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Related Concept Videos

Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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

The Concept of Multiple Allelism
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
Pleiotropy01:33

Pleiotropy

Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...

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Related Experiment Video

Updated: May 16, 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

Multilocus association analysis under polygenic models.

Jurg Ott1, Dandan Sun

  • 1Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 4A Datun Road, Beijing 100101, China. ottjurg@psych.ac.cn

International Journal of Data Mining and Bioinformatics
|November 20, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new genome-wide test statistic for genetic association studies. It effectively identifies disease-related variants, even those with small individual effects, by analyzing cumulative risk allele differences.

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

Published on: June 21, 2018

Related Experiment Videos

Last Updated: May 16, 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

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:

  • Genetics and Genomics
  • Statistical Genetics
  • Disease Association Studies

Background:

  • Genome-wide association studies (GWAS) aim to identify genetic variants associated with diseases.
  • Traditional single-locus analyses may miss variants with small individual effects.
  • A need exists for methods that can detect cumulative genetic risk across multiple variants.

Purpose of the Study:

  • To develop a novel genome-wide statistical test for identifying disease-associated variants.
  • To create a method that enhances the detection of genetic risk, particularly for variants with subtle effects.
  • To establish a robust test statistic and significance level determination through permutation analysis.

Main Methods:

  • Defining risk alleles based on odds ratios greater than 1 for each variant.
  • Calculating the difference in the number of risk alleles between cases and controls for sets of variants.
  • Performing successive sums of these differences for the top variants and obtaining associated p-values.
  • Determining the smallest p-value as the genome-wide test statistic.
  • Utilizing permutation analysis to establish an empirical significance level for the test statistic.

Main Results:

  • The proposed method generates a genome-wide test statistic based on cumulative risk allele differences.
  • Associated p-values are derived for successive combinations of variants.
  • Empirical significance levels are established using permutation analysis.
  • The approach demonstrated success in identifying significant associations in disease datasets, even with minimal single-locus effects.

Conclusions:

  • The developed genome-wide test statistic provides a powerful approach for genetic association studies.
  • This method is effective in detecting disease-related variants, outperforming single-locus tests when effects are small.
  • The approach offers improved power for identifying complex genetic architectures underlying diseases.