Genomic Medicine in Developing Countries and Resource-Limited Environments

Genomic Medicine in Developing Countries and Resource-Limited Environments

Chapter 26 Genomic Medicine in Developing Countries and Resource-Limited Environments T. Katsila1, a, K. Mitropoulos2, a, Z. Mohamed3, D.A. Forero4, ...

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Chapter 26

Genomic Medicine in Developing Countries and Resource-Limited Environments T. Katsila1, a, K. Mitropoulos2, a, Z. Mohamed3, D.A. Forero4, P. Laissue5, A. Wonkam6, C. Lopez-Correa7, W. Chantratita8, A. Llerena9, B.R. Ali10 and G.P. Patrinos1, 10 1

University of Patras School of Health Sciences, Patras, Greece; 2The Golden Helix Foundation, London, United Kingdom; 3University of Malaya, Kuala Lumpur, Malaysia; 4Universidad Antonio Nariño, Bogotá, Colombia; 5Del Rosario University, Bogotá, Colombia; 6University of Cape Town, Cape Town, South Africa; 7Genome Quebec, Montreal, QC, Canada; 8Mahidol University, Bangkok, Thailand; 9Extremadura University Hospital and Medical School, Badajoz, Spain;


United Arab Emirates University, Al-Ain, United Arab Emirates

26.1 INTRODUCTION The advent of genomics and related technologies has revolutionized mainstream medical practices (McCarthy et al., 2013). The advent of genome-wide studies has catalyzed the translation of genomic findings into health care (Mardis, 2011). New technologies, particularly nextgeneration sequencing (NGS) approaches, are being adopted by diagnostic laboratories and hospitals in the United States and Western Europe. In terms of regulation, several guidelines from the United States Food and Drug Administration ( and the European Medicines Agency ( are being announced regarding the translation of genomic medicine into the clinic. However, genomic medicine is implemented at a different pace when developing and resource-limited countries are considered. In these countries, significant barriers exist, which often relate to limited resources and a lack of technology and knowledge transfer. As such, the potential of genomic medicine is often hardly understood by biomedical scientists and healthcare professionals. Considering that approximately 85% of the world’s population lives in developing/resource-limited countries, access to genomic medicine becomes fundamental. Today, there have been some examples from the successful a

implementation of genomic medicine in developing countries in Europe and Asia that rely on several related and intersected disciplines (population genomics, pharmacogenomics (PGx), informatics, and public health genomics). We feel that the examples described in the following relate to the previously mentioned disciplines and can serve as model cases toward the implementation of genomic medicine in resource-limited environments.

26.1.1 Euro-PGx Project: A European-Wide Pharmacogenomics Map In Europe, PGx is implemented in the various health systems at a rather heterogeneous pace. This is due to the lack of harmonization of the national guidelines within Europe and, most importantly, differences in resource availability (Mitropoulos et al., 2011). Furthermore, taking into account that the pharmacogenomic biomarker allele frequencies in various European populations are hardly known, it becomes challenging to define the actionable pharmacogenomic biomarkers on which drugedose recommendations will be set. The Euro-PGx project (http://www.goldenhelix. org/index.php/research/pharmacogenomics-in-europe) focuses on the determination of the varying pharmacogenomic biomarker allele frequencies in a large number of mostly developing European countries to produce druge dose recommendations. Preliminary findings from a large-

These authors contributed equally to this work.

Molecular Diagnostics. Copyright © 2017 Elsevier Ltd. All rights reserved.


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scale genotyping effort using the Drug-Metabolizing Enzymes and Transporters Plus microarray (Affymetrix, Santa Clara, CA, USA) suggest that several pharmacogenomic biomarker allele frequencies vary significantly, despite the strong Caucasian genetic component of the vast majority of the European populations. Data access to the scientific community is anticipated through the FINDbase database ( via the microattribution approach (Giardine et al., 2011). The Euro-PGx project also facilitates the organization of PGx educational activities in various European countries (as of May 2016, 16 different events have been organized in 10 European, mostly developing, countries). These educational events, also known as the Golden Helix PGx Days (http://, aim to educate healthcare professionals and increase PGx awareness.

26.1.2 Implementation of Pharmacogenomics in Clinical Settings In Southeast Asia, particularly Taiwan, the associations of the HLA-B*1502 allele with Stevens-Johnson Syndrome/ Toxic Epidermal Necrolysis (SJS/10) upon carbamazepine administration (Chung et al., 2004) as well as that of the HLA-B*5801 allele with allopurinol-induced severe cutaneous adverse reactions (Hung et al., 2005) support the value of PGx in tailor-made therapeutics. A large clinical study that confirmed the benefit of HLA-B*1502 screening to prospectively identify subjects at genetic risk for the previously mentioned condition (Chen et al., 2011) led the Taiwanese government to begin reimbursing the screening costs in 2010. Today, several PGx biomarkers have been found to be correlated with interindividual drug efficacy and/or toxicity. Nevertheless, health professionals often lack genomics education, so it is urgent to make PGx knowledge readily available in a user-friendly format. The DruGeVar database ( (Dalabira et al., 2014) was developed to serve as an online knowledge portal for clinical PGx with the aim of triangulating drugsegenesePGx biomarkers (those approved by regulatory agencies) (Fig. 26.1). In Southeast Asia, a pharmacogenomic card has been proposed to record patients’ pharmacogenomic biomarkers. Similarly, the Ramathibodi Hospital in Thailand has launched a “PGx” wallet card. The latter summarizes patients’ HLA gene variant information to predict the risk of developing SJS/10 (Borchers et al., 2008). Such PGx cards could be readily expandable toward tailor-made therapeutics (Fig. 26.2).

