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1.  Shared genetic susceptibility to ischemic stroke and coronary artery disease – a genome-wide analysis of common variants 
Summary
Background and Purpose
Ischemic stroke (IS) and coronary artery disease (CAD) share several risk factors and each have a substantial heritability. We conducted a genome-wide analysis to evaluate the extent of shared genetic determination of the two diseases.
Methods
Genome-wide association data were obtained from the METASTROKE, CARDIoGRAM, and C4D consortia. We first analyzed common variants reaching a nominal threshold of significance (p<0.01) for CAD for their association with IS and vice versa. We then examined specific overlap across phenotypes for variants that reached a high threshold of significance. Finally, we conducted a joint meta-analysis on the combined phenotype of IS or CAD. Corresponding analyses were performed restricted to the 2,167 individuals with the ischemic large artery stroke (LAS) subtype.
Results
Common variants associated with CAD at p<0.01 were associated with a significant excess risk for IS and for LAS and vice versa. Among the 42 known genome-wide significant loci for CAD, three and five loci were significantly associated with IS and LAS, respectively. In the joint meta-analyses, 15 loci passed genome-wide significance (p<5×10-8) for the combined phenotype of IS or CAD and 17 loci passed genome-wide significance for LAS or CAD. Since these loci had prior evidence for genome-wide significance for CAD we specifically analyzed the respective signals for IS and LAS and found evidence for association at chr12q24/SH2B3 (pIS=1.62×10-07) and ABO (pIS =2.6×10-4) as well as at HDAC9 (pLAS=2.32×10-12), 9p21 (pLAS =3.70×10-6), RAI1-PEMT-RASD1 (pLAS =2.69×10-5), EDNRA (pLAS =7.29×10-4), and CYP17A1-CNNM2-NT5C2 (pLAS =4.9×10-4).
Conclusions
Our results demonstrate substantial overlap in the genetic risk of ischemic stroke and particularly the large artery stroke subtype with coronary artery disease.
doi:10.1161/STROKEAHA.113.002707
PMCID: PMC4112102  PMID: 24262325
2.  A unifying framework for robust association testing, estimation, and genetic model selection using the generalized linear model 
European Journal of Human Genetics  2013;21(12):1442-1448.
The analysis of genome-wide genetic association studies generally starts with univariate statistical tests of each single-nucleotide polymorphism. The standard approach is the Cochran-Armitage trend test or its logistic regression equivalent although this approach can lose considerable power if the underlying genetic model is not additive. An alternative is the MAX test, which is robust against the three basic modes of inheritance. Here, the asymptotic distribution of the MAX test is derived using the generalized linear model together with the Delta method and multiple contrasts. The approach is applicable to binary, quantitative, and survival traits. It may be used for unrelated individuals, family-based studies, and matched pairs. The approach provides point and interval effect estimates and allows selecting the most plausible genetic model using the minimum P-value. R code is provided. A Monte-Carlo simulation study shows that the asymptotic MAX test framework meets type I error levels well, has good power, and good model selection properties for minor allele frequencies ≥0.3. Pearson's χ2-test is superior for lower minor allele frequencies with low frequencies for the rare homozygous genotype. In these cases, the model selection procedure should be used with caution. The use of the MAX test is illustrated by reanalyzing findings from seven genome-wide association studies including case–control, matched pairs, and quantitative trait data.
doi:10.1038/ejhg.2013.62
PMCID: PMC3820468  PMID: 23572026
family-based association; genetic association; genome-wide association; indirect mapping; MAX test
3.  Effects of a rater training on rating accuracy in a physical examination skills assessment  
Background: The accuracy and reproducibility of medical skills assessment is generally low. Rater training has little or no effect. Our knowledge in this field, however, relies on studies involving video ratings of overall clinical performances. We hypothesised that a rater training focussing on the frame of reference could improve accuracy in grading the curricular assessment of a highly standardised physical head-to-toe examination.
Methods: Twenty-one raters assessed the performance of 242 third-year medical students. Eleven raters had been randomly assigned to undergo a brief frame-of-reference training a few days before the assessment. 218 encounters were successfully recorded on video and re-assessed independently by three additional observers. Accuracy was defined as the concordance between the raters' grade and the median of the observers' grade. After the assessment, both students and raters filled in a questionnaire about their views on the assessment.
Results: Rater training did not have a measurable influence on accuracy. However, trained raters rated significantly more stringently than untrained raters, and their overall stringency was closer to the stringency of the observers. The questionnaire indicated a higher awareness of the halo effect in the trained raters group. Although the self-assessment of the students mirrored the assessment of the raters in both groups, the students assessed by trained raters felt more discontent with their grade.
Conclusions: While training had some marginal effects, it failed to have an impact on the individual accuracy. These results in real-life encounters are consistent with previous studies on rater training using video assessments of clinical performances. The high degree of standardisation in this study was not suitable to harmonize the trained raters’ grading. The data support the notion that the process of appraising medical performance is highly individual. A frame-of-reference training as applied does not effectively adjust the physicians' judgement on medical students in real-live assessments.
doi:10.3205/zma000933
PMCID: PMC4259060  PMID: 25489341
rater training; rating accuracy; skills assessment; physical examination skills; randomised controlled trial
4.  A comprehensive evaluation of collapsing methods using simulated and real data: excellent annotation of functionality and large sample sizes required 
Frontiers in Genetics  2014;5:323.
The advent of next generation sequencing (NGS) technologies enabled the investigation of the rare variant-common disease hypothesis in unrelated individuals, even on the genome-wide level. Analysis of this hypothesis requires tailored statistical methods as single marker tests fail on rare variants. An entire class of statistical methods collapses rare variants from a genomic region of interest (ROI), thereby aggregating rare variants. In an extensive simulation study using data from the Genetic Analysis Workshop 17 we compared the performance of 15 collapsing methods by means of a variety of pre-defined ROIs regarding minor allele frequency thresholds and functionality. Findings of the simulation study were additionally confirmed by a real data set investigating the association between methotrexate clearance and the SLCO1B1 gene in patients with acute lymphoblastic leukemia. Our analyses showed substantially inflated type I error levels for many of the proposed collapsing methods. Only four approaches yielded valid type I errors in all considered scenarios. None of the statistical tests was able to detect true associations over a substantial proportion of replicates in the simulated data. Detailed annotation of functionality of variants is crucial to detect true associations. These findings were confirmed in the analysis of the real data. Recent theoretical work showed that large power is achieved in gene-based analyses only if large sample sizes are available and a substantial proportion of causing rare variants is present in the gene-based analysis. Many of the investigated statistical approaches use permutation requiring high computational cost. There is a clear need for valid, powerful and fast to calculate test statistics for studies investigating rare variants.
doi:10.3389/fgene.2014.00323
PMCID: PMC4164031  PMID: 25309579
collapsing; rare variants; simulation study; comparison; burden test; SLCO1B1
5.  Association Tests for X-Chromosomal Markers – A Comparison of Different Test Statistics 
Human Heredity  2011;71(1):23-36.
Objective
Genome-wide association studies have successfully elucidated the genetic background of complex diseases, but X chromosomal data have usually not been analyzed. A reason for this is that there is no consensus approach for the analysis taking into account the specific features of X chromosomal data. This contribution evaluates test statistics proposed for X chromosomal markers regarding type I error frequencies and power.
Methods
We performed extensive simulation studies covering a wide range of different settings. Besides characteristics of the general population, we investigated sex-balanced or unbalanced sampling procedures as well as sex-specific effect sizes, allele frequencies and prevalence. Finally, we applied the test statistics to an association data set on Crohn's disease.
Results
Simulation results imply that in addition to standard quality control, sex-specific allele frequencies should be checked to control for type I errors. Furthermore, we observed distinct differences in power between test statistics which are determined by sampling design and sex specificity of effect sizes. Analysis of the Crohn's disease data detects two previously unknown genetic regions on the X chromosome.
