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Related Concept Videos

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.
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...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

Updated: Jun 2, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
09:06

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Association studies for next-generation sequencing.

Li Luo1, Eric Boerwinkle, Momiao Xiong

  • 1Human Genetics Center, University of Texas School of Public Health, Houston, TX 77030, USA.

Genome Research
|April 28, 2011
PubMed
Summary
This summary is machine-generated.

New statistical methods using functional principal component analysis (FPCA) effectively detect associations between rare genetic variants and diseases. These FPCA-based approaches also demonstrate superior performance in filtering sequencing errors from genetic data.

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genome-wide association studies (GWAS) identify common variants for complex diseases but explain limited heritability.
  • Rare variants are a potential source of missing heritability, but their analysis is challenging due to large numbers, sequencing errors, and missing data.
  • Next-generation sequencing generates vast amounts of rare variant data, necessitating advanced analytical methods.

Purpose of the Study:

  • To develop and evaluate novel statistical methods for rare variant association analysis using resequencing data.
  • To address the challenges posed by large numbers of rare variants, sequence errors, and missing data in genetic association studies.
  • To compare the performance of functional principal component analysis (FPCA)-based methods against other established statistics.

Main Methods:

  • Utilized a genome continuum model and functional principal components for developing new association analysis methods.
  • Simulated data to calculate type I error rates and power for nine different statistical methods, including FPCA-based, MPCA-based, WSS, VT, generalized T(2), collapsing, and CMC methods.
  • Assessed the impact of sequence errors on the type I error rates of these statistics.
  • Applied the nine statistics to the ANGPTL4 resequencing data from the Dallas Heart Study.

Main Results:

  • FPCA-based statistics demonstrated higher power in detecting associations between rare variants and phenotypes compared to other methods.
  • FPCA-based statistics showed a stronger ability to filter sequence errors, improving data reliability.
  • The study compared the performance of FPCA against MPCA, WSS, VT, generalized T(2), collapsing, and CMC methods.

Conclusions:

  • FPCA-based statistical methods offer a powerful approach for analyzing rare variants in resequencing data.
  • These methods are effective in identifying disease-associated rare variants and mitigating the impact of sequencing errors.
  • The findings suggest FPCA-based statistics are a valuable tool for uncovering the genetic basis of complex diseases through rare variant analysis.