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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,...

Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

Genome-wide Association Studies-GWAS

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

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Published on: June 21, 2018

Tailoring sparse multivariable regression techniques for prognostic single-nucleotide polymorphism signatures.

H Binder¹, A Benner, L Bullinger

¹Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Johannes Gutenberg University Mainz, Mainz, Germany. binderh@uni-mainz.de

Statistics in Medicine

|July 13, 2012

Summary

This summary is machine-generated.

This study introduces a novel boosting approach for analyzing single-nucleotide polymorphism (SNP) data to identify prognostic markers. The method efficiently handles millions of genetic variables for improved patient outcome prediction.

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Area of Science:

Genomics
Biostatistics
Computational Biology

Background:

Genomic data, particularly single-nucleotide polymorphisms (SNPs), offers vast potential for patient prognosis.
Analyzing millions of SNPs simultaneously presents significant statistical and computational challenges.

Purpose of the Study:

To adapt sparse multivariable regression techniques for SNP data analysis.
To develop a computationally efficient method for identifying prognostic molecular signatures and SNP-treatment interactions.

Main Methods:

Proposed component-wise likelihood-based boosting approach tailored for SNP data.
Developed variants to handle SNPs with varying minor allele frequencies.
Introduced a heuristic for efficient computation with millions of covariates.

Main Results:

Demonstrated the approach's effectiveness using acute myeloid leukemia patient data.
Evaluated models using prediction error curves and resampling.
Achieved increased model stability by interpreting results at the gene level.

Conclusions:

The proposed boosting approach offers a robust strategy for linking SNP data to time-to-event endpoints.
The method facilitates the identification of prognostic markers and interactions in large-scale genomic studies.
This approach enhances the utility of genomic measurements for personalized medicine.