Actively Protective Combinatorial Analysis: a Scalable Novel Method for Detecting Variants that Contribute to Reduced Disease Prevalence in High-Risk Individuals

Abstract

We present a novel method for routinely identifying disease resilience associations that offers powerful insights for the discovery of a new class of disease protective targets. We show how this can be used to identify mechanisms in the background of normal cellular biology that work to slow or stop progression of complex, chronic diseases.

Actively protective combinatorial analysis identifies combinations of features that contribute to reducing risk of disease in individuals who remain healthy even though their genomic profile suggests that they have high risk of developing disease. These protective signatures can potentially be used to identify novel drug targets, pharmacogenomic and/or therapeutic mRNA opportunities and to better stratify patients by overall disease risk and mechanistic subtype.

We describe the method and illustrate how it offers increased power for detecting disease-associated genetic variants relative to traditional methods. We exemplify this by identifying individuals who remain healthy despite possessing several disease signatures associated with increased risk of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) or amyotrophic lateral sclerosis (ALS). We then identify combinations of SNP-genotypes significantly associated with reduced disease prevalence in these high-risk protected cohorts.

We discuss how actively protective combinatorial analysis generates novel insights into the genetic drivers of established disease biology and detects gene-disease associations missed by standard statistical approaches such as meta-GWAS. The results support the mechanism of action hypotheses identified in our original causative disease analyses. They also illustrate the potential for development of precision medicine approaches that can increase healthspan by reducing the progression of disease.