Publications by authors named "Yasaman Saba"

30 Publications

  • Page 1 of 1

Cerebral small vessel disease genomics and its implications across the lifespan.

Nat Commun 2020 12 8;11(1):6285. Epub 2020 Dec 8.

University of Alabama at Birmingham School of Medicine, Birmingham, AL, 35233, USA.

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.
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http://dx.doi.org/10.1038/s41467-020-19111-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722866PMC
December 2020

Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults.

Nat Commun 2020 09 22;11(1):4796. Epub 2020 Sep 22.

Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.

Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.
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http://dx.doi.org/10.1038/s41467-020-18367-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508833PMC
September 2020

Association of common genetic variants with brain microbleeds: A genome-wide association study.

Neurology 2020 12 10;95(24):e3331-e3343. Epub 2020 Sep 10.

From the Departments of Epidemiology (M.J.K., H.H.H.A., D.V., S.J.v.d.L., P.Y., M.W.V., N.A., C.M.v.D., M.A.I.), Radiology and Nuclear Medicine (H.H.H.A., P.Y., A.v.d.L., M.W.V.), and Clinical Genetics (H.H.H.A.), Erasmus MC University Medical Center, Rotterdam, the Netherlands; Stroke Research Group, Department of Clinical Neurosciences (D.L., M.T., J.L., D.J.T., H.S.M.), University of Cambridge, UK; Department of Neurology (J.R.J.R., C.L.S., J.J.H., A.S.B., C.D., S. Seshadri), Boston University School of Medicine; The Framingham Heart Study (J.R.J.R., C.L.S., J.J.H., A.S.B., S. Seshadri), MA; Department of Biostatistics (A.V.S.), University of Michigan, Ann Arbor; Icelandic Heart Association (A.V.S., S. Sigurdsson, V.G.), Kopavogur, Iceland; Brown Foundation Institute of Molecular Medicine, McGovern Medical School (M.F.), and Human Genetics Center, School of Public Health (M.F.), University of Texas Health Science Center at Houston; Clinical Division of Neurogeriatrics, Department of Neurology (E.H., L.P., R.S.), Institute for Medical Informatics, Statistics and Documentation (E.H.), and Gottfried Schatz Research Center, Department of Molecular Biology and Biochemistry (Y.S., H.S.), Medical University of Graz, Austria; Center of Cerebrovascular Diseases, Department of Neurology (J.L.), West China Hospital, Sichuan University, Chengdu; Stroke Research Centre, Queen Square Institute of Neurology (I.C.H., D.W., H.H., D.J.W.), University College London, UK; Department of Neurosurgery (I.C.H.), Klinikum rechts der Isar, University of Munich, Germany; Centre for Cognitive Ageing and Cognitive Epidemiology, Psychology (M.L., D.C.M.L., M.E.B., I.J.D., J.M.W.), and Centre for Clinical Brain Sciences, Edinburgh Imaging, UK Dementia Research Institute (M.E.B., J.M.W.), University of Edinburgh, UK; Department of Internal Medicine, Section of Gerontology and Geriatrics (S.T.), Department of Cardiology (S.T., J.v.d.G., J.W.J.), Section of Molecular Epidemiology, Biomedical Data Sciences (E.B.v.d.A., M.B., P.E.S.), Leiden Computational Biology Center, Biomedical Data Sciences (E.B.v.d.A.), Department of Radiology (J.v.d.G.), and Einthoven Laboratory for Experimental Vascular Medicine (J.W.J.), Leiden University Medical Center, the Netherlands; Department of Neurology (A.-K.G., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Memory Aging and Cognition Center (S.H., C.C.), National University Health System, Singapore; Department of Pharmacology (S.H., C.C.) and Saw Swee Hock School of Public Health (S.H.), National University of Singapore and National University Health System, Singapore; Pattern Recognition & Bioinformatics (E.B.v.d.A.), Delft University of Technology, the Netherlands; Department of Biostatistics (S.L., J.J.H., Q.Y., A.S.B.), Boston University School of Public Health, MA; Department of Radiology (C.R.J., K.K.), Mayo Clinic, Rochester, MN; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (C.L.S., S. Seshadri), UT Health San Antonio, TX; Department of Medicine, Division of Geriatrics (B.G.W., T.H.M), and Memory Impairment and Neurodegenerative Dementia (MIND) Center (T.H.M.), University of Mississippi Medical Center, Jackson; Singapore Eye Research Institute (C.Y.C., J.Y.K., T.Y.W.); Department of Neuroradiology (Z.M., J.M.W.), NHS Lothian, Edinburgh; Institute of Cardiovascular and Medical Sciences (D.J.S.), College of Medical, Veterinary and Life Sciences, University of Glasgow, UK; Division of Cerebrovascular Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Department of Neuroradiology (A.D.M.), Atkinson Morley Neurosciences Centre, St George's NHS Foundation Trust, London, UK; Department of Neurology (C.D.), University of California at Davis; Nuffield Department of Population Health (C.M.v.D.), University of Oxford, UK; Laboratory of Epidemiology and Population Sciences (L.J.L.), National Institute on Aging, Baltimore, MD; and Faculty of Medicine (V.G.), University of Iceland, Reykjavik, Iceland.