In Latin America, the Iberian American Network of Pharmacogenetics and Pharmacogenomics (RIBEF), created in 2006, aims to promote collaborative PGx research. RIBEF, which consists of 43 research groups and more than 200 researchers, aims to promote scientific studies among its members as well as the clinical implementation of PGx to support the healthcare needs of neglected populations. RIBEF teaching programs and human resources training activities include over 400 events all over Latin America. Moreover, the RIBEF network develops research projects that include Iberoamerican population PGx studies. The Consorcio Europeo e Iberoamericano de Farmacogenética de Poblaciones Consortium (CEIBA) was established among the RIBEF members for this purpose. The MESTIFAR project aimed to determine the variability of polymorphisms in genes involved in the response to drugs in populations of different ethnic origin (Native Americans [Amerindian] and Mestizos [the result of post-Columbian admixture]). In addition to population PGx, RIBEF has projects that relate to clinical PGx in Neurology, Psychiatry, Cardiovascular, and/or Infectious Diseases, resulting in a total of 31 scientific articles being published so far. In Africa, a disproportionate burden of disease is observed (HIV/AIDS, tuberculosis, and malaria) against a backdrop of an increasing burden of noncommunicable diseases (Niemz et al., 2011). Genomic data have supported the notion that several genetic variants can provide an increased resistance or susceptibility to HIV infections (Sirugo et al., 2008). Notably, a huge variability has been evident regarding the pattern of genetic variations in the CYP genes among African populations. The latter was translated into differential drug responses (Dandara et al., 2014). In the Middle East, particularly the United Arab Emirates (UAE), PGx research was initially conducted on erythrocyte glucose-6-phosphate dehydrogenase deficiency (G-6-PD) and its association to drug-induced hemolytic anemia (Bayoumi et al., 1996) and later arylamine Nacetyltransferase (NAT2) (Woolhouse et al., 1997). CYP2D6 allele frequencies were also investigated in the Emirati population, reporting four novel CYP2D6 variants (Qumsieh et al., 2011), while a warfarin PGx study is under way for the Emirati population (Fortina et al., 2014; AlJaibeji et al. unpublished). These studies sparked an interest from Dubai Hospital toward the integration of PGx information for chemotherapeutic agents, while the UAE Health Authority policy of reporting adverse drug reactions in the UAE requires expert pharmacogenomic recommendations within the first 24 h related to each adverse drug reaction reported (Abu Dhabi). PGx awareness is gaining

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FIGURE 26.1 DruGeVar database. (A) Overview of data entries (colored boxes) on the basis of Microsoft’s PivotViewer and Silverlight technologies. The querying interface, by which the user can further exploit the database content, is shown on the left. (B) Display items that correspond to a variant of interest in relation to drug toxicity (red sign at the bottom left of the item Dark Gray in print versions) or efficacy (green sign at the bottom left of the item Light Gray in print versions).

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FIGURE 26.2 “Pharmacogenomics (PGx)” wallet card that assists in clinical decision making. The card shares information regarding the pharmacogenetics test ordered (HLA-B genotyping) and its outcome (HLA-B*58:01/15:02). Pharmacogenetics data interpretation informs the clinician about the associated risk of allopurinol, carbamazepine, and oxcarbazepine treatment. Such a pharmacogenomic card has been successfully implemented in clinical practice in Thailand at the Laboratory for PGx, Somdech Phra Debaratana Medical Center, Ramathibodi Hospital.

ground in Middle Eastern countries, such as Saudi Arabia (Abu-Elmagd et al., 2015), Oman (Pathare et al., 2012), Lebanon (Ossaily and Zgheib, 2014), and Qatar (Elewa et al., 2015).

26.1.3 Mapping of Stakeholders in Genomic Medicine The genomic medicine puzzle is comprised of several key players and stakeholders, and notably, their genomics awareness and views vary significantly. A systematic mapping of those views and different awareness levels would positively impact a better understanding of the policy environment as well as the role of the relevant key stakeholders in the field. Mitropoulou and coworkers undertook such an initiative and assessed the level of support or opposition to PGx and genomic medicine in Greece

(Mitropoulou et al., 2014). Similarly, an analysis is currently underway in Middle Eastern countries to determine the stakeholders and their views. The smooth incorporation of genomic medicine into clinical practice is hindered by insufficient genomics education and a lack of genomics awareness among healthcare professionals and the general public (Reydon et al., 2012). The low genomic literacy of the broader public (and patients) is especially challenging for public health genomics as well as health literacy (Syurina et al., 2011). On top of this, genomics education is not uniformly provided in the various academic institutions worldwide (Pisanu et al., 2014; Mai et al., 2014). Such studies might provide a basis for harmonizing PGx education in southeast European countries with those of northwest European countries to create a smoother and more timely integration of PGx into mainstream medical practice. In Latin America there are

