Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Analysis: Overview

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

Statistical Methods to Analyze Parametric Data: ANOVA

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...

Statistical Significance

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...

Biostatistics: Overview

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...

Relationship Formation

Relationship Formation

What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...

Statistical Package for the Social Sciences (SPSS)

Statistical Package for the Social Sciences (SPSS)

The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...

You might also read

Related Articles

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

Sort by

Same author

Gene x environment interaction analysis confirms genetic modifier effects on steroid efficacy via TGF-β pathway in Duchenne muscular dystrophy.

European journal of human genetics : EJHG·2026

Same author

Operationalizing a research-oriented learning healthcare system across covered entities: cross-institutional strategies and innovations.

npj health systems·2025

Same author

Enhancing Efficiency, Reducing Length of Stay and Costs in Pediatric Cardiology Rounds Through Simulation-Based Optimization.

Applied clinical informatics·2025

Same author

A multifaceted approach to advancing data quality and fitness standards in multi-institutional networks.

Journal of the American Medical Informatics Association : JAMIA·2025

Same author

Language-Related Barriers to Digital Health Engagement in Pediatric Primary Care.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association·2025

Same author

Virtual Reality for Workplace Violence Training of Health Care Workers: Pilot Mixed Methods Usability Study.

JMIR serious games·2025

Same journal

Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight.

Genetic epidemiology·2026

Same journal

Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease.

Genetic epidemiology·2026

Same journal

DRIVE v3: Command Line Application for Identity-by-Descent Haplotype Clustering in Large Biobank Scale Data.

Genetic epidemiology·2026

Same journal

Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.

Genetic epidemiology·2026

Same journal

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Genetic epidemiology·2026

Same journal

Outcome and Exposure Polygenic Risk Scores Can Help Reduce Information Bias and Selection Bias in Regression Estimates From Biobank Data.

Genetic epidemiology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

Association statistics under the PPL framework.

Yungui Huang¹, Veronica J Vieland

¹The Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio 43205, USA. Yungui.Huang@nationwidechildrens.org

Genetic Epidemiology

|November 9, 2010

Summary

This summary is machine-generated.

This study introduces novel linkage disequilibrium (LD) statistics for case-control (CC) and family data analysis. These statistics measure LD evidence directly, avoiding multiple testing corrections and enhancing genetic analysis flexibility.

More Related Videos

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

Related Experiment Videos

Last Updated: Jun 6, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Linkage Disequilibrium (LD) is crucial for genetic association studies.
Existing methods often require multiple testing corrections and struggle with diverse family structures.
The PPL (Penalized Partial Likelihood) framework offers a robust statistical foundation.

Purpose of the Study:

Extend the PPL framework for case-control (CC) data analysis.
Introduce novel LD statistics that measure evidence for or against LD.
Enable seamless analysis of diverse family data structures within a unified framework.

Main Methods:

Developed three new LD statistics within the PPL framework.
Statistics measure LD evidence directly, bypassing null hypothesis testing.
Implemented sequential updating for accumulating evidence across heterogeneous data.
Incorporated flexible trait likelihoods (dominant, recessive, additive) and two-locus epistasis modeling.
Integrated linkage information as prior distributions.

Main Results:

The new LD statistics provide direct evidence for or against LD, avoiding multiple testing issues.
The PPL framework accommodates various family data structures (trios to complex pedigrees).
Sequential updating allows efficient accumulation of evidence from heterogeneous data.
The framework supports general trait models and epistasis analysis.
Incorporation of linkage information as priors is demonstrated.

Conclusions:

The proposed LD statistics and PPL framework extension offer a flexible and powerful tool for genetic analysis.
This approach simplifies the analysis of diverse genetic data, including CC and family studies.
The method enhances the ability to detect and characterize LD and epistasis.