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Updated: Jun 18, 2026

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

Supporting genotype-to-phenotype association studies with grid-enabled knowledge discovery workflows.

Lefteris Koumakis¹, Vassilis Moustakis, Manolis Tsiknakis

¹Institute of Computer Science, FORTH, N. Plastira 100, 70013 Heraklion, Crete, Greece. koumakis@ics.foth.gr

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|December 8, 2009

Summary

This summary is machine-generated.

This study introduces a Grid-enabled Genotype-to-Phenotype (GG2P) workflow for efficient knowledge discovery. It successfully identifies about 100 Single Nucleotide Polymorphisms (SNPs) associated with breast cancer, demonstrating high classification performance.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Web Services and Grid-enabled scientific workflows are crucial for efficient and secure knowledge discovery.
Integrating diverse SNP genotype data sources and discovering genotype-to-phenotype associations are key challenges.

Purpose of the Study:

To present a Grid-enabled Genotype-to-Phenotype (GG2P) discovery scenario realized through a scientific workflow.
To support seamless integration of SNP genotype data and discover genotype-to-phenotype association models.

Main Methods:

Development of a Grid-enabled Genotype-to-Phenotype (GG2P) scientific workflow.
Wrapping custom-made Web Services for data integration and model discovery.
Application of the GG2P workflow on a whole-genome SNP-genotyping experiment comparing breast cancer versus control phenotypes.

Main Results:

Successful application of the GG2P workflow on a breast cancer whole-genome SNP-genotyping experiment.
Identification of approximately 100 indicative Single Nucleotide Polymorphisms (SNPs).
Achieved very high classification performance for genotype-to-phenotype association models.

Conclusions:

The GG2P workflow enables efficient and secure genotype-to-phenotype discovery.
The identified SNPs show high potential for breast cancer classification.
The biological relevance of the findings is supported by existing literature.