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very few postgraduate programs focusing on genomics (Palacios and Collado-Vides, 2007). In Africa, the high cost of genomic services and low private investment are compounded by a relatively low level of medical professionals with an understanding of genomics (Wonkam et al., 2006). An attempt in sub-Saharan Africa to triangulate the views of multiple stakeholders related to prenatal diagnosis of sickle cell disease showed several discrepancies that signal potential value-based conflicts and can usefully inform future policy actions (Wonkam and Hurst, 2014). In fact, African-based scientists are participating in studies focusing on the genomics of monogenic diseases (Mercier et al., 2013; Mtatiro et al., 2014; Wonkam et al., 2014; Wonkam, 2015) and multifactorial conditions (Tekola Ayele et al., 2012). These data are concretely assisting the effective practice of genomic medicine that is well established in South Africa (Beighton et al., 2012), in some Northern African countries (Chaabouni-Bouhamed, 2008; El-Beshlawy et al., 2012), and recently initiated in Central Africa (Wonkam et al., 2011). Regional initiatives, such as the Southern Africa Human Genome Project (Pepper, 2011), have been boosted by international funding agencies and academic institutions through major programs, such as the Malaria Genomic Epidemiology Network (http://www., the Human Heredity and Health in Africa program (H3Africa Consortium et al., 2014), and the African Genome Variation Project (Gurdasani et al., 2015). Latin America populations are characterized by high and heterogeneous levels of admixture that arises from their history and corresponds to different patterns of mating between Native American, European, and African individuals (Ruiz-Linares et al., 2014). The National Institute of Genomic Medicine was built in Mexico with public funds (Jimenez-Sanchez et al., 2008), having several publications in the fields of population genomics and medical genomics (Silva-Zolezzi et al., 2009; Moreno-Estrada et al., 2014). Brazil and Colombia have also successfully implemented genomic approaches for the study of several human diseases with a high epidemiological impact in those resource-limited countries (Passos-Bueno et al., 2014; Ortega-Recalde et al., 2014; Pinto et al., 2015; Benitez et al., 2010), while the availability of commercial tests by service providers abroad has been considered to be fundamental toward the implementation of genomic medicine.

26.1.4 Advent of Next-Generation Sequencing in Genomic Medicine The advent of NGS technology has marked the beginning of a new era in the analysis of human genome sequences (Hodges et al., 2007; Albert et al., 2007; Shendure and Ji, 2008; Gnirke et al., 2009; see also Chapter 9). Before NGS, Sanger sequencing was widely used for screening, even


though analyses involving numerous genes or large genomic regions were particularly challenging, e.g., a read length encompassing up to 700 base-pairs (bp) per reaction. The NGS approach allows for the simultaneous analysis of millions of bp in only hours, facilitating large-scale explorations of the human genome (McCarthy et al., 2013). Successful screening attempts focus on researching novel recessive disease-related sequence variants, particularly those caused by homozygous mutations as well as monogenic dominant Mendelian disorder. NGS has not been widely used for some complex pathologies in which several variants might contribute toward the phenotype, because data analysis highlights a remarkable complexity, especially for simultaneous interactive network exploration. Three main approaches are employed, which depend on the length of the genome region being analyzed: wholegenome sequencing (WGS), whole-exome sequencing (WES), and custom target sequencing microarrays (TSMs) (McCarthy et al., 2013). WGS is mainly used for research, whereas WES and TSM are used for both research and diagnostic purposes. Although most academic and private technological platforms for the previously mentioned are located in high-income countries, resource-limited countries have performed interesting studies by using NGS outsourcing services (Pinto et al., 2015; Benitez et al., 2010; Diggle et al., 2012). Innovative diagnostic approaches have also been proposed for pathologies with overlapping phenotypes caused by several genes (Pinto et al., 2015; Benitez et al., 2010). Knowledge of the genomes of mammalian species has enabled large-scale comparative genomic approaches, resulting in dissecting loci related to evolution mechanisms, which may contribute to human diseases (Prada and Laissue, 2014). Complex pathologies, such as female infertility, have also been explored via NGS (Fonseca et al., 2015).

26.1.5 Is Genomics-Guided Therapy CostEffective? Genome-based drug treatment is expected to reduce national healthcare expenditures. In resource-limited countries in particular, which in many cases have vast fiscal deficits, the economic evaluation of PGx is fundamental (Snyder et al., 2014). Although the field of economic evaluation in genomic medicine, PGx, and public health genomics is currently in its infancy, several studies indicate that genotype-guided therapy can be cost-effective and of a high cost benefit. Focusing on resource-limited countries, initial economic evaluation studies in the Thai population indicated that HLA-B*1502-guided carbamazepine treatment is costeffective compared to conventional treatment and can reduce carbamazepine-induced severe adverse drug

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FIGURE 26.3 Encouraging collaboration between developed and developing/resource-limited countries in the field of Genomic Medicine. Developing countries are expected to benefit from training opportunities, knowledge transfer, and/or expanding research networks. Developed countries may also benefit through comparative work as well as multicenter projects on rare diseases or unique clinical features from well-defined populations.

reactions (Rattanavipapong et al., 2013; Grosse, 2008). Similar findings were reported for the Singaporean population, when the cost-effectiveness of HLA-B*1502 genotyping in adult patients with newly diagnosed epilepsy was assessed (Dong et al., 2012). Another study to evaluate cost-effectiveness of warfarin treatment in Croatian elderly ischemic stroke patients with atrial fibrillation indicated that PGx-guided warfarin treatment represented a cost-effective therapy option for the management of those patients (Mitropoulou et al., 2015).

26.2 CONCLUSIONS AND FUTURE PERSPECTIVES When resource-limited environments are considered, genomic medicine can only be implemented via a stronger collaboration in genomics research between developed and developing/resource-limited countries, which is likely to create benefits for all parties. Developing countries will benefit from training opportunities, knowledge transfer, and/or expanding transnational networks, and developed countries may benefit through comparative work and multicenter projects on families with rare diseases and/or unique clinical features Fig. 26.3 (Cooper et al., 2014). Developing countries may suffer from limited resources, but they are also potential-rich in producing data (in the context of genomic medicine-related disciplines, from the perspective of public health genomics). We feel that the success stories presented in this chapter set the paradigm for replication in other countries to acquire more and better insights toward the implementation of genomic medicine and harmonizing the strategies and policies if a fast and smooth adoption of genomic medicine practices occurs in the various national healthcare systems.

