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

You might also read

Related Articles

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

Sort by
Same author

Metal-support interactions <i>via</i> multidimensional regulation in key electrocatalytic reactions.

Chemical science·2026
Same author

Aging, Genetic Susceptibility and Risk of Chronic Inflammatory Upper Airway Diseases: Finding From A Prospective Cohort Study.

Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology·2026
Same author

Improving Genetic Risk Prediction of CAD in Chinese by Multi-ancestry and Multi-trait GWAS Integration.

Genomics, proteomics & bioinformatics·2026
Same author

<i>Clumppling 2.0</i>: A Clustering Alignment Program for Population Structure Analyses.

Human population genetics and genomics·2026
Same author

Integrated spatial multi-omics delineates fatty acid degradation fuels malignant evolution at the tumour periphery in cervical squamous cell carcinoma.

EBioMedicine·2026
Same author

Discovery of gene-alcohol interaction loci influencing blood pressure in 1.1 million individuals from multiple populations.

Research square·2026

Related Experiment Video

Updated: May 20, 2026

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

A maximum-likelihood method to correct for allelic dropout in microsatellite data with no replicate genotypes.

Chaolong Wang1, Kari B Schroeder, Noah A Rosenberg

  • 1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. chaolong@umich.edu

Genetics
|August 2, 2012
PubMed
Summary

Allelic dropout, a common issue in genetic analysis, can skew results. This study introduces a new computational method to accurately estimate dropout rates and correct heterozygosity bias without needing repeated DNA testing.

More Related Videos

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

Methods to Increase the Sensitivity of High Resolution Melting Single Nucleotide Polymorphism Genotyping in Malaria
10:27

Methods to Increase the Sensitivity of High Resolution Melting Single Nucleotide Polymorphism Genotyping in Malaria

Published on: November 10, 2015

Related Experiment Videos

Last Updated: May 20, 2026

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

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

Methods to Increase the Sensitivity of High Resolution Melting Single Nucleotide Polymorphism Genotyping in Malaria
10:27

Methods to Increase the Sensitivity of High Resolution Melting Single Nucleotide Polymorphism Genotyping in Malaria

Published on: November 10, 2015

Area of Science:

  • Genetics
  • Population Genetics
  • Molecular Ecology

Background:

  • Allelic dropout (AD) is a significant source of missing data in microsatellite genotyping.
  • AD leads to biased estimates of heterozygosity and inbreeding, particularly with low-quality DNA.
  • Current methods to mitigate AD often require costly replicate genotyping.

Purpose of the Study:

  • To develop a computational method for estimating allelic dropout rates and allele frequencies from nonreplicated genotypes.
  • To correct for biases in heterozygosity and inbreeding estimates caused by allelic dropout.
  • To provide a cost-effective alternative to replicate genotyping for managing missing genetic data.

Main Methods:

  • A maximum-likelihood approach combined with an expectation-maximization algorithm was employed.
  • The method jointly estimates sample-specific and locus-specific allelic dropout rates.
  • Multiple imputation is used to correct heterozygosity bias, accounting for inbreeding (deviation from Hardy-Weinberg equilibrium).

Main Results:

  • The method accurately reproduces missing data patterns and heterozygosity observed in real datasets.
  • Model parameters, including dropout rates and inbreeding coefficients, are estimated effectively.
  • The downward bias in observed heterozygosity estimation is successfully corrected.

Conclusions:

  • The proposed method provides a robust and cost-effective solution for addressing allelic dropout in genetic data analysis.
  • It accurately estimates key population genetic parameters and corrects for data biases.
  • The approach is valuable for diverse applications in population genetics and molecular ecology, even with potential violations of model assumptions.