Bio

Professional Education


  • Bachelor of Science, Seoul National University (2005)
  • Master of Science, University of Michigan Ann Arbor (2011)
  • Doctor of Philosophy, University of Michigan Ann Arbor (2015)
  • Master of Science, Seoul National University (2007)
  • PhD, University of Michigan, Genetic Epidemiology (2015)
  • MS, University of Michigan, Biostatistics (2011)
  • MS, Seoul National University, Bioinformatics (2007)
  • BS, Seoul National University, Animal Science and Biotechnology (minor in Computer Science) (2005)

Stanford Advisors


Publications

All Publications


  • An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis. Nature communications Liu, J., Carnero-Montoro, E., van Dongen, J., Lent, S., Nedeljkovic, I., Ligthart, S., Tsai, P. C., Martin, T. C., Mandaviya, P. R., Jansen, R., Peters, M. J., Duijts, L., Jaddoe, V. W., Tiemeier, H., Felix, J. F., Willemsen, G., de Geus, E. J., Chu, A. Y., Levy, D., Hwang, S. J., Bressler, J., Gondalia, R., Salfati, E. L., Herder, C., Hidalgo, B. A., Tanaka, T., Moore, A. Z., Lemaitre, R. N., Jhun, M. A., Smith, J. A., Sotoodehnia, N., Bandinelli, S., Ferrucci, L., Arnett, D. K., Grallert, H., Assimes, T. L., Hou, L., Baccarelli, A., Whitsel, E. A., van Dijk, K. W., Amin, N., Uitterlinden, A. G., Sijbrands, E. J., Franco, O. H., Dehghan, A., Spector, T. D., Dupuis, J., Hivert, M. F., Rotter, J. I., Meigs, J. B., Pankow, J. S., van Meurs, J. B., Isaacs, A., Boomsma, D. I., Bell, J. T., Demirkan, A., van Duijn, C. M. 2019; 10 (1): 2581

    Abstract

    Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.

    View details for DOI 10.1038/s41467-019-10487-4

    View details for PubMedID 31197173

  • Meta-analysis of epigenome-wide association studies of cognitive abilities MOLECULAR PSYCHIATRY Marioni, R. E., McRae, A. F., Bressler, J., Colicino, E., Hannon, E., Li, S., Prada, D., Smith, J. A., Trevisi, L., Tsai, P., Vojinovic, D., Simino, J., Levy, D., Liu, C., Mendelson, M., Satizabal, C. L., Yang, Q., Jhun, M. A., Kardia, S. R., Zhao, W., Bandinelli, S., Ferrucci, L., Hernandez, D. G., Singleton, A. B., Harrisl, S. E., Starr, J. M., Kie, D. P., McLean, R. R., Just, A. C., Schwartz, J., Spiro, A., Vokonas, P., Amin, N., Ikram, M., Uitterlinden, A. G., van Meurs, J. J., Spector, T. D., Steves, C., Baccarelli, A. A., Bell, J. T., van Duijn, C. M., Fornage, M., Hsu, Y., Mill, J., Mosley, T. H., Seshadri, S., Deary, I. J. 2018; 23 (11): 2133?44

    Abstract

    Cognitive functions are important correlates of health outcomes across the life-course. Individual differences in cognitive functions are partly heritable. Epigenetic modifications, such as DNA methylation, are susceptible to both genetic and environmental factors and may provide insights into individual differences in cognitive functions. Epigenome-wide meta-analyses for blood-based DNA methylation levels at ~420,000 CpG sites were performed for seven measures of cognitive functioning using data from 11 cohorts. CpGs that passed a Bonferroni correction, adjusting for the number of CpGs and cognitive tests, were assessed for: longitudinal change; being under genetic control (methylation QTLs); and associations with brain health (structural MRI), brain methylation and Alzheimer's disease pathology. Across the seven measures of cognitive functioning (meta-analysis n range: 2557-6809), there were epigenome-wide significant (P?

    View details for DOI 10.1038/s41380-017-0008-y

    View details for Web of Science ID 000452399200007

    View details for PubMedID 29311653

    View details for PubMedCentralID PMC6035894

  • DNA methylation age is associated with an altered hemostatic profile in a multiethnic meta-analysis BLOOD Ward-Caviness, C. K., Huffman, J. E., Everett, K., Germain, M., van Dongen, J., Hill, W., Jhun, M. A., Brody, J. A., Ghanbari, M., Du, L., Roetker, N. S., de Vries, P. S., Waldenberger, M., Gieger, C., Wolf, P., Prokisch, H., Koenig, W., O'Donnell, C. J., Levy, D., Liu, C., Vinh Truong, Wells, P. S., Tregouet, D., Tang, W., Morrison, A. C., Boerwinkle, E., Wiggins, K. L., McKnight, B., Guo, X., Psaty, B. M., Sotoodenia, N., Boomsma, D. I., Willemsen, G., Ligthart, L., Deary, I. J., Zhao, W., Ware, E. B., Kardia, S. R., Van Meurs, J. J., Uitterlinden, A. G., Franco, O. H., Eriksson, P., Franco-Cereceda, A., Pankow, J. S., Johnson, A. D., Gagnon, F., Morange, P., de Geus, E. C., Starr, J. M., Smith, J. A., Dehghan, A., Bjorck, H. M., Smith, N. L., Peters, A. 2018; 132 (17): 1842?50

    Abstract

    Many hemostatic factors are associated with age and age-related diseases; however, much remains unknown about the biological mechanisms linking aging and hemostatic factors. DNA methylation is a novel means by which to assess epigenetic aging, which is a measure of age and the aging processes as determined by altered epigenetic states. We used a meta-analysis approach to examine the association between measures of epigenetic aging and hemostatic factors, as well as a clotting time measure. For fibrinogen, we performed European and African ancestry-specific meta-analyses which were then combined via a random effects meta-analysis. For all other measures we could not estimate ancestry-specific effects and used a single fixed effects meta-analysis. We found that 1-year higher extrinsic epigenetic age as compared with chronological age was associated with higher fibrinogen (0.004 g/L/y; 95% confidence interval, 0.001-0.007; P = .01) and plasminogen activator inhibitor 1 (PAI-1; 0.13 U/mL/y; 95% confidence interval, 0.07-0.20; P = 6.6 × 10-5) concentrations, as well as lower activated partial thromboplastin time, a measure of clotting time. We replicated PAI-1 associations using an independent cohort. To further elucidate potential functional mechanisms, we associated epigenetic aging with expression levels of the PAI-1 protein encoding gene (SERPINE1) and the 3 fibrinogen subunit-encoding genes (FGA, FGG, and FGB) in both peripheral blood and aorta intima-media samples. We observed associations between accelerated epigenetic aging and transcription of FGG in both tissues. Collectively, our results indicate that accelerated epigenetic aging is associated with a procoagulation hemostatic profile, and that epigenetic aging may regulate hemostasis in part via gene transcription.

    View details for DOI 10.1182/blood-2018-02-831347

    View details for Web of Science ID 000448328300015

    View details for PubMedID 30042098

    View details for PubMedCentralID PMC6202911

  • Testing cross-phenotype effects of rare variants in longitudinal studies of complex traits GENETIC EPIDEMIOLOGY Rudra, P., Broadaway, K., Ware, E. B., Jhun, M. A., Bielak, L. F., Zhao, W., Smith, J. A., Peyser, P. A., Kardia, S. R., Epstein, M. P., Ghosh, D. 2018; 42 (4): 320?32

    Abstract

    Many gene mapping studies of complex traits have identified genes or variants that influence multiple phenotypes. With the advent of next-generation sequencing technology, there has been substantial interest in identifying rare variants in genes that possess cross-phenotype effects. In the presence of such effects, modeling both the phenotypes and rare variants collectively using multivariate models can achieve higher statistical power compared to univariate methods that either model each phenotype separately or perform separate tests for each variant. Several studies collect phenotypic data over time and using such longitudinal data can further increase the power to detect genetic associations. Although rare-variant approaches exist for testing cross-phenotype effects at a single time point, there is no analogous method for performing such analyses using longitudinal outcomes. In order to fill this important gap, we propose an extension of Gene Association with Multiple Traits (GAMuT) test, a method for cross-phenotype analysis of rare variants using a framework based on the distance covariance. The approach allows for both binary and continuous phenotypes and can also adjust for covariates. Our simple adjustment to the GAMuT test allows it to handle longitudinal data and to gain power by exploiting temporal correlation. The approach is computationally efficient and applicable on a genome-wide scale due to the use of a closed-form test whose significance can be evaluated analytically. We use simulated data to demonstrate that our method has favorable power over competing approaches and also apply our approach to exome chip data from the Genetic Epidemiology Network of Arteriopathy.

