Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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.
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Rare protein-coding variation and the genetic architecture of height in >1.4 million individuals.

medRxiv : the preprint server for health sciences·2026
Same author

Population-scale repeat expansions elucidate disease risk and brain atrophy.

Nature·2026
Same author

Humans with function-disrupting variants in the myostatin gene (MSTN) have increased skeletal muscle mass and strength, and less adiposity.

Nature communications·2026
Same author

Rare coding variants in CHRNB3 associate with reduced daily cigarette smoking across ancestries.

Nature communications·2026
Same author

Computationally efficient meta-analysis of gene-based tests using summary statistics in large-scale genetic studies.

Nature genetics·2025
Same author

Variant Classification Using Proteomics-Informed Large Language Models Increases Power of Rare Variant Association Studies and Enhances Target Discovery.

Genetic epidemiology·2025
Same journal

Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight.

Genetic epidemiology·2026
Same journal

Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease.

Genetic epidemiology·2026
Same journal

DRIVE v3: Command Line Application for Identity-by-Descent Haplotype Clustering in Large Biobank Scale Data.

Genetic epidemiology·2026
Same journal

Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.

Genetic epidemiology·2026
Same journal

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Genetic epidemiology·2026
Same journal

Outcome and Exposure Polygenic Risk Scores Can Help Reduce Information Bias and Selection Bias in Regression Estimates From Biobank Data.

Genetic epidemiology·2026
See all related articles

Related Experiment Video

Updated: May 20, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Joint genotype calling with array and sequence data.

Jared O'Connell1, Jonathan Marchini

  • 1Wellcome Trust Center of Human Genetics, Oxford, United Kingdom. marchini@stats.ox.ac.uk

Genetic Epidemiology
|July 24, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces Chiamante, a new method for accurate genotype calling from genetic data. It improves accuracy, especially for rare single-nucleotide polymorphisms (SNPs), by intelligently combining array and sequencing data.

More Related Videos

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
09:16

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Related Experiment Videos

Last Updated: May 20, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
09:16

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Rare variant analysis is crucial for understanding human disease genetics.
  • Current genotyping technologies (microarrays, sequencing) have limitations in accuracy, particularly for rare single-nucleotide polymorphisms (SNPs).
  • Increasing availability of both array and sequence data necessitates methods to integrate these sources for improved accuracy.

Purpose of the Study:

  • To develop and evaluate a novel genotype calling method, Chiamante, capable of integrating array and sequence data.
  • To adapt the method to varying data quality and selectively ignore unreliable data.
  • To demonstrate improved performance, especially for rare SNPs, compared to existing methods.

Main Methods:

  • Chiamante employs a model that adapts to data quality, allowing it to weigh array and sequence data differently for each SNP.
  • The method can call genotypes using only array data, only sequence data, or a combination of both.
  • The approach was validated using Phase I data from the 1000 Genomes Project.

Main Results:

  • Chiamante demonstrates improved genotype calling accuracy, particularly for rare SNPs.
  • The method outperforms existing approaches when using only array data.
  • Application to 1000 Genomes Project data confirmed its enhanced performance.

Conclusions:

  • Chiamante offers a robust framework for fusing genetic data from diverse sources, enhancing genotype accuracy.
  • This method is foundational for future studies combining exome sequencing and exome microarrays.
  • The approach has significant implications for genetic disease research and variant discovery.