Objective: To identify common genetic variants associated with the presence of brain microbleeds (BMBs).

Methods: We performed genome-wide association studies in 11 population-based cohort studies and 3 case-control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMBs were rated on susceptibility-weighted or T2*-weighted gradient echo MRI sequences, and further classified as lobar or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of ε2 and ε4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs.

Results: BMBs were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the region reached genome-wide significance for its association with BMB (lead rs769449; odds ratio [OR] [95% confidence interval (CI)] 1.33 [1.21-1.45]; = 2.5 × 10). ε4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19-1.50]; = 1.0 × 10) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86-1.25]; = 0.68). ε2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB.

Conclusions: Genetic variants in the region are associated with the presence of BMB, most likely due to the ε4 allele count related to a higher number of strictly lobar BMBs. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers.
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http://dx.doi.org/10.1212/WNL.0000000000010852DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7836652PMC
December 2020

Global and Regional Development of the Human Cerebral Cortex: Molecular Architecture and Occupational Aptitudes.

Cereb Cortex 2020 06;30(7):4121-4139

Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04109 Leipzig, Germany.

We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade-albeit in a very limited manner-to behaviors as complex as the choice of one's occupation.
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http://dx.doi.org/10.1093/cercor/bhaa035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947185PMC
June 2020

Associations of autozygosity with a broad range of human phenotypes.

Nat Commun 2019 10 31;10(1):4957. Epub 2019 Oct 31.

Department of Neurology, Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht University, Utrecht, 3584 CX, The Netherlands.

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F) for >1.4 million individuals, we show that F is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F are confirmed within full-sibling pairs, where the variation in F is independent of all environmental confounding.
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http://dx.doi.org/10.1038/s41467-019-12283-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823371PMC
October 2019

Genetic architecture of subcortical brain structures in 38,851 individuals.

Nat Genet 2019 11 21;51(11):1624-1636. Epub 2019 Oct 21.

Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
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http://dx.doi.org/10.1038/s41588-019-0511-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055269PMC
November 2019

Evaluation of the Performance of AmpliSeq and SureSelect Exome Sequencing Libraries for Ion Proton.

Front Genet 2019 25;10:856. Epub 2019 Sep 25.

Research Unit-Genetic Epidemiology, Gottfried Schatz Research Centre for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University Graz, Graz, Austria.

Library preparation for whole-exome sequencing is a critical step serving the enrichment of the regions of interest. For Ion Proton, there are only two exome library preparation methods available, AmpliSeq and SureSelect. Although of major interest, a comparison of the two methods is missing in the literature. Here, we systematically evaluate the performance of AmpliSeq and SureSelect and present an improved variant calling pipeline. We used 12 in-house DNA samples with genome-wide and exome microarray data and a commercially available reference DNA (NA12878) for evaluation. Both methods had a high concordance (>97%) with microarray genotypes and, when validating against NA12878, a sensitivity and positive predictive values of >93% and >80%, respectively. Application of our variant calling pipeline decreased the number of false positive variants dramatically by 90% and resulted in positive predictive value of 97%. This improvement is highly relevant in research as well as clinical setting.
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http://dx.doi.org/10.3389/fgene.2019.00856DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6774276PMC
September 2019

Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels.

Nat Genet 2019 10 2;51(10):1459-1474. Epub 2019 Oct 2.

Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden.

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.
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http://dx.doi.org/10.1038/s41588-019-0504-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6858555PMC
October 2019

A genome-wide association study identifies genetic loci associated with specific lobar brain volumes.

Commun Biol 2019 2;2:285. Epub 2019 Aug 2.

17Department of Biomedical Data Sciences, Statistical Genetics, Leiden University Medical Center, Leiden, 2333ZA the Netherlands.

Brain lobar volumes are heritable but genetic studies are limited. We performed genome-wide association studies of frontal, occipital, parietal and temporal lobe volumes in 16,016 individuals, and replicated our findings in 8,789 individuals. We identified six genetic loci associated with specific lobar volumes independent of intracranial volume. Two loci, associated with occipital (6q22.32) and temporal lobe volume (12q14.3), were previously reported to associate with intracranial and hippocampal volume, respectively. We identified four loci previously unknown to affect brain volumes: 3q24 for parietal lobe volume, and 1q22, 4p16.3 and 14q23.1 for occipital lobe volume. The associated variants were located in regions enriched for histone modifications ( and ), or close to genes causing Mendelian brain-related diseases ( and ). No genetic overlap between lobar volumes and neurological or psychiatric diseases was observed. Our findings reveal part of the complex genetics underlying brain development and suggest a role for regulatory regions in determining brain volumes.
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http://dx.doi.org/10.1038/s42003-019-0537-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677735PMC
April 2020

A catalog of genetic loci associated with kidney function from analyses of a million individuals.

