Modeling Growth Characteristics of Meat-Type Guinea Fowl

This study was conducted to describe the growth pattern of the French guinea fowl, a meat-type variety. Using BW data from hatch to 9 wk, 2 nonlinear mathematical functions (Gompertz and logistic) were used to estimate growth patterns of the French guinea fowl. The French guinea fowl did not exhibit sexual dimorphism for growth characteristics. From the Gompertz model, the asymptotic BW, growth rate, and age at maximum growth were 2.05 kg, 0.25 kg/wk, and 5.74 wk in males, respectively, and 2.03 kg, 0.25 kg/wk, and 5.72 wk in females, respectively. The ages at maximum growth were 5.75 and 5.74 wk for males and females, respectively, using the logistic model. Differences in asymptotic BW between males and females were not significant in both Gompertz and logistic models. However, the average asymptotic BW of about 1.50 kg for both sexes predicted by the logistic model was below the average predicted BW from the Gompertz model (2.04 kg) at 9 wk. Also, the logistic model overestimated hatching weight (0.06 kg) more than the Gompertz model (0.03 kg), suggesting that the growth pattern of the French guinea fowl is Gompertz. The inverse relationship between the asymptotic weight and age at maximum growth of the French guinea fowl is similar to that of the pearl gray guinea fowl, chickens, quail, and ducks. Understanding the growth characteristics of French guinea fowl will contribute to the efforts of improving production efficiency of this least studied avian species

Gompertz-Laird model prediction of optimum utilization of crude protein and metabolizable energy by French guinea fowl broilers

This study was conducted to assess the influence of dietary CP and ME on growth parameters of the French guinea fowl, a meat-type variety. In a 2 × 3 × 3 factorial arrangement, 297 one-day-old French guinea keets (162 females and 135 males) were randomly assigned to experimental diets comprising 3,050, 3,100, and 3,150 kcal of ME/kg, each containing 21, 23, and 25% CP from hatch to 4 wk of age (WOA), and 3,100, 3150, and 3,200 kcal of ME/kg, each containing 19, 21, and 23% CP at 5 to 8 WOA. Using BW and G:F data from hatch to 8 WOA, the Gompertz-Laird growth model was employed to estimate growth patterns of the French guinea fowl. Mean differences in exponential growth rate, age of maximum growth, and asymptotic BW among dietary CP and ME levels were not significant. However, instantaneous growth rate and weight at inflection point were significantly higher (P \u3c 0.05) in birds on the 25% CP diet than those on the 21% CP diet at hatch to 4 WOA (1.12 kg/wk and 0.79 kg vs. 1.04 kg/wk and 0.74 kg, respectively). The exponential growth rate was also higher (P \u3c 0.05) in birds fed the 3,050 kcal of ME/kg diet with either 23 or 25% CP than those fed diets containing 3,050 kcal of ME/kg and 21% CP. Mean G:F was higher (P \u3c 0.05) in birds fed diets containing 3,050 kcal of ME/kg and either 21 or 23% CP than those in other dietary treatments. Therefore, based on the Gompertz-Laird growth model estimates, feeding 21 and 23% CP and 3,100 kcal of ME/kg at hatch to 4 WOA and 19 and 21% CP with 3,150 kcal of ME/kg at 5 to 8 WOA can be recommended as adequate for growth for the French guinea fowl broilers

Correction to: High density marker panels, SNPs prioritizing and accuracy of genomic selection

Correction to: BMC Genetics (2018) 19:4 DOI: 10.1186/s12863-017-0595-2 The original version of this article [1], published on 5 January 2018, contained 3 formatting errors. In this Correction the affected parts of the article are shown. The original article has been updated

High density marker panels, SNPs prioritizing and accuracy of genomic selection

Abstract Background The availability of high-density (HD) marker panels, genome wide variants and sequence data creates an unprecedented opportunity to dissect the genetic basis of complex traits, enhance genomic selection (GS) and identify causal variants of disease. The disproportional increase in the number of parameters in the genetic association model compared to the number of phenotypes has led to further deterioration in statistical power and an increase in co-linearity and false positive rates. At best, HD panels do not significantly improve GS accuracy and, at worst, reduce accuracy. This is true for both regression and variance component approaches. To remedy this situation, some form of single nucleotide polymorphisms (SNP) filtering or external information is needed. Current methods for prioritizing SNP markers (i.e. BayesB, BayesCπ) are sensitive to the increased co-linearity in HD panels which could limit their performance. Results In this study, the usefulness of FST, a measure of allele frequency variation among populations, as an external source of information in GS was evaluated. A simulation was carried out for a trait with heritability of 0.4. Data was divided into three subpopulations based on phenotype distribution (bottom 5%, middle 90%, top 5%). Marker data were simulated to mimic a 770 K and 1.5 million SNP marker panel. A ten-chromosome genome with 200 K and 400 K SNPs was simulated. Several scenarios with varying distributions for the quantitative trait loci (QTL) effects were simulated. Using all 200 K markers and no filtering, the accuracy of genomic prediction was 0.77. When marker effects were simulated from a gamma distribution, SNPs pre-selected based on the 99.5, 99.0 and 97.5% quantile of the FST score distribution resulted in an accuracy of 0.725, 0.797, and 0.853, respectively. Similar results were observed under other simulation scenarios. Clearly, the accuracy obtained using all SNPs can be easily achieved using only 0.5 to 1% of all markers. Conclusions These results indicate that SNP filtering using already available external information could increase the accuracy of GS. This is especially important as next-generation sequencing technology becomes more affordable and accessible to human, animal and plant applications

Growth Characteristics of Pearl Gray Guinea Fowl as Predicted by the Richards, Gompertz, and Logistic Models

This study was undertaken to describe the growth pattern of the pearl gray Guinea fowl. Using BW data from hatch to 22 wk, 3 nonlinear mathematical functions (Richards, Gompertz, and logistic) were used to estimate growth patterns of the pearl gray guinea fowl. The logistic and Gompertz models are a special case of the Richards model, which has a variable point of inflection defined by the shape or growth trajectory parameter, m. The shape parameter m was 1.08 and 0.98 in males and females, respectively, suggesting that the growth pattern of the pearl gray female guinea fowl is Gompertz. The pearl gray guinea fowl exhibited sexual dimorphism for their growth characteristics. From the Gompertz model, the asymptotic BW, growth rate, and age at maximum growth were 1.62 kg, 0.22 kg/wk, and 6.65 wk in males, respectively, and 1.70 kg, 0.19 kg/wk, and 6.70 wk in females, respectively. The ages at maximum growth were 6.65, 6.47, and 8.12 wk for the Richards, Gompertz, and logistic models, respectively. The pearl gray guinea fowl females have a higher asymptotic BW compared with the males. The average asymptotic BW of about 1.57 kg for both sexes predicted by the logistic model was below the average predicted BW from the Richards (1.66 kg) and Gompertz (1.67 kg) models, respectively, at 22 wk of age. The inverse relationship between the asymptotic weight and both relative growth and age at maximum growth of the pearl gray guinea fowl is similar to that of chickens, quail, and ducks. Success in studying the growth characteristics of guinea fowl will contribute to the efforts of genetically improving this least-studied avian species