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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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.
GWAS does not require the identification of the target gene involved in...
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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Genetic Screens02:46

Genetic Screens

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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

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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...
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Related Experiment Video

Updated: Nov 16, 2025

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

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GW-SEM 2.0: Efficient, Flexible, and Accessible Multivariate GWAS.

Joshua N Pritikin1,2, Michael C Neale1,2,3, Elizabeth C Prom-Wormley4

  • 1The Department of Psychiatry, Virginia Commonwealth University, Richmond, USA.

Behavior Genetics
|February 19, 2021
PubMed
Summary
This summary is machine-generated.

Multivariate genome-wide association studies (GWAS) using GW-SEM 2.0 reveal novel genetic insights into substance use behaviors. This approach enhances understanding of the genomic architecture for complex traits by analyzing multiple measures simultaneously.

Keywords:
GWASGeneticsGenome-wide association studySEMStructural equation modelingWeighted least squares

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Area of Science:

  • Genetics
  • Psychology
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) typically analyze single traits, limiting insights into complex genetic architectures.
  • Multivariate analyses of GWAS data are computationally demanding and difficult to specify, hindering their adoption.
  • Existing methods struggle to differentiate variance common to multiple measures from measure-specific variance.

Purpose of the Study:

  • To introduce GW-SEM 2.0, a software tool designed to simplify model specification and overcome computational challenges in multivariate GWAS.
  • To enable accurate modeling of ordinal data within a GWAS context, particularly for behavioral and psychological research.
  • To demonstrate the utility of multivariate GWAS for uncovering novel genetic associations and understanding complex trait architectures.

Main Methods:

  • Developed GW-SEM 2.0 with enhanced computational efficiency and user-friendly model specification.
  • Implemented capabilities for modeling ordinal items and selecting appropriate fit functions.
  • Expanded compatibility with standard genomic data formats and improved output for post-GWAS analysis.
  • Conducted multivariate GWAS on substance use items from the UK Biobank, timing studies, and Type I Error rate studies.

Main Results:

  • Multivariate GWAS identified novel patterns of associations between genomic loci and specific substance use behaviors.
  • Analyses highlighted the importance of distinguishing between substance-specific use and polysubstance use.
  • Timing studies confirmed reasonable computational time, and Type I Error studies validated the accuracy of latent variable models.

Conclusions:

  • GW-SEM 2.0 facilitates more insightful multivariate GWAS, overcoming previous limitations in computational demand and model specification.
  • This approach offers substantially deeper insights into the genomic architecture of multivariate behavioral and psychological systems compared to standard GWAS.
  • The software is available for broader adoption in genetic research, with tutorials provided.