Nat Genet 2019 06 31;51(6):957-972. Epub 2019 May 31.

Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clincial Sciences in Malmö, Lund University, Malmö, Sweden.

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
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http://dx.doi.org/10.1038/s41588-019-0407-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6698888PMC
June 2019

Author Correction: Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function.

Nat Commun 2019 May 1;10(1):2068. Epub 2019 May 1.

Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, 00014, Finland.

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article.
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http://dx.doi.org/10.1038/s41467-019-10160-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494826PMC
May 2019

Association of variants in HTRA1 and NOTCH3 with MRI-defined extremes of cerebral small vessel disease in older subjects.

Brain 2019 04;142(4):1009-1023

The University of Texas Health Science Center at Houston, Houston, TX, USA.

We report a composite extreme phenotype design using distribution of white matter hyperintensities and brain infarcts in a population-based cohort of older persons for gene-mapping of cerebral small vessel disease. We demonstrate its application in the 3C-Dijon whole exome sequencing (WES) study (n = 1924, nWESextremes = 512), with both single variant and gene-based association tests. We used other population-based cohort studies participating in the CHARGE consortium for replication, using whole exome sequencing (nWES = 2,868, nWESextremes = 956) and genome-wide genotypes (nGW = 9924, nGWextremes = 3308). We restricted our study to candidate genes known to harbour mutations for Mendelian small vessel disease: NOTCH3, HTRA1, COL4A1, COL4A2 and TREX1. We identified significant associations of a common intronic variant in HTRA1, rs2293871 using single variant association testing (Pdiscovery = 8.21 × 10-5, Preplication = 5.25 × 10-3, Pcombined = 4.72 × 10-5) and of NOTCH3 using gene-based tests (Pdiscovery = 1.61 × 10-2, Preplication = 3.99 × 10-2, Pcombined = 5.31 × 10-3). Follow-up analysis identified significant association of rs2293871 with small vessel ischaemic stroke, and two blood expression quantitative trait loci of HTRA1 in linkage disequilibrium. Additionally, we identified two participants in the 3C-Dijon cohort (0.4%) carrying heterozygote genotypes at known pathogenic variants for familial small vessel disease within NOTCH3 and HTRA1. In conclusion, our proof-of-concept study provides strong evidence that using a novel composite MRI-derived phenotype for extremes of small vessel disease can facilitate the identification of genetic variants underlying small vessel disease, both common variants and those with rare and low frequency. The findings demonstrate shared mechanisms and a continuum between genes underlying Mendelian small vessel disease and those contributing to the common, multifactorial form of the disease.
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http://dx.doi.org/10.1093/brain/awz024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6439324PMC
April 2019

Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing.

Nat Genet 2019 03 28;51(3):414-430. Epub 2019 Feb 28.

Research Center and Memory Clinic of Fundació ACE, Institut Català de Neurociències Aplicades-Universitat Internacional de Catalunya, Barcelona, Spain.

Risk for late-onset Alzheimer's disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer's or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer's disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education.
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http://dx.doi.org/10.1038/s41588-019-0358-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6463297PMC
March 2019

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.

Neurology 2019 Jan 16. Epub 2019 Jan 16.

Objective: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.

Methods: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI.

Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, = 1.77 × 10; and LINC00539/ZDHHC20, = 5.82 × 10. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits ( value for BI, = 9.38 × 10; = 5.23 × 10 for hypertension), smoking ( = 4.4 × 10; = 1.2 × 10), diabetes ( = 1.7 × 10; = 2.8 × 10), previous cardiovascular disease ( = 1.0 × 10; = 2.3 × 10), stroke ( = 3.9 × 10; = 3.2 × 10), and MRI-defined white matter hyperintensity burden ( = 1.43 × 10; = 3.16 × 10), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI ( ≤ 0.0022), without indication of directional pleiotropy.

Conclusion: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.
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http://dx.doi.org/10.1212/WNL.0000000000006851DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369905PMC
January 2019

GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes.

Nat Commun 2018 12 3;9(1):5141. Epub 2018 Dec 3.

Department of Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA.

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.
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http://dx.doi.org/10.1038/s41467-018-07340-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277418PMC
December 2018

Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders.

Am J Hum Genet 2018 11;103(5):691-706

Department of Epidemiology and Prevention, Public Health Sciences, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.

C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.
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http://dx.doi.org/10.1016/j.ajhg.2018.09.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6218410PMC
November 2018

Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume.

Nat Commun 2018 09 26;9(1):3945. Epub 2018 Sep 26.

Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald 17475, Germany.