    View details for DOI 10.1002/gepi.22121

    View details for Web of Science ID 000435882500001

    View details for PubMedID 29601641

    View details for PubMedCentralID PMC5980726

  • DNA Methylation Analysis Identifies Loci for Blood Pressure Regulation AMERICAN JOURNAL OF HUMAN GENETICS Richard, M. A., Huan, T., Ligthart, S., Gondalia, R., Jhun, M. A., Brody, J. A., Irvin, M. R., Marioni, R., Shen, J., Tsai, P., Montasser, M. E., Jia, Y., Syme, C., Salfati, E. L., Boerwinkle, E., Guan, W., Mosley, T. H., Bressler, J., Morrison, A. C., Liu, C., Mendelson, M. M., Uitterlinden, A. G., van Meurs, J. B., Franco, O. H., Zhang, G., Li, Y., Stewart, J. D., Bis, J. C., Psaty, B. M., Chen, Y., Kardia, S. R., Zhao, W., Turner, S. T., Absher, D., Aslibekyan, S., Starr, J. M., Mcrae, A. F., Hou, L., Just, A. C., Schwartz, J. D., Vokonas, P. S., Menni, C., Spector, T. D., Shuldiner, A., Damcott, C. M., Rotter, J. I., Palmas, W., Liu, Y., Paus, T., Horvath, S., O'Connell, J. R., Guo, X., Pausova, Z., Assimes, T. L., Sotoodehnia, N., Smith, J. A., Arnett, D. K., Deary, I. J., Baccarelli, A. A., Bell, J. T., Whitsel, E., Dehghan, A., Levy, D., Fornage, M., BIOS Consortium 2017; 101 (6): 888?902

    Abstract

    Genome-wide association studies have identified hundreds of genetic variants associated with blood pressure (BP), but sequence variation accounts for a small fraction of the phenotypic variance. Epigenetic changes may alter the expression of genes involved in BP regulation and explain part of the missing heritability. We therefore conducted a two-stage meta-analysis of the cross-sectional associations of systolic and diastolic BP with blood-derived genome-wide DNA methylation measured on the Infinium HumanMethylation450 BeadChip in 17,010 individuals of European, African American, and Hispanic ancestry. Of 31 discovery-stage cytosine-phosphate-guanine (CpG) dinucleotides, 13 replicated after Bonferroni correction (discovery: N = 9,828, p < 1.0 × 10-7; replication: N = 7,182, p < 1.6 × 10-3). The replicated methylation sites are heritable (h2 > 30%) and independent of known BP genetic variants, explaining an additional 1.4% and 2.0% of the interindividual variation in systolic and diastolic BP, respectively. Bidirectional Mendelian randomization among up to 4,513 individuals of European ancestry from 4 cohorts suggested that methylation at cg08035323 (TAF1B-YWHAQ) influences BP, while BP influences methylation at cg00533891 (ZMIZ1), cg00574958 (CPT1A), and cg02711608 (SLC1A5). Gene expression analyses further identified six genes (TSPAN2, SLC7A11, UNC93B1, CPT1A, PTMS, and LPCAT3) with evidence of triangular associations between methylation, gene expression, and BP. Additional integrative Mendelian randomization analyses of gene expression and DNA methylation suggested that the expression of TSPAN2 is a putative mediator of association between DNA methylation at cg23999170 and BP. These findings suggest that heritable DNA methylation plays a role in regulating BP independently of previously known genetic variants.

    View details for PubMedID 29198723

    View details for PubMedCentralID PMC5812919

  • Modeling the Causal Role of DNA Methylation in the Association Between Cigarette Smoking and Inflammation in African Americans: A 2-Step Epigenetic Mendelian Randomization Study AMERICAN JOURNAL OF EPIDEMIOLOGY Jhun, M. A., Smith, J. A., Ware, E. B., Kardia, S. R., Mosley, T. H., Turner, S. T., Peyser, P. A., Park, S. 2017; 186 (10): 1149?58

    Abstract

    The association between cigarette smoking and inflammation is well known. However, the biological mechanisms behind the association are not fully understood, particularly the role of DNA methylation, which is known to be affected by smoking. Using 2-step epigenetic Mendelian randomization, we investigated the role of DNA methylation in the association between cigarette smoking and inflammation. In 822 African Americans from the Genetic Epidemiology Network of Arteriopathy, phase 2 (Jackson, Mississippi; 2000-2005), study population, we examined the association of cigarette smoking with DNA methylation using single nucleotide polymorphisms identified in previous genome-wide association studies of cigarette smoking. We then investigated the association of DNA methylation with levels of inflammatory markers using cis-methylation quantitative trait loci single nucleotide polymorphisms. We found that current smoking status was associated with the DNA methylation levels (M values) of cg03636183 in the coagulation factor II (thrombin) receptor-like 3 gene (F2RL3) (M = -0.64, 95% confidence interval (CI): -0.84, -0.45) and of cg19859270 in the G protein-coupled receptor 15 gene (GPR15) (M = -0.21, 95% CI: -0.27, -0.15). The DNA methylation levels of cg03636183 in F2RL3 were associated with interleukin-18 concentration (-0.11 pg/mL, 95% CI: -0.19, -0.04). These combined negative effects suggest that cigarette smoking increases interleukin-18 levels through the decrease in DNA methylation levels of cg03636183 in F2RL3.

    View details for DOI 10.1093/aje/kwx181

    View details for Web of Science ID 000416392200006

    View details for PubMedID 29149250

    View details for PubMedCentralID PMC5860475

  • Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits NATURE COMMUNICATIONS Justice, A. E., Winkler, T. W., Feitosa, M. F., Graff, M., Fisher, V. A., Young, K., Barata, L., Deng, X., Czajkowski, J., Hadley, D., Ngwa, J. S., Ahluwalia, T. S., Chu, A. Y., Heard-Costa, N. L., Lim, E., Perez, J., Eicher, J. D., Kutalik, Z., Xue, L., Mahajan, A., Renstrom, F., Wu, J., Qi, Q., Ahmad, S., Alfred, T., Amin, N., Bielak, L. F., Bonnefond, A., Bragg, J., Cadby, G., Chittani, M., Coggeshall, S., Corre, T., Direk, N., Eriksson, J., Fischer, K., Gorski, M., Harder, M. N., Horikoshi, M., Huang, T., Huffman, J. E., Jackson, A. U., Justesen, J. M., Kanoni, S., Kinnunen, L., Kleber, M. E., Komulainen, P., Kumari, M., Lim, U., Luan, J., Lyytikainen, L., Mangino, M., Manichaikul, A., Marten, J., Middelberg, R. P., Mueller-Nurasyid, M., Navarro, P., Perusse, L., Pervjakova, N., Sarti, C., Smith, A. V., Smith, J. A., Stancakova, A., Strawbridge, R. J., Stringham, H. M., Sung, Y. J., Tanaka, T., Teumer, A., Trompet, S., van der Laan, S. W., van der Most, P. J., van Vliet-Ostaptchouk, J. V., Vedantam, S. L., Verweij, N., Vink, J. M., Vitart, V., Wu, Y., Yengo, L., Zhang, W., Zhao, J. H., Zimmermann, M. E., Zubair, N., Abecasis, G. R., Adair, L. S., Afaq, S., Afzal, U., Bakker, S. J., Bartz, T. M., Beilby, J., Bergman, R. N., Bergmann, S., Biffar, R., Blangero, J., Boerwinkle, E., Bonnycastle, L. L., Bottinger, E., Braga, D., Buckley, B. M., Buyske, S., Campbell, H., Chambers, J. C., Collins, F. S., Curran, J. E., de Borst, G. J., de Craen, A. J., de Geus, E. J., Dedoussis, G., Delgado, G. E., Den Ruijter, H. M., Eiriksdottir, G., Eriksson, A. L., Esko, T., Faul, J. D., Ford, I., Forrester, T., Gertow, K., Gigante, B., Glorioso, N., Gong, J., Grallert, H., Grammer, T. B., Grarup, N., Haitjema, S., Hallmans, G., Hamsten, A., Hansen, T., Harris, T. B., Hartman, C. A., Hassinen, M., Hastie, N. D., Heath, A. C., Hernandez, D., Hindorff, L., Hocking, L. J., Hollensted, M., Holmen, O. L., Homuth, G., Hottenga, J. J., Huang, J., Hung, J., Hutri-Kahonen, N., Ingelsson, E., James, A. L., Jansson, J., Jarvelin, M., Jhun, M. A., Jorgensen, M. E., Juonala, M., Kahonen, M., Karlsson, M., Koistinen, H. A., Kolcic, I., Kolovou, G., Kooperberg, C., Kramer, B. K., Kuusisto, J., Kvaloy, K., Lakka, T. A., Langenberg, C., Launer, L. J., Leander, K., Lee, N. R., Lind, L., Lindgren, C. M., Linneberg, A., Lobbens, S., Loh, M., Lorentzon, M., Luben, R., Lubke, G., Ludolph-Donislawski, A., Lupoli, S., Madden, P. A., Mannikko, R., Marques-Vidal, P., Martin, N. G., McKenzie, C. A., McKnight, B., Mellstrom, D., Menni, C., Montgomery, G. W., Musk, A. W., Narisu, N., Nauck, M., Nolte, I. M., Oldehinkel, A. J., Olden, M., Ong, K. K., Padmanabhan, S., Peyser, P. A., Pisinger, C., Porteous, D. J., Raitakari, O. T., Rankinen, T., Rao, D. C., Rasmussen-Torvik, L. J., Rawal, R., Rice, T., Ridker, P. M., Rose, L. M., Bien, S. A., Rudan, I., Sanna, S., Sarzynski, M. A., Sattar, N., Savonen, K., Schlessinger, D., Scholtens, S., Schurmann, C., Scott, R. A., Sennblad, B., Siemelink, M. A., Silbernagel, G., Slagboom, P. E., Snieder, H., Staessen, J. A., Stott, D. J., Swertz, M. A., Swift, A. J., Taylor, K. D., Tayo, B. O., Thorand, B., Thuillier, D., Tuomilehto, J., Uitterlinden, A. G., Vandenput, L., Vohl, M., Volzke, H., Vonk, J. M., Waeber, G., Waldenberger, M., Westendorp, R. G., Wild, S., Willemsen, G., Wolffenbuttel, B. H., Wong, A., Wright, A. F., Zhao, W., Zillikens, M. C., Baldassarre, D., Balkau, B., Bandinelli, S., Boger, C. A., Boomsma, D. I., Bouchard, C., Bruinenberg, M., Chasman, D. I., Chen, Y. I., Chines, P. S., Cooper, R. S., Cucca, F., Cusi, D., de Faire, U., Ferrucci, L., Franks, P. W., Froguel, P., Gordon-Larsen, P., Grabe, H., Gudnason, V., Haiman, C. A., Hayward, C., Hveem, K., Johnson, A. D., Jukema, W., Kardia, S. L., Kivimaki, M., Kooner, J. S., Kuh, D., Laakso, M., Lehtimaki, T., Le Marchand, L., Marz, W., McCarthy, M. I., Metspalu, A., Morris, A. P., Ohlsson, C., Palmer, L. J., Pasterkamp, G., Pedersen, O., Peters, A., Peters, U., Polasek, O., Psaty, B. M., Qi, L., Rauramaa, R., Smith, B. H., Sorensen, T. I., Strauch, K., Tiemeier, H., Tremoli, E., van der Harst, P., Vestergaard, H., Vollenweider, P., Wareham, N. J., Weir, D. R., Whitfield, J. B., Wilson, J. F., Tyrrell, J., Frayling, T. M., Barroso, I., Boehnke, M., Deloukas, P., Fox, C. S., Hirschhorn, J. N., Hunter, D. J., Spector, T. D., Strachan, D. P., van Duijn, C. M., Heid, I. M., Mohlke, K. L., Marchini, J., Loos, R. J., Kilpelainen, T. O., Liu, C., Borecki, I. B., North, K. E., Cupples, L. A. 2017; 8

