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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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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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Association Tests for Rare Variants.

Dan L Nicolae1

  • 1Departments of Medicine and Statistics, University of Chicago, Chicago, Illinois 60637;

Annual Review of Genomics and Human Genetics
|May 6, 2016
PubMed
Summary
This summary is machine-generated.

Researchers are developing statistical methods to identify rare variants affecting human traits. This review covers association testing methods, study design, and challenges in rare variant analysis.

Keywords:
burden testeffect sizeexome sequencinggenetic association studiesrare variantsstudy designvariance componentsvariant annotationwhole-genome sequencing

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Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genetics

Background:

  • Growing interest in identifying rare genetic variants influencing human phenotypes.
  • Need for robust statistical methods to analyze rare variants in genetic studies.

Purpose of the Study:

  • To review statistical methods for rare variant association testing.
  • To discuss key considerations in rare variant study design, analysis, and interpretation.
  • To highlight challenges and future directions in the field.

Main Methods:

  • Review of existing statistical methodologies for rare variant association tests.
  • Discussion of experimental design principles for rare variant studies.
  • Exploration of interpretation and validation strategies.

Main Results:

  • Overview of various statistical approaches for rare variant analysis.
  • Identification of critical factors influencing the choice of analytical methods.
  • Summary of challenges, limitations, and potential research avenues.

Conclusions:

  • Effective rare variant studies require careful method selection and study design.
  • Addressing challenges in rare variant analysis is crucial for advancing genetic research.
  • Future research should focus on refining methods and overcoming current limitations.