The volume of the lateral ventricles (LV) increases with age and their abnormal enlargement is a key feature of several neurological and psychiatric diseases. Although lateral ventricular volume is heritable, a comprehensive investigation of its genetic determinants is lacking. In this meta-analysis of genome-wide association studies of 23,533 healthy middle-aged to elderly individuals from 26 population-based cohorts, we identify 7 genetic loci associated with LV volume. These loci map to chromosomes 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, and 22q13.1 and implicate pathways related to tau pathology, S1P signaling, and cytoskeleton organization. We also report a significant genetic overlap between the thalamus and LV volumes (ρ = -0.59, p-value = 3.14 × 10), suggesting that these brain structures may share a common biology. These genetic associations of LV volume provide insights into brain morphology.
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http://dx.doi.org/10.1038/s41467-018-06234-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158214PMC
September 2018

Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits.

Nat Genet 2018 10 17;50(10):1412-1425. Epub 2018 Sep 17.

Laboratory of Genetics and Genomics, NIA/NIH, Baltimore, MD, USA.

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.
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http://dx.doi.org/10.1038/s41588-018-0205-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284793PMC
October 2018

Exome Chip Analysis Identifies Low-Frequency and Rare Variants in MRPL38 for White Matter Hyperintensities on Brain Magnetic Resonance Imaging.

Stroke 2018 08;49(8):1812-1819

Department of Biochemistry (D.W.B., N.D.P.), Wake Forest School of Medicine, Winston-Salem, NC.

Background and Purpose- White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. Methods- In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. Results- At 17q25, we confirmed the association of multiple common variants in TRIM65, FBF1, and ACOX1 ( P<6×10). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; P=4.5×10) partially independent of known common signal ( P=1.4×10). We further identified a locus at 2q33 containing common variants in NBEAL1, CARF, and WDR12 (lead, rs2351524; P=1.9×10). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants ( P=2.8×10). Conclusions- Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH.
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http://dx.doi.org/10.1161/STROKEAHA.118.020689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202149PMC
August 2018

Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function.

Nat Commun 2018 05 29;9(1):2098. Epub 2018 May 29.

Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, 00014, Finland.

General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P < 5 × 10) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.
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http://dx.doi.org/10.1038/s41467-018-04362-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5974083PMC
May 2018

Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity.

Nat Commun 2017 10 13;8(1):910. Epub 2017 Oct 13.

Centre for Epidemiology, Division of Population Health, Health Services Research & Primary Care, The University of Manchester, Manchester, Greater, Manchester, M13 9PL, UK.

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.Variability in human longevity is genetically influenced. Using genetic data of parental lifespan, the authors identify associations at HLA-DQA/DRB1 and LPA and find that genetic variants that increase educational attainment have a positive effect on lifespan whereas increasing BMI negatively affects lifespan.
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http://dx.doi.org/10.1038/s41467-017-00934-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5715013PMC
October 2017

Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney.

Hypertension 2017 Jul 24. Epub 2017 Jul 24.

