Figures
Abstract
Background
Familial aggregation of Chagas cardiac disease in T. cruzi–infected persons suggests that human genetic variation may be an important determinant of disease progression.
Objective
To perform a GWAS using a well-characterized cohort to detect single nucleotide polymorphisms (SNPs) and genes associated with cardiac outcomes.
Methods
A retrospective cohort study was developed by the NHLBI REDS-II program in Brazil. Samples were collected from 499 T. cruzi seropositive blood donors who had donated between1996 and 2002, and 101 patients with clinically diagnosed Chagas cardiomyopathy. In 2008–2010, all subjects underwent a complete medical examination. After genotype calling, quality control filtering with exclusion of 20 cases, and imputation of 1,000 genomes variants; association analysis was performed for 7 cardiac and parasite related traits, adjusting for population stratification.
Results
The cohort showed a wide range of African, European, and modest Native American admixture proportions, consistent with the recent history of Brazil. No SNPs were found to be highly (P<10−8) associated with cardiomyopathy. The two mostly highly associated SNPs for cardiomyopathy (rs4149018 and rs12582717; P-values <10−6) are located on Chromosome 12p12.2 in the SLCO1B1 gene, a solute carrier family member. We identified 44 additional genic SNPs associated with six traits at P-value <10-6: Ejection Fraction, PR, QRS, QT intervals, antibody levels by EIA, and parasitemia by PCR.
Conclusion
This GWAS identified suggestive SNPs that may impact the risk of progression to cardiomyopathy. Although this Chagas cohort is the largest examined by GWAS to date, (580 subjects), moderate sample size may explain in part the limited number of significant SNP variants. Enlarging the current sample through expanded cohorts and meta-analyses, and targeted studies of candidate genes, will be required to confirm and extend the results reported here. Future studies should also include exposed seronegative controls to investigate genetic associations with susceptibility or resitance to T. cruzi infection and non-Chagas cardiomathy.
Citation: Deng X, Sabino EC, Cunha-Neto E, Ribeiro AL, Ianni B, Mady C, et al. (2013) Genome Wide Association Study (GWAS) of Chagas Cardiomyopathy in Trypanosoma cruzi Seropositive Subjects. PLoS ONE 8(11): e79629. https://doi.org/10.1371/journal.pone.0079629
Editor: Herbert B. Tanowitz, Albert Einstein College of Medicine, United States of America
Received: May 17, 2013; Accepted: September 24, 2013; Published: November 20, 2013
Copyright: © 2013 Deng et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study is supported by National Heart, Lung, and Blood Institute Retrovirus Epidemiology Donor Study-II (REDS-II), International Component Contract (HHSN268200417175C). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Chagas disease is caused by T. cruzi, a parasite which is naturally transmitted through several species of haematophagous reduviid bugs. The infection/disease is prevalent in most Latin American countries, where approximately 10 million people are infected with T. cruzi and at least 120 million are at risk [1], [2]. In the recent decades, migration from endemic areas brought hundreds of thousands of T. cruzi-infected patients to the USA and Europe, turning Chagas disease into a global health concern [1], [2]. Most untreated acute cases evolve into the so-called indeterminate stage of chronic Chagas disease (seropositive but no evidence of the cardiac or digestive forms of the disease as evaluated by clinical examination, ECG and X ray studies) [2].
The progression from the indeterminate to “clinical” stage of chronic Chagas disease, i.e., cardiomyopathy and the mega-syndromes, generally occurs 10 to 20 years after acute infection at a rate of approximately 1–2% per year [3]. Chronic Chagas cardiomyopathy (CCC), the most clinically relevant manifestation of human Chagas disease, affects 20–40% of patients in the chronic phase of the disease, and manifests as heart failure, arrhythmia, heart block, thromboembolism, stroke, and sudden death [2]. These abnormalities generally occur in combination and CCC is characterized by its severity, as well as by a worse prognosis when compared with other cardiomyopathies [2], [4].
The pathogenesis of chronic CCC is not completely understood. There is evidence to indicate that persistent parasitism of heart tissue induces T cell-mediated inflammation, which has been implicated in myocardial tissue damage [5], [6]. In addition, there is evidence to suggest that auto-immunity may contribute significantly to the inflammatory damage to heart cells and the conduction system [6], [7].
