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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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Frequency-dependent Selection01:21

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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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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Comparing Copy Number Variations and SNPs02:26

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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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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Nonlinear post-selection inference for genome-wide association studies.

Lotfi Slim1, Clément Chatelain, Chloé-Agathe Azencott

  • 1CBIO Mines ParisTech, PSL Research University, Paris, F-75006, France, lslim@nvidia.com.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces kernelPSI, a novel two-step method for genome-wide association studies (GWAS). kernelPSI enhances statistical power and interpretability by focusing on strongly associated single nucleotide polymorphisms (SNPs) within genes.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) face challenges with statistical power and interpretability.
  • Current gene-level analyses can mask important association signals by including all single nucleotide polymorphisms (SNPs).

Purpose of the Study:

  • To develop a novel two-step strategy to improve statistical power and interpretability in quantitative GWAS.
  • To adapt the kernelPSI framework for gene-level association testing in GWAS.

Main Methods:

  • A two-step approach involving SNP selection within genes followed by joint effect testing.
  • Adaptation of the kernelPSI framework for quantitative GWAS, incorporating kernels for epistasis and post-selection inference.
  • Application to continuous phenotypes from the UKBiobank dataset.

Main Results:

  • kernelPSI successfully identifies genes associated with phenotypes by focusing on strongly associated SNP regions.
  • Demonstrated increased statistical power compared to existing gene-based GWAS tools like SKAT and MAGMA.
  • Effective modeling of non-linear relationships and epistatic interactions between neighboring SNPs.

Conclusions:

  • kernelPSI is an effective tool for combining SNP-based and gene-based GWAS analyses.
  • The method improves both statistical performance and interpretability of GWAS findings.
  • kernelPSI offers a powerful approach for dissecting complex genetic architectures of traits.