I read with interest a recent publication by Khandpur et al in Science Translational Medicine on NETosis in the pathogenesis of rheumatoid arthritis (download PDF here). It made me think about “cause vs consequence” in scientific discovery. That is, how does one determine whether a biological process observed in patients with active disease is a cause of disease rather than a consequence of disease?
In reading the article, I learned about how neutrophils cause tissue damage and promote autoimmunity through the aberrant formation of neutrophil extracellular traps (NETs). Released via a novel form of cell death called NETosis, NETs consist of a chromatin meshwork decorated with antimicrobial peptides typically present in neutrophil granules. (Read more about NETs on Wikipedia here.)
Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in non-experimental studies (read more here). It is a powerful to determine if an observation in patients is causal. For example, if autoantibodies are pathogenic in RA, then DNA variants that influence the formation of autoantibodies should also be associated with risk of RA. This is indeed the case, as exemplified by variants in a gene, PADI4, the codes for an enzyme involved in peptide citrullination (see here). Similarly, if NETosis is a cause and not a consequence of RA, then validated RA genetic risk factors should be associated with NETosis. This has not yet been performed, as the phenomenon of NETosis in RA is only now emerging (thanks to elegant studies such as that by Khandpur and colleagues).
One of the most powerful examples of Mendelian randomization is that of teasing apart the causal relationship between HDL cholesterol and risk of myocardial infarction (MI). For decades, human epidemiology has pointed to the causal relationship between HDL cholesterol and MI. Drugs have been developed for the purpose of raising HDL cholesterol to decrease risk of MI. However, these drugs have not yet shown clinical utility, suggesting that there is not a causal relationship (see here). Through Mendelian randomization, human genetics supports the observation that there is NOT a causal link between HDL cholesterol and MI risk (see here).
Another interesting recent example is the association between telomere length and human disease, published recently in Nature Genetics (see here). Interindividual variation in mean leukocyte telomere length (LTL) is associated with cancer and several age-associated diseases. In a GWAS of LTL, seven loci were identified at a genome-wide level of significance. Lead SNPs at two loci (TERC and TERT) associate with several cancers and other diseases, including idiopathic pulmonary fibrosis. Moreover, a genetic risk score analysis combining lead variants at all 7 loci in 22,233 coronary artery disease cases and 64,762 controls showed an association of the alleles associated with shorter LTL with increased risk of coronary artery disease (P = 0.01).
A key concept in all studies is “allelic pleiotropy” (see here). This feature makes human genetics an incredibly powerful tool to differentiate between cause and consequence. Further, pleiotropy enables the connection of two human traits (e.g., disease and a biological process) in a way that is quite unique among scientific disciplines.
Mendelian randomization is underutilized in pharmacogenomic (PGx) traits involving efficacy and toxicity. We have tried to associate RA risk alleles with response to anti-TNF therapy. To date, there is no obvious connection (see Plenge Publications section on Precision Medicine here). If anyone know of good examples, please post them in the Discussion section below!
As GWAS of biological “endophenotypes” such as NETosis, HDL cholesterol and telomere length continue to emerge, it will be extremely interesting and important to connect the genetics of endophenotypes with the genetics of human disease. In this way, Mendelian Randomization will be a powerful ally in understanding fundamental biological processes that lead to disease in the model organism that matters the most, humans.