Canadian Journal of Infectious Diseases and Medical Microbiology

Canadian Journal of Infectious Diseases and Medical Microbiology / 2018 / Article
Special Issue

Computational Tools for Investigating Pathogen, Pathogen-Host Interaction, and Infectious Disease

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Review Article | Open Access

Volume 2018 |Article ID 1875217 |

Gerald Mboowa, Ivan Sserwadda, Marion Amujal, Norah Namatovu, "Human Genomic Loci Important in Common Infectious Diseases: Role of High-Throughput Sequencing and Genome-Wide Association Studies", Canadian Journal of Infectious Diseases and Medical Microbiology, vol. 2018, Article ID 1875217, 9 pages, 2018.

Human Genomic Loci Important in Common Infectious Diseases: Role of High-Throughput Sequencing and Genome-Wide Association Studies

Academic Editor: José Ramón Blanco
Received09 Sep 2017
Accepted07 Mar 2018
Published20 Mar 2018


HIV/AIDS, tuberculosis (TB), and malaria are 3 major global public health threats that undermine development in many resource-poor settings. Recently, the notion that positive selection during epidemics or longer periods of exposure to common infectious diseases may have had a major effect in modifying the constitution of the human genome is being interrogated at a large scale in many populations around the world. This positive selection from infectious diseases increases power to detect associations in genome-wide association studies (GWASs). High-throughput sequencing (HTS) has transformed both the management of infectious diseases and continues to enable large-scale functional characterization of host resistance/susceptibility alleles and loci; a paradigm shift from single candidate gene studies. Application of genome sequencing technologies and genomics has enabled us to interrogate the host-pathogen interface for improving human health. Human populations are constantly locked in evolutionary arms races with pathogens; therefore, identification of common infectious disease-associated genomic variants/markers is important in therapeutic, vaccine development, and screening susceptible individuals in a population. This review describes a range of host-pathogen genomic loci that have been associated with disease susceptibility and resistant patterns in the era of HTS. We further highlight potential opportunities for these genetic markers.

1. Introduction

HIV/AIDS, tuberculosis, and malaria are 3 major global public health threats causing substantial morbidity, mortality, negative socioeconomic impact, and human suffering [1]. Whole-genome sequencing (WGS) of hosts and their cognate pathogens has transformed our understanding of the contribution of genomics in infectious disease processes. As the antibiotic era changed our understanding of infections so has the genomic revolution. The host-pathogen coevolutionary “arms race” is a phenomenon that has been described to result from interaction within host innate, adaptive immune responses, exposure to antibiotics, and competition with commensal microbiota [2, 3]. When positive selection in one geographical region causes large allele frequency differences between populations than those expected for neutrally evolving alleles, high frequency of the derived allele (i.e., when a new allele increases to a frequency higher than that expected under genetic drift) [4] and HTS technologies are harnessed and used to decipher the nature and dynamics of these signals. The dynamics of host-pathogen interactions (e.g., length of exposure, geographical spread, morbidity, mortality, and cooccurring environmental events) influence the genetic architecture of resistance variants in modern populations [4]. The application of genomic sequence-based approaches to understand the host-pathogen interface continues to provide us with important clues for our survival. Human genetic variation is a major determinant of genetic susceptibility to many common infectious diseases [5]. Malaria, HIV/AIDS, and tuberculosis are some of the common infectious diseases in which a range of genetic susceptibilities and resistant conferring loci have been identified using both traditional molecular-based approaches and HTS technologies. HTS has enabled us to identify genomic signatures from these interactions. These markers identified through infectious disease genome-wide association studies (GWASs) have considerable significances that can be exploited to understand host protective mechanisms against pathogens and identify new molecular targets for diagnostic, prophylactic, and therapeutic interventions (Table 1).

DiseasePopulationVariantAnnotationGenome-wide significance ( value)AssociationReferences

HIV-1/AIDSAfrican Americansrs2523608HLA-B57035.6 × 10−10Viral load set point[6]
European ancestryrs2395029HCP5/B57019.7 × 10−26Long-term nonprogression and viral load set point[79]
European ancestryrs9264942HLA-C2.8 × 10−35Long-term nonprogression and viral load set point[7]
European ancestryrs4418214MICA1.4 × 10−34Long-term nonprogression[7]
European ancestryrs3131018PSORS1C34.2 × 10−16Long-term nonprogression
African Americanrs2255221Intergenic3.5 × 10−14Long-term nonprogression
rs2523590HLA-B1.7 × 10−13Long-term nonprogression
rs9262632Intergenic1.0 × 10−8Long-term nonprogression
European ancestryrs9261174ZNRD1, RNF391.8 × 10–8Disease progression[8, 10]
rs11884476PARD3B3.4 × 10–9Disease progression[11]
rs2234358CXCR69.7 × 10–10Long-term nonprogression[12]
MalariaGhanaianrs10900585ATP2B46.1 × 10−9Protective[13]
Ghanaianrs2334880MARVELD33.9 × 10−8Protective[13]
Ghanaianrs8176719ABO2.9 × 10−13Protective[13]
Gambianrs11036238HBB3.7 × 10–11Susceptibility[14]
Gambianrs334HBB4 × 10−14Protective[13, 14]
TuberculosisMoroccanrs17590261AGMO2 × 10−6Age-at-onset of TB[15]
Ghanaian and Gambianrs433142618q11.26.8 × 10−9Susceptibility[16]

