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1. Genome-wide analysis of mitochondrial DNA copy number reveals loci implicated in nucleotide metabolism, platelet activation, and megakaryocyte proliferation

Author

Longchamps, R. J., Yang, S. Y., Castellani, C. A., Shi, W., Lane, J., Grove, M. L., Bartz, T. M., Sarnowski, C., Liu, C., Burrows, K., Guyatt, A. L., Gaunt, T. R., Kacprowski, T., Yang, J., De Jager, P. L., Yu, L., Bergman, A., Xia, R., Fornage, M., Feitosa, M. F., Wojczynski, M. K., Kraja, A. T., Province, M. A., Amin, N., Rivadeneira, F., Tiemeier, H., Uitterlinden, A. G., Broer, L., Van Meurs, J. B. J., Van Duijn, C. M., Raffield, L. M., Lange, L., Rich, S. S., Lemaitre, R. N., Goodarzi, M. O., Sitlani, C. M., Mak, A. C. Y., Bennett, D. A., Rodriguez, S., Murabito, J. M., Lunetta, K. L., Sotoodehnia, N., Atzmon, G., Ye, K., Barzilai, N., Brody, J. A., Psaty, B. M., Taylor, K. D., Rotter, J. I., Boerwinkle, E., Pankratz, N., and Arking, D. E.

Published
2022
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2. Prediction of venous thromboembolism incidence in the general adult population using two published genetic risk scores

Author

Hong, C.-P., Pankratz, N., Cushman, M., Rosamond, W.D., Lane, J.A., Folsom, A.R., and Tang, W.

Abstract

Introduction Most strategies for prevention of venous thromboembolism focus on preventing recurrent events. Yet, primary prevention might be possible through approaches targeting the whole population or high-risk patients. To inform possible prevention strategies, population-based information on the ability of genetic risk scores to identify risk of incident venous thromboembolism is needed. Materials and methods We used proportional hazards regression to relate two published genetic risk scores (273-variants versus 5-variants) with venous thromboembolism incidence in the Atherosclerosis Risk in Communities Study (ARIC) cohort (n = 11,292), aged 45–64 at baseline, drawn from 4 US communities. Results Over a median of 28 years, ARIC identified 788 incident venous thromboembolism events. Incidence rates rose more than two-fold across quartiles of the 273-variant genetic risk score: 1.7, 2.7, 3.4 and 4.0 per 1,000 person-years. For White participants, age, sex, and ancestry-adjusted hazard ratios (95% confidence intervals) across quartiles were strong [1 (reference), 1.30 (0.99,1.70), 1.85 (1.43,2.40), and 2.58 (2.04,3.28)] but weaker for Black participants [1, 1.05 (0.63,1.75), 1.37 (0.84,2.22), and 1.32 (0.80,2.20)]. The 5-variant genetic risk score showed a less steep gradient, with hazard ratios in Whites of 1, 1.17 (0.89,1.54), 1.48 (1.14,1.92), and 2.18 (1.71,2.79). Models including the 273-variant genetic risk score plus lifestyle and clinical factors had a c-statistic of 0.67. Conclusions In the general population, middle-aged adults in the highest quartile of either genetic risk score studied have approximately two-fold higher risk of an incident venous thromboembolism compared with the lowest quartile. The genetic risk scores show a weaker association with venous thromboembolism for Black people.

Published
2023
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3. Generalization and fine mapping of European ancestry-based central adiposity variants in African ancestry populations

Author

Yoneyama, S, Yao, J, Guo, X, Fernandez-Rhodes, L, Lim, U, Boston, J, Buzková, P, Carlson, C S, Cheng, I, Cochran, B, Cooper, R, Ehret, G, Fornage, M, Gong, J, Gross, M, Gu, C C, Haessler, J, Haiman, C A, Henderson, B, Hindorff, L A, Houston, D, Irvin, M R, Jackson, R, Kuller, L, Leppert, M, Lewis, C E, Li, R, Le Marchand, L, Matise, T C, Nguyen, K-DH, Chakravarti, A, Pankow, J S, Pankratz, N, Pooler, L, Ritchie, M D, Bien, S A, Wassel, C L, Chen, Y-DI, Taylor, K D, Allison, M, Rotter, J I, Schreiner, P J, Schumacher, F, Wilkens, L, Boerwinkle, E, Kooperberg, C, Peters, U, Buyske, S, Graff, M, and North, K E

Published
2017
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4. Cross-ancestry investigation of venousc genomic predictors

Author

Thibord, F., Klarin, D., Brody, J.A., Chen, M.H., Levin, M.G., Chasman, D.I., Goode, E.L., Hveem, K., Teder-Laving, M., Martinez-Perez, A., Aissi, D., Daian-Bacq, D., Ito, K., Natarajan, P., Lutsey, P.L., Nadkarni, G.N., Vries, P.S. de, Cuellar-Partida, G., Wolford, B.N., Pattee, J.W., Kooperberg, C., Braekkan, S.K., Li-Gao, R.F., Saut, N., Sept, C., Germain, M., Judy, R.L., Wiggins, K.L., Ko, D., O'Donnell, C.J., Taylor, K.D., Giulianini, F., Andrade, M. de, Nost, T.H., Boland, A., Empana, J.P., Koyama, S., Gilliland, T., R. do, Huffman, J.E., Wang, X., Zhou, W., Soria, J.M., Souto, J.C., Pankratz, N., Haessler, J., Hindberg, K., Rosendaal, F.R., Turman, C., Olaso, R., Kember, R.L., Bartz, T.M., Lynch, J.A., Heckbert, S.R., Armasu, S.M., Brumpton, B., Smadja, D.M., Jouven, X., Komuro, I., Clapham, K.R., Loos, R.J.F., Willer, C.J., Sabater-Lleal, M., Pankow, J.S., Reiner, A.P., Morelli, V.M., Ridker, P.M., Vlieg, A.V., Deleuze, J.F., Kraft, P., Rader, D.J., Lee, K.M., Psaty, B.M., Skogholt, A.H., Emmerich, J., Suchon, P., Rich, S.S., Vy, H.T., Tang, W.H., Jackson, R.D., Hansen, J.B., Morange, P.E., Kabrhel, C., Tregouet, D.A., Damrauer, S.M., Johnson, A.D., and Smith, N.L.

Subjects
meta-analysis, genome-wide association study, venous thromboembolism, genetics, venous thrombosis
Abstract

Background: Venous thromboembolism (VTE) is a life-threatening vascular event with environmental and genetic determinants. Recent VTE genome-wide association studies (GWAS) meta-analyses involved nearly 30 000 VTE cases and identified up to 40 genetic loci associated with VTE risk, including loci not previously suspected to play a role in hemostasis. The aim of our research was to expand discovery of new genetic loci associated with VTE by using cross-ancestry genomic resources. Methods: We present new cross-ancestry meta-analyzed GWAS results involving up to 81 669 VTE cases from 30 studies, with replication of novel loci in independent populations and loci characterization through in silico genomic interrogations. Results: In our genetic discovery effort that included 55 330 participants with VTE (47 822 European, 6320 African, and 1188 Hispanic ancestry), we identified 48 novel associations, of which 34 were replicated after correction for multiple testing. In our combined discovery-replication analysis (81 669 VTE participants) and ancestry-stratified meta-analyses (European, African, and Hispanic), we identified another 44 novel associations, which are new candidate VTE-associated loci requiring replication. In total, across all GWAS meta-analyses, we identified 135 independent genomic loci significantly associated with VTE risk. A genetic risk score of the significantly associated loci in Europeans identified a 6-fold increase in risk for those in the top 1% of scores compared with those with average scores. We also identified 31 novel transcript associations in transcriptome-wide association studies and 8 novel candidate genes with protein quantitative-trait locus Mendelian randomization analyses. In silico interrogations of hemostasis and hematology traits and a large phenome-wide association analysis of the 135 GWAS loci provided insights to biological pathways contributing to VTE, with some loci contributing to VTE through well-characterized coagulation pathways and others providing new data on the role of hematology traits, particularly platelet function. Many of the replicated loci are outside of known or currently hypothesized pathways to thrombosis. Conclusions: Our cross-ancestry GWAS meta-analyses identified new loci associated with VTE. These findings highlight new pathways to thrombosis and provide novel molecules that may be useful in the development of improved antithrombosis treatments.

Published
2022

5. Genomic and transcriptomic association studies identify 16 novel susceptibility loci for venous thromboembolism

Author

Lindstrom, S., Wang, L., Smith, E.N., Gordon, W., Vlieg, A.V., Andrade, M. de, Brody, J.A., Pattee, J.W., Haessler, J., Brumpton, B., Chasman, D.I., Suchon, P., Chen, M.H., Turman, C., Germain, M., Wiggins, K.L., MacDonald, J., Braekkan, S.K., Armasu, S.M., Pankratz, N., Jackson, R.D., Nielsen, J.B., Giulianini, F., Puurunen, M.K., Ibrahim, M., Heckbert, S.R., Damrauer, S.M., Natarajan, P., Klarin, D., Vries, P.S. de, Sabater-Lleal, M., Huffman, J.E., Bammler, T.K., Frazer, K.A., McCauley, B.M., Taylor, K., Pankow, J.S., Reiner, A.P., Gabrielsen, M.E., Deleuze, J.F., O'Donnell, C.J., Kim, J., McKnight, B., Kraft, P., Hansen, J.B., Rosendaal, F.R., Heit, J.A., Psaty, B.M., Tang, W.H., Kooperberg, C., Hveem, K., Ridker, P.M., Morange, P.E., Johnson, A.D., Kabrhel, C., Tregouet, D.A., Smith, N.L., Busenkell, E., Judy, R., Lynch, J., Levin, M., Aragam, J.H.K., Chaffin, M., Haas, M., Assimes, T.L., Huang, J., Lee, K.M., Shao, Q., Huang, Y.F., Sun, Y.V., Vujkovic, M., Saleheen, D., Miller, D.R., Reaven, P., DuVall, S., Boden, W., Pyarajan, S., Henke, P., Gaziano, J.M., Concato, J., Rader, D.J., Cho, K., Chang, K.M., Wilson, P.W.F., Tsao, P.S., Kathiresan, S., Obi, A., Million Veteran Program, CHARGE Hemostasis Working Grp, INVENT Consortium, Program in Genetic Epidemiology and Statistical Genetics (PGESG - BOSTON), Harvard School of Public Health, University of Washington [Seattle], The Scripps Translational Science Institute and Scripps Health, Department of Thrombosis and Haemostasis, Leiden University Medical Center (LUMC), Department of Health Sciences Research [Mayo Clinic] (HSR), Mayo Clinic, University of Minnesota System, Fred Hutchinson Cancer Research Center [Seattle] (FHCRC), Brigham and Women's Hospital [Boston], Centre recherche en CardioVasculaire et Nutrition = Center for CardioVascular and Nutrition research (C2VN), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'hématologie biologique [Hôpital de la Timone - Hôpital Nord - APHM], Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE), National Institutes of Health [Bethesda] (NIH), Harvard T.H. Chan School of Public Health, Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Institute of cardiometabolism and nutrition (ICAN), Université Pierre et Marie Curie - Paris 6 (UPMC)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Biorobotics Lab (University of Washington), University of South-Eastern Norway (USN), Department of Health Sciences Research, Ohio State University [Columbus] (OSU), Nutrition, obésité et risque thrombotique (NORT), Aix Marseille Université (AMU)-Institut National de la Recherche Agronomique (INRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Los Angeles Biomedical Research Institute (LA BioMed), University of Augsburg [Augsburg], Centre National de Génotypage (CNG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Department of Epidemiology, Division of Cardiovascular Diseases, Mayo Clinic College of Medicine, University of Minnesota [Twin Cities] (UMN), Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Department of Emergency Medicine, Massachusetts General Hospital [Boston], Universiteit Leiden-Universiteit Leiden, Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Institut National de la Recherche Agronomique (INRA)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Augsburg (UNIA), and Universiteit Leiden

Subjects
0301 basic medicine, Genetics, [SDV]Life Sciences [q-bio], Immunology, Genome-wide association study, Cell Biology, Hematology, 030204 cardiovascular system & hematology, Biology, equipment and supplies, Biochemistry, 3. Good health, Transcriptome, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Mendelian randomization, Expression quantitative trait loci, Gene expression, cardiovascular diseases, Gene, Genetic association, Whole blood
Abstract

Venous thromboembolism (VTE) is a significant contributor to morbidity and mortality. To advance our understanding of the biology contributing to VTE, we conducted a genome-wide association study (GWAS) of VTE and a transcriptome-wide association study (TWAS) based on imputed gene expression from whole blood and liver. We meta-analyzed GWAS data from 18 studies for 30 234 VTE cases and 172 122 controls and assessed the association between 12 923 718 genetic variants and VTE. We generated variant prediction scores of gene expression from whole blood and liver tissue and assessed them for association with VTE. Mendelian randomization analyses were conducted for traits genetically associated with novel VTE loci. We identified 34 independent genetic signals for VTE risk from GWAS meta-analysis, of which 14 are newly reported associations. This included 11 newly associated genetic loci (C1orf198, PLEK, OSMR-AS1, NUGGC/SCARA5, GRK5, MPHOSPH9, ARID4A, PLCG2, SMG6, EIF5A, and STX10) of which 6 replicated, and 3 new independent signals in 3 known genes. Further, TWAS identified 5 additional genetic loci with imputed gene expression levels differing between cases and controls in whole blood (SH2B3, SPSB1, RP11-747H7.3, RP4-737E23.2) and in liver (ERAP1). At some GWAS loci, we found suggestive evidence that the VTE association signal for novel and previously known regions colocalized with expression quantitative trait locus signals. Mendelian randomization analyses suggested that blood traits may contribute to the underlying risk of VTE. To conclude, we identified 16 novel susceptibility loci for VTE; for some loci, the association signals are likely mediated through gene expression of nearby genes.

Published
2019

6. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations

Author

Cai, J., NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Qian, H., Montgomery, C., Kelly, T.N., Cho, M.H., Weiss, S.T., Loos, R.J.F., Johnson, A.D., de Vries, P.S., Li, Y., Becker, L.C., Peralta, J.M., Wiggins, K.L., Bowden, D.W., Lasky-Su, J.A., Buyske, S., Shan, Y., Moon, J.-Y., Jorgenson, E., Cushman, M., Tiwari, H.K., Kooperberg, C., Faraday, N., Tapia, A.L., TOPMed Hematology & Hemostasis Working Group, Thornton, T.A., Choquet, H., Barnes, K.C., Bis, J.C., Hodonsky, C.J., Mathias, R.A., Wang, T., Taylor, K.D., He, J., Kaplan, R., Gupta, N., Lubitz, S.A., Smith, N.L., Daya, M., Rich, S.S., Peyser, P.A., Palmer, N.D., Silverman, E.K., Arnett, D.K., Choi, S.H., Cupples, L. A., Reiner, A.P., Argos, M., Boerwinkle, E., Hou, Z., Auer, P.L., Bien, S.A., Hidalgo, B., Ellinor, P.T., Heckbert, S.R., Gabriel, S., Tracy, R.P., Avery, C., Yanek, L.R., Raffield, L.M., Papanicolaou, G.J., Fornage, M., Z��llner, S., Graff, M., Wilson, J.G., Smith, J.A., Weng, L.-C., Morrison, A.C., Rosen, J.D., Irvin, M.R., North, K.E., Kardia, S.L.R., Pankratz, N., Rotter, J.I., Blangero, J., McHugh, C.P., Jain, D., Kowalski, M.H., and Ganesh, S.K.

Abstract

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count 86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.

