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Data in large-scale genetic studies of complex human diseases, such as substance use disorders, are often incomplete. Despite great progress in genotype imputation, e.g., the IMPUTE2 method, considerably less progress has been made in inferring phenotypes. We designed a novel approach to integrate individuals' comorbid conditions with their genotype data to infer missing (unreported) diagnostic criteria...
Improving feed efficiency in dairy production is an important endeavor, as it can reduce feed costs and negative impacts of production on the environment. Feed efficiency is a multivariate phenotype that is characterized by a variety of phenotypic variables, such as dry matter intake, body weight gain, and milk yield. Currently, there is no consensus method for quantifying the feed efficiency of lactating...
Soybean is one of the most important crops for food, feed and bio-energy world-wide. The study of soybean phenotypic variation at different geographical locations can help the understanding of soybean domestication, population structure of soybean, and the conservation of soybean biodiversity. We investigate if soybean varieties can be identified that they differ from other varieties on multiple traits...
An important approach to reducing missing heritability and enhancing success of genome-wide association studies (GWAS) for complex diseases is the identification of traits that are highly heritable and homogeneous in their etiology. Many approaches have been proposed to define such traits based on either cluster analysis or pedigree-based heritable component analysis. None of the existing methods,...
Genetic association analysis of complex diseases has been limited by heterogeneity in their clinical manifestations and genetic etiology. Research has made it possible to differentiate homogeneous subtypes of the disease phenotype. Currently, the most sophisticated subtyping methods perform unsupervised cluster analysis using only clinical features of a disorder, resulting in subtypes for which genetic...
Complex disorders exhibit great heterogeneity in both clinical manifestation and genetic etiology. This heterogeneity substantially limits the identification of geneotype-phenotype associations. Differentiating homogeneous subtypes of a complex phenotype will enable the detection of genetic variants contributing to the effect of subtypes that cannot be detected by the non-differentiated phenotype...
Identifying genetic variations that underlie human disease is very important to advance our understanding of the disease's pathophysiology and promote its personalized treatment. However, many disease phenotypes have complex clinical manifestations and a complicated etiology. Gene finding efforts for complex diseases have had limited success to date. Research results suggest that one way to enhance...
Numerous approaches have been proposed to relax the conditional independence assumption of naive Bayes, the accuracy performance was indeed improved relative to naive Bayes when the assumption is violated. But most of the previous approaches treated the attribute relation in the same way for all class labels. In practice, this relation may be different for different class labels. This paper proposes...
This paper proposes a new classification approach; we call the graph augmented Bayes classifier (GAB). We show that naive Bayes classifier is a special case of GAB under the conditional independence assumption. GAB relaxes the conditional independence assumptions and takes into account of the influences on an attribute from all other attributes, and extends naive Bayes with the capability in expressiveness...
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