In the past, candidate gene studies have been used to identify disease susceptibility genetic loci for many major human infections. But with the advent of HTS approaches, many new loci have been and continue to be identified in diverse populations. A paradigm shift from candidate gene studies to GWAS and HTS has ushered in a “big data” genomic revolution era enabling us to redefine the genomic architecture of host-pathogen disease susceptibility. Both exome and whole-genome sequencing approaches are proving more successful and affordable. Host genetics influence clinical course of infectious diseases as well as genetic variation of the pathogens determining their survival in presence of selective pressure from the host and environment (antimicrobials) and identify genetic markers of drug-resistant pathogens and parasites. Furthermore, HTS has given us unprecedented resolution of understanding the role of host genetics to infectious diseases susceptibility. This genomic revolution has generated data informative for understanding the frequency of many genetic traits, including those that cause disease susceptibility in African populations and populations of recent African descent [17].

Pathogens have always been a major cause of human mortality, so they impose strong selective pressure on the human genome [18, 19]. HTS applied to screening populations of host immune-specific cells and their respective pathogens can highlight the host-pathogen unique genetic signatures important in host-pathogen coevolution, profiling immunological history, pathogen-induced immunodominance genetic patterns, predicting clinical outcomes of common infections (such as HIV/AIDS disease progression phenotypes like long-term nonprogressors and rapid progressors, as well as highly exposed persistently seronegative group), rapid diagnosis plus screening outbreaks involving Risk Group 4 highly infectious pathogens, and genetic characterization of live-attenuated vaccine vectors (Figures 1(a) and 1(b)).

GWASs demand recruitment of large well-phenotyped clinical cohorts within appropriate study designs and settings. However, they are very expensive, and therefore few funding agencies are able to finance such studies yet they may offer unique opportunities to unveil genetically important signals. Currently, there are a growing number of communicable disease-specific research initiatives that are specifically interested in looking at the stages of the infections and GWAS of disease progression. A classic example is the Collaborative African Genomics Network (CAfGEN), a H3Africa-funded consortium probing host genetic factors that are important to the progression of HIV and HIV-TB infection in sub-Saharan African children ( CAfGEN is specifically looking at both rapid and long-term HIV/AIDS progression status while utilizing a unique protocol that applies exome sequencing of AIDS extreme clinical phenotypes. The study designs and the protocols being utilized are valuable resources for future genomic research involving common infectious diseases. In this review, we describe the impact of HTS and genomics in understanding the human host-pathogen interaction.

2. Common Infectious Diseases

Infectious diseases account for 15 million deaths per year worldwide, and disproportionately affect the young, elderly people, and the poorest sections of society making them a high priority [20]. The World Health Organization in 2016 estimated the global mortality for tuberculosis, HIV/AIDS, and malaria to be at 3.23 million with most deaths occurring in sub-Saharan Africa. This region has continued to lead in both prevalence and incidence of these major infectious killer diseases [21]. Research investment in infectious diseases was poorly matched. Data show that funding does not correspond closely with burden [22]. Review of findings to date suggests that the genetic architecture of infectious disease susceptibility may be importantly different from that of noninfectious diseases [20]. Other authors have extensively reviewed the genetic susceptibility to diseases [4, 5, 13, 16, 1820, 2349]. The ancient biological “arms race” between microbial pathogens and humans has shaped genetic variation in modern populations, and this has important implications for the growing field of medical genomics [4]. As humans migrated throughout the world, populations encountered distinct pathogens, and natural selection increased the prevalence of alleles that are advantageous in the new ecosystems in both host and pathogens [4]. Temporal patterns supporting evidence of host-pathogen coevolution have been reported [50]. Common infectious diseases have shown geographical disparities, for example, Mycobacterium tuberculosis lineage 4 comprises globally distributed and geographically restricted sublineages [51]. Molecular epidemiological studies show that, with the exception of sub-Saharan Africa, almost all HIV-1 subtypes, circulating recombinant forms, and several unique recombinant forms have been detected, but there is a specific geographic distribution pattern for HIV-1 subtypes [5254]. HIV diversity plays a central role in the HIV pandemic [55] and has significant implications for diagnosis, vaccine development, and clinical management of patients [56]. High levels of Plasmodium falciparum malaria endemicity are common in Africa [57]. Formal characterization of disease-causing agents has always been fundamental to understanding the evolution of pathogens and the epidemiology of infectious disease. Linking this information with temporal, spatial, and clinical data can bring understanding of evolution, geographical spread, and disease associations for the pathogen, providing vital information for identifying sources of infection as well as designing interventions to prevent and treat disease [58].

