Analysis of 17,576 Potentially Functional SNPs in Three Case–Control Studies of Myocardial Infarction

Myocardial infarction (MI) is a common complex disease with a genetic component. While several single nucleotide polymorphisms (SNPs) have been reported to be associated with risk of MI, they do not fully explain the observed genetic component of MI. We have been investigating the association between MI and SNPs that are located in genes and have the potential to affect gene function or expression. We have previously published studies that tested about 12,000 SNPs for association with risk of MI, early-onset MI, or coronary stenosis. In the current study we tested 17,576 SNPs that could affect gene function or expression. In order to use genotyping resources efficiently, we staged the testing of these SNPs in three case–control studies of MI. In the first study (762 cases, 857 controls) we tested 17,576 SNPs and found 1,949 SNPs that were associated with MI (P<0.05). We tested these 1,949 SNPs in a second study (579 cases and 1159 controls) and found that 24 SNPs were associated with MI (1-sided P<0.05) and had the same risk alleles in the first and second study. Finally, we tested these 24 SNPs in a third study (475 cases and 619 controls) and found that 5 SNPs in 4 genes (ENO1, FXN (2 SNPs), HLA-DPB2, and LPA) were associated with MI in the third study (1-sided P<0.05), and had the same risk alleles in all three studies. The false discovery rate for this group of 5 SNPs was 0.23. Thus, we have identified 5 SNPs that merit further examination for their potential association with MI. One of these SNPs (in LPA), has been previously shown to be associated with risk of cardiovascular disease in other studies

NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Taking the pulse of Earth's tropical forests using networks of highly distributed plots

Tropical forests are the most diverse and productive ecosystems on Earth. While better understanding of these forests is critical for our collective future, until quite recently efforts to measure and monitor them have been largely disconnected. Networking is essential to discover the answers to questions that transcend borders and the horizons of funding agencies. Here we show how a global community is responding to the challenges of tropical ecosystem research with diverse teams measuring forests tree-by-tree in thousands of long-term plots. We review the major scientific discoveries of this work and show how this process is changing tropical forest science. Our core approach involves linking long-term grassroots initiatives with standardized protocols and data management to generate robust scaled-up results. By connecting tropical researchers and elevating their status, our Social Research Network model recognises the key role of the data originator in scientific discovery. Conceived in 1999 with RAINFOR (South America), our permanent plot networks have been adapted to Africa (AfriTRON) and Southeast Asia (T-FORCES) and widely emulated worldwide. Now these multiple initiatives are integrated via ForestPlots.net cyber-infrastructure, linking colleagues from 54 countries across 24 plot networks. Collectively these are transforming understanding of tropical forests and their biospheric role. Together we have discovered how, where and why forest carbon and biodiversity are responding to climate change, and how they feedback on it. This long-term pan-tropical collaboration has revealed a large long-term carbon sink and its trends, as well as making clear which drivers are most important, which forest processes are affected, where they are changing, what the lags are, and the likely future responses of tropical forests as the climate continues to change. By leveraging a remarkably old technology, plot networks are sparking a very modern revolution in