ACKNOWLEDGMENTS This chapter was encouraged by the Genomic Medicine Alliance Public Health Genomics Working Group.

FINANCIAL AND COMPETING INTERESTS KM is a scientific advisor of the Golden Helix Foundation and GPP is a member of the Scientific Advisory Committee of the Genomic Medicine Alliance. DAF is supported by research grants from Colciencias and VCTI-UAN. The authors declare that they have no competing interests.

REFERENCES Abu Dhabi, H.A.A., Reporting Adverse Reaction. haad/Portals/0/ReportingAdverseReactions-updated24-june.pdf . Abu-Elmagd, M., Assidi, M., Schulten, H.J., Dallol, A., Pushparaj, P., Ahmed, F., Scherer, S.W., Al-Qahtani, M., 2015. Individualized medicine enabled by genomics in Saudi Arabia. BMC Med. Genom. 8 (Suppl. 1), S3. Albert, T.J., Molla, M.N., Muzny, D.M., Nazareth, L., Wheeler, D., Song, X., Richmond, T.A., Middle, C.M., Rodesch, M.J., Packard, C.J., Weinstock, G.M., Gibbs, R.A., 2007. Direct selection of human genomic loci by microarray hybridization. Nat. Methods 4 (11), 903e905. Borchers, A.T., Lee, J.L., Naguwa, S.M., Cheema, G.S., Gershwin, M.E., 2008. Stevens-Johnson syndrome and toxic epidermal necrolysis. Autoimmun. Rev. 7 (8), 598e605. Bayoumi, R.A., Nur-E-Kamal, M.S., Tadayyon, M., Mohamed, K.K., Mahboob, B.H., Qureshi, M.M., Lakhani, M.S., Awaad, M.O., Kaeda, J., Vulliamy, T.J., Luzzatto, L., 1996. Molecular characterization of erythrocyte glucose-6-phosphate dehydrogenase deficiency in Al-Ain District, United Arab Emirates. Hum. Hered. 46 (3), 136e141. Beighton, P., Fieggen, K., Wonkam, A., Ramesar, R., Greenberg, J., 2012. UCT’s contribution to medical genetics in Africa - from the past into the future. S. Afr. Med. J. 102 (6), 446e448. Benitez, B.A., Forero, D.A., Arboleda, G.H., Granados, L.A., Yunis, J.J., Fernandez, W., Arboleda, H., 2010. Exploration of genetic susceptibility factors for Parkinson’s disease in a South American sample. J. Genet. 89 (2), 229e232. Chung, W.H., Hung, S.I., Hong, H.S., Hsih, M.S., Yang, L.C., Ho, H.C., Wu, J.Y., Chen, Y.T., 2004. Medical genetics: a marker for StevensJohnson syndrome. Nature 428 (6982), 486. Chen, P., Lin, J.J., Lu, C.S., Ong, C.T., Hsieh, P.F., Yang, C.C., Tai, C.T., Wu, S.L., Lu, C.H., Hsu, Y.C., Yu, H.Y., Ro, L.S., Lu, C.T., Chu, C.C., Tsai, J.J., Su, Y.H., Lan, S.H., Sung, S.F., Lin, S.Y., Chuang, H.P., Huang, L.C., Chen, Y.J., Tsai, P.J., Liao, H.T.,

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Lin, Y.H., Chen, C.H., Chung, W.H., Hung, S.I., Wu, J.Y., Chang, C.F., Chen, L., Chen, Y.T., Shen, C.Y., Taiwan SJS Consortium, 2011. Carbamazepine-induced toxic effects and HLAB*1502 screening in Taiwan. N. Engl. J. Med. 364 (12), 1126e1133. Chaabouni-Bouhamed, H., 2008. Tunisia: communities and community genetics. Community Genet. 11 (6), 313e323. Cooper, D.N., Brand, A., Dolzan, V., Fortina, P., Innocenti, F., Lee, M.T., Macek Jr., M., Al-Mulla, F., Prainsack, B., Squassina, A., Vayena, E., Vozikis, A., Williams, M.S., Patrinos, G.P., 2014. Bridging genomics research between developed and developing countries: the Genomic Medicine Alliance. Per. Med. 11 (7), 615e623. Dalabira, E., Viennas, E., Daki, E., Komianou, A., Bartsakoulia, M., Poulas, K., Katsila, T., Tzimas, G., Patrinos, G.P., 2014. DruGeVar: an online resource triangulating drugs with genes and genomic biomarkers for clinical pharmacogenomics. Public Health Genom. 17 (5e6), 265e271. Dandara, C., Swart, M., Mpeta, B., Wonkam, A., Masimirembwa, C., 2014. Cytochrome P450 pharmacogenetics in African populations: implications for public health. Expert Opin. Drug Metab. Toxicol. 10 (6), 769e785. Diggle, C.P., Parry, D.A., Logan, C.V., Laissue, P., Rivera, C., Restrepo, C.M., Fonseca, D.J., Morgan, J.E., Allanore, Y., Fontenay, M., Wipff, J., Varret, M., Gibault, L., Dalantaeva, N., Korbonits, M., Zhou, B., Yuan, G., Harifi, G., Cefle, K., Palanduz, S., Akoglu, H., Zwijnenburg, P.J., Lichtenbelt, K.D., Aubry-Rozier, B., Superti-Furga, A., Dallapiccola, B., Accadia, M., Brancati, F., Sheridan, E.G., Taylor, G.R., Carr, I.M., Johnson, C.A., Markham, A.F., Bonthron, D.T., 2012. Prostaglandin transporter mutations cause pachydermoperiostosis with myelofibrosis. Hum. Mutat. 33 (8), 1175e1181. Dong, D., Sung, C., Finkelstein, E.A., 2012. Cost-effectiveness of HLAB*1502 genotyping in adult patients with newly diagnosed epilepsy in Singapore. Neurology 79 (12), 1259e1267. Elewa, H., Alkhiyami, D., Alsahan, D., Abdel-Aziz, A., August 2015. A survey on the awareness and attitude of pharmacists and doctors towards the application of pharmacogenomics and its challenges in Qatar. J. Eval. Clin. Pract. 21 (4), 703e709. El-Beshlawy, A., El-Shekha, A., Momtaz, M., Said, F., Hamdy, M., Osman, O., et al., 2012. Prenatal diagnosis for thalassaemia in Egypt: what changed parents’ attitude? Prenat. Diagn. 32 (8), 777e782. Fortina, P., Al Khaja, N., Al Ali, M.T., Hamzeh, A.R., Nair, P., Innocenti, F., Patrinos, G.P., Kricka, L.J., 2014. Genomics into healthcare: the 5th Pan Arab Human Genetics Conference and 2013 Golden Helix Symposium. Hum. Mutat. 35 (5), 637e640. Fonseca, D.J., Patiño, L.C., Suárez, Y.C., de Jesús Rodríguez, A., Mateus, H.E., Jiménez, K.M., Ortega-Recalde, O., Díaz-Yamal, I., Laissue, P., July 2015. Next generation sequencing in women affected by nonsyndromic premature ovarian failure displays new potential causative genes and mutations. Fertil. Steril. 104 (1), 154e162 e2. Giardine, B., Borg, J., Higgs, D.R., Peterson, K.R., Philipsen, S., Maglott, D., Singleton, B.K., Anstee, D.J., Basak, A.N., Clark, B., Costa, F.C., Faustino, P., Fedosyuk, H., Felice, A.E., Francina, A., Galanello, R., Gallivan, M.V., Georgitsi, M., Gibbons, R.J., Giordano, P.C., Harteveld, C.L., Hoyer, J.D., Jarvis, M., Joly, P., Kanavakis, E., Kollia, P., Menzel, S., Miller, W., Moradkhani, K., Old, J., Papachatzopoulou, A., Papadakis, M.N., Papadopoulos, P.,


