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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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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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Related Experiment Video

Updated: Jul 5, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Beyond the factor indeterminacy problem using genome-wide association data.

Margaret L Clapp Sullivan1, Ted Schwaba2, K Paige Harden3,4

  • 1Department of Psychology, University of Texas at Austin, Austin, TX, USA. mclapp@utexas.edu.

Nature Human Behaviour
|January 15, 2024
PubMed
Summary
This summary is machine-generated.

Latent factor theories in social science can be indeterminate. New genomic methods using genetic variants help rigorously test the validity of these factors, improving behavioral science understanding.

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Area of Science:

  • Behavioral Science
  • Social Science
  • Genomics

Background:

  • Latent factors are crucial in social science for constructs like intelligence and depression.
  • These factors are inferred from correlations in measured data, but this inference is not always certain.

Purpose of the Study:

  • To address the factor indeterminacy problem in social science.
  • To explore how genomic data can validate latent factors.

Main Methods:

  • Reviewing historical critiques of latent factor models.
  • Utilizing associations between genetic variants and measured variables.
  • Applying genomic approaches to test latent factor validity.

Main Results:

  • Demonstrating how genetic data can overcome factor indeterminacy.
  • Providing a rigorous method to test latent factor validity.
  • Advancing the understanding of behavioral constructs.

Conclusions:

  • Genomic insights offer a powerful tool to validate latent factors in social science.
  • This approach strengthens the scientific basis of behavioral research.
  • Future research can leverage genetics to refine understanding of complex human traits.