tropical forest science. In the future, humanity can benefit greatly by nurturing the grassroots communities now collectively capable of generating unique, long-term understanding of Earth's most precious forests.Additional co-authors: Susan Laurance, William Laurance, Francoise Yoko Ishida, Andrew Marshall, Catherine Waite, Hannsjoerg Woell, Jean-Francois Bastin, Marijn Bauters, Hans Beeckman, Pfascal Boeckx, Jan Bogaert, Charles De Canniere, Thales de Haulleville, Jean-Louis Doucet, Olivier Hardy, Wannes Hubau, Elizabeth Kearsley, Hans Verbeeck, Jason Vleminckx, Steven W. Brewer, Alfredo Alarcón, Alejandro Araujo-Murakami, Eric Arets, Luzmila Arroyo, Ezequiel Chavez, Todd Fredericksen, René Guillén Villaroel, Gloria Gutierrez Sibauty, Timothy Killeen, Juan Carlos Licona, John Lleigue, Casimiro Mendoza, Samaria Murakami, Alexander Parada Gutierrez, Guido Pardo, Marielos Peña-Claros, Lourens Poorter, Marisol Toledo, Jeanneth Villalobos Cayo, Laura Jessica Viscarra, Vincent Vos, Jorge Ahumada, Everton Almeida, Jarcilene Almeida, Edmar Almeida de Oliveira, Wesley Alves da Cruz, Atila Alves de Oliveira, Fabrício Alvim Carvalho, Flávio Amorim Obermuller, Ana Andrade, Fernanda Antunes Carvalho, Simone Aparecida Vieira, Ana Carla Aquino, Luiz Aragão, Ana Claudia Araújo, Marco Antonio Assis, Jose Ataliba Mantelli Aboin Gomes, Fabrício Baccaro, Plínio Barbosa de Camargo, Paulo Barni, Jorcely Barroso, Luis Carlos Bernacci, Kauane Bordin, Marcelo Brilhante de Medeiros, Igor Broggio, José Luís Camargo, Domingos Cardoso, Maria Antonia Carniello, Andre Luis Casarin Rochelle, Carolina Castilho, Antonio Alberto Jorge Farias Castro, Wendeson Castro, Sabina Cerruto Ribeiro, Flávia Costa, Rodrigo Costa de Oliveira, Italo Coutinho, John Cunha, Lola da Costa, Lucia da Costa Ferreira, Richarlly da Costa Silva, Marta da Graça Zacarias Simbine, Vitor de Andrade Kamimura, Haroldo Cavalcante de Lima, Lia de Oliveira Melo, Luciano de Queiroz, José Romualdo de Sousa Lima, Mário do Espírito Santo, Tomas Domingues, Nayane Cristina dos Santos Prestes, Steffan Eduardo Silva Carneiro, Fernando Elias, Gabriel Eliseu, Thaise Emilio, Camila Laís Farrapo, Letícia Fernandes, Gustavo Ferreira, Joice Ferreira, Leandro Ferreira, Socorro Ferreira, Marcelo Fragomeni Simon, Maria Aparecida Freitas, Queila S. García, Angelo Gilberto Manzatto, Paulo Graça, Frederico Guilherme, Eduardo Hase, Niro Higuchi, Mariana Iguatemy, Reinaldo Imbrozio Barbosa, Margarita Jaramillo, Carlos Joly, Joice Klipel, Iêda Leão do Amaral, Carolina Levis, Antonio S. Lima, Maurício Lima Dan, Aline Lopes, Herison Madeiros, William E. Magnusson, Rubens Manoel dos Santos, Beatriz Marimon, Ben Hur Marimon Junior, Roberta Marotti Martelletti Grillo, Luiz Martinelli, Simone Matias Reis, Salomão Medeiros, Milton Meira-Junior, Thiago Metzker, Paulo Morandi, Natanael Moreira do Nascimento, Magna Moura, Sandra Cristina Müller, Laszlo Nagy, Henrique Nascimento, Marcelo Nascimento, Adriano Nogueira Lima, Raimunda Oliveira de Araújo, Jhonathan Oliveira Silva, Marcelo Pansonato, Gabriel Pavan Sabino, Karla Maria Pedra de Abreu, Pablo José Francisco Pena Rodrigues, Maria Piedade, Domingos Rodrigues, José Roberto Rodrigues Pinto, Carlos Quesada, Eliana Ramos, Rafael Ramos, Priscyla Rodrigues, Thaiane Rodrigues de Sousa, Rafael Salomão, Flávia Santana, Marcos Scaranello, Rodrigo Scarton Bergamin, Juliana Schietti, Jochen Schöngart, Gustavo Schwartz, Natalino Silva, Marcos Silveira, Cristiana Simão Seixas, Marta Simbine, Ana Claudia Souza, Priscila Souza, Rodolfo Souza, Tereza Sposito, Edson Stefani Junior, Julio Daniel do Vale, Ima Célia Guimarães Vieira, Dora Villela, Marcos Vital, Haron Xaud, Katia Zanini, Charles Eugene Zartman, Nur Khalish Hafizhah Ideris, Faizah binti Hj Metali, Kamariah Abu Salim, Muhd Shahruney Saparudin, Rafizah Mat Serudin, Rahayu Sukmaria Sukri, Serge