Pavlovic, S., Perseu, L., Radmilovic, M., Riemer, C., Satta, S., Schrijver, I., Stojiljkovic, M., Thein, S.L., Traeger-Synodinos, J., Tully, R., Wada, T., Waye, J.S., Wiemann, C., Zukic, B., Chui, D.H., Wajcman, H., Hardison, R.C., Patrinos, G.P., 2011. Systematic documentation and analysis of human genetic variation in hemoglobinopathies using the microattribution approach. Nat. Genet. 43 (4), 295e301. Gurdasani, D., Carstensen, T., Tekola-Ayele, F., Pagani, L., Tachmazidou, I., Hatzikotoulas, K., Karthikeyan, S., Iles, L., Pollard, M.O., Choudhury, A., Ritchie, G.R., Xue, Y., Asimit, J., Nsubuga, R.N., Young, E.H.1, Pomilla, C.1, Kivinen, K., Rockett, K., Kamali, A., Doumatey, A.P., Asiki, G., Seeley, J., Sisay-Joof, F., Jallow, M., Tollman, S., Mekonnen, E., Ekong, R., Oljira, T., Bradman, N., Bojang, K., Ramsay, M., Adeyemo, A., Bekele, E., Motala, A., Norris, S.A., Pirie, F., Kaleebu, P., Kwiatkowski, D., Tyler-Smith, C., Rotimi, C., Zeggini, E., Sandhu, M.S., 2015. The African genome variation project shapes medical genetics in Africa. Nature 517 (7534), 327e332. Gnirke, A., Melnikov, A., Maguire, J., Rogov, P., LeProust, E.M., Brockman, W., Fennell, T., Giannoukos, G., Fisher, S., Russ, C., Gabriel, S., Jaffe, D.B., Lander, E.S., Nusbaum, C., 2009. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat. Biotechnol. 27 (2), 182e189. Grosse, S.D., 2008. Assessing cost-effectiveness in healthcare: history of the $50,000 per QALY threshold. Expert Rev. Pharmacoecon. Outcomes Res. 8 (2), 165e178. Hung, S.I., Chung, W.H., Liou, L.B., Chu, C.C., Lin, M., Huang, H.P., Lin, Y.L., Lan, J.L., Yang, L.C., Hong, H.S., Chen, M.J., Lai, P.C., Wu, M.S., Chu, C.Y., Wang, K.H., Chen, C.H., Fann, C.S., Wu, J.Y., Chen, Y.T., 2005. HLA-B*5801 allele as a genetic marker for severe cutaneous adverse reactions caused by allopurinol. Proc. Natl. Acad. Sci. U. S. A. 102 (11), 4134e4139. H3Africa Consortium, Rotimi, C., Abayomi, A., Abimiku, A., Adabayeri, V.M., Adebamowo, C., Adebiyi, E., Ademola, A.D., Adeyemo, A., Adu, D., Affolabi, D., Agongo, G., Ajayi, S., AkaroloAnthony, S., Akinyemi, R., Akpalu, A., Alberts, M., Alonso Betancourt, O., Alzohairy, A.M., Ameni, G., Amodu, O., Anabwani, G., Andersen, K., Arogundade, F., Arulogun, O., Asogun, D., Bakare, R., Balde, N., Baniecki, M.L., Beiswanger, C., Benkahla, A., Bethke, L., Boehnke, M., Boima, V., Brandful, J., Brooks, A.I., Brosius, F.C., Brown, C., Bucheton, B., Burke, D.T., Burnett, B.G., Carrington-Lawrence, S., Carstens, N., Chisi, J., Christoffels, A., Cooper, R., Cordell, H., Crowther, N., Croxton, T., de Vries, J., Derr, L., Donkor, P., Doumbia, S., Duncanson, A., Ekem, I., El Sayed, A., Engel, M.E., Enyaru, J.C., Everett, D., Fadlelmola, F.M., Fakunle, E., Fischbeck, K.H., Fischer, A., Folarin, O., Gamieldien, J., Garry, R.F., Gaseitsiwe, S., Gbadegesin, R., Ghansah, A., Giovanni, M., Goesbeck, P., GomezOlive, F.X., Grant, D.S., Grewal, R., Guyer, M., Hanchard, N.A., Happi, C.T., Hazelhurst, S., Hennig, B.J., Hertz- C, F., Hide, W., Hilderbrandt, F., Hugo-Hamman, C., Ibrahim, M.E., James, R., Jaufeerally-Fakim, Y., Jenkins, C., Jentsch, U., Jiang, P.P., Joloba, M., Jongeneel, V., Joubert, F., Kader, M., Kahn, K., Kaleebu, P., Kapiga, S.H., Kassim, S.K., Kasvosve, I., Kayondo, J., Keavney, B., Kekitiinwa, A., Khan, S.H., Kimmel, P., King, M.C., Kleta, R., Koffi, M., Kopp, J., Kretzler, M., Kumuthini, J., Kyobe, S.,

