scholarly journals Genetics of Common Polygenic Ischaemic Stroke: Current Understanding and Future Challenges

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Steve Bevan ◽  
Hugh S. Markus

Stroke is the third commonest cause of death and the major cause of adult neurological disability worldwide. While much is known about conventional risk factors such as hypertension, diabetes and incidence of smoking, these environmental factors only account for a proportion of stroke risk. Up to 50% of stroke risk can be attributed to genetic risk factors, although to date no single risk allele has been convincingly identified as contributing to this risk. Advances in the field of genetics, most notably genome wide association studies (GWAS), have revealed genetic risks in other cardiovascular disease and these techniques are now being applied to ischaemic stroke. This paper covers previous genetic studies in stroke including candidate gene studies, discusses the genome wide association approach, and future techniques such as next generation sequencing and the post-GWAS era. The review also considers the overlap from other cardiovascular diseases and whether findings from these may also be informative in ischaemic stroke.

2020 ◽  
Author(s):  
Jingshu Wang ◽  
Qingyuan Zhao ◽  
Jack Bowden ◽  
Gilbran Hemani ◽  
George Davey Smith ◽  
...  

Over a decade of genome-wide association studies have led to the finding that significant genetic associations tend to spread across the genome for complex traits. The extreme polygenicity where "all genes affect every complex trait" complicates Mendelian Randomization studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing Mendelian Randomization methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using summary statistics from genome-wide association studies, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, adjust for confounding risk factors, and determine the causal direction. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and the potential pleiotropic pathways.


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