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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Predicting pathogenicity of missense variants with weakly supervised regression.

Yue Cao1, Yuanfei Sun1, Mostafa Karimi1

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas.

Human Mutation
|May 31, 2019
PubMed
Summary

A novel weakly supervised regression (WSR) model accurately predicts the clinical significance of genetic variants and reveals their molecular mechanisms. This computational approach enhances genomic interpretation for variants of unknown clinical significance.

Keywords:
clinical significancegenetic variationgenome medicinemachine learningmodel interpretabilitymolecular mechanismweak supervision

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Genetics

Background:

  • Genetic variation data is rapidly expanding, necessitating advanced computational methods for predicting clinical significance and understanding molecular mechanisms.
  • Variants of unknown clinical significance pose challenges in genetic diagnosis and research.

Purpose of the Study:

  • To develop and validate a novel weakly supervised regression (WSR) model for predicting the clinical significance (pathogenicity) of genetic variants.
  • To infer variant-specific molecular mechanisms underlying pathogenicity.
  • To improve upon existing classification methods using a new computational framework.

Main Methods:

  • Development of a kernelized weakly supervised regression (WSR) model utilizing inexact training annotations (pathogenicity class).
  • Application of the WSR model on the Critical Assessment of Genome Interpretation (CAGI) platform and the ENIGMA Challenge dataset.
  • Comparison of WSR model performance against multiclass logistic regression using binary and ordinal multiclass AUC metrics.
  • Integration of WSR model interpretation with protein structural analysis to elucidate molecular mechanisms.

Main Results:

  • The kernelized WSR model significantly improved predictive performance compared to multiclass logistic regression.
  • Achieved a binary area under the receiver operating characteristic curve (AUC) of 0.97, up from 0.72.
  • Achieved an ordinal multiclass AUC of 0.80, up from 0.64.
  • Identified specific molecular mechanisms for pathogenic BRCA1 variants, including metal-binding disruption, protein-binding disruption, and structure destabilization.

Conclusions:

  • The developed WSR model offers a robust computational tool for predicting genetic variant pathogenicity and uncovering molecular mechanisms.
  • This approach enhances the interpretation of genetic variation data, particularly for variants of unknown clinical significance.
  • The synergy between WSR model interpretation and structural biology provides mechanistic insights into variant effects.