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Kyobutungi, C., Lackland, D.T., Lacourciere, K.A., Landouré, G., Lawlor, R., Lehner, T., Lesosky, M., Levitt, N., Littler, K., Lombard, Z., Loring, J.F., Lyantagaye, S., Macleod, A., Madden, E.B., Mahomva, C.R., Makani, J., Mamven, M., Marape, M., Mardon, G., Marshall, P., Martin, D.P., Masiga, D., Mason, R., Mate-Kole, M., Matovu, E., Mayige, M., Mayosi, B.M., Mbanya, J.C., McCurdy, S.A., McCarthy, M.I., McIlleron, H., Mc’Ligeyo, S.O., Merle, C., Mocumbi, A.O., Mondo, C., Moran, J.V., Motala, A., Moxey-Mims, M., Mpoloka, W.S., Msefula, C.L., Mthiyane, T., Mulder, N., Mulugeta, G., Mumba, D., Musuku, J., Nagdee, M., Nash, O., Ndiaye, D., Nguyen, A.Q., Nicol, M., Nkomazana, O., Norris, S., Nsangi, B., Nyarko, A., Nyirenda, M., Obe, E., Obiakor, R., Oduro, A., Ofori-Acquah, S.F., Ogah, O., Ogendo, S., Ohene-Frempong, K., Ojo, A., Olanrewaju, T., Oli, J., Osafo, C., Ouwe Missi Oukem-Boyer, O., Ovbiagele, B., Owen, A., Owolabi, M.O., Owolabi, L., Owusu-Dabo, E., Pare, G., Parekh, R., Patterton, H.G., Penno, M.B., Peterson, J., Pieper, R., PlangeRhule, J., Pollak, M., Puzak, J., Ramesar, R.S., Ramsay, M., Rasooly, R., Reddy, S., Sabeti, P.C., Sagoe, K., Salako, T., Samassékou, O., Sandhu, M.S., Sankoh, O., Sarfo, F.S., Sarr, M., Shaboodien, G., Sidibe, I., Simo, G., Simuunza, M., Smeeth, L., Sobngwi, E., Soodyall, H., Sorgho, H., Sow Bah, O., Srinivasan, S., Stein, D.J., Susser, E.S., Swanepoel, C., Tangwa, G., Tareila, A., Tastan Bishop, O., Tayo, B., Tiffin, N., Tinto, H., Tobin, E., Tollman, S.M., Traoré, M., Treadwell, M.J., Troyer, J., TsimakoJohnstone, M., Tukei, V., Ulasi, I., Ulenga, N., van Rooyen, B., Wachinou, A.P., Waddy, S.P., Wade, A., Wayengera, M., Whitworth, J., Wideroff, L., Winkler, C.A., Winnicki, S., Wonkam, A., Yewondwos, M., sen, T., Yozwiak, N., Zar, H., 2014. Research capacity. Enabling the genomic revolution in Africa. Science 344 (6190), 1346e1348. Hodges, E., Xuan, Z., Balija, V., Kramer, M., Molla, M.N., Smith, S.W., Middle, C.M., Rodesch, M.J., Albert, T.J., Hannon, G.J., McCombie, W.R., 2007. Genome-wide in situ exon capture for selective resequencing. Nat. Genet. 39 (12), 1522e1527. Jimenez-Sanchez, G., Silva-Zolezzi, I., Hidalgo, A., March, S., 2008. Genomic medicine in Mexico: initial steps and the road ahead. Genome Res. 18 (8), 1191e1198. McCarthy, J.J., McLeod, H.L., Ginsburg, G.S., 2013. Genomic medicine: a decade of successes, challenges, and opportunities. Sci. Transl. Med. 5 (189), 189sr4. Mardis, E.R., 2011. A decade’s perspective on DNA sequencing technology. Nature 470 (7333), 198e203. Mitropoulos, K., Johnson, L., Vozikis, A., Patrinos, G.P., 2011. Relevance of pharmacogenomics for developing countries in Europe. Drug Metabol. Drug Interact. 26 (4), 143e146. Mitropoulou, C., Mai, Y., van Schaik, R.H., Vozikis, A., Patrinos, G.P., 2014. Stakeholder analysis in pharmacogenomics and genomic medicine in Greece. Public Health Genom. 17 (5e6), 280e286. Mai, Y., Mitropoulou, C., Papadopoulou, X.E., Vozikis, A., Cooper, D.N., van Schaik, R.H., Patrinos, G.P., 2014. Critical appraisal of the views of healthcare professionals with respect to pharmacogenomics and personalized medicine in Greece. Pers. Med. 11 (1), 15e26. Mercier, S., Küry, S., Shaboodien, G., Houniet, D.T., Khumalo, N.P., Bou-Hanna, C., Bodak, N., Cormier-Daire, V., David, A., Faivre, L., Figarella-Branger, D., Gherardi, R.K., Glen, E., Hamel, A., Laboisse, C., Le Caignec, C., Lindenbaum, P., Magot, A.,

