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Applied Bio’s Automated Genotyping Pipeline


By Kevin Davies

July 14, 2008 | While the field of genome-wide association studies (GWAS) is still dominated by large centers and teams, a new genome analysis pipeline, developed by Francisco De La Vega and colleagues from Applied Biosystems (ABI), and researchers from the University Hospital of the Christian-Albrechts-University in Kiel, Germany, led by Stefan Schreiber, focuses on the identification of SNPs encoding amino acid changes that predispose to complex diseases. The work earned the Basic Research Best Practices Award. (see Best Practices )

The Kiel group validated their approach by mapping genes underlying Crohn’s disease (CD). An area of intense interest in both public and private sectors (see “The Galileo Code,” Bio-IT World, February 2005), the group identified a novel susceptibility gene after performing a GWAS with some 20,000 coding SNPs (cSNPs). The cSNP mapping strategy offered several advantages, including fewer statistical tests required to identify disease associations. The functional role of the identified variant provides new insights in the etiology of this chronic, debilitating disease that may lead to new therapies. The study was published last year in the journal Nature Genetics.

Schreiber’s group developed a discovery pipeline and algorithm that progressively promotes the stronger SNP candidates to follow-on replication studies. Schreiber calls the pipeline “a blend of pragmatic genetic association methodology and technology to ensure the success of subsequent steps,” beginning with an informative panel of cSNPs that presumably impact the structure or function of the corresponding protein. The panel was compiled from various sources, including SNP databases from Celera, Applera, and NCBI’s dbSNP.

Using multiplex genotyping assays for almost 20,000 cSNPs using the ABI SNPlex system, Schreiber’s group developed an automated genotyping pipeline that included LIMS and analysis tools. The Kiel team’s initial GWAS study was on 735 CD patients and 368 controls. SNPs that met statistical significance were followed up in additional cases and controls, as well as 380 CD trios. The tests revealed a strong, replicated association with CD and SNP rs2241880. This variant encodes a Thr300Ala substitution in the ATG16L1 gene, which codes for a protein that processes intracellular bacteria, suggesting new insights into the role of bacteria in disease pathogenesis. Computer modeling of the ATG16L1 protein hints that the CD mutation destabilizes the protein structure.

Schreiber’s group performed its data analysis using a homegrown open source statistical analysis package, GENOMIZER. Selection of supplementary tagging SNPs and development of additional SNPlex assays was enabled by the Applied Biosystems SNPbrowser software. Schreiber says that more than 3,000 of the cSNPs from the Applera database are now publicly available for the first time in dbSNP.

Schreiber says his group’s pipeline represents “an efficient algorithm for the discovery of susceptibility variants of complex, common disease.” It permits easier analysis and cost-effective replication, providing a quick route to identifying candidate causal mutations. Such common variants, “often are not predictive enough in isolation about disease risk for diagnostic purposes. However, the ability to identify causative, disease-associated genetic variation provides new insights on the pathways involved in the diseases.”

De la Vega, a distinguished scientific fellow with ABI, stresses that the newly discovered variants are fairly common and have little diagnostic value individually, leaving much of the genetic contribution to disease unaccounted for. “The next frontier will be to uncover the role of rare variants in complex/common disease,” he says. Together with the Kiel group, “We are approaching this new area through next-generation sequencing to sequence associated regions in larger samples.”

With the strategy catching on in other disease areas, the team appears justified in its assertion that “the pipeline and algorithm… is a generic, best practice approach that can be deployed in other research areas.”

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This article appeared in Bio-IT World Magazine.

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