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

Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
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...
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,...
Incomplete Dominance01:43

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

You might also read

Related Articles

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

Sort by
Same author

Ovarian cancer tumor immune profiles associated with intrauterine device and oral contraceptive use.

British journal of cancer·2026
Same author

Spatial Clustering of Recently Activated Cytotoxic Lymphocytes Improves Association with Overall Survival in Women with High-Grade Serous Ovarian Cancer.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Spatial Clustering of Recently Activated Cytotoxic Lymphocytes Improves Association with Overall Survival in Women with High-Grade Serous Ovarian Cancer.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

FastPCA: An R package for fast singular value decomposition.

Journal of open source software·2026
Same author

scSpatialSIM: a simulator of spatial single-cell molecular data.

SoftwareX·2026
Same author

Longer-term aspirin use and subsequent ovarian cancer risk in the Ovarian Cancer Cohort Consortium.

International journal of epidemiology·2026

Related Experiment Video

Updated: Jun 17, 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

Single versus multiple imputation for genotypic data.

Brooke L Fridley1, Shannon K McDonnell, Kari G Rabe

  • 1Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA. fridley.brooke@mayo.edu.

BMC Proceedings
|December 19, 2009
PubMed
Summary
This summary is machine-generated.

This study compared single and multiple imputation methods for genetic data. Results suggest multiple imputation may not be necessary for accurate genotype imputation and data combination.

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

Related Experiment Videos

Last Updated: Jun 17, 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

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Combining multi-study genetic data and imputing missing genotypes are crucial for genetic association studies.
  • Current single imputation methods do not account for imputation uncertainty.
  • Multiple imputation offers an alternative by incorporating imputation variability.

Purpose of the Study:

  • To assess the variation in genotypic data imputation using both single and multiple imputation methods.
  • To compare the necessity of multiple imputation versus single imputation in genetic data analysis.

Main Methods:

  • Employed MACH, a hidden Markov model imputation method, for both single and multiple imputations.
  • Utilized genome-wide data from the North American Rheumatoid Arthritis Consortium.
  • Analyzed four chromosomal regions with varying linkage disequilibrium and association signals.

Main Results:

  • Assessed two scenarios: imputation of untyped markers and combining data from two studies.
  • Findings from four regions suggest multiple imputation may not be essential.
  • Single imputation might be sufficient, contrary to initial expectations.

Conclusions:

  • The study's limited scope indicates that multiple imputation might not always be necessary for genotype imputation and data combination.
  • Further research is warranted to validate these findings across diverse genetic datasets and scenarios.