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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...
Single Nucleotide Polymorphisms-SNPs01:05

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,...
Incomplete Dominance01:43

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
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...
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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Updated: Jun 5, 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

Statistical issues in gene association studies.

Richard M Watanabe1

  • 1Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. rwatanab@usc.edu

Methods in Molecular Biology (Clifton, N.J.)
|January 5, 2011
PubMed
Summary
This summary is machine-generated.

This review covers statistical challenges in gene association studies, including study design and analysis. It aims to simplify complex topics like population stratification and multiple testing for a broader audience.

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Area of Science:

  • Genetics
  • Biostatistics
  • Population Genetics

Background:

  • Gene association studies are crucial for understanding genetic contributions to diseases.
  • Statistical methodologies are fundamental to the validity and interpretation of these studies.
  • A need exists for accessible explanations of statistical concepts in genetic research.

Purpose of the Study:

  • To provide a review of key statistical issues in gene association studies.
  • To simplify complex statistical concepts for researchers without extensive statistical backgrounds.
  • To illustrate statistical challenges using examples from type 2 diabetes genetics.

Main Methods:

  • Review of statistical concepts relevant to both genome-wide and candidate gene studies.
  • Discussion of critical elements including study design, power, sample size, and data checking.
  • Explanation of advanced topics such as population stratification and multiple testing.

Main Results:

  • Identifies and explains common statistical pitfalls in gene association studies.
  • Highlights the importance of appropriate study design and analysis techniques.
  • Demonstrates practical application of statistical principles through real-world examples.

Conclusions:

  • Effective statistical practices are essential for reliable gene association study outcomes.
  • Understanding statistical issues improves the rigor and reproducibility of genetic research.
  • This review serves as a guide for researchers navigating the statistical complexities of gene association studies.