The genetic basis for differential rates of T. cruzi infection and disease progression was evaluated by Williams-Blangero and colleagues who studied pedigrees from a highly endemic region in the State of Goias, Brazil [8]. They showed that half of the variability in susceptibility to infection (presence of antibody to T. cruzi) could be attributed to genetic factors. They also showed that ECG measurements altered by CCC such as PR, QRS and QT intervals were influenced by genetic variation. Using a similar approach, Silva-Grecco et al. showed similar evidence of familial aggregation for T. cruzi seropositivity in the city of Agua Comprida, Minas Gerais, Brazil [9].
A number of candidate gene association studies have been performed in Chagas disease, comparing polymorphism frequencies in patients with CCC and asymptomatic carriers. Due to the obvious importance of the Th1 T cell-rich myocarditis in the pathogenesis of CCC, the focus has been on genes involved in the innate and adaptive immune responses. However, these studies were usually small and led to conflicting results when populations of different ethnicity were studied [10].
As part of the National Heart, Lung and Blood Institute (NHLBI) Retrovirus Epidemiological Donor Study-II (REDS-II), we developed a retrospective cohort study to characterize the natural history of clinical Chagas disease in T. cruzi seropositive blood donors3. We performed a GWAS study using this well established cohort. All phenotype and genotype data is available upon request.
Methods
This study is approved by the UCSF CHR, Comissão de Ética para Análise de Projetos de Pesquisa (CAPPesq), Comitê de Ética em Pesquisa da Fundação Hemominas (CEP Hemominas) and National IRB - Brasília: A Comissão Nacional de Ética em Pesquisa (CONEP). Written consent was given by the patients for their information to be stored in the hospital database and used for research.
Study Design
This study population was derived from a retrospective cohort study, in which 499 T. cruzi seropositive (SP) blood donors (cases) identified by blood bank screening (255 from the city of São Paulo and 244 from the city of Montes Claros in the State of Minas Gerais) and 488 seronegative (SN) donors matched by site, donation date (year), age and gender were enrolled3. This cohort was supplemented with a total of 101 previously diagnosed cases of cardiomyopathyfrom the Heart Institute of University of Sao Paulo Medical School. From July 2008 to October 2010, all individuals (blood donors and patients with CCC) were characterized by demographic survey (including questions about skin color classification, which is comparable to a race-ethnicity classification in Brazil) and by a health questionnaire and medical evaluation, including electrocardiogram (ECG), echocardiogram (Echo) and laboratory tests. Results of ECG and Echo were reviewed under code at centralized reading centers [11]. The presence of CCC was determined by an expert panel composed of three Brazilian cardiologists based on the evaluation of clinical, laboratory, EKG and Echo findings, as described elsewhere [3]. In brief, a pre-defined set of abnormalities in the Echo or ECG measurements triggered the panel to review cardiac findings blinded to the subject’s serostatus. Each physician is asked to answer if, based on available data for a triggered case, he would diagnose CCC; discordances among the examiners were resolved by consensus. Classification rules were used as general guidance for the diagnosis, as well as the physicians’ experience and clinical expertise. The diagnostic accuracy of this algorithm was very good, with a sensitivity of 98% for the detection of previously diagnosed CCC and specificity of 95%, considering 5% of “false positive” diagnosis of CCC in T. cruzi seronegatives donors [3]. In addition to Cardiomyopathy as the main trait, we also evaluated a limited number of specific parameters, including Ejection Fraction (EF), PR interval, QRS duration (QRS), corrected QT interval (QTc), EIA signal/cutoff levels, and T. cruzi PCR status [12], for genome-wide association. Of the 600 T. cruzi seropositive donors/cases, 221 were classified as having CCC; 311 had no cardiomyopathy and 68 were inconclusive. All samples were submitted for DNA extraction.
DNA Sample Preparation
DNA was obtained using QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany), and quantified using NanoDrop (NanoDrop Technologies, Inc.,Wilmington, DE USA). The concentration accepted was between 50 ng/uL and 100 ng/uL.
Genotyping
The 600 DNA samples were sent to the Genomics Core Facility at UCSF for genotyping using the Affymetrix Axiom Genome-Wide Latino (Axiom GW LAT 1) array (Affymetrix, Santa Clara, CA), which consists of 818,154 SNPs and is optimized for individuals with ancestry from Europe, West Africa, and Native Americans.
