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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Interrogating the major histocompatibility complex with high-throughput genomics.

Paul I W de Bakker¹, Soumya Raychaudhuri

¹Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands. pdebakker@umcutrecht.nl

Human Molecular Genetics

|September 15, 2012

Summary

The major histocompatibility complex (MHC) is linked to many diseases, but its function is unclear. New methods using genome-wide association study (GWAS) data can help pinpoint causal variants within the MHC region.

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Area of Science:

Genetics
Immunology
Genomics

Background:

The Major Histocompatibility Complex (MHC) on chromosome 6 is associated with numerous diseases.
Understanding the functional basis of these MHC associations is challenging due to high genetic diversity and linkage disequilibrium.
Identifying causal variants within the MHC is crucial for understanding disease mechanisms.

Purpose of the Study:

To review strategies for fine-mapping disease associations within the MHC region.
To highlight the potential of leveraging existing genome-wide association study (GWAS) datasets for MHC analysis.
To discuss advanced methods for identifying independent effects and causal variants in the MHC.

Main Methods:

Utilizing imputation methods to analyze classical human leukocyte antigen (HLA) loci within GWAS data.
Leveraging large GWAS datasets to disentangle effects at different MHC loci.
Querying genetic variation at individual amino acid positions for association analysis.

Main Results:

Imputation of GWAS data enables comprehensive interrogation of HLA loci.
Sufficiently large GWAS datasets allow for the disentanglement of independent effects within the MHC.
Amino acid-level variation analysis offers a powerful expansion beyond traditional HLA typing.

Conclusions:

Existing GWAS datasets offer a valuable opportunity to investigate MHC associations in depth.
Advanced analytical approaches can overcome challenges posed by MHC diversity and linkage disequilibrium.
Identifying causal variants in the MHC can be achieved through imputation and amino acid-level analyses.