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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...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
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Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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Related Experiment Video

Updated: Jun 26, 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

Flexible designs for genomewide association studies.

André Scherag1, Johannes Hebebrand, Helmut Schäfer

  • 1Institute of Medical Biometry and Epidemiology, Philipps-University, Marburg, Germany.

Biometrics
|January 29, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible new method for genomewide association studies (GWAS) that controls statistical errors while allowing modifications to marker density and sample size. This approach enhances efficiency in genetic research for complex traits.

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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

Area of Science:

  • Genetics
  • Statistical genetics
  • Genomics

Background:

  • Genomewide association studies (GWAS) are crucial for understanding the genetic basis of complex traits.
  • Sequential genotyping strategies are commonly used in GWAS, involving initial marker screening in a subset of subjects followed by genotyping promising markers in the larger cohort.
  • Existing methods for sequential genotyping have limitations in flexibility and error rate control.

Purpose of the Study:

  • To introduce a novel, flexible method for sequential genotyping designs in GWAS.
  • To enable strong control of the family-wise type I error rate while allowing for modifications in marker density and sample proportions.
  • To improve upon existing GWAS designs by offering greater flexibility and efficiency.

Main Methods:

  • Development of a novel statistical method for sequential genotyping in GWAS.
  • Redistribution of conditional type I error rates for discarded markers to maintain efficiency.
  • Integration with cost-optimal designs and development of a new algorithm for practical application.
  • Evaluation through simulations and illustration with a real dataset.

Main Results:

  • The proposed method strongly controls the family-wise type I error rate in sequential GWAS designs.
  • Efficiency is maintained by redistributing type I error rates of markers not selected for further genotyping.
  • The approach offers greater flexibility, allowing for adjustments in marker density, sample proportions, and sample size.
  • The developed algorithm demonstrates practical applicability and is validated through simulations.

Conclusions:

  • The novel method provides a flexible and statistically rigorous framework for sequential genotyping in GWAS.
  • It allows investigators to adapt study designs based on biological or statistical criteria and modify parameters during the project.
  • This approach enhances the power and cost-effectiveness of genetic studies for complex traits.