. The basic idea is that only a subset of the complete set of possible haplotypes is present in the population. Conclusion Line origin of approximately 95% of the alleles at heterozygous sites was assessed correctly in both simulated and real data. Comparing accuracy of haplotype frequencies inferred with the new algorithm to the accuracy of haplotype frequencies inferred with PHASE, an existing algorithm for haplotype inference, showed that the DP algorithm outperformed PHASE in situations of crossbreeding and that PHASE performed better in situations of random mating.

Abstract Background One of the main limitations of many livestock breeding programs is that selection is in pure breeds housed in high-health environments but the aim is to improve crossbred performance under field conditions. Genomic selection (GS) using high-density genotyping could be used to address this. However in crossbred populations, 1) effects of SNPs may be breed specific, and 2) linkage disequilibrium may not be restricted to markers that are tightly linked to the QTL. In this study we apply GS to select for commercial crossbred performance and compare a model with breed-specific effects of SNP alleles (BSAM) to a model where SNP effects are assumed the same across breeds (ASGM). The impact of breed relatedness (generations since separation), size of the population used for training, and marker density were evaluated. Trait phenotype was controlled by 30 QTL and had a heritability of 0.30 for crossbred individuals. A Bayesian method (Bayes-B) was used to estimate the SNP effects in the crossbred training population and the accuracy of resulting GS breeding values for commercial crossbred performance was validated in the purebred population. Results Results demonstrate that crossbred data can be used to evaluate purebreds for commercial crossbred performance. Accuracies based on crossbred data were generally not much lower than accuracies based on pure breed data and almost identical when the breeds crossed were closely related breeds. The accuracy of both models (ASGM and BSAM) increased with marker density and size of the training data. Accuracies of both models also tended to decrease with increasing distance between breeds. However the effect of marker density, training data size and distance between breeds differed between the two models. BSAM only performed better than AGSM when the number of markers was small (500), the number of records used for training was large (4000), and when breeds were distantly related or unrelated. Conclusion In conclusion, GS can be conducted in crossbred population and models that fit breed-specific effects of SNP alleles may not be necessary, especially with high marker density. This opens great opportunities for genetic improvement of purebreds for performance of their crossbred descendents in the field, without the need to track pedigrees through the system.

Abstract Background Marek's disease (MD) is a T-cell lymphoma of chickens caused by the Marek's disease virus (MDV), an oncogenic avian herpesvirus. MD is a major cause of economic loss to the poultry industry and the most serious and persistent infectious disease concern. A full-sib intercross population, consisting of five independent families was generated by crossing and repeated intercrossing of two partially inbred commercial White Leghorn layer lines known to differ in genetic resistance to MD. At the F6 generation, a total of 1615 chicks were produced (98 to 248 per family) and phenotyped for MD resistance measured as survival time in days after challenge with a very virulent plus (vv+) strain of MDV. Results QTL affecting MD resistance were identified by selective DNA pooling using a panel of 15 SNPs and 217 microsatellite markers. Since MHC blood type (BT) is known to affect MD resistance, a total of 18 independent pool pairs were constructed according to family × BT combination, with some combinations represented twice for technical reasons. Twenty-one QTL regions (QTLR) affecting post-challenge survival time were identified, distributed among 11 chromosomes (GGA1, 2, 3, 4, 5, 8, 9, 15, 18, 26 and Z), with about two-thirds of the MD resistance alleles derived from the more MD resistant parental line. Eight of the QTLR associated with MD resistance, were previously identified in a backcross (BC) mapping study with the same parental lines. Of these, 7 originated from the more resistant line, and one from the less resistant line. Conclusion There was considerable evidence suggesting that MD resistance alleles tend to be recessive. The width of the QTLR for these QTL appeared to be reduced about two-fold in the F6 as compared to that found in the previous BC study. These results provide a firm basis for high-resolution linkage disequilibrium mapping and positional cloning of the resistance genes.

Abstract Background Specific knowledge of the molecular pathways controlling host-pathogen interactions can increase our understanding of immune response biology as well as provide targets for drug development and genetic improvement of disease resistance. Toward this end, we have characterized the porcine transcriptional response to Salmonella enterica serovar Choleraesuis (S. Choleraesuis), a Salmonella serovar that predominately colonizes swine, yet can cause serious infections in human patients. Affymetrix technology was used to screen for differentially expressed genes in pig mesenteric lymph nodes (MLN) responding to infection with S. Choleraesuis at acute (8 hours (h), 24 h and 48 h post-inoculation (pi)) and chronic stages (21 days (d) pi). Results Analysis of variance with false discovery rate control identified 1,853 genes with significant changes in expression level (p-value < 0.01, q-value < 0.26, and fold change (FC) > 2) during infection as compared to un-inoculated control pigs. Down-regulation of translation-related genes at 8 hpi and 24 hpi implied that S. Choleraesuis repressed host protein translation. Genes involved in the Th1, innate immune/inflammation response and apoptosis pathways were induced significantly. However, antigen presentation/dendritic cell (DC) function pathways were not affected significantly during infection. A strong NFκB-dependent response was observed, as 58 known NFκB target genes were induced at 8, 24 and/or 48 hpi. Quantitative-PCR analyses confirmed the microarray data for 21 of 22 genes tested. Based on expression patterns, these target genes can be classified as an "Early" group (induced at either 8 or 24 hpi) and a "Late" group (induced only at 48 hpi). Cytokine activity or chemokine activity were enriched within the Early group genes GO annotations, while the Late group was predominantly composed of signal transduction and cell metabolism annotated genes. Regulatory motif analysis of the human orthologous promoters for both Early and Late genes revealed that 241 gene promoters were predicted to contain NFκB binding sites, and that of these, 51 Early and 145 Late genes were previously not known to be NFκB targets. Conclusion Our study provides novel genome-wide transcriptional profiling data on the porcine response to S. Choleraesuis and expands the understanding of NFκB signaling in response to Salmonella infection. Comparison of the magnitude and timing of porcine MLN transcriptional response to different Salmonella serovars, S. Choleraesuis and S. Typhimurium, clearly showed a larger but later transcriptional response to S. Choleraesuis. Both microarray and QPCR data provided evidence of a strong NFκB-dependent host transcriptional response during S. Choleraesuis infection. Our data indicate that a lack of strong DC-mediated antigen presentation in the MLN may cause S. Choleraesuis infected pigs to develop a systemic infection, and our analysis predicts nearly 200 novel NFκB target genes which may be applicable across mammalian species.