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• W2892400988 abstract "Assessing pedigreed broiler lines for ascites resistance in an industry setting is time consuming. Further, the use of sibling selection implies study subjects are not used in the breeding program, and instead, siblings take their place in pedigree systems, which reduces overall genetic accuracy. The purpose of this study is to evaluate the effectiveness of prediction models produced with SNP with the goal of predicting ascites incidence. Ascites is the manifestation of a series of adverse changes in a broiler beginning with hypoxia. Increased blood pressure, accumulation of fluid in the abdominal cavity, and death can result. Ascites results in losses estimated at $100 million/year in the USA. A multi-generational genome wide association study in an unselected line maintained at the University of Arkansas since the 1990s identified chromosomal regions associated with ascites incidence in males when challenged at high altitude. From the identified regions of significance 20 SNP were selected to construct a predictive model (8 SNP on chromosome 11, and 12 SNP on chromosome Z). Ascites phenotype and genotype data were obtained for 295 male and female individuals from the REL line. Five modeling techniques were compared for their ascites predictive ability using a 70/30 split between training and validation. For both males and females, the artificial neural network model was the best fit prediction model due to the large area under the curve value of 0.997 and 0.997, respectively, as well as a low misclassification ratio of 0.027 and 0.037, respectively. Using a parameter decreasing method, the total number of SNP inputs used to construct artificial neural network (ANN) models was reduced. A 13 SNP male ANN model and an 18 SNP female ANN model were constructed with equally high levels of prediction accuracy compared with the 20 SNP input models. The construction of predictive ANN models indicates that we have found the genetic predictors to ascites outcome in male and female broilers from an elite line of the 1990s with a high level of accuracy." @default.
• W2892400988 created "2018-10-05" @default.
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• W2892400988 date "2018-11-01" @default.
• W2892400988 modified "2023-09-26" @default.
• W2892400988 title "Predicting ascites incidence in a simulated altitude-challenge using single nucleotide polymorphisms identified in multi-generational genome wide association studies" @default.
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• W2892400988 doi "https://doi.org/10.3382/ps/pey273" @default.
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