From the Department of Health Sciences (L.V.W., A.M.E., N. Shrine, C.B., T.B., M.D.T.), and Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre (C.P.N., P.S.B., N.J.S.), University of Leicester, United Kingdom; Department of Epidemiology (A.V., P.J.v.d.M., I.M.N., H. Snieder), Division of Nephrology, Department of Internal Medicine (M.H.d.B., M.A.S.), Interdisciplinary Center Psychopathology and Emotion Regulation (IPCE) (A.J.O., H.R., C.A.H.), Department of Genetics, (M.S.), and Department of Cardiology (P.v.d.H.), University of Groningen, University Medical Center Groningen, The Netherlands; Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Iran (A.V.); Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands (R. Jansen); Hebrew SeniorLife, Harvard Medical School, Boston, MA (R. Joehanes); National Heart, Lung and Blood Institute's Framingham Heart Study, MA (R. Joehanes, A.D.J., M. Larson); Institute of Psychiatry, Psychology and Neuroscience (P.F.O.), and Department of Twin Research and Genetic Epidemiology (M.M., C. Menni, T.D.S.), King's College London, United Kingdom; Clinical Pharmacology, William Harvey Research Institute (C.P.C., H.R.W., M.R.B., M. Brown, B.M., M.R., P.B.M., M.J.C.) and NIHR Barts Cardiovascular Biomedical Research Unit (C.P.C., H.R.W., M.R.B., M. Brown, P.B.M., M.J.C.), Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom; Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA (L.M.R., F.G., P.M.R., D.I.C.); Department of Epidemiology (G.C.V., A. Hofman, A.G.U., O.H.F.), Genetic Epidemiology Unit, Department of Epidemiology (N.A., B.A.O., C.M.v.D.), and Department of Internal Medicine (A.G.U.), Erasmus MC, Rotterdam, The Netherlands; Department of Biological Psychology, Vrije Universiteit, Amsterdam, EMGO+ Institute, VU University Medical Center, The Netherlands (J.-J.H., E.J.d.G., G.W., D.I.B.); Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden (R.J.S., M. Frånberg, A. Hamsten); Centre for Molecular Medicine, Karolinska Universitetsjukhuset, Solna, Sweden (R.J.S., M. Frånberg, A. Hamsten); Estonian Genome Center (T.E., E.O., A. Metspalu), Institute of Biomedicine and Translational Medicine (S.S., M. Laan), and Estonian Genome Center (M.P.), University of Tartu, Estonia; Divisions of Endocrinology/Children's Hospital, Boston, MA (T.E.); Broad Institute of Harvard and MIT, Cambridge, MA (T.E., C.M.L., C.N.-C.); Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (D.E.A., P.N., A. Chakravarti, G.B.E.); The Population Science Branch, Division of Intramural Research, National Heart Lung and Blood Institute (S.-J.H., D.L.), Laboratory of Neurogenetics, National Institute on Aging (M.A.N.), Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute (F.C.), and Center for Information Technology (Y.D., P.J.M., Q.T.N.), National Institutes of Health, Bethesda, MD; The Framingham Heart Study, Framingham, MA (S.-J.H., D.L.); The Institute for Translational Genomics and Population Sciences, Department of Pediatrics (X.G., J.Y.), and The Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine (J.I.R.), LABioMed at Harbor-UCLA Medical Center, Torrance, CA; Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland (Z.K., M. Bochud); Swiss Institute of Bioinformatics, Lausanne, Switzerland (Z.K.); Department of Cardiology (S. Trompet, J.W.J.) Department of Gerontology and Geriatrics (S. Trompet), Department of Clinical Epidemiology (R.L.-G., R.d.M., D.O.M.-K.), Department of Molecular Epidemiology (J.D.), and Department of Public Health and Primary Care (D.O.M.-K.), Leiden University Medical Center, The Netherlands; Institute for Community Medicine (A.T.), Department of Internal Medicine B (M.D.), and Interfaculty Institute for Genetics and Functional Genomics (U.V.), University Medicine Greifswald, Germany; DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Germany (A.T., M.D., U.V.); Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany (J.S.R., A. Peters); Cardiovascular Health Research Unit, Department of Medicine (J.C.B., B.M.P.) and Departments of Biostatistics (K.R.), Epidemiology (B.M.P.), and Health Services (B.M.P.), University of Washington, Seattle; Icelandic Heart Association, Kopavogur, Iceland (A.V.S., V. Gudnason); Faculty of Medicine, University of Iceland, Reykjavik, Iceland (A.V.S., V. Gudnason); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (L.-P.L., T.L.); Department of Clinical Chemistry, Faculty of Medicine and Life Sciences, University of Tampere, Finland (L.-P.L., T.L.); Wellcome Trust Centre for Human Genetics (A. Mahajan, A.G., M. Farrall, T.F., C.M.L., H.W., A.P.M.), and Division of Cardiovascular Medicine, Radcliffe Department of Medicine (A.G., M. Farrall, H.W.), University of Oxford, United Kingdom; MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, United Kingdom (N.J.W., J.L., C.L., R.J.F.L., R.A.S., J.H.Z.); Clinical Division of Neurogeriatrics, Department of Neurology (E.H., R. Schmidt), Institute of Medical Informatics, Statistics and Documentation (E.H.), and Department of Neurology (H. Schmidt), Medical University Graz, Austria; Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics (P.K.J., H.C., I.R., S.W., J.F.W.), Centre for Cognitive Ageing and Cognitive Epidemiology (L.M.L., S.E.H., G.D., A.J.G., D.C.M.L., J.M.S., I.J.D.), Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine (A. Campbell), Generation Scotland, Centre for Genomic and Experimental Medicine (A. Campbell, S.P., C.H.), Department of Psychology (G.D., D.C.M.L., A. Pattie, I.J.D.), Alzheimer Scotland Dementia Research Centre (J.M.S.), and Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine (C.H.), University of Edinburgh, Scotland, United Kingdom; Department of Health (K.K., A.S.H., T. Niiranen, P.J., A.J., S. Koskinen, P.K., V.S., M.P.), and Chronic Disease Prevention Unit (J.T.), National Institute for Health and Welfare (THL), Helsinki, Finland; Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milano, Italy (M.T., C.M.B., C.F.S., D.T.); Data Tecnica International, Glen Echo, MD (M.A.N.); Medical Genetics, IRCCS-Burlo Garofolo Children Hospital, Trieste, Italy (D.V., G.G., P.G.); Department of Medical, Surgical and Health Sciences, University of Trieste, Italy (D.V., I.G., M. Brumat, M. Cocca, A. Morgan, G.G., P.G.); Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy (F.D.G.M., P.P.P., A.S.P., A.A.H.); Department of Genetics and Genomic Sciences (K.L.A.), The Charles Bronfman Institute for Personalized Medicine (Y.L., E.P.B., R.J.F.L.), and Mindich Child health Development Institute (R.J.F.L.), Icahn School of Medicine at Mount Sinai, New York; Cardiovascular Epidemiology and Genetics, IMIM, and CIBERCV, Barcelona, Spain (J. Marrugat, R.E.); Institute of Genetics and Biophysics A. Buzzati-Traverso, CNR, Napoli, Italy (D.R., T. Nutile, R. Sorice, M. Ciullo); Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin (L.M.L.); UCD Conway Institute, Centre for Proteome Research (L.M.L.), and School of Medicine, Conway Institute (D.C.S.), University College Dublin, Belfield, Ireland; Department of Immunology, Genetics and Pathology, Uppsala Universitet, Science for Life Laboratory, Sweden (S.E., Å. Johansson, U.G.); Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor (A.U.J., M. Boehnke); NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester United Kingdom (C.P.N., P.S.B., N.J.S.); MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine (J.E.H., V.V., J. Marten, A.F.W., J.F.W.), and Medical Genetics Section, Centre for Genomic and Experimental Medicine and MRC Institute of Genetics and Molecular Medicine (S.E.H.), University of Edinburgh, Western General Hospital, Scotland, United Kingdom; Department of Epidemiology and Biostatistics, School of Public Health (W.Z., E.E., J.C.C., H.G., B.L., I.T., A.