    Abstract

    Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.

    View details for DOI 10.1038/ncomms14977

    View details for PubMedID 28443625

  • Genome-wide physical activity interactions in adiposity. A meta-analysis of 200,452 adults PLOS GENETICS Graff, M., Scott, R. A., Justice, A. E., Young, K. L., Feitosa, M. F., Barata, L., Winkler, T. W., Chu, A. Y., Mahajan, A., Hadley, D., Xue, L., Workalemahu, T., Heard-Costa, N. L., den Hoed, M., Ahluwalia, T. S., Qi, Q., Ngwa, J. S., Renstrom, F., Quaye, L., Eicher, J. D., Hayes, J. E., Cornelis, M., Kutalik, Z., Lim, E., Luan, J., Huffman, J. E., Zhang, W., Zhao, W., Griffin, P. J., Haller, T., Ahmad, S., Marques-Vidal, P. M., Bien, S., Yengo, L., Teumer, A., Smith, A., Kumari, M., Harder, M., Justesen, J., Kleber, M. E., Hollensted, M., Lohman, K., Rivera, N. V., Whitfield, J. B., Zhao, J., Stringham, H. M., Lyytikainen, L., Huppertz, C., Willemsen, G., Peyrot, W. J., Wu, Y., Kristiansson, K., Demirkan, A., Fornage, M., Hassinen, M., Bielak, L. F., Cadby, G., Tanaka, T., Magl, R., Van der Most, P. J., Jackson, A. U., Bragg-Gresham, J. L., Vitart, V., Marten, J., Navarro, P., Bellis, C., Pasko, D., Johansson, A., Snitker, S., Cheng, Y., Eriksson, J., Lim, U., Aadahl, M., Adair, L. S., Amin, N., Balkau, B., Auvinen, J., Beilby, J., Bergman, R. N., Bergmann, S., Bertoni, A. G., Blangero, J., Bonnefond, A., Bonnycastle, L. L., Borja, J. B., Brage, S., Busonero, F., Buyske, S., Campbell, H., Chines, P. S., Collins, F. S., Corre, T., Smith, G., Delgado, G. E., Dueker, N., Doerr, M., Ebeling, T., Eiriksdottir, G., Esko, T., Faul, J. D., Fu, M., Faerch, K., Gieger, C., Glaeser, S., Gong, J., Gordon-Larsen, P., Grallert, H., Grammer, T. B., Grarup, N., van Grootheest, G., Harald, K., Hastie, N. D., Havulinna, A. S., Hernandez, D., Hindorff, L., Hocking, L. J., Holmens, O. L., Holzapfel, C., Hottenga, J., Huang, J., Huang, T., Hui, J., Huth, C., Hutri-Kahonen, N., James, A. L., Jansson, J., Jhun, M. A., Juonala, M., Kinnunen, L., Koistinen, H. A., Kolcic, I., Komulainen, P., Kuusisto, J., Kvaloy, K., Kahonen, M., Lakka, T. A., Launer, L. J., Lehne, B., Lindgren, C. M., Lorentzon, M., Luben, R., Marre, M., Milaneschi, Y., Monda, K. L., Montgomery, G. W., De Moor, M. M., Mulas, A., Mueller-Nurasyid, M., Musk, A. W., Mannikko, R., Mannisto, S., Narisu, N., Nauck, M., Nettleton, J. A., Nolte, I. M., Oldehinkel, A. J., Olden, M., Ong, K. K., Padmanabhan, S., Paternoster, L., Perez, J., Perola, M., Peters, A., Peters, U., Peyser, P. A., Prokopenko, I., Puolijoki, H., Raitakari, O. T., Rankinen, T., Rasmussen-Torvik, L. J., Rawal, R., Ridker, P. M., Rose, L. M., Rudan, I., Sarti, C., Sarzynski, M. A., Savonen, K., Scott, W. R., Sanna, S., Shuldiner, A. R., Sidney, S., Silbernagel, G., Smith, B. H., Smith, J. A., Snieder, H., Stancakova, A., Sternfeld, B., Swift, A. J., Tammelin, T., Tan, S., Thorand, B., Thuillier, D., Vandenput, L., Vestergaard, H., van Vliet-Ostaptchouk, J. V., Vohl, M., Voelker, U., Waeber, G., Walker, M., Wild, S., Wong, A., Wright, A. F., Zillikens, M., Zubair, N., Haiman, C. A., Lemarchand, L., Gyllensten, U., Ohlsson, C., Ohlsson, C., Hofman, A., Rivadeneira, F., Uitterlinden, A. G., Perusse, L., Wilson, J. F., Hayward, C., Polasek, O., Cucca, F., Hveem, K., Hartman, C. A., Toenjes, A., Bandinelli, S., Palmer, L. J., Kardia, S. R., Rauramaa, R., Sorensen, T. A., Tuomilehto, J., Salomaa, V., Penninx, B. H., de Geus, E. C., Boomsma, D. I., Lehtimaki, T., Mangino, M., Laakso, M., Bouchard, C., Martin, N. G., Kuh, D., Liu, Y., Linneberg, A., Maerz, W., Strauch, K., Kivimaki, M., Harris, T. B., Gudnason, V., Voelzke, H., Qi, L., Jarvelin, M., Chambers, J. C., Kooner, J. S., Froguel, P., Kooperberg, C., Vollenweider, P., Hallmans, G., Hansen, T., Pedersen, O., Metspalu, A., Wareham, N. J., Langenberg, C., Weir, D. R., Porteous, D. J., Boerwinkle, E., Chasman, D. I., Abecasis, G. R., Barroso, I., McCarthy, M. I., Frayling, T. M., O'Connell, J. R., van Duijn, C. M., Boehnke, M., Heid, I. M., Mohlke, K. L., Strachan, D. P., Fox, C. S., Liu, C., Hirschhorn, J. N., Klein, R. J., Johnson, A. D., Borecki, I. B., Franks, P. W., North, K. E., Cupples, L., Loos, R. F., Kilpelainen, T. O., CHARGE Consortium, EPIC-InterAct Consortium, PAGE Consortium 2017; 13 (4): e1006528

    Abstract

    Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.

