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MAQC Adds Genome-Wide Association Studies to Phase II To-Do List

CARY, NC (GenomeWeb News) — The MicroArray Quality Control Consortium has expanded its scope by adding a new workgroup that will identify “best practices” for genome-wide association studies.
 
Leming Shi, a researcher at the US Food and Drug Administration’s National Center for Toxicological Research and a coordinator of the MAQC initiative, announced the new group during the MAQC’s seventh face-to-face project meeting held here on the campus of the SAS Institute last week.
 
The new workgroup will be the fifth under the auspices of the second phase of the MAQC project, dubbed MAQC-II. The consortium published the results of the first phase of its study, which evaluated the reproducibility of microarray experiments across different labs and platforms, in Nature Biotechnology last fall.
 
In phase II, the consortium is addressing the challenges of developing and confirming predictive models that use gene-expression profiles to predict outcomes for individuals, including disease recurrence, prognosis, drug response, or the like.
 
The First Four
 
Before last week’s meeting, the project comprised four workgroups: the Clinical Working Group, which is analyzing patient data from large-scale clinical studies; the Toxicogenomics Working Group, which is doing the same for toxicogenomics experiments; the Titrations Working Group, which is following up on titration samples from MAQC-I; and the Regulatory Biostatistics Working Group, which is advising the Clinical and Toxicogenomics groups on ways to evaluate the performance of predictive models and classifiers.
 
However, according to several speakers at last week’s meeting, the recent rise of array-based genome-wide association studies has presented a number of issues that the consortium would be better off addressing sooner rather than later.
 
In a presentation arguing in favor of forming the working group, Nick Xiao of SAIC Frederick said that “MAQC has successfully proven that microarray technology can be used for biomarker discovery,” and noted that the group can apply many of the lessons learned from MAQC-I to show that “genotyping technology can be just as trusted and just as robust.”
 
In a similar presentation, Federico Goodsaid, senior staff scientist in the genomics group at FDA’s Office of Clinical Pharmacology, said that the FDA has been getting “a number” of genome-wide association studies under its Voluntary Exploratory Data Submissions guidelines. These GWAS experiments have “enormous sources of variability at each analysis step,” Goodsaid said, yet there is currently “no framework to tell us why [submitters] are doing this or that.”
 
These examples are “amazingly parallel” to the issues MAQC-I explored for gene expression analysis, he said.
 
Goodsaid suggested that the goal of the GWAS working group would be to publish “best practices for analyzing whole-genome analysis data.”
 
After the discussion, Shi announced the formation of the fifth workgroup and named Goodsaid and Xiao as coordinators. The first task for the group, he said, will be to identify experts in academia, industry, and government who will be willing to assist with the project.
 
Shi noted that GWAS experiments present several “new challenges” for the microarray community, “but a lot of similarity with what we saw in gene expression.”
 
Ultimately, he said, the objectives of the new working group align with the broader goal of MAQC-II, which is to predict health outcomes based on microarray measurements of biological samples.
 
“For personalized medicine to be realized, we have to be able to make a prediction for each patient,” he said.

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