Published
2019
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7. A genome-wide association study identifies new loci for factor VII and implicates factor VII in ischemic stroke etiology

Author

DE VRIES, P. S., SABATER-LLEAL, M., HUFFMAN, J. E., MARTEN, J., Song, C., PANKRATZ, N., BARTZ, T. M., DE HAAN, H. G., DELGADO, G. E., EICHER, J. D., MARTINEZ-PEREZ, A., WARD-CAVINESS, C. K., BRODY, J. A., CHEN, M. H., DE MAAT, M. P. M., Franberg, M., Gill, D., KLEBER, M. E., Rivadeneira, F., SORIA, J. M., Tang, W., TOFLER, G. H., UITTERLINDEN, A. G., van Hylckama Vlieg, A., SESHADRI, S., BOERWINKLE, E., DAVIES, N. M., GIESE, A. K., IKRAM, M. K., KITTNER, S. J., MCKNIGHT, B., PSATY, B. M., REINER, A. P., Sargurupremraj, Muralidharan, TAYLOR, K. D., CONSORTIUM, Invent, FORNAGE, M., HAMSTEN, A., MARZ, W., ROSENDAAL, F. R., SOUTO, J. C., DEHGHAN, A., JOHNSON, A. D., MORRISON, A. C., O'DONNELL, C. J., SMITH, N. L., Bordeaux population health (BPH), and Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
VINTAGE, hemic and lymphatic diseases, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, cardiovascular diseases
Abstract

Factor VII (FVII) is an important component of the coagulation cascade. Few genetic loci regulating FVII activity and/or levels have been discovered to date. We conducted a meta-analysis of nine genome-wide association studies of plasma FVII levels (seven FVII activity and two FVII antigen) among 27,495 participants of European and African ancestry. Each study performed ancestry-specific association analyses. Inverse variance weighted meta-analysis was performed within each ancestry group and then combined for a trans-ancestry meta-analysis. Our primary analysis included the seven studies that measured FVII activity, and a secondary analysis included all nine studies. We provided functional genomic validation for newly identified significant loci by silencing candidate genes in a human liver cell line (HuH7) using siRNA and then measuring F7 mRNA and FVII protein expression. Lastly, we used meta-analysis results to perform Mendelian randomization analysis to estimate the causal effect of FVII activity on coronary artery disease, ischemic stroke, and venous thromboembolism. We identified two novel (REEP3 and JAZF1-AS1) and six known loci associated with FVII activity, explaining 19.0% of the phenotypic variance. Adding FVII antigen data to the meta-analysis did not result in the discovery of further loci. Silencing REEP3 in HuH7 cells upregulated FVII, while silencing JAZF1 downregulated FVII. Mendelian randomization analyses suggest that FVII activity has a positive causal effect on the risk of ischemic stroke. Variants at REEP3 and JAZF1 contribute to FVII activity by regulating F7 expression levels. FVII activity appears to contribute to the etiology of ischemic stroke in the general population.

Published
2019

10. Erratum: Meta-analysis of dense genecentric association studies reveals common and uncommon variants associated with height ((The American Journal of Human Genetics (2010) 88 (6-18))

Author

Lanktree, M, Guo, Y, Murtaza, M, Glessner, J, Bailey, S, Onland-Moret, N, Lettre, G, Ongen, H, Rajagopalan, R, Johnson, T, Shen, H, Nelson, C, Klopp, N, Baumert, J, Padmanabhan, S, Pankratz, N, Pankow, J, Shah, S, Taylor, K, Barnard, J, Peters, B, Maloney, C, Lobmeyer, M, Stanton, A, and Zafarmand, M

Published
2016

11. Rare and low-frequency variants and their association with plasma levels of fibrinogen, FVII, FVIII, and vWF

Author

Huffman, JE, De Vries, PS, Morrison, AC, Sabater-Lleal, M, Kacprowski, T, Auer, PL, Brody, JA, Chasman, DI, Chen, MH, Guo, X, Lin, LA, Marioni, RE, Müller-Nurasyid, M, Yanek, LR, Pankratz, N, Grove, ML, De Maat, MPM, Cushman, M, Wiggins, KL, Qi, L, Sennblad, B, Harris, SE, Polasek, O, Riess, H, Rivadeneira, F, Rose, LM, Goel, A, Taylor, KD, Teumer, A, Uitterlinden, AG, Vaidya, D, Yao, J, Tang, W, Levy, D, Waldenberger, M, Becker, DM, Folsom, AR, Giulianini, F, Greinacher, A, Hofman, A, Huang, CC, Kooperberg, C, Silveira, A, Starr, JM, Strauch, K, Strawbridge, RJ, Wright, AF, McKnight, B, Franco, OH, Zakai, N, Mathias, RA, Psaty, BM, Ridker, PM, Tofler, GH, Völker, U, Watkins, H, Fornage, M, Hamsten, A, Deary, IJ, Boerwinkle, E, Koenig, W, Rotter, JI, Hayward, C, Dehghan, A, Reiner, AP, and O'Donnell, CJ

Abstract

© 2015, American Society of Hematology. All rights reserved. Fibrinogen, coagulation factor VII (FVII), and factor VIII (FVIII) and its carrier von Willebrand factor (vWF) play key roles in hemostasis. Previously identified common variants explain only a small fraction of the trait heritabilities, and additional variations may be explained by associations with rarer variants with larger effects. The aim of this study was to identify low-frequency (minor allele frequency [MAF] ≥0.01 and

Published
2015

13. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease

Author

Nalls, M.A. Pankratz, N. Lill, C.M. Do, C.B. Hernandez, D.G. Saad, M. Destefano, A.L. Kara, E. Bras, J. Sharma, M. Schulte, C. Keller, M.F. Arepalli, S. Letson, C. Edsall, C. Stefansson, H. Liu, X. Pliner, H. Lee, J.H. Cheng, R. Ikram, M.A. Ioannidis, J.P.A. Hadjigeorgiou, G.M. Bis, J.C. Martinez, M. Perlmutter, J.S. Goate, A. Marder, K. Fiske, B. Sutherland, M. Xiromerisiou, G. Myers, R.H. Clark, L.N. Stefansson, K. Hardy, J.A. Heutink, P. Chen, H. Wood, N.W. Houlden, H. Payami, H. Brice, A. Scott, W.K. Gasser, T. Bertram, L. Eriksson, N. Foroud, T. Singleton, A.B. Plagnol, V. Sheerin, U.-M. Simón-Sánchez, J. Lesage, S. Sveinbjörnsdóttir, S. Barker, R. Ben-Shlomo, Y. Berendse, H.W. Berg, D. Bhatia, K. de Bie, R.M.A. Biffi, A. Bloem, B. Bochdanovits, Z. Bonin, M. Bras, J.M. Brockmann, K. Brooks, J. Burn, D.J. Charlesworth, G. Chinnery, P.F. Chong, S. Clarke, C.E. Cookson, M.R. Cooper, J.M. Corvol, J.C. Counsell, C. Damier, P. Dartigues, J.-F. Deloukas, P. Deuschl, G. Dexter, D.T. van Dijk, K.D. Dillman, A. Durif, F. Dürr, A. Edkins, S. Evans, J.R. Foltynie, T. Dong, J. Gardner, M. Gibbs, J.R. Gray, E. Guerreiro, R. Harris, C. van Hilten, J.J. Hofman, A. Hollenbeck, A. Holton, J. Hu, M. Huang, X. Wurster, I. Mätzler, W. Hudson, G. Hunt, S.E. Huttenlocher, J. Illig, T. Jónsson, P.V. Lambert, J.-C. Langford, C. Lees, A. Lichtner, P. Limousin, P. Lopez, G. Lorenz, D. McNeill, A. Moorby, C. Moore, M. Morris, H.R. Morrison, K.E. Mudanohwo, E. O’sullivan, S.S. Pearson, J. Pétursson, H. Pollak, P. Post, B. Potter, S. Ravina, B. Revesz, T. Riess, O. Rivadeneira, F. Rizzu, P. Ryten, M. Sawcer, S. Schapira, A. Scheffer, H. Shaw, K. Shoulson, I. Sidransky, E. Smith, C. Spencer, C.C.A. Stefánsson, H. Bettella, F. Stockton, J.D. Strange, A. Talbot, K. Tanner, C.M. Tashakkori-Ghanbaria, A. Tison, F. Trabzuni, D. Traynor, B.J. Uitterlinden, A.G. Velseboer, D. Vidailhet, M. Walker, R. van de Warrenburg, B. Wickremaratchi, M. Williams, N. Williams-Gray, C.H. Winder-Rhodes, S. Stefánsson, K. Hardy, J. Factor, S. Higgins, D. Evans, S. Shill, H. Stacy, M. Danielson, J. Marlor, L. Williamson, K. Jankovic, J. Hunter, C. Simon, D. Ryan, P. Scollins, L. Saunders-Pullman, R. Boyar, K. Costan-Toth, C. Ohmann, E. Sudarsky, L. Joubert, C. Friedman, J. Chou, K. Fernandez, H. Lannon, M. Galvez-Jimenez, N. Podichetty, A. Thompson, K. Lewitt, P. Deangelis, M. O'brien, C. Seeberger, L. Dingmann, C. Judd, D. Marder, K. Fraser, J. Harris, J. Bertoni, J. Peterson, C. Rezak, M. Medalle, G. Chouinard, S. Panisset, M. Hall, J. Poiffaut, H. Calabrese, V. Roberge, P. Wojcieszek, J. Belden, J. Jennings, D. Marek, K. Mendick, S. Reich, S. Dunlop, B. Jog, M. Horn, C. Uitti, R. Turk, M. Ajax, T. Mannetter, J. Sethi, K. Carpenter, J. Dill, B. Hatch, L. Ligon, K. Narayan, S. Blindauer, K. Abou-Samra, K. Petit, J. Elmer, L. Aiken, E. Davis, K. Schell, C. Wilson, S. Velickovic, M. Koller, W. Phipps, S. Feigin, A. Gordon, M. Hamann, J. Licari, E. Marotta-Kollarus, M. Shannon, B. Winnick, R. Simuni, T. Videnovic, A. Kaczmarek, A. Williams, K. Wolff, M. Rao, J. Cook, M. Fernandez, M. Kostyk, S. Hubble, J. Campbell, A. Reider, C. Seward, A. Camicioli, R. Carter, J. Nutt, J. Andrews, P. Morehouse, S. Stone, C. Mendis, T. Grimes, D. Alcorn-Costa, C. Gray, P. Haas, K. Vendette, J. Sutton, J. Hutchinson, B. Young, J. Rajput, A. Klassen, L. Shirley, T. Manyam, B. Simpson, P. Whetteckey, J. Wulbrecht, B. Truong, D. Pathak, M. Frei, K. Luong, N. Tra, T. Tran, A. Vo, J. Lang, A. Kleiner-Fisman, G. Nieves, A. Johnston, L. So, J. Podskalny, G. Giffin, L. Atchison, P. Allen, C. Martin, W. Wieler, M. Suchowersky, O. Furtado, S. Klimek, M. Hermanowicz, N. Niswonger, S. Shults, C. Fontaine, D. Aminoff, M. Christine, C. Diminno, M. Hevezi, J. Dalvi, A. Kang, U. Richman, J. Uy, S. Sahay, A. Gartner, M. Schwieterman, D. Hall, D. Leehey, M. Culver, S. Derian, T. Demarcaida, T. Thurlow, S. Rodnitzky, R. Dobson, J. Lyons, K. Pahwa, R. Gales, T. Thomas, S. Shulman, L. Weiner, W. Dustin, K. Singer, C. Zelaya, L. Tuite, P. Hagen, V. Rolandelli, S. Schacherer, R. Kosowicz, J. Gordon, P. Werner, J. Serrano, C. Roque, S. Kurlan, R. Berry, D. Gardiner, I. Hauser, R. Sanchez-Ramos, J. Zesiewicz, T. Delgado, H. Price, K. Rodriguez, P. Wolfrath, S. Pfeiffer, R. Davis, L. Pfeiffer, B. Dewey, R. Hayward, B. Johnson, A. Meacham, M. Estes, B. Walker, F. Hunt, V. O'neill, C. Racette, B. Swisher, L. Dijamco, C. Conley, E.D. Dorfman, E. Tung, J.Y. Hinds, D.A. Mountain, J.L. Wojcicki, A. Lew, M. Klein, C. Golbe, L. Growdon, J. Wooten, G.F. Watts, R. Guttman, M. Goldwurm, S. Saint-Hilaire, M.H. Baker, K. Litvan, I. Nicholson, G. Nance, M. Drasby, E. Isaacson, S. Burn, D. Pramstaller, P. Al-Hinti, J. Moller, A. Sherman, S. Roxburgh, R. Slevin, J. Perlmutter, J. Mark, M.H. Huggins, N. Pezzoli, G. Massood, T. Itin, I. Corbett, A. Chinnery, P. Ostergaard, K. Snow, B. Cambi, F. Kay, D. Samii, A. Agarwal, P. Roberts, J.W. Higgins, D.S. Molho, E. Rosen, A. Montimurro, J. Martinez, E. Griffith, A. Kusel, V. Yearout, D. Factor, S. Zabetian, C. Clark, L.N. Liu, X. Lee, J.H. Cheng Taub, R. Louis, E.D. Cote, L.J. Waters, C. Ford, B. Fahn, S. Vance, J.M. Beecham, G.W. Martin, E.R. Nuytemans, K. Pericak-Vance, M.A. Haines, J.L. Destefano, A. Seshadri, S. Choi, S.H. Frank, S. Bis, J.C. Psaty, B.M. Rice, K. Longstreth, W.T., Jr. Ton, T.G.N. Jain, S. van Duijn, C.M. Uitterlinden, A.G. Verlinden, V.J. Koudstaal, P.J. Singleton, A. Cookson, M. Gibbs, J.R. Hernandez, D. Nalls, M. Zonderman, A. Ferrucci, L. Johnson, R. Longo, D. O'brien, R. Traynor, B. Troncoso, J. van der Brug, M. Zielke, R. Weale, M. Ramasamy, A. Dardiotis, E. Tsimourtou, V. Spanaki, C. Plaitakis, A. Bozi, M. Stefanis, L. Vassilatis, D. Koutsis, G. Panas, M. Hadjigeorgiou, G.M. Lunnon, K. Lupton, M. Powell, J. Parkkinen, L. Ansorge, O. International Parkinson's Disease Genomics Consortium (IPDGC) Parkinson's Study Group (PSG) Parkinson's Research: The Organized GENetics Initiative (PROGENI) 23andMe GenePD NeuroGenetics Research Consortium (NGRC) Hussman Institute of Human Genomics (HIHG) The Ashkenazi Jewish Dataset Investigator Cohorts for Health Aging Research in Genetic Epidemiology (CHARGE) North American Brain Expression Consortium (NABEC) United Kingdom Brain Expression Consortium (UKBEC) Greek Parkinson's Disease Consortium Alzheimer Genetic Analysis Group

Abstract

We conducted a meta-analysis of Parkinson's disease genome-wide association studies using a common set of 7,893,274 variants across 13,708 cases and 95,282 controls. Twenty-six loci were identified as having genome-wide significant association; these and 6 additional previously reported loci were then tested in an independent set of 5,353 cases and 5,551 controls. Of the 32 tested SNPs, 24 replicated, including 6 newly identified loci. Conditional analyses within loci showed that four loci, including GBA, GAK-DGKQ, SNCA and the HLA region, contain a secondary independent risk variant. In total, we identified and replicated 28 independent risk variants for Parkinson's disease across 24 loci. Although the effect of each individual locus was small, risk profile analysis showed substantial cumulative risk in a comparison of the highest and lowest quintiles of genetic risk (odds ratio (OR) = 3.31, 95% confidence interval (CI) = 2.55-4.30; P = 2 × 10-16). We also show six risk loci associated with proximal gene expression or DNA methylation. © 2014 Nature America, Inc. All rights reserved.

