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...

You might also read

Related Articles

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

Sort by

Same author

The New York Genome Center ALS Consortium resource integrates postmortem tissue transcriptomics and whole genome sequencing to empower biological discovery.

medRxiv : the preprint server for health sciences·2026

Same author

Mechanistic analysis of an IRF7-dependent pathway in virus-induced fibrosis in chronic lung allograft dysfunction.

EBioMedicine·2026

Same author

Publisher Correction: A meta-analysis of single-nucleus expression quantitative trait loci linking genetic risk to brain disorders.

Nature genetics·2026

Same author

A meta-analysis of single-nucleus expression quantitative trait loci linking genetic risk to brain disorders.

Nature genetics·2026

Same author

Elucidating genetic backgrounds of myasthenia gravis in Japanese by genome-wide association studies and multi-omics analyses of thymoma.

Nature communications·2026

Same author

Spatial transcriptomics reveals coordinated endothelial and epithelial activator protein-1 activation in chronic lung allograft dysfunction.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation·2026

Search research articles

Related Experiment Video

Updated: Jun 23, 2025

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Published on: September 6, 2017

Deep Learning-Based HLA Allele Imputation Applicable to GWAS.

Tatsuhiko Naito¹

¹Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Osaka, Japan. tnaito@sg.med.osaka-u.ac.jp.

Methods in Molecular Biology (Clifton, N.J.)

|June 22, 2024

Summary

Deep*HLA uses deep learning for accurate human leukocyte antigen (HLA) imputation from genetic data. This method improves understanding of genetic traits by providing a reliable tool for HLA allele identification.

Keywords:

Deep learning Fine-mapping GWAS HLA HLA imputation MHC

More Related Videos

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: Jun 23, 2025

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Published on: September 6, 2017

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Genetics
Bioinformatics
Immunogenetics

Background:

Human leukocyte antigen (HLA) imputation is crucial for genome-wide association studies, especially when investigating HLA gene associations.
Existing HLA imputation methods vary in accuracy and computational efficiency.
Accurate HLA imputation is vital for understanding the genetic basis of human traits.

Purpose of the Study:

To introduce Deep*HLA, a novel deep learning-based method for imputing human leukocyte antigen (HLA) alleles.
To provide a detailed protocol for utilizing the Deep*HLA tool.

Main Methods:

Deep*HLA employs deep learning algorithms to impute HLA alleles using regional single nucleotide variants.
The method was trained and validated on reference panels from diverse ancestries.
The protocol covers data preprocessing, model training, and the imputation process.

Main Results:

Deep*HLA demonstrates high accuracy in imputing HLA alleles.
The method shows a relatively small decrease in imputation accuracy for rare alleles.
The tool is implemented in Python 3 and is publicly accessible.

Conclusions:

Deep*HLA offers an accurate and efficient approach for HLA imputation.
This method can enhance the genetic analysis of human traits by improving HLA allele identification.
The availability of Deep*HLA facilitates further research in immunogenetics and related fields.