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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.
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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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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: Jun 16, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Mendelian randomization analysis identified potential genes pleiotropically associated with gout.

Yu Wang1, Jiahao Chen2, Hang Yao3

  • 1Graduate School of Jiangxi University of Traditional Chinese Medicine, Nanchang, China.

Frontiers in Genetics
|August 20, 2024
PubMed
Summary

This study used Summary Data-based Mendelian Randomization (SMR) to identify genes linked to gout risk. It found 14 gene probes associated with gout, highlighting potential therapeutic targets for this complex disease.

Keywords:
expression quantitative trait locigenome-wide association studygoutpleotropic associationsummary data-based mendelian randomization

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Area of Science:

  • Genetics
  • Genomics
  • Metabolic Diseases

Background:

  • Gout is a complex metabolic disease with multifactorial origins.
  • Identifying genetic factors contributing to gout pathogenesis is crucial for understanding disease mechanisms.

Purpose of the Study:

  • To prioritize genes potentially involved in gout using genetic association and expression data.
  • To identify novel genetic loci associated with gout risk.

Main Methods:

  • Employed the Summary Data-based Mendelian Randomization (SMR) approach.
  • Analyzed expression quantitative trait loci (eQTL) data from blood and renal tissues.
  • Utilized genome-wide association study (GWAS) data for gout from the FinnGen R10 release.
  • Performed heterogeneity testing using the HEIDI test and adjusted for False Discovery Rate (FDR).

Main Results:

  • Identified 14 gene probes significantly associated with gout in blood cis-eQTL data.
  • Top associated genes include THBS3, THBS3-AS1, KRTCAP2, KAT5, and PGAP3.
  • Observed differential risk association: increased KRTCAP2 and PGAP3 expression linked to higher gout risk, while THBS3, THBS3-AS1, and KAT5 expression linked to reduced risk.
  • No significant associations were found in renal tissue eQTL data, potentially due to limited sample size.

Conclusions:

  • Highlighted several genes potentially involved in gout pathogenesis.
  • Provided insights into the genetic mechanisms underlying gout.
  • Identified potential therapeutic targets for gout treatment.