3. High-Throughput Sequencing (HTS) and Computational Tools

New sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome, and many computational and statistical approaches to identify disease association signals have emerged [59]. Foremost is a better understanding of disease pathogenesis and resistance in the expectation that this will lead in time to improved interventions such as better drugs or vaccines to prevent or attenuate the great global burden of infectious disease morbidity and mortality. With over 10 million deaths annually from infectious diseases and the threat of new epidemics and pandemics, this is a very high priority [20]. During the past decade, we have also witnessed the emergence of many new pathogens not previously detected in humans, such as the avian influenza virus, severe acute respiratory syndrome (SARS), and Ebola [60]. Now that the scientific community has access to complete genomes of infectious diseases though application of HTS, priority should be the dissection of host-pathogen interactions through development of powerful computational tools. HTS has profoundly altered our understanding of human diversity and disease. The interaction between hosts and pathogens is a coevolution process which may simply be described as “shooting a moving target.” HTS and computational modelling tools will offer potential to understand this interaction leading to better vaccine designs and therapeutic targets. Sanger DNA sequencing is limited in throughput and high cost as compared to HTS platforms, which differ in their details but typically follow a similar general paradigm: template preparation, clonal amplification, followed by cyclical rounds of massively parallel sequencing [61]. Nanopore-based sequencing approaches such as single molecule, real-time (SMRT) sequencing technologies have been developed and consistently produce some of the longest average read lengths compared to HTS. SMRT sequencing is particularly useful for projects involving de novo assembly of small bacterial and viral genomes as well as large genome finishing [62]. Many important sequencing platforms are reviewed in great depth [61, 63].

Computational tools are an important integral part of genomics. New computational methods are constantly being developed to collect, process, and extract useful biological information from a variety of samples and complex datasets [64]. The scale and complexity of genomic data is ever-expanding, requiring biologists to apply increasingly more sophisticated computational tools in the generation, analyses, interpretation, and storage of this data. The data are generated in different sizes, formats, and structures requiring a wide range of tools to manipulate. Despite the level of specialization needed in bioinformatics, it is important that life-scientists have a good understanding of it for a correct experimental design which allows them to reveal the information in a metagenome [64]. HTS technologies are generating an astonishing amount of unprecedented information in the history of Biology which has spurred a Biomedical Big Data to Knowledge (B2D2K) revolution. Thus, a new exhilarating rapidly evolving scientific field, Bioinformatics (Biology meets computer programming), has recently emerged and uses novel computational approaches to help solve important biological problems. Bioinformatics is a set of activities: data acquisition, database development, data analysis, data integration, and analysis of integrated data. The majority of available bioinformatics software requires some knowledge of the text-based command line of the UNIX or Linux operating systems, allowing custom programming scripts and pipelines to automate data manipulation and analysis in a single step [65]. Although bioinformatics tools/software are both “open sources” and commercially available, clinicians have limited bioinformatics knowledge [6669]. It is clear that user-friendly bioinformatics pipelines are key to facilitating more widespread use of WGS, with more widespread bioinformatics expertise [65].

4. Signatures of Selection on the Genomes

Positive selection (also known as Darwinian selection) in genes and genomes can point to the evolutionary basis for differences among species and among races within a species [70]. Many other aspects of human biology not necessarily related to the “branding” of our species, for instance, host-pathogen interactions, reproduction, dietary adaptation, and physical appearance, have also been the substrate of varying levels of positive selection. Comparative genetics/genomics studies in recent years have uncovered a growing list of genes that might have experienced positive selection during the evolution of humans and/or primates [71]. Microbial genome evolution is shaped by a variety of selective pressures. Understanding how these processes occur can help to address important problems in microbiology by explaining observed differences in phenotypes, including virulence and resistance to antibiotics. Greater access to whole-genome sequencing provides microbiologists with the opportunity to perform large-scale analyses of selection in novel settings such as within individual hosts [72].

