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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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%...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific primer.
Since the...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Related Experiment Video

Updated: May 31, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

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

Published on: January 16, 2019

Rare-variant association testing for sequencing data with the sequence kernel association test.

Michael C Wu1, Seunggeun Lee, Tianxi Cai

  • 1Department of Biostatistics, The University of North Carolina at Chapel Hill, 27599, USA.

American Journal of Human Genetics
|July 9, 2011
PubMed
Summary
This summary is machine-generated.

The Sequence Kernel Association Test (SKAT) offers a powerful new method for analyzing rare genetic variants in complex traits. This computationally efficient approach significantly outperforms existing tests in identifying associations relevant to human health.

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Last Updated: May 31, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

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

Published on: January 16, 2019

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Identifying rare variants associated with complex traits is crucial but challenging.
  • Classical single-marker association analysis lacks power for rare variants.

Purpose of the Study:

  • To introduce the Sequence Kernel Association Test (SKAT), a novel method for genetic association studies.
  • To provide a flexible, computationally efficient tool for analyzing common and rare variants.

Main Methods:

  • SKAT is a supervised, score-based variance-component test using regression.
  • It analytically calculates p-values, enabling efficient genome-wide application.
  • The method easily adjusts for covariates and handles continuous or dichotomous traits.

Main Results:

  • SKAT analysis of a 1000-individual genome-wide study was completed in 7 hours on a laptop.
  • Simulations and real-world data (Dallas Heart Study triglycerides) demonstrated SKAT's superior performance over alternative rare-variant tests.
  • Analytic power and sample-size calculations were developed for study design.

Conclusions:

  • SKAT provides a powerful, efficient, and flexible approach for rare variant association studies.
  • It significantly enhances the ability to detect genetic associations with complex traits.
  • SKAT is a valuable tool for designing and analyzing candidate-gene, whole-exome, and whole-genome sequencing studies.