    View details for DOI 10.1371/journal.pgen.1006528

    View details for Web of Science ID 000402549200002

    View details for PubMedID 28448500

    View details for PubMedCentralID PMC5407576

  • Rare and low-frequency coding variants alter human adult height. Nature Marouli, E., Graff, M., Medina-Gomez, C., Lo, K. S., Wood, A. R., Kjaer, T. R., Fine, R. S., Lu, Y., Schurmann, C., Highland, H. M., Rüeger, S., Thorleifsson, G., Justice, A. E., Lamparter, D., Stirrups, K. E., Turcot, V., Young, K. L., Winkler, T. W., Esko, T., Karaderi, T., Locke, A. E., Masca, N. G., Ng, M. C., Mudgal, P., Rivas, M. A., Vedantam, S., Mahajan, A., Guo, X., Abecasis, G., Aben, K. K., Adair, L. S., Alam, D. S., Albrecht, E., Allin, K. H., Allison, M., Amouyel, P., Appel, E. V., Arveiler, D., Asselbergs, F. W., Auer, P. L., Balkau, B., Banas, B., Bang, L. E., Benn, M., Bergmann, S., Bielak, L. F., Blüher, M., Boeing, H., Boerwinkle, E., Böger, C. A., Bonnycastle, L. L., Bork-Jensen, J., Bots, M. L., Bottinger, E. P., Bowden, D. W., Brandslund, I., Breen, G., Brilliant, M. H., Broer, L., Burt, A. A., Butterworth, A. S., Carey, D. J., Caulfield, M. J., Chambers, J. C., Chasman, D. I., Chen, Y. I., Chowdhury, R., Christensen, C., Chu, A. Y., Cocca, M., Collins, F. S., Cook, J. P., Corley, J., Galbany, J. C., Cox, A. J., Cuellar-Partida, G., Danesh, J., Davies, G., de Bakker, P. I., de Borst, G. J., De Denus, S., de Groot, M. C., de Mutsert, R., Deary, I. J., Dedoussis, G., Demerath, E. W., den Hollander, A. I., Dennis, J. G., Di Angelantonio, E., Drenos, F., Du, M., Dunning, A. M., Easton, D. F., Ebeling, T., Edwards, T. L., Ellinor, P. T., Elliott, P., Evangelou, E., Farmaki, A., Faul, J. D., Feitosa, M. F., Feng, S., Ferrannini, E., Ferrario, M. M., Ferrieres, J., Florez, J. C., Ford, I., Fornage, M., Franks, P. W., Frikke-Schmidt, R., Galesloot, T. E., Gan, W., Gandin, I., Gasparini, P., Giedraitis, V., Giri, A., Girotto, G., Gordon, S. D., Gordon-Larsen, P., Gorski, M., Grarup, N., Grove, M. L., Gudnason, V., Gustafsson, S., Hansen, T., Harris, K. M., Harris, T. B., Hattersley, A. T., Hayward, C., He, L., Heid, I. M., Heikkilä, K., Helgeland, Ø., Hernesniemi, J., Hewitt, A. W., Hocking, L. J., Hollensted, M., Holmen, O. L., Hovingh, G. K., Howson, J. M., Hoyng, C. B., Huang, P. L., Hveem, K., Ikram, M. A., Ingelsson, E., Jackson, A. U., Jansson, J., Jarvik, G. P., Jensen, G. B., Jhun, M. A., Jia, Y., Jiang, X., Johansson, S., Jørgensen, M. E., Jørgensen, T., Jousilahti, P., Jukema, J. W., Kahali, B., Kahn, R. S., Kähönen, M., Kamstrup, P. R., Kanoni, S., Kaprio, J., Karaleftheri, M., Kardia, S. L., Karpe, F., Kee, F., Keeman, R., Kiemeney, L. A., Kitajima, H., Kluivers, K. B., Kocher, T., Komulainen, P., Kontto, J., Kooner, J. S., Kooperberg, C., Kovacs, P., Kriebel, J., Kuivaniemi, H., Küry, S., Kuusisto, J., La Bianca, M., Laakso, M., Lakka, T. A., Lange, E. M., Lange, L. A., Langefeld, C. D., Langenberg, C., Larson, E. B., Lee, I., Lehtimäki, T., Lewis, C. E., Li, H., Li, J., Li-Gao, R., Lin, H., Lin, L., Lin, X., Lind, L., Lindström, J., Linneberg, A., Liu, Y., Liu, Y., Lophatananon, A., Luan, J., Lubitz, S. A., Lyytikäinen, L., Mackey, D. A., Madden, P. A., Manning, A. K., Männistö, S., Marenne, G., Marten, J., Martin, N. G., Mazul, A. L., Meidtner, K., Metspalu, A., Mitchell, P., Mohlke, K. L., Mook-Kanamori, D. O., Morgan, A., Morris, A. D., Morris, A. P., Müller-Nurasyid, M., Munroe, P. B., Nalls, M. A., Nauck, M., Nelson, C. P., Neville, M., Nielsen, S. F., Nikus, K., Njølstad, P. R., Nordestgaard, B. G., Ntalla, I., O'Connel, J. R., Oksa, H., Loohuis, L. M., Ophoff, R. A., Owen, K. R., Packard, C. J., Padmanabhan, S., Palmer, C. N., Pasterkamp, G., Patel, A. P., Pattie, A., Pedersen, O., Peissig, P. L., Peloso, G. M., Pennell, C. E., Perola, M., Perry, J. A., Perry, J. R., Person, T. N., Pirie, A., Polasek, O., Posthuma, D., Raitakari, O. T., Rasheed, A., Rauramaa, R., Reilly, D. F., Reiner, A. P., Renström, F., Ridker, P. M., Rioux, J. D., Robertson, N., Robino, A., Rolandsson, O., Rudan, I., Ruth, K. S., Saleheen, D., Salomaa, V., Samani, N. J., Sandow, K., Sapkota, Y., Sattar, N., Schmidt, M. K., Schreiner, P. J., Schulze, M. B., Scott, R. A., Segura-Lepe, M. P., Shah, S., Sim, X., Sivapalaratnam, S., Small, K. S., Smith, A. V., Smith, J. A., Southam, L., Spector, T. D., Speliotes, E. K., Starr, J. M., Steinthorsdottir, V., Stringham, H. M., Stumvoll, M., Surendran, P., 't Hart, L. M., Tansey, K. E., Tardif, J., Taylor, K. D., Teumer, A., Thompson, D. J., Thorsteinsdottir, U., Thuesen, B. H., Tönjes, A., Tromp, G., Trompet, S., Tsafantakis, E., Tuomilehto, J., Tybjaerg-Hansen, A., Tyrer, J. P., Uher, R., Uitterlinden, A. G., Ulivi, S., van der Laan, S. W., van der Leij, A. R., van Duijn, C. M., van Schoor, N. M., van Setten, J., Varbo, A., Varga, T. V., Varma, R., Edwards, D. R., Vermeulen, S. H., Vestergaard, H., Vitart, V., Vogt, T. F., Vozzi, D., Walker, M., Wang, F., Wang, C. A., Wang, S., Wang, Y., Wareham, N. J., Warren, H. R., Wessel, J., Willems, S. M., Wilson, J. G., Witte, D. R., Woods, M. O., Wu, Y., Yaghootkar, H., Yao, J., Yao, P., Yerges-Armstrong, L. M., Young, R., Zeggini, E., Zhan, X., Zhang, W., Zhao, J. H., Zhao, W., Zhao, W., Zheng, H., Zhou, W., Rotter, J. I., Boehnke, M., Kathiresan, S., McCarthy, M. I., Willer, C. J., Stefansson, K., Borecki, I. B., Liu, D. J., North, K. E., Heard-Costa, N. L., Pers, T. H., Lindgren, C. M., Oxvig, C., Kutalik, Z., Rivadeneira, F., Loos, R. J., Frayling, T. M., Hirschhorn, J. N., Deloukas, P., Lettre, G. 2017; 542 (7640): 186-190

    Abstract

    Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2?centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2?centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.