Conclusion
Although no test is uniformly most powerful under all settings, recommendations are offered as to which test performs best under certain conditions.
doi:10.1159/000323768
PMCID: PMC3089425  PMID: 21325864
Crohn's disease; Genetic association; Genome-wide association; Sex specific; X chromosome
6.  Multiple Testing in High-Throughput Sequence Data: Experiences from Group 8 of Genetic Analysis Workshop 17 
Genetic Epidemiology  2011;35(Suppl 1):S61-S66.
The use of high-throughput sequence data in genetic epidemiology allows the investigation of common and rare variants in the entire genome, thus increasing the amount of information and the potential number of statistical tests performed within one study. As a consequence, the problem of multiple testing may become even more pressing than in previous studies. As an important challenge, the exact number of statistical tests depends on the actual statistical method used. Furthermore, many statistical approaches for the analysis of sequence data require permutation. Thus it may be difficult to also use permutation to estimate correct type I error levels as in genome-wide association studies. In view of this, a separate group at Genetic Analysis Workshop 17 was formed with a focus on multiple testing. Here, we present the approaches used for the workshop. Apart from tackling the multiple testing problem, the new group focused on different issues. Some contributors developed and investigated modifications of existing collapsing methods. Others aimed at improving the identification of functional variants through a reduction and analysis of the underlying data dimensions. Two research groups investigated the overall accumulation of rare variation across the genome and its value in predicting phenotypes. Finally, other investigators left the path of traditional statistical analyses by reversing null and alternative hypotheses and by proposing a novel resampling method. We describe and discuss all these approaches.
doi:10.1002/gepi.20651
PMCID: PMC3265920  PMID: 22128061
next-generation sequencing; resampling; collapsing methods; rare sequence variants
7.  A comparison of two collapsing methods in different approaches 
BMC Proceedings  2014;8(Suppl 1):S8.
Sequencing technologies have enabled the investigation of whole genomes of many individuals in parallel. Studies have shown that the joint consideration of multiple rare variants may explain a relevant proportion of the genetic basis for disease so that grouping of rare variants, termed collapsing, can enrich the association signal.
Following this assumption, we investigate the type I error and the power of two proposed collapsing methods (combined multivariate and collapsing method and the functional principal component analysis [FPCA]-based statistic) using the case-control data provided for the Genetic Analysis Workshop 18 with knowledge of the true model. Variants with a minor allele frequency (MAF) of 0.05 or less were collapsed per gene for combined multivariate and collapsing. Neither of the methods detected any of the truly associated genes reliably. Although combined multivariate and collapsing identified one gene with a power of 0.66, it had an unacceptably high false-positive rate of 75%. In contrast, FPCA covered the type I error level well but at the cost of low power. A strict filtering of variants by small MAF might lead to a better performance of the collapsing methods. Furthermore, the inclusion of information on functionality of the variants could be helpful.
doi:10.1186/1753-6561-8-S1-S8
PMCID: PMC4143760  PMID: 25519408
8.  Genetic Analysis Workshop 18: Methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees 
BMC Proceedings  2014;8(Suppl 1):S1.
Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted for publication.
doi:10.1186/1753-6561-8-S1-S1
PMCID: PMC4143625  PMID: 25519310
9.  A unifying framework for robust association testing, estimation, and genetic model selection using the generalized linear model 
European journal of human genetics : EJHG  2013;21(12):10.1038/ejhg.2013.62.
The analysis of genome-wide genetic association studies generally starts with univariate statistical tests of each single nucleotide polymorphism. The standard approach is the Cochran-Armitage trend test or its logistic regression equivalent although this approach can lose considerable power if the underlying genetic model is not additive. An alternative is the MAX test which is robust against the three basic modes of inheritance. Here, the asymptotic distribution of the MAX test is derived using the generalized linear model together with the Delta method and multiple contrasts. The approach is applicable to binary, quantitative, and survival traits. It may be used for unrelated individuals, family-based studies, and matched pairs. The approach provides point and interval effect estimates and allows selecting the most plausible genetic model using the minimum p-value. R code is provided. A Monte-Carlo simulation study shows that the asymptotic MAX test framework meets type I error levels well, has good power and good model selection properties for minor allele frequencies ≥0.3. Pearson’s chi-square test is superior for lower minor allele frequencies with low frequencies for the rare homozygous genotype. In these cases, the model selection procedure should be used with caution. The use of the MAX test is illustrated by re-analyzing findings from 7 genome-wide association studies including case-control, matched pairs, and quantitative trait data.
doi:10.1038/ejhg.2013.62
PMCID: PMC3820468  PMID: 23572026
Family-based association; genetic association; genome-wide association; indirect mapping; MAX test
10.  Genetic predisposition to higher blood pressure increases coronary artery disease risk 
Hypertension  2013;61(5):10.1161/HYPERTENSIONAHA.111.00275.
Hypertension is a risk factor for coronary artery disease. Recent genome-wide association studies have identified 30 genetic variants associated with higher blood pressure at genome-wide significance (p<5×10−8). If elevated blood pressure is a causative factor for coronary artery disease, these variants should also increase coronary artery disease risk. Analyzing genome-wide association data from 22,233 coronary artery disease cases and 64,762 controls, we observed in the Coronary artery disease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) consortium that 88% of these blood pressure-associated polymorphisms were likewise positively associated with coronary artery disease, i.e. they had an odds ratio >1 for coronary artery disease, a proportion much higher than expected by chance (p=4.10−5). The average relative coronary artery disease risk increase per each of the multiple blood pressure-raising alleles observed in the consortium was 3.0% for systolic blood pressure-associated polymorphisms (95% confidence interval, 1.8 to 4.3%) and 2.9% for diastolic blood pressure-associated polymorphisms (95% confidence interval, 1.7 to 4.1%). In sub-studies, individuals carrying most systolic blood pressure- and diastolic blood pressure-related risk alleles (top quintile of a genetic risk score distribution) had 70% (95% confidence interval, 50-94%) and 59% (95% confidence interval, 40-81%) higher odds of having coronary artery disease, respectively, as compared to individuals in the bottom quintile. In conclusion, most blood pressure-associated polymorphisms also confer an increased risk for coronary artery disease. These findings are consistent with a causal relationship of increasing blood pressure to coronary artery disease. Genetic variants primarily affecting blood pressure contribute to the genetic basis of coronary artery disease.
