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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.
GWAS does not require the identification of the target gene involved in...
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Related Experiment Video

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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
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HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics.

Jie Zheng1,2, Santiago Rodriguez1,2, Charles Laurin1,2

  • 1MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol BS8 6BN, UK.

Bioinformatics (Oxford, England)
|September 4, 2016
PubMed
Summary
This summary is machine-generated.

Fine mapping using summary statistics is improved by HAPRAP, a novel method utilizing haplotype information. This approach enhances accuracy and robustness, especially with limited data or low-frequency variants.

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Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Fine mapping identifies causal variants at disease-associated loci, traditionally requiring individual-level genotype data.
  • Existing summary-level fine mapping methods rely on pairwise correlation coefficients, which are less biologically representative than haplotypes.
  • Haplotypes, not pairwise correlations, accurately reflect linkage disequilibrium (LD) among multiple loci.

Purpose of the Study:

  • To introduce the Haplotype Regional Association analysis Program (HAPRAP), a novel empirical iterative method for genetic fine mapping.
  • To enable fine mapping using readily available summary statistics and haplotype information from reference panels.
  • To overcome limitations of existing methods by leveraging haplotype structure for improved accuracy and robustness.

Main Methods:

  • Developed HAPRAP, an empirical iterative method for fine mapping.
  • Utilizes summary statistics and haplotype information from an individual-level reference panel.
  • Evaluated performance through simulations with individual-level and summary-level data, and a parametric simulation using human height data.

Main Results:

  • HAPRAP demonstrated high consistency with multiple regression on individual-level genotypes.
  • In simulations with summary-level data, HAPRAP showed reduced sensitivity to inaccurate LD estimates.
  • HAPRAP performed well with small sample sizes (<2000) and was robust to single nucleotide polymorphisms (SNPs) with low minor allele frequencies.
  • Applied to meta-analyses of human height, QTc interval, and gallbladder disease, HAPRAP replicated known associations and identified two novel variants for human height.

Conclusions:

  • HAPRAP provides a robust and accurate method for genetic fine mapping using summary statistics and haplotype information.
  • The method is particularly valuable given the increasing availability of summary-level data for large-scale genetic studies.
  • HAPRAP is expected to facilitate future applications such as functional prediction and the identification of instruments for Mendelian randomization.