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Single Nucleotide Polymorphisms-SNPs01:05

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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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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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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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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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Generalized multi-SNP mediation intersection-union test.

Wujuan Zhong1, Toni Darville2, Xiaojing Zheng1,2

  • 1Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Biometrics
|December 14, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to understand how multiple genetic variants influence traits by testing mediation effects. The approach offers significant power gains for genetic research and drug discovery.

Keywords:
intersection-union testmediation analysismultiple correlated SNPsnon-Gaussian outcome

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Area of Science:

  • Genetics and Genomics
  • Statistical Bioinformatics
  • Molecular Biology

Background:

  • Genome-wide association studies (GWAS) identify genetic variants linked to various traits.
  • Understanding the molecular mechanisms and causal pathways from DNA to phenotype remains a challenge.
  • Existing mediation analysis methods may not accommodate multiple genetic variants or diverse outcome types.

Purpose of the Study:

  • To develop a novel and flexible statistical method for testing mediation effects of multiple genetic variants.
  • To accommodate various phenotypic outcomes including binary, continuous, count, and survival data.
  • To elucidate molecular mechanisms underlying GWAS-identified genetic variants.

Main Methods:

  • Intersection-union test combined with the likelihood ratio test for mediation detection.
  • Fitting high-dimensional generalized linear mixed models under a mediation framework.
  • Utilizing Laplace approximation for marginal likelihood computation and coordinate descent for parameter estimation.

Main Results:

  • Extensive simulations confirm the validity of the proposed method.
  • Demonstrated substantial power gains, up to 97%, compared to alternative methods.
  • Successful application to real-world data, including the study of Chlamydia trachomatis infection.

Conclusions:

  • The proposed method provides a powerful tool for mediation analysis in the post-GWAS era.
  • Facilitates disentangling causal mechanisms from DNA to phenotype.
  • Offers potential for advancing new drug discovery and personalized medicine.