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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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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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Related Experiment Video

Updated: Jul 10, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Strategies in Aggregation Tests for Rare Variants.

Farid Rajabli1, Brian W Kunkle1

  • 1Dr. John T. Macdonald Foundation Department of Human Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA.

Current Protocols
|November 21, 2023
PubMed
Summary

This study introduces statistical methods for analyzing rare genetic variants in complex diseases, addressing limitations of traditional genome-wide association studies (GWAS) and offering practical R scripts for researchers.

Keywords:
aggregation testsannotationkernel methodspermutation testsrare variant

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Area of Science:

  • Genetics and Genomics
  • Statistical Bioinformatics
  • Complex Disease Research

Background:

  • Genome-wide association studies (GWAS) have identified common variants but explain limited heritability in complex diseases.
  • High-throughput sequencing enables rare variant analysis, yet their low frequency poses statistical challenges.
  • Existing common variant methods lack power for detecting rare variant associations.

Purpose of the Study:

  • To provide an overview of statistical approaches for analyzing the aggregate effect of rare variants in complex diseases.
  • To offer practical, step-by-step protocols using R scripts for rare variant association testing.
  • To discuss essential concepts and related bioinformatics topics for rare variant analysis.

Main Methods:

  • Focuses on aggregate testing methods for multiple rare variants within genetic regions.
  • Details four categories of statistical tests: burden tests, adaptive burden tests, variance-component tests, and combined tests.
  • Includes explanations of permutation tests, kernel methods, and genetic variant annotation.

Main Results:

  • Presents a comprehensive guide to statistical approaches for rare variant analysis.
  • Provides hands-on R script examples for implementing various aggregation tests.
  • Discusses practical considerations including bioinformatics tools, family-based designs, population stratification, and meta-analysis.

Conclusions:

  • The developed methods and protocols address the underpower of traditional GWAS for rare variants.
  • This work equips researchers with practical tools and knowledge for effective rare variant association studies.
  • Facilitates a deeper understanding of the genetic architecture of complex diseases by incorporating rare variants.