    View details for DOI 10.1038/nature21039

    View details for PubMedID 28146470

  • Gene expression signature of Gleason score is associated with prostate cancer outcomes in a radical prostatectomy cohort. Oncotarget Jhun, M. A., Geybels, M. S., Wright, J. L., Kolb, S., April, C., Bibikova, M., Ostrander, E. A., Fan, J. B., Feng, Z., Stanford, J. L. 2017; 8 (26): 43035?47

    Abstract

    Prostate cancer (PCa) is a leading cause of cancer-related mortality worldwide. Gleason score (GS) is one of the best predictors of PCa aggressiveness, but additional tumor biomarkers may improve its prognostic accuracy. We developed a gene expression signature of GS to enhance the prediction of PCa outcomes. Elastic net was used to construct a gene expression signature by contrasting GS 8-10 vs. ?6 tumors in The Cancer Genome Atlas (TCGA) dataset. The constructed signature was then evaluated for its ability to predict recurrence and metastatic-lethal (ML) progression in a Fred Hutchinson (FH) patient cohort (N=408; NRecurrence=109; NMLprogression=27). The expression signature included transcripts representing 49 genes. In the FH cohort, a 25% increase in the signature was associated with a hazard ratio (HR) of 1.51 (P=2.7×10-5) for recurrence. The signature's area under the curve (AUC) for predicting recurrence and ML progression was 0.68 and 0.76, respectively. Compared to a model with age at diagnosis, pathological stage and GS, the gene expression signature improved the AUC for recurrence (3%) and ML progression (6%). Higher levels of the signature were associated with increased expression of genes in cell cycle-related pathways and decreased expression of genes in androgen response, estrogen response, oxidative phosphorylation, and apoptosis. This gene expression signature based on GS may improve the prediction of recurrence as well as ML progression in PCa patients after radical prostatectomy.

    View details for DOI 10.18632/oncotarget.17428

    View details for PubMedID 28496006

    View details for PubMedCentralID PMC5522125

  • SOS2 and ACP1 Loci Identified through Large-Scale Exome Chip Analysis Regulate Kidney Development and Function. Journal of the American Society of Nephrology : JASN Li, M., Li, Y., Weeks, O., Mijatovic, V., Teumer, A., Huffman, J. E., Tromp, G., Fuchsberger, C., Gorski, M., Lyytikäinen, L. P., Nutile, T., Sedaghat, S., Sorice, R., Tin, A., Yang, Q., Ahluwalia, T. S., Arking, D. E., Bihlmeyer, N. A., Böger, C. A., Carroll, R. J., Chasman, D. I., Cornelis, M. C., Dehghan, A., Faul, J. D., Feitosa, M. F., Gambaro, G., Gasparini, P., Giulianini, F., Heid, I., Huang, J., Imboden, M., Jackson, A. U., Jeff, J., Jhun, M. A., Katz, R., Kifley, A., Kilpeläinen, T. O., Kumar, A., Laakso, M., Li-Gao, R., Lohman, K., Lu, Y., Mägi, R., Malerba, G., Mihailov, E., Mohlke, K. L., Mook-Kanamori, D. O., Robino, A., Ruderfer, D., Salvi, E., Schick, U. M., Schulz, C. A., Smith, A. V., Smith, J. A., Traglia, M., Yerges-Armstrong, L. M., Zhao, W., Goodarzi, M. O., Kraja, A. T., Liu, C., Wessel, J., Boerwinkle, E., Borecki, I. B., Bork-Jensen, J., Bottinger, E. P., Braga, D., Brandslund, I., Brody, J. A., Campbell, A., Carey, D. J., Christensen, C., Coresh, J., Crook, E., Curhan, G. C., Cusi, D., de Boer, I. H., de Vries, A. P., Denny, J. C., Devuyst, O., Dreisbach, A. W., Endlich, K., Esko, T., Franco, O. H., Fulop, T., Gerhard, G. S., Glümer, C., Gottesman, O., Grarup, N., Gudnason, V., Hansen, T., Harris, T. B., Hayward, C., Hocking, L., Hofman, A., Hu, F. B., Husemoen, L. L., Jackson, R. D., Jørgensen, T., Jørgensen, M. E., Kähönen, M., Kardia, S. L., König, W., Kooperberg, C., Kriebel, J., Launer, L. J., Lauritzen, T., Lehtimäki, T., Levy, D., Linksted, P., Linneberg, A., Liu, Y., Loos, R. J., Lupo, A., Meisinger, C., Melander, O., Metspalu, A., Mitchell, P., Nauck, M., Nürnberg, P., Orho-Melander, M., Parsa, A., Pedersen, O., Peters, A., Peters, U., Polasek, O., Porteous, D., Probst-Hensch, N. M., Psaty, B. M., Qi, L., Raitakari, O. T., Reiner, A. P., Rettig, R., Ridker, P. M., Rivadeneira, F., Rossouw, J. E., Schmidt, F., Siscovick, D., Soranzo, N., Strauch, K., Toniolo, D., Turner, S. T., Uitterlinden, A. G., Ulivi, S., Velayutham, D., Völker, U., Völzke, H., Waldenberger, M., Wang, J. J., Weir, D. R., Witte, D., Kuivaniemi, H., Fox, C. S., Franceschini, N., Goessling, W., Köttgen, A., Chu, A. Y. 2017; 28 (3): 981?94

    Abstract

    Genome-wide association studies have identified >50 common variants associated with kidney function, but these variants do not fully explain the variation in eGFR. We performed a two-stage meta-analysis of associations between genotypes from the Illumina exome array and eGFR on the basis of serum creatinine (eGFRcrea) among participants of European ancestry from the CKDGen Consortium (nStage1: 111,666; nStage2: 48,343). In single-variant analyses, we identified single nucleotide polymorphisms at seven new loci associated with eGFRcrea (PPM1J, EDEM3, ACP1, SPEG, EYA4, CYP1A1, and ATXN2L; PStage1<3.7×10-7), of which most were common and annotated as nonsynonymous variants. Gene-based analysis identified associations of functional rare variants in three genes with eGFRcrea, including a novel association with the SOS Ras/Rho guanine nucleotide exchange factor 2 gene, SOS2 (P=5.4×10-8 by sequence kernel association test). Experimental follow-up in zebrafish embryos revealed changes in glomerular gene expression and renal tubule morphology in the embryonic kidney of acp1- and sos2-knockdowns. These developmental abnormalities associated with altered blood clearance rate and heightened prevalence of edema. This study expands the number of loci associated with kidney function and identifies novel genes with potential roles in kidney formation.

    View details for PubMedID 27920155

  • DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases GENOME BIOLOGY Ligthart, S., Marzi, C., Aslibekyan, S., Mendelson, M. M., Conneely, K. N., Tanaka, T., Colicino, E., Waite, L. L., Joehanes, R., Guan, W., Brody, J. A., Elks, C., Marioni, R., Jhun, M. A., Agha, G., Bressler, J., Ward-Caviness, C. K., Chen, B. H., Huan, T., Bakulski, K., Salfati, E. L., Wahl, S., Schramm, K., Sha, J., Hernandez, D. G., Just, A. C., Smith, J. A., Sotoodehnia, N., Pilling, L. C., Pankow, J. S., Tsao, P. S., Liu, C., Zhao, W., Guarrera, S., Michopoulos, V. J., Smith, A. K., Peters, M. J., Melzer, D., Vokonas, P., Fornage, M., Prokisch, H., Bis, J. C., Chu, A. Y., Herder, C., Grallert, H., Yao, C., Shah, S., McRae, A. F., Lin, H., Horvath, S., Fallin, D., Hofman, A., Wareham, N. J., Wiggins, K. L., Feinberg, A. P., Starr, J. M., Visscher, P. M., Murabito, J. M., Kardia, S. L., Absher, D. M., Binder, E. B., Singleton, A. B., Bandinelli, S., Peters, A., Waldenberger, M., Matullo, G., Schwartz, J. D., Demerath, E. W., Uitterlinden, A. G., van Meurs, J. B., Franco, O. H., Chen, Y. I., Levy, D., Turner, S. T., Deary, I. J., Ressler, K. J., Dupuis, J., Ferrucci, L., Ong, K. K., Assimes, T. L., Boerwinkle, E., Koenig, W., Arnett, D. K., Baccarelli, A. A., Benjamin, E. J., Dehghan, A. 2016; 17

    Abstract

    Chronic low-grade inflammation reflects a subclinical immune response implicated in the pathogenesis of complex diseases. Identifying genetic loci where DNA methylation is associated with chronic low-grade inflammation may reveal novel pathways or therapeutic targets for inflammation.We performed a meta-analysis of epigenome-wide association studies (EWAS) of serum C-reactive protein (CRP), which is a sensitive marker of low-grade inflammation, in a large European population (n?=?8863) and trans-ethnic replication in African Americans (n?=?4111). We found differential methylation at 218 CpG sites to be associated with CRP (P?

