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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...

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Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

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Published on: September 6, 2017

GRIMM-II: A Two-Stage Real-Time Algorithm for Nine-Locus HLA Imputation and Matching with Up to Three Mismatches.

Ofek Kirshenboim¹, Amit Kabya¹, Regev Yehezkel-Imra¹

¹Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel.

Biorxiv : the Preprint Server for Biology

|April 10, 2026

Summary

This summary is machine-generated.

GRIMM-II enhances hematopoietic stem cell transplantation by enabling real-time matching of donors with up to three human leukocyte antigen (HLA) mismatches. This advanced system improves donor identification, especially for underrepresented ethnic minorities.

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Last Updated: Apr 11, 2026

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Published on: September 6, 2017

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

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Published on: January 9, 2020

Area of Science:

Immunogenetics
Computational Biology
Transplantation Science

Background:

Hematopoietic stem cell transplantation (HSCT) success relies on HLA matching.
Extended HLA typing (nine loci) is increasingly important.
Current algorithms struggle with real-time, multi-locus mismatch identification.

Purpose of the Study:

Develop GRIMM-II, a novel system for HLA imputation and matching.
Enable real-time identification of donors with up to three HLA mismatches.
Improve donor pool accessibility for diverse patient populations.

Main Methods:

Developed ML-GRIM for multi-locus HLA imputation.
Developed ML-GRMA for real-time donor-patient matching.
Utilized a two-stage graph-theoretic approach for efficiency.

Main Results:

GRIMM-II successfully imputed genotypes and identified more suitable donors than traditional methods.
ML-GRMA demonstrated 100% sensitivity and specificity in validation.
Real-time performance achieved, with matching times under 13 seconds for large databases.

Conclusions:

GRIMM-II offers a scalable, efficient solution for HLA imputation and matching.
The system supports evolving clinical criteria and registry updates.
GRIMM-II expands the donor pool, benefiting patients with limited matching options.