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Genome-wide Association Studies-GWAS01:11

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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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Related Experiment Video

Updated: Jul 12, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Learning the kernel for rare variant genetic association test.

Isak Falk1,2, Millie Zhao3, Juba Nait Saada3

  • 1Department of Computer Science, University College London, London, United Kingdom.

Frontiers in Genetics
|October 27, 2023
PubMed
Summary

The new ecSKAT method improves rare variant association studies by optimally combining genetic data, outperforming existing methods for both continuous and binary traits. This approach enhances power and corrects for confounders like age and sex.

Keywords:
GWASSKATWESkernel learningreproducing kernel Hilbert spacescore testingtarget alignment

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Single-marker association analysis is underpowered for rare variants.
  • Set-based analyses for rare variants are crucial for capturing missing heritability.
  • Existing methods like cSKAT can be extended to more complex models.

Purpose of the Study:

  • To extend the convex-optimized SKAT (cSKAT) method to the Generalised Linear Model (GLM) setting.
  • To develop an extended cSKAT (ecSKAT) that incorporates non-genetic covariates.
  • To improve power in rare variant association studies.

Main Methods:

  • Extended cSKAT (ecSKAT) by incorporating arbitrary non-genetic covariates into the GLM framework.
  • Formulated the optimization problem as a quadratic programming problem solvable with no additional computational cost.
  • Evaluated performance using simulations for continuous and binary traits and real data from UK Biobank.

Main Results:

  • ecSKAT optimizes kernel combinations, related to p-value upper bounds.
  • The method effectively corrects for confounders such as age, sex, and population structure.
  • In UK Biobank data, ecSKAT yielded lower p-values compared to burden tests and SKAT for both quantitative and binary traits.

Conclusions:

  • ecSKAT provides a powerful and flexible framework for rare variant association studies.
  • The method enhances power and corrects for covariates in both quantitative and binary trait analyses.
  • ecSKAT represents a significant advancement for genetic association studies utilizing whole exome sequencing data.