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Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

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,...

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Updated: Sep 2, 2025

Methods to Increase the Sensitivity of High Resolution Melting Single Nucleotide Polymorphism Genotyping in Malaria

Methods to Increase the Sensitivity of High Resolution Melting Single Nucleotide Polymorphism Genotyping in Malaria

Published on: November 10, 2015

Risk score prediction model based on single nucleotide polymorphism for predicting malaria: a machine learning

Kah Yee Tai¹, Jasbir Dhaliwal², KokSheik Wong¹

¹School of Information Technology, Monash University Malaysia, Subang Jaya, Selangor, Malaysia.

BMC Bioinformatics

|August 7, 2022

Summary

This summary is machine-generated.

This study introduces a novel machine learning approach to predict malaria susceptibility using Single Nucleotide Polymorphisms (SNPs) and weighted genetic risk scores (wGRS). The findings highlight SNP rs334 as a key predictor, with LightGBM models showing superior performance in malaria risk assessment.

Keywords:

Feature extraction algorithm Genetic risk factors Machine learning Malaria Single nucleotide polymorphisms Weighted genetic risk score

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Area of Science:

Genetics
Computational Biology
Epidemiology

Background:

Current malaria risk prediction relies on statistical methods and epidemiological data.
Machine learning has been applied to malaria using blood smear images and environmental factors.
No prior studies have utilized machine learning to analyze the contribution of Single Nucleotide Polymorphisms (SNPs) to malaria risk.

Purpose of the Study:

To quantify an individual's susceptibility to malaria development using risk scores derived from SNPs.
To develop and evaluate machine learning models for malaria risk prediction based on genetic data.
To identify the most significant SNPs contributing to malaria susceptibility.

Main Methods:

Developed an SNP-based feature extraction algorithm incorporating malaria susceptibility information.
Reduced feature dimensionality using Logistic Regression and Recursive Feature Elimination (LR-RFE) to select impactful SNPs.
Calculated weighted genetic risk scores (wGRS) as target variables, comparing wGRS-only models with wGRS combined with genotype frequency (wGRS + GF).
Utilized Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Ridge regression for model development.

Main Results:

Identified SNP rs334 as the most significant contributing feature (importance score: 6.224), a known major genetic risk factor for malaria.
LightGBM achieved the highest model performance for wGRS-only prediction (Mean Absolute Error [MAE]: 0.0373).
Models incorporating wGRS + GF demonstrated significantly improved performance over wGRS alone, with LightGBM achieving the best MAE score of 0.0033.

Conclusions:

The proposed machine learning approach effectively predicts malaria risk by analyzing SNP contributions.
SNP rs334 is confirmed as a critical genetic factor influencing malaria susceptibility.
Machine learning models, particularly LightGBM, offer a powerful tool for malaria risk prediction, with combined genetic risk scores and genotype frequency yielding optimal results.