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Gene-Environment Interactions01:20

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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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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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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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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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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SNPxE: SNP-environment interaction pattern identifier.

Hui-Yi Lin1, Po-Yu Huang2, Tung-Sung Tseng3

  • 1Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA. hlin1@lsuhsc.edu.

BMC Bioinformatics
|September 8, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces SNPxE, a novel statistical tool to identify complex patterns of single nucleotide polymorphism (SNP) and environment interactions in disease. SNPxE enhances detection accuracy, offering insights into complex disease pathogenesis and the heritability gap.

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Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Complex diseases arise from interactions between genetic variations, such as single nucleotide polymorphisms (SNPs), and environmental factors.
  • Current statistical methods for evaluating SNP-environment interactions are limited, often leading to false negatives due to an inability to detect diverse interaction patterns.
  • There is a need for advanced statistical tools to accurately identify various SNP-environment interaction patterns crucial for understanding disease pathogenesis.

Purpose of the Study:

  • To develop and introduce SNPxE, a statistical tool designed to comprehensively identify and evaluate multiple SNP-environment interaction patterns.
  • To improve the accuracy of detecting SNP-environment interactions compared to conventional methods.
  • To provide a tool that can offer insights into the 'missing heritability' of complex diseases.

Main Methods:

  • SNPxE evaluates multiple interaction patterns for each SNP-environment pair, considering 27 patterns for ordinal environments and 18 for categorical environments.
  • The tool integrates model structure, SNP inheritance mode, and risk direction to detect interactions.
  • The best interaction pattern is selected using the Bayesian information criterion or the smallest p-value, applicable to both numeric and binary phenotypes.

Main Results:

  • SNPxE successfully identifies various SNP-environment interaction patterns, offering a more exhaustive analysis than traditional methods.
  • The tool can identify specific risk sub-groups based on combined SNP and environmental factor effects.
  • Heat-tables can be generated for clear interpretation of outcome proportions within identified sub-groups.

Conclusions:

  • SNPxE is a valuable tool for in-depth evaluation of SNP-environment interactions, significantly advancing the field.
  • Findings from SNPxE can contribute to understanding and potentially solving the 'missing heritability' problem in complex diseases.
  • The SNPxE R function is publicly available on GitHub for researchers to utilize.