Begne, George Chuyong, Marie Noel Djuikouo, Christelle Gonmadje, Murielle Simo-Droissart, Bonaventure Sonké, Hermann Taedoumg, Lise Zemagho, Sean Thomas, Fidèle Baya, Gustavo Saiz, Javier Silva Espejo, Dexiang Chen, Alan Hamilton, Yide Li, Tushou Luo, Shukui Niu, Han Xu, Zhang Zhou, Esteban Álvarez-Dávila, Juan Carlos Andrés Escobar, Henry Arellano-Peña, Jaime Cabezas Duarte, Jhon Calderón, Lina Maria Corrales Bravo, Borish Cuadrado, Hermes Cuadros, Alvaro Duque, Luisa Fernanda Duque, Sandra Milena Espinosa, Rebeca Franke-Ante, Hernando García, Alejandro Gómez, Roy González-M., Álvaro Idárraga-Piedrahíta, Eliana Jimenez, Rubén Jurado, Wilmar López Oviedo, René López-Camacho, Omar Aurelio Melo Cruz, Irina Mendoza Polo, Edwin Paky, Karen Pérez, Angel Pijachi, Camila Pizano, Adriana Prieto, Laura Ramos, Zorayda Restrepo Correa, James Richardson, Elkin Rodríguez, Gina M. Rodriguez M., Agustín Rudas, Pablo Stevenson, Markéta Chudomelová, Martin Dancak, Radim Hédl, Stanislav Lhota, Martin Svatek, Jacques Mukinzi, Corneille Ewango, Terese Hart, Emmanuel Kasongo Yakusu, Janvier Lisingo, Jean-Remy Makana, Faustin Mbayu, Benjamin Toirambe, John Tshibamba Mukendi, Lars Kvist, Gustav Nebel, Selene Báez, Carlos Céron, Daniel M. Griffith, Juan Ernesto Guevara Andino, David Neill, Walter Palacios, Maria Cristina Peñuela-Mora, Gonzalo Rivas-Torres, Gorky Villa, Sheleme Demissie, Tadesse Gole, Techane Gonfa, Kalle Ruokolainen, Michel Baisie, Fabrice Bénédet, Wemo Betian, Vincent Bezard, Damien Bonal, Jerôme Chave, Vincent Droissart, Sylvie Gourlet-Fleury, Annette Hladik, Nicolas Labrière, Pétrus Naisso, Maxime Réjou-Méchain, Plinio Sist, Lilian Blanc, Benoit Burban, Géraldine Derroire, Aurélie Dourdain, Clement Stahl, Natacha Nssi Bengone, Eric Chezeaux, Fidèle Evouna Ondo, Vincent Medjibe, Vianet Mihindou, Lee White, Heike Culmsee, Cristabel Durán Rangel, Viviana Horna, Florian Wittmann, Stephen Adu-Bredu, Kofi Affum-Baffoe, Ernest Foli, Michael Balinga, Anand Roopsind, James Singh, Raquel Thomas, Roderick Zagt, Indu K. Murthy, Kuswata Kartawinata, Edi Mirmanto, Hari Priyadi, Ismayadi Samsoedin, Terry Sunderland, Ishak Yassir, Francesco Rovero, Barbara Vinceti, Bruno Hérault, Shin-Ichiro Aiba, Kanehiro Kitayama, Armandu Daniels, Darlington Tuagben, John T. Woods, Muhammad Fitriadi, Alexander Karolus, Kho Lip Khoon, Noreen Majalap, Colin Maycock, Reuben Nilus, Sylvester Tan, Almeida Sitoe, Indiana Coronado G., Lucas Ojo, Rafael de Assis, Axel Dalberg Poulsen, Douglas Sheil, Karen Arévalo Pezo, Hans Buttgenbach Verde, Victor Chama Moscoso, Jimmy Cesar Cordova Oroche, Fernando Cornejo Valverde, Massiel Corrales Medina, Nallaret Davila Cardozo, Jano de Rutte Corzo, Jhon del Aguila Pasquel, Gerardo Flores Llampazo, Luis Freitas, Darcy Galiano Cabrera, Roosevelt García Villacorta, Karina Garcia Cabrera, Diego García Soria, Leticia Gatica Saboya, Julio Miguel Grandez Rios, Gabriel Hidalgo Pizango, Eurídice Honorio Coronado, Isau Huamantupa-Chuquimaco, Walter Huaraca Huasco, Yuri Tomas Huillca Aedo, Jose Luis Marcelo Peña, Abel Monteagudo Mendoza, Vanesa Moreano Rodriguez, Percy Núñez Vargas, Sonia Cesarina Palacios Ramos, Nadir Pallqui Camacho, Antonio Peña Cruz, Freddy Ramirez Arevalo, José Reyna Huaymacari, Carlos Reynel Rodriguez, Marcos Antonio Ríos Paredes, Lily Rodriguez Bayona, Rocio del Pilar Rojas Gonzales, Maria Elena Rojas Peña, Norma Salinas Revilla, Yahn Carlos Soto Shareva, Raul Tupayachi Trujillo, Luis Valenzuela Gamarra, Rodolfo Vasquez Martinez, Jim Vega Arenas, Christian Amani, Suspense Averti Ifo, Yannick Bocko, Patrick Boundja, Romeo Ekoungoulou, Mireille Hockemba, Donatien Nzala, Alusine Fofanah, David Taylor, Guillermo Bañares-de Dios, Luis Cayuela, Íñigo Granzow-de la Cerda, Manuel Macía, Juliana Stropp, Maureen Playfair, Verginia Wortel, Toby Gardner, Robert Muscarella, Hari Priyadi, Ervan