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Single Nucleotide Polymorphisms-SNPs

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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,...

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An empirical workflow for genome-wide single nucleotide polymorphism-based predictive modeling.

Charalampos S Floudas¹, Jeya Balaji Balasubramanian, Marjorie Romkes

¹Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA;

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|December 5, 2013

Summary

We developed a workflow for analyzing high-dimensional genetic data to predict survival in non-small cell lung cancer (NSCLC). This method efficiently processes genome-wide association study (GWAS) data, yielding accurate predictive models.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

High-dimensional data analysis is crucial for advancing technology and personalized medicine.
Genome-wide association studies (GWAS) generate vast amounts of single nucleotide polymorphism (SNP) data.
Predictive modeling for non-small cell lung cancer (NSCLC) survival is vital for treatment decisions.

Purpose of the Study:

To develop and validate an empirical workflow for predictive modeling using SNP data from GWAS.
To address challenges in processing Affymetrix® GWAS datasets for model construction.
To assess the predictive accuracy of SNP-based models for NSCLC survival.

Main Methods:

Developed a computational workflow for processing GWAS data.
Utilized a Bayesian rule learner system (BRL+) for predictive modeling.
Applied the workflow to SNP data for predicting NSCLC survival.

Main Results:

The workflow proved feasible and efficient for handling complex GWAS data.
SNP-based models demonstrated high predictive accuracy through cross-validation.
Successfully outlined the data processing pipeline from raw hybridization results to model building.

Conclusions:

The developed workflow enables efficient analysis of high-dimensional GWAS data.
SNP-based predictive models can achieve high accuracy for NSCLC survival prediction.
This approach facilitates the application of genetic data in clinical decision-making.