Identification of positive selection genetic signatures in the genomes can help us to understand the kinetics and directions of continuing host-pathogen coadaptation and impact on their diagnosis, transmission, fitness, immunogenicity, and pathogenicity. Given the potential for strong selective pressure, that genetic programs controlling host-pathogen interactions in humans and other species are littered with signatures of positive selection [73]. The high mortality and widespread impact of malaria has resulted in this disease being the strongest evolutionary selective force in recent human history, and the genes that confer resistance to malaria provide some of the best-known case studies of strong positive selection in modern humans [27]. A number of specific genomic variants including β-globin locus, G6PD deficiency, Duffy, ovalocytosis, ABO, and human leukocyte antigen confer resistance to malaria in the human host. Elevated frequencies of hemoglobinopathies such as thalassemia and sickle cell disease, which are caused by mutations at the β-globin locus, are maintained via balancing selection (“the malarial hypothesis”) [7476]. This hypothesis suggests that some human diseases such as thalassemia are polymorphisms which provide heterozygote advantage because of the trade-offs between the advantages of resistance to malaria and negative effects due to the disease [77], where the hemoglobin S (HbS) homozygote disadvantage is recompensed through the malaria resistance of the heterozygote (HbAS) in regions of malaria endemicity [78, 79]. The ∆32 mutation at the CCR5 locus is a well-studied example of natural selection acting in humans. The mutation is found principally in Europe and western Asia, with higher frequencies generally in the north. Homozygous carriers of the ∆32 mutation are resistant to HIV-1 infection because the mutation prevents functional expression of the CCR5 chemokine receptor normally used by HIV-1 to enter CD4+ T cells [8087]. Host genetic factors play important roles in susceptibility to tuberculosis infection, and different gene polymorphisms in different ethnicity and genetic backgrounds may lead to different effects on tuberculosis risk [88]. Polymorphisms in natural resistance-associated macrophage protein 1 (NRAMP1), toll-like receptor 2 (TLR2), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), interleukin-1 receptor antagonist (IL-1RA), IL-10, vitamin D receptor (VDR), dendritic cell-specific ICAM-3-grabbing nonintegrin (DC-SIGN), monocyte chemoattractant protein-1 (MCP-1), nucleotide oligomerization binding domain 2 (NOD2), interferon-gamma (IFN-γ), inducible nitric oxide synthase (iNOS), mannose-binding lectin (MBL), and surfactant proteins A (SP-As) genes have been variably associated with tuberculosis infection, and there is strong evidence indicating that host genetic factors play critical roles in tuberculosis susceptibility, severity, and development among different populations [8991]. Several NRAMP1 polymorphisms were significantly associated with PTB in African and Asian populations, but not in populations of European descent [92, 93].

5. Genome-Wide Association Studies in Infectious Diseases

GWASs are based on the “common disease, common variant” hypothesis, and they have been performed largely using single nucleotide polymorphism (SNP) arrays that focus only on common genetic polymorphisms (for which the minor allele frequency is >5%) [35, 9496]. GWAS approach has potential to provide candidates for the development of control measures against infectious diseases in humans [13]. For over 50 years, candidate gene studies have been used to identify loci for many major causes of human infectious mortality, including malaria, tuberculosis, and HIV-1 [20]. The first successful GWAS was published in 2005 ushering genome-wide approaches that have identified loci in diverse populations. Common genetic variants have also been demonstrated to regulate susceptibility/resistance to infectious diseases, for example, the CCR5∆32 polymorphism that modulates HIV/AIDS disease progression [97]. Genome-wide association study approaches are being increasingly utilized to define genetic variants underlying susceptibility to major infectious diseases [34]. Infectious diseases follow a series of stages right from acquisition, disease development, rate of progression, convalescence, and asymptomatic carrier state. Therefore, every stage will be influenced by one or a set of mutations in a population or individual. Different populations will have mutations that affect diseases at different stages. This is where GWAS has and will continue to play an important role in identifying these mutations. A recent study suggested that host genetic risk in TB is depended upon the pathogen’s genetic background and demonstrated the importance of analyzing the interaction between host and pathogen genomes in TB [98]. Studies are exploiting unique designs like extreme phenotype designs to identify complex trait genomic loci, while others have identified genetic associations of infectious diseases by integrating estimation of population admixture events to detect disease susceptibility loci after teasing out different ancestries and allelic, genotypic, or haplotype risk ratios.

A genomic database for all mutations identified to be conferring resistance to infectious diseases in different populations is a vital product of more than 10 years of GWAS. More studies should be appropriately designed to identify new potential infectious disease resistance-conferring mutations in human hosts. We searched the PubMed database for studies published since January 2005 using the terms “malaria,” “tuberculosis,” “HIV/AIDS,” “genome-wide association.” Search terms included combinations of ((disease-query[Title] AND Genome-wide association[Title])) where a disease-query was either a communicable or noncommunicable disease. Two search restrictions were set: publication date set (from 2005/01/01) and species (humans). Figure 2 indicates more GWASs carried out in noncommunicable diseases than infectious diseases. There is an urgent need for a major increase in funding for communicable disease control in the developing world and for more balanced allocation of the resources already provided [22].

6. Future Directions

Genomics and whole-genome sequencing have the capacity to greatly enhance knowledge and understanding of infectious diseases and clinical microbiology [65]. Human genetics is an indispensable tool for enhancing the understanding of the molecular basis of many common diseases [99]. Over 4,500 SNPs have been associated with a variety of human traits and complex diseases [59]. We envisage that with the reducing costs, HTS and genomics will become an indispensable component of every health-care system. HTS and computational tools offer a potential to stratify populations for risk of infectious disease based on genomic profiling thereby prioritizing interventions such as vaccines and therapeutics to the “most-at-risk” populations since there is no “one-size-fits-all” approach to treating infectious diseases. With the growing number of sequencing facilities on every continent, the future will offer a less costly approach that will integrate a genomic profile in routine patient management, improving management of diseases, and therapeutic development.

