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

Quartile01:15

Quartile

Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure 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 cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...

You might also read

Related Articles

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

Sort by
Same author

Genetic Analysis of Heat Stress for Conception Rate across Parities in the Netherlands.

Journal of dairy science·2026
Same author

Determining crossover count and position in two pig lines with different selection histories.

Genetics, selection, evolution : GSE·2026
Same author

A k-mer-based genome-wide association study approach empowering gene mining in polyploids.

Nature genetics·2026
Same author

Restructuring breeding programs 2: Assortative mating for improved commercial genetic gain when using optimum contribution selection and diversity introduction.

Genetics, selection, evolution : GSE·2026
Same author

Methods to Detect Selection History in a Population under Ongoing Directional Selection.

Genetics·2026
Same author

Restructuring breeding programs 1: Integration of diversity.

Genetics, selection, evolution : GSE·2026
Same journal

Abstracts from Specialized Centers of Research Excellence (SCORE) on Sex Differences 2025 annual meeting.

BMC proceedings·2026
Same journal

Conference abstracts the 1st UDOM scientific conference on health: healthy lives and well-being for all: opportunities and challenges.

BMC proceedings·2026
Same journal

Entrepreneurship beyond the lab: commercializing your creative outputs.

BMC proceedings·2026
Same journal

The need to strengthen laboratory leadership, systems, and networks to enhance outbreak detection and resilience in Africa: proceedings of a regional workshop.

BMC proceedings·2026
Same journal

Abstracts from the Globesync Community Research and Sustainability 2025 (GlobeCoReS 2025).

BMC proceedings·2026
Same journal

Bauru International Craniofacial Symposium: Comprehensive Care, Policy and Advocacy Proceedings.

BMC proceedings·2026
See all related articles

Related Experiment Video

Updated: Jun 14, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

QTLMAS 2009: simulated dataset.

Albart Coster1, John W M Bastiaansen, Mario P L Calus

  • 1Animal Breeding and Genomics Centre, Wageningen University, Wageningen, The Netherlands. albart.coster@wur.nl.

BMC Proceedings
|April 13, 2010
PubMed
Summary
This summary is machine-generated.

This study simulated data for growth curves influenced by quantitative trait loci (QTL). The simulation included genetic markers, phenotypes, and pedigree for a large population, providing valuable resources for QTL analysis.

More Related Videos

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

Related Experiment Videos

Last Updated: Jun 14, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

Area of Science:

  • Quantitative genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Data simulation for the QTLMAS 2009 Workshop.
  • Growth curve modeling influenced by quantitative trait loci (QTL).

Purpose of the Study:

  • Simulate observations from a logistic growth curve.
  • Incorporate the influence of multiple QTL on growth curve parameters.

Main Methods:

  • Simulated genotypes for 2,025 individuals across 5 chromosomes.
  • Generated phenotypes based on a logistic growth curve influenced by six QTL per parameter.
  • Included a pedigree for 25 related parents and 2,000 offspring.

Main Results:

  • Phenotypes were simulated for 1,000 offspring, reflecting a logistic growth curve.
  • Each growth curve parameter was influenced by six QTL with varying effects (one large, five small).
  • Variance of large-effect QTL was five times that of small-effect QTL.

Conclusions:

  • The simulated dataset provides a resource for studying QTL effects on growth curves.
  • The data allows for the investigation of genetic architecture underlying complex traits.
  • Facilitates methods development and validation in quantitative trait locus mapping.