Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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%...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Growing Up with Parents Who Smoke: Genetic and Environmental Effects on Offspring Substance Use and Externalizing Psychopathology From a 20-Year Minnesota Study of Adoptive Families.

Behavior genetics·2026

Same author

Highly Constrained Kinetic Models for Single-Cell Gene Expression Analysis.

bioRxiv : the preprint server for biology·2026

Same author

How I Treat: Chronic granulomatous disease.

Journal of human immunity·2026

Same author

Durable Lessons on Intelligence from Twin and Adoption Research.

Behavior genetics·2026

Same author

Autoinflammatory disease and severe neutropenia due to <i>de novo</i> variant of PSTPIP1 with increased binding to pyrin.

Journal of human immunity·2026

Same author

2025 Inborn errors of immunity practice parameter: Guidance from the Joint Task Force on Practice Parameters, the American Academy of Allergy, Asthma & Immunology (AAAAI), the American College of Allergy, Asthma and Immunology (ACAAI) and the Clinical Immunology Society (CIS).

Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology·2026

Search research articles

Related Experiment Video

Updated: Apr 27, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Applying compressed sensing to genome-wide association studies.

Shashaank Vattikuti¹, James J Lee², Christopher C Chang³

¹Mathematical Biology Section, Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, South Drive, Bethesda, MD 20814, USA.

|July 9, 2014

Summary

Compressed sensing (CS) theory enables efficient identification of genetic markers in genome-wide association studies (GWAS), even with more markers than samples. Sufficient sample size ensures accurate selection of significant DNA variants.

Keywords:

Compressed sensing GWAS Genomic selection Lasso Phase transition Sparsity Underdetermined system

More Related Videos

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

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Related Experiment Videos

Last Updated: Apr 27, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

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

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Area of Science:

Genetics and Genomics
Statistical Genetics
Computational Biology

Background:

Genome-wide association studies (GWAS) aim to identify DNA markers for traits of interest.
A key challenge in GWAS is the large number of genetic markers compared to available samples.
Compressed sensing (CS) is a signal recovery theory applicable when predictor variables exceed sample size.

Purpose of the Study:

To investigate the applicability of compressed sensing (CS) theory to genome-wide association studies (GWAS).
To determine if CS can efficiently identify genetic markers in GWAS, overcoming sample size limitations.

Main Methods:

Applied compressed sensing (CS) theory and an efficient algorithm to analyze genome-wide association study (GWAS) data.
Investigated marker selection performance under varying heritability (h^2) and sample sizes.
Assessed signal recovery robustness to linkage disequilibrium (LD).

Main Results:

CS theory enables identification of all markers with non-zero coefficients if they are sparse relative to sample size.
A sharp phase transition for marker selection occurs with increasing sample size when heritability (h^2) is 1.
For h^2 ≈ 0.5, a sample size ~30 times the number of non-zero coefficient markers is sufficient for full selection.
Signal recovery is robust to linkage disequilibrium (LD) between causal variants and nearby markers.

Conclusions:

Compressed sensing (CS) provides an efficient method for marker selection in genome-wide association studies (GWAS).
Increasing penalization in CS can reveal phase transitions and recover subsets of significant markers.
CS analysis of height data showed high correlation (70-100%) with markers identified by the GIANT Consortium.