It is less likely to find a population or individual who carries mutations conferring resistance to an infectious disease at every stage of the infection. Different individuals have different mutations that offer resistance to different stages of the infections. A genomic catalogue of these mutations identified through HTS, computational tools, and GWAS now combined with the rapidly growing genome-editing technology known as CRISPR/Cas9 will enable introduction of an array of disease-stage-specific resistance-conferring mutations. This will offer interventions at all levels of the disease process unlike traditional vaccines.


The views expressed in this publication are those of the authors and not necessarily those of the AAS, NEPAD Agency, Wellcome Trust, or the United Kingdom government.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

All authors participated in writing the manuscript. They further reviewed and approved the final manuscript.


This work was supported through the DELTAS Africa Initiative (Grant no. DEL-15-011) to THRiVE-2 (the Training Health Researchers into Vocational Excellence in East Africa). The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences’ (AAS) Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa’s Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust (Grant no. 107742/Z/15/Z) and the UK government.


  1. M. Vitoria, R. Granich, C. F. Gilks et al., “The global fight against HIV/AIDS, tuberculosis, and malaria: current status and future perspectives,” American Journal of Clinical Pathology, vol. 131, no. 6, pp. 844–848, 2009. View at: Publisher Site | Google Scholar
  2. K. A. Bliven and A. T. Maurelli, “Evolution of bacterial pathogens within the human host,” Microbiology Spectrum, vol. 4, no. 1, 2016. View at: Publisher Site | Google Scholar
  3. R. Dawkins and J. R. Krebs, “Arms races between and within species,” Proceedings of the Royal Society of London B: Biological Sciences, vol. 205, no. 1161, pp. 489–511, 1979. View at: Publisher Site | Google Scholar
  4. E. K. Karlsson, D. P. Kwiatkowski, and P. C. Sabeti, “Natural selection and infectious disease in human populations,” Nature Reviews Genetics, vol. 15, no. 6, pp. 379–393, 2014. View at: Publisher Site | Google Scholar
  5. A. V. Hill, “Genetics and genomics of infectious disease susceptibility,” British Medical Bulletin, vol. 55, no. 2, pp. 401–413, 1999. View at: Publisher Site | Google Scholar
  6. K. Pelak, D. B. Goldstein, N. M. Walley et al., “Host determinants of HIV-1 control in African Americans,” Journal of Infectious Diseases, vol. 201, no. 8, pp. 1141–1149, 2010. View at: Publisher Site | Google Scholar
  7. The International HIV Controllers Study, “The major genetic determinants of HIV-1 control affect HLA class I peptide presentation,” Science, vol. 330, no. 6010, pp. 1551–1557, 2010. View at: Google Scholar
  8. J. Fellay, K. V. Shianna, D. Ge et al., “A whole-genome association study of major determinants for host control of HIV-1,” Science, vol. 317, no. 5840, pp. 944–947, 2007. View at: Publisher Site | Google Scholar
  9. S. Limou, S. L. Clerc, C. Coulonges et al., “Genomewide association study of an AIDS-nonprogression cohort emphasizes the role played by HLA genes (ANRS Genomewide Association Study 02),” Journal of Infectious Diseases, vol. 199, no. 3, pp. 419–426, 2009. View at: Publisher Site | Google Scholar
  10. J. Fellay, D. Ge, K. V. Shianna et al., “Common genetic variation and the control of HIV-1 in humans,” PLoS Genetics, vol. 5, no. 12, p. e1000791, 2009. View at: Publisher Site | Google Scholar
  11. J. L. Troyer, G. W. Nelson, J. A. Lautenberger et al., “Genome-wide association study implicates PARD3B-based AIDS restriction,” Journal of Infectious Diseases, vol. 203, no. 10, pp. 1491–1502, 2011. View at: Publisher Site | Google Scholar
  12. S. Limou, C. Coulonges, J. T. Herbeck et al., “Multiple-cohort genetic association study reveals CXCR6 as a new chemokine receptor involved in long-term nonprogression to AIDS,” Journal of Infectious Diseases, vol. 202, no. 6, pp. 908–915, 2010. View at: Publisher Site | Google Scholar
  13. C. Timmann, T. Thye, M. Vens et al., “Genome-wide association study indicates two novel resistance loci for severe malaria,” Nature, vol. 489, no. 7416, pp. 443–446, 2012. View at: Publisher Site | Google Scholar
