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Predicting pathogenicity of missense variants with weakly supervised regression.

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Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas.

Quickly growing genetic variation data of unknown clinical significance demand computational methods that can reliably predict clinical phenotypes and deeply unravel molecular mechanisms. On the platform enabled by the Critical Assessment of Genome Interpretation (CAGI), we develop a novel "weakly supervised" regression (WSR) model that not only predicts precise clinical significance (probability of pathogenicity) from inexact training annotations (class of pathogenicity) but also infers underlying molecular mechanisms in a variant-specific manner. Compared to multiclass logistic regression, a representative multiclass classifier, our kernelized WSR improves the performance for the ENIGMA Challenge set from 0.

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Germ-line mutations in the breast cancer susceptibility genes BRCA1 and BRCA2 account for a large proportion of hereditary breast/ovarian cancer families. A large number of disease-causing germ-line mutations and variants of unknown pathological significance have been identified in both genes. The majority of these variants have been studied only in genomic DNA and their effects at the mRNA level have not been reported.

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Germline mutations in the breast and ovarian cancer susceptibility gene BRCA1 are responsible for the majority of cases involving hereditary breast and ovarian cancer. Whereas all truncating mutations are considered as functionally deleterious, most of the missense variants identified to date cannot be readily distinguished as either disease-associated mutations or benign polymorphisms. The C-terminal domain of BRCA1 displays an intrinsic transactivation activity, and mutations linked to disease predisposition have been shown to confer loss of such activity in yeast and mammalian cells.

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