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

Probability Laws01:49

Probability Laws

Overview
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.In the early 20th century,...
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.
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

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

Sort by
Same author

Genome wide association study meta-analysis of neuropathologic lesions of Alzheimer's disease and related dementias in a multi-site autopsy cohort.

PLoS genetics·2026
Same author

Evidence of APOE4-related brain vulnerabilities in verbal memory systems in midlife women.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Combining post-mortem and neuroimaging measures of brain amyloidosis to accelerate genomic discovery.

Brain : a journal of neurology·2026
Same author

CNS-selective plasma p-tau217 accurately captures Alzheimer's disease pathology and progression.

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

Physical activity, aerobic fitness, and AD blood biomarkers: The IGNITE study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Lifespan exposure to hormone therapies and structural brain morphometry in older women.

NeuroImage·2026
Same journal

Comparative profiles of pediatric Mendeliome: A Single-Centre 572-Whole-Exome Sequencing Study in Xinjiang.

Human heredity·2026
Same journal

Erratum.

Human heredity·2026
Same journal

Exploratory Analysis of HMGB1 Genetic Variants and Their Potential Association with Lung Cancer Susceptibility and Chemotherapy Response in a Chinese Population.

Human heredity·2025
Same journal

Weighted Burden Analysis of Rare Genetic Variants Identifies Novel Genes with Effects on BMI.

Human heredity·2025
Same journal

Generalized Stable Population and Agent-Based Models of Phenotypic Transmission in Human Populations, with an Application to Body Size.

Human heredity·2025
Same journal

Proteinase-activated receptor 2 (PAR-2) expression and F2RL1 genetic variants are associated with asthma: a case-control study in the Chinese population.

Human heredity·2025
See all related articles

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

A Likelihood-Based Approach for Missing Genotype Data.

Gina M D'Angelo1, M Ilyas Kamboh, Eleanor Feingold

  • 1Division of Biostatistics, Washington University School of Medicine, St. Louis, Mo., USA.

Human Heredity
|January 14, 2010
PubMed
Summary
This summary is machine-generated.

Missing genotype data in genetic studies hinders analysis. The expectation-maximization algorithm and multiple imputation offer superior methods for handling missing SNP data, improving efficiency and reducing bias in association studies.

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

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Missing genotype data is a common challenge in genetic association studies.
  • Complete case analysis, a frequent approach, can lead to reduced sample size, bias, and inefficiency.
  • Standard missing data handling methods are underutilized in this context.

Purpose of the Study:

  • To describe and adapt standard missing data handling methods for candidate gene association studies.
  • To compare the performance of these methods via simulation.
  • To identify optimal methods for managing missing SNP data in genetic analyses.

Main Methods:

  • Application and adaptation of several standard missing data techniques.
  • Simulation study to evaluate method performance.
  • Demonstration using an Alzheimer's disease association dataset.

Main Results:

  • The expectation-maximization (EM) algorithm demonstrated strong performance.
  • Multiple imputation utilizing a bootstrapped EM sampling algorithm also yielded excellent results.
  • These methods outperformed other estimators in the study.

Conclusions:

  • The EM algorithm and multiple imputation are effective strategies for handling missing genotype data.
  • These advanced methods offer improved efficiency and reduced bias compared to complete case analysis.
  • Their application can enhance the reliability of genetic association studies.