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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...

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,...

Pharmacogenomics: Identification of New Drug Targets

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

Comparing Copy Number Variations and SNPs

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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%...

Statistical Software for Data Analysis and Clinical Trials

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Principles of Pharmacogenetics: Types of Genetic Variants

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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Computational intelligence in bioinformatics: SNP/haplotype data in genetic association study for common diseases.

Arpad Kelemen¹, Athanasios V Vasilakos, Yulan Liang

¹Department of Organizational Systems and Adult Health, University of Maryland, Baltimore, MD 21201, USA. akele001@umaryland.edu

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society

|June 27, 2009

Summary

Computational intelligence (CI) methods show promise for analyzing genetic variations, like single-nucleotide polymorphisms (SNPs), to understand complex diseases. These approaches help overcome challenges in genome-wide association studies.

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

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

Published on: June 21, 2018

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Genome-wide association studies (GWAS) are crucial for understanding common complex diseases.
The Human Genome Project and HapMap Project have advanced the study of genetic variations.
Computational intelligence (CI) is emerging as a key scientific method alongside theory and experimentation.

Purpose of the Study:

To review recent developments in computational intelligence (CI) approaches for genetic association studies.
To highlight the application of CI in analyzing single-nucleotide polymorphism (SNP) and haplotype data for complex diseases.
To address challenges in genomic association studies, including gene-gene/environment interactions and high dimensionality.

Main Methods:

Review of computational intelligence (CI) techniques applied to genetic association studies.
Analysis of single-nucleotide polymorphism (SNP) and haplotype data.
Exploration of CI's role in tackling the "curse of dimensionality" and interaction effects.

Main Results:

CI methods demonstrate significant promise in disease mapping using SNP and haplotype data.
CI effectively addresses complex challenges in genomic association studies.
Recent developments show increasing interest and application of CI in human genome research.

Conclusions:

Computational intelligence offers powerful tools for analyzing complex genetic data in disease research.
CI is vital for advancing our understanding of the genetic basis of common complex diseases.
The integration of CI in GWAS is essential for future discoveries in human genomics.