Published
2014

14. Gene-centric meta-analyses of 108 912 individuals confirm known body mass index loci and reveal three novel signals

Author

Guo, Y., Lanktree, M. B., Taylor, K. C., Hakonarson, H., Lange, L. A., Keating, B. J., Fairfax, B. P., Elbers, C. C., Barnard, J., Farrall, M., Padmanabhan, S., Baumert, J., Castillo, B. A., Gaunt, T. R., Gong, Y., Rajagopalan, R., Romaine, S. P., Kumari, M., Rafelt, S., Smith, E. N., Li, Y. R., Sivapalaratnam, S., van Iperen, E. P., Speliotes, E. K., Toskala, E., Zhang, L., Ochs-Balcom, H. M., Bhangale, T. R., Chandrupatla, H. R., Drenos, F., Gieger, C., Gupta, J., Johnson, T., Kleber, M. E., Makino, S., Mangino, M., Meng, Y., Nelson, C. P., Pankow, J. S., Pankratz, N., Price, T. S., Shaffer, J., Shen, H., Tischfield, S., Tomaszewski, M., Atwood, L. D., Bailey, K. M., Balasubramanyam, A., Baldwin, C. T., Basart, H., Bauer, F., Behr, E. R., Beitelshees, A. L., Berenson, G. S., Beresford, S. A., Bezzina, C. R., Bhatt, D. L., Boer, J. M., Braund, P. S., Burke, G. L., Burkley, B., Carty, C., Chen, W., Clarke, R., Cooper-DeHoff, R. M., Curtis, S. P., de Bakker, P. I., de Jong, J. S., Delles, C., Dominiczak, A. F., Duggan, D., Feldman, H. I., Furlong, C. E., Gorski, M. M., Gums, J. G., Hardwick, R., Hastie, C., Heid, I. M., Huang, G.-H., Huggins, G. S., Humphries, S. E., Kirkland, S. A., Kivimaki, M., Klein, R., Klein, B. E., Knowler, W. C., Kottke-Marchant, K., LaCroix, A. Z., Langaee, T. Y., Li, M., Lyon, H. N., Maiwald, S., Marshall, J. K., Mehta, A., Meijs, M. F., Melander, O., Meyer, N., Mitra, N., Molony, C. M., Morrow, D. A., Murugesan, G., Newhouse, S. J., Nieto, J. F., Onland-Moret, N. C., Ouwehand, W. H., Palmen, J., Pepine, C. J., Ranchalis, J., Rosas, S. E., Rosenthal, E. A., Scharnagl, H., Schork, N. J., Schreiner, P. J., Shah, T., Shashaty, M., Shimbo, D., Srinivasan, S. R., Thomas, F., Tobin, M. D., Tsai, M. Y., Verschuren, W. M. M., Wagenknecht, L. E., Winkelmann, B. R., Young, T., Yusuf, S., Zafarmand, M. H., Zmuda, J. M., Zwinderman, A. H., Anand, S. S., Balmforth, A. J., Boehm, B. O., Boerwinkle, E., Burton, P. R., Cappola, T. P., Casas, J. P., Caulfield, M. J., Christiani, D. C., Christie, J., Cruickshanks, K. J., Davey-Smith, G., Davidson, K. W., Day, I. N., Doevendans, P. A., Dorn, G. W., FitzGerald, G. A., Hall, A. S., Hingorani, A. D., Hirschhorn, J. N., Hofker, M. H., Hovingh, K. G., Illig, T., Jamshidi, Y., Jarvik, G. P., Johnson, J. A., Kanetsky, P. A., Kastelein, J. J., Koenig, W., Lawlor, D. A., Marz, W., McCaffery, J., Mega, J. L., Mitchell, B. D., Murray, S. S., O'Connell, J. R., Patel, S. R., Peters, A., Pettinger, M., Rader, D. J., Redline, S., Reilly, M. P., Sabatine, M. S., Schadt, E. E., Shuldiner, A. R., Silverstein, R. L., Spector, T. D., Taylor, H. A., Thorand, B., Trip, M. D., Watkins, H., Wichmann, H.- E., Fox, C. S., Grant, S. F., Peter, I., Talmud, P. J., Munroe, P. B., Wilson, J. G., Knight, J. C., Samani, N. J., Hegele, R. A., Asselbergs, F. W., Monda, K. L., van der Schouw, Y. T., Demerath, E. W., Wijmenga, C., Timpson, N. J., Reiner, A. P., North, K. E., Papanicolaou, G. J., Lange , L. A., Keating , B. J., Vascular Medicine, Amsterdam Public Health, Epidemiology and Data Science, Graduate School, Other departments, Amsterdam Cardiovascular Sciences, Cardiology, Other Research, and Public and occupational health

Subjects
Population, Single-nucleotide polymorphism, Genome-wide association study, Biology, Polymorphism, Single Nucleotide, Body Mass Index, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, SH2B1, Genotype, Ethnicity, Genetics, Humans, education, Molecular Biology, Gene, Genetics (clinical), 030304 developmental biology, Genetic association, 0303 health sciences, education.field_of_study, Association Studies Articles, General Medicine, Melanocortin 4 receptor, 030217 neurology & neurosurgery
Abstract

Recent genetic association studies have made progress in uncovering components of the genetic architecture of the body mass index (BMI). We used the ITMAT-Broad-Candidate Gene Association Resource (CARe) (IBC) array comprising up to 49 320 single nucleotide polymorphisms (SNPs) across ~2100 metabolic and cardiovascular-related loci to genotype up to 108 912 individuals of European ancestry (EA), African-Americans, Hispanics and East Asians, from 46 studies, to provide additional insight into SNPs underpinning BMI. We used a five-phase study design: Phase I focused on meta-analysis of EA studies providing individual level genotype data; Phase II performed a replication of cohorts providing summary level EA data; Phase III meta-analyzed results from the first two phases; associated SNPs from Phase III were used for replication in Phase IV; finally in Phase V, a multi-ethnic meta-analysis of all samples from four ethnicities was performed. At an array-wide significance (P < 2.40E-06), we identify novel BMI associations in loci translocase of outer mitochondrial membrane 40 homolog (yeast) - apolipoprotein E - apolipoprotein C-I (TOMM40-APOE-APOC1) (rs2075650, P = 2.95E-10), sterol regulatory element binding transcription factor 2 (SREBF2, rs5996074, P = 9.43E-07) and neurotrophic tyrosine kinase, receptor, type 2 [NTRK2, a brain-derived neurotrophic factor (BDNF) receptor gene, rs1211166, P = 1.04E-06] in the Phase IV meta-analysis. Of 10 loci with previous evidence for BMI association represented on the IBC array, eight were replicated, with the remaining two showing nominal significance. Conditional analyses revealed two independent BMI-associated signals in BDNF and melanocortin 4 receptor (MC4R) regions. Of the 11 array-wide significant SNPs, three are associated with gene expression levels in both primary B-cells and monocytes; with rs4788099 in SH2B adaptor protein 1 (SH2B1) notably being associated with the expression of multiple genes in cis. These multi-ethnic meta-analyses expand our knowledge of BMI genetics.

Published
2013

15. Erratum: Meta-analysis of dense genecentric association studies reveals common and uncommon variants associated with height ((The American Journal of Human Genetics (2010) 88 (6-18))

Author

Lanktree, MB, Guo, Y, Murtaza, M, Glessner, JT, Bailey, SD, Onland-Moret, NC, Lettre, G, Ongen, H, Rajagopalan, R, Johnson, T, Shen, H, Nelson, CP, Klopp, N, Baumert, J, Padmanabhan, S, Pankratz, N, Pankow, JS, Shah, S, Taylor, K, Barnard, J, Peters, BJ, Maloney, CM, Lobmeyer, MT, Stanton, A, Zafarmand, MH, Romaine, SPR, Mehta, A, Van Iperen, EPA, Gong, Y, Price, TS, Smith, EN, Kim, CE, Li, YR, Asselbergs, FW, Atwood, LD, Bailey, KM, Bhatt, D, Bauer, F, Behr, ER, Bhangale, T, Boer, JMA, Boehm, BO, Bradfield, JP, Brown, M, Braund, PS, Burton, PR, Carty, C, Chandrupatla, HR, Chen, W, Connell, J, Dalgeorgou, C, De Boer, A, Drenos, F, Elbers, CC, Fang, JC, Fox, CS, Frackelton, EC, Fuchs, B, Furlong, CE, Gibson, Q, Gieger, C, Goel, A, Grobbee, DE, Hastie, C, Howard, PJ, Huang, G-H, Johnson, WC, Li, Q, Kleber, ME, Klein, BEK, Klein, R, Kooperberg, C, Ky, B, Lacroix, A, Lanken, P, Lathrop, M, Li, M, Marshall, V, Melander, O, Mentch, FD, Meyer, NJ, Monda, KL, Montpetit, A, Murugesan, G, Nakayama, K, Nondahl, D, Onipinla, A, Rafelt, S, Newhouse, SJ, Otieno, FG, Patel, SR, Putt, ME, Rodriguez, S, Safa, RN, Sawyer, DB, Schreiner, PJ, Simpson, C, Sivapalaratnam, S, Srinivasan, SR, Suver, C, Swergold, G, Sweitzer, NK, Thomas, KA, Thorand, B, Timpson, NJ, Tischfield, S, Tobin, M, Tomaszewski, M, Verschuren, WMM, Wallace, C, Winkelmann, B, Zhang, H, Zheng, D, Zhang, L, Zmuda, JM, Clarke, R, Balmforth, AJ, Danesh, J, Day, IN, Schork, NJ, De Bakker, PIW, Delles, C, Duggan, D, Hingorani, AD, Hirschhorn, JN, Hofker, MH, Humphries, SE, Kivimaki, M, Lawlor, DA, Kottke-Marchant, K, Mega, JL, Mitchell, BD, Morrow, DA, Palmen, J, Redline, S, Shields, DC, Shuldiner, AR, Sleiman, PM, Smith, GD, Farrall, M, Jamshidi, Y, Christiani, DC, Casas, JP, Hall, AS, Doevendans, PA, Christie, JD, Berenson, GS, Murray, SS, Illig, T, Dorn, GW, Cappola, TP, Boerwinkle, E, Sever, P, Rader, DJ, Reilly, MP, Caulfield, M, Talmud, PJ, Topol, E, Engert, JC, Wang, K, Dominiczak, A, Hamsten, A, Curtis, SP, Silverstein, RL, Lange, LA, Sabatine, MS, Trip, M, Saleheen, D, Peden, JF, Cruickshanks, KJ, März, W, O'Connell, JR, Klungel, OH, Wijmenga, C, Maitland-Van Der Zee, AH, Schadt, EE, Johnson, JA, Jarvik, GP, Papanicolaou, GJ, Grant, SFA, Munroe, PB, North, KE, Samani, NJ, Koenig, W, Gaunt, TR, Anand, SS, Van Der Schouw, YT, Soranzo, N, Fitzgerald, GA, Reiner, A, Hegele, RA, Hakonarson, H, and Keating, BJ

Published
2012

16. Comprehensive research synopsis and systematic meta-analyses in Parkinson's disease genetics: The PDGene database

Author

Lill, C., Roehr, C., McQueen, M., Kavvoura, F., Bagade, S., Schjeide, B., Schjeide, L., Meissner, E., Zauft, U., Allen, N., Liu, T., Schilling, M., Anderson, K., Beecham, G., Berg, D., Biernacka, J., Brice, A., DeStefano, A., Do, C., Eriksson, N., Factor, S., Farrer, M., Foroud, T., Gasser, T., Hamza, T., Hardy, J., Heutink, P., Hill-Burns, E., Klein, C., Latourelle, J., Maraganore, D., Martin, E., Martinez, M., Myers, R., Nalls, M., Pankratz, N., Payami, H., Satake, W., Scott, W., Sharma, M., Singleton, A., Stefansson, K., Toda, T., Tung, J., Vance, J., Wood, N., Zabetian, C., Young, P., Tanzi, R., Khoury, M., Zipp, F., Lehrach, H., Ioannidis, J., Bertram, L., Parkinson's, G., IPDGC, Consortium, P., WTCCC2, 23andMe, The Genetic Epidemiology of Parkinson's Disease (GEO-PD) Consortium, The International Parkinson's Disease Genomics Consortium (IPDGC), The Parkinson's Disease GWAS Consortium, The Wellcome Trust Case Control Consortium 2 (WTCCC2), Human genetics, and NCA - Neurodegeneration

Subjects
Internet, Epidemiology, Genome, Human, Parkinson Disease, QH426-470, Polymorphism, Single Nucleotide, Neurology, genetics [Parkinson Disease], Databases, Genetic, Genetics, Medicine, Humans, ddc:610, Research Article, Genome-Wide Association Study
Abstract

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of ∼27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P, Author Summary The genetic basis of Parkinson's disease is complex, i.e. it is determined by a number of different disease-causing and disease-predisposing genes. Especially the latter have proven difficult to find, evidenced by more than 800 published genetic association studies, typically showing discrepant results. To facilitate the interpretation of this large and continuously increasing body of data, we have created a freely available online database (“PDGene”: http://www.pdgene.org) which provides an exhaustive account of all published genetic association studies in PD. One particularly useful feature is the calculation and display of up-to-date summary statistics of published data for overlapping DNA sequence variants (polymorphisms). These meta-analyses revealed eleven gene loci that showed a statistically very significant (P

Published
2012

22. Genetic screening for a single common LRRK2 mutation in familial Parkinson's disease.

Author

Nichols WC, Pankratz N, Hernandez D, Paisán-Ruíz C, Jain S, Halter CA, Michaels VE, Reed T, Rudolph A, Shults CW, Singleton A, Foroud T, Parkinson Study Group-PROGENI Investigators, Nichols, William C, Pankratz, Nathan, Hernandez, Dena, Paisán-Ruíz, Coro, Jain, Shushant, Halter, Cheryl A, and Michaels, Veronika E

Abstract

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene cause some forms of autosomal dominant Parkinson's disease. We measured the frequency of a novel mutation (Gly2019 ser) in familial Parkinson's disease by screening genomic DNA of patients and controls. Of 767 affected individuals from 358 multiplex families, 35 (5%) individuals were either heterozygous (34) or homozygous (one) for the mutation, and had typical clinical findings of idiopathic Parkinson's disease. Thus, our results suggest that a single LRRK2 mutation causes Parkinson's disease in 5% of individuals with familial disease. Screening for this mutation should be a component of genetic testing for Parkinson's disease. [ABSTRACT FROM AUTHOR]

Published
2005
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23. Maternal inheritance and mitochondrial DNA variants in familial Parkinson's disease

Author

Pfeiffer Ronald F, Rudolph Alice, Halter Cheryl A, Pauciulo Michael W, Kissell Diane K, Pankratz Nathan, Simon David K, Nichols William C, and Foroud Tatiana

Subjects
Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract Background Mitochondrial function is impaired in Parkinson's disease (PD) and may contribute to the pathogenesis of PD, but the causes of mitochondrial impairment in PD are unknown. Mitochondrial dysfunction is recapitulated in cell lines expressing mitochondrial DNA (mtDNA) from PD patients, implicating mtDNA variants or mutations, though the role of mtDNA variants or mutations in PD risk remains unclear. We investigated the potential contribution of mtDNA variants or mutations to the risk of PD. Methods We examined the possibility of a maternal inheritance bias as well as the association between mitochondrial haplogroups and maternal inheritance and disease risk in a case-control study of 168 multiplex PD families in which the proband and one parent were diagnosed with PD. 2-tailed Fisher Exact Tests and McNemar's tests were used to compare allele frequencies, and a t-test to compare ages of onset. Results The frequency of affected mothers of the proband with PD (83/167, 49.4%) was not significantly different from the frequency of affected females of the proband generation (115/259, 44.4%) (Odds Ratio 1.22; 95%CI 0.83 - 1.81). After correcting for multiple tests, there were no significant differences in the frequencies of mitochondrial haplogroups or of the 10398G complex I gene polymorphism in PD patients compared to controls, and no significant associations with age of onset of PD. Mitochondrial haplogroup and 10398G polymorphism frequencies were similar in probands having an affected father as compared to probands having an affected mother. Conclusions These data fail to demonstrate a bias towards maternal inheritance in familial PD. Consistent with this, we find no association of common haplogroup-defining mtDNA variants or for the 10398G variant with the risk of PD. However, these data do not exclude a role for mtDNA variants in other populations, and it remains possible that other inherited mitochondrial DNA variants, or somatic mDNA mutations, contribute to the risk of familial PD.

