A Manhattan plot showing results of a typical genome wide association study. Source: “Four Novel Loci Influence the Microcirculation In Vivo.” Ikram MK et al, 2010, http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1001184

Biologists and medical doctors everywhere will be surprised to hear that a few canny geneticists have discovered a new disease risk etiology unlike any other thus far. Stunning new research published in Nature Genetics promises to shed light on the non-Mendelian genetic factors involved in disease risk by examining the physical interactions between risk SNPs and chromatin regulatory systems.1 The new research uses genome wide association studies (GWAS) to identify disease risk clusters that are independent from traditionally identified risk SNPs, meaning that it examined only genes which don’t canonically promote a given disease. Like in all other GWAS studies, the new research relied extremely heavily on powerful information technology to make its discovery.

Quantifying disease risk is an uncertain science, but is subject to a great amount of research and attention because of its applications to human health. Making claims about genetic yet non-Mendelian nor epigenetic disease risk requires extraordinary evidence, and the new research rises to the occasion. After identifying the new set of risk SNPs, the research developed a new hypothesis which explains the association between risk SNPs and disease progression: physical interactions between the new risk SNPs and chromatin remodeling machinery. This new understanding is predicted to throw researchers, doctors, and insurance companies alike into a frenzy of follow-up studies, and software that accommodates this level of fresh inquiry is going to be needed.

Finding a hole in genetic disease risk hypotheses

This new research blows the old genetic disease risk hypotheses to pieces. Prior to publication of this most recent study, genetic disease risk was broken down somewhat easily:

  • Mendelian inheritance via SNPs in genetic disorders
  • Mendelian inheritance of risk SNPs in combination with environmental factors
  • Mitochondrial DNA inheritance of risk SNPs in genetic disorders
  • Mitochondrial DNA inheritance of risk SNPs in combination with environmental factors
  • Chromosomal malformation in genetic disorders
  • Epigenetic risk factors, if any

Simply put, the new research has identified a totally new pathway to understand disease risk using public data, math, and information technology. First, the researchers performed their GWAS, using disease outcomes and respective SNPs correlated with six separate autoimmune diseases: arthritis, lupus, Crohn’s disease, MS, ulcerative colitis, and celiac disease. These diseases were picked because they are known to be rich in known genetic risk etiologies so as to not accidentally stumble upon a traditionally understood risk SNP whose presence causes a transcriptional difference which leads to disease.

After performing the GWAS, the researchers looked for disease which developed in absence of the traditionally identified risk SNPs by performing a linkage disequilibrium analysis.2 The analysis found that certain genomic areas with multifunctional enhancer variants were hotspots of disease development that were not correlated with risk SNPs. By mapping the enhancer variant areas, the researchers developed the hypothesis that physical interactions between the multifunctional enhancers and the newly identified SNPs were responsible for increasing disease risk. Though the researchers didn’t perform a simulation to validate their hypothesis of the physical interaction, it’s likely that they will as a confirmatory follow-up.

Peeking into “the interactome”

Physical genetic interactions haven’t been studied for very long, and the concept of a genetic interactome is still forming, but the basic pattern of a genetic physical interaction isn’t hard to understand despite being unfamiliar to most within the field of biology.3 A physical interaction occurs when regulatory structures involved in chromatin remodeling enter into such close proximity with nucleotides that their electrostatic forces intersect and push off of each other. Historically, researchers have identified physical interactions statistically or probabilistically, though some newer research has turned to simulating interactions. 4

Simulating interactions between genetic components will likely be the tact that future researchers use to flesh out the new pathway of disease risk discovered by the GWAS. What’s clear is that detecting and understanding physical interactions has long been the domain of sophisticated software and laborious analysis of old data sets rather than wetware experimentation.

A new disease risk hypothesis?

In the wake of the new research, the list detailed above gets quite a bit more complicated, however:

  • Traditionally identified Mendelian-inherited risk SNPs in combination or isolation from environmental factors
  • Traditionally identified mitochondrial DNA risk SNPs in combination or isolation from environmental factors
  • Traditionally identified chromosomal malformations
  • Traditional Mendelian-inherited risk SNPs in combination with newly identified risk SNPs caused by physical interactions of genomic DNA in chromatin
  • Traditional mitochondrial DNA risk SNPs in combination with newly identified risk SNPs caused by physical interactions of genomic DNA in chromatin and potentially also in mitochondria
  • Traditional chromosomal malformations in combination with newly identified risk SNPs in chromatin, if any
  • Epigenetic risk factors in combination with newly identified risk SNPs during the risk SNPs replication and interaction with chromatin remodeling machinery

It’s clear that to even approach GWAS or understanding physical interactions, competitive labs will need a powerful information technology platform. Even if your current software platform can handle the many data sets required for GWAS, it’s unlikely that it allows you to simulate interactions between genetic factors as well. Luckily, there is such a platform which you can use to model physical interactions that your GWAS study implicates as novel regions involved in disease risk.

BIOVIA Pipeline Pilot is the physical interaction simulation suite that your lab needs to make the most out of probably risk SNPs identified by GWAS. A graphical scientific workflow authoring application, Pipeline Pilot allows you to capitalize on advanced modeling, data tracking, and physical interaction validation software and find new risk clusters for diseases. Contact us today to find out how you can use Pipeline Pilot to start exploring the next frontier of human disease risk.

  1.  “Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry.” September 2016, https://www.researchgate.net/publication/308341873_Modeling_disease_risk_through_analysis_of_physical_interactions_between_genetic_variants_within_chromatin_regulatory_circuitry
  2.  “Linkage disequilibrium– understanding the evolutionary past and mapping the medical future.” June 2008, https://www.ncbi.nlm.nih.gov/pubmed/18427557
  3. “The genetic landscape of a cell.” January 2010, https://www.ncbi.nlm.nih.gov/pubmed/20093466
  4.  “Grasping at molecular interactions and genetic networks in Drosophila melanogaster using FlyNets, an Internet database.” January 1999, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC148104/