Genotype Calling, Sample and SNP Filtering
We performed genotype calling using Affymetrix Power Tools (APT) genotyping module. We followed the manufacturer’s genotyping analysis guidelines to remove samples with DishQC below 0.82. QC genotype filtering was performed on a set of SNPs whose genotypes are representative of the expected performance of the standard tiling design, samples with call rate <0.97 were identified and removed. For plate-level QC, all plates passed with plate pass rates >95% and the average call rate of passing samples >99%. Final genotype calling was performed on samples across all SNPs using generic priors. Additional problematic sample were removed from further analysis: samples with Identify By Descent (IBD) score >0.5 and, samples with unknown or contradicted computed gender compared to documented gender. A total of 20 (3%) samples were excluded using these filters.
A series of SNP filtering procedures were performed on the remaining 580 samples. To begin with, the total number of SNPs was 818,154 on array. The filters and their thresholds were: SNP call rate >97%; FLD >3.6; HetSO>3.6; HomRO 2-cluster >0.3; HomRO>3-cluster −0.9; Hardy-Weinberg test P-value >10−9; and MAF >0.5%. In all, 142,436 SNPs were removed. The final genotype data set contained 580 samples across 675,718 SNPs that passed all the above filters.
Genotype Imputation
Genotype imputation was performed using Impute2 version 2.3.0 with 1000 Genomes Phase I integrated variant set as reference panel [13], [14]. A total of 5,767,018 SNPs (including the typed SNPs) were imputed into the Chagas dataset. Imputed genotypes with probability <0.8, SNP call rate <0.7, Hardy-Weinberg test P-value >10−9; and MAF >0.5% were removed. The final imputed dataset consisted of 5,486,770 genotypes across the 580 samples.
Genotype Multidimensional Scaling (MDS)
Assessment for possible population stratification was conducted using Plink-MDS [15]. The Chagas samples were combined with Hapmap populations and MDS was performed on the combined genotypes. The first 4 dimensions were kept as covariates in the subsequent GWAS.
Phenotyping and GWAS
Genome wide association tests were performed on seven phenotypes: Cardiomyopathy (as defined by the expert panel) [3], PCR, anti- T cruzi antibody level (as defined by S/CO value obtained using Ortho T. cruzi EIA test system (Raritan,NJ)) [12], ECG measurements (PR, QRS, corrected QT intervals) and ejection fraction (EF) as defined by echocardiogram. For cardiomyopathy, we excluded individuals with inconclusive results determined by the expert panel (total of 67), and analyzed 207 CCC and 306 non-CCC samples. GWAS was performed using snptest_v2.4.1 with the expected genotype counts method (genotype dosages) [13], adjusting for population stratification using the first four dimensions generated in MDS.
Ancestry Proportion Estimation
Individual ancestry and admixture proportions were estimated using Frappe [16]. Based on visually inspecting Chagas admixture proportions, along with Hapmap populations (Figure 1), four ancestral populations (K = 4) (ASW: African ancestry in Southwest USA; CEU: Utah residents with Northern and Western European ancestry from the CEPH collection; CHB: Han Chinese in Beijing, China; and MEX: Mexican ancestry in Los Angeles, California) were identified and then combined with Chagas genotype data for a total of 239,530 common SNPs. We randomly chose 5% of SNPs for 1000 steps of expectation maximization (EM) optimization to obtain the ancestry proportion estimates for all individuals.
Each point on the plot represents an individual; each population is coded in a different color. The populations are: ASW: African ancestry in Southwest USA; CEU: Utah residents with Northern and Western European ancestry from the CEPH collection; CHB: Han Chinese in Beijing, China; CHD: Chinese in Metropolitan Denver, Colorado; GIH: Gujarati Indians in Houston, Texas; JPT: Japanese in Tokyo, Japan; LWK: Luhya in Webuye, Kenya; MEX: Mexican ancestry in Los Angeles, California; MKK: Maasai in Kinyawa, Kenya; TSI: Toscans in Italy; YRI: Yoruba in Ibadan, Nigeria.A) Dimension 1 vs. Dimension 2; B) Dimension 2 vs Dimension 3.
Results and Discussion
We obtained genome-wide genotypes for 580 Chagas seropositive donors and cases. More than 675,000 SNPs were directly genotyped,and more than 5 million additional genotypes were obtained through genotype imputation. The missing genotype frequency was less than 1% in the final dataset. The average pair-wise concordance of the single control sample across 7 plates was greater than 99.6%.
