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BioData Min. 2014 Jun 30;7:10. doi: 10.1186/1756-0381-7-10. eCollection 2014.

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

Author information

1
Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
2
Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755, USA.
3
Center for Systems Genomics, Pennsylvania State University, University Park, PA 16802, USA.
4
Department of Biochemistry & Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA.
5
Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232-0700, USA.
6
Community and Family Medicine, Section of Biostatistics & Epidemiology, Geisel School of Medicine, Hanover, NH 03766, USA.
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Contributed equally

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.

KEYWORDS:

Complex disease; False negatives; False positives; GWAS; Heterogeneity; Omics; Replication; Type 1 error; Type 2 error; Validation

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