doi:10.1161/HYPERTENSIONAHA.111.00275
PMCID: PMC3855241  PMID: 23478099
Blood pressure; polymorphism; genetics; coronary artery disease
11.  Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture 
Berndt, Sonja I. | Gustafsson, Stefan | Mägi, Reedik | Ganna, Andrea | Wheeler, Eleanor | Feitosa, Mary F. | Justice, Anne E. | Monda, Keri L. | Croteau-Chonka, Damien C. | Day, Felix R. | Esko, Tõnu | Fall, Tove | Ferreira, Teresa | Gentilini, Davide | Jackson, Anne U. | Luan, Jian’an | Randall, Joshua C. | Vedantam, Sailaja | Willer, Cristen J. | Winkler, Thomas W. | Wood, Andrew R. | Workalemahu, Tsegaselassie | Hu, Yi-Juan | Lee, Sang Hong | Liang, Liming | Lin, Dan-Yu | Min, Josine L. | Neale, Benjamin M. | Thorleifsson, Gudmar | Yang, Jian | Albrecht, Eva | Amin, Najaf | Bragg-Gresham, Jennifer L. | Cadby, Gemma | den Heijer, Martin | Eklund, Niina | Fischer, Krista | Goel, Anuj | Hottenga, Jouke-Jan | Huffman, Jennifer E. | Jarick, Ivonne | Johansson, Åsa | Johnson, Toby | Kanoni, Stavroula | Kleber, Marcus E. | König, Inke R. | Kristiansson, Kati | Kutalik, Zoltán | Lamina, Claudia | Lecoeur, Cecile | Li, Guo | Mangino, Massimo | McArdle, Wendy L. | Medina-Gomez, Carolina | Müller-Nurasyid, Martina | Ngwa, Julius S. | Nolte, Ilja M. | Paternoster, Lavinia | Pechlivanis, Sonali | Perola, Markus | Peters, Marjolein J. | Preuss, Michael | Rose, Lynda M. | Shi, Jianxin | Shungin, Dmitry | Smith, Albert Vernon | Strawbridge, Rona J. | Surakka, Ida | Teumer, Alexander | Trip, Mieke D. | Tyrer, Jonathan | Van Vliet-Ostaptchouk, Jana V. | Vandenput, Liesbeth | Waite, Lindsay L. | Zhao, Jing Hua | Absher, Devin | Asselbergs, Folkert W. | Atalay, Mustafa | Attwood, Antony P. | Balmforth, Anthony J. | Basart, Hanneke | Beilby, John | Bonnycastle, Lori L. | Brambilla, Paolo | Bruinenberg, Marcel | Campbell, Harry | Chasman, Daniel I. | Chines, Peter S. | Collins, Francis S. | Connell, John M. | Cookson, William | de Faire, Ulf | de Vegt, Femmie | Dei, Mariano | Dimitriou, Maria | Edkins, Sarah | Estrada, Karol | Evans, David M. | Farrall, Martin | Ferrario, Marco M. | Ferrières, Jean | Franke, Lude | Frau, Francesca | Gejman, Pablo V. | Grallert, Harald | Grönberg, Henrik | Gudnason, Vilmundur | Hall, Alistair S. | Hall, Per | Hartikainen, Anna-Liisa | Hayward, Caroline | Heard-Costa, Nancy L. | Heath, Andrew C. | Hebebrand, Johannes | Homuth, Georg | Hu, Frank B. | Hunt, Sarah E. | Hyppönen, Elina | Iribarren, Carlos | Jacobs, Kevin B. | Jansson, John-Olov | Jula, Antti | Kähönen, Mika | Kathiresan, Sekar | Kee, Frank | Khaw, Kay-Tee | Kivimaki, Mika | Koenig, Wolfgang | Kraja, Aldi T. | Kumari, Meena | Kuulasmaa, Kari | Kuusisto, Johanna | Laitinen, Jaana H. | Lakka, Timo A. | Langenberg, Claudia | Launer, Lenore J. | Lind, Lars | Lindström, Jaana | Liu, Jianjun | Liuzzi, Antonio | Lokki, Marja-Liisa | Lorentzon, Mattias | Madden, Pamela A. | Magnusson, Patrik K. | Manunta, Paolo | Marek, Diana | März, Winfried | Mateo Leach, Irene | McKnight, Barbara | Medland, Sarah E. | Mihailov, Evelin | Milani, Lili | Montgomery, Grant W. | Mooser, Vincent | Mühleisen, Thomas W. | Munroe, Patricia B. | Musk, Arthur W. | Narisu, Narisu | Navis, Gerjan | Nicholson, George | Nohr, Ellen A. | Ong, Ken K. | Oostra, Ben A. | Palmer, Colin N.A. | Palotie, Aarno | Peden, John F. | Pedersen, Nancy | Peters, Annette | Polasek, Ozren | Pouta, Anneli | Pramstaller, Peter P. | Prokopenko, Inga | Pütter, Carolin | Radhakrishnan, Aparna | Raitakari, Olli | Rendon, Augusto | Rivadeneira, Fernando | Rudan, Igor | Saaristo, Timo E. | Sambrook, Jennifer G. | Sanders, Alan R. | Sanna, Serena | Saramies, Jouko | Schipf, Sabine | Schreiber, Stefan | Schunkert, Heribert | Shin, So-Youn | Signorini, Stefano | Sinisalo, Juha | Skrobek, Boris | Soranzo, Nicole | Stančáková, Alena | Stark, Klaus | Stephens, Jonathan C. | Stirrups, Kathleen | Stolk, Ronald P. | Stumvoll, Michael | Swift, Amy J. | Theodoraki, Eirini V. | Thorand, Barbara | Tregouet, David-Alexandre | Tremoli, Elena | Van der Klauw, Melanie M. | van Meurs, Joyce B.J. | Vermeulen, Sita H. | Viikari, Jorma | Virtamo, Jarmo | Vitart, Veronique | Waeber, Gérard | Wang, Zhaoming | Widén, Elisabeth | Wild, Sarah H. | Willemsen, Gonneke | Winkelmann, Bernhard R. | Witteman, Jacqueline C.M. | Wolffenbuttel, Bruce H.R. | Wong, Andrew | Wright, Alan F. | Zillikens, M. Carola | Amouyel, Philippe | Boehm, Bernhard O. | Boerwinkle, Eric | Boomsma, Dorret I. | Caulfield, Mark J. | Chanock, Stephen J. | Cupples, L. Adrienne | Cusi, Daniele | Dedoussis, George V. | Erdmann, Jeanette | Eriksson, Johan G. | Franks, Paul W. | Froguel, Philippe | Gieger, Christian | Gyllensten, Ulf | Hamsten, Anders | Harris, Tamara B. | Hengstenberg, Christian | Hicks, Andrew A. | Hingorani, Aroon | Hinney, Anke | Hofman, Albert | Hovingh, Kees G. | Hveem, Kristian | Illig, Thomas | Jarvelin, Marjo-Riitta | Jöckel, Karl-Heinz | Keinanen-Kiukaanniemi, Sirkka M. | Kiemeney, Lambertus A. | Kuh, Diana | Laakso, Markku | Lehtimäki, Terho | Levinson, Douglas F. | Martin, Nicholas G. | Metspalu, Andres | Morris, Andrew D. | Nieminen, Markku S. | Njølstad, Inger | Ohlsson, Claes | Oldehinkel, Albertine J. | Ouwehand, Willem H. | Palmer, Lyle J. | Penninx, Brenda | Power, Chris | Province, Michael A. | Psaty, Bruce M. | Qi, Lu | Rauramaa, Rainer | Ridker, Paul M. | Ripatti, Samuli | Salomaa, Veikko | Samani, Nilesh J. | Snieder, Harold | Sørensen, Thorkild I.A. | Spector, Timothy D. | Stefansson, Kari | Tönjes, Anke | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | van der Harst, Pim | Vollenweider, Peter | Wallaschofski, Henri | Wareham, Nicholas J. | Watkins, Hugh | Wichmann, H.-Erich | Wilson, James F. | Abecasis, Goncalo R. | Assimes, Themistocles L. | Barroso, Inês | Boehnke, Michael | Borecki, Ingrid B. | Deloukas, Panos | Fox, Caroline S. | Frayling, Timothy | Groop, Leif C. | Haritunian, Talin | Heid, Iris M. | Hunter, David | Kaplan, Robert C. | Karpe, Fredrik | Moffatt, Miriam | Mohlke, Karen L. | O’Connell, Jeffrey R. | Pawitan, Yudi | Schadt, Eric E. | Schlessinger, David | Steinthorsdottir, Valgerdur | Strachan, David P. | Thorsteinsdottir, Unnur | van Duijn, Cornelia M. | Visscher, Peter M. | Di Blasio, Anna Maria | Hirschhorn, Joel N. | Lindgren, Cecilia M. | Morris, Andrew P. | Meyre, David | Scherag, André | McCarthy, Mark I. | Speliotes, Elizabeth K. | North, Kari E. | Loos, Ruth J.F. | Ingelsson, Erik
Nature genetics  2013;45(5):501-512.