    View details for DOI 10.1186/s13059-016-1119-5

    View details for PubMedID 27955697

  • Multiethnic Exome-Wide Association Study of Subclinical Atherosclerosis CIRCULATION-CARDIOVASCULAR GENETICS Natarajan, P., Bis, J. C., Bielak, L. F., Cox, A. J., Dorr, M., Feitosa, M. F., Franceschini, N., Guo, X., Hwang, S., Isaacs, A., Jhun, M. A., Kavousi, M., Li-Gao, R., Lyytikainen, L., Marioni, R. E., Schminke, U., Stitziel, N. O., Tada, H., van Setten, J., Smith, A. V., Vojinovic, D., Yanek, L. R., Yao, J., Yerges-Armstrong, L. M., Amin, N., Baber, U., Borecki, I. B., Carr, J. J., Chen, Y. I., Cupples, L. A., de Jong, P. A., de Koning, H., de Vos, B. D., Demirkan, A., Fuster, V., Franco, O. H., Goodarzi, M. O., Harris, T. B., Heckbert, S. R., Heiss, G., Hoffmann, U., Hofman, A., Isgum, I., Jukema, J. W., Kahonen, M., Kardia, S. L., Kral, B. G., Launer, L. J., Massaro, J., Mehran, R., Mitchell, B. D., Jr, T. H., de Mutsert, R., Newman, A. B., Nguyen, K., North, K. E., O'Connell, J. R., Oudkerk, M., Pankow, J. S., Peloso, G. M., Post, W., Province, M. A., Raffield, L. M., Raitakari, O. T., Reilly, D. F., Rivadeneira, F., Rosendaal, F., Sartori, S., Taylor, K. D., Teumer, A., Trompet, S., Turner, S. T., Uitterlinden, A. G., Vaidya, D., van der Lugt, A., Volker, U., Wardlaw, J. M., Wassel, C. L., Weiss, S., Wojczynski, M. K., Becker, D. M., Becker, L. C., Boerwinkle, E., Bowden, D. W., Deary, I. J., Dehghan, A., Felix, S. B., Gudnason, V., Lehtimaki, T., Mathias, R., Mook-Kanamori, D. O., Psaty, B. M., Rader, D. J., Rotter, J. I., Wilson, J. G., van Duijn, C. M., Volzke, H., Kathiresan, S., Peyser, P. A., O'Donnell, C. J. 2016; 9 (6): 511-?

    Abstract

    The burden of subclinical atherosclerosis in asymptomatic individuals is heritable and associated with elevated risk of developing clinical coronary heart disease. We sought to identify genetic variants in protein-coding regions associated with subclinical atherosclerosis and the risk of subsequent coronary heart disease.We studied a total of 25?109 European ancestry and African ancestry participants with coronary artery calcification (CAC) measured by cardiac computed tomography and 52?869 participants with common carotid intima-media thickness measured by ultrasonography within the CHARGE Consortium (Cohorts for Heart and Aging Research in Genomic Epidemiology). Participants were genotyped for 247?870 DNA sequence variants (231?539 in exons) across the genome. A meta-analysis of exome-wide association studies was performed across cohorts for CAC and carotid intima-media thickness. APOB p.Arg3527Gln was associated with 4-fold excess CAC (P=3×10(-)(10)). The APOE ?2 allele (p.Arg176Cys) was associated with both 22.3% reduced CAC (P=1×10(-)(12)) and 1.4% reduced carotid intima-media thickness (P=4×10(-)(14)) in carriers compared with noncarriers. In secondary analyses conditioning on low-density lipoprotein cholesterol concentration, the ?2 protective association with CAC, although attenuated, remained strongly significant. Additionally, the presence of ?2 was associated with reduced risk for coronary heart disease (odds ratio 0.77; P=1×10(-)(11)).Exome-wide association meta-analysis demonstrates that protein-coding variants in APOB and APOE associate with subclinical atherosclerosis. APOE ?2 represents the first significant association for multiple subclinical atherosclerosis traits across multiple ethnicities, as well as clinical coronary heart disease.

    View details for DOI 10.1161/CIRCGENETICS.116.001572

    View details for Web of Science ID 000391823900007

    View details for PubMedID 27872105

    View details for PubMedCentralID PMC5418659

  • A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants AMERICAN JOURNAL OF HUMAN GENETICS Broadaway, K., Cutler, D. J., Duncan, R., Moore, J. L., Ware, E. B., Jhun, M. A., Bielak, L. F., Zhao, W., Smith, J. A., Peyser, P. A., Kardia, S. R., Ghosh, D., Epstein, M. P. 2016; 98 (3): 525?40

    Abstract

    Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.

    View details for DOI 10.1016/j.ajhg.2016.01.017

    View details for Web of Science ID 000372383100015

    View details for PubMedID 26942286

    View details for PubMedCentralID PMC4800053

  • A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration HUMAN MOLECULAR GENETICS de Vries, P. S., Chasman, D. I., Sabater-Lleal, M., Chen, M., Huffman, J. E., Steri, M., Tang, W., Teumer, A., Marioni, R. E., Grossmann, V., Hottenga, J. J., Trompet, S., Mueller-Nurasyid, M., Zhao, J., Brody, J. A., Kleber, M. E., Guo, X., Wang, J., Auer, P. L., Attia, J. R., Yanek, L. R., Ahluwalia, T. S., Lahti, J., Venturini, C., Tanaka, T., Bielak, L. F., Joshi, P. K., Rocanin-Arjo, A., Kolcic, I., Navarro, P., Rose, L. M., Oldmeadow, C., Riess, H., Mazur, J., Basu, S., Goel, A., Yang, Q., Ghanbari, M., Willemsen, G., Rumley, A., Fiorillo, E., de Craen, A. M., Grotevendt, A., Scott, R., Taylor, K. D., Delgado, G. E., Yao, J., Kifley, A., Kooperberg, C., Qayyum, R., Lopez, L. M., Berentzen, T. L., Raikkonen, K., Mangino, M., Bandinelli, S., Peyser, P. A., Wild, S., Tregouet, D., Wright, A. F., Marten, J., Zemunik, T., Morrison, A. C., Sennblad, B., Tofler, G., de Maat, M. M., de Geus, E. C., Lowe, G. D., Zoledziewska, M., Sattar, N., Binder, H., Voelker, U., Waldenberger, M., Khaw, K., Mcknight, B., Huang, J., Jenny, N. S., Holliday, E. G., Qi, L., Mcevoy, M. G., Becker, D. M., Starr, J. M., Sarin, A., Hysi, P. G., Hernandez, D. G., Jhun, M. A., Campbell, H., Hamsten, A., Rivadeneira, F., Mcardle, W. L., Slagboom, P., Zeller, T., Koenig, W., Psaty, B. M., Haritunians, T., Liu, J., Palotie, A., Uitterlinden, A. G., Stott, D. J., Hofman, A., Franco, O. H., Polasek, O., Rudan, I., Morange, P., Wilson, J. F., Kardia, S. R., Ferrucci, L., Spector, T. D., Eriksson, J. G., Hansen, T., Deary, I. J., Becker, L. C., Scott, R. J., Mitchell, P., Maerz, W., Wareham, N. J., Peters, A., Greinacher, A., Wild, P. S., Jukema, J., Boomsma, D. I., Hayward, C., Cucca, F., Tracy, R., Watkins, H., Reiner, A. P., Folsom, A. R., Ridker, P. M., O'Donnell, C. J., Smith, N. L., Strachan, D. P., Dehghan, A. 2016; 25 (2): 358?70

    Abstract

    Genome-wide association studies have previously identified 23 genetic loci associated with circulating fibrinogen concentration. These studies used HapMap imputation and did not examine the X-chromosome. 1000 Genomes imputation provides better coverage of uncommon variants, and includes indels. We conducted a genome-wide association analysis of 34 studies imputed to the 1000 Genomes Project reference panel and including ?120 000 participants of European ancestry (95 806 participants with data on the X-chromosome). Approximately 10.7 million single-nucleotide polymorphisms and 1.2 million indels were examined. We identified 41 genome-wide significant fibrinogen loci; of which, 18 were newly identified. There were no genome-wide significant signals on the X-chromosome. The lead variants of five significant loci were indels. We further identified six additional independent signals, including three rare variants, at two previously characterized loci: FGB and IRF1. Together the 41 loci explain 3% of the variance in plasma fibrinogen concentration.

