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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

342
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...
342
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.3K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.3K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

955
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
955
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

727
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
727
Longitudinal Studies01:26

Longitudinal Studies

698
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
698
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Pleiotropic shared heritability quantifies the shared genetic variance of common diseases.

Nature genetics·2026
Same author

The Biobank Rare Variant consortium powers the discovery of rare genetic associations through global collaboration.

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

Mechanism of age-related accumulation of mtDNA mutations in human blood.

Nature·2026
Same author

Systematic common and rare variant association testing in 392,030 whole genomes in <i>All of Us</i>.

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

Effect of ancestry and shared genetic architecture of serious mental illness on symptoms and cognition in an admixed Latin American population.

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

Functionally informed cis and trans proteome-wide association studies prioritize disease-critical genes.

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

Mutational scanning reveals substrate-assisted autoregulation of the WNT destruction complex.

Nature genetics·2026
Same journal

Spatial transcriptomic analyses highlight distinct erythroid niches in mice and humans.

Nature genetics·2026
Same journal

Building up pangenome analysis block by block.

Nature genetics·2026
Same journal

Mutations in splicing factor gene U2AF1 rescue defective oncogene splicing in KRAS-mutant cancers.

Nature genetics·2026
Same journal

Assessing the effect of immune surveillance on clonal expansions in the blood.

Nature genetics·2026
Same journal

Improved heritability partitioning and enrichment analyses using summary statistics with graphREML.

Nature genetics·2026
See all related articles

Related Experiment Video

Updated: Apr 18, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Efficient Bayesian mixed-model analysis increases association power in large cohorts.

Po-Ru Loh1, George Tucker2, Brendan K Bulik-Sullivan3

  • 11] Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA. [2] Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

Nature Genetics
|February 3, 2015
PubMed
Summary
This summary is machine-generated.

BOLT-LMM is a faster mixed-model association method for genetic studies. It improves power in large cohorts by using a more realistic genetic model, outperforming existing methods.

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.8K
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

10.9K

Related Experiment Videos

Last Updated: Apr 18, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.8K
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

10.9K

Area of Science:

  • Genetics
  • Statistical Genetics
  • Computational Biology

Background:

  • Linear mixed models are crucial for genetic association studies, but computationally intensive for large datasets.
  • Existing methods have limitations in scalability and statistical power due to assumptions of infinitesimal genetic architecture.

Purpose of the Study:

  • To develop a computationally efficient mixed-model association method that enhances statistical power for large cohorts.
  • To address the limitations of existing methods in terms of time complexity and power optimization.

Main Methods:

  • Introduced BOLT-LMM, a novel mixed-model association method with O(MN) time complexity.
  • Implemented a Bayesian mixture prior on marker effect sizes to model non-infinitesimal genetic architectures.
  • Applied the method to 9 quantitative traits in a large cohort (23,294 samples).

Main Results:

  • BOLT-LMM demonstrated significant increases in statistical power across multiple quantitative traits.
  • Simulations confirmed the enhanced power and efficiency of BOLT-LMM compared to existing methods.
  • The power improvement of BOLT-LMM scales positively with increasing cohort size.

Conclusions:

  • BOLT-LMM offers a computationally efficient and powerful alternative for genome-wide association studies in large cohorts.
  • The method's ability to model realistic genetic architectures makes it suitable for advancing genetic discovery.
  • BOLT-LMM is particularly appealing for future large-scale genetic association studies.