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

32
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...
32
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.2K
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...
13.2K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

123
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
123
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

156
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...
156
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

313
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:
313

You might also read

Related Articles

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

Sort by
Same author

An Ensemble Classifier for Ordinal Outcomes in High-Dimensional Genomics Data.

Pharmaceutical statistics·2026
Same author

Match Accuracy of Burned Teeth: A pilot study of allied dental professionals.

Journal of dental hygiene : JDH·2025
Same author

Improving Dental Hygiene Students' Knowledge, Attitudes, and Confidence Toward Prenatal Oral Health Through Experiential Learning: A pilot study.

Journal of dental hygiene : JDH·2025
Same author

Allied Dental Students' Perceived Knowledge, Confidence, and Attitudes Regarding Disaster Victim Identification Topics.

Journal of dental hygiene : JDH·2024
Same author

Plasma protein signatures of adult asthma.

Allergy·2024
Same author

Populational Variations of Cheiloscopy Patterns: A cross-sectional observation pilot study.

Journal of dental hygiene : JDH·2023
Same journal

Balanced mediated pathway detection in genomic data.

Statistical applications in genetics and molecular biology·2026
Same journal

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same journal

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.

Statistical applications in genetics and molecular biology·2026
Same journal

BLOG: Bayesian longitudinal omics with group constraints.

Statistical applications in genetics and molecular biology·2026
Same journal

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer.

Statistical applications in genetics and molecular biology·2026
Same journal

Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data.

Statistical applications in genetics and molecular biology·2026
See all related articles

Related Experiment Video

Updated: Jun 11, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.6K

Empirically adjusted fixed-effects meta-analysis methods in genomic studies.

Wimarsha T Jayanetti1, Sinjini Sikdar2

  • 1Department of Statistical Sciences, Wake Forest University, Winston-Salem, NC 27109, USA.

Statistical Applications in Genetics and Molecular Biology
|September 28, 2024
PubMed
Summary
This summary is machine-generated.

Genomic meta-analysis using METAL can yield incorrect results due to its reliance on theoretical null distributions. Modifying METAL with an empirical null distribution significantly improves statistical detection, especially with hidden confounders.

Keywords:
empirical null distributiongenomic studieslarge-scale hypothesis testingmeta-analysis

More Related Videos

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

448
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K

Related Experiment Videos

Last Updated: Jun 11, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.6K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

448
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Meta-analysis of genomic studies enhances statistical power compared to individual studies.
  • Combining effect size estimates (e.g., using METAL) is statistically more powerful than combining significance measures.
  • METAL is a popular tool for fixed-effects meta-analysis in genomic research.

Purpose of the Study:

  • To identify limitations of the METAL tool stemming from its dependence on theoretical null distributions.
  • To demonstrate the benefits of employing an empirical null distribution within METAL for improved significance testing.
  • To compare different approaches for estimating empirical null distributions and identify optimal scenarios.

Main Methods:

  • Simulation studies were conducted to evaluate METAL's performance.
  • Real genomic data were analyzed to assess the practical impact of the proposed modifications.
  • The study focused on modifying z-scores by incorporating an empirical null distribution.

Main Results:

  • METAL's reliance on theoretical null distributions can lead to incorrect significance testing.
  • Modifying METAL with an empirical null distribution significantly improves results, particularly in the presence of hidden confounders.
  • Two distinct empirical null distribution estimation approaches were evaluated, with varying performance depending on the scenario.

Conclusions:

  • Using an empirical null distribution is crucial for accurate fixed-effects meta-analysis in genomics.
  • Researchers should carefully consider the choice of empirical null distribution estimation method for optimal results.
  • This work provides insights for researchers to enhance the reliability of genomic meta-analysis.