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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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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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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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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Meta-Analysis of SNP-Environment Interaction With Overlapping Data.

Qinqin Jin1,2, Gang Shi1

  • 1State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China.

Frontiers in Genetics
|February 22, 2020
PubMed
Summary

This study introduces a new meta-analysis method to accurately test gene-environment interactions in genome-wide association studies with overlapping data. The method controls false positives and maintains statistical power without needing individual-level data.

Keywords:
correlation matrixgene-environment interactionmeta-analysismeta-regressionoverlapping data

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Meta-analysis is crucial for genome-wide association studies (GWAS).
  • Overlapping data in GWAS meta-analyses can lead to false positive results.
  • Existing methods address overlapping data for genetic main effects but not for gene-environment interactions.

Purpose of the Study:

  • To develop a novel meta-analysis method for testing gene-environment interactions in the presence of overlapping data.
  • To address limitations of current methods that do not account for data overlap in gene-environment interaction analyses.

Main Methods:

  • Proposed an overlapping meta-regression method generalizing covariance matrices using Lin's and Han's correlation structures.
  • Developed statistical significance tests for gene-environment interactions and joint effects.
  • Utilized simulations to evaluate Type I errors and statistical power under varying overlap levels.

Main Results:

  • The proposed method effectively controls Type I errors.
  • Statistical power is comparable to the data-splitting method.
  • Ignoring overlapping data inflates Type I errors.

Conclusions:

  • The new method accurately handles overlapping data in gene-environment interaction meta-analyses.
  • It offers a statistically efficient alternative to data-splitting, without requiring individual-level data.
  • This approach improves the reliability of GWAS findings concerning gene-environment interactions.