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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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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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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Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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A novel hypothesis-generating approach for detecting phenotypic associations using epigenetic data.

Florence Z Martin1,2, Kayleigh E Easey1,2,3, Laura D Howe1,2

  • 1MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.

Epigenomics
|July 17, 2024
PubMed
Summary
This summary is machine-generated.

Hypothesis-free epigenome-wide association studies (EWAS) identified novel CpG sites linked to dysmenorrhea and heavy menstrual bleeding (HMB). These findings can guide future research into the causes of these common gynecological conditions.

Keywords:
ALSPACARIESdysmenorrheaepigenome-wide association studyheavy menstrual bleedinghypothesis-generatinghypothesis-testing

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Area of Science:

  • Epigenetics
  • Gynecological Health

Background:

  • Causal analyses for clinical and policy implications require robust hypotheses.
  • Epigenetic approaches offer hypothesis-free methods to identify potential causal factors.

Purpose of the Study:

  • To explore the utility of hypothesis-free epigenome-wide association studies (EWAS) for identifying phenotypes related to dysmenorrhea and heavy menstrual bleeding (HMB).
  • To investigate potential epigenetic markers associated with these gynecological conditions.

Main Methods:

  • Minimally adjusted epigenome-wide association studies (EWAS) were conducted using ALSPAC data for dysmenorrhea and HMB.
  • Differentially methylated CpGs were identified via EWAS Catalog, and associated traits were analyzed.
  • Traits were compared between individuals with and without the specified conditions.

Main Results:

  • Seven CpG sites were significantly associated with dysmenorrhea.
  • Two CpG sites were significantly associated with heavy menstrual bleeding (HMB).
  • Smoking and adverse childhood experiences were identified as potential confounding factors associated with both conditions.

Conclusions:

  • Hypothesis-generating EWAS are effective in identifying novel associations for future research.
  • Epigenetic insights can inform the understanding and potential treatment of gynecological disorders like dysmenorrhea and HMB.