Genetic variation in PLEKHG1 is associated with white matter hyperintensities (n = 11,226)

Objective To identify novel genetic associations with white matter hyperintensities (WMH). Methods We performed a genome-wide association meta-analysis of WMH volumes in 11,226 individuals, including 8,429 population-based individuals from UK Biobank and 2,797 stroke patients. Replication of novel loci was performed in an independent dataset of 1,202 individuals. In all studies, WMH were quantified using validated automated or semi-automated methods. Imputation was to either the Haplotype Reference Consortium or 1,000 Genomes Phase 3 panels. Results We identified a locus at genome-wide significance in an intron of PLEKHG1 (rs275350, β [SE] = 0.071 [0.013]; p = 1.6 × 10−8), a Rho guanine nucleotide exchange factor that is involved in reorientation of cells in the vascular endothelium. This association was validated in an independent sample (overall p value, 2.4 × 10−9). The same single nucleotide polymorphism was associated with all ischemic stroke (odds ratio [OR] [95% confidence interval (CI)] 1.07 [1.03–1.12], p = 0.00051), most strongly with the small vessel subtype (OR [95% CI] 1.09 [1.00–1.19], p = 0.044). Previous associations at 17q25 and 2p16 reached genome-wide significance in this analysis (rs3744020; β [SE] = 0.106 [0.016]; p = 1.2 × 10−11 and rs7596872; β [SE] = 0.143 [0.021]; p = 3.4 × 10−12). All identified associations with WMH to date explained 1.16% of the trait variance in UK Biobank, equivalent to 6.4% of the narrow-sense heritability. Conclusions Genetic variation in PLEKHG1 is associated with WMH and ischemic stroke, most strongly with the small vessel subtype, suggesting it acts by promoting small vessel arteriopathy.

[1]  L. Wilkins.  Genetic variation in PLEKHG1 is associated with white matter hyperintensities (n = 11,226) , 2019, Neurology.

[2]  George Davey Smith,et al.  Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference , 2018, Nature Communications.

[3]  Ludovica Griffanti,et al.  Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank , 2017, NeuroImage.

[4]  Jesse M. Engreitz,et al.  A Genetic Variant Associated with Five Vascular Diseases Is a Distal Regulator of Endothelin-1 Gene Expression , 2017, Cell.

[5]  P. Donnelly,et al.  Genome-wide genetic data on ~500,000 UK Biobank participants , 2017, bioRxiv.

[6]  Xiaofeng Zhu,et al.  Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations , 2017, PLoS genetics.

[7]  Christian Gieger,et al.  Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets , 2017, Nature Genetics.

[8]  Jonathan M. Cairns,et al.  Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters , 2016, Cell.

[9]  Ludovica Griffanti,et al.  BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities , 2016, NeuroImage.

[10]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[11]  D. Roden,et al.  Phenome-Wide Association Studies as a Tool to Advance Precision Medicine. , 2016, Annual review of genomics and human genetics.

[12]  Steve D. M. Brown,et al.  High-throughput discovery of novel developmental phenotypes , 2017 .

[13]  Stephen Burgess,et al.  PhenoScanner: a database of human genotype–phenotype associations , 2016, Bioinform..

[14]  Eric S. Lander,et al.  Direct Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter Assay , 2016, Cell.

[15]  C. Sudlow,et al.  Genetic Associations With White Matter Hyperintensities Confer Risk of Lacunar Stroke , 2016, Stroke.

[16]  C. Sudlow,et al.  Low-frequency and common genetic variation in ischemic stroke , 2016, Neurology.

[17]  H. Stöhr,et al.  Reducing Timp3 or vitronectin ameliorates disease manifestations in CADASIL mice , 2016, Annals of neurology.

[18]  Kaitlin M. Fitzpatrick,et al.  Genome-wide meta-analysis of cerebral white matter hyperintensities in patients with stroke , 2016, Neurology.

[19]  J. Danesh,et al.  A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease , 2016 .

[20]  Philip A. Ewels,et al.  Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C , 2015, Nature Genetics.

[21]  G. Kempermann.  Faculty Opinions recommendation of Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. , 2015 .

[22]  Jun S. Liu,et al.  The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans , 2015, Science.

[23]  Masaaki Sato,et al.  Rho guanine nucleotide exchange factors involved in cyclic‐stretch‐induced reorientation of vascular endothelial cells , 2015, Journal of Cell Science.

[24]  Lorna M. Lopez,et al.  Multiethnic Genome-Wide Association Study of Cerebral White Matter Hyperintensities on MRI , 2015, Circulation. Cardiovascular genetics.

[25]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[26]  Lisa J. Martin,et al.  Meta-analysis of genome-wide association studies identifies 1q22 as a susceptibility locus for intracerebral hemorrhage. , 2014, American journal of human genetics.

[27]  J. Shendure,et al.  A general framework for estimating the relative pathogenicity of human genetic variants , 2014, Nature Genetics.

[28]  Nick C Fox,et al.  Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease , 2013, Nature Genetics.

[29]  Xiaofeng Zhu,et al.  Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations. , 2013, American journal of human genetics.

[30]  Nick C Fox,et al.  Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration , 2013, The Lancet Neurology.

[31]  Jake K. Byrnes,et al.  Bayesian refinement of association signals for 14 loci in 3 common diseases , 2012, Nature Genetics.

[32]  Mark I McCarthy,et al.  Genomic inflation factors under polygenic inheritance , 2011, European Journal of Human Genetics.

[33]  Eric E. Smith,et al.  White matter hyperintensity volume is increased in small vessel stroke subtypes , 2010, Neurology.

[34]  H. Markus,et al.  The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis , 2010, BMJ : British Medical Journal.

[35]  Yun Li,et al.  METAL: fast and efficient meta-analysis of genomewide association scans , 2010, Bioinform..

[36]  Jon Wakefield,et al.  Bayes factors for genome‐wide association studies: comparison with P‐values , 2009, Genetic epidemiology.

[37]  Y. W. Chen,et al.  Progression of white matter lesions and hemorrhages in cerebral amyloid angiopathy , 2006, Neurology.

[38]  W. Schiemann,et al.  Fibulins 3 and 5 antagonize tumor angiogenesis in vivo. , 2006, Cancer research.

[39]  Charles DeCarli,et al.  Genetic Variation in White Matter Hyperintensity Volume in the Framingham Study , 2004, Stroke.

[40]  M. Fornage,et al.  Heritability of Leukoaraiosis in Hypertensive Sibships , 2004, Hypertension.

[41]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[42]  K. Roeder,et al.  Genomic Control for Association Studies , 1999, Biometrics.

[43]  G. Barker,et al.  Quantification of MRI lesion load in multiple sclerosis: a comparison of three computer-assisted techniques. , 1996, Magnetic resonance imaging.