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Anna Wolc,
Chris Stricker,
Jesus Arango,
Petek Settar,
Janet Fulton,
Neil O&apos,
Sullivan,
Rudolf Preisinger,
David Habier,
Rohan Fernando,
Dorian Garrick, Susan Lamont,
Jack Dekkers
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ABSTRACT: 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.
Genetics Selection Evolution. 01/2011;
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ABSTRACT: Abstract Background Macrophages play essential roles in both innate and adaptive immune responses. Bacteria require endotoxin, a complex lipopolysaccharide, for outer membrane permeability and the host interprets endotoxin as a signal to initiate an innate immune response. The focus of this study is kinetic and global transcriptional analysis of the chicken macrophage response to in vitro stimulation with endotoxin from Salmonella typhimurium-798. Results The 38535-probeset Affymetrix GeneChip Chicken Genome array was used to profile transcriptional response to endotoxin 1, 2, 4, and 8 hours post stimulation (hps). Using a maximum FDR (False Discovery Rate) of 0.05 to declare genes as differentially expressed (DE), we found 13, 33, 1761 and 61 DE genes between endotoxin-stimulated versus non-stimulated cells at 1, 2, 4 and 8 hps, respectively. QPCR demonstrated that endotoxin exposure significantly affected the mRNA expression of IL1B, IL6, IL8, and TLR15, but not IL10 and IFNG in HD 11 cells. Ingenuity Pathway Analysis showed that 10% of the total DE genes were involved in inflammatory response. Three, 9.7, 96.8, and 11.8% of the total DE inflammatory response genes were significantly differentially expressed with endotoxin stimulation at 1, 2, 4 and 8 hps, respectively. The NFKBIA, IL1B, IL8 and CCL4 genes were consistently induced at all times after endotoxin treatment. NLRC5 (CARD domain containing, NOD-like receptor family, RCJMB04_18i2), an intracellular receptor, was induced in HD11 cells treated with endotoxin. Conclusions As above using an in vitro model of chicken response to endotoxin, our data revealed the kinetics of gene networks involved in host response to endotoxin and extend the known complexity of networks in chicken immune response to Gram-negative bacteria such as Salmonella. The induction of NFKBIA, IL1B, IL8, CCL4 genes is a consistent signature of host response to endotoxin over time. We make the first report of induction of a NOD-like receptor family member in response to Salmonella endotoxin in chicken macrophages.
BMC Genomics. 01/2010;
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Behnam Abasht,
Erin Sandford,
Jesus Arango,
Petek Settar,
Janet Fulton,
Neil O'Sullivan,
Abebe Hassen,
David Habier,
Rohan Fernando,
Jack Dekkers, Susan Lamont
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ABSTRACT: Abstract
Background
The genome sequence and a high-density SNP map are now available for the chicken and can be used to identify genetic markers for use in marker-assisted selection (MAS). Effective MAS requires high linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), and sustained marker-QTL LD over generations. This study used data from a 3,000 SNP panel to assess the level and consistency of LD between single nucleotide polymorphisms (SNPs) over consecutive years in two egg-layer chicken lines, and analyzed one line by two methods (SNP-wise association and genome-wise Bayesian analysis) to identify markers associated with egg-quality and egg-production phenotypes.
Results
The LD between markers pairs was high at short distances (r<sup>2 </sup>> 0.2 at < 2 Mb) and remained high after one generation (correlations of 0.80 to 0.92 at < 5 Mb) in both lines. Single- and 3-SNP regression analyses using a mixed model with SNP as fixed effect resulted in 159 and 76 significant tests (P < 0.01), respectively, across 12 traits. A Bayesian analysis called BayesB, that fits all SNPs simultaneously as random effects and uses model averaging procedures, identified 33 SNPs that were included in the model >20% of the time ( φ > 0.2) and an additional ten 3-SNP windows that had a sum of φ greater than 0.35. Generally, SNPs included in the Bayesian model also had a small P -value in the 1-SNP analyses.
Conclusion
High LD correlations between markers at short distances across two generations indicate that such markers will retain high LD with linked QTL and be effective for MAS. The different association analysis methods used provided consistent results. Multiple single SNPs and 3-SNP windows were significantly associated with egg-related traits, providing genomic positions of QTL that can be useful for both MAS and to identify causal mutations.
BMC Genomics. 01/2009;
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ABSTRACT: 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.
Genetics Selection Evolution. 01/2007;
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ABSTRACT: 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.
Genetics Selection Evolution. 01/2006;