  14. M. Jallow, Y. Y. Teo, K. S. Small et al., “Genome-wide and fine-resolution association analysis of malaria in West Africa,” Nature Genetics, vol. 41, no. 6, pp. 657–665, 2009. View at: Publisher Site | Google Scholar
  15. A. Grant, A. Sabri, A. Abid et al., “A genome-wide association study of pulmonary tuberculosis in Morocco,” Human genetics, vol. 135, no. 3, pp. 299–307, 2016. View at: Publisher Site | Google Scholar
  16. T. Thye, F. O. Vannberg, S. H. Wong et al., “Genome-wide association analyses identifies a susceptibility locus for tuberculosis on chromosome 18q11. 2,” Nature Genetics, vol. 42, no. 9, pp. 739–741, 2010. View at: Publisher Site | Google Scholar
  17. F. Gomez, J. Hirbo, and S. A. Tishkoff, “Genetic variation and adaptation in Africa: implications for human evolution and disease,” Cold Spring Harbor Perspectives in Biology, vol. 6, no. 7, p. a008524, 2014. View at: Publisher Site | Google Scholar
  18. L. B. Barreiro and L. Quintana-Murci, “From evolutionary genetics to human immunology: how selection shapes host defence genes,” Nature Reviews Genetics, vol. 11, no. 1, pp. 17–30, 2010. View at: Publisher Site | Google Scholar
  19. R. Cagliani and M. Sironi, “Pathogen-driven selection in the human genome,” International Journal of Evolutionary Biology, vol. 2013, Article ID 204240, 6 pages, 2013. View at: Publisher Site | Google Scholar
  20. A. V. Hill, “Evolution, revolution and heresy in the genetics of infectious disease susceptibility,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 367, no. 1590, pp. 840–849, 2012. View at: Publisher Site | Google Scholar
  21. G. Mboowa, “Genetics of sub-Saharan African human population: implications for HIV/AIDS, tuberculosis, and malari,” International Journal of Evolutionary Biology, vol. 2014, Article ID 108291, 8 pages, 2014. View at: Publisher Site | Google Scholar
  22. J. Shiffman, “Donor funding priorities for communicable disease control in the developing world,” Health Policy and Planning, vol. 21, no. 6, pp. 411–420, 2006. View at: Publisher Site | Google Scholar
  23. D. Weatherall, J. Clegg, and D. Kwiatkowski, “The role of genomics in studying genetic susceptibility to infectious disease,” Genome Research, vol. 7, no. 10, pp. 967–973, 1997. View at: Publisher Site | Google Scholar
  24. A. V. Hill, “The genomics and genetics of human infectious disease susceptibility,” Annual Review of Genomics and Human Genetics, vol. 2, no. 1, pp. 373–400, 2001. View at: Publisher Site | Google Scholar
  25. J.-L. Casanova and L. Abel, “The genetic theory of infectious diseases: a brief history and selected illustrations,” Annual Review of Genomics and Human Genetics, vol. 14, no. 1, pp. 215–243, 2013. View at: Publisher Site | Google Scholar
  26. S. Eridani, “Sickle cell protection from malaria,” Hematology Reports, vol. 3, no. 3, 2011. View at: Publisher Site | Google Scholar
  27. P. W. Hedrick, “Population genetics of malaria resistance in humans,” Heredity, vol. 107, no. 4, pp. 283–304, 2011. View at: Publisher Site | Google Scholar
  28. K. K. Singh and S. A. Spector, “Host genetic determinants of HIV infection and disease progression in children,” Pediatric Research, vol. 65, no. 5, pp. 55–63, 2009. View at: Publisher Site | Google Scholar
  29. B. D. Walker and G. Y. Xu, “Unravelling the mechanisms of durable control of HIV-1,” Nature Reviews Immunology, vol. 13, no. 7, pp. 487–498, 2013. View at: Publisher Site | Google Scholar
  30. P. Kumar, “Long term non-progressor (LTNP) HIV infection,” Indian Journal of Medical Research, vol. 138, no. 3, pp. 291–293, 2013. View at: Google Scholar
  31. M. P. Martin and M. Carrington, “Immunogenetics of HIV disease,” Immunological Reviews, vol. 254, no. 1, pp. 245–264, 2013. View at: Publisher Site | Google Scholar
  32. P. Singh, G. Kaur, G. Sharma, and N. K. Mehra, “Immunogenetic basis of HIV-1 infection, transmission and disease progression,” Vaccine, vol. 26, no. 24, pp. 2966–2980, 2008. View at: Publisher Site | Google Scholar
  33. S. R. Grossman, K. G. Andersen, I. Shlyakhter et al., “Identifying recent adaptations in large-scale genomic data,” Cell, vol. 152, no. 4, pp. 703–713, 2013. View at: Publisher Site | Google Scholar
  34. F. O. Vannberg, S. J. Chapman, and A. V. Hill, “Human genetic susceptibility to intracellular pathogens,” Immunological Reviews, vol. 240, no. 1, pp. 105–116, 2011. View at: Publisher Site | Google Scholar
