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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Multiple Allele Traits01:49

Multiple Allele Traits

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Multiple Allele Traits01:49

Multiple Allele Traits

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Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

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)...
Heritability01:06

Heritability

Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic" a trait is,...

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Related Experiment Video

Updated: Jun 22, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Multivariate association test using haplotype trend regression.

Yu-Fang Pei1, Lei Zhang, Jianfeng Liu

  • 1The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P. R. China.

Annals of Human Genetics
|June 4, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new haplotype-based method for analyzing multiple genetic traits together. The approach enhances statistical power by considering correlations between traits, especially when gene and residual correlations oppose each other.

Related Experiment Videos

Last Updated: Jun 22, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Population genetics
  • Statistical genetics
  • Quantitative trait analysis

Background:

  • Haplotype association analyses can offer greater power than single-marker analyses.
  • Analyzing multiple correlated traits simultaneously can leverage additional information compared to single-trait analyses.

Purpose of the Study:

  • To propose a novel haplotype-based association test for multivariate quantitative traits in unrelated individuals.
  • To extend the population-based haplotype trend regression (HTR) approach to accommodate multiple traits.

Main Methods:

  • Developed a bivariate haplotype trend regression (HTR) method for multivariate quantitative traits.
  • Utilized simulation studies to evaluate the proposed method's performance.

Main Results:

  • The proposed bivariate HTR method demonstrated correct pre-specified type-I error rates in simulations.
  • Statistical power was significantly influenced by the magnitude and origin of correlations between traits.
  • Maximum power was achieved when the correlation of a specific gene opposed the residual correlation between traits.

Conclusions:

  • The extended HTR approach provides a valid statistical framework for haplotype association testing in multivariate quantitative trait studies.
  • Understanding trait correlations is crucial for optimizing the power of genetic association analyses.