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

Introduction to Normal Distributions01:29

Introduction to Normal Distributions

Standardized test scores often follow a symmetric distribution that can be modeled with the normal distribution, a fundamental concept in statistics. This distribution is particularly useful for interpreting test performance fairly across populations, as it provides a mathematical framework for understanding variability and central tendency in large datasets.From Histogram to Frequency DistributionRaw test data are often displayed using histograms, where the height of each bar represents the...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Introduction to the Sign Test01:10

Introduction to the Sign Test

The sign test is an important tool in nonparametric statistics, offering a straightforward yet effective method for analyzing matched pairs, nominal data, or hypotheses concerning the median of a population. It transforms data points into positive or negative signs, avoiding the need for assumptions about data distribution and instead focusing on the direction of change. It is particularly valuable when data does not conform to the normal distribution requirements of many parametric tests. For...
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
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

KIF21A regulates breast cancer aggressiveness and is prognostic of patient survival and tumor recurrence.

Breast cancer research and treatment·2021
Same author

Formation of the Australia and New Zealand Vasculitis Society to improve the care of patients with Vasculitis in Australian and New Zealand.

Internal medicine journal·2020
Same author

ADP ribosyl-cyclases (CD38/CD157), social skills and friendship.

Psychoneuroendocrinology·2017
Same author

Standard Codon Substitution Models Overestimate Purifying Selection for Nonstationary Data.

Genome biology and evolution·2017
Same author

Statistical Methods for Identifying Sequence Motifs Affecting Point Mutations.

Genetics·2016
Same author

Genetic distance for a general non-stationary markov substitution process.

Systematic biology·2014

Related Experiment Video

Updated: Jun 17, 2026

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

Effects of normalization on quantitative traits in association test.

Liang Goh1, Von Bing Yap

  • 1Cancer & Stem Cell Biology Program, Duke-National University of Singapore Graduate Medical School, Singapore. liang.goh@duke-nus.edu.sg

BMC Bioinformatics
|December 17, 2009
PubMed
Summary

Rank-based transformation is the best method for quantitative trait loci analysis when sample sizes are small. Normalization may not be necessary for large sample sizes or genetic effects.

More Related Videos

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Related Experiment Videos

Last Updated: Jun 17, 2026

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

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Area of Science:

  • Genetics
  • Statistical Genetics

Background:

  • Quantitative trait loci (QTL) analysis typically assumes normal trait distribution, which is often not met in practice.
  • Trait transformation is a common strategy, but optimal methods and required normality levels remain unclear for association studies.

Purpose of the Study:

  • To evaluate the effectiveness of different trait normalization methods in quantitative trait loci (QTL) analysis.
  • To investigate the impact of sample size and genetic effects on normalization strategies.

Main Methods:

  • Simulations were conducted using four common quantitative trait types.
  • Logarithm, Box-Cox, and rank-based transformations were evaluated.
  • The influence of sample size and genetic effect magnitude was assessed.

Main Results:

  • Rank-based transformation demonstrated superior and consistent performance in identifying causal polymorphisms and ranking them in association tests.
  • A slight increase in the false positive rate was observed with rank-based transformation.
  • Normalization benefits were less pronounced with large sample sizes and strong genetic effects.

Conclusions:

  • Rank transformation enhances sensitivity, particularly for small sample sizes or genetic effects, outweighing a minor increase in false positives.
  • For large sample sizes and genetic effects, the modest sensitivity gains may not justify normalization.
  • The choice of normalization strategy should consider sample size and genetic effect strength in QTL studies.