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Home
Genetic Background Shapes Ai-predicted Variant Effects.

Home
Genetic Background Shapes Ai-predicted Variant Effects.

Related Experiment Video

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Genetic background shapes AI-predicted variant effects.

Brian M Schilder¹, Zhihan Liu¹, John J Desmarais¹

¹Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.

Biorxiv : the Preprint Server for Biology

|April 17, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Genetic background significantly impacts variant effects, with the personalized variant effect predictor (pVEP) showing the same variant can be pathogenic in some individuals and benign in others. This highlights the importance of personalized genomic context in variant interpretation.

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Predicting the functional impact of genetic variants is crucial in biomedicine.
Current methods often overlook the influence of genetic diversity on variant effects by using a single reference genome.

Purpose of the Study:

To introduce the personalized variant effect predictor (pVEP) framework.
To quantify how diverse human genetic backgrounds influence computational predictions of clinical variant effects.

Main Methods:

Developed the pVEP framework to analyze variant effects across thousands of globally diverse human genomes.
Applied deep learning models to assess impacts on protein structure, splicing, and noncoding regulation.

Main Results:

Identified that many clinical variants have heterogeneous predicted effects across different genetic backgrounds (haplotypes).
Observed that the same variant can be predicted as pathogenic in one genetic context and benign in another.
Found molecular evidence supporting these findings, including altered protein contacts and splice-site recognition.

Conclusions:

Personalized genomic context is a critical, yet underappreciated, factor in variant annotation and clinical interpretation.
This approach has significant implications for understanding genetic variation in diverse populations.