Rutishauser, Kuo-Jung Chao, Pantaleo Munishi, Olaf Bánki, Frans Bongers, Rene Boot, Gabriella Fredriksson, Jan Reitsma, Hans ter Steege, Tinde van Andel, Peter van de Meer, Peter van der Hout, Mark van Nieuwstadt, Bert van Ulft, Elmar Veenendaal, Ronald Vernimmen, Pieter Zuidema, Joeri Zwerts, Perpetra Akite, Robert Bitariho, Colin Chapman, Eilu Gerald, Miguel Leal, Patrick Mucunguzi, Miguel Alexiades, Timothy R. Baker, Karina Banda, Lindsay Banin, Jos Barlow, Amy Bennett, Erika Berenguer, Nicholas Berry, Neil M. Bird, George A. Blackburn, Francis Brearley, Roel Brienen, David Burslem, Lidiany Carvalho, Percival Cho, Fernanda Coelho, Murray Collins, David Coomes, Aida Cuni-Sanchez, Greta Dargie, Kyle Dexter, Mat Disney, Freddie Draper, Muying Duan, Adriane Esquivel-Muelbert, Robert Ewers, Belen Fadrique, Sophie Fauset, Ted R. Feldpausch, Filipe França, David Galbraith, Martin Gilpin, Emanuel Gloor, John Grace, Keith Hamer, David Harris, Tommaso Jucker, Michelle Kalamandeen, Bente Klitgaard, Aurora Levesley, Simon L. Lewis, Jeremy Lindsell, Gabriela Lopez-Gonzalez, Jon Lovett, Yadvinder Malhi, Toby Marthews, Emma McIntosh, Karina Melgaço, William Milliken, Edward Mitchard, Peter Moonlight, Sam Moore, Alexandra Morel, Julie Peacock, Kelvin Peh, Colin Pendry, R. Toby Pennington, Luciana de Oliveira Pereira, Carlos Peres, Oliver L. Phillips, Georgia Pickavance, Thomas Pugh, Lan Qie, Terhi Riutta, Katherine Roucoux, Casey Ryan, Tiina Sarkinen, Camila Silva Valeria, Dominick Spracklen, Suzanne Stas, Martin Sullivan, Michael Swaine, Joey Talbot, James Taplin, Geertje van der Heijden, Laura Vedovato, Simon Willcock, Mathew Williams, Luciana Alves, Patricia Alvarez Loayza, Gabriel Arellano, Cheryl Asa, Peter Ashton, Gregory Asner, Terry Brncic, Foster Brown, Robyn Burnham, Connie Clark, James Comiskey, Gabriel Damasco, Stuart Davies, Tony Di Fiore, Terry Erwin, William Farfan-Rios, Jefferson Hall, David Kenfack, Thomas Lovejoy, Roberta Martin, Olga Martha Montiel, John Pipoly, Nigel Pitman, John Poulsen, Richard Primack, Miles Silman, Marc Steininger, Varun Swamy, John Terborgh, Duncan Thomas, Peter Umunay, Maria Uriarte, Emilio Vilanova Torre, Ophelia Wang, Kenneth Young, Gerardo A. Aymard C., Lionel Hernández, Rafael Herrera Fernández, Hirma Ramírez-Angulo, Pedro Salcedo, Elio Sanoja, Julio Serrano, Armando Torres-Lezama, Tinh Cong Le, Trai Trong Le, Hieu Dang Tra

Use of low density lipoprotein particle number levels as an aid in statin treatment decisions for intermediate risk patients: A cost-effectiveness analysis

BACKGROUND: The 2013 ACC/AHA guideline recommended either no statin therapy or moderate-intensity statin therapy (MST) for intermediate risk patients—those with 5–7.5% 10-year risk and without cardiovascular disease (CVD), hypercholesterolemia or diabetes. The guideline further suggested that the therapy choice be based on patient-clinician discussions of risks and benefits. Since low-density lipoprotein particle (LDL-P) levels were reported to be associated with CVD independently of traditional risk factors in intermediate and low risk patients, we investigated the cost-effectiveness of using LDL-P levels to identify intermediate risk patients likely to benefit from initiating or intensifying statin therapy. METHODS: We evaluated 5 care strategies for intermediate risk patients. These included the strategies suggested by the guideline: no-statin therapy and MST. We compared each of these strategies to a related strategy that incorporated LDL-P testing. No-statin therapy was compared with the strategy of MST for those with high LDL-P levels and no statin therapy for all other patients (test-and-MST). MST was compared with the strategy of high-intensity statin therapy (HST) for those with high LDL-P levels and MST for all other patients (test-and-HST). We also evaluated the strategy of