Statistical Models for Predicting Genetic Variability and Disease Susceptibility

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Year : 2025 | Volume :14 | Issue : 01 | Page : –
By
vector

Tufail Ahmad,

  1. Student, Department of Biotechnology, Amity University Gurgaon,, Haryana, India

Abstract

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Differences in genetics are key to understanding why some individuals are more prone to certain diseases than others. Recent advancements in genomic research, combined with statistical modeling techniques, have made significant strides in predicting disease risk based on genetic factors. This review explores the application of statistical models for predicting genetic variability and their role in disease susceptibility. We discuss traditional methods like linear regression and genome-wide association studies (GWAS), as well as newer techniques such as polygenic risk scores (PRS) and machine learning algorithms (e.g., random forests, support vector machines, and neural networks). While these models have enhanced our ability to predict diseases like coronary artery disease, diabetes, and cancer, challenges remain, particularly in integrating environmental factors and genetic data across diverse populations. Polygenic risk scores, which aggregate the effects of numerous genetic variants, have shown promise but often exhibit limited accuracy when applied to different populations, highlighting the need for large, diverse datasets. Moreover, the integration of multi-omic data (including genomic, transcriptomic, proteomic, and epigenomic data) is increasingly seen as a way to improve prediction models by capturing complex gene-environment interactions. Despite the potential, issues such as model overfitting, data quality, and ethical concerns regarding genetic data privacy need to be addressed. The review emphasizes the future of statistical models in predicting disease risk and suggests that the combination of genetic data with lifestyle, environmental, and multi-omic factors could lead to more accurate and personalized predictions of disease susceptibility.

Keywords: Genetic Variability, Disease Susceptibility, Statistical Models, Polygenic Risk Scores (PRS), Genome-Wide Association Studies (GWAS),

[This article belongs to Research & Reviews : Journal of Computational Biology (rrjocb)]

How to cite this article:
Tufail Ahmad. Statistical Models for Predicting Genetic Variability and Disease Susceptibility. Research & Reviews : Journal of Computational Biology. 2025; 14(01):-.
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Tufail Ahmad. Statistical Models for Predicting Genetic Variability and Disease Susceptibility. Research & Reviews : Journal of Computational Biology. 2025; 14(01):-. Available from: https://journals.stmjournals.com/rrjocb/article=2025/view=0

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Regular Issue Subscription Review Article
Volume 14
Issue 01
Received 26/12/2024
Accepted 15/01/2025
Published 20/01/2025
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