Standing on the shoulders of giants: the EPICURE GWAS on Idiopathic Generalized Epilepsy

Pushing the reset button. The history of epilepsy genetics can broadly be distinguished into two major eras: the time before September 4th, 2012 and everything after this. September 4th, 2012 was the date that the first large genome-wide association study in IGE/GGE was published online in Human Molecular Genetics. Each of the >100 association studies in IGE listed in PubMed is now dated and needs to measure up against the current study, which will likely be remembered as the “EPICURE study”. The results of the EPICURE study are surprising and upset our conventional wisdom of what causes one of the most common forms of epilepsy. Continue reading

The heritability of schizophrenia, as told by common SNPs

Heritability 2.0. Genome-wide association studies (GWAS) have acquired a slightly negative connotation in the last two years as the results of the enormous efforts were moderate at best. Even though several hundreds of variants have been identified as susceptibility genes for various diseases, the identified genetic risk factors only explain a tiny fraction of the risk for these diseases. Much of what causes common and rare diseases is still unknown – there is a vast discrepancy between population estimates of the genetic contribution and the contribution explained through identified genetic risk factors. This phenomenon has been labeled the “missing heritability”. Now, a recent study using novel statistical tools for GWAS data finds that there is not that much missing after all… Continue reading

No use in studying gene-gene and gene-environment effects in complex diseases?

Genome-wide association studies (GWAS) have improved our insight into the genetics of  complex diseases but have fallen short of initial expectations, leaving the majority of the heritabililty to be explained. Interactions of genes with the environments and with each other receive a fair share of the blame for the lack of progress despite the widespread efforts. The large number of possible interactions, however, currently still limits progress in this field.  A dedicated and growing group of computer scientists and geneticists now study gene-gene effects in the hope of shedding light on complex diseases. Initial results were hopeful, even in the field of epilepsy genetics.

Now, a group of Harvard based biostatisticians presented simulations for breast cancer, type 2 diabetes and rheumatoid arthritis that include gene-gene and gene-environment effects. Their interpretation reads bleak: little predictive power can be gained by including the additional dependencies, which means that all the CPU time consumed currently for their analysis is only warming the planet and the hearts of computer scientists.

Diabetes in the US

The large number of cases diabetes and many other complex widespread diseases are not explained easily. And the Aschard study suggests that it will remain so for the immediate future despite the progress in sequencing technology.

Negative predictions from experts for their own domain usually receive a negative backlash. The study could probably be attacked on the grounds that the authors selected a large number of parameters, some from probably little more than thin air. But the geneticists on twitter remained silent. Is this acceptance already? Maybe the critics still lie exhausted from attacking Vogelstein’s negative predictions from a couples of months ago.

If the statistical model and parameter choices find widespread acceptance, it would mean that it is virtually impossible to explain many complex diseases from genetics alone to a sufficient degree. As individual studies of the interactions of two SNPs are difficult enough, many cases of complex diseases will remain unexplained. Despite all the efforts, it would be almost as dark as before we had high-throughput sequencing facilities.