    View details for DOI 10.1093/hmg/ddv454

    View details for Web of Science ID 000372148200014

    View details for PubMedID 26561523

    View details for PubMedCentralID PMC4715256

  • Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin. American journal of human genetics Liu, C. T., Raghavan, S., Maruthur, N., Kabagambe, E. K., Hong, J., Ng, M. C., Hivert, M. F., Lu, Y., An, P., Bentley, A. R., Drolet, A. M., Gaulton, K. J., Guo, X., Armstrong, L. L., Irvin, M. R., Li, M., Lipovich, L., Rybin, D. V., Taylor, K. D., Agyemang, C., Palmer, N. D., Cade, B. E., Chen, W. M., Dauriz, M., Delaney, J. A., Edwards, T. L., Evans, D. S., Evans, M. K., Lange, L. A., Leong, A., Liu, J., Liu, Y., Nayak, U., Patel, S. R., Porneala, B. C., Rasmussen-Torvik, L. J., Snijder, M. B., Stallings, S. C., Tanaka, T., Yanek, L. R., Zhao, W., Becker, D. M., Bielak, L. F., Biggs, M. L., Bottinger, E. P., Bowden, D. W., Chen, G., Correa, A., Couper, D. J., Crawford, D. C., Cushman, M., Eicher, J. D., Fornage, M., Franceschini, N., Fu, Y. P., Goodarzi, M. O., Gottesman, O., Hara, K., Harris, T. B., Jensen, R. A., Johnson, A. D., Jhun, M. A., Karter, A. J., Keller, M. F., Kho, A. N., Kizer, J. R., Krauss, R. M., Langefeld, C. D., Li, X., Liang, J., Liu, S., Lowe, W. L., Mosley, T. H., North, K. E., Pacheco, J. A., Peyser, P. A., Patrick, A. L., Rice, K. M., Selvin, E., Sims, M., Smith, J. A., Tajuddin, S. M., Vaidya, D., Wren, M. P., Yao, J., Zhu, X., Ziegler, J. T., Zmuda, J. M., Zonderman, A. B., Zwinderman, A. H., Adeyemo, A., Boerwinkle, E., Ferrucci, L., Hayes, M. G., Kardia, S. L., Miljkovic, I., Pankow, J. S., Rotimi, C. N., Sale, M. M., Wagenknecht, L. E., Arnett, D. K., Chen, Y. I., Nalls, M. A., Province, M. A., Kao, W. H., Siscovick, D. S., Psaty, B. M., Wilson, J. G., Loos, R. J., Dupuis, J., Rich, S. S., Florez, J. C., Rotter, J. I., Morris, A. P., Meigs, J. B. 2016; 99 (1): 56?75

    Abstract

    Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loci.

    View details for PubMedID 27321945

  • The association between lower educational attainment and depression owing to shared genetic effects? Results in similar to 25 000 subjects MOLECULAR PSYCHIATRY Peyrot, W. J., Lee, S. H., Milaneschi, Y., Abdellaoui, A., Byrne, E. M., Esko, T., de Geus, E. J., Hemani, G., Hottenga, J. J., Kloiber, S., Levinson, D. F., Lucae, S., Martin, N. G., Medland, S. E., Metspalu, A., Milani, L., Noethen, M. M., Potash, J. B., Rietschel, M., Rietveld, C. A., Ripke, S., Shi, J., Willemsen, G., Zhu, Z., Boomsma, D. I., Wray, N. R., Penninx, B. W. 2015; 20 (6): 735-743

    Abstract

    An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14,949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15,138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884,105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ~120,000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status.

    View details for DOI 10.1038/mp.2015.50

    View details for Web of Science ID 000354890200012

    View details for PubMedID 25917368

  • A Statistical Approach for Rare-Variant Association Testing in Affected Sibships AMERICAN JOURNAL OF HUMAN GENETICS Epstein, M. P., Duncan, R., Ware, E. B., Jhun, M. A., Bielak, L. F., Zhao, W., Smith, J. A., Peyser, P. A., Kardia, S. R., Satten, G. A. 2015; 96 (4): 543?54

    Abstract

    Sequencing and exome-chip technologies have motivated development of novel statistical tests to identify rare genetic variation that influences complex diseases. Although many rare-variant association tests exist for case-control or cross-sectional studies, far fewer methods exist for testing association in families. This is unfortunate, because cosegregation of rare variation and disease status in families can amplify association signals for rare variants. Many researchers have begun sequencing (or genotyping via exome chips) familial samples that were either recently collected or previously collected for linkage studies. Because many linkage studies of complex diseases sampled affected sibships, we propose a strategy for association testing of rare variants for use in this study design. The logic behind our approach is that rare susceptibility variants should be found more often on regions shared identical by descent by affected sibling pairs than on regions not shared identical by descent. We propose both burden and variance-component tests of rare variation that are applicable to affected sibships of arbitrary size and that do not require genotype information from unaffected siblings or independent controls. Our approaches are robust to population stratification and produce analytic p values, thereby enabling our approach to scale easily to genome-wide studies of rare variation. We illustrate our methods by using simulated data and exome chip data from sibships ascertained for hypertension collected as part of the Genetic Epidemiology Network of Arteriopathy (GENOA) study.

    View details for DOI 10.1016/j.ajhg.2015.01.020

    View details for Web of Science ID 000352212600003

    View details for PubMedID 25799106

    View details for PubMedCentralID PMC4385187

  • Effect modification by vitamin D receptor genetic polymorphisms in the association between cumulative lead exposure and pulse pressure: a longitudinal study ENVIRONMENTAL HEALTH Jhun, M. A., Hu, H., Schwartz, J., Weisskopf, M. G., Nie, L. H., Sparrow, D., Vokonas, P. S., Park, S. 2015; 14: 5

    Abstract

    Although the association between lead and cardiovascular disease is well established, potential mechanisms are still poorly understood. Calcium metabolism plays a role in lead toxicity and thus, vitamin D receptor (VDR) polymorphisms have been suggested to modulate the association between lead and health outcomes. We investigated effect modification by VDR genetic polymorphisms in the association between cumulative lead exposure and pulse pressure, a marker of arterial stiffness.We examined 727 participants (3,100 observations from follow-ups from 1991 to 2011) from the Normative Aging Study (NAS), a longitudinal study of aging. Tibia and patella bone lead levels were measured using K-x-ray fluorescence. Four single nucleotide polymorphisms (SNPs) in the VDR gene, Bsm1, Taq1, Apa1, and Fok1, were genotyped. Linear mixed effects models with random intercepts were implemented to take into account repeated measurements.Adjusting for potential confounders, pulse pressure was 2.5 mmHg (95% CI: 0.4-4.7) and 1.9 mmHg (95% CI: 0.1-3.8) greater per interquartile range (IQR) increase in tibia lead (15 ?g/g) and patella lead (20 ?g/g), respectively, in those with at least one minor frequency allele in Bsm1 compared with those with major frequency allele homozygotes. The observed interaction effect between bone lead and the Bsm1 genotype persists over time during the follow-up. Similar results were observed in effect modification by Taq1.This study suggests that subjects with the minor frequency alleles of VDR Bsm1 or Taq1 may be more susceptible to cumulative lead exposure-related elevated pulse pressure.

    View details for DOI 10.1186/1476-069X-14-5

    View details for Web of Science ID 000349042800001

    View details for PubMedID 25582168

    View details for PubMedCentralID PMC4417283

  • Characterization of European Ancestry Nonalcoholic Fatty Liver Disease-Associated Variants in Individuals of African and Hispanic Descent HEPATOLOGY Palmer, N. D., Musani, S. K., Yerges-Armstrong, L. M., Feitosa, M. F., Bielak, L. F., Hernaez, R., Kahali, B., Carr, J., Harris, T. B., Jhun, M. A., Kardia, S. R., Langefeld, C. D., Mosley, T. H., Norris, J. M., Smith, A. V., Taylor, H. A., Wagenknecht, L. E., Liu, J., Borecki, I. B., Peyser, P. A., Speliotes, E. K. 2013; 58 (3): 966?75

    Abstract

    Nonalcoholic fatty liver disease (NAFLD) is an obesity-related condition affecting over 50% of individuals in some populations and is expected to become the number one cause of liver disease worldwide by 2020. Common, robustly associated genetic variants in/near five genes were identified for hepatic steatosis, a quantifiable component of NAFLD, in European ancestry individuals. Here we tested whether these variants were associated with hepatic steatosis in African- and/or Hispanic-Americans and fine-mapped the observed association signals. We measured hepatic steatosis using computed tomography in five African American (n?=?3,124) and one Hispanic American (n?=?849) cohorts. All analyses controlled for variation in age, age(2) , gender, alcoholic drinks, and population substructure. Heritability of hepatic steatosis was estimated in three cohorts. Variants in/near PNPLA3, NCAN, LYPLAL1, GCKR, and PPP1R3B were tested for association with hepatic steatosis using a regression framework in each cohort and meta-analyzed. Fine-mapping across African American cohorts was conducted using meta-analysis. African- and Hispanic-American cohorts were 33.9/37.5% male, with average age of 58.6/42.6 years and body mass index of 31.8/28.9 kg/m(2) , respectively. Hepatic steatosis was 0.20-0.34 heritable in African- and Hispanic-American families (P?

