“Intriguing Findings Are for Romance Novels”

         October 29, 2013

A report from the World Congress of Psychiatric Genetics

Earlier this month, while much of the genetics community was scrambling to edit and print their posters for ASHG, I had the opportunity to attend WCPG, the World Congress of Psychiatric Genetics, in Boston.  This was my second trip to WCPG and it is becoming one of my favorite events to attend.  WCPG stands out to me as one of the largest conferences where the majority of content is reports of applied gene-finding activities.  It seemed like almost every speaker told about the results of a GWAS or sequencing experiment.  It is energizing to hear so many success stories about analysis projects that turned out well and informative to hear the cautionary tales about challenges encountered by others.  It’s impossible to recount everything that I heard and learned, but I’ll try to share a few highlights and quotable quotes, mostly from the final day of the conference.

“Intriguing findings are for romance novels”
This juicy quote came from Patrick Sullivan during the plenary panel discussion on the future of psychiatric genetics.  He continued by pointing out that, according to some estimates, about 70% of human genes may be of interest to a psychiatrist.  As a result, many marginal results in GWAS and other studies might be considered “intriguing,” but the community simply can’t waste time pursuing them all.  We need to focus on improving study design and study power and focus on the truly significant results.  To paraphrase, he said that we all need to “stop picking up pretty seashells” and get serious about scientific rigor.

“Over 8,000 independent loci may affect schizophrenia risk”
This figure was quoted by Dr. Sullivan in the same talk, and comes from a recent PGC publication (more on that below).  Those who know me won’t be surprised by my initial response: “That’s more than 2 variants per megabase… How can they really be independent?”  Of course, I was ignoring the bigger picture: that complex diseases are complicated!  There may be 1,000 genes involved in Schizophrenia.  If this estimate is even close to correct, it’s no wonder that we struggle to find the causes for complex diseases when we are using methods based on individual loci.  Unraveling complex diseases requires a more holistic approach, analyzing pathways and networks to understand the disease at a systems level.

“GWAS effect size is not directly related to therapeutic value”
This statement came from Peter Donnelly.  We often speak negatively about the fact that most significant GWAS findings explain a very small proportion of disease risk.  Donnelly said we should be more optimistic.  Understanding that we are working with complex diseases, even SNPs with mild effects can be important clues about the biological processes that control a particular disease.  Knowing a little about the biology of a disease can tell us a lot about what treatments and drugs might be effective for it.

“Sequence-based discovery is fundamentally harder”
This quote also comes from Peter Donnelly, and effectively summarizes the message I heard from several speakers working with rare variants.  GWAS is a mature technology, and analytics for common variants are well defined.  Sequence analysis, particularly with regard to rare variants, is challenging due to the amount of noise involved.  Most rare variation is benign, and it’s difficult to separate the signal from the noise.  I heard a similar message from speakers analyzing exome chip data.  Multiple speakers getting their first exposure to exome chips warned the audience that the data behaves very differently than GWAS data, and the data noise in the rare variants is a big part of that.

“At least the genome is finite”
I don’t recall who said this, but it was in response to a discussion about environmental factors in disease etiology.  Somebody asked why we focus so much on the genetics of psychiatric disorders when there is so much evidence that they have a major environmental component.  I don’t know if this response was intended to be ironic, but it certainly struck me that way.  Not very long ago the genome was viewed as a great, unknown frontier.  Now we increasingly look at genomics as being simpler than traditional epidemiology to explain disease risks.  I sometimes wonder how much money is spent on unnecessary genomic analysis of diseases simply to satisfy the demands of grant reviewers, who often tend to favor new technology over traditional methods.

The Psychiatric Genetics Consortium (PGC)

'Figure 1: Manhattan plot of the Swedish and PGC schizophrenia meta-analysis results' from 'Genome-wide association analysis identifies 13 new risk loci for schizophrenia' in Nature Genetics

‘Figure 1: Manhattan plot of the Swedish and PGC schizophrenia meta-analysis results’ from ‘Genome-wide association analysis identifies 13 new risk loci for schizophrenia’ in Nature Genetics

I was previously aware of the PGC, but I don’t think that I fully appreciated the scope and scale of the PGC prior to this conference.  The consortium currently has GWAS data for about 170K samples representing several important psychiatric phenotypes.  About 80K more samples will soon be added to that figure as they receive data from the new Illumina “Psych Chip” (designed by PGC).  The PGC recently published a schizophrenia GWAS based on nearly 60K samples, and reported 22 loci with genome-wide significance.  PGC is continuing to press forward with schizophrenia, using it as the model disease to define the process for analyzing other diseases in the future.  Large consortium efforts like this are becoming more common in complex diseases, and indeed they may be the best or only way to unravel the genetics of many diseases with available technology.

Colocation
WCPG was held in Boston immediately prior to ASHG this year.  The WCPG organizers hoped that this would boost attendance, although attending both conferences would require an exhausting 10-day trip.  During the WCPG business meeting, somebody asked how many attendees were staying for ASHG, and at least half the audience raised their hands.  The next question asked how many attendees would not have come to WCPG if it wasn’t colocated with ASHG, and only 2 or 3 people responded.  I found the whole subject interesting because there was a similar conversation at IGES (International Genetic Epidemiology Society) this year as attendees were polled about colocation and other ideas to increase attendance.  Personally, I think that there could be some good synergy between these two conferences–one group (IGES) is very focused on methodology for complex trait analysis, while the other (WCPG) is very focused on the application of the same methods.  Having attended both, I am surprised by the minimal overlap in attendees between the two, despite the inherent similarity in the programs. Last year (2012) the two meetings were actually held during the same week, but on different continents!  I for one would like to see them get together at least once and see what happens.  In 2014, IGES is scheduled for Vienna (with GAW-19 and ISCB), while WCPG will be in Copenhagen.  I really hope to attend at least one of them.

Closing Thoughts
There were a lot of great presentations given at WCPG this year, including many thought-provoking statements from luminary scientists.  I did not attend ASHG this year, but I’ve heard a lot of good reports from those who did.  If you attended either conference (or another recent conference for that matter), we’d love to hear your thoughts.  Did you notice a theme in the talks you heard?  Did you hear any provocative statements that you want to share?  How do you feel about conferences colocating?  Are there any conferences that you think could really generate synergy together? Let us know in the comments section below! Till next time…

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