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Multiple Allele Traits01:49

Multiple Allele Traits

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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.
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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...

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Related Experiment Video

Updated: May 29, 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 mapping using generalized ridge logistic regression.

Zhe Liu1, Yuanyuan Shen, Jurg Ott

  • 1Department of Statistics, University of Chicago, IL 60637, USA.

BMC Bioinformatics
|October 1, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sliding window method for genome-wide association studies. It effectively identifies contiguous single-nucleotide polymorphisms (SNPs) to predict disease status, outperforming existing methods.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Last Updated: May 29, 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
  • Statistical genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) benefit from multilocus methods over single-nucleotide polymorphism (SNP) analysis.
  • Scan statistics are effective for detecting disease susceptibility regions and mapping genes.

Purpose of the Study:

  • Propose a novel sliding window-based method for GWAS.
  • Identify parsimonious subsets of contiguous SNPs for disease prediction.

Main Methods:

  • Utilize a sliding window approach inspired by scan statistics.
  • Employ forward model selection with generalized ridge logistic regression within windows.
  • Compare performance against five existing methods via power simulations.

Main Results:

  • The proposed method demonstrated superior performance in power simulations.
  • Averaged power across conditions showed the new method outperforming others.
  • The method proved useful in identifying causal SNPs in two published datasets.

Conclusions:

  • The method integrates local genomic information and accounts for linkage disequilibrium between SNPs.
  • It addresses limitations of traditional scan statistics approaches.
  • The method shows promise for genome-wide case-control association studies.