Plenge Lab
Date posted: May 17, 2013 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

After all, baseball is a metaphor for life.

Bill James developed the “Keltner list” to serve as a series of gut-check questions to test a baseball player’s suitability for the Hall of Fame (see here).  The list comprises 15 questions designed to aid in the thought process, where each question is designed to be relatively easy to answer.  As a subjective method, the Keltner list is not designed to yield an undeniable answer about a player’s worthiness.  Says James: “You can’t total up the score and say that everybody who is at eight or above should be in, or anything like that.”

The Keltner list concept has been adapted to address to serve as a common sense assessment of non-baseball events, including political scandals (see here) and rock bands like Devo (see here).

Here, I try out this concept for genetics and drug discovery.  That is, I ask a series of question designed to answer the question: “Would a drug against the product of this gene be a useful drug?”  I use PCSK9 as one of the best examples (see brief PCSK9 slide deck here).  I also used in on our recent study of CD40 in rheumatoid arthritis, published in PLoS Genetics (see here).…

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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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