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

Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...

You might also read

Related Articles

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

Sort by
Same author

Contact Hypersensitivity to Topical Antimicrobial and Antifungal Agents.

Indian journal of dermatology, venereology and leprology·2017
Same author

Arginase I gene single-nucleotide polymorphism is associated with decreased risk of pulmonary hypertension in bronchopulmonary dysplasia.

Acta paediatrica (Oslo, Norway : 1992)·2014
Same author

A genome-wide linkage and association study of musical aptitude identifies loci containing genes related to inner ear development and neurocognitive functions.

Molecular psychiatry·2014
Same author

Measurement of statistical evidence on an absolute scale following thermodynamic principles.

Theory in biosciences = Theorie in den Biowissenschaften·2013
Same author

Novel cleft susceptibility genes in chromosome 6q.

Journal of dental research·2010
Same author

A posterior probability of linkage-based re-analysis of schizophrenia data yields evidence of linkage to chromosomes 1 and 17.

Human heredity·2006
Same journal

Comparative profiles of pediatric Mendeliome: A Single-Centre 572-Whole-Exome Sequencing Study in Xinjiang.

Human heredity·2026
Same journal

Erratum.

Human heredity·2026
Same journal

Exploratory Analysis of HMGB1 Genetic Variants and Their Potential Association with Lung Cancer Susceptibility and Chemotherapy Response in a Chinese Population.

Human heredity·2025
Same journal

Weighted Burden Analysis of Rare Genetic Variants Identifies Novel Genes with Effects on BMI.

Human heredity·2025
Same journal

Generalized Stable Population and Agent-Based Models of Phenotypic Transmission in Human Populations, with an Application to Body Size.

Human heredity·2025
Same journal

Proteinase-activated receptor 2 (PAR-2) expression and F2RL1 genetic variants are associated with asthma: a case-control study in the Chinese population.

Human heredity·2025
See all related articles

Related Experiment Video

Updated: Jul 3, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Practical considerations for dividing data into subsets prior to PPL analysis.

M Govil1, V J Vieland

  • 1Department of Oral Biology and Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA. govil@pitt.edu

Human Heredity
|July 10, 2008
PubMed
Summary
This summary is machine-generated.

Exploring different data subsetting strategies for the PPL (PPL) statistic in human complex trait genetic mapping, this study found that realistic subsetting improves linkage evidence under heterogeneity without inflating results at unlinked loci.

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Related Experiment Videos

Last Updated: Jul 3, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • The PPL statistic is crucial for complex trait genetic mapping in humans.
  • It uses Bayesian sequential updating to assess linkage evidence across diverse data subsets.
  • Understanding optimal subsetting is key to maximizing PPL's utility.

Purpose of the Study:

  • To systematically evaluate various data subsetting approaches for PPL calculation.
  • To determine the most effective strategies for accumulating linkage evidence.
  • To assess the impact of subsetting on PPL's performance under data heterogeneity.

Main Methods:

  • Simulated genotypes across three pedigree structures (sib pairs, 2-3 generations, >=4 generations).
  • Generated 100 replicates per pedigree set with varying heterogeneity levels (1000 under 'no linkage').
  • Compared subsetting methods: random (RAND2, RAND4), true linkage (TRUE), realistic (REAL), pedigree-specific (PED), and no subsetting (NONE).

Main Results:

  • Realistic (REAL) subsetting yielded higher PPL values under linkage compared to NONE, RAND2, RAND4, or PED.
  • Under 'no linkage' conditions, RAND2, RAND4, and PED subsetting resulted in PPLs similar to no subsetting (NONE).

Conclusions:

  • Different subsetting strategies significantly impact the sampling behavior of the PPL statistic.
  • Identifying variables for more homogeneous data subsets enhances PPL's utility.
  • Sequential updating with appropriate subsetting is beneficial for detecting linkage in heterogeneous data without inflating false positives.