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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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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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Related Experiment Video

Updated: Aug 20, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Prioritizing autoimmunity risk variants for functional analyses by fine-mapping mutations under natural selection.

Vasili Pankratov1, Milyausha Yunusbaeva2, Sergei Ryakhovsky2

  • 1University of Tartu, Institute of Genomics, Centre for Genomics, Evolution and Medicine, Tartu, 51010, Estonia.

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|November 18, 2022
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Summary

Pathogen-driven selection has influenced genetic variants linked to inflammatory disorders. This study identifies specific risk loci under selection, revealing insights into disease mechanisms and potential functional tests for environmental stressors.

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Area of Science:

  • Evolutionary genetics
  • Human genetics
  • Immunology

Background:

  • Pathogen-driven selection is hypothesized to shape adaptive mutations in immunity genes.
  • Understanding these adaptive variants can illuminate inflammatory disorder biology and evolutionary history.
  • Pinpointing adaptive mutations within selection footprints has been a significant research challenge.

Purpose of the Study:

  • To identify and characterize adaptive mutations in genetic risk loci associated with inflammatory disorders.
  • To investigate the role of selection in shaping population-specific genetic variations.
  • To provide a foundation for functional studies linking environmental stressors to genetic risk.

Main Methods:

  • Utilized a local-tree-based approach to detect footprints of selection in genetic data.
  • Analyzed 535 risk loci across 21 inflammatory disorders.
  • Employed statistical methods to distinguish between selection targets and hitchhiker variants.

Main Results:

  • Identified selection footprints in 28% (153/535) of inflammatory disorder risk loci, with some being population-specific.
  • Found that in 19% of selected risk loci, candidate disease variants are hitchhikers, not direct targets of selection.
  • Determined that only 39% of selected variants are likely direct targets of selection, with predicted functions and examples of antagonistic pleiotropy.

Conclusions:

  • A significant proportion of inflammatory disorder risk loci show evidence of past selection.
  • Distinguishing between direct selection targets and hitchhikers is crucial for understanding disease genetics.
  • Identified specific variants under selection that warrant functional investigation to explore links between environmental factors and inflammatory diseases.