Approaches exploiting extremes of the trait distribution may reveal novel loci for common traits, but it is unknown whether such loci are generalizable to the general population. In a genome-wide search for loci associated with upper vs. lower 5th percentiles of body mass index, height and waist-hip ratio, as well as clinical classes of obesity including up to 263,407 European individuals, we identified four new loci (IGFBP4, H6PD, RSRC1, PPP2R2A) influencing height detected in the tails and seven new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3, ZZZ3) for clinical classes of obesity. Further, we show that there is large overlap in terms of genetic structure and distribution of variants between traits based on extremes and the general population and little etiologic heterogeneity between obesity subgroups.
doi:10.1038/ng.2606
PMCID: PMC3973018  PMID: 23563607
12.  Dense genotyping of immune-related disease regions identifies nine new risk loci for primary sclerosing cholangitis 
Liu, Jimmy Z. | Hov, Johannes Roksund | Folseraas, Trine | Ellinghaus, Eva | Rushbrook, Simon M. | Doncheva, Nadezhda T. | Andreassen, Ole A. | Weersma, Rinse K. | Weismüller, Tobias J. | Eksteen, Bertus | Invernizzi, Pietro | Hirschfield, Gideon M. | Gotthardt, Daniel Nils | Pares, Albert | Ellinghaus, David | Shah, Tejas | Juran, Brian D. | Milkiewicz, Piotr | Rust, Christian | Schramm, Christoph | Müller, Tobias | Srivastava, Brijesh | Dalekos, Georgios | Nöthen, Markus M. | Herms, Stefan | Winkelmann, Juliane | Mitrovic, Mitja | Braun, Felix | Ponsioen, Cyriel Y. | Croucher, Peter J. P. | Sterneck, Martina | Teufel, Andreas | Mason, Andrew L. | Saarela, Janna | Leppa, Virpi | Dorfman, Ruslan | Alvaro, Domenico | Floreani, Annarosa | Onengut-Gumuscu, Suna | Rich, Stephen S. | Thompson, Wesley K. | Schork, Andrew J. | Næss, Sigrid | Thomsen, Ingo | Mayr, Gabriele | König, Inke R. | Hveem, Kristian | Cleynen, Isabelle | Gutierrez-Achury, Javier | Ricaño-Ponce, Isis | van Heel, David | Björnsson, Einar | Sandford, Richard N. | Durie, Peter R. | Melum, Espen | Vatn, Morten H | Silverberg, Mark S. | Duerr, Richard H. | Padyukov, Leonid | Brand, Stephan | Sans, Miquel | Annese, Vito | Achkar, Jean-Paul | Boberg, Kirsten Muri | Marschall, Hanns-Ulrich | Chazouillères, Olivier | Bowlus, Christopher L. | Wijmenga, Cisca | Schrumpf, Erik | Vermeire, Severine | Albrecht, Mario | Rioux, John D. | Alexander, Graeme | Bergquist, Annika | Cho, Judy | Schreiber, Stefan | Manns, Michael P. | Färkkilä, Martti | Dale, Anders M. | Chapman, Roger W. | Lazaridis, Konstantinos N. | Franke, Andre | Anderson, Carl A. | Karlsen, Tom H.
Nature genetics  2013;45(6):670-675.
doi:10.1038/ng.2616
PMCID: PMC3667736  PMID: 23603763
genetic association study; disease genetics; immunogenetics; liver
13.  Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure 
Wain, Louise V | Verwoert, Germaine C | O’Reilly, Paul F | Shi, Gang | Johnson, Toby | Johnson, Andrew D | Bochud, Murielle | Rice, Kenneth M | Henneman, Peter | Smith, Albert V | Ehret, Georg B | Amin, Najaf | Larson, Martin G | Mooser, Vincent | Hadley, David | Dörr, Marcus | Bis, Joshua C | Aspelund, Thor | Esko, Tõnu | Janssens, A Cecile JW | Zhao, Jing Hua | Heath, Simon | Laan, Maris | Fu, Jingyuan | Pistis, Giorgio | Luan, Jian’an | Arora, Pankaj | Lucas, Gavin | Pirastu, Nicola | Pichler, Irene | Jackson, Anne U | Webster, Rebecca J | Zhang, Feng | Peden, John F | Schmidt, Helena | Tanaka, Toshiko | Campbell, Harry | Igl, Wilmar | Milaneschi, Yuri | Hotteng, Jouke-Jan | Vitart, Veronique | Chasman, Daniel I | Trompet, Stella | Bragg-Gresham, Jennifer L | Alizadeh, Behrooz Z | Chambers, John C | Guo, Xiuqing | Lehtimäki, Terho | Kühnel, Brigitte | Lopez, Lorna M | Polašek, Ozren | Boban, Mladen | Nelson, Christopher P | Morrison, Alanna C | Pihur, Vasyl | Ganesh, Santhi K | Hofman, Albert | Kundu, Suman | Mattace-Raso, Francesco US | Rivadeneira, Fernando | Sijbrands, Eric JG | Uitterlinden, Andre G | Hwang, Shih-Jen | Vasan, Ramachandran S | Wang, Thomas J | Bergmann, Sven | Vollenweider, Peter | Waeber, Gérard | Laitinen, Jaana | Pouta, Anneli | Zitting, Paavo | McArdle, Wendy L | Kroemer, Heyo K | Völker, Uwe | Völzke, Henry | Glazer, Nicole L | Taylor, Kent D | Harris, Tamara B | Alavere, Helene | Haller, Toomas | Keis, Aime | Tammesoo, Mari-Liis | Aulchenko, Yurii | Barroso, Inês | Khaw, Kay-Tee | Galan, Pilar | Hercberg, Serge | Lathrop, Mark | Eyheramendy, Susana | Org, Elin | Sõber, Siim | Lu, Xiaowen | Nolte, Ilja M | Penninx, Brenda W | Corre, Tanguy | Masciullo, Corrado | Sala, Cinzia | Groop, Leif | Voight, Benjamin F | Melander, Olle | O’Donnell, Christopher J | Salomaa, Veikko | d’Adamo, Adamo Pio | Fabretto, Antonella | Faletra, Flavio | Ulivi, Sheila | Del Greco, M Fabiola | Facheris, Maurizio | Collins, Francis S | Bergman, Richard N | Beilby, John P | Hung, Joseph | Musk, A William | Mangino, Massimo | Shin, So-Youn | Soranzo, Nicole | Watkins, Hugh | Goel, Anuj | Hamsten, Anders | Gider, Pierre | Loitfelder, Marisa | Zeginigg, Marion | Hernandez, Dena | Najjar, Samer S | Navarro, Pau | Wild, Sarah H | Corsi, Anna Maria | Singleton, Andrew | de Geus, Eco JC | Willemsen, Gonneke | Parker, Alex N | Rose, Lynda M | Buckley, Brendan | Stott, David | Orru, Marco | Uda, Manuela | van der Klauw, Melanie M | Zhang, Weihua | Li, Xinzhong | Scott, James | Chen, Yii-Der Ida | Burke, Gregory L | Kähönen, Mika | Viikari, Jorma | Döring, Angela | Meitinger, Thomas | Davies, Gail | Starr, John M | Emilsson, Valur | Plump, Andrew | Lindeman, Jan H | ’t Hoen, Peter AC | König, Inke R | Felix, Janine F | Clarke, Robert | Hopewell, Jemma C | Ongen, Halit | Breteler, Monique | Debette, Stéphanie | DeStefano, Anita L | Fornage, Myriam | Mitchell, Gary F | Smith, Nicholas L | Holm, Hilma | Stefansson, Kari | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Samani, Nilesh J | Preuss, Michael | Rudan, Igor | Hayward, Caroline | Deary, Ian J | Wichmann, H-Erich | Raitakari, Olli T | Palmas, Walter | Kooner, Jaspal S | Stolk, Ronald P | Jukema, J Wouter | Wright, Alan F | Boomsma, Dorret I | Bandinelli, Stefania | Gyllensten, Ulf B | Wilson, James F | Ferrucci, Luigi | Schmidt, Reinhold | Farrall, Martin | Spector, Tim D | Palmer, Lyle J | Tuomilehto, Jaakko | Pfeufer, Arne | Gasparini, Paolo | Siscovick, David | Altshuler, David | Loos, Ruth JF | Toniolo, Daniela | Snieder, Harold | Gieger, Christian | Meneton, Pierre | Wareham, Nicholas J | Oostra, Ben A | Metspalu, Andres | Launer, Lenore | Rettig, Rainer | Strachan, David P | Beckmann, Jacques S | Witteman, Jacqueline CM | Erdmann, Jeanette | van Dijk, Ko Willems | Boerwinkle, Eric | Boehnke, Michael | Ridker, Paul M | Jarvelin, Marjo-Riitta | Chakravarti, Aravinda | Abecasis, Goncalo R | Gudnason, Vilmundur | Newton-Cheh, Christopher | Levy, Daniel | Munroe, Patricia B | Psaty, Bruce M | Caulfield, Mark J | Rao, Dabeeru C | Tobin, Martin D | Elliott, Paul | van Duijn, Cornelia M
Nature genetics  2011;43(10):1005-1011.