HST for all. Costs (payer perspective) and utilities were assessed over a 5-year time horizon in a Markov model of 100,000 hypothetical intermediate risk patients. RESULTS: HST dominated all other strategies, costing less and—despite causing 739 more cases of diabetes than did MST—resulting in more quality adjusted life-years (QALYs). For patient-clinician discussions that would otherwise lead to the MST strategy, we found the test-and-HST strategy reduced costs by $4.67 MM and resulted in 134 fewer CVD events and 115 additional QALYs. For patient-clinician discussions that would otherwise lead to no statin therapy, we found that the test-and-MST strategy reduced costs by$3.25 MM, resulted in 97 fewer CVD events and 44 additional QALYs. CONCLUSIONS: The HST strategy was cost saving and improved outcomes in intermediate risk patients. For patient and clinicians concerned about the adverse events associated with HST, using LDL-P levels to target intensified statin therapy could improve outcomes and reduce costs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12872-016-0429-6) contains supplementary material, which is available to authorized users

Analysis of 17,576 potentially functional SNPs in three case-control studies of myocardial infarction.

Myocardial infarction (MI) is a common complex disease with a genetic component. While several single nucleotide polymorphisms (SNPs) have been reported to be associated with risk of MI, they do not fully explain the observed genetic component of MI. We have been investigating the association between MI and SNPs that are located in genes and have the potential to affect gene function or expression. We have previously published studies that tested about 12,000 SNPs for association with risk of MI, early-onset MI, or coronary stenosis. In the current study we tested 17,576 SNPs that could affect gene function or expression. In order to use genotyping resources efficiently, we staged the testing of these SNPs in three case-control studies of MI. In the first study (762 cases, 857 controls) we tested 17,576 SNPs and found 1,949 SNPs that were associated with MI (P&lt;0.05). We tested these 1,949 SNPs in a second study (579 cases and 1159 controls) and found that 24 SNPs were associated with MI (1-sided P&lt;0.05) and had the same risk alleles in the first and second study. Finally, we tested these 24 SNPs in a third study (475 cases and 619 controls) and found that 5 SNPs in 4 genes (ENO1, FXN (2 SNPs), HLA-DPB2, and LPA) were associated with MI in the third study (1-sided P&lt;0.05), and had the same risk alleles in all three studies. The false discovery rate for this group of 5 SNPs was 0.23. Thus, we have identified 5 SNPs that merit further examination for their potential association with MI. One of these SNPs (in LPA), has been previously shown to be associated with risk of cardiovascular disease in other studies

Investigation of KIF6 Trp719Arg in a Case-Control Study of Myocardial Infarction: A Costa Rican Population

Background and Methodology The 719Arg allele of KIF6 (rs20455) was associated with coronary events in Caucasian participants of five prospective studies. We investigated whether this KIF6 variant was associated with non-fatal myocardial infarction (MI) in a case-control study of an admixed population from the Central Valley of Costa Rica. Genotypes of the KIF6 variant were determined for 4,134 men and women. Cases (1,987) had survived a first MI; controls (2,147) had no history of MI and were matched to cases by age, sex, and area of residence. We tested the association between the KIF6 719Arg allele and non-fatal MI by conditional logistic regression and adjusted for admixture of founder populations. Principal Findings Compared with the reference Trp/Trp homozygotes, KIF6 719Arg carriers were not at significantly higher risk for