Published
2010
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24. Genomewide association study for onset age in Parkinson disease

Author

Halter Cheryl, DeStefano Anita L, Mariani Claudio B, Pezzoli Gianni, Goldwurm Stefano, Wilk Jemma B, Dumitriu Alexandra, Pankratz Nathan, Latourelle Jeanne C, Gusella James F, Nichols William C, Myers Richard H, and Foroud Tatiana

Subjects
Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract Background Age at onset in Parkinson disease (PD) is a highly heritable quantitative trait for which a significant genetic influence is supported by multiple segregation analyses. Because genes associated with onset age may represent invaluable therapeutic targets to delay the disease, we sought to identify such genetic modifiers using a genomewide association study in familial PD. There have been previous genomewide association studies (GWAS) to identify genes influencing PD susceptibility, but this is the first to identify genes contributing to the variation in onset age. Methods Initial analyses were performed using genotypes generated with the Illumina HumanCNV370Duo array in a sample of 857 unrelated, familial PD cases. Subsequently, a meta-analysis of imputed SNPs was performed combining the familial PD data with that from a previous GWAS of 440 idiopathic PD cases. The SNPs from the meta-analysis with the lowest p-values and consistency in the direction of effect for onset age were then genotyped in a replication sample of 747 idiopathic PD cases from the Parkinson Institute Biobank of Milan, Italy. Results Meta-analysis across the three studies detected consistent association (p < 1 × 10-5) with five SNPs, none of which reached genomewide significance. On chromosome 11, the SNP with the lowest p-value (rs10767971; p = 5.4 × 10-7) lies between the genes QSER1 and PRRG4. Near the PARK3 linkage region on chromosome 2p13, association was observed with a SNP (rs7577851; p = 8.7 × 10-6) which lies in an intron of the AAK1 gene. This gene is closely related to GAK, identified as a possible PD susceptibility gene in the GWAS of the familial PD cases. Conclusion Taken together, these results suggest an influence of genes involved in endocytosis and lysosomal sorting in PD pathogenesis.

Published
2009
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25. The Familial Intracranial Aneurysm (FIA) study protocol

Author

Meissner Irene, Pankratz Nathan, Huston John, Foroud Tatiana, Sauerbeck Laura R, Broderick Joseph P, and Brown Robert D

Subjects
Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract Background Subarachnoid hemorrhage (SAH) due to ruptured intracranial aneurysms (IAs) occurs in about 20,000 people per year in the U.S. annually and nearly half of the affected persons are dead within the first 30 days. Survivors of ruptured IAs are often left with substantial disability. Thus, primary prevention of aneurysm formation and rupture is of paramount importance. Prior studies indicate that genetic factors are important in the formation and rupture of IAs. The long-term goal of the Familial Intracranial Aneurysm (FIA) Study is to identify genes that underlie the development and rupture of intracranial aneurysms (IA). Methods/Design The FIA Study includes 26 clinical centers which have extensive experience in the clinical management and imaging of intracerebral aneurysms. 475 families with affected sib pairs or with multiple affected relatives will be enrolled through retrospective and prospective screening of potential subjects with an IA. After giving informed consent, the proband or their spokesperson invites other family members to participate. Each participant is interviewed using a standardized questionnaire which covers medical history, social history and demographic information. In addition blood is drawn from each participant for DNA isolation and immortalization of lymphocytes. High- risk family members without a previously diagnosed IA undergo magnetic resonance angiography (MRA) to identify asymptomatic unruptured aneurysms. A 10 cM genome screen will be performed to identify FIA susceptibility loci. Due to the significant mortality of affected individuals, novel approaches are employed to reconstruct the genotype of critical deceased individuals. These include the intensive recruitment of the spouse and children of deceased, affected individuals. Discussion A successful, adequately-powered genetic linkage study of IA is challenging given the very high, early mortality of ruptured IA. Design features in the FIA Study that address this challenge include recruitment at a large number of highly active clinical centers, comprehensive screening and recruitment techniques, non-invasive vascular imaging of high-risk subjects, genome reconstruction of dead affected individuals using marker data from closely related family members, and inclusion of environmental covariates in the statistical analysis.

Published
2005
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26. Genome-wide association meta-analysis identifies five novel loci for age-related hearing impairment

Author

Adolfo Correa, Dragana Vuckovic, Chuang Ming Li, Sheila R. Pratt, Vilmundur Gudnason, Robert C. Kaplan, Massimiliano Cocca, Giorgia Girotto, Frances M K Williams, Nona Sotoodehnia, Christopher Spankovich, T. Ryan Price, Charles E. Bishop, Johanna Jakobsdottir, W. T. Longstreth, Howard J. Hoffman, Karen Valle, Mark W. Christiansen, Erik Fransen, Helena R R Wells, John M. Schweinfurth, André G. Uitterlinden, Mohammad Arfan Ikram, Guy Van Camp, Daniel S. Evans, Paolo Gasparini, Linda Broer, Gregory J. Tranah, Marco Brumat, Andries Paul Nagtegaal, Mike A. Nalls, Mary Rachel Stimson, Sudha Seshadri, Gudny Eiriksdottir, Karen J. Cruickshanks, Yan Gao, Nancy L. Heard-Costa, Nuno R. Zilhão, James G. Wilson, Nathan Pankratz, André Goedegebure, Claire J. Steves, Otorhinolaryngology and Head and Neck Surgery, Internal Medicine, Epidemiology, Nagtegaal, A. P., Broer, L., Zilhao, N. R., Jakobsdottir, J., Bishop, C. E., Brumat, M., Christiansen, M. W., Cocca, M., Gao, Y., Heard-Costa, N. L., Evans, D. S., Pankratz, N., Pratt, S. R., Price, T. R., Spankovich, C., Stimson, M. R., Valle, K., Vuckovic, D., Wells, H., Eiriksdottir, G., Fransen, E., Ikram, M. A., Li, C. -M., Longstreth, W. T., Steves, C., Van Camp, G., Correa, A., Cruickshanks, K. J., Gasparini, P., Girotto, G., Kaplan, R. C., Nalls, M., Schweinfurth, J. M., Seshadri, S., Sotoodehnia, N., Tranah, G. J., Uitterlinden, A. G., Wilson, J. G., Gudnason, V., Hoffman, H. J., Williams, F. M. K., and Goedegebure, A.

Subjects
0301 basic medicine, Male, Aging, Auditory Pathways, Genome-wide association study, Audiology, Genome-wide association studies, Mice, 0302 clinical medicine, Genetics research, GWAS, age-related hearing lo, Multidisciplinary, Middle Aged, Phenotype, age-related hearing loss, Meta-analysis, Medicine, Female, medicine.symptom, medicine.medical_specialty, Hearing loss, Science, Single-nucleotide polymorphism, Biology, Article, ARHL, 03 medical and health sciences, medicine, otorhinolaryngologic diseases, SNP, Animals, Humans, Genetic Predisposition to Disease, Hearing Loss, Gene, Reproducibility of Results, Molecular Sequence Annotation, Genetic architecture, 030104 developmental biology, Gene Expression Regulation, Genetic Loci, Human medicine, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Previous research has shown that genes play a substantial role in determining a person’s susceptibility to age-related hearing impairment. The existing studies on this subject have different results, which may be caused by difficulties in determining the phenotype or the limited number of participants involved. Here, we have gathered the largest sample to date (discovery n = 9,675; replication n = 10,963; validation n = 356,141), and examined phenotypes that represented low/mid and high frequency hearing loss on the pure tone audiogram. We identified 7 loci that were either replicated and/or validated, of which 5 loci are novel in hearing. Especially the ILDR1 gene is a high profile candidate, as it contains our top SNP, is a known hearing loss gene, has been linked to age-related hearing impairment before, and in addition is preferentially expressed within hair cells of the inner ear. By verifying all previously published SNPs, we can present a paper that combines all new and existing findings to date, giving a complete overview of the genetic architecture of age-related hearing impairment. This is of importance as age-related hearing impairment is highly prevalent in our ageing society and represents a large socio-economic burden.

