Clinical OMICs - Issue 3 - (Page 7)

Guidelines to Prevent Genomics Data from Amounting to So Many Tea Leaves he reading of tea leaves won't provide more reliable predictions if the reader simply brews more cups of tea, so as to peruse more and more leaves. A similar problem can arise in other pursuits, even genomics analysis. Granted, tasseography is far from genomics, but one may still encounter narrative bias, the tendency to find meaning where none is to be found, when one is trying to perceive mutation-disease patterns against a noisy sequencing background. Simply analyzing more data may not be a solution. To find ways to suppress narrative bias, leading genomics researchers participated in a workshop convened by the National Human Genome Research Institute. The workshop, entitled "Implicating Sequence Vari- ants in Human Disease," took place in 2012. Afterwards, many of the workshop's participants continued working together. Ultimately, they produced an article, which was published April 23 in Nature, outlining how researchers and clinicians may ensure the quality of genomics data and avoid false assignments of pathogenicity, particularly in investigations of rare genetic variants, or changes detected in a person's genome. The article, entitled "Guidelines for investigating causality of sequence variants in human disease," examines how the flood of genome sequence data can be handled. In particular, it explains how analysts can go about confidently distinguishing between variants that seem likely to contribute Rare Gene Variants Linked to Hematological Traits that the loci detected by GWASs explain little of inferred genetic variance. In light of this "missing heritability" problem, the scientists have moved on. Increasingly, they are looking to reconcile a couple of different views-first: disease risk can be attributed to a large number of smalleffect common variants (the infinitesimal view); second: a large number of large-effect rare variants (the rare allele view). As part of this broad effort to redraw the genomic map of disease susceptibility, researchers at the Montreal Heart Institute (MHI) have been weighing data from GWASs, deep DNA resequencing studies, and exome-wide genotyping. Most L ike cartographers of old who ended up altering the shapes of large land masses to accommodate information from increasingly detailed surveys, genomic scientists are revising their ideas about the respective roles of common and rare genetic variants in complex disease processes. To date, thousands of common variants have been identified by means of genomewide association studies (GWASs). But these variants are no longer expected to support the simple common disease/common variant (CDCV) hypothesis. Genomic scientists have observed www.clinicalomics.com Photo : Sergey Nivens-Fotolia.com T A recent article in Nature outlined how researchers and clinicians can ensure the quality of genomics data and avoid false assignments of pathogenicity. disease and variants that don't (so far as anyone is currently aware). Recommendations in the article focus on several key areas, such as study design, gene- and variant-level implication, databases, and implications for diagnosis. (continued on p. 28) recently, they have uncovered findings that suggest these approaches can complement each other in defining the allelic architecture of complex traits. At least, they have demonstrated that this appears to be the case with hematological traits. The MHI researchers organized an international collaboration that identified a dozen mutations in the human genome that are involved in significant changes in complete blood counts and that explain the onset of sometimes severe biological disorders. These researchers, led by Guillaume Lettre, Ph.D., an assistant professor of medicine at the Montreal Heart Institute and the Université de Montréal, (continued on p. 29) May 15, 2014 Clinical OMICs 7 http://www.Fotolia.com http://www.clinicalomics.com

Table of Contents for the Digital Edition of Clinical OMICs - Issue 3

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Clinical OMICs - Issue 3