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Genome-wide Association Studies-GWAS

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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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Updated: Mar 29, 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

Rare variants analysis using penalization methods for whole genome sequence data.

Akram Yazdani¹, Azam Yazdani², Eric Boerwinkle^3,4

¹Human Genetics Center, University of Texas Health Science Center at Houston, TX, USA. akram.yazdani@uth.tmc.edu.

BMC Bioinformatics

|December 6, 2015

Summary

This summary is machine-generated.

This study introduces a novel statistical method for analyzing rare genetic variants in whole genome sequencing data. The approach enhances association testing power while effectively controlling the false discovery rate, offering improved insights into complex genetic architectures.

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Published on: June 23, 2012

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

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Published on: August 21, 2016

Area of Science:

Genomics
Statistical Genetics
Bioinformatics

Background:

Whole genome sequencing (WGS) presents statistical challenges for analyzing rare variants due to data sparseness.
Existing statistical methods for rare variant analysis require further refinement for optimal performance.

Purpose of the Study:

To develop and evaluate a new statistical approach for rare variant analysis in WGS data.
To improve the power of association testing and control the false discovery rate.

Main Methods:

Restricted principal component analysis with convex penalization.
Concave penalized regression for predictor selection.
Estimation of genomic region impact on phenotype.

Main Results:

The proposed method demonstrates good power for association testing.
Maintains a low false discovery rate across various genetic architectures.
Outperforms commonly applied methods in illustrative data analyses.

Conclusions:

The novel method effectively addresses linkage disequilibrium and data sparseness.
Improves statistical power and controls the false discovery rate in rare variant analyses.
Offers a promising advancement for genomic data interpretation.