Published
2019

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

Author

Symen Ligthart, Ahmad Vaez, Urmo Võsa, Maria G. Stathopoulou, Paul S. de Vries, Bram P. Prins, Peter J. Van der Most, Toshiko Tanaka, Elnaz Naderi, Lynda M. Rose, Ying Wu, Robert Karlsson, Maja Barbalic, Honghuang Lin, René Pool, Gu Zhu, Aurélien Macé, Carlo Sidore, Stella Trompet, Massimo Mangino, Maria Sabater-Lleal, John P. Kemp, Ali Abbasi, Tim Kacprowski, Niek Verweij, Albert V. Smith, Tao Huang, Carola Marzi, Mary F. Feitosa, Kurt K. Lohman, Marcus E. Kleber, Yuri Milaneschi, Christian Mueller, Mahmudul Huq, Efthymia Vlachopoulou, Leo-Pekka Lyytikäinen, Christopher Oldmeadow, Joris Deelen, Markus Perola, Jing Hua Zhao, Bjarke Feenstra, Marzyeh Amini, Jari Lahti, Katharina E. Schraut, Myriam Fornage, Bhoom Suktitipat, Wei-Min Chen, Xiaohui Li, Teresa Nutile, Giovanni Malerba, Jian’an Luan, Tom Bak, Nicholas Schork, Fabiola Del Greco M., Elisabeth Thiering, Anubha Mahajan, Riccardo E. Marioni, Evelin Mihailov, Joel Eriksson, Ayse Bilge Ozel, Weihua Zhang, Maria Nethander, Yu-Ching Cheng, Stella Aslibekyan, Wei Ang, Ilaria Gandin, Loïc Yengo, Laura Portas, Charles Kooperberg, Edith Hofer, Kumar B. Rajan, Claudia Schurmann, Wouter den Hollander, Tarunveer S. Ahluwalia, Jing Zhao, Harmen H.M. Draisma, Ian Ford, Nicholas Timpson, Alexander Teumer, Hongyan Huang, Simone Wahl, YongMei Liu, Jie Huang, Hae-Won Uh, Frank Geller, Peter K. Joshi, Lisa R. Yanek, Elisabetta Trabetti, Benjamin Lehne, Diego Vozzi, Marie Verbanck, Ginevra Biino, Yasaman Saba, Ingrid Meulenbelt, Jeff R. O’Connell, Markku Laakso, Franco Giulianini, Patrik K.E. Magnusson, Christie M. Ballantyne, Jouke Jan Hottenga, Grant W. Montgomery, Fernando Rivadineira, Rico Rueedi, Maristella Steri, Karl-Heinz Herzig, David J. Stott, Cristina Menni, Mattias Frånberg, Beate St. Pourcain, Stephan B. Felix, Tune H. Pers, Stephan J.L. Bakker, Peter Kraft, Annette Peters, Dhananjay Vaidya, Graciela Delgado, Johannes H. Smit, Vera Großmann, Juha Sinisalo, Ilkka Seppälä, Stephen R. Williams, Elizabeth G. Holliday, Matthijs Moed, Claudia Langenberg, Katri Räikkönen, Jingzhong Ding, Harry Campbell, Michele M. Sale, Yii-Der I. Chen, Alan L. James, Daniela Ruggiero, Nicole Soranzo, Catharina A. Hartman, Erin N. Smith, Gerald S. Berenson, Christian Fuchsberger, Dena Hernandez, Carla M.T. Tiesler, Vilmantas Giedraitis, David Liewald, Krista Fischer, Dan Mellström, Anders Larsson, Yunmei Wang, William R. Scott, Matthias Lorentzon, John Beilby, Kathleen A. Ryan, Craig E. Pennell, Dragana Vuckovic, Beverly Balkau, Maria Pina Concas, Reinhold Schmidt, Carlos F. Mendes de Leon, Erwin P. Bottinger, Margreet Kloppenburg, Lavinia Paternoster, Michael Boehnke, A.W. Musk, Gonneke Willemsen, David M. Evans, Pamela A.F. Madden, Mika Kähönen, Zoltán Kutalik, Magdalena Zoledziewska, Ville Karhunen, Stephen B. Kritchevsky, Naveed Sattar, Genevieve Lachance, Robert Clarke, Tamara B. Harris, Olli T. Raitakari, John R. Attia, Diana van Heemst, Eero Kajantie, Rossella Sorice, Giovanni Gambaro, Robert A. Scott, Andrew A. Hicks, Luigi Ferrucci, Marie Standl, Cecilia M. Lindgren, John M. Starr, Magnus Karlsson, Lars Lind, Jun Z. Li, John C. Chambers, Trevor A. Mori, Eco J.C.N. de Geus, Andrew C. Heath, Nicholas G. Martin, Juha Auvinen, Brendan M. Buckley, Anton J.M. de Craen, Melanie Waldenberger, Konstantin Strauch, Thomas Meitinger, Rodney J. Scott, Mark McEvoy, Marian Beekman, Cristina Bombieri, Paul M. Ridker, Karen L. Mohlke, Nancy L. Pedersen, Alanna C. Morrison, Dorret I. Boomsma, John B. Whitfield, David P. Strachan, Albert Hofman, Peter Vollenweider, Francesco Cucca, Marjo-Riitta Jarvelin, J. Wouter Jukema, Tim D. Spector, Anders Hamsten, Tanja Zeller, André G. Uitterlinden, Matthias Nauck, Vilmundur Gudnason, Lu Qi, Harald Grallert, Ingrid B. Borecki, Jerome I. Rotter, Winfried März, Philipp S. Wild, Marja-Liisa Lokki, Michael Boyle, Veikko Salomaa, Mads Melbye, Johan G. Eriksson, James F. Wilson, Brenda W.J.H. Penninx, Diane M. Becker, Bradford B. Worrall, Greg Gibson, Ronald M. Krauss, Marina Ciullo, Gianluigi Zaza, Nicholas J. Wareham, Albertine J. Oldehinkel, Lyle J. Palmer, Sarah S. Murray, Peter P. Pramstaller, Stefania Bandinelli, Joachim Heinrich, Erik Ingelsson, Ian J. Deary, Reedik Mägi, Liesbeth Vandenput, Pim van der Harst, Karl C. Desch, Jaspal S. Kooner, Claes Ohlsson, Caroline Hayward, Terho Lehtimäki, Alan R. Shuldiner, Donna K. Arnett, Lawrence J. Beilin, Antonietta Robino, Philippe Froguel, Mario Pirastu, Tine Jess, Wolfgang Koenig, Ruth J.F. Loos, Denis A. Evans, Helena Schmidt, George Davey Smith, P. Eline Slagboom, Gudny Eiriksdottir, Andrew P. Morris, Bruce M. Psaty, Russell P. Tracy, Ilja M. Nolte, Eric Boerwinkle, Sophie Visvikis-Siest, Alex P. Reiner, Myron Gross, Joshua C. Bis, Lude Franke, Oscar H. Franco, Emelia J. Benjamin, Daniel I. Chasman, Josée Dupuis, Harold Snieder, Abbas Dehghan, Behrooz Z. Alizadeh, H. Marike Boezen, Gerjan Navis, Marianne Rots, Morris Swertz, Bruce H.R. Wolffenbuttel, Cisca Wijmenga, Emelia Benjamin, Tarunveer Singh Ahluwalia, James Meigs, Russell Tracy, Josh Bis, Nathan Pankratz, Alex Rainer, James G. Wilson, Josee Dupuis, Bram Prins, Urmo Vaso, Maria Stathopoulou, Terho Lehtimaki, Yalda Jamshidi, Sophie Siest, Andre G. Uitterlinden, Mohammadreza Abdollahi, Renate Schnabel, Ursula M. Schick, Aldi Kraja, Yi-Hsiang Hsu, Daniel S. Tylee, Alyson Zwicker, Rudolf Uher, George Davey-Smith, Andrew Hicks, Cornelia M. van Duijn, Cavin Ward-Caviness, J. Rotter, Ken Rice, Leslie Lange, Eco de Geus, Kari Matti Makela, David Stacey, Johan Eriksson, Tim M. Frayling, Eline P. Slagboom, Erasmus University Medical Center [Rotterdam] (Erasmus MC), University Medical Center Groningen [Groningen] (UMCG), University of Isfahan, University of Tartu, Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), The University of Texas Health Science Center at Houston (UTHealth), National Institute on Aging [Bethesda, USA] (NIA), National Institutes of Health [Bethesda] (NIH), Brigham and Women's Hospital [Boston], University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], University of Split, Boston University School of Medicine (BUSM), Boston University [Boston] (BU), Process & Energy Laboratory, Delft University of Technology (TU Delft), Grand Lyon : communauté urbaine de Lyon, Interuniversity Cardiology Institute Netherlands, Department of Twin Research and Genetic Epidemiology, King's College London, London, Huazhong University of Science and Technology [Wuhan] (HUST), Division of Statistical Genomics, Washington University School of Medicine, Department of Psychiatry, VU University Medical Center [Amsterdam], Institut fuer Theoretische Physik (Institut fuer Theoretische Physik), Universität Heidelberg [Heidelberg] = Heidelberg University, Molecular Epidemiology, MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Mahidol University [Bangkok], Northwest A and F University, Laboratoire d'Optimisation des Systèmes Industriels (LOSI), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Institute of Genetics and Biophysics, CNR, Naples, Università degli studi di Verona = University of Verona (UNIVR), Department of Molecular Medicine [Scripps Research Institute], The Scripps Research Institute [La Jolla, San Diego], Department of Physics, Indian Institute of Technology Kanpur (IIT Kanpur), Deptartment of Medical Biochemistry and Microbiology, Uppsala University, Department of Electrical and Computer Engineering [Waterloo] (ECE), University of Waterloo [Waterloo], University of Maryland School of Medicine, University of Maryland System, Institut National de l'Environnement Industriel et des Risques (INERIS), Institute of Pop. Genetics, CNR, Sassari, Interfaculty Institute for Genetics and Functional Genomics, Universität Greifswald - University of Greifswald, IT University of Copenhagen (ITU), Robertson Centre for Biostatistics, University of Glasgow, Centre for Causal Analyses in Translational Epidemiology, University of Bristol [Bristol]-Medical Research Council, King‘s College London, Jinan University [Guangzhou], Institute of Oceanology [China], School Medicine, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE)-Pennsylvania Commonwealth System of Higher Education (PCSHE), General Internal Medicine, Johns Hopkins School of Medicine, Johns Hopkins University School of Medicine [Baltimore], Shardna life science Pula Cagliari, Section Molecular Epidemiology, Leiden University Medical Center (LUMC), Department of Medicine, University of Eastern Finland-Kuopio University Hospital, Medstar Research Institute, Department of Cardiology, Ernst-Moritz-Arndt University, Center for Biological Sequence Analysis [Lyngby], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Department of Internal Medicine, University of Groningen and University Medical Center Groningen, Department of Epidemiology, Harvard School of Public Health, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Metacohorts Consortium, INEOS Technologies (SWITZERLAND), MRC Epidemiology Unit, University of Cambridge [UK] (CAM)-Institute of Metabolic Science, University of Edinburgh, School of Population Health [Crawley, Western Australia], The University of Western Australia (UWA), Institute of Genetics and Biophysics, National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), The Scripps Translational Science Institute and Scripps Health, Tulane Center for Cardiovascular Health, Tulane University Health Sciences Center, Centre for Population Health Sciences, Genomic Research Laboratory, Service of Infectious Disease, Hôpitaux Universitaires de Genève (HUG), Infectious diseases division, Department of internal medicine, Washington University in Saint Louis (WUSTL), Luleå University of Technology (LUT), Recherche en épidémiologie et biostatistique, Université Paris-Sud - Paris 11 (UP11)-Institut National de la Santé et de la Recherche Médicale (INSERM), Austrian Institute of Technology [Vienna] (AIT), Icahn School of Medicine at Mount Sinai [New York] (MSSM), Department of Rheumatology and Clinical Epidemiology, Leiden University Medical Center (LUMC), Department of Rheumatology and Clinical Epidemiology [Leiden University Medical Center] (LUMC), Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden-Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden, Department of Biostatistics and Center for Statistical Genetics, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, University of Virginia, Tampere University Hospital, Department of Medical Genetics, Université de Lausanne = University of Lausanne (UNIL), Department of Pathological Biochemistry, Royal Infirmary, Oxford University, University of Oxford, University of Newcastle [Callaghan, Australia] (UoN), Department of neurology, Institute of Metabolic Science, MRC, The Wellcome Trust Centre for Human Genetics [Oxford], Uppsala Universitet [Uppsala], QIMR Berghofer Medical Research Institute, Institute of Genetic Epidemiology [Neuherberg, Germany], Institute of Human Genetics, Helmholtz Zentrum München = German Research Center for Environmental Health, Schizophrenia Research Institute [Sydney], Department of Genetics, University of North Carolina System (UNC)-University of North Carolina System (UNC), Vrije Universiteit Brussel (VUB), Population Health Sciences and Education, St George's University of London, Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), Institute of Health Sciences and Biocenter Oulu, University of Oulu, Medizinische Klinik und Poliklinik, Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), Institute of Clinical Chemistry and Laboratory Medicine, Icelandic Heart Association, Heart Preventive Clinic and Research Institute, Departments of Epidemiology and Nutrition, Institute of Epidemiology [Neuherberg] (EPI), Medical University Graz, Transplantation Laboratory [Helsinki], Haartman Institute [Helsinki], Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Department of Chronic Disease Prevention, National Institute for Health and Welfare [Helsinki], Dept. of Epidemiology Research, Statens Serum Institut [Copenhagen], CLinical Psychology, Genetics and Pathology, Geriatric Rehabilitation Unit, Azienda Sanitaria Firenze, Center For Narcolepsy, Stanford University, Centre for Bone and Arthritis Research, University of Gothenburg (GU)-Institute of Medicine, MRC Human Gentics Unit, Inst Genet and Mol Med, Western General Hospital, Edinburgh, University of Maryland School of Medicine [Baltimore, MD, USA], Génétique des maladies multifactorielles (GMM), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), Department of Physics [Stockholm], Stockholm University, University of Bristol [Bristol], Universiteit Leiden, Department of Epidemiology, University of Washington, University of Washington [Seattle], Department of Epidemiology [Rotterdam], University of Groningen [Groningen], Dutch Initiative on Crohn and Colitis (ICC), Icelandic Heart Association [Kopavogur, Iceland] (IHA), Department of Physiology and Biophysics [Jackson, MS, USA], University of Southern Mississippi (USM), Human Genetics Branch, National Institutes of Health [Bethesda] (NIH)-National Institute of Mental Health (NIMH), Faculty of Medicine and Life Sciences [Tampere], University of Tampere [Finland], German Center for Cardiovascular Research (DZHK), Berlin Institute of Health (BIH), MRC Centre for Neuropsychiatric Genetics and Genomics, Medical Research Council-Cardiff University, Department of Social Medicine, School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Department of Medicine [Aurora, CO, USA], University of Colorado [Denver], Institute for Molecular Medicine Finland [Helsinki] (FIMM), Helsinki Institute of Life Science (HiLIFE), Mathematical Institute [Oxford] (MI), Institute of Psychiatry, Psychology & Neuroscience, King's College London, LifeLines Cohort Study, CHARGE Inflammation Working Group, Ligthart, S., Vaez, A., Vosa, U., Stathopoulou, M. G., de Vries, P. S., Prins, B. P., Van der Most, P. J., Tanaka, T., Naderi, E., Rose, L. M., Wu, Y., Karlsson, R., Barbalic, M., Lin, H., Pool, R., Zhu, G., Mace, A., Sidore, C., Trompet, S., Mangino, M., Sabater-Lleal, M., Kemp, J. P., Abbasi, A., Kacprowski, T., Verweij, N., Smith, A. V., Huang, T., Marzi, C., Feitosa, M. F., Lohman, K. K., Kleber, M. E., Milaneschi, Y., Mueller, C., Huq, M., Vlachopoulou, E., Lyytikainen, L. -P., Oldmeadow, C., Deelen, J., Perola, M., Zhao, J. H., Feenstra, B., Alizadeh, B. Z., Boezen, H. M., Franke, L., van der Harst, P., Navis, G., Rots, M., Snieder, H., Swertz, M., Wolffenbuttel, B. H. R., Wijmenga, C., Amini, M., Benjamin, E., Chasman, D. I., Dehghan, A., Ahluwalia, T. S., Meigs, J., Tracy, R., Bis, J., Eiriksdottir, G., Pankratz, N., Gross, M., Rainer, A., Wilson, J. G., Psaty, B. M., Dupuis, J., Prins, B., Vaso, U., Stathopoulou, M., Lehtimaki, T., Koenig, W., Jamshidi, Y., Siest, S., Uitterlinden, A. G., Abdollahi, M., Schnabel, R., Schick, U. M., Nolte, I. M., Kraja, A., Hsu, Y. -H., Tylee, D. S., Zwicker, A., Uher, R., Davey-Smith, G., Morrison, A. C., Hicks, A., van Duijn, C. M., Ward-Caviness, C., Boerwinkle, E., Rotter, J., Rice, K., Lange, L., de Geus, E., Morris, A. P., Makela, K. M., Stacey, D., Eriksson, J., Frayling, T. M., Slagboom, E. P., Lahti, J., Schraut, K. E., Fornage, M., Suktitipat, B., Chen, W. -M., Li, X., Nutile, T., Malerba, G., Luan, J., Bak, T., Schork, N., Del Greco, M. F., Thiering, E., Mahajan, A., Marioni, R. E., Mihailov, E., Ozel, A. B., Zhang, W., Nethander, M., Cheng, Y. -C., Aslibekyan, S., Ang, W., Gandin, I., Yengo, L., Portas, L., Kooperberg, C., Hofer, E., Rajan, K. B., Schurmann, C., den Hollander, W., Zhao, J., Draisma, H. H. M., Ford, I., Timpson, N., Teumer, A., Huang, H., Wahl, S., Liu, Y., Huang, J., Uh, H. -W., Geller, F., Joshi, P. K., Yanek, L. R., Trabetti, E., Lehne, B., Vozzi, D., Verbanck, M., Biino, G., Saba, Y., Meulenbelt, I., O'Connell, J. R., Laakso, M., Giulianini, F., Magnusson, P. K. E., Ballantyne, C. M., Hottenga, J. J., Montgomery, G. W., Rivadineira, F., Rueedi, R., Steri, M., Herzig, K. -H., Stott, D. J., Menni, C., Franberg, M., S, t. Pourcain B., Felix, S. B., Pers, T. H., Bakker, S. J. L., Kraft, P., Peters, A., Vaidya, D., Delgado, G., Smit, J. H., Grossmann, V., Sinisalo, J., Seppala, I., Williams, S. R., Holliday, E. G., Moed, M., Langenberg, C., Raikkonen, K., Ding, J., Campbell, H., Sale, M. M., Chen, Y. -D. I., James, A. L., Ruggiero, D., Soranzo, N., Hartman, C. A., Smith, E. N., Berenson, G. S., Fuchsberger, C., Hernandez, D., Tiesler, C. M. T., Giedraitis, V., Liewald, D., Fischer, K., Mellstrom, D., Larsson, A., Wang, Y., Scott, W. R., Lorentzon, M., Beilby, J., Ryan, K. A., Pennell, C. E., Vuckovic, D., Balkau, B., Concas, M. P., Schmidt, R., Mendes de Leon, C. F., Bottinger, E. P., Kloppenburg, M., Paternoster, L., Boehnke, M., Musk, A. W., Willemsen, G., Evans, D. M., Madden, P. A. F., Kahonen, M., Kutalik, Z., Zoledziewska, M., Karhunen, V., Kritchevsky, S. B., Sattar, N., Lachance, G., Clarke, R., Harris, T. B., Raitakari, O. T., Attia, J. R., van Heemst, D., Kajantie, E., Sorice, R., Gambaro, G., Scott, R. A., Hicks, A. A., Ferrucci, L., Standl, M., Lindgren, C. M., Starr, J. M., Karlsson, M., Lind, L., Li, J. Z., Chambers, J. C., Mori, T. A., de Geus, E. J. C. N., Heath, A. C., Martin, N. G., Auvinen, J., Buckley, B. M., de Craen, A. J. M., Waldenberger, M., Strauch, K., Meitinger, T., Scott, R. J., Mcevoy, M., Beekman, M., Bombieri, C., Ridker, P. M., Mohlke, K. L., Pedersen, N. L., Boomsma, D. I., Whitfield, J. B., Strachan, D. P., Hofman, A., Vollenweider, P., Cucca, F., Jarvelin, M. -R., Jukema, J. W., Spector, T. D., Hamsten, A., Zeller, T., Nauck, M., Gudnason, V., Qi, L., Grallert, H., Borecki, I. B., Rotter, J. I., Marz, W., Wild, P. S., Lokki, M. -L., Boyle, M., Salomaa, V., Melbye, M., Eriksson, J. G., Wilson, J. F., Penninx, B. W. J. H., Becker, D. M., Worrall, B. B., Gibson, G., Krauss, R. M., Ciullo, M., Zaza, G., Wareham, N. J., Oldehinkel, A. J., Palmer, L. J., Murray, S. S., Pramstaller, P. P., Bandinelli, S., Heinrich, J., Ingelsson, E., Deary, I. J., Magi, R., Vandenput, L., Desch, K. C., Kooner, J. S., Ohlsson, C., Hayward, C., Shuldiner, A. R., Arnett, D. K., Beilin, L. J., Robino, A., Froguel, P., Pirastu, M., Jess, T., Loos, R. J. F., Evans, D. A., Schmidt, H., Slagboom, P. E., Tracy, R. P., Visvikis-Siest, S., Reiner, A. P., Bis, J. C., Franco, O. H., Benjamin, E. J., AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, Graduate School, Epidemiology, Internal Medicine, Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Groningen Kidney Center (GKC), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Cardiovascular Centre (CVC), Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), VU University medical center, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, APH - Methodology, APH - Digital Health, Biological Psychology, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Universität Heidelberg [Heidelberg], University of Verona (UNIVR), Department of Molecular and Experimental Medicine, The Scripps Research Institute, The Scripps Research Institute, Université Grenoble Alpes - UFR Sciences de l'Homme et de la Société (UGA UFR SHS), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), IT University of Copenhagen, Technical University of Denmark [Lyngby] (DTU), Consiglio Nazionale delle Ricerche (CNR), University of Virginia [Charlottesville], Université de Lausanne (UNIL), University of Oxford [Oxford], University of Newcastle [Australia] (UoN), Centre d'économie industrielle i3 (CERNA i3), Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Helmholtz-Zentrum München (HZM), Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Universitätsmedizin der Johannes-Gutenberg Universität Mainz, University of Helsinki-University of Helsinki-Faculty of Medecine [Helsinki], University of Helsinki-University of Helsinki, Cardiff University-Medical Research Council, University of California-University of California, and DE CARVALHO, Philippe

Subjects
0301 basic medicine, Male, Netherlands Twin Register (NTR), Bipolar Disorder, LD SCORE REGRESSION, [SDV]Life Sciences [q-bio], Genome-wide association study, [SDV.GEN] Life Sciences [q-bio]/Genetics, Body Mass Index, inflammatory disorder, 80 and over, WIDE ASSOCIATION, EPIDEMIOLOGY, ta318, International HapMap Project, Child, Genetics (clinical), 2. Zero hunger, Genetics, Genetics & Heredity, Aged, 80 and over, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, C-reactive proteingenome-wide association studyinflammationMendelian randomizationinflammatory disordersDEPICTcoronary artery diseaseschizophreniasystem biology, system biology, DEPICT, Mendelian Randomization Analysis, 11 Medical And Health Sciences, Middle Aged, C-reactive protein, coronary artery disease, genome-wide association study, inflammation, inflammatory disorders, Mendelian randomization, schizophrenia, Adolescent, Adult, Aged, Biomarkers, C-Reactive Protein, Female, Genetic Loci, Genome-Wide Association Study, Humans, Inflammation, Liver, Metabolic Networks and Pathways, Schizophrenia, Young Adult, 3. Good health, [SDV] Life Sciences [q-bio], Medical genetics, Biomarker (medicine), Life Sciences & Biomedicine, Human, medicine.medical_specialty, CHARGE Inflammation Working Group, Biology, IMMUNITY, ta3111, Article, 03 medical and health sciences, SDG 3 - Good Health and Well-being, medicine, CORONARY-HEART-DISEASE, Mendelian Randomization Analysi, 1000 Genomes Project, METAANALYSIS, Genetic association, [SDV.GEN]Life Sciences [q-bio]/Genetics, Science & Technology, ta1184, Metabolic Networks and Pathway, Biomarker, INSTRUMENTS, 06 Biological Sciences, 030104 developmental biology, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, LifeLines Cohort Study
Abstract

International audience; 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 × 10-8). 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.Copyright © 2018 American Society of Human Genetics. All rights reserved.