The Brazilian population represented in the Chagas cohort displayed a wide range of genetic diversity. Figure 1 shows that the majority of individuals spanned the complete range of admixture proportions between European (CEU and TSI) and African (ASW) populations, with a few individuals of apparent East Asian ancestry (CHB, JPT), and a few individuals showing similarity to Mexican populations (MEX), presumably due mostly to Native American ancestry. Ancestry analysis further quantified the ancestry proportions for each individual in the Chagas cohort (Figure 2). Although 176 individuals self-reported as white race, the majority of them show a high degree of multi-racial ancestry, the same was observed for the self-reported black individuals. Therefore self-reported race is a highly subjective and individual cultural decision, with individuals identifying with both major races displaying a wide range of admixture proportions, which is consistent with previous reports on Brazilian populations [17], [18]. Based on the median of chi-square values resulting from logistic regression in PLINK [15], the genomic inflation factor is 1.086 without adjustment for population stratification. The genomic inflation factor is 1.019 after adjusting for population stratification using the first 4 dimensions generated in MDS.
X-axis shows individuals and y-axis shows ancestry proportions. A) 4 related Hapmap populations: CEU, CHB, ASW, and MEX; B) Chagas cohorts stratified by self-reported races.
We detected two SNPs (rs4149018 and rs12582717) which were associated with cardiomyopathy at P<10−6. The SNP rs12582717 is located in an intron of the SLCO1B1 gene, while rs4149018 is located in its 5′UTR. SLCO1B1 is a membrane transporter that belongs to a solute carrier family and plays a role in drug metabolism. It is expressed in the liver, brain, heart and kidney, and transports organic anions, such as digoxin, bilirubin, methothrexate and statins. In addition, loss-of function mutations may be associated with impaired drug action in target tissues [19]. Previous studies reported that common variants in SLCO1B1 are strongly associated with an increased risk of statin-induced myopathy [20], [21]. This is the first report of its association with CCC, and while it fails to reach genome-wide significance, the previous associations with a form of myopathy increases the likelihood of its involvement with CCC.
We found a cluster of 12 SNPs within introns of COL14A1 that is associated with PCR positivity. COL14A1is a fibril-associated collagen which interacts with the fibril surface and regulates fibrillogenesis [22]. A previous study showed that COL14A1 harbors a SNP that is associated with HIV-1 viral load during the asymptomatic set-point period of infection [23].
All SNPs that are significantly associated with any of the seven traits can be found in Table S1. Table 1 lists a total of 46 genic SNPs that are associated with the seven traits with P<10−6. None of the traits are associated with SNPs with P<10−7, except PR interval, which shows association with six SNPs at P<10−7. The six SNPs are located in six different genes: ABCB5, COL1A2, HSPB8, ACCN1, LEPREL4, and LPIN2.
HSPB8/Hsp22/H11 is a small heat shock protein whose heart-specific overexpression induces myocardial hypertrophy [24]. Furthermore, HSPB8-transgenic mice bearing the K141N mutation expressed myocardial hypertrophy, ventricular dysfunction and apical fibrosis-the latter being a hallmark of heart involvement in CCC [25]. Significantly, expression of HSPB8 is selectively increased in myocardial tissue from CCC patients, rather than in idiopathic dilated cardiomyopathy patients [26].
ACCN1/ASIC2 is an acid-sensing ion channel which has been implicated as a mechanoreceptor/baroreceptor in afferent sympathetic nerve fibers. ACCN1/ASIC2 -null mice show an exaggerated sympathetic and depressed parasympathetic control of the circulation, indicative of an impaired baroreceptor reflex. Multiple measures of baroreceptor activity each suggest that mechano-sensitivity is diminished in ASIC2 null mice. This recapitulates the pathological dysautonomia seen in heart failure [27]. Significantly, dysautonomia with reduced baroreflex sensitivity is a hallmark of Chagas disease and progresses with disease severity and may be directly linked to the increased PR interval phenotype [28], [29].
COL1A2 was found to be increased in hearts from idiopathic dilated cardiomyopathy patients [30]. ABCB5 is another membrane transporter that belongs to the ATP-binding cassette (ABC) transporter family, which has been implicated in cancer progression and resistance to chemotherapy [31]. Polymorphisms in the LPIN2 gene are associated with metabolic disease traits, including insulin sensitivity, diabetes, and blood pressure [32]. LEPREL4 is a nucleolar protein that is associated with chromosomes during mitosis [33].