Munnich, A., Mussini, J.M., Pillay, K., Rahman, T., Redon, R., Salort-Campana, E., Santibanez-Koref, M., Thauvin, C., Barbarot, S., Keavney, B., Bézieau, S., Mayosi, B.M., 2013. Mutations in FAM111B cause hereditary fibrosing poikiloderma with tendon contracture, myopathy, and pulmonary fibrosis. Am. J. Hum. Genet. 93 (6), 1100e1107. Mtatiro, S.N., Singh, T., Rooks, H., Mgaya, J., Mariki, H., Soka, D., Mmbando, B., Msaki, E., Kolder, I., Thein, S.L., Menzel, S., Cox, S.E., Makani, J., Barrett, J.C., 2014. Genome wide association study of fetal hemoglobin in sickle cell anemia in Tanzania. PLoS One 9 (11), e111464. Moreno-Estrada, A., Gignoux, C.R., Fernández-López, J.C., Zakharia, F., Sikora, M., Contreras, A.V., Acuña-Alonzo, V., Sandoval, K., Eng, C., Romero-Hidalgo, S., Ortiz-Tello, P.4, Robles, V., Kenny, E.E., NuñoArana, I., Barquera-Lozano, R., Macín-Pérez, G., GranadosArriola, J., Huntsman, S., Galanter, J.M., Via, M., Ford, J.G., Chapela, R.2, Rodriguez-Cintron, W., Rodríguez-Santana, J.R., Romieu, I., Sienra-Monge, J.J., del Rio Navarro, B., London, S.J., RuizLinares, A., Garcia-Herrera, R., Estrada, K., Hidalgo-Miranda, A., Jimenez-Sanchez, G., Carnevale, A., Soberón, X., CanizalesQuinteros, S., Rangel-Villalobos, H., Silva-Zolezzi, I., Burchard, E.G., Bustamante, C.D., 2014. Human genetics. The genetics of Mexico recapitulates Native American substructure and affects biomedical traits. Science 344 (6189), 1280e1285. Mitropoulou, C., Fragoulakis, V., Bozina, N., Vozikis, A., Supe, S., Bozina, T., Poljakovic, Z., van Schaik, R.H., Patrinos, G.P., 2015. Economic evaluation of pharmacogenomic-guided warfarin treatment for elderly Croatian atrial fibrillation patients with ischemic stroke. Pharmacogenomics 16 (2), 137e148. Niemz, A., Ferguson, T.M., Boyle, D.S., 2011. Point-of-care nucleic acid testing for infectious diseases. Trends Biotechnol. 29 (5), 240e250. Ossaily, S., Zgheib, N.K., 2014. The pharmacogenetics of drug metabolizing enzymes in the Lebanese population. Drug Metabol. Drug Interact. 29 (2), 81e90. Ortega-Recalde, O., Vergara, J.I., Fonseca, D.J., Ríos, X., Mosquera, H., Bermúdez, O.M., Medina, C.L., Vargas, C.I., Pallares, A.E., Restrepo, C.M., Laissue, P., 2014. Whole-exome sequencing enables rapid determination of xeroderma pigmentosum molecular etiology. PLoS One 8 (6), e64692. Pathare, A.V., Al Zadjali, S., Misquith, R., Alkindi, S.S., Panjwani, V., Lapoumeroulie, C., Pravin, S., Paldi, A., Krishnamoorthy, R., February 2012. Warfarin pharmacogenetics: polymorphisms of the CYP2C9, CYP4F2, and VKORC1 loci in a genetically admixed Omani population. Hum. Biol. 84 (1), 67e77. Pisanu, C., Tsermpini, E.E., Mavroidi, E., Katsila, T., Patrinos, G.P., Squassina, A., 2014. Assessment of the pharmacogenomics educational environment in Southeast Europe. Public Health Genom. 17 (5e6), 272e279. Palacios, R., Collado-Vides, J., 2007. Development of genomic sciences in Mexico: a good start and a long way to go. PLoS Comput. Biol. 3 (9), 1670e1673. Pepper, M.S., 2011. Launch of the Southern African Human Genome Programme. South Afr. Med. J. 101 (5), 287e288. Passos-Bueno, M.R., Bertola, D., Horovitz, D.D., de Faria Ferraz, V.E., Brito, L.A., 2014. Genetics and genomics in Brazil: a promising future. Mol. Genet. Genomic Med. 2 (4), 280e291. Pinto, W.B., Oliveira, A.S., Souza, P.V., 2015. Whole exome sequencing identifies three recessive FIG4-mutations in an

