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

Heritability01:06

Heritability

977
Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
977
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.9K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.9K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

9.9K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
9.9K
Uncertainty: Overview00:59

Uncertainty: Overview

1.6K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.6K
Probability Laws01:49

Probability Laws

29.7K
Overview
29.7K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.4K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Multiple instance fine-mapping: Predicting causal regulatory variants with a deep sequence model.

PLoS genetics·2026
Same author

Assessing VirScan serosurvey epitope profiling variability between in-clinic venous blood draw and capillary blood self-sampling device.

Microbiology spectrum·2026
Same author

Prediction and risk evaluation of delirium after surgery in older patients: development and internal validation of an algorithm from the prospective BioCog cohort study.

British journal of anaesthesia·2026
Same author

Large language models improve transferability of electronic health record-based predictions across countries and coding systems.

NPJ digital medicine·2026
Same author

A Pre-Trained Model Customization Framework for Accelerated PET/MR Segmentation of Abdominal Fat in Obstructive Sleep Apnea.

Diagnostics (Basel, Switzerland)·2025
Same author

The AIR·MS data platform for artificial intelligence in healthcare.

JAMIA open·2025
Same journal

Haplotype analysis of spinocerebellar ataxia type 36 suggests a shared permissive core haplotype across populations.

Journal of human genetics·2026
Same journal

Activation of cryptic donor splice site due to an exonic MYPN variant in congenital myopathy.

Journal of human genetics·2026
Same journal

The importance of integrating genetic testing into reproductive medicine: a retrospective observational study investigating the monogenic causes of human infertility in couples considering ICSI.

Journal of human genetics·2026
Same journal

Functional effect predictions for ion channel missense variants using a protein language model.

Journal of human genetics·2026
Same journal

A trio-based long-read sequencing workflow identifies a pathogenic transposable element insertion in a previously undiagnosed patient.

Journal of human genetics·2026
Same journal

Recombinant GBA1 alleles presenting as exon-level deletions by short-read NGS in Parkinson disease: Implications for diagnostic approaches.

Journal of human genetics·2026
See all related articles

Related Experiment Video

Updated: May 1, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

9.4K

Quantifying the uncertainty in heritability.

Nicholas A Furlotte1, David Heckerman2, Christoph Lippert2

  • 11] Microsoft Research, Los Angeles, CA, USA [2] Computer Science Department, University of California, Los Angeles, CA, USA.

Journal of Human Genetics
|March 28, 2014
PubMed
Summary
This summary is machine-generated.

This study compares frequentist and Bayesian methods for estimating heritability, finding that while they align in large samples, Bayesian approaches offer a more robust quantification of uncertainty in genetic estimates, especially in real-world cohorts.

More Related Videos

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
10:08

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Published on: August 12, 2019

13.6K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

7.6K

Related Experiment Videos

Last Updated: May 1, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

9.4K
Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
10:08

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Published on: August 12, 2019

13.6K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

7.6K

Area of Science:

  • Quantitative Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Mixed models are widely used for estimating narrow-sense heritability and SNP heritability.
  • Quantifying the variability and uncertainty in heritability estimates remains a significant challenge.

Purpose of the Study:

  • To develop a computationally efficient Bayesian approach for quantifying uncertainty in heritability estimates.
  • To compare the developed Bayesian method with the traditional frequentist approach.

Main Methods:

  • Developed a Bayesian method to estimate heritability and its posterior distribution.
  • Implemented computational efficiencies for the Bayesian approach.
  • Compared Bayesian results with frequentist (maximum likelihood with asymptotic normal approximation) estimates using theoretical analysis and empirical data.

Main Results:

  • Theoretically, frequentist and Bayesian approaches yield similar results for large sample sizes and intermediate heritability values.
  • Empirically, using the Atherosclerosis Risk in Communities cohort, differences between the two approaches were observed.
  • The study highlights that substantial uncertainty can persist in heritability estimates, even with large datasets.

Conclusions:

  • The Bayesian approach provides a valuable alternative for quantifying uncertainty in heritability estimates.
  • Empirical evidence suggests potential discrepancies between frequentist and Bayesian methods in real-world genetic studies.
  • Accurate assessment of heritability uncertainty is crucial for reliable genetic inferences.