Numerous genetic loci influence systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Europeans 1-3. We now report genome-wide association studies of pulse pressure (PP) and mean arterial pressure (MAP). In discovery (N=74,064) and follow-up studies (N=48,607), we identified at genome-wide significance (P= 2.7×10-8 to P=2.3×10-13) four novel PP loci (at 4q12 near CHIC2/PDGFRAI, 7q22.3 near PIK3CG, 8q24.12 in NOV, 11q24.3 near ADAMTS-8), two novel MAP loci (3p21.31 in MAP4, 10q25.3 near ADRB1) and one locus associated with both traits (2q24.3 near FIGN) which has recently been associated with SBP in east Asians. For three of the novel PP signals, the estimated effect for SBP was opposite to that for DBP, in contrast to the majority of common SBP- and DBP-associated variants which show concordant effects on both traits. These findings indicate novel genetic mechanisms underlying blood pressure variation, including pathways that may differentially influence SBP and DBP.
doi:10.1038/ng.922
PMCID: PMC3445021  PMID: 21909110
14.  Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study 
Voight, Benjamin F | Peloso, Gina M | Orho-Melander, Marju | Frikke-Schmidt, Ruth | Barbalic, Maja | Jensen, Majken K | Hindy, George | Hólm, Hilma | Ding, Eric L | Johnson, Toby | Schunkert, Heribert | Samani, Nilesh J | Clarke, Robert | Hopewell, Jemma C | Thompson, John F | Li, Mingyao | Thorleifsson, Gudmar | Newton-Cheh, Christopher | Musunuru, Kiran | Pirruccello, James P | Saleheen, Danish | Chen, Li | Stewart, Alexandre FR | Schillert, Arne | Thorsteinsdottir, Unnur | Thorgeirsson, Gudmundur | Anand, Sonia | Engert, James C | Morgan, Thomas | Spertus, John | Stoll, Monika | Berger, Klaus | Martinelli, Nicola | Girelli, Domenico | McKeown, Pascal P | Patterson, Christopher C | Epstein, Stephen E | Devaney, Joseph | Burnett, Mary-Susan | Mooser, Vincent | Ripatti, Samuli | Surakka, Ida | Nieminen, Markku S | Sinisalo, Juha | Lokki, Marja-Liisa | Perola, Markus | Havulinna, Aki | de Faire, Ulf | Gigante, Bruna | Ingelsson, Erik | Zeller, Tanja | Wild, Philipp | de Bakker, Paul I W | Klungel, Olaf H | Maitland-van der Zee, Anke-Hilse | Peters, Bas J M | de Boer, Anthonius | Grobbee, Diederick E | Kamphuisen, Pieter W | Deneer, Vera H M | Elbers, Clara C | Onland-Moret, N Charlotte | Hofker, Marten H | Wijmenga, Cisca | Verschuren, WM Monique | Boer, Jolanda MA | van der Schouw, Yvonne T | Rasheed, Asif | Frossard, Philippe | Demissie, Serkalem | Willer, Cristen | Do, Ron | Ordovas, Jose M | Abecasis, Gonçalo R | Boehnke, Michael | Mohlke, Karen L | Daly, Mark J | Guiducci, Candace | Burtt, Noël P | Surti, Aarti | Gonzalez, Elena | Purcell, Shaun | Gabriel, Stacey | Marrugat, Jaume | Peden, John | Erdmann, Jeanette | Diemert, Patrick | Willenborg, Christina | König, Inke R | Fischer, Marcus | Hengstenberg, Christian | Ziegler, Andreas | Buysschaert, Ian | Lambrechts, Diether | Van de Werf, Frans | Fox, Keith A | El Mokhtari, Nour Eddine | Rubin, Diana | Schrezenmeir, Jürgen | Schreiber, Stefan | Schäfer, Arne | Danesh, John | Blankenberg, Stefan | Roberts, Robert | McPherson, Ruth | Watkins, Hugh | Hall, Alistair S | Overvad, Kim | Rimm, Eric | Boerwinkle, Eric | Tybjaerg-Hansen, Anne | Cupples, L Adrienne | Reilly, Muredach P | Melander, Olle | Mannucci, Pier M | Ardissino, Diego | Siscovick, David | Elosua, Roberto | Stefansson, Kari | O'Donnell, Christopher J | Salomaa, Veikko | Rader, Daniel J | Peltonen, Leena | Schwartz, Stephen M | Altshuler, David | Kathiresan, Sekar
Lancet  2012;380(9841):572-580.
Summary
Background
High plasma HDL cholesterol is associated with reduced risk of myocardial infarction, but whether this association is causal is unclear. Exploiting the fact that genotypes are randomly assigned at meiosis, are independent of non-genetic confounding, and are unmodified by disease processes, mendelian randomisation can be used to test the hypothesis that the association of a plasma biomarker with disease is causal.
Methods
We performed two mendelian randomisation analyses. First, we used as an instrument a single nucleotide polymorphism (SNP) in the endothelial lipase gene (LIPG Asn396Ser) and tested this SNP in 20 studies (20 913 myocardial infarction cases, 95 407 controls). Second, we used as an instrument a genetic score consisting of 14 common SNPs that exclusively associate with HDL cholesterol and tested this score in up to 12 482 cases of myocardial infarction and 41 331 controls. As a positive control, we also tested a genetic score of 13 common SNPs exclusively associated with LDL cholesterol.
Findings
Carriers of the LIPG 396Ser allele (2·6% frequency) had higher HDL cholesterol (0·14 mmol/L higher, p=8×10−13) but similar levels of other lipid and non-lipid risk factors for myocardial infarction compared with non-carriers. This difference in HDL cholesterol is expected to decrease risk of myocardial infarction by 13% (odds ratio [OR] 0·87, 95% CI 0·84–0·91). However, we noted that the 396Ser allele was not associated with risk of myocardial infarction (OR 0·99, 95% CI 0·88–1·11, p=0·85). From observational epidemiology, an increase of 1 SD in HDL cholesterol was associated with reduced risk of myocardial infarction (OR 0·62, 95% CI 0·58–0·66). However, a 1 SD increase in HDL cholesterol due to genetic score was not associated with risk of myocardial infarction (OR 0·93, 95% CI 0·68–1·26, p=0·63). For LDL cholesterol, the estimate from observational epidemiology (a 1 SD increase in LDL cholesterol associated with OR 1·54, 95% CI 1·45–1·63) was concordant with that from genetic score (OR 2·13, 95% CI 1·69–2·69, p=2×10−10).
Interpretation
Some genetic mechanisms that raise plasma HDL cholesterol do not seem to lower risk of myocardial infarction. These data challenge the concept that raising of plasma HDL cholesterol will uniformly translate into reductions in risk of myocardial infarction.