The genes harboring the SNPs found to be associated with diagnosed CCC and cardiac phenotypes in this GWAS showed no overlap with the hypothesis-driven candidate genes studied so far in CCC. Due to the relevance of inflammation and myocarditis in the pathogenesis of CCC, most of the candidate genes and SNPs chosen for study were previously known or suspected to lead to variations in the intensity of the innate or acquired immune response, as well as inflammatory cell migration (reviewed by Teixeira [34]). Among the innate and adaptive immunity candidate genes studied, alleles of HLA class I (HLA-C locus) and class II (DRB1 and DQB1 loci), and SNPs in IL1B, IL1RN, IL10, IL12B, TNFA, LTA, BAT-1/UAP56, NFkBIL-1 (the latter four located in the MHC class III region in chromosome 6), and MAL/TIRAP [35] have been found previously to be associated to CCC. The TNFA −308 promoter polymorphism is associated with survival in CCC patients with ventricular dysfunction [36].
Among genes encoding chemokines and chemokine receptors, polymorphisms associated with CCC have been found, including MCP1/CCL2 [37]. CCR5, MIG/CXCL9, and IP10/CXCL10 (the latter associated with severe CCC, with ventricular dysfuction). Significantly, the CXCL9 polymorphism was associated with increased levels of myocardial CXCL9 expression and myocarditis; conversely, myocardial CXCL9 levels were found to correlate with intensity of myocarditis [38].
In summary, our GWAS results suggest that polymorphisms in the SLCO1B1 gene are associated with the cardiomyopathy phenotype, whereas polymorphisms in heat shock protein HSPB8, the ion channel ACCN1, and alpha-2 type I collagen COL1A2, involved in cardiac hypertrophy, baroreflex sensitivity and autonomic control of the circulation, and fibrosis, respectively, are associated with the PR interval. However, none of the loci detected in this study have been linked to PR interval in previous primary or meta-analysis studies of cardiac disease [39]. This may suggest that the mechanism of CCC may be different from previous studies or our statistical power to detect true association is limited by the moderate sample size and high degree of genetic diversity of our cohort. Together with the available literature, results indicate that both cardiovascular-related and immune-related gene polymorphisms may play a role in the genetic predisposition to CCC development.
Supporting Information
Table S1.
SNPs that are significantly associated with any of the seven traits with P<10−5.
https://doi.org/10.1371/journal.pone.0079629.s001
(DOCX)
Acknowledgments
The REDS II Chagas study group from the NHLBI Retrovirus Epidemiology Donor Study-II (REDS-II), International Component consists of the following investigators.
Brazil:
Fundação Pró-Sangue/Hemocentro São Paulo (São Paulo):C Almeida-Neto, GM Patavino, NA Salles, M Otani.
Hemominas (Belo Horizonte, Minas Gerais): ABF Carneiro-Proietti,
HeartInstitute/Universityof São Paulo: V Salemi, B Ianni, LNastari, F Fernandes, J C A Ferreira-Filho, C Mady
Centro de Ciências Biológicas e da Saúde, Prontosocor de Montes Claros, Minas Gerais:Antunes AP, Menezes M
Federal Universityof Sao Joao del Rey, Divinópolis,(Minas Gerais) : CL Oliveira
Hospital das Clínicas/Faculdade de Medicina, Universidade Federal de Minas Gerais: AL Ribeiro
Department of Infectious Disease/Institute of Tropical Medicine/University of São Paulo:EC Sabino, L Capuani.
US investigators:
Blood Systems Research Institute andUniversity of California San Francisco: M.P. Busch, E.L. Murphy, B. Custer, M Seielstad, X Deng, TH Lee, SM Keating and T. Gonçalez
Coordinating center:
Westat, Inc.: J. Schulman, M. King, and K. Kavounis
National Heart, Lung, and Blood Institute, NIH
V Sachdeva
S.A. Glynn
Author Contributions
Conceived and designed the experiments: MS ECS ECN MB. Analyzed the data: XD. Contributed reagents/materials/analysis tools: ALR BI CM. Wrote the paper: XD ECS ECN ALR MB MS.
References
- 1. Schofield CJ, Jannin J, Salvatella R (2006) The future of Chagas disease control. Trends Parasitol 22(12): 583–8.
