Plenge Lab

At the Spring PGRN meeting last week, there were a number of interesting talks about the need for new databases to foster genetics research.  One talk was from Scott Weiss on Gene Insight (see here).  I gave a talk about our “RA Responder” Crowdsourcing Challenge (complete slide deck here).  Here are a few general thoughts about the databases we need for genomics research.

(1) Silo’s are so last year

Too often, data from one interesting pharmacogenomic study (e.g., GWAS data on treatment response) are completely separate from another dataset that can be used to interpret the data (e.g., RNA-sequencing). Yes, specialized labs that generated the data can integrate the data for their own analysis. And yes, they can release individual datasets into the public for others to stitch together. But is this really what we need? Somehow, we need to make data available in a manner that is fully integrated and interoperable. One simple example of this is GWAS for autoimmune diseases. Since 2006, a large number of genetic data have been published. Still, there is no single place to go see results for all autoimmune diseases, despite the fact that there is tremendous shared overlap among the genetic basis for these diseases.…

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I read with interest a recent publication by Khandpur et al in Science Translational Medicine on NETosis in the pathogenesis of rheumatoid arthritis (download PDF here).  It made me think about “cause vs consequence” in scientific discovery.  That is, how does one determine whether a biological process observed in patients with active disease is a cause of disease rather than a consequence of disease?

In reading the article, I learned about how neutrophils cause tissue damage and promote autoimmunity through the aberrant formation of neutrophil extracellular traps (NETs).  Released via a novel form of cell death called NETosis, NETs consist of a chromatin meshwork decorated with antimicrobial peptides typically present in neutrophil granules.  (Read more about NETs on Wikipedia here.) 

Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in non-experimental studies (read more here).  It is a powerful to determine if an observation in patients is causal.  For example, if autoantibodies are pathogenic in RA, then DNA variants that influence the formation of autoantibodies should also be associated with risk of RA.  This is indeed the case, as exemplified by variants in a gene, PADI4, the codes for an enzyme involved in peptide citrullination (see here). …

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