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Related Concept Videos

Pedigree Analysis01:35

Pedigree Analysis

Overview
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Using pre-training and interaction modeling for ancestry-specific disease prediction using multiomics data from the

Thomas Le Menestrel1, Erin Craig2, Robert Tibshirani3,2

  • 1Institute for Computational and Mathematical Engineering (ICME), School of Engineering, Stanford University, Stanford, California, United States of America.

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|December 1, 2025
PubMed
Summary

Genetic prediction models show improved accuracy for diverse populations by incorporating interaction modeling and pretraining. These methods offer modest gains for diseases like diabetes and asthma, but performance varies across conditions.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) often lack diversity, leading to underperformance in non-European populations.
  • Existing disease prediction models struggle to generalize across ancestries, potentially widening health disparities.

Purpose of the Study:

  • To evaluate if interaction modeling and pretraining enhance disease prediction accuracy in diverse ancestries.
  • To assess the performance of glinternet and pretrained lasso models using multiomic data.

Main Methods:

  • Utilized Group-LASSO INTERaction-NET (glinternet) and pretrained lasso models.
  • Trained and validated models on multiomic data from UK Biobank participants (>96,000 individuals) across diverse ancestries.
  • Evaluated predictive performance for 8 common diseases using ROC-AUC scores.

Main Results:

  • 16 out of 96 models showed statistically significant improvements in predictive performance (ROC-AUC).
  • Enhanced accuracy was observed for diseases including diabetes, arthritis, gallstones, cystitis, asthma, and osteoarthritis.
  • The benefits of interaction terms and pretraining were modest and inconsistent across all evaluated diseases.

Conclusions:

  • Interaction modeling and pretraining can offer incremental improvements in disease prediction accuracy for diverse populations.
  • The effectiveness of these methods is disease-specific and requires further investigation.
  • The study highlights the need for more inclusive genetic research and improved predictive modeling strategies.