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

Chi-square Distribution01:10

Chi-square Distribution

7.1K
How does one determine if bingo numbers are evenly distributed or if some numbers occurred with a greater frequency? Or if the types of movies people preferred were different across different age groups or if a coffee machine dispensed approximately the same amount of coffee each time. These questions can be addressed by conducting a hypothesis test. One distribution that can be used to find answers to such questions is known as the chi-square distribution. The chi-square distribution has...
7.1K
Test for Homogeneity01:23

Test for Homogeneity

2.4K
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...
2.4K
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

8.3K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
8.3K
Chi-square Analysis02:46

Chi-square Analysis

44.4K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
44.4K
Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

4.6K
Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
4.6K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

9.3K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
9.3K

You might also read

Related Articles

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

Sort by
Same author

Genome-wide identification of SNPs in microRNA genes and the SNP effects on microRNA target binding and biogenesis.

Human mutation·2011
Same author

A case of intimal sarcoma of the pulmonary artery successfully treated with chemotherapy.

International journal of clinical oncology·2011
Same author

Exome sequencing identifies frequent mutation of ARID1A in molecular subtypes of gastric cancer.

Nature genetics·2011
Same author

Energetic salts based on dipicrylamine and its amino derivative.

Chemistry (Weinheim an der Bergstrasse, Germany)·2011
Same author

Biophysical properties of slow potassium channels in human embryonic stem cell derived cardiomyocytes implicate subunit stoichiometry.

The Journal of physiology·2011
Same author

Natural variation of folate content and composition in spinach (Spinacia oleracea) germplasm.

Journal of agricultural and food chemistry·2011
Same journal

Inherited long telomeres induce a genome-wide transcriptional response in budding yeast.

Genetics·2026
Same journal

Adaptive Dynamics of Quantitative Traits in a Steadily Changing Environment.

Genetics·2026
Same journal

Functional Landscape of Zebrafish Gonadotropins and Receptors: A Comprehensive Genetic Analysis.

Genetics·2026
Same journal

Synergistic actions of Nup43 and Myosin VI drive actin cone assembly during Drosophila spermiogenesis.

Genetics·2026
Same journal

Identification of two Cryptococcus neoformans heme transporters involved in Fhb1-mediated nitrosative stress protection in a fission yeast model.

Genetics·2026
Same journal

Analysis of a hypomorphic mei-P26 mutation reveals coordination between developmental programming of germ cells and meiotic chromosome dynamics.

Genetics·2026
See all related articles

Related Experiment Video

Updated: Feb 22, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

19.1K

A Powerful Variant-Set Association Test Based on Chi-Square Distribution.

Zhongxue Chen1, Tong Lin2, Kai Wang3

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Indiana 47405 zc3@indiana.edu.

Genetics
|September 16, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical test for detecting associations between genetic variants and phenotypes. The proposed method demonstrates superior power compared to existing approaches in simulations and real-world data analysis.

Keywords:
chi-square distributiongene-set analysisprincipal component analysis

More Related Videos

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

15.8K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.4K

Related Experiment Videos

Last Updated: Feb 22, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

19.1K
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

15.8K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.4K

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Identifying associations between genetic variants and phenotypes is crucial but challenging.
  • Existing statistical methods for variant-phenotype association detection require improvement in power.

Purpose of the Study:

  • To develop a novel and powerful statistical test for detecting associations between single nucleotide polymorphisms (SNPs) and phenotypes.
  • To evaluate the performance of the proposed test against existing popular methods.

Main Methods:

  • The proposed test utilizes principal component analysis to combine information from individual SNPs.
  • It does not rely on eigenvalues associated with principal components.
  • Performance is assessed via simulation studies and real data applications.

Main Results:

  • The new test generally exhibits higher statistical power than competing methods.
  • Substantial gains in detection power are observed in various scenarios.
  • The approach is robust and effective in identifying SNP-phenotype associations.

Conclusions:

  • The developed statistical test offers a powerful new tool for genetic association studies.
  • This method can significantly enhance the ability to detect complex genetic influences on phenotypes.
  • It represents a valuable advancement in statistical genetics methodologies.