Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genome-wide Association Studies-GWAS

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-SNPs

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,...

Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...

Principles of Pharmacogenetics: Types of Genetic Variants

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...

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Genetic underpinnings of the heterogeneous impact of obesity on lipid levels and cardiovascular disease.

Genome medicine·2025

Same author

Polygenic risk score for type 2 diabetes shows context-dependent effects across populations.

Nature communications·2025

Same author

Genetic architecture and analysis practices of circulating metabolites in the NHLBI Trans-Omics for Precision Medicine Program.

American journal of human genetics·2025

Same author

Genome-wide association study provides novel insight into the genetic architecture of severe obesity.

PLoS genetics·2025

Same author

Whole genome sequence analysis of low-density lipoprotein cholesterol across 246 K individuals.

Genome biology·2025

Same author

An Efficient Lasso Framework for Admixture-Aware Polygenic Scores.

bioRxiv : the preprint server for biology·2025

Search research articles

Related Experiment Video

Updated: Jul 19, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Imputation methods to improve inference in SNP association studies.

James Y Dai¹, Ingo Ruczinski, Michael LeBlanc

¹Department of Biostatistics, University of Washington, Seattle, Washington, USA. yud@u.washington.edu

Genetic Epidemiology

|September 21, 2006

Summary

Imputing missing single nucleotide polymorphisms (SNPs) in genetic studies improves analysis efficiency compared to ignoring data. Developed imputation methods enhance SNP-disease association inference, with a tree-based approach offering computational advantages.

Related Experiment Videos

Last Updated: Jul 19, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

Genetics
Statistical genetics
Bioinformatics

Background:

Missing single nucleotide polymorphisms (SNPs) are prevalent in genetic association studies.
Discarding subjects with missing SNPs can significantly bias SNP-disease association inference.

Purpose of the Study:

To develop and evaluate novel imputation approaches for handling missing SNPs in genetic association studies.
To compare the performance of imputation methods against the standard practice of ignoring missing data.

Main Methods:

Developed two haplotype-based imputation methods (EM and WEM algorithms) and one tree-based imputation method (CART and Gibbs sampler).
Employed multiple imputation to address imputation uncertainty.
Applied methods to simulated data and a developmental dyslexia case-control study.

Main Results:

Imputation generally enhances analytical efficiency compared to ignoring missing data.
The tree-based imputation approach demonstrates comparable performance to haplotype-based methods with improved computational efficiency.
The weighted EM (WEM) algorithm minimizes bias but may increase variance.

Conclusions:

Imputation is a valuable strategy for managing missing SNP data in genetic association studies.
The tree-based imputation method offers a computationally efficient alternative.
Method selection should consider the trade-off between bias and variance.