-C.V.), MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health (M.-R.J., P.E.), School of Public Health (N.P.), International Centre for Circulatory Health (S. Thom), and National Heart and Lung Institute (P.S.), Imperial College London, United Kingdom; Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Southall, United Kingdom (W.Z., J.C.C., J.S.K.); Department of Medical Biology, Faculty of Medicine, University of Split, Croatia (T.Z.); Department of Hygiene and Epidemiology, University of Ioannina Medical School, Greece (E.E.); Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Scotland, United Kingdom (N. Shah, A.S.F.D., C.N.A.P.); Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, Pakistan (N. Shah); National Institute for Health Research Biomedical Research Centre, London, United Kingdom (M.M.); Department of Human Genetics, Wellcome Trust Sanger Institute, United Kingdom (B.P.P., E.Z.); INSERM U 1219, Bordeaux Population Health Center, France (G.C., C.T., S.D.); Bordeaux University, France (G.C., C.T., S.D.); Hunter Medical Research Institute, New Lambton, NSW, Australia (C.O., E.G.H., R. Scott, J.A.); Center for Statistical Genetics, Department of Biostatistics, Ann Arbor, MI (G.A.); Department of Genetics and Molecular Biology, Isfahan University of Medical Sciences, Iran (M.A.); Busselton Population Medical Research Institute, Western Australia (J.B., J.H.); PathWest Laboratory Medicine of Western Australia, Nedlands (J.B., J.H.); School of Pathology and Laboratory Medicine (J.B., J.H.), School of Population and Global Health (J.H.), and School of Medicine and Pharmacology (A. James), The University of Western Australia, Nedlands; Imperial College Healthcare NHS Trust, London, United Kingdom (J.C.C., J.S.K.); University of Dundee, Ninewells Hospital & Medical School, United Kingdom (J.C.); Institute of Genetic Medicine (H.J.C.), and Institute of Health and Society (C. Mamasoula), Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Pathology, Amsterdam Medical Center, The Netherlands (J.J.D.); Department of Numerical Analysis and Computer Science, Stockholm University, Sweden (M. Frånberg); Department of Public Health and Caring Sciences, Geriatrics, Uppsala, Sweden (V. Giedraitis); Helmholtz Zentrum Muenchen, Deutsches Forschungszentrum fuer Gesundheit und Umwelt (GmbH), Neuherberg, Germany (C.G.); Department of Psychology, School of Social Sciences, Heriot-Watt University, Edinburgh, United Kingdom (A.J.G.); Intramural Research Program, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging (T.B.H., L.J.L.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (A. Hofman); Center For Life-Course Health Research (M.-R.J.), and Biocenter Oulu (M.-R.J.), University of Oulu, Finland; Unit of Primary Care, Oulu University Hospital, Finland (M.-R.J.); National Heart, Lung and Blood Institute, Cardiovascular Epidemiology and Human Genomics Branch, Bethesda, MD (A.D.J.); Department of Clinical Physiology, Tampere University Hospital, Finland (M.K.); Department of Clinical Physiology, Faculty of Medicine and Life Sciences, University of Tampere, Finland (M.K.); Cardiovascular Research Center (S. Kathiresan, C.N.-C.); Center for Human Genetics (S. Kathiresan), and Center for Human Genetic Research (C.N.-C.), Massachusetts General Hospital, Boston; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA (S. Kathiresan, C.N.-C.); Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, United Kingdom (K.-T.K.); Department of Public Health, Faculty of Medicine, University of Split, Croatia (I.K., O.P.); Cardiology, Department of Specialties of Medicine, Geneva University Hospital, Switzerland (L. Lin, F.M., G.B.E.); Department of Medical Sciences, Cardiovascular Epidemiology (L. Lind, J.S.), and Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory (E.I.), Uppsala University, Sweden; Department of Psychiatry, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands (Y.M., B.W.J.H.P.); School of Molecular, Genetic and Population Health Sciences, University of Edinburgh, Medical School, Teviot Place, Scotland, United Kingdom (A.D.M.); Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston (A.C.M.); British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences (S.P.), and Institute of Cardiovascular and Medical Sciences, Faculty of Medicine (D.J.S.), University of Glasgow, United Kingdom; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland (A. Palotie, S.R., A.-P.S., M.P.); Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada (G.P., S. Thériault); Department of Neurology, General Central Hospital, Bolzano, Italy (P.P.P.); Department of Neurology, University of Lübeck, Germany (P.P.P.); Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Finland (O.T.R.); Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Finland (O.T.R.); Department of Cardiology, Fujian Provincial Hospital, Fujian Medical University, Fuzhou, China (M.R.); Harvard Medical School, Boston, MA (P.M.R., D.I.C.); Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste, Italy (A.R.); Institute of Molecular Biology and Biochemistry, Centre for Molecular Medicine, Medical University of Graz, Austria (Y.S., H. Schmidt); INSERM U1078, Etablissement Français du Sang, Brest Cedex, France (A.S.P.); Faculty of Health, University of Newcastle, Callaghan, NSW, Australia (R. Scott, J.A.); John Hunter Hospital, New Lambton, NSW, Australia (R. Scott, J.A.); The New York Academy of Medicine, New York (D.S.); IRCCS Neuromed, Pozzilli, Isernia, Italy (R. Sorice, M. Ciullo); Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin 2, Ireland (A.S.); Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA (K.D.T.); Division of Genetic Outcomes, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA (K.D.T.); Department of Public Health (C.T.), and Department of Neurology (S.D.), Bordeaux University Hospital, France; Department of Internal Medicine, Lausanne University Hospital, CHUV, Switzerland (P.V.); Population Health Research Institute, McMaster University, Hamilton Ontario, Canada (D.C.); National Heart and Lung Institute, Imperial College London, Hammersmith Hospital Campus, United Kingdom (J.S.K.); Dasman Diabetes Institute, Kuwait (J.T.); Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia (J.T.); Department of Neurosciences and Preventive Medicine, Danube-University Krems, Austria (J.T.); Division of Cardiovascular Sciences, The University of Manchester and Central Manchester University Hospitals NHS Foundation Trust, United Kingdom (B.D.K.); Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem (Y.M.L.); Kaiser Permanent Washington Health Research Institute, Seattle, WA (B.M.P.); Institute of Physiology, University Medicine Greifswald, Karlsburg, Germany (R.R); Department of Pulmonary Physiology and Sleep, Sir Charles Gairdner Hospital, Nedlands, Western Australia (A. James); Population Health Research Institute, St George's, University of London, United Kingdom (D.P.S.); Department of Medicine, Columbia University Medical Center, New York (W.P.); Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, CA (E.I.); Data Science Institute and Lancaster Medical School, Lancaster University, United Kingdom (J.K.); and Department of Biostatistics, University of Liverpool, United Kingdom (A.P.M.).

