Microsatellite polymorphism between and within broiler populations

Two independent broiler chicken populations were genotyped with microsatellite markers to determine genetic polymorphisms within and among broiler populations. Birds were genotyped with primers from the US Poultry Genome Mapping Kits 1 and 2. The 59 primer sets selected for this study provided wide genomic coverage. All 59 primer sets amplified a polymerase chain reaction product in Population L, whereas 57 primer sets produced a product in Population C. The average allele number per line per microsatellite was 2.8 and 2.9 for Populations L and C, respectively. Considering the 57 primer pairs generating product in both lines, 72.3% of the total alleles were unique to one or the other population. This study illustrates the high polymorphism level in broiler populations of microsatellites amplified from primers developed from Red Jungle Fowl or White Leghorn sequences

Temporal transcriptome changes induced by MDV in marek's disease-resistant and -susceptible inbred chickens

<p>Abstract</p> <p>Background</p> <p>Marek's disease (MD) is a lymphoproliferative disease in chickens caused by Marek's disease virus (MDV) and characterized by T cell lymphoma and infiltration of lymphoid cells into various organs such as liver, spleen, peripheral nerves and muscle. Resistance to MD and disease risk have long been thought to be influenced both by genetic and environmental factors, the combination of which contributes to the observed outcome in an individual. We hypothesize that after MDV infection, genes related to MD-resistance or -susceptibility may exhibit different trends in transcriptional activity in chicken lines having a varying degree of resistance to MD.</p> <p>Results</p> <p>In order to study the mechanisms of resistance and susceptibility to MD, we performed genome-wide temporal expression analysis in spleen tissues from MD-resistant line 6₃, susceptible line 7₂and recombinant congenic strain M (RCS-M) that has a phenotype intermediate between lines 6₃and 7₂after MDV infection. Three time points of the MDV life cycle in chicken were selected for study: 5 days post infection (dpi), 10dpi and 21dpi, representing the early cytolytic, latent and late cytolytic stages, respectively. We observed similar gene expression profiles at the three time points in line 6₃and RCS-M chickens that are both different from line 7₂. Pathway analysis using Ingenuity Pathway Analysis (IPA) showed that MDV can broadly influence the chickens irrespective of whether they are resistant or susceptible to MD. However, some pathways like cardiac arrhythmia and cardiovascular disease were found to be affected only in line 7₂; while some networks related to cell-mediated immune response and antigen presentation were enriched only in line 6₃and RCS-M. We identified 78 and 30 candidate genes associated with MD resistance, at 10 and 21dpi respectively, by considering genes having the same trend of expression change after MDV infection in lines 6₃and RCS-M. On the other hand, by considering genes with the same trend of expression change after MDV infection in lines 7₂and RCS-M, we identified 78 and 43 genes at 10 and 21dpi, respectively, which may be associated with MD-susceptibility.</p> <p>Conclusions</p> <p>By testing temporal transcriptome changes using three representative chicken lines with different resistance to MD, we identified 108 candidate genes for MD-resistance and 121 candidate genes for MD-susceptibility over the three time points. Genes included in our resistance or susceptibility genes lists that are also involved in more than 5 biofunctions, such as <it>CD8α</it>, <it>IL8</it>, <it>USP18</it>, and <it>CTLA4</it>, are considered to be important genes involved in MD-resistance or -susceptibility. We were also able to identify several biofunctions related with immune response that we believe play an important role in MD-resistance.</p

Detecting parent of origin and dominant QTL in a two-generation commercial poultry pedigree using variance component methodology

<p>Abstract</p> <p>Introduction</p> <p>Variance component QTL methodology was used to analyse three candidate regions on chicken chromosomes 1, 4 and 5 for dominant and parent-of-origin QTL effects. Data were available for bodyweight and conformation score measured at 40 days from a two-generation commercial broiler dam line. One hundred dams were nested in 46 sires with phenotypes and genotypes on 2708 offspring. Linear models were constructed to simultaneously estimate fixed, polygenic and QTL effects. Different genetic models were compared using likelihood ratio test statistics derived from the comparison of full with reduced or null models. Empirical thresholds were derived by permutation analysis.</p> <p>Results</p> <p>Dominant QTL were found for bodyweight on chicken chromosome 4 and for bodyweight and conformation score on chicken chromosome 5. Suggestive evidence for a maternally expressed QTL for bodyweight and conformation score was found on chromosome 1 in a region corresponding to orthologous imprinted regions in the human and mouse.</p> <p>Conclusion</p> <p>Initial results suggest that variance component analysis can be applied within commercial populations for the direct detection of segregating dominant and parent of origin effects.</p

Microsatellite polymorphism between and within broiler populations

Two independent broiler chicken populations were genotyped with microsatellite markers to determine genetic polymorphisms within and among broiler populations. Birds were genotyped with primers from the US Poultry Genome Mapping Kits 1 and 2. The 59 primer sets selected for this study provided wide genomic coverage. All 59 primer sets amplified a polymerase chain reaction product in Population L, whereas 57 primer sets produced a product in Population C. The average allele number per line per microsatellite was 2.8 and 2.9 for Populations L and C, respectively. Considering the 57 primer pairs generating product in both lines, 72.3% of the total alleles were unique to one or the other population. This study illustrates the high polymorphism level in broiler populations of microsatellites amplified from primers developed from Red Jungle Fowl or White Leghorn sequences.This article is published as Kaiser, M. G., N. Yonash, A. Cahaner, and S. J. Lamont. "Microsatellite polymorphism between and within broiler populations." Poultry Science 79, no. 5 (2000): 626-628. DOI: 10.1093/ps/79.5.626. Posted with permission.</p

Microsatellite Markers Linked to Salmonella enterica Serovar Enteritidis Vaccine Response in Young F1 Broiler-Cross Chicks

Reduction in Salmonella enteritidis (SE) contamination is of importance for poultry production as well as for food safety. The objectives of this study were to identify potential genetic markers of antibody response to SE vaccine in young broiler chicks and then to confirm this linkage in broiler-cross offspring, as well as to explore interactions of marker alleles with dam line and sex. The initial identification of suggestive quantitative trait loci (QTL) markers for antibody response to SE vaccine was conducted by using bulked segregant analysis (BSA) with 58 microsatellite markers in a broiler breeder male line. Four unlinked microsatellites that had allele frequency differences between the high and low antibody response DNA pools were selected for subsequent analysis in a linkage study. Antibody response was measured in an F1 population (n = 379) that was derived by crossing each of four males of the broiler line with several dams from four genetically distant, highly inbred lines (Spanish, Fayoumi, and MHC-congenic G-B1 and G-B2 Leghorn). These crosses enabled us to evaluate the broiler sire QTL-marker allele effects and to explore QTL interactions with the dam lines by individual genotyping. Each of the four microsatellites identified by BSA in the broiler population had a significant (P This article is published as Kaiser, M. G., N. Deeb, and S. J. Lamont. "Microsatellite markers linked to Salmonella enterica serovar enteritidis vaccine response in young F1 broiler-cross chicks." Poultry Science 81, no. 2 (2002): 193-201. DOI: 10.1093/ps/81.2.193. Posted with permission.</p