Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors

Abstract

In omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot completely eliminate false positive conclusions, and it can also mask many true associations that might otherwise advance our understanding of pathology. These issues beg the question: How can we increase the amount of knowledge gained from high throughput genetic data? To address this challenge, we present an approach that complements standard statistical validation methods by drawing attention to both potential false negative and false positive conclusions, as well as providing broad information for directing future research. The Diverse Convergent Evidence approach (DiCE) we propose integrates information from multiple sources (omics, informatics, and laboratory experiments) to estimate the strength of the available corroborating evidence supporting a given association. This process is designed to yield an evidence metric that has utility when etiologic heterogeneity, variable risk factor frequencies, and a variety of observational data imperfections might lead to false conclusions. We provide proof of principle examples in which DiCE identified strong evidence for associations that have established biological importance, when standard validation methods alone did not provide support. If used as an adjunct to standard validation methods this approach can leverage multiple distinct data types to improve genetic risk factor discovery/validation, promote effective science communication, and guide future research directions.

Learning to cope with stress modulates anterior cingulate cortex stargazin expression in monkeys and mice

“…Convergent evidence from diverse sources avoids problems with adjustments in P-value thresholds (Ciesielski et al, 2014; Nuzzo, 2014). Different stress coping conditions were designed on the basis of evidence that mild intermittent but not minimal nor severe stress exposure provides opportunities to learn, practice, and improve coping as described by U-shaped functions (Seery et al, 2010; Russo et al, 2012; Sapolsky, 2015).…”

“…Direct regulation of differential gene expression by glucocorticoids is considered in our analysis but indirect pathways that involve various glucocorticoid receptor co-regulators are not examined (Ratman et al, 2013). The pursuit of convergent evidence in monkeys and mice minimizes false positive findings and may enhance translational relevance (Ciesielski et al, 2014) but also increases the risk of falsely disregarding important species differences as negative results.…”

“…Genes potentially regulated by glucocorticoids and that serve relevant neural functions (see Discussion section below) for learning to cope modeled in monkeys were then assessed in mice. Diverse convergent evidence from monkeys and mice was sought to minimize false positive findings and enhance translational relevance (Ciesielski et al, 2014). Translation has commanded considerable attention because of recent uncertainties about generalization across different species given inevitable biological variation, the use of diverse experimental manipulations, and various ways to operationalize complex outcomes of interest (Institute of Medicine, 2013).…”

Transdisciplinary approaches enhance the production of translational knowledge

“…For example, DiCE (Diverse Convergent Evidence) is an evidence integration process that combines information from observational association studies, bioinformatics, and laboratory experiments to yield a metric that reflects the likelihood that a given genetic factor is involved in the disease pathophysiology. 52 As a proof of principle, this metric identified the role of Hemoglobin S in severe malaria resistance 52–54 and the role of PPAR-gamma in type 2 diabetes 52, 55–57 when standard GWAS validation criteria alone failed to detect these etiologically relevant factors. DiCE can also highlight potential false positive findings in GWAS analyses, including those that reach canonical thresholds for statistical significance, and suggest future research to address the ambiguous evidence.…”

“…It is becoming recognized that, when utilized in isolation, GWAS analyses have a variety of weaknesses that can hinder the discovery of genetic risk factors. 46–52 Essentially GWAS, like all epidemiologic analyses, are prone to both type-1 and type-2 error, as well as the influence of unrecognized biases. However, if GWAS data is systematically evaluated in the context of relevant evidence from diverse areas, it can be part of a larger process that more effectively discovers and vets genetic risk factors for disease.…”