Breast cancer prediction using genome wide single nucleotide polymorphism data.
BMC Bioinformatics
|November 26, 2013
Summary
This study developed a breast cancer prediction model using SNP profiles, achieving 59.55% accuracy in internal validation and 60.25% in external validation, outperforming baseline models.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Breast cancer prediction remains a challenge.
- Genome-wide association studies (GWAS) offer potential for predictive modeling.
- Single Nucleotide Polymorphism (SNP) profiles are key genetic markers.
Purpose of the Study:
- To develop and validate a predictive model for breast cancer risk using SNP data.
- To evaluate the efficacy of feature selection and machine learning methods for breast cancer prediction.
- To compare the developed model against existing methods and datasets.
Main Methods:
- Genotyping of 696 female subjects (348 cases, 348 controls) using Affymetrix Human SNP 6.0 arrays.
- Application of EIGENSTRAT for population stratification correction and stringent SNP filtering.
- Utilized MeanDiff feature selection combined with K-Nearest Neighbors (KNN) learning algorithm.
Main Results:
- Achieved 59.55% Leave-One-Out Cross-Validation (LOOCV) accuracy with the developed model, significantly better than the 51.52% baseline.
- External validation on the CGEMS dataset yielded 60.25% LOOCV accuracy, surpassing its 50.06% baseline.
- The MeanDiff and KNN combination outperformed other tested feature selection and learning method combinations.
Conclusions:
- The developed SNP-based predictive model shows promising accuracy for breast cancer risk assessment.
- Future improvements may involve larger cohorts, detailed phenotyping, and integration of diverse genomic and non-genetic data.
- Further research is needed to enhance prediction accuracy by accounting for breast cancer heterogeneity and other risk factors.
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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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