Published
2018

28. Whole-genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles.

Author

Huffman JE, Nicholas J, Hahn J, Heath AS, Raffield LM, Yanek LR, Brody JA, Thibord F, Almasy L, Bartz TM, Bielak LF, Bowler RP, Carrasquilla GD, Chasman DI, Chen MH, Emmert DB, Ghanbari M, Haessler J, Hottenga JJ, Kleber ME, Le NQ, Lee J, Lewis JP, Li-Gao R, Luan J, Malmberg AL, Mangino M, Marioni R, Martinez-Perez A, Pankratz N, Polasek O, Richmond A, Rodriguez BAT, Rotter JI, Steri M, Suchon P, Trompet S, Weiss S, Zare M, Auer PL, Cho M, Christofidou P, Davies G, de Geus EJ, Deleuze JF, Delgado GE, Ekunwe L, Faraday N, Gogele M, Greinacher A, Gao H, Howard TE, Joshi PK, Kilpeläinen TO, Lahti J, Linneberg A, Naitza S, Noordam R, Vergés FP, Rich SS, Rosendaal FR, Rudan I, Ryan KA, Souto JCC, van Rooij FJA, Wang H, Zhao W, Becker L, Beswick A, Brown MR, Cade B, Campbell H, Cho K, Crapo J, Curran J, de Maat MPM, Doyle MF, Elliott P, Floyd JS, Fuchsberger C, Grarup N, Guo X, Harris S, Hou L, Kolcic I, Kooperberg C, Menni C, Nauck M, O'Connell JR, Orru V, Psaty BM, Räikkönen K, Smith JA, Soria JM, Stott D, van Hylckama Vlieg A, Watkins H, Willemsen G, Wilson PW, Ben-Shlomo Y, Blangero J, Boomsma D, Cox SR, Dehghan A, Eriksson JG, Fiorillo E, Fornage M, Hansen T, Hayward C, Ikram MA, Jukema JW, Kardia S, Lange L, Maerz W, Mathias R, Mitchell BD, Mook-Kanamori DO, Morange PE, Pedersen O, Pramstaller PP, Redline S, Reiner AP, Ridker PM, Silverman EK, Spector TD, Volker U, Wareham N, Wilson J, Yao J, Tregouet DA, Johnson AD, Wolberg AS, de Vries PS, Sabater-Lleal M, Morrison A, and Smith NL

Abstract

Genetic studies have identified numerous regions associated with plasma fibrinogen levels in Europeans, yet missing heritability and limited inclusion of non-Europeans necessitates further studies with improved power and sensitivity. Compared with array-based genotyping, whole genome sequencing (WGS) data provides better coverage of the genome and better representation of non-European variants. To better understand the genetic landscape regulating plasma fibrinogen levels, we meta-analyzed WGS data from the NHLBI's Trans-Omics for Precision Medicine (TOPMed) program (n=32,572), with array-based genotype data from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (n=131,340) imputed to the TOPMed or Haplotype Reference Consortium panel. We identified 18 loci that have not been identified in prior genetic studies of fibrinogen. Of these, four are driven by common variants of small effect with reported MAF at least 10 percentage points higher in African populations. Three signals (SERPINA1, ZFP36L2, and TLR10) contain predicted deleterious missense variants. Two loci, SOCS3 and HPN, each harbor two conditionally distinct, non-coding variants. The gene region encoding the fibrinogen protein chain subunits (FGG;FGB;FGA), contains 7 distinct signals, including one novel signal driven by rs28577061, a variant common in African ancestry populations but extremely rare in Europeans (MAFAFR=0.180; MAFEUR=0.008). Through phenome-wide association studies in the VA Million Veteran Program, we found associations between fibrinogen polygenic risk scores and thrombotic and inflammatory disease phenotypes, including an association with gout. Our findings demonstrate the utility of WGS to augment genetic discovery in diverse populations and offer new insights for putative mechanisms of fibrinogen regulation., (Copyright © 2024 American Society of Hematology.)

Published
2024
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29. Multi-ancestry polygenic risk scores for venous thromboembolism.

Author

Jee YH, Thibord F, Dominguez A, Sept C, Boulier K, Venkateswaran V, Ding Y, Cherlin T, Verma SS, Faro VL, Bartz TM, Boland A, Brody JA, Deleuze JF, Emmerich J, Germain M, Johnson AD, Kooperberg C, Morange PE, Pankratz N, Psaty BM, Reiner AP, Smadja DM, Sitlani CM, Suchon P, Tang W, Trégouët DA, Zöllner S, Pasaniuc B, Damrauer SM, Sanna S, Snieder H, Kabrhel C, Smith NL, and Kraft P

Subjects
Female, Humans, Male, Black or African American genetics, Case-Control Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Polymorphism, Single Nucleotide, White genetics, Genetic Risk Score, Venous Thromboembolism genetics, Venous Thromboembolism epidemiology
Abstract

Venous thromboembolism (VTE) is a significant contributor to morbidity and mortality, with large disparities in incidence rates between Black and White Americans. Polygenic risk scores (PRSs) limited to variants discovered in genome-wide association studies in European-ancestry samples can identify European-ancestry individuals at high risk of VTE. However, there is limited evidence on whether high-dimensional PRS constructed using more sophisticated methods and more diverse training data can enhance the predictive ability and their utility across diverse populations. We developed PRSs for VTE using summary statistics from the International Network against Venous Thrombosis (INVENT) consortium genome-wide association studies meta-analyses of European- (71 771 cases and 1 059 740 controls) and African-ancestry samples (7482 cases and 129 975 controls). We used LDpred2 and PRS-CSx to construct ancestry-specific and multi-ancestry PRSs and evaluated their performance in an independent European- (6781 cases and 103 016 controls) and African-ancestry sample (1385 cases and 12 569 controls). Multi-ancestry PRSs with weights tuned in European-ancestry samples slightly outperformed ancestry-specific PRSs in European-ancestry test samples (e.g. the area under the receiver operating curve [AUC] was 0.609 for PRS-CSx&#95;combinedEUR and 0.608 for PRS-CSxEUR [P = 0.00029]). Multi-ancestry PRSs with weights tuned in African-ancestry samples also outperformed ancestry-specific PRSs in African-ancestry test samples (PRS-CSxAFR: AUC = 0.58, PRS-CSx&#95;combined AFR: AUC = 0.59), although this difference was not statistically significant (P = 0.34). The highest fifth percentile of the best-performing PRS was associated with 1.9-fold and 1.68-fold increased risk for VTE among European- and African-ancestry subjects, respectively, relative to those in the middle stratum. These findings suggest that the multi-ancestry PRS might be used to improve performance across diverse populations to identify individuals at highest risk for VTE., (© The Author(s) 2024. Published by Oxford University Press.)

Published
2024
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30. Extreme phenotype sampling and next generation sequencing to identify genetic variants associated with tacrolimus in African American kidney transplant recipients.

Author

Mohamed ME, Guo B, Wu B, Schladt DP, Muthusamy A, Guan W, Abrahante JE, Onyeaghala G, Saqr A, Pankratz N, Agarwal G, Mannon RB, Matas AJ, Oetting WS, Remmel RP, Israni AK, Jacobson PA, and Dorr CR

Subjects
Adult, Female, Humans, Male, Middle Aged, Cytochrome P-450 CYP3A genetics, Genetic Variation, High-Throughput Nucleotide Sequencing, Pharmacogenomic Variants, Phenotype, Transplant Recipients, Black or African American genetics, Immunosuppressive Agents pharmacokinetics, Kidney Transplantation, Tacrolimus pharmacokinetics, Tacrolimus therapeutic use
Abstract

African American (AA) kidney transplant recipients (KTRs) have poor outcomes, which may in-part be due to tacrolimus (TAC) sub-optimal immunosuppression. We previously determined the common genetic regulators of TAC pharmacokinetics in AAs which were CYP3A5 *3, *6, and *7. To identify low-frequency variants that impact TAC pharmacokinetics, we used extreme phenotype sampling and compared individuals with extreme high (n = 58) and low (n = 60) TAC troughs (N = 515 AA KTRs). Targeted next generation sequencing was conducted in these two groups. Median TAC troughs in the high group were 7.7 ng/ml compared with 6.3 ng/ml in the low group, despite lower daily doses of 5 versus 12 mg, respectively. Of 34,542 identified variants across 99 genes, 1406 variants were suggestively associated with TAC troughs in univariate models (p-value < 0.05), however none were significant after multiple testing correction. We suggest future studies investigate additional sources of TAC pharmacokinetic variability such as drug-drug-gene interactions and pharmacomicrobiome., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Published
2024
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31. Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Author

Jiang MZ, Gaynor SM, Li X, Van Buren E, Stilp A, Buth E, Wang FF, Manansala R, Gogarten SM, Li Z, Polfus LM, Salimi S, Bis JC, Pankratz N, Yanek LR, Durda P, Tracy RP, Rich SS, Rotter JI, Mitchell BD, Lewis JP, Psaty BM, Pratte KA, Silverman EK, Kaplan RC, Avery C, North KE, Mathias RA, Faraday N, Lin H, Wang B, Carson AP, Norwood AF, Gibbs RA, Kooperberg C, Lundin J, Peters U, Dupuis J, Hou L, Fornage M, Benjamin EJ, Reiner AP, Bowler RP, Lin X, Auer PL, and Raffield LM

Subjects
Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genetic Predisposition to Disease, Female, Interleukin-6 genetics, Precision Medicine methods, Biomarkers, Inflammation genetics, Genome-Wide Association Study methods, Whole Genome Sequencing methods
Abstract

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Published
2024
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32. Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes.

Author

Keener R, Chhetri SB, Connelly CJ, Taub MA, Conomos MP, Weinstock J, Ni B, Strober B, Aslibekyan S, Auer PL, Barwick L, Becker LC, Blangero J, Bleecker ER, Brody JA, Cade BE, Celedon JC, Chang YC, Cupples LA, Custer B, Freedman BI, Gladwin MT, Heckbert SR, Hou L, Irvin MR, Isasi CR, Johnsen JM, Kenny EE, Kooperberg C, Minster RL, Naseri T, Viali S, Nekhai S, Pankratz N, Peyser PA, Taylor KD, Telen MJ, Wu B, Yanek LR, Yang IV, Albert C, Arnett DK, Ashley-Koch AE, Barnes KC, Bis JC, Blackwell TW, Boerwinkle E, Burchard EG, Carson AP, Chen Z, Chen YI, Darbar D, de Andrade M, Ellinor PT, Fornage M, Gelb BD, Gilliland FD, He J, Islam T, Kaab S, Kardia SLR, Kelly S, Konkle BA, Kumar R, Loos RJF, Martinez FD, McGarvey ST, Meyers DA, Mitchell BD, Montgomery CG, North KE, Palmer ND, Peralta JM, Raby BA, Redline S, Rich SS, Roden D, Rotter JI, Ruczinski I, Schwartz D, Sciurba F, Shoemaker MB, Silverman EK, Sinner MF, Smith NL, Smith AV, Tiwari HK, Vasan RS, Weiss ST, Williams LK, Zhang Y, Ziv E, Raffield LM, Reiner AP, Arvanitis M, Greider CW, Mathias RA, and Battle A

Subjects
Humans, K562 Cells, Polymorphism, Single Nucleotide, Gene Expression Regulation, CRISPR-Cas Systems, Genome-Wide Association Study, Telomere genetics, Telomere metabolism, Telomere Homeostasis genetics
Abstract

Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation., (© 2024. The Author(s).)

Published
2024
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33. Determinants of mosaic chromosomal alteration fitness.

Author

Pershad Y, Mack T, Poisner H, Jakubek YA, Stilp AM, Mitchell BD, Lewis JP, Boerwinkle E, Loos RJF, Chami N, Wang Z, Barnes K, Pankratz N, Fornage M, Redline S, Psaty BM, Bis JC, Shojaie A, Silverman EK, Cho MH, Yun JH, DeMeo D, Levy D, Johnson AD, Mathias RA, Taub MA, Arnett D, North KE, Raffield LM, Carson AP, Doyle MF, Rich SS, Rotter JI, Guo X, Cox NJ, Roden DM, Franceschini N, Desai P, Reiner AP, Auer PL, Scheet PA, Jaiswal S, Weinstock JS, and Bick AG

Subjects
Humans, Male, Female, Genome-Wide Association Study, Janus Kinase 2 genetics, Telomerase genetics, Telomerase metabolism, Loss of Heterozygosity, Cross-Sectional Studies, Mutation, Middle Aged, Hematopoietic Stem Cells metabolism, Polymorphism, Single Nucleotide, Aged, Mosaicism, Chromosome Aberrations, Clonal Hematopoiesis genetics
Abstract

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R 2  = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT., (© 2024. The Author(s).)

Published
2024
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34. A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels.

Author

de Vries PS, Reventun P, Brown MR, Heath AS, Huffman JE, Le NQ, Bebo A, Brody JA, Temprano-Sagrera G, Raffield LM, Ozel AB, Thibord F, Jain D, Lewis JP, Rodriguez BAT, Pankratz N, Taylor KD, Polasek O, Chen MH, Yanek LR, Carrasquilla GD, Marioni RE, Kleber ME, Trégouët DA, Yao J, Li-Gao R, Joshi PK, Trompet S, Martinez-Perez A, Ghanbari M, Howard TE, Reiner AP, Arvanitis M, Ryan KA, Bartz TM, Rudan I, Faraday N, Linneberg A, Ekunwe L, Davies G, Delgado GE, Suchon P, Guo X, Rosendaal FR, Klaric L, Noordam R, van Rooij F, Curran JE, Wheeler MM, Osburn WO, O'Connell JR, Boerwinkle E, Beswick A, Psaty BM, Kolcic I, Souto JC, Becker LC, Hansen T, Doyle MF, Harris SE, Moissl AP, Deleuze JF, Rich SS, van Hylckama Vlieg A, Campbell H, Stott DJ, Soria JM, de Maat MPM, Almasy L, Brody LC, Auer PL, Mitchell BD, Ben-Shlomo Y, Fornage M, Hayward C, Mathias RA, Kilpeläinen TO, Lange LA, Cox SR, März W, Morange PE, Rotter JI, Mook-Kanamori DO, Wilson JF, van der Harst P, Jukema JW, Ikram MA, Blangero J, Kooperberg C, Desch KC, Johnson AD, Sabater-Lleal M, Lowenstein CJ, Smith NL, and Morrison AC

Subjects
Humans, Polymorphism, Single Nucleotide, Human Umbilical Vein Endothelial Cells metabolism, Mendelian Randomization Analysis, Genome-Wide Association Study, Thrombosis genetics, Thrombosis blood, Genetic Association Studies, Male, Endothelial Cells metabolism, Female, von Willebrand Factor genetics, von Willebrand Factor metabolism, Factor VIII genetics, Factor VIII metabolism, Kininogens, Receptors, Cell Surface, Cell Adhesion Molecules, Lectins, C-Type
Abstract

Abstract: Coagulation factor VIII (FVIII) and its carrier protein von Willebrand factor (VWF) are critical to coagulation and platelet aggregation. We leveraged whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program along with TOPMed-based imputation of genotypes in additional samples to identify genetic associations with circulating FVIII and VWF levels in a single-variant meta-analysis, including up to 45 289 participants. Gene-based aggregate tests were implemented in TOPMed. We identified 3 candidate causal genes and tested their functional effect on FVIII release from human liver endothelial cells (HLECs) and VWF release from human umbilical vein endothelial cells. Mendelian randomization was also performed to provide evidence for causal associations of FVIII and VWF with thrombotic outcomes. We identified associations (P < 5 × 10-9) at 7 new loci for FVIII (ST3GAL4, CLEC4M, B3GNT2, ASGR1, F12, KNG1, and TREM1/NCR2) and 1 for VWF (B3GNT2). VWF, ABO, and STAB2 were associated with FVIII and VWF in gene-based analyses. Multiphenotype analysis of FVIII and VWF identified another 3 new loci, including PDIA3. Silencing of B3GNT2 and the previously reported CD36 gene decreased release of FVIII by HLECs, whereas silencing of B3GNT2, CD36, and PDIA3 decreased release of VWF by HVECs. Mendelian randomization supports causal association of higher FVIII and VWF with increased risk of thrombotic outcomes. Seven new loci were identified for FVIII and 1 for VWF, with evidence supporting causal associations of FVIII and VWF with thrombotic outcomes. B3GNT2, CD36, and PDIA3 modulate the release of FVIII and/or VWF in vitro.