non-fatal MI in this study after adjustment for traditional risk factors or admixture (OR = 1.12; 95%CI, 0.98–1.28). Heterozygotes of the KIF6 Trp719Arg variant were at increased risk of non-fatal MI: the adjusted odds ratio was 1.16 (95% confidence interval, 1.01–1.34), but this association would not be significant after a multiple testing correction. Conclusions/Significance We found that carriers of the KIF6 719Arg allele were not at increased risk of non-fatal MI in a case-control study of Costa Ricans living in the Central Valley of Costa Rica. Introduction Kinesins are a superfamily of homodimeric motor proteins that transport cellular cargos (e.g., proteins, vesicles, or organelles) along microtubules in an ATP dependent process. A single nucleotide polymorphism (SNP) in the gene for the KIF6 protein—a member of the kinesin 9 family—has been reported to be associated with coronary heart disease (CHD) and event reduction during statin therapy[2]. In Caucasians, carriers of the 719Arg allele of this SNP (rs20455) were at increased risk for coronary events in 5 prospective studies: the Atherosclerosis Risk in Communities (ARIC) study, the Cardiovascular Health Study (CHS) , the Women's Health Study (WHS), and in the placebo groups of both the Cholesterol and Recurrent Events (CARE) study and the West of Scotland Coronary Prevention Study (WOSCOPS), In contrast to these prospective studies, KIF6 Trp719Arg was not associated with coronary artery disease (CAD) in two recently reported case-control studies: the Ottawa Heart Genomics study and the Welcome Trust Case-Control Consortium study Only limited data are available regarding whether the KIF6 719Arg allele is associated with CHD in other ethnic populations. For example, among African Americans in ARIC, each allele of KIF6 719Arg increased the risk for incident CHD by 1.23-fold (95%CI, 0.99–1.52), and among African Americans in CHS, carriers of the KIF6 719Arg allele had a hazard ratio for incident myocardial infarction (MI) of 4.14 (90%CI, 0.79–21.77), compared with noncarriers[4]. Thus, although the associations between the KIF6 719Arg allele and CHD were not statistically significant in the small African American study populations in ARIC and CHS, these results suggested that the KIF6 719Arg allele may be associated with CHD in ethnic populations other than Caucasians. Therefore, we asked whether the KIF6 719Arg allele is associated with MI in a Hispanic population from Costa Rica. Results The characteristics of cases with myocardial infarction and population-based controls from the Central Valley of Costa Rica used in this study are shown in Table 1. Age, sex, and residence (i.e., matched characteristics) did not differ significantly between cases and controls (Table 1). Other CHD risk factors (smoking, history of hypertension, family history of MI, and waist-to-hip ratio) were higher in cases than in controls (Table 1). The genotypes of the KIF6 SNP (rs20455) that cause a Trp719Arg variation did not deviate from the distribution expected under Hardy–Weinberg equilibrium (P = 0.38); the frequency of the minor allele (KIF6 719Arg) was 0.36 in the control subjects. Characteristics of Cases and Controls. We found that carriers of one or two copies of the KIF6 719Arg variant were not at increased risk of MI (OR = 1.12; 95%CI, 0.98–1.28; Table 2). Although the risk estimate for heterozygotes of the KIF6 variant (Trp/Arg), compared with major allele homozygotes (Trp/Trp), was 1.15 (95%CI 1.00–1.33; Table 2) after adjusting for potentially confounding risk factors and admixture in this Costa Rican population, this association would not be significant if corrected for the testing of multiple KIF6 719Arg carrier groups. Homozygotes of the KIF6 variant (Arg/Arg) were not at increased risk of MI, compared with major allele homozygotes (Trp/Trp)