Funding
US National Institutes of Health, The Wellcome Trust, European Union, British Heart Foundation, and the German Federal Ministry of Education and Research.
doi:10.1016/S0140-6736(12)60312-2
PMCID: PMC3419820  PMID: 22607825
15.  A Genome-wide Association Study Identifies LIPA as a Susceptibility Gene for Coronary Artery Disease 
Wild, Philipp S | Zeller, Tanja | Schillert, Arne | Szymczak, Silke | Sinning, Christoph R | Deiseroth, Arne | Schnabel, Renate B | Lubos, Edith | Keller, Till | Eleftheriadis, Medea S | Bickel, Christoph | Rupprecht, Hans J | Wilde, Sandra | Rossmann, Heidi | Diemert, Patrick | Cupples, L Adrienne | Perret, Claire | Erdmann, Jeanette | Stark, Klaus | Kleber, Marcus E | Epstein, Stephen E | Voight, Benjamin F | Kuulasmaa, Kari | Li, Mingyao | Schäfer, Arne S | Klopp, Norman | Braund, Peter S | Sager, Hendrik B | Demissie, Serkalem | Proust, Carole | König, Inke R | Wichmann, Heinz-Erich | Reinhard, Wibke | Hoffmann, Michael M | Virtamo, Jarmo | Burnett, Mary Susan | Siscovick, David | Wiklund, Per Gunnar | Qu, Liming | El Mokthari, Nour Eddine | Thompson, John R | Peters, Annette | Smith, Albert V | Yon, Emmanuelle | Baumert, Jens | Hengstenberg, Christian | März, Winfried | Amouyel, Philippe | Devaney, Joseph | Schwartz, Stephen M | Saarela, Olli | Mehta, Nehal N | Rubin, Diana | Silander, Kaisa | Hall, Alistair S | Ferrieres, Jean | Harris, Tamara B | Melander, Olle | Kee, Frank | Hakonarson, Hakon | Schrezenmeir, Juergen | Gudnason, Vilmundur | Elosua, Roberto | Arveiler, Dominique | Evans, Alun | Rader, Daniel J | Illig, Thomas | Schreiber, Stefan | Bis, Joshua C | Altshuler, David | Kavousi, Maryam | Witteman, Jaqueline CM | Uitterlinden, Andre G | Hofman, Albert | Folsom, Aaron R | Barbalic, Maja | Boerwinkle, Eric | Kathiresan, Sekar | Reilly, Muredach P | O'Donnell, Christopher J | Samani, Nilesh J | Schunkert, Heribert | Cambien, Francois | Lackner, Karl J | Tiret, Laurence | Salomaa, Veikko | Munzel, Thomas | Ziegler, Andreas | Blankenberg, Stefan
Background
eQTL analyses are important to improve the understanding of genetic association results. Here, we performed a genome-wide association and global gene expression study to identify functionally relevant variants affecting the risk of coronary artery disease (CAD).
Methods and Results
In a genome-wide association analysis of 2,078 CAD cases and 2,953 controls, we identified 950 single nucleotide polymorphisms (SNPs) that were associated with CAD at P<10-3. Subsequent in silico and wet-lab replication stages and a final meta-analysis of 21,428 CAD cases and 38,361 controls revealed a novel association signal at chromosome 10q23.31 within the LIPA (Lysosomal Acid Lipase A) gene (P=3.7×10-8; OR 1.1; 95% CI: 1.07-1.14). The association of this locus with global gene expression was assessed by genome-wide expression analyses in the monocyte transcriptome of 1,494 individuals. The results showed a strong association of this locus with expression of the LIPA transcript (P=1.3×10-96). An assessment of LIPA SNPs and transcript with cardiovascular phenotypes revealed an association of LIPA transcript levels with impaired endothelial function (P=4.4×10-3).
Conclusions
The use of data on genetic variants and the addition of data on global monocytic gene expression led to the identification of the novel functional CAD susceptibility locus LIPA, located on chromosome 10q23.31. The respective eSNPs associated with CAD strongly affect LIPA gene expression level, which itself was related to endothelial dysfunction, a precursor of CAD.
doi:10.1161/CIRCGENETICS.110.958728
PMCID: PMC3157552  PMID: 21606135
coronary artery disease; genome-wide association studies; gene expression; genetic variation; genomics; eQTL; eSNP; LIPA
16.  Risk estimation and risk prediction using machine-learning methods 
Human Genetics  2012;131(10):1639-1654.
After an association between genetic variants and a phenotype has been established, further study goals comprise the classification of patients according to disease risk or the estimation of disease probability. To accomplish this, different statistical methods are required, and specifically machine-learning approaches may offer advantages over classical techniques. In this paper, we describe methods for the construction and evaluation of classification and probability estimation rules. We review the use of machine-learning approaches in this context and explain some of the machine-learning algorithms in detail. Finally, we illustrate the methodology through application to a genome-wide association analysis on rheumatoid arthritis.
Electronic supplementary material
The online version of this article (doi:10.1007/s00439-012-1194-y) contains supplementary material, which is available to authorized users.
doi:10.1007/s00439-012-1194-y
PMCID: PMC3432206  PMID: 22752090
17.  No association of vitamin D metabolism-related polymorphisms and melanoma risk as well as melanoma prognosis: a case–control study 
Archives of Dermatological Research  2012;304(5):353-361.
Melanoma is one of the most aggressive human cancers. The vitamin D system contributes to the pathogenesis and prognosis of malignancies including cutaneous melanoma. An expression of the vitamin D receptor (VDR) and an anti-proliferative effect of vitamin D in melanocytes and melanoma cells have been shown in vitro. Studies examining associations of polymorphisms in genes coding for vitamin D metabolism-related proteins (1α-hydroxylase [CYP27B1], 1,25(OH)2D-24hydroxylase [CYP24A1], vitamin D-binding protein [VDBP]) and cancer risk are scarce, especially with respect to melanoma. Mainly VDR polymorphisms regarding melanoma risk and prognosis were examined although other vitamin D metabolism-related genes may also be crucial. In our hospital-based case–control study including 305 melanoma patients and 370 healthy controls single nucleotide polymorphisms in the genes CYP27B1 (rs4646536), CYP24A1 (rs927650), VDBP (rs1155563, rs7041), and VDR (rs757343, rs731236, rs2107301, rs7975232) were analyzed for their association with melanoma risk and prognosis. Except VDR rs731236 and VDR rs2107301, the other six polymorphisms have not been analyzed regarding melanoma before. To further improve the prevention as well as the treatment of melanoma, it is important to identify further genetic markers for melanoma risk as well as prognosis in addition to the crude phenotypic, demographic, and environmental markers used in the clinic today. A panel of genetic risk markers could help to better identify individuals at risk for melanoma development or worse prognosis. We, however, found that none of the polymorphisms tested was associated with melanoma risk as well as prognosis in logistic and linear regression models in our study population.
doi:10.1007/s00403-012-1243-3
PMCID: PMC3382284  PMID: 22576141
Melanoma; Vitamin D; Vitamin D receptor (VDR); 25-hydroxyvitamin D-1α-hydroxylase (CYP27B1); 1,25-dihydroxyvitamin D-24-hydroxylase (CYP24A1); Vitamin D binding protein (VDBP); Gene polymorphism
18.  Brief Review of Regression-Based and Machine Learning Methods in Genetic Epidemiology: The Genetic Analysis Workshop 17 Experience 
Genetic Epidemiology  2011;35(Suppl 1):S5-11.
Genetics Analysis Workshop 17 provided common and rare genetic variants from exome sequencing data and simulated binary and quantitative traits in 200 replicates. We provide a brief review of the machine learning and regression-based methods used in the analyses of these data. Several regression and machine learning methods were used to address different problems inherent in the analyses of these data, which are high-dimension, low-sample-size data typical of many genetic association studies. Unsupervised methods, such as cluster analysis, were used for data segmentation and subset selection. Supervised learning methods, which include regression-based methods (e.g., generalized linear models, logic regression, and regularized regression) and tree-based methods (e.g., decision trees and random forests), were used for variable selection (selecting genetic and clinical features most associated or predictive of outcome) and prediction (developing models using common and rare genetic variants to accurately predict outcome), with the outcome being case-control status or quantitative trait value. We include a discussion of cross-validation for model selection and assessment and a description of available software resources for these methods.
doi:10.1002/gepi.20642
PMCID: PMC3345521  PMID: 22128059
unsupervised learning; supervised learning; cluster analysis; logistic regression; Poisson regression; logic regression; LASSO; ridge regression; decision trees; random forests; cross-validation; software
19.  A Genome Wide Association Study for Coronary Artery Disease Identifies a Novel Susceptibility Locus in the Major Histocompatibility Complex 
Background
Recent genome-wide association studies (GWAS) have identified several novel loci that reproducibly associate with CAD and/or MI risk. However, known common CAD risk variants explain only 10% of the predicted genetic heritability of the disease, suggesting that important genetic signals remain to be discovered.