  35. S. J. Chapman and A. V. Hill, “Human genetic susceptibility to infectious disease,” Nature Reviews Genetics, vol. 13, no. 3, pp. 175–188, 2012. View at: Publisher Site | Google Scholar
  36. S. J. O’Brien and S. L. Hendrickson, “Host genomic influences on HIV/AIDS,” Genome Biology, vol. 14, no. 1, p. 201, 2013. View at: Publisher Site | Google Scholar
  37. P. R. Shea, K. V. Shianna, M. Carrington, D. B. Goldstein et al., “Host genetics of HIV acquisition and viral control,” Annual Review of Medicine, vol. 64, no. 1, pp. 203–217, 2013. View at: Publisher Site | Google Scholar
  38. D. Burgner, S. E. Jamieson, and J. M. Blackwell, “Genetic susceptibility to infectious diseases: big is beautiful, but will bigger be even better?” Lancet Infectious Diseases, vol. 6, no. 10, pp. 653–663, 2006. View at: Publisher Site | Google Scholar
  39. M. Clementi and E. Di Gianantonio, “Genetic susceptibility to infectious diseases,” Reproductive Toxicology, vol. 21, no. 4, pp. 345–349, 2006. View at: Publisher Site | Google Scholar
  40. S. Segal and A. V. Hill, “Genetic susceptibility to infectious disease,” Trends in Microbiology, vol. 11, no. 9, pp. 445–448, 2003. View at: Publisher Site | Google Scholar
  41. A. Driss, J. M. Hibbert, N. O. Wilson, S. A. Iqbal, T. V. Adamkiewicz, and J. K. Stiles, “Genetic polymorphisms linked to susceptibility to malaria,” Malaria Journal, vol. 10, no. 1, p. 271, 2011. View at: Publisher Site | Google Scholar
  42. L. Abel and A. J. Dessein, “Genetic epidemiology of infectious diseases in humans: design of population-based studies,” Emerging Infectious Diseases, vol. 4, no. 4, pp. 593–603, 1998. View at: Publisher Site | Google Scholar
  43. R. S. Sobota, C. M. Stein, N. Kodaman et al., “A locus at 5q33. 3 confers resistance to tuberculosis in highly susceptible individuals,” American Journal of Human Genetics, vol. 98, no. 3, pp. 514–524, 2016. View at: Publisher Site | Google Scholar
  44. K. Ding, M. de Andrade, T. A. Manolio et al., “Genetic variants that confer resistance to malaria are associated with red blood cell traits in African-Americans: an electronic medical record-based genome-wide association study,” G3: Genes, Genomes, Genetics, vol. 3, no. 7, pp. 1061–1068, 2013. View at: Publisher Site | Google Scholar
  45. S. N. Redmond, K. Eiglmeier, C. Mitri et al., “Association mapping by pooled sequencing identifies TOLL 11 as a protective factor against Plasmodium falciparum in Anopheles gambiae,” BMC Genomics, vol. 16, no. 1, p. 779, 2015. View at: Publisher Site | Google Scholar
  46. Malaria Genomic Epidemiology Network, G. Band, K. A. Rockett, C. C. Spencer, and D. P. Kwiatkowski, “A novel locus of resistance to severe malaria in a region of ancient balancing selection,” Nature, vol. 526, no. 7572, pp. 253–257, 2015. View at: Publisher Site | Google Scholar
  47. M. J. Newport and C. Finan, “Genome-wide association studies and susceptibility to infectious diseases,” Briefings in Functional Genomics, vol. 10, no. 2, pp. 98–107, 2011. View at: Publisher Site | Google Scholar
  48. J. N. Hirschhorn and M. J. Daly, “Genome-wide association studies for common diseases and complex traits,” Nature Reviews Genetics, vol. 6, no. 2, pp. 95–108, 2005. View at: Publisher Site | Google Scholar
  49. B. Penman, C. Buckee, S. Gupta, and S. Nee, “Genome-wide association studies in Plasmodium species,” BMC Biology, vol. 8, no. 1, p. 90, 2010. View at: Publisher Site | Google Scholar
  50. M. E. Woolhouse, J. P. Webster, E. Domingo, B. Charlesworth, and B. R. Levin, “Biological and biomedical implications of the co-evolution of pathogens and their hosts,” Nature Genetics, vol. 32, no. 4, pp. 569–577, 2002. View at: Publisher Site | Google Scholar
  51. D. Stucki, D. Brites, L. Jeljeli et al., “Mycobacterium tuberculosis lineage 4 comprises globally distributed and geographically restricted sublineages,” Nature Genetics, vol. 48, no. 12, pp. 1535–1543, 2016. View at: Publisher Site | Google Scholar
  52. L. Buonaguro, M. Tornesello, and F. Buonaguro, “Human immunodeficiency virus type 1 subtype distribution in the worldwide epidemic: pathogenetic and therapeutic implications,” Journal of Virology, vol. 81, no. 19, pp. 10209–10219, 2007. View at: Publisher Site | Google Scholar
