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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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...
Genetic Screens02:46

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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Evolutionary Relationships through Genome Comparisons02:54

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...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...
Genomics02:02

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...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

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Related Experiment Video

Updated: Jun 2, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

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

Published on: July 27, 2021

An Expectation and Maximization Algorithm for Multivariate Genome-wide Association Studies (EMmvGWAS).

Chin-Sheng Teng1, Xuesong Wang2, Cheng Liu3

  • 1Department of Statistics, University of California, Riverside, CA 92521, USA.

Genetics
|June 1, 2026
PubMed
Summary
This summary is machine-generated.

Multivariate Genome-Wide Association Studies (GWAS) jointly analyze multiple traits to boost power and identify shared genetic factors. The EMmvGWAS R package offers an efficient computational framework for these complex analyses.

Keywords:
Expectation and maximization algorithmGenome-wide association studiesMultivariate linear mixed modelRestricted maximum likelihood method

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

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Last Updated: Jun 2, 2026

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08:27

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

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
  • Biostatistics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) traditionally analyze one quantitative trait at a time.
  • Joint analysis of multiple traits can enhance statistical power and reveal shared genetic architecture.
  • Existing multivariate GWAS methods face significant computational challenges.

Purpose of the Study:

  • To present EMmvGWAS, an efficient R-based computational framework for multivariate GWAS.
  • To reduce computational time for analyzing multiple traits without compromising statistical power.
  • To facilitate the identification of pleiotropic effects and shared genetic underpinnings of complex traits.

Main Methods:

  • Utilizes an Expectation-Maximization (EM) algorithm to estimate genetic and environmental covariance matrices.
  • Employs a semi-exact method by treating the ratio of covariance matrices as a constant for efficient genome-wide scanning.
  • Enables closed-form solutions for marker effects and residual covariance matrices.

Main Results:

  • EMmvGWAS significantly reduces computational time for multivariate GWAS.
  • The method demonstrates scalability and robust performance in simulation studies and real datasets (rice, mice, human).
  • The framework supports multivariate analyses and can accommodate univariate analyses.

Conclusions:

  • EMmvGWAS provides an efficient and powerful approach for multivariate GWAS.
  • The R package is publicly available, promoting wider adoption in genetic research.
  • This method aids in uncovering complex trait genetic architecture and pleiotropic effects.