Methods and Results
We performed a discovery meta-analysis of 5 GWASs involving 13,949 subjects (7123 cases, 6826 controls) imputed at approximately 5 million SNPs using pilot 1000 Genomes based haplotypes. Promising loci were followed up in an additional 5 studies with 11,032 subjects (5211 cases, 5821 controls). A novel CAD locus on chromosome 6p21.3 in the major histocompatibility complex (MHC) between HCG27 and HLA-C was identified and achieved genome wide significance in the combined analysis (rs3869109; pdiscovery=3.3×10−7, preplication=5.3×10−4 pcombined=1.12×10−9). A sub-analysis combining discovery GWASs showed an attenuation of significance when stringent corrections for European population structure were employed (p=4.1×10-10 versus 3.2×10-7) suggesting the observed signal is partly confounded due to population stratification. This gene dense region plays an important role in inflammation, immunity and self cell recognition. To determine whether the underlying association was driven by MHC class I alleles, we statistically imputed common HLA alleles into the discovery subjects; however, no single common HLA type contributed significantly or fully explained the observed association.
Conclusions
We have identified a novel locus in the MHC associated with CAD. MHC genes regulate inflammation and T cell responses that contribute importantly to the initiation and propagation of atherosclerosis. Further laboratory studies will be required to understand the biological basis of this association and identify the causative allele(s).
doi:10.1161/CIRCGENETICS.111.961243
PMCID: PMC3335297  PMID: 22319020
coronary artery disease; myocardial infarction; meta-analysis; genetics
20.  A Genome Wide Association Study for Coronary Artery Disease Identifies a Novel Susceptibility Locus in the Major Histocompatibility Complex 
Background
Recent genome-wide association studies (GWAS) have identified several novel loci that reproducibly associate with CAD and/or MI risk. However, known common CAD risk variants explain only 10% of the predicted genetic heritability of the disease, suggesting that important genetic signals remain to be discovered.
Methods and Results
We performed a discovery meta-analysis of 5 GWASs involving 13,949 subjects (7123 cases, 6826 controls) imputed at approximately 5 million SNPs using pilot 1000 Genomes based haplotypes. Promising loci were followed up in an additional 5 studies with 11,032 subjects (5211 cases, 5821 controls). A novel CAD locus on chromosome 6p21.3 in the major histocompatibility complex (MHC) between HCG27 and HLA-C was identified and achieved genome wide significance in the combined analysis (rs3869109; pdiscovery=3.3×10−7, preplication=5.3×10−4 pcombined=1.12×10−9). A sub-analysis combining discovery GWASs showed an attenuation of significance when stringent corrections for European population structure were employed (p=4.1×10−10 versus 3.2×10−7) suggesting the observed signal is partly confounded due to population stratification. This gene dense region plays an important role in inflammation, immunity and self cell recognition. To determine whether the underlying association was driven by MHC class I alleles, we statistically imputed common HLA alleles into the discovery subjects; however, no single common HLA type contributed significantly or fully explained the observed association.
Conclusion
We have identified a novel locus in the MHC associated with CAD. MHC genes regulate inflammation and T cell responses that contribute importantly to the initiation and propagation of atherosclerosis. Further laboratory studies will be required to understand the biological basis of this association and identify the causative allele(s).
doi:10.1161/CIRCGENETICS.111.961243
PMCID: PMC3335297  PMID: 22319020
Coronary Artery Disease; Myocardial Infarction; Meta-Analysis; Genetics
22.  Identifying rare variants from exome scans: the GAW17 experience 
BMC Proceedings  2011;5(Suppl 9):S1.
Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.
doi:10.1186/1753-6561-5-S9-S1
PMCID: PMC3287821  PMID: 22373325
23.  Comparison of collapsing methods for the statistical analysis of rare variants 
BMC Proceedings  2011;5(Suppl 9):S115.
Novel technologies allow sequencing of whole genomes and are considered as an emerging approach for the identification of rare disease-associated variants. Recent studies have shown that multiple rare variants can explain a particular proportion of the genetic basis for disease. Following this assumption, we compare five collapsing approaches to test for groupwise association with disease status, using simulated data provided by Genetic Analysis Workshop 17 (GAW17). Variants are collapsed in different scenarios per gene according to different minor allele frequency (MAF) thresholds and their functionality. For comparing the different approaches, we consider the family-wise error rate and the power. Most of the methods could maintain the nominal type I error levels well for small MAF thresholds, but the power was generally low. Although the methods considered in this report are common approaches for analyzing rare variants, they performed poorly with respect to the simulated disease phenotype in the GAW17 data set.
doi:10.1186/1753-6561-5-S9-S115
PMCID: PMC3287839  PMID: 22373249
24.  Lack of association between the Trp719Arg polymorphism in kinesin-like protein 6 and coronary artery disease in 19 case-control studies 
Assimes, Themistocles L | Hólm, Hilma | Kathiresan, Sekar | Reilly, Muredach P | Thorleifsson, Gudmar | Voight, Benjamin F | Erdmann, Jeanette | Willenborg, Christina | Vaidya, Dhananjay | Xie, Changchun | Patterson, Chris C | Morgan, Thomas M | Burnett, Mary Susan | Li, Mingyao | Hlatky, Mark A | Knowles, Joshua W | Thompson, John R | Absher, Devin | Iribarren, Carlos | Go, Alan | Fortmann, Stephen P | Sidney, Stephen | Risch, Neil | Tang, Hua | Myers, Richard M | Berger, Klaus | Stoll, Monika | Shah, Svati H. | Thorgeirsson, Gudmundur | Andersen, Karl | Havulinna, Aki S | Herrera, J. Enrique | Faraday, Nauder | Kim, Yoonhee | Kral, Brian G. | Mathias, Rasika | Ruczinski, Ingo | Suktitipat, Bhoom | Wilson, Alexander F | Yanek, Lisa R. | Becker, Lewis C | Linsel-Nitschke, Patrick | Lieb, Wolfgang | König, Inke R | Hengstenberg, Christian | Fischer, Marcus | Stark, Klaus | Reinhard, Wibke | Winogradow, Janina | Grassl, Martina | Grosshennig, Anika | Preuss, Michael | Eifert, Sandra | Schreiber, Stefan | Wichmann, H-Erich | Meisinger, Christa | Yee, Jean | Friedlander, Yechiel | Do, Ron | Meigs, James B | Williams, Gordon | Nathan, David M | MacRae, Calum A | Qu, Liming | Wilensky, Robert L | Matthai, William H. | Qasim, Atif N | Hakonarson, Hakon | Pichard, Augusto D | Kent, Kenneth M | Satler, Lowell | Lindsay, Joseph M | Waksman, Ron | Knouff, Christopher W | Waterworth, Dawn M | Walker, Max C | Mooser, Vincent | Marrugat, Jaume | Lucas, Gavin | Subirana, Isaac | Sala, Joan | Ramos, Rafael | Martinelli, Nicola | Olivieri, Oliviero | Trabetti, Elisabetta | Malerba, Giovanni | Pignatti, Pier Franco | Guiducci, Candace | Mirel, Daniel | Parkin, Melissa | Hirschhorn, Joel N | Asselta, Rosanna | Duga, Stefano | Musunuru, Kiran | Daly, Mark J | Purcell, Shaun | Braund, Peter S | Wright, Benjamin J | Balmforth, Anthony J | Ball, Stephen G | Ouwehand, Willem H | Deloukas, Panos | Scholz, Michael | Cambien, Francois | Huge, Andreas | Scheffold, Thomas | Salomaa, Veikko | Girelli, Domenico | Granger, Christopher B. | Peltonen, Leena | McKeown, Pascal P | Altshuler, David | Melander, Olle | Devaney, Joseph M | Epstein, Stephen E | Rader, Daniel J | Elosua, Roberto | Engert, James C | Anand, Sonia S | Hall, Alistair S | Ziegler, Andreas | O’Donnell, Christopher J | Spertus, John A | Siscovick, David | Schwartz, Stephen M | Becker, Diane | Thorsteinsdottir, Unnur | Stefansson, Kari | Schunkert, Heribert | Samani, Nilesh J | Quertermous, Thomas
Objectives
We sought to replicate the association between the kinesin-like protein 6 (KIF6) Trp719Arg polymorphism (rs20455) and clinical coronary artery disease (CAD).