Published
2024
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35. Extreme Phenotype Sampling and Next Generation Sequencing to Identify Genetic Variants Associated with Tacrolimus in African American Kidney Transplant Recipients.

Author

Mohamed M, Guo B, Wu B, Schladt D, Muthusamy A, Guan W, Abrahante J, Onyeaghala G, Saqr A, Pankratz N, Agarwal G, Mannon R, Matas A, Oetting W, Remmel R, Israni A, Jacobson P, and Dorr C

Abstract

African American (AA) kidney transplant recipients (KTRs) have poor outcomes, which may in-part be due to tacrolimus (TAC) sub-optimal immunosuppression. We previously determined the common genetic regulators of TAC pharmacokinetics in AAs which were CYP3A5 *3, *6, and *7. To identify low-frequency variants that impact TAC pharmacokinetics, we used extreme phenotype sampling and compared individuals with extreme high (n=58) and low (n=60) TAC troughs (N=515 AA KTRs). Targeted next generation sequencing was conducted in these two groups. Median TAC troughs in the high group were 7.7 ng/ml compared with 6.3 ng/ml in the low group, despite lower daily doses of 5 versus 12mg, respectively. Of 34,542 identified variants across 99 genes, 1,406 variants were suggestively associated with TAC troughs in univariate models (p-value <0.05), however none were significant after multiple testing correction. We suggest future studies investigate additional sources of TAC pharmacokinetic variability such as drug-drug-gene interactions and pharmacomicrobiome., Competing Interests: COMPETING INTERESTS Authors declare no competing interests.

Published
2024
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36. Parkinson Disease Genetics Extended to African and Hispanic Ancestries in the VA Million Veteran Program.

Author

Pankratz N, Cole BR, Beutel KM, Liao KP, and Ashe J

Abstract

Background and Objectives: Nearly all genetic analyses of Parkinson disease (PD) have been in populations of European ancestry. We sought to test the ability of a machine learning method to extract accurate PD diagnoses from an electronic medical record (EMR) system, to see whether genetic variants identified in European populations generalize to individuals of African and Hispanic ancestries, and to compare the rates of PD across ancestries., Methods: A machine learning method using natural language processing was applied to EMRs of US veterans participating in the VA Million Veteran Program (MVP) to identify individuals with PD. These putative cases were vetted via blind chart review by a movement disorder specialist. A polygenic risk score (PRS) of 90 established genetic variants whose genotypes were imputed from a customized Axiom Biobank Array was evaluated in different case groups., Results: The EMR prediction scores had a distinct trimodal distribution, with 97% of the high group and only 30% of the middle group having a credible diagnosis of PD. Using the 3,542 cases from the high group matched 4:1 to controls, the PRS was highly predictive in individuals of European ancestry (n = 3,137 cases; OR = 1.82; p = 8.01E-48), and nearly identical effect sizes were seen in individuals of African (n = 184; OR = 2.07; p = 3.4E-4) and Hispanic ancestries (n = 221; OR = 2.13; p = 3.9E-6). The PRS was much less predictive for the 2,757 European ancestry cases who had an ICD code for PD but for whom the machine learning method had a lower confidence in their diagnosis. No novel ancestry-specific genetic variants were identified. Individuals with African ancestry had one-quarter the rate of PD compared with European or Hispanic ancestries aged 60-70 years and one half the rate in the 70-80 years age range. African American cases had a higher proportion of their DNA originating in Europe compared with African American controls., Discussion: Machine learning can reliably classify PD using data from a large EMR. Larger studies of non-European populations are required to confirm the generalizability of PD risk variants identified in populations of European ancestry and the increased risk coming from a higher proportion of European DNA in African Americans., Competing Interests: The authors report no relevant disclosures. Go to Neurology.org/NG for full disclosures., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Published
2023
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37. Genome-wide association meta-analysis identifies risk loci for abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target.

Author

Roychowdhury T, Klarin D, Levin MG, Spin JM, Rhee YH, Deng A, Headley CA, Tsao NL, Gellatly C, Zuber V, Shen F, Hornsby WE, Laursen IH, Verma SS, Locke AE, Einarsson G, Thorleifsson G, Graham SE, Dikilitas O, Pattee JW, Judy RL, Pauls-Verges F, Nielsen JB, Wolford BN, Brumpton BM, Dilmé J, Peypoch O, Juscafresa LC, Edwards TL, Li D, Banasik K, Brunak S, Jacobsen RL, Garcia-Barrio MT, Zhang J, Rasmussen LM, Lee R, Handa A, Wanhainen A, Mani K, Lindholt JS, Obel LM, Strauss E, Oszkinis G, Nelson CP, Saxby KL, van Herwaarden JA, van der Laan SW, van Setten J, Camacho M, Davis FM, Wasikowski R, Tsoi LC, Gudjonsson JE, Eliason JL, Coleman DM, Henke PK, Ganesh SK, Chen YE, Guan W, Pankow JS, Pankratz N, Pedersen OB, Erikstrup C, Tang W, Hveem K, Gudbjartsson D, Gretarsdottir S, Thorsteinsdottir U, Holm H, Stefansson K, Ferreira MA, Baras A, Kullo IJ, Ritchie MD, Christensen AH, Iversen KK, Eldrup N, Sillesen H, Ostrowski SR, Bundgaard H, Ullum H, Burgess S, Gill D, Gallagher K, Sabater-Lleal M, Surakka I, Jones GT, Bown MJ, Tsao PS, Willer CJ, and Damrauer SM

Subjects
Humans, Animals, Mice, Proprotein Convertase 9 genetics, Proprotein Convertase 9 metabolism, Subtilisin, Proprotein Convertases, Genome-Wide Association Study, Aortic Aneurysm, Abdominal genetics
Abstract

Abdominal aortic aneurysm (AAA) is a common disease with substantial heritability. In this study, we performed a genome-wide association meta-analysis from 14 discovery cohorts and uncovered 141 independent associations, including 97 previously unreported loci. A polygenic risk score derived from meta-analysis explained AAA risk beyond clinical risk factors. Genes at AAA risk loci indicate involvement of lipid metabolism, vascular development and remodeling, extracellular matrix dysregulation and inflammation as key mechanisms in AAA pathogenesis. These genes also indicate overlap between the development of AAA and other monogenic aortopathies, particularly via transforming growth factor β signaling. Motivated by the strong evidence for the role of lipid metabolism in AAA, we used Mendelian randomization to establish the central role of nonhigh-density lipoprotein cholesterol in AAA and identified the opportunity for repurposing of proprotein convertase, subtilisin/kexin-type 9 (PCSK9) inhibitors. This was supported by a study demonstrating that PCSK9 loss of function prevented the development of AAA in a preclinical mouse model., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Published
2023
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38. Mosaic chromosomal alterations in blood across ancestries using whole-genome sequencing.

Author

Jakubek YA, Zhou Y, Stilp A, Bacon J, Wong JW, Ozcan Z, Arnett D, Barnes K, Bis JC, Boerwinkle E, Brody JA, Carson AP, Chasman DI, Chen J, Cho M, Conomos MP, Cox N, Doyle MF, Fornage M, Guo X, Kardia SLR, Lewis JP, Loos RJF, Ma X, Machiela MJ, Mack TM, Mathias RA, Mitchell BD, Mychaleckyj JC, North K, Pankratz N, Peyser PA, Preuss MH, Psaty B, Raffield LM, Vasan RS, Redline S, Rich SS, Rotter JI, Silverman EK, Smith JA, Smith AP, Taub M, Taylor KD, Yun J, Li Y, Desai P, Bick AG, Reiner AP, Scheet P, and Auer PL

Subjects
Humans, Black People genetics, Hispanic or Latino genetics, Precision Medicine, Genome-Wide Association Study, Mosaicism, Genome, Human
Abstract

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis., (© 2023. The Author(s).)

Published
2023
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39. Determinants of mosaic chromosomal alteration fitness.

Author

Pershad Y, Mack T, Poisner H, Jakubek YA, Stilp AM, Mitchell BD, Lewis JP, Boerwinkle E, Loos RJ, Chami N, Wang Z, Barnes K, Pankratz N, Fornage M, Redline S, Psaty BM, Bis JC, Shojaie A, Silverman EK, Cho MH, Yun J, DeMeo D, Levy D, Johnson A, Mathias R, Taub M, Arnett D, North K, Raffield LM, Carson A, Doyle MF, Rich SS, Rotter JI, Guo X, Cox N, Roden DM, Franceschini N, Desai P, Reiner A, Auer PL, Scheet P, Jaiswal S, Weinstock JS, and Bick AG

Abstract

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well-understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our estimates of mCA fitness were correlated (R 2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using a theoretical probability distribution. Individuals with lymphoid-associated mCAs had a significantly higher white blood cell count and faster clonal expansion rate. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified TCL1A , NRIP1 , and TERT locus variants as modulators of mCA clonal expansion rate.

Published
2023
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40. Association Between Whole Blood-Derived Mitochondrial DNA Copy Number, Low-Density Lipoprotein Cholesterol, and Cardiovascular Disease Risk.

Author

Liu X, Sun X, Zhang Y, Jiang W, Lai M, Wiggins KL, Raffield LM, Bielak LF, Zhao W, Pitsillides A, Haessler J, Zheng Y, Blackwell TW, Yao J, Guo X, Qian Y, Thyagarajan B, Pankratz N, Rich SS, Taylor KD, Peyser PA, Heckbert SR, Seshadri S, Boerwinkle E, Grove ML, Larson NB, Smith JA, Vasan RS, Fitzpatrick AL, Fornage M, Ding J, Carson AP, Abecasis G, Dupuis J, Reiner A, Kooperberg C, Hou L, Psaty BM, Wilson JG, Levy D, Rotter JI, Bis JC, Satizabal CL, Arking DE, and Liu C

Subjects
Humans, DNA, Mitochondrial genetics, Risk Factors, Cholesterol, LDL, DNA Copy Number Variations, Cross-Sectional Studies, Cholesterol, HDL, Obesity, Cardiovascular Diseases epidemiology, Cardiovascular Diseases genetics, Coronary Disease genetics, Diabetes Mellitus, Hypertension epidemiology, Hypertension genetics
Abstract

Background The relationship between mitochondrial DNA copy number (mtDNA CN) and cardiovascular disease remains elusive. Methods and Results We performed cross-sectional and prospective association analyses of blood-derived mtDNA CN and cardiovascular disease outcomes in 27 316 participants in 8 cohorts of multiple racial and ethnic groups with whole-genome sequencing. We also performed Mendelian randomization to explore causal relationships of mtDNA CN with coronary heart disease (CHD) and cardiometabolic risk factors (obesity, diabetes, hypertension, and hyperlipidemia). P <0.01 was used for significance. We validated most of the previously reported associations between mtDNA CN and cardiovascular disease outcomes. For example, 1-SD unit lower level of mtDNA CN was associated with 1.08 (95% CI, 1.04-1.12; P <0.001) times the hazard for developing incident CHD, adjusting for covariates. Mendelian randomization analyses showed no causal effect from a lower level of mtDNA CN to a higher CHD risk (β=0.091; P =0.11) or in the reverse direction (β=-0.012; P =0.076). Additional bidirectional Mendelian randomization analyses revealed that low-density lipoprotein cholesterol had a causal effect on mtDNA CN (β=-0.084; P <0.001), but the reverse direction was not significant ( P =0.059). No causal associations were observed between mtDNA CN and obesity, diabetes, and hypertension, in either direction. Multivariable Mendelian randomization analyses showed no causal effect of CHD on mtDNA CN, controlling for low-density lipoprotein cholesterol level ( P =0.52), whereas there was a strong direct causal effect of higher low-density lipoprotein cholesterol on lower mtDNA CN, adjusting for CHD status (β=-0.092; P <0.001). Conclusions Our findings indicate that high low-density lipoprotein cholesterol may underlie the complex relationships between mtDNA CN and vascular atherosclerosis.

Published
2023
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41. Genome Sequencing of Consanguineous Family Implicates Ubiquitin-Specific Protease 53 ( USP53 ) Variant in Psychosis/Schizophrenia: Wild-Type Expression in Murine Hippocampal CA 1-3 and Granular Dentate with AMPA Synapse Interactions.

Author

Kanwal A, Sheikh SA, Aslam F, Yaseen S, Beetham Z, Pankratz N, Clabots CR, Naz S, and Pardo JV

Subjects
Child, Humans, Animals, Mice, Adult, Adolescent, Consanguinity, alpha-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid, Hippocampus, Ubiquitin-Specific Proteases genetics, Schizophrenia genetics, Psychotic Disorders genetics
Abstract

Psychosis is a severe mental disorder characterized by abnormal thoughts and perceptions (e.g., hallucinations) occurring quintessentially in schizophrenia and in several other neuropsychiatric disorders. Schizophrenia is widely considered as a neurodevelopmental disorder that onsets during teenage/early adulthood. A multiplex consanguineous Pakistani family was afflicted with severe psychosis and apparent autosomal recessive transmission. The first-cousin parents and five children were healthy, whereas two teenage daughters were severely affected. Structured interviews confirmed the diagnosis of DSM-V schizophrenia. Probands and father underwent next-generation sequencing. All available relatives were subjected to confirmatory Sanger sequencing. Homozygosity mapping and directed a priori filtering identified only one rare variant [MAF < 5(10) -5 ] at a residue conserved across vertebrates. The variant was a non-catalytic deubiquitinase, USP53 (p.Cys228Arg), predicted in silico as damaging. Genome sequencing did not identify any other potentially pathogenic single nucleotide variant or structural variant. Since the literature on USP53 lacked relevance to mental illness or CNS expression, studies were conducted which revealed USP53 localization in regions of the hippocampus (CA 1-3) and granular dentate. The staining pattern was like that seen with GRIA2/GluA2 and GRIP2 antibodies. All three proteins coimmunoprecipitated. These findings support the glutamate hypothesis of schizophrenia as part of the AMPA-R interactome. If confirmed, USP53 appears to be one of the few Mendelian variants potentially causal to a common-appearing mental disorder that is a rare genetic form of schizophrenia.

Published
2023
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42. Predicted leukocyte telomere length and risk of myeloid neoplasms.