- 2. Ribeiro AL, Nunes MP, Teixeira MM, Rocha MO (2012) Diagnosis and management of Chagas disease and cardiomyopathy. Nat Rev Cardiol 9(10): 576–89.
- 3. Sabino EC, Ribeiro AL, Salemi VMC, Di Lorenzo Oliveira C, Antunes AP, et al. (2013) Ten-year Incidence of Chagas cardiomyopathy among asymptomatic, T. cruzi seropositive former blood donors. Circulation 127(10): 1105–15.
- 4. Pereira Nunes Mdo C, Barbosa MM, Ribeiro AL, Amorim Fenelon LM, Rocha MO (2010) Predictors of mortality in patients with dilated cardiomyopathy: relevance of chagas disease as an etiological factor. Rev Esp Cardiol 63(7): 788–97.
- 5. Basquiera AL, Sembaj A, Aguerri AM, Omelianiuk M, Guzman S, et al. (2003) Risk progression to chronic Chagas cardiomyopathy: influence of male sex and of parasitaemia detected by polymerase chain reaction. Heart 89(10): 1186–90.
- 6. Cunha-Neto E, Teixeira PC, Nogueira LG, Kalil J (2011) Autoimmunity. Adv Parasitol 76: 129–52.
- 7. Cunha-Neto E, Kalil J (2001) Heart-infiltrating and peripheral T cells in the pathogenesis of human Chagas’ disease cardiomyopathy. Autoimmunity 34(3): 187–92.
- 8. Williams-Blangero S, VandeBerg JL, Blangero J, Correa-Oliveira R (2011) Genetic epidemiology of Chagas disease. Adv Parasitol 75: 147–67.
- 9. Silva-Grecco RL, Balarin MA, Correia D, Prata A, Rodrigues V Jr (2010) Familial analysis of seropositivity to Trypanosoma cruzi and of clinical forms of Chagas disease. Am J Trop Med Hyg 82(1): 45–8.
- 10. Cunha-Neto E, Nogueira LG, Teixeira PC, Ramasawmy R, Drigo SA, et al. (2009) Immunological and non-immunological effects of cytokines and chemokines in the pathogenesis of chronic Chagas disease cardiomyopathy. Mem Inst Oswaldo Cruz 104 Suppl 1252–8.
- 11.
Ribeiro AL, Sabino EC, Marcolino MS, Salemi VMC, Ianni BM, et al.. (2013) Electrocardiographic abnormalities in Trypanosoma cruzi seropositive and seronegative former blood donors. PLOS Neglected Tropical Disease in press.
- 12. Sabino EC, Lee TH, Montalvo L, Nguyen ML, Leiby DA, et al. (2013) Antibody levels correlate with detection of Trypanosoma cruzi DNA by sensitive PCR assays in seropositive blood donors and possible resolution of infection over tim. Transfusion 53(6): 1257–65.
- 13. Marchini J, Howie B, Myers S, McVean G, Donnelly P (2007) A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39(7): 906–13.
- 14. Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5(6): e1000529.
- 15. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3): 559–75.
- 16. Tang H, Peng J, Wang P, Risch NJ (2005) Estimation of individual admixture: analytical and study design considerations. Genet Epidemiol 28(4): 289–301.
- 17. Lins TC, Vieira RG, Abreu BS, Gentil P, Moreno-Lima R, et al. (2011) Genetic heterogeneity of self-reported ancestry groups in an admixed Brazilian population. J Epidemiol 21(4): 240–5.
- 18. Parra FC, Amado RC, Lambertucci JR, Rocha J, Antunes CM, et al. (2003) Color and genomic ancestry in Brazilians. Proc Natl Acad Sci U S A 100(1): 177–82.
- 19. Ishikawa T (2012) Genetic variants in the human SLCO1B1 gene and individual variations in methotrexate clearance. Pharmacogenomics 13(9): 993–4.
- 20. Furihata T, Satoh N, Ohishi T, Ugajin M, Kameyama Y, et al. (2009) Functional analysis of a mutation in the SLCO1B1 gene (c.1628T>G) identified in a Japanese patient with pravastatin-induced myopathy. Pharmacogenomics J 9(3): 185–93.
- 21. Link E, Parish S, Armitage J, Bowman L, Heath S, et al. (2008) SLCO1B1 variants and statin-induced myopathy–a genomewide study. N Engl J Med 359(8): 789–99.