Elevated blood pressure is a major risk factor for cardiovascular disease and has a substantial genetic contribution. Genetic variation influencing blood pressure has the potential to identify new pharmacological targets for the treatment of hypertension. To discover additional novel blood pressure loci, we used 1000 Genomes Project-based imputation in 150 134 European ancestry individuals and sought significant evidence for independent replication in a further 228 245 individuals. We report 6 new signals of association in or near , , , , , and , and provide new replication evidence for a further 2 signals in and Combining large whole-blood gene expression resources totaling 12 607 individuals, we investigated all novel and previously reported signals and identified 48 genes with evidence for involvement in blood pressure regulation that are significant in multiple resources. Three novel kidney-specific signals were also detected. These robustly implicated genes may provide new leads for therapeutic innovation.
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http://dx.doi.org/10.1161/HYPERTENSIONAHA.117.09438DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5783787PMC
July 2017

1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.

Sci Rep 2017 04 28;7:45040. Epub 2017 Apr 28.

Department of Nephrology, University Hospital Regensburg, Regensburg, Germany.

HapMap imputed genome-wide association studies (GWAS) have revealed >50 loci at which common variants with minor allele frequency >5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 × 10 previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until whole-genome sequencing becomes feasible in large samples.
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http://dx.doi.org/10.1038/srep45040DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408227PMC
April 2017

Gray matter heritability in family-based and population-based studies using voxel-based morphometry.

Hum Brain Mapp 2017 05 1;38(5):2408-2423. Epub 2017 Feb 1.

Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.

Background: The combination of genetics and imaging has improved their understanding of the brain through studies of aggregate measures obtained from high-resolution structural imaging. Voxel-wise analyses have the potential to provide more detailed information of genetic influences on the brain. Here they report a large-scale study of the heritability of gray matter at voxel resolution (1 × 1 × 1 mm).