Author

Sullivan SM, Cole B, Lane J, Meredith JJ, Langer E, Hooten AJ, Roesler M, McGraw KL, Pankratz N, and Poynter JN

Subjects
Humans, Genetic Predisposition to Disease, Risk Factors, Leukocytes metabolism, Genetic Risk Score, Telomere genetics, Mendelian Randomization Analysis, Genome-Wide Association Study, Leukemia, Myeloid, Acute genetics, Leukemia, Myeloid, Acute metabolism
Abstract

Maintenance of telomere length has long been established to play a role in the biology of cancer and several studies suggest that it may be especially important in myeloid malignancies. To overcome potential bias in confounding and reverse causation of observational studies, we use both a polygenic risk score (PRS) and inverse-variance weighted (IVW) Mendelian randomization (MR) analyses to estimate the relationship between genetically predicted leukocyte telomere length (LTL) and acute myeloid leukemia (AML) risk in 498 cases and 2099 controls and myelodysplastic syndrome (MDS) risk in 610 cases and 1759 controls. Genetic instruments derived from four recent studies explaining 1.23-4.57% of telomere variability were considered. We used multivariable logistic regression to estimate odds ratios (OR, 95% confidence intervals [CI]) as the measure of association between individual single-nucleotide polymorphisms and myeloid malignancies. We observed a significant association between a PRS of longer predicted LTL and AML using three genetic instruments (OR = 4.03 per ~1200 base pair [bp] increase in LTL, 95% CI: 1.65, 9.85 using Codd et al. [Codd, V., Nelson, C.P., Albrecht, E., Mangino, M., Deelen, J., Buxton, J.L., Hottenga, J.J., Fischer, K., Esko, T., Surakka, I. et al. (2013) Identification of seven loci affecting mean telomere length and their association with disease. Nat. Genet., 45, 422-427 427e421-422.], OR = 3.48 per one-standard deviation increase in LTL, 95% CI: 1.74, 6.97 using Li et al. [Li, C., Stoma, S., Lotta, L.A., Warner, S., Albrecht, E., Allione, A., Arp, P.P., Broer, L., Buxton, J.L., Alves, A.D.S.C. et al. (2020) Genome-wide association analysis in humans links nucleotide metabolism to leukocyte telomere length. Am. J. Hum. Genet., 106, 389-404.] and OR = 2.59 per 1000 bp increase in LTL, 95% CI: 1.03, 6.52 using Taub et al. [Taub, M.A., Conomos, M.P., Keener, R., Iyer, K.R., Weinstock, J.S., Yanek, L.R., Lane, J., Miller-Fleming, T.W., Brody, J.A., Raffield, L.M. et al. (2022) Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed. Cell Genom., 2.] genetic instruments). MR analyses further indicated an association between LTL and AML risk (PIVW ≤ 0.049) but not MDS (all PIVW ≥ 0.076). Findings suggest variation in genes relevant to telomere function and maintenance may be important in the etiology of AML but not MDS., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Published
2023
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43. Exome sequencing findings in children with annular pancreas.

Author

Pitsava G, Pankratz N, Lane J, Yang W, Rigler S, Shaw GM, and Mills JL

Subjects
Humans, Male, Female, Infant, Homeodomain Proteins genetics, Trans-Activators genetics, Heterozygote, Child, Pancreas abnormalities, Pancreas pathology, Mutation, Missense, ras GTPase-Activating Proteins genetics, Pancreatic Diseases genetics, Pancreatic Diseases pathology, Exome Sequencing, Forkhead Transcription Factors genetics
Abstract

Background: Annular pancreas (AP) is a congenital defect of unknown cause in which the pancreas encircles the duodenum. Theories include abnormal migration and rotation of the ventral bud, persistence of ectopic pancreatic tissue, and inappropriate fusion of the ventral and dorsal buds before rotation. The few reported familial cases suggest a genetic contribution., Methods: We conducted exome sequencing in 115 affected infants from the California birth defects registry., Results: Seven cases had a single heterozygous missense variant in IQGAP1, five of them with CADD scores >20; seven other infants had a single heterozygous missense variant in NRCAM, five of them with CADD scores >20. We also looked at genes previously associated with AP and found two rare heterozygous missense variants, one each in PDX1 and FOXF1., Conclusion: IQGAP1 and NRCAM are crucial in cell polarization and migration. Mutations result in decreased motility which could possibly cause the ventral bud to not migrate normally. To our knowledge, this is the first study reporting a possible association for IQGAP1 and NRCAM with AP. Our findings of rare genetic variants involved in cell migration in 15% of our population raise the possibility that AP may be related to abnormal cell migration., (© 2023 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals LLC.)

Published
2023
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44. Deleterious heteroplasmic mitochondrial mutations are associated with an increased risk of overall and cancer-specific mortality.

Author

Hong YS, Battle SL, Shi W, Puiu D, Pillalamarri V, Xie J, Pankratz N, Lake NJ, Lek M, Rotter JI, Rich SS, Kooperberg C, Reiner AP, Auer PL, Heard-Costa N, Liu C, Lai M, Murabito JM, Levy D, Grove ML, Alonso A, Gibbs R, Dugan-Perez S, Gondek LP, Guallar E, and Arking DE

Subjects
Humans, DNA, Mitochondrial genetics, Heteroplasmy, Mutation, Mitochondria genetics, Leukemia genetics
Abstract

Mitochondria carry their own circular genome and disruption of the mitochondrial genome is associated with various aging-related diseases. Unlike the nuclear genome, mitochondrial DNA (mtDNA) can be present at 1000 s to 10,000 s copies in somatic cells and variants may exist in a state of heteroplasmy, where only a fraction of the DNA molecules harbors a particular variant. We quantify mtDNA heteroplasmy in 194,871 participants in the UK Biobank and find that heteroplasmy is associated with a 1.5-fold increased risk of all-cause mortality. Additionally, we functionally characterize mtDNA single nucleotide variants (SNVs) using a constraint-based score, mitochondrial local constraint score sum (MSS) and find it associated with all-cause mortality, and with the prevalence and incidence of cancer and cancer-related mortality, particularly leukemia. These results indicate that mitochondria may have a functional role in certain cancers, and mitochondrial heteroplasmic SNVs may serve as a prognostic marker for cancer, especially for leukemia., (© 2023. Springer Nature Limited.)

Published
2023
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45. Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium.

Author

Jiang MZ, Gaynor SM, Li X, Van Buren E, Stilp A, Buth E, Wang FF, Manansala R, Gogarten SM, Li Z, Polfus LM, Salimi S, Bis JC, Pankratz N, Yanek LR, Durda P, Tracy RP, Rich SS, Rotter JI, Mitchell BD, Lewis JP, Psaty BM, Pratte KA, Silverman EK, Kaplan RC, Avery C, North K, Mathias RA, Faraday N, Lin H, Wang B, Carson AP, Norwood AF, Gibbs RA, Kooperberg C, Lundin J, Peters U, Dupuis J, Hou L, Fornage M, Benjamin EJ, Reiner AP, Bowler RP, Lin X, Auer PL, and Raffield LM

Abstract

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Published
2023
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46. Whole genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles.

Author

Huffman JE, Nicolas J, Hahn J, Heath AS, Raffield LM, Yanek LR, Brody JA, Thibord F, Almasy L, Bartz TM, Bielak LF, Bowler RP, Carrasquilla GD, Chasman DI, Chen MH, Emmert DB, Ghanbari M, Haessle J, Hottenga JJ, Kleber ME, Le NQ, Lee J, Lewis JP, Li-Gao R, Luan J, Malmberg A, Mangino M, Marioni RE, Martinez-Perez A, Pankratz N, Polasek O, Richmond A, Rodriguez BA, Rotter JI, Steri M, Suchon P, Trompet S, Weiss S, Zare M, Auer P, Cho MH, Christofidou P, Davies G, de Geus E, Deleuze JF, Delgado GE, Ekunwe L, Faraday N, Gögele M, Greinacher A, He G, Howard T, Joshi PK, Kilpeläinen TO, Lahti J, Linneberg A, Naitza S, Noordam R, Paüls-Vergés F, Rich SS, Rosendaal FR, Rudan I, Ryan KA, Souto JC, van Rooij FJ, Wang H, Zhao W, Becker LC, Beswick A, Brown MR, Cade BE, Campbell H, Cho K, Crapo JD, Curran JE, de Maat MP, Doyle M, Elliott P, Floyd JS, Fuchsberger C, Grarup N, Guo X, Harris SE, Hou L, Kolcic I, Kooperberg C, Menni C, Nauck M, O'Connell JR, Orrù V, Psaty BM, Räikkönen K, Smith JA, Soria JM, Stott DJ, van Hylckama Vlieg A, Watkins H, Willemsen G, Wilson P, Ben-Shlomo Y, Blangero J, Boomsma D, Cox SR, Dehghan A, Eriksson JG, Fiorillo E, Fornage M, Hansen T, Hayward C, Ikram MA, Jukema JW, Kardia SL, Lange LA, März W, Mathias RA, Mitchell BD, Mook-Kanamori DO, Morange PE, Pedersen O, Pramstaller PP, Redline S, Reiner A, Ridker PM, Silverman EK, Spector TD, Völker U, Wareham N, Wilson JF, Yao J, Trégouët DA, Johnson AD, Wolberg AS, de Vries PS, Sabater-Lleal M, Morrison AC, and Smith NL

Abstract

Genetic studies have identified numerous regions associated with plasma fibrinogen levels in Europeans, yet missing heritability and limited inclusion of non-Europeans necessitates further studies with improved power and sensitivity. Compared with array-based genotyping, whole genome sequencing (WGS) data provides better coverage of the genome and better representation of non-European variants. To better understand the genetic landscape regulating plasma fibrinogen levels, we meta-analyzed WGS data from the NHLBI's Trans-Omics for Precision Medicine (TOPMed) program (n=32,572), with array-based genotype data from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (n=131,340) imputed to the TOPMed or Haplotype Reference Consortium panel. We identified 18 loci that have not been identified in prior genetic studies of fibrinogen. Of these, four are driven by common variants of small effect with reported MAF at least 10% higher in African populations. Three ( SERPINA1, ZFP36L2 , and TLR10) signals contain predicted deleterious missense variants. Two loci, SOCS3 and HPN , each harbor two conditionally distinct, non-coding variants. The gene region encoding the protein chain subunits ( FGG;FGB;FGA ), contains 7 distinct signals, including one novel signal driven by rs28577061, a variant common (MAF=0.180) in African reference panels but extremely rare (MAF=0.008) in Europeans. Through phenome-wide association studies in the VA Million Veteran Program, we found associations between fibrinogen polygenic risk scores and thrombotic and inflammatory disease phenotypes, including an association with gout. Our findings demonstrate the utility of WGS to augment genetic discovery in diverse populations and offer new insights for putative mechanisms of fibrinogen regulation., Key Points: Largest and most diverse genetic study of plasma fibrinogen identifies 54 regions (18 novel), housing 69 conditionally distinct variants (20 novel).Sufficient power achieved to identify signal driven by African population variant.Links to (1) liver enzyme, blood cell and lipid genetic signals, (2) liver regulatory elements, and (3) thrombotic and inflammatory disease.

Published
2023
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47. Associations between MICA and MICB Genetic Variants, Protein Levels, and Colorectal Cancer: Atherosclerosis Risk in Communities (ARIC).

Author

Wang S, Onyeaghala GC, Pankratz N, Nelson HH, Thyagarajan B, Tang W, Norby FL, Ugoji C, Joshu CE, Gomez CR, Couper DJ, Coresh J, Platz EA, and Prizment AE

Subjects
Female, Humans, Male, Histocompatibility Antigens Class I genetics, Histocompatibility Antigens Class I metabolism, Colorectal Neoplasms epidemiology, Colorectal Neoplasms genetics, Polymorphism, Single Nucleotide
Abstract

Background: The MHC class I chain-related protein A (MICA) and protein B (MICB) participate in tumor immunosurveillance and may be important in colorectal cancer, but have not been examined in colorectal cancer development., Methods: sMICA and sMICB blood levels were measured by SomaScan in Visit 2 (1990-92, baseline) and Visit 3 (1993-95) samples in cancer-free participants in the Atherosclerosis Risk in Communities Study. We selected rs1051792, rs1063635, rs2516448, rs3763288, rs1131896, rs2596542, and rs2395029 that were located in or in the vicinity of MICA or MICB and were associated with cancer or autoimmune diseases in published studies. SNPs were genotyped by the Affymetrix Genome-Wide Human SNP Array. We applied linear and Cox proportional hazards regressions to examine the associations of preselected SNPs with sMICA and sMICB levels and colorectal cancer risk (236 colorectal cancers, 8,609 participants) and of sMICA and sMICB levels with colorectal cancer risk (312 colorectal cancers, 10,834 participants). In genetic analyses, estimates adjusted for ancestry markers were meta-analyzed., Results: Rs1051792-A, rs1063635-A, rs2516448-C, rs3763288-A, rs2596542-T, and rs2395029-G were significantly associated with decreased sMICA levels. Rs2395029-G, in the vicinity of MICA and MICB, was also associated with increased sMICB levels. Rs2596542-T was significantly associated with decreased colorectal cancer risk. Lower sMICA levels were associated with lower colorectal cancer risk in males (HR = 0.68; 95% confidence interval, 0.49-0.96) but not in females (Pinteraction = 0.08)., Conclusions: Rs2596542-T associated with lower sMICA levels was associated with decreased colorectal cancer risk. Lower sMICA levels were associated with lower colorectal cancer risk in males., Impact: These findings support an importance of immunosurveillance in colorectal cancer., (©2023 American Association for Cancer Research.)

Published
2023
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49. Exploratory Study of the Association of Genetic Factors With Recovery of Adrenal Function in Cushing Disease.

Author

Nguyen MH, Zhang W, Pankratz N, Lane J, Chitiboina P, Faucz FR, Mills JL, Stratakis CA, and Tatsi C

Abstract

Successful treatment of endogenous Cushing disease (CD) is often followed by a period of adrenal insufficiency (AI). We performed an exploratory study on genetic factors potentially involved in the hypothalamic-pituitary-adrenal (HPA) axis recovery in patients with CD after remission. We identified 90 patients who achieved remission after surgery and had a minimum of 3 months follow-up. Variants in a selected panel of genes that were rare in the general population and predicted as damaging in silico were retrieved from whole exome sequencing analysis. We did not identify any variant with significant correlation with recovery time after adjusting for multiple comparisons. On gene-specific analysis the BAG1 gene showed a correlation with shorter duration of postsurgical AI, but both patients with BAG1 variants later experienced a recurrence. After excluding patients with recurrence, no statistical association was recorded. To conclude, we did not identify a strong genetic modifier of HPA recovery in this exploratory study., (Published by Oxford University Press on behalf of the Endocrine Society 2023.)

Published
2023
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50. Whole Genome Analysis of Venous Thromboembolism: the Trans-Omics for Precision Medicine Program.

Author

Seyerle AA, Laurie CA, Coombes BJ, Jain D, Conomos MP, Brody J, Chen MH, Gogarten SM, Beutel KM, Gupta N, Heckbert SR, Jackson RD, Johnson AD, Ko D, Manson JE, McKnight B, Metcalf GA, Morrison AC, Reiner AP, Sofer T, Tang W, Wiggins KL, Boerwinkle E, de Andrade M, Gabriel SB, Gibbs RA, Laurie CC, Psaty BM, Vasan RS, Rice K, Kooperberg C, Pankow JS, Smith NL, and Pankratz N

Subjects
Humans, Precision Medicine, Genetic Predisposition to Disease, Gene Frequency, Genome-Wide Association Study, Venous Thromboembolism genetics
Abstract

Background: Risk for venous thromboembolism has a strong genetic component. Whole genome sequencing from the TOPMed program (Trans-Omics for Precision Medicine) allowed us to look for new associations, particularly rare variants missed by standard genome-wide association studies., Methods: The 3793 cases and 7834 controls (11.6% of cases were individuals of African, Hispanic/Latino, or Asian ancestry) were analyzed using a single variant approach and an aggregate gene-based approach using our primary filter (included only loss-of-function and missense variants predicted to be deleterious) and our secondary filter (included all missense variants)., Results: Single variant analyses identified associations at 5 known loci. Aggregate gene-based analyses identified only PROC (odds ratio, 6.2 for carriers of rare variants; P =7.4×10 -14 ) when using our primary filter. Employing our secondary variant filter led to a smaller effect size at PROC (odds ratio, 3.8; P =1.6×10 -14 ), while excluding variants found only in rare isoforms led to a larger one (odds ratio, 7.5). Different filtering strategies improved the signal for 2 other known genes: PROS1 became significant (minimum P =1.8×10 -6 with the secondary filter), while SERPINC1 did not (minimum P =4.4×10 -5 with minor allele frequency <0.0005). Results were largely the same when restricting the analyses to include only unprovoked cases; however, one novel gene, MS4A1 , became significant ( P =4.4×10 -7 using all missense variants with minor allele frequency <0.0005)., Conclusions: Here, we have demonstrated the importance of using multiple variant filtering strategies, as we detected additional genes when filtering variants based on their predicted deleteriousness, frequency, and presence on the most expressed isoforms. Our primary analyses did not identify new candidate loci; thus larger follow-up studies are needed to replicate the novel MS4A1 locus and to identify additional rare variation associated with venous thromboembolism.

Published
2023
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