Background
Recent prospective studies suggest that carriers of the 719Arg allele in KIF6 are at increased risk of clinical CAD compared with non-carriers.
Methods
The KIF6 Trp719Arg polymorphism (rs20455) was genotyped in nineteen case-control studies of non-fatal CAD either as part of a genome-wide association study or in a formal attempt to replicate the initial positive reports.
Results
Over 17 000 cases and 39 000 controls of European descent as well as a modest number of South Asians, African Americans, Hispanics, East Asians, and admixed cases and controls were successfully genotyped. None of the nineteen studies demonstrated an increased risk of CAD in carriers of the 719Arg allele compared with non-carriers. Regression analyses and fixed effect meta-analyses ruled out with high degree of confidence an increase of ≥2% in the risk of CAD among European 719Arg carriers. We also observed no increase in the risk of CAD among 719Arg carriers in the subset of Europeans with early onset disease (<50 years of age for males and <60 years for females) compared with similarly aged controls as well as all non-European subgroups.
Conclusions
The KIF6 Trp719Arg polymorphism was not associated with the risk of clinical CAD in this large replication study.
doi:10.1016/j.jacc.2010.06.022
PMCID: PMC3084526  PMID: 20933357
kinesin-like protein 6; KIF6; coronary artery disease; myocardial infarction; polymorphism
25.  Large-scale association analyses identifies 13 new susceptibility loci for coronary artery disease 
Schunkert, Heribert | König, Inke R. | Kathiresan, Sekar | Reilly, Muredach P. | Assimes, Themistocles L. | Holm, Hilma | Preuss, Michael | Stewart, Alexandre F. R. | Barbalic, Maja | Gieger, Christian | Absher, Devin | Aherrahrou, Zouhair | Allayee, Hooman | Altshuler, David | Anand, Sonia S. | Andersen, Karl | Anderson, Jeffrey L. | Ardissino, Diego | Ball, Stephen G. | Balmforth, Anthony J. | Barnes, Timothy A. | Becker, Diane M. | Becker, Lewis C. | Berger, Klaus | Bis, Joshua C. | Boekholdt, S. Matthijs | Boerwinkle, Eric | Braund, Peter S. | Brown, Morris J. | Burnett, Mary Susan | Buysschaert, Ian | Carlquist, Cardiogenics, John F. | Chen, Li | Cichon, Sven | Codd, Veryan | Davies, Robert W. | Dedoussis, George | Dehghan, Abbas | Demissie, Serkalem | Devaney, Joseph M. | Do, Ron | Doering, Angela | Eifert, Sandra | El Mokhtari, Nour Eddine | Ellis, Stephen G. | Elosua, Roberto | Engert, James C. | Epstein, Stephen E. | Faire, Ulf de | Fischer, Marcus | Folsom, Aaron R. | Freyer, Jennifer | Gigante, Bruna | Girelli, Domenico | Gretarsdottir, Solveig | Gudnason, Vilmundur | Gulcher, Jeffrey R. | Halperin, Eran | Hammond, Naomi | Hazen, Stanley L. | Hofman, Albert | Horne, Benjamin D. | Illig, Thomas | Iribarren, Carlos | Jones, Gregory T. | Jukema, J.Wouter | Kaiser, Michael A. | Kaplan, Lee M. | Kastelein, John J.P. | Khaw, Kay-Tee | Knowles, Joshua W. | Kolovou, Genovefa | Kong, Augustine | Laaksonen, Reijo | Lambrechts, Diether | Leander, Karin | Lettre, Guillaume | Li, Mingyao | Lieb, Wolfgang | Linsel-Nitschke, Patrick | Loley, Christina | Lotery, Andrew J. | Mannucci, Pier M. | Maouche, Seraya | Martinelli, Nicola | McKeown, Pascal P. | Meisinger, Christa | Meitinger, Thomas | Melander, Olle | Merlini, Pier Angelica | Mooser, Vincent | Morgan, Thomas | Mühleisen, Thomas W. | Muhlestein, Joseph B. | Münzel, Thomas | Musunuru, Kiran | Nahrstaedt, Janja | Nelson, Christopher P. | Nöthen, Markus M. | Olivieri, Oliviero | Patel, Riyaz S. | Patterson, Chris C. | Peters, Annette | Peyvandi, Flora | Qu, Liming | Quyyumi, Arshed A. | Rader, Daniel J. | Rallidis, Loukianos S. | Rice, Catherine | Rosendaal, Frits R. | Rubin, Diana | Salomaa, Veikko | Sampietro, M. Lourdes | Sandhu, Manj S. | Schadt, Eric | Schäfer, Arne | Schillert, Arne | Schreiber, Stefan | Schrezenmeir, Jürgen | Schwartz, Stephen M. | Siscovick, David S. | Sivananthan, Mohan | Sivapalaratnam, Suthesh | Smith, Albert | Smith, Tamara B. | Snoep, Jaapjan D. | Soranzo, Nicole | Spertus, John A. | Stark, Klaus | Stirrups, Kathy | Stoll, Monika | Tang, W. H. Wilson | Tennstedt, Stephanie | Thorgeirsson, Gudmundur | Thorleifsson, Gudmar | Tomaszewski, Maciej | Uitterlinden, Andre G. | van Rij, Andre M. | Voight, Benjamin F. | Wareham, Nick J. | Wells, George A. | Wichmann, H.-Erich | Wild, Philipp S. | Willenborg, Christina | Witteman, Jaqueline C. M. | Wright, Benjamin J. | Ye, Shu | Zeller, Tanja | Ziegler, Andreas | Cambien, Francois | Goodall, Alison H. | Cupples, L. Adrienne | Quertermous, Thomas | März, Winfried | Hengstenberg, Christian | Blankenberg, Stefan | Ouwehand, Willem H. | Hall, Alistair S. | Deloukas, Panos | Thompson, John R. | Stefansson, Kari | Roberts, Robert | Thorsteinsdottir, Unnur | O’Donnell, Christopher J. | McPherson, Ruth | Erdmann, Jeanette | Samani, Nilesh J.
Nature genetics  2011;43(4):333-338.
We performed a meta-analysis of 14 genome-wide association studies of coronary artery disease (CAD) comprising 22,233 cases and 64,762 controls of European descent, followed by genotyping of top association signals in 60,738 additional individuals. This genomic analysis identified 13 novel loci harboring one or more SNPs that were associated with CAD at P<5×10−8 and confirmed the association of 10 of 12 previously reported CAD loci. The 13 novel loci displayed risk allele frequencies ranging from 0.13 to 0.91 and were associated with a 6 to 17 percent increase in the risk of CAD per allele. Notably, only three of the novel loci displayed significant association with traditional CAD risk factors, while the majority lie in gene regions not previously implicated in the pathogenesis of CAD. Finally, five of the novel CAD risk loci appear to have pleiotropic effects, showing strong association with various other human diseases or traits.
doi:10.1038/ng.784
PMCID: PMC3119261  PMID: 21378990

Results 1-25 (50)