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

719
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"...
719

You might also read

Related Articles

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

Sort by
Same author

Improving calls of differentially transcribed enhancers and their upstream regulators.

Bioinformatics advances·2026
Same author

Genetic Modulation of Oxycodone Self-Administration Trajectories: From Initiation to Escalating Burst Patterns.

bioRxiv : the preprint server for biology·2026
Same author

Genome-wide association mapping and targeted loss of function studies identify <i>Shroom3</i> as a driver of hyperpolyploidy and ventricular dilation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Mitochondrial signatures of infant mesenchymal stem cells predict child adiposity: The Healthy Start Study.

Research square·2026
Same author

Differences in immune cell profiles around the time of islet autoimmunity seroconversion in children with and without type 1 diabetes.

BMJ open diabetes research & care·2026
Same author

Cross-assay RNA modeling reveals cancer biomarkers.

bioRxiv : the preprint server for biology·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Mar 6, 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

10.8K

Model based heritability scores for high-throughput sequencing data.

Pratyaydipta Rudra1, W Jenny Shi2, Brian Vestal1

  • 1Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO 80045, USA.

BMC Bioinformatics
|March 4, 2017
PubMed
Summary
This summary is machine-generated.

We developed new statistical models to calculate heritability for high-throughput sequencing data. The compound Poisson mixed model (CP-fit) demonstrated the best confidence interval coverage for assessing molecular trait heritability in mice.

Keywords:
Compound Poisson mixed modelHeritabilityNegative binomial mixed modelRNAseqRecombinant inbred panelVariance partition coefficient

More Related Videos

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

38.1K
High-throughput Screening for Protein-based Inheritance in S. cerevisiae
08:12

High-throughput Screening for Protein-based Inheritance in S. cerevisiae

Published on: August 8, 2017

6.8K

Related Experiment Videos

Last Updated: Mar 6, 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

10.8K
Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

38.1K
High-throughput Screening for Protein-based Inheritance in S. cerevisiae
08:12

High-throughput Screening for Protein-based Inheritance in S. cerevisiae

Published on: August 8, 2017

6.8K

Area of Science:

  • Genetics and Genomics
  • Bioinformatics
  • Statistical Modeling

Background:

  • Heritability quantifies the proportion of phenotypic or molecular trait variance due to genotypic variance, crucial in breeding and genetics.
  • Existing methods for calculating heritability are limited for high-throughput sequencing data.
  • Accurate heritability estimation is vital for understanding genetic contributions to complex traits.

Purpose of the Study:

  • To propose and evaluate novel statistical models for computing and testing heritability in high-throughput sequencing data.
  • To compare the performance of different mixed-effects models for heritability estimation.
  • To apply these methods to real sequencing data for molecular traits in mice.

Main Methods:

  • Development of statistical models based on linear and generalized linear mixed effects models.
  • Implementation of methods for hypothesis testing and interval estimation of heritability.
  • Application of negative binomial mixed model (NB-fit), compound Poisson mixed model (CP-fit), and variance stabilizing transformed linear mixed model (VST) to sequencing data.

Main Results:

  • NB-fit, CP-fit, and VST models outperformed the voom-transformed linear mixed model (voom) for heritability estimation.
  • NB-fit and VST showed robustness, while CP-fit offered the best confidence interval coverage.
  • Analysis of microRNA (miRNA) and messenger RNA (mRNA) sequencing data revealed high heritability of expression traits in mice, with some heritable features linked to expression quantitative trait loci.

Conclusions:

  • The proposed models are applicable to sequencing experiments with biological replicates and controlled environmental variation.
  • The compound Poisson mixed model (CP-fit) approach for heritability assessment was newly implemented.
  • An R package, HeritSeq, is available, providing the methods and tools for data simulation and analysis.