- 22. Ansorge HL, Meng X, Zhang G, Veit G, Sun M, et al. (2009) Type XIV Collagen Regulates Fibrillogenesis: premature collagen fibril growth and tissue dysfunction in null mice. J Biol Chem 284(13): 8427–38.
- 23. Fellay J, Shianna KV, Ge D, Colombo S, Ledergerber B, et al. (2007) A whole-genome association study of major determinants for host control of HIV-1. Science 317(5840): 944–7.
- 24. Depre C, Hase M, Gaussin V, Zajac A, Wang L, et al. (2002) H11 kinase is a novel mediator of myocardial hypertrophy in vivo. Circ Res 91(11): 1007–14.
- 25. Sanbe A, Marunouchi T, Abe T, Tezuka Y, Okada M, et al. (2013) Phenotype of cardiomyopathy in cardiac-specific heat shock protein B8 K141N transgenic mouse. J Biol Chem 288(13): 8910–21.
- 26. Cunha-Neto E, Dzau VJ, Allen PD, Stamatiou D, Benvenutti L, et al. (2005) Cardiac gene expression profiling provides evidence for cytokinopathy as a molecular mechanism in Chagas’ disease cardiomyopathy. Am J Pathol 167(2): 305–13.
- 27. Lu Y, Ma X, Sabharwal R, Snitsarev V, Morgan D, et al. (2009) The ion channel ASIC2 is required for baroreceptor and autonomic control of the circulation. Neuron 64(6): 885–97.
- 28. Lopes HF, Bortolotto LA, Szlejf C, Kamitsuji CS, Krieger EM (2001) Hemodynamic and metabolic profile in offspring of malignant hypertensive parents. Hypertension 38(3 Pt 2): 616–20.
- 29. Marin-Neto JA, Cunha-Neto E, Maciel BC, Simoes MV (2007) Pathogenesis of chronic Chagas heart disease. Circulation 115(9): 1109–23.
- 30. Aharinejad S, Krenn K, Paulus P, Schafer R, Zuckermann A, et al. (2005) Differential role of TGF-beta1/bFGF and ET-1 in graft fibrosis in heart failure patients. Am J Transplant 5(9): 2185–92.
- 31. Yang M, Li W, Fan D, Yan Y, Zhang X, et al. (2012) Expression of ABCB5 gene in hematological malignances and its significance. Leuk Lymphoma 53(6): 1211–5.
- 32. Reue K (2009) The lipin family: mutations and metabolism. Curr Opin Lipidol 20(3): 165–70.
- 33. Fossa A, Siebert R, Aasheim HC, Maelandsmo GM, Berner A, et al. (2000) Identification of nucleolar protein No55 as a tumour-associated autoantigen in patients with prostate cancer. Br J Cancer 83(6): 743–9.
- 34. Teixeira PC, Frade AF, Nogueira LG, Kalil J, Cunha-Neto E (2012) Pathogenesis of Chagas disease cardiomyopathy. World J Clin Infect Dis 2(3): 39–53.
- 35. Ramasawmy R, Cunha-Neto E, Fae KC, Borba SC, Teixeira PC, et al. (2009) Heterozygosity for the S180L variant of MAL/TIRAP, a gene expressing an adaptor protein in the Toll-like receptor pathway, is associated with lower risk of developing chronic Chagas cardiomyopathy. J Infect Dis 199(12): 1838–45.
- 36. Drigo SA, Cunha-Neto E, Ianni B, Cardoso MR, Braga PE, et al. (2006) TNF gene polymorphisms are associated with reduced survival in severe Chagas’ disease cardiomyopathy patients. Microbes Infect 8(3): 598–603.
- 37. Ramasawmy R, Cunha-Neto E, Fae KC, Muller NG, Cavalcanti VL, et al. (2006) BAT1, a putative anti-inflammatory gene, is associated with chronic Chagas cardiomyopathy. J Infect Dis 193(10): 1394–9.
- 38. Nogueira LG, Santos RH, Ianni BM, Fiorelli AI, Mairena EC, et al. (2012) Myocardial chemokine expression and intensity of myocarditis in Chagas cardiomyopathy are controlled by polymorphisms in CXCL9 and CXCL10. PLoS Negl Trop Dis 6(10): e1867.
- 39. Pfeufer A, van Noord C, Marciante KD, Arking DE, Larson MG, et al. (2010) Genome-wide association study of PR interval. Nat Genet 42(2): 153–9.