Publications by authors named "Brent W Woodward"
Background: Artificial neural networks (ANN) mimic the function of the human brain and are capable of performing massively parallel computations for data processing and knowledge representation. ANN can capture nonlinear relationships between predictors and responses and can adaptively learn complex functional forms, in particular, for situations where conventional regression models are ineffective. In a previous study, ANN with Bayesian regularization outperformed a benchmark linear model when predicting milk yield in dairy cattle or grain yield of wheat.
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- The study explores how polygenic models can better identify genomic regions under moderate selection, using a 50-year analysis of US Angus beef cattle to examine multi-trait selection effects.
- By employing "Birth Date Selection Mapping," the researchers found significant changes in allele frequencies across 44,817 SNP loci related to 16 production traits routinely monitored by breeders.
- The findings highlight that a mixed model approach can address biases in data sampling, showing that selection has gradually altered allele frequencies, with certain traits selected together despite small effects of many quantitative trait loci.
Summary Imputation of moderate-density genotypes from low-density panels is of increasing interest in genomic selection, because it can dramatically reduce genotyping costs. Several imputation software packages have been developed, but they vary in imputation accuracy, and imputed genotypes may be inconsistent among methods. An AdaBoost-like approach is proposed to combine imputation results from several independent software packages, i.
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