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apparently dominant pedigree with Charcot-Marie-Tooth disease. Neuromuscul. Disord. 25 (4), 359e360. Prada, C.F., Laissue, P., 2014. A high resolution map of mammalian X chromosome fragile regions assessed by large-scale comparative genomics. Mamm. Genome 25 (11e12), 618e635. Qumsieh, R.Y., Ali, B.R., Abdulrazzaq, Y.M., Osman, O., Akawi, N.A., Bastaki, S.M., 2011. Identification of new alleles and the determination of alleles and genotypes frequencies at the CYP2D6 gene in Emiratis. PLoS One 6 (12), e28943. Reydon, T.A., Kampourakis, K., Patrinos, G.P., 2012. Genetics, genomics and society: the responsibilities of scientists for science communication and education. Pers. Med. 9 (6), 633e643. Ruiz-Linares, A., Adhikari, K., Acuña-Alonzo, V., Quinto-Sanchez, M., Jaramillo, C., Arias, W., Fuentes, M., Pizarro, M., Everardo, P., de Avila, F., Gómez-Valdés, J., León-Mimila, P., Hunemeier, T., Ramallo, V., Silva de Cerqueira, C.C., Burley, M.W., Konca, E., de Oliveira, M.Z., Veronez, M.R., Rubio-Codina, M., Attanasio, O., Gibbon, S., Ray, N., Gallo, C., Poletti, G., Rosique, J., SchulerFaccini, L., Salzano, F.M., Bortolini, M.C., Canizales-Quinteros, S., Rothhammer, F., Bedoya, G., Balding, D., Gonzalez-José, R., 2014. Admixture in Latin America: geographic structure, phenotypic diversity and self-perception of ancestry based on 7,342 individuals. PLoS Genet. 10 (9), e1004572. Rattanavipapong, W., Koopitakkajorn, T., Praditsitthikorn, N., Mahasirimongkol, S., Teerawattananon, Y., 2013. Economic evaluation of HLA-B*15:02 screening for carbamazepine-induced severe adverse drug reactions in Thailand. Epilepsia 54 (9), 1628e1638. Sirugo, G., Hennig, B.J., Adeyemo, A.A., Matimba, A., Newport, M.J., Ibrahim, M.E., Ryckman, K.K., Tacconelli, A., MarianiCostantini, R., Novelli, G., Soodyall, H., Rotimi, C.N., Ramesar, R.S., Tishkoff, S.A., Williams, S.M., 2008. Genetic studies of African populations: an overview on disease susceptibility and response to vaccines and therapeutics. Hum. Genet. 123 (6), 557e598. Syurina, E., Brankovic, I., Probst-Hensch, N., Brand, A., 2011. Genomebased health literacy: a new challenge for public health genomics. Public Health Genom. 14 (4e5), 201e210. Silva-Zolezzi, I., Hidalgo-Miranda, A., Estrada-Gil, J., FernandezLopez, J.C., Uribe-Figueroa, L., Contreras, A., Balam-Ortiz, E., del Bosque-Plata, L., Velazquez-Fernandez, D., Lara, C., Goya, R.,


Hernandez-Lemus, E., Davila, C., Barrientos, E., March, S., JimenezSanchez, G., 2009. Analysis of genomic diversity in Mexican Mestizo populations to develop genomic medicine in Mexico. Proc. Natl. Acad. Sci. U. S. A. 106 (21), 8611e8616. Shendure, J., Ji, H., 2008. Next-generation DNA sequencing. Nat. Biotechnol. 26 (10), 1135e1145. Snyder, S.R., Mitropoulou, C., Patrinos, G.P., Williams, M.S., 2014. Economic evaluation of pharmacogenomics: a value-based approach to pragmatic decision making in the face of complexity. Public Health Genom. 17 (5e6), 256e264. Tekola Ayele, F., Adeyemo, A., Finan, C., Hailu, E., Sinnott, P., Burlinson, N.D., Aseffa, A., Rotimi, C.N., Newport, M.J., Davey, G., 2012. HLA class II locus and susceptibility to podoconiosis. N. Engl. J. Med. 366 (13), 1200e1208. Woolhouse, N.M., Qureshi, M.M., Bastaki, S.M., Patel, M., Abdulrazzaq, Y., Bayoumi, R.A., 1997. Polymorphic N-acetyltransferase (NAT2) genotyping of Emiratis. Pharmacogenetics 7 (1), 73e82. Wonkam, A., Njamnshi, A.K., Angwafo 3rd, F.F., 2006. Knowledge and attitudes concerning medical genetics amongst physicians and medical students in Cameroon (sub-Saharan Africa). Genet. Med. 8 (6), 331e338. Wonkam, A., Hurst, S., 2014. A call for policy action in sub-Saharan Africa to rethink diagnostics for pregnancy affected by sickle cell disease: differential views of medical doctors, parents and adult patients predict value conflicts in Cameroon. Omics J. Integr. Biol. 18 (7), 472e480. Wonkam, A., Ngo Bitoungui, V.J., Vorster, A.A., Ramesar, R., Cooper, R.S., Tayo, B., Lettre, G., Ngogang, J., 2014. Association of variants at BCL11A and HBS1L-MYB with hemoglobin F and hospitalization rates among sickle cell patients in Cameroon. PLoS One 9 (3), e92506. Wonkam, A., 2015. Letter to the editor regarding “GJB2, GJB6 or GJA1 genes should not be investigated in routine in non syndromic deafness in people of sub-Saharan African descent”. Int. J. Pediatr. otorhinolaryngology 79 (4), 632e633. Wonkam, A., Tekendo, C.N., Sama, D.J., Zambo, H., Dahoun, S., Béna, F., Morris, M.A., 2011. Initiation of a medical genetics service in sub-Saharan Africa: experience of prenatal diagnosis in Cameroon. Eur. J. Med. Genet. 54 (4), e399e404.