Methods: Validated voxel-based morphometry (VBM) protocols were applied to process magnetic resonance imaging data of 3,239 unrelated subjects from a population-based study and 491 subjects from two family-based studies. Genome-wide genetic data was used to estimate voxel-wise gray matter heritability of the unrelated subjects and pedigree-structure was used to estimate heritability in families. They subsequently associated two genetic variants, known to be linked with subcortical brain volume, with most heritable voxels to determine if this would enhance their association signals.

Results: Voxels significantly heritable in both estimates mapped to subcortical structures, but also voxels in the language areas of the left hemisphere were found significantly heritable. When comparing regional patterns of heritability, family-based estimates were higher than population-based estimates. However, regional consistency of the heritability measures across study designs was high (Pearson's correlation coefficient = 0.73, P = 2.6 × 10 ). They further show enhancement of the association signal of two previously discovered genetic loci with subcortical volume by using only the most heritable voxels.

Conclusion: Gray matter voxel-wise heritability can be reliably estimated with different methods. Combining heritability estimates from multiple studies is feasible to construct reliable heritability maps of gray matter voxels. Hum Brain Mapp 38:2408-2423, 2017. © 2017 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/hbm.23528DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6867052PMC
May 2017

Genome-wide Trans-ethnic Meta-analysis Identifies Seven Genetic Loci Influencing Erythrocyte Traits and a Role for RBPMS in Erythropoiesis.

Am J Hum Genet 2017 Jan 22;100(1):51-63. Epub 2016 Dec 22.

Icahn Institute for Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Genome-wide association studies (GWASs) have identified loci for erythrocyte traits in primarily European ancestry populations. We conducted GWAS meta-analyses of six erythrocyte traits in 71,638 individuals from European, East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in allelic effects and variation in the structure of linkage disequilibrium between ethnicities. We identified seven loci for erythrocyte traits including a locus (RBPMS/GTF2E2) associated with mean corpuscular hemoglobin and mean corpuscular volume. Statistical fine-mapping at this locus pointed to RBPMS at this locus and excluded nearby GTF2E2. Using zebrafish morpholino to evaluate loss of function, we observed a strong in vivo erythropoietic effect for RBPMS but not for GTF2E2, supporting the statistical fine-mapping at this locus and demonstrating that RBPMS is a regulator of erythropoiesis. Our findings show the utility of trans-ethnic GWASs for discovery and characterization of genetic loci influencing hematologic traits.
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http://dx.doi.org/10.1016/j.ajhg.2016.11.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5223059PMC
January 2017

Genome-wide analysis identifies 12 loci influencing human reproductive behavior.

Nat Genet 2016 12 31;48(12):1462-1472. Epub 2016 Oct 31.

Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands.

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits.
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http://dx.doi.org/10.1038/ng.3698DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5695684PMC
December 2016

Developmental dysplasia of the hip: impact of sonographic newborn hip screening on the outcome of early treated decentered hip joints-a single center retrospective comparative cohort study based on Graf's method of hip ultrasonography.

J Child Orthop 2011 Dec 18;5(6):415-24. Epub 2011 Sep 18.

General and Orthopaedic Hospital Stolzalpe, 8852 Stolzalpe, Austria.

Unlabelled: PURPOSE/BACKGROUND/INTRODUCTION: The aim of this study was to retrospectively evaluate the impact of neonatal sonographic hip screening using Graf's method for the management and outcome of orthopaedic treatment of decentered hip joints with developmental dysplasia of the hip (DDH), using three decades (1978-2007) of clinical information compiled in a medical database.

Methods: Three representative cohorts of consecutive cases of decentered hip joints were selected according to different search criteria and inclusion and exclusion parameters: (1) cohort 1 (1978-1982; n = 80), without sonographic screening; (2) cohort 2.1 (1994-1996; n = 91), with nationwide established general sonographic screening according to the Graf-method; (3) cohort 2.2 (2003-2005; n = 91), with sonographic screening including referred cases for open reduction from non-screened populations. These three cohorts were compared for the following parameters: age at initial treatment, successful closed reduction, necessary overhead traction, necessary adductor-tenotomy, rate of open reduction, rate of avascular necrosis (AVN) and rate of secondary acetabuloplasty.

Results: The age at initial treatment was reduced from 5.5 months in the first cohort to 2 months in the two subsequent two cohorts and the rate of successful closed reduction increased from 88.7 to 98.9 and 95.6%, respectively. There was a statistically significant improvement in six out of seven parameters with sonographic hip screening; only the rate of secondary acetabuloplasty did not improve significantly.

Conclusion: Compared to the era before the institution of a sonographic hip screening programme according to the Graf-method in Austria in 1992, ultrasound screening based-treatment of decentered hip joints has become safer, shorter and simpler: "safer" means lower rate of AVN, "shorter" means less treatment time due to earlier onset and "simpler" means that the devices are now less invasive and highly standardized.
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http://dx.doi.org/10.1007/s11832-011-0366-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3221760PMC
December 2011
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