Recommendations From the International Stroke Genetics Consortium, Part 1: Standardized Phenotypic Data Collection

Risk and clinical outcome of stroke, as for nearly all complex conditions, is polygenic.1 Discovering influential genetic variants offers the promise of new and personalized treatments that will substantially reduce the devastating effects of stroke on global health. Adequate power to detect multiple genetic risk alleles requires large sample sizes. Although stroke is the second leading cause of death worldwide and a major contributor to adult disability,2 no individual center can collect sufficient samples on its own. Recognizing this challenge, in 2007, stroke researchers from around the world formed the International Stroke Genetics Consortium (ISGC, http://www.strokegenetics.org). The ISGC mission is to identify genetic factors influencing stroke risk, prognosis, and treatment response by studying patients enrolled at centers around the globe. Although there has been notable early success,3–5 much work remains not only to achieve the ultimate goal of personalized medicine in stroke, finding genetic risk alleles, but also, more importantly, to develop comprehensive stroke risk assessments with actionable clinical results.6 Judging from developments in other complex diseases, such as diabetes mellitus and coronary artery disease, sample sizes of the order of 100 000 to 200 000 will be needed to identify the full range of genetic variation involved in stroke. Achieving such sample sizes requires even larger collaboration. We propose a standard methodology for data collection in stroke genetics studies to establish a best practice approach, sharing lessons learned through the ISGC. We outline the appropriate selection of case and control subjects and delineate the phenotypic data to collect, including minimum and preferred data points. Minimum requirements are prerequisites for inclusion in basic stroke genetic studies. Preferred data elements enable centers to participate in a broader variety of collaborations, such as those exploring gene–environment interactions, imaging endophenotypes, such as white matter hyperintensities, and functional …

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