    View details for DOI 10.1002/hep.26440

    View details for Web of Science ID 000329284000018

    View details for PubMedID 23564467

    View details for PubMedCentralID PMC3782998

  • SNP Set Association Analysis for Familial Data GENETIC EPIDEMIOLOGY Schifano, E. D., Epstein, M. P., Bielak, L. F., Jhun, M. A., Kardia, S. R., Peyser, P. A., Lin, X. 2012; 36 (8): 797?810

    Abstract

    Genome-wide association studies (GWAS) are a popular approach for identifying common genetic variants and epistatic effects associated with a disease phenotype. The traditional statistical analysis of such GWAS attempts to assess the association between each individual single-nucleotide polymorphism (SNP) and the observed phenotype. Recently, kernel machine-based tests for association between a SNP set (e.g., SNPs in a gene) and the disease phenotype have been proposed as a useful alternative to the traditional individual-SNP approach, and allow for flexible modeling of the potentially complicated joint SNP effects in a SNP set while adjusting for covariates. We extend the kernel machine framework to accommodate related subjects from multiple independent families, and provide a score-based variance component test for assessing the association of a given SNP set with a continuous phenotype, while adjusting for additional covariates and accounting for within-family correlation. We illustrate the proposed method using simulation studies and an application to genetic data from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.

    View details for DOI 10.1002/gepi.21676

    View details for Web of Science ID 000311055200002

    View details for PubMedID 22968922

    View details for PubMedCentralID PMC3683469

  • A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance NATURE GENETICS Manning, A. K., Hivert, M., Scott, R. A., Grimsby, J. L., Bouatia-Naji, N., Chen, H., Rybin, D., Liu, C., Bielak, L. F., Prokopenko, I., Amin, N., Barnes, D., Cadby, G., Hottenga, J., Ingelsson, E., Jackson, A. U., Johnson, T., Kanoni, S., Ladenvall, C., Lagou, V., Lahti, J., Lecoeur, C., Liu, Y., Martinez-Larrad, M. T., Montasser, M. E., Navarro, P., Perry, J. R., Rasmussen-Torvik, L. J., Salo, P., Sattar, N., Shungin, D., Strawbridge, R. J., Tanaka, T., van Duijn, C. M., An, P., de Andrade, M., Andrews, J. S., Aspelund, T., Atalay, M., Aulchenko, Y., Balkau, B., Bandinelli, S., Beckmann, J. S., Beilby, J. P., Bellis, C., Bergman, R. N., Blangero, J., Boban, M., Boehnke, M., Boerwinkle, E., Bonnycastle, L. L., Boomsma, D. I., Borecki, I. B., Boettcher, Y., Bouchard, C., Brunner, E., Budimir, D., Campbell, H., Carlson, O., Chines, P. S., Clarke, R., Collins, F. S., Corbaton-Anchuelo, A., Couper, D., de Faire, U., Dedoussis, G. V., Deloukas, P., Dimitriou, M., Egan, J. M., Eiriksdottir, G., Erdos, M. R., Eriksson, J. G., Eury, E., Ferrucci, L., Ford, I., Forouhi, N. G., Fox, C. S., Franzosi, M. G., Franks, P. W., Frayling, T. M., Froguel, P., Galan, P., de Geus, E., Gigante, B., Glazer, N. L., Goel, A., Groop, L., Gudnason, V., Hallmans, G., Hamsten, A., Hansson, O., Harris, T. B., Hayward, C., Heath, S., Hercberg, S., Hicks, A. A., Hingorani, A., Hofman, A., Hui, J., Hung, J., Jarvelin, M., Jhun, M. A., Johnson, P. C., Jukema, J. W., Jula, A., Kao, W. H., Kaprio, J., Kardia, S. L., Keinanen-Kiukaanniemi, S., Kivimaki, M., Kolcic, I., Kovacs, P., Kumari, M., Kuusisto, J., Kyvik, K. O., Laakso, M., Lakka, T., Lannfelt, L., Lathrop, G. M., Launer, L. J., Leander, K., Li, G., Lind, L., Lindstrom, J., Lobbens, S., Loos, R. J., Luan, J., Lyssenko, V., Magi, R., Magnusson, P. K., Marmot, M., Meneton, P., Mohlke, K. L., Mooser, V., Morken, M. A., Miljkovic, I., Narisu, N., O'Connell, J., Ong, K. K., Oostra, B. A., Palmer, L. J., Palotie, A., Pankow, J. S., Peden, J. F., Pedersen, N. L., Pehlic, M., Peltonen, L., Penninx, B., Pericic, M., Perola, M., Perusse, L., Peyser, P. A., Polasek, O., Pramstaller, P. P., Province, M. A., Raikkonen, K., Rauramaa, R., Rehnberg, E., Rice, K., Rotter, J. I., Rudan, I., Ruokonen, A., Saaristo, T., Sabater-Lleal, M., Salomaa, V., Savage, D. B., Saxena, R., Schwarz, P., Seedorf, U., Sennblad, B., Serrano-Rios, M., Shuldiner, A. R., Sijbrands, E. J., Siscovick, D. S., Smit, J. H., Small, K. S., Smith, N. L., Smith, A. V., Stancakova, A., Stirrups, K., Stumvoll, M., Sun, Y. V., Swift, A. J., Toenjes, A., Tuomilehto, J., Trompet, S., Uitterlinden, A. G., Uusitupa, M., Vikstrom, M., Vitart, V., Vohl, M., Voight, B. F., Vollenweider, P., Waeber, G., Waterworth, D. M., Watkins, H., Wheeler, E., Widen, E., Wild, S. H., Willems, S. M., Willemsen, G., Wilson, J. F., Witteman, J. C., Wright, A. F., Yaghootkar, H., Zelenika, D., Zemunik, T., Zgaga, L., Wareham, N. J., McCarthy, M. I., Barroso, I., Watanabe, R. M., Florez, J. C., Dupuis, J., Meigs, J. B., Langenberg, C. 2012; 44 (6): 659-U81

    Abstract

    Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and ?-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.

    View details for DOI 10.1038/ng.2274

    View details for Web of Science ID 000304551100012

    View details for PubMedID 22581228

  • Association of the Vitamin D Metabolism Gene CYP24A1 With Coronary Artery Calcification ARTERIOSCLEROSIS THROMBOSIS AND VASCULAR BIOLOGY Shen, H., Bielak, L. F., Ferguson, J. F., Streeten, E. A., Yerges-Armstrong, L. M., Liu, J., Post, W., O'Connell, J. R., Hixson, J. E., Kardia, S. L., Sun, Y. V., Jhun, M. A., Wang, X., Mehta, N. N., Li, M., Koller, D. L., Hakonarson, H., Keating, B. J., Rader, D. J., Shuldiner, A. R., Peyser, P. A., Reilly, M. P., Mitchell, B. D. 2010; 30 (12): 2648-U587

    Abstract

    The vitamin D endocrine system is essential for calcium homeostasis, and low levels of vitamin D metabolites have been associated with cardiovascular disease risk. We hypothesized that DNA sequence variation in genes regulating vitamin D metabolism and signaling pathways might influence variation in coronary artery calcification (CAC).We genotyped single-nucleotide polymorphisms (SNPs) in GC, CYP27B1, CYP24A1, and VDR and tested their association with CAC quantity, as measured by electron beam computed tomography. Initial association studies were carried out in a discovery sample comprising 697 Amish subjects, and SNPs nominally associated with CAC quantity (4 SNPs in CYP24A1, P=0.008 to 0.00003) were then tested for association with CAC quantity in 2 independent cohorts of subjects of white European ancestry (Genetic Epidemiology Network of Arteriopathy study [n=916] and the Penn Coronary Artery Calcification sample [n=2061]). One of the 4 SNPs, rs2762939, was associated with CAC quantity in both the Genetic Epidemiology Network of Arteriopathy (P=0.007) and Penn Coronary Artery Calcification (P=0.01) studies. In all 3 populations, the rs2762939 C allele was associated with lower CAC quantity. Metaanalysis for the association of this SNP with CAC quantity across all 3 studies yielded a P value of 2.9×10(-6).A common SNP in the CYP24A1 gene was associated with CAC quantity in 3 independent populations. This result suggests a role for vitamin D metabolism in the development of CAC quantity.

    View details for DOI 10.1161/ATVBAHA.110.211805

    View details for Web of Science ID 000284309000053

    View details for PubMedID 20847308

    View details for PubMedCentralID PMC2988112

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