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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...

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%...

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Related Experiment Video

Updated: Mar 27, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

FINEMAP: efficient variable selection using summary data from genome-wide association studies.

Christian Benner¹, Chris C A Spencer², Aki S Havulinna³

¹Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland, Department of Public Health, University of Helsinki, Helsinki, Finland.

Bioinformatics (Oxford, England)

|January 17, 2016

Summary

This summary is machine-generated.

FINEMAP is a new software tool that efficiently identifies causal variants in complex disease genetics. It significantly speeds up analysis of genome-wide association studies data.

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Area of Science:

Genetics
Genomics
Computational Biology

Background:

Fine-mapping genomic regions is crucial for identifying causal variants linked to complex diseases and traits.
Current fine-mapping methods using genome-wide association studies (GWAS) summary data are computationally intensive due to exhaustive searches of causal configurations.

Purpose of the Study:

To develop an efficient software package for fine-mapping genomic regions.
To accelerate the identification of causal variants and their underlying molecular mechanisms.

Main Methods:

Introduction of FINEMAP, a software package utilizing a shotgun stochastic search algorithm.
FINEMAP efficiently explores key causal configurations within genomic regions.

Main Results:

FINEMAP achieves accurate fine-mapping results.
The software significantly reduces processing time compared to existing methods.
FINEMAP is suitable for large-scale GWAS and sequencing data analysis.

Conclusions:

FINEMAP offers a computationally efficient and accurate solution for genetic fine-mapping.
This tool is valuable for advancing research in complex diseases and traits using large genomic datasets.