Dr. Aslibekyan and colleagues propose that an alternative to replication of genetic association studies be considered in an article titled
“To Replicate or Not to Replicate: The Case of Pharmacogenetic Studies” in Circulation: Cardiovascular Genetics
(2013). It is understandable why
replicating the positive results of a genetic association study is, as the
authors state, “the gold standard” of validation. Replication is a useful tool
to confirm the likelihood of an association from a previous genetic association
study.
However, the results of genetic association studies, including
genome wide association studies (GWAS), are often difficult to reproduce as
evidenced in pharmacogenomics. The authors provide multiple reasons why
false positives and false negatives in genetic association studies prevent
results from being reproduced. Much of the data in pharmacogenomic studies come
from groups of patients who may have differing drug regimens and intervention
strategies. Other factors include low minor allele frequencies, small effect
size of the variants, limited sample sizes, and differences in phenotype
definitions.
The authors conclude that as an alternative to replication a
combination of methods should be used to validate results. They’ve termed this
multi-method validation of genetic association studies “triangulation”. They propose to validate results through a combination of functional validation in
vitro and in animal models, joint analyses of several populations, and
simulation-based methods, and cite a precedent for methodological
triangulation in the social sciences. Social science research involves highly
complex systems, and the inherent ethical limitations in human subjects
research render reproducibility an impractical method of validation.
In a rebuttal, Dr. John Ioannidis argues that what pharmacogenetic
association studies actually require are “better, more rigorous methods, and
even more stringent replication, and clinical validation”. He proposes several
strategies to yield better results from pharmacogenetic association studies
including mining data from biobanks and electronic medical records, more
stringent criteria for replication, improved methods of detecting and
validating rare variants, focus on polygenic markers rather than on single
genes, and validation with large, randomized clinical trials.
In a response, Dr. Aslibekyan and colleagues agreed with much of
Dr. Ioannidis’ argument. They did, however, reiterate the concern that lowering
P-value thresholds for single nucleotide variants
could cause many single gene variants with true associations to be overlooked due to factors such as
gene-environment interactions and epistasis.
Read the articles here:
