New paper on genomics

James Lee and I have a new paper out: Lee and Chow, Conditions for the validity of SNP-based heritability estimation, Human Genetics, 2014. As I summarized earlier (e.g. see here and here), heritability is a measure of the proportion of the variance of some trait (like height or cholesterol levels) due to genetic factors. The classical way to estimate heritability is to regress standardized (mean zero, standard deviation one) phenotypes of close relatives against each other. In 2010, Jian Yang, Peter Visscher and colleagues developed a way to estimate heritability directly from the data obtained in Genome Wide Association Studies (GWAS), sometimes called GREML.  Shashaank Vattikuti and I quickly adopted this method and computed the heritability of metabolic syndrome traits as well as the genetic correlations between the traits (link here). Unfortunately, our methods section has a lot of typos but the corrected Methods with the Matlab code can be found here. However, I was puzzled by the derivation of the method provided by the Yang et al. paper.  This paper is our resolution.  The technical details are below the fold.


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Saving US biomedical research

Bruce Alberts, Marc Kirschner, Shirley Tilghman, and Harold Varmus have an opinion piece in PNAS (link here) summarizing their concerns for the future of US biomedical research and suggesting some fixes. Their major premise is that medical research is predicated on an ever continuing expansion and we’re headed for a crisis if we don’t change immediately. As an NIH intramural investigator, I am shielded from the intense grant writing requirements of those on the outside. However, I am well aware of the difficulties in obtaining grant support and more than cognizant of the fact that a simple way to resolve the recent 8% cut in NIH funding is to eliminate the NIH intramural program. I have also noticed that medical schools keep expanding and hiring faculty on “soft money”, which requires them to raise their own salaries through grants. Soft money faculty essentially run independent businesses who rent lab space from institutions. The problem is that the market is a monopsony, where the sole buyer is the NIH. In order to keep their businesses running, they need lots of low paid labour, in the form of grad students and postdocs, many of whom have no hope of ever becoming independent investigators. One of the proposed solutions is to increase the salary of post docs and increase the numbers of permanent staff scientist positions. The premise is that by increasing unit costs, a labour equilibrium can be achieved. There is much more in the article and anyone involved in science should read it.

Big Data backlash

I predicted that there would be an eventual push back on Big Data and it seems that it has begun. Gary Marcus and Ernest Davis of NYU had an op-ed in the Times yesterday outlining nine issues with Big Data. I think one way to encapsulate many of the critiques is that you will never be able to do true prior free data modeling. The number of combinations in a data set grows as the factorial of the number of elements, which grows faster than an exponential. Hence, Moore’s law can never catch up. At some point, someone will need to exercise some judgement in which case Big Data is not really different from the ordinary data that we deal with all the time.