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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.
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Related Experiment Video

Updated: May 27, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Detecting multiple causal rare variants in exome sequence data.

Kenny Q Ye¹, Corinne D Engelman

¹Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.

Genetic Epidemiology

|December 1, 2011

Summary

This summary is machine-generated.

Detecting rare genetic variants is challenging. New methods combine multiple rare variants using genes or pathways to improve statistical power for genetic analysis.

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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

Area of Science:

Genetics
Bioinformatics
Statistical genomics

Background:

Advances in sequencing technology offer opportunities but pose challenges for detecting rare causal variants.
Limited statistical power in current methods hinders the identification of single rare variants with practical sample sizes.

Purpose of the Study:

To develop and evaluate methods for detecting the combined signal of multiple causal rare variants.
To improve the detection of genetic associations by aggregating information from rare variants.

Main Methods:

Utilized genes, genomic location proximity, and genetic pathways as units for combining variant information.
Applied five distinct approaches to analyze exome sequence data from Genetic Analysis Workshop 17.
Focused on methods to enhance statistical power by considering multiple rare variants collectively.

Main Results:

Evaluated the strengths and weaknesses of five different methods for rare variant analysis.
Demonstrated the utility of combining multiple rare variants to overcome limitations of single-variant tests.
Highlighted the impact of chosen biological units (genes, pathways) on detection power.

Conclusions:

Combining multiple rare variants in biologically meaningful units is a promising strategy for genetic discovery.
The developed methods offer improved statistical power compared to single-variant approaches for rare variant detection.
Further research is needed to refine these methods and address limitations of exome sequence data.