  53. S. Osmanov, C. Pattou, N. Walker, B. Schwardländer, and J. Esparza, “Estimated global distribution and regional spread of HIV-1 genetic subtypes in the year 2000,” Journal of Acquired Immune Deficiency Syndromes, vol. 29, no. 2, pp. 184–190, 2002. View at: Publisher Site | Google Scholar
  54. J. Hemelaar, E. Gouws, P. D. Ghys, and S. Osmanov, “Global and regional distribution of HIV-1 genetic subtypes and recombinants in 2004,” AIDS, vol. 20, no. 16, pp. W13–W23, 2006. View at: Publisher Site | Google Scholar
  55. M. M. Santoro and C. F. Perno, “HIV-1 genetic variability and clinical implications,” ISRN Microbiology, vol. 2013, Article ID 481314, 20 pages, 2013. View at: Publisher Site | Google Scholar
  56. M. T. Pyne, J. Hackett, V. Holzmayer, and D. R. Hillyard, “Large-scale analysis of the prevalence and geographic distribution of HIV-1 non-B variants in the United States,” Journal of Clinical Microbiology, vol. 51, no. 8, pp. 2662–2669, 2013. View at: Publisher Site | Google Scholar
  57. S. I. Hay, C. A. Guerra, P. W. Gething et al., “A world malaria map: Plasmodium falciparum endemicity in 2007,” PLoS Medicine, vol. 6, no. 3, p. e1000048, 2009. View at: Publisher Site | Google Scholar
  58. S. D. Bentley and J. Parkhill, “Genomic perspectives on the evolution and spread of bacterial pathogens,” Proceedings of the Royal Society B: Biological Sciences, vol. 282, no. 1821, p. 20150488, 2015. View at: Publisher Site | Google Scholar
  59. N. O. Stitziel, A. Kiezun, and S. Sunyaev, “Computational and statistical approaches to analyzing variants identified by exome sequencing,” Genome Biology, vol. 12, no. 9, p. 227, 2011. View at: Publisher Site | Google Scholar
  60. E. C. Berglund, B. Nystedt, and S. G. Andersson, “Computational resources in infectious disease: limitations and challenges,” PLoS Computational Biology, vol. 5, no. 10, p. e1000481, 2009. View at: Publisher Site | Google Scholar
  61. J. A. Reuter, D. V. Spacek, and M. P. Snyder, “High-throughput sequencing technologies,” Molecular Cell, vol. 58, no. 4, pp. 586–597, 2015. View at: Publisher Site | Google Scholar
  62. A. C. English, S. Richards, Y. Han, M. Wang et al., “Mind the gap: upgrading genomes with Pacific Biosciences RS long-read sequencing technology,” PLoS One, vol. 7, no. 11, Article ID e47768, 2012. View at: Publisher Site | Google Scholar
  63. S. Goodwin, J. D. McPherson, and W. R. McCombie, “Coming of age: ten years of next-generation sequencing technologies,” Nature Reviews Genetics, vol. 17, no. 6, pp. 333–351, 2016. View at: Publisher Site | Google Scholar
  64. A. Escobar-Zepeda, A. V.-P. de León, and A Sanchez-Flores, “The road to metagenomics: from microbiology to DNA sequencing technologies and bioinformatics,” Frontiers in Genetics, vol. 6, 2015. View at: Publisher Site | Google Scholar
  65. J. C. Kwong, N. Mccallum, V. Sintchenko, and B. P. Howden, “Whole genome sequencing in clinical and public health microbiology,” Pathology, vol. 47, no. 3, pp. 199–210, 2015. View at: Publisher Site | Google Scholar
  66. D. Blankenberg, N. Coraor, G. Von Kuster, J. Taylor, and A. Nekrutenko, “Integrating diverse databases into an unified analysis framework: a Galaxy approach,” Database, vol. 2011, p. bar011, 2011. View at: Publisher Site | Google Scholar
  67. R. Lazarus, A. Kaspi, and M. Ziemann, “Creating reusable tools from scripts: the Galaxy Tool Factory,” Bioinformatics, vol. 28, no. 23, pp. 3139-3140, 2012. View at: Publisher Site | Google Scholar
  68. S. K. Gupta, B. R. Padmanabhan, S. M. Diene et al., “ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes,” Antimicrobial Agents and Chemotherapy, vol. 58, no. 1, pp. 212–220, 2014. View at: Publisher Site | Google Scholar
  69. E. Zankari, H. Hasman, S. Cosentino et al., “Identification of acquired antimicrobial resistance genes,” Journal of Antimicrobial Chemotherapy, vol. 67, no. 11, pp. 2640–2644, 2012. View at: Publisher Site | Google Scholar
  70. A. Wagner, “Rapid detection of positive selection in genes and genomes through variation clusters,” Genetics, vol. 176, no. 4, pp. 2451–2463, 2007. View at: Publisher Site | Google Scholar
  71. E. J. Vallender and B. T. Lahn, “Positive selection on the human genome,” Human Molecular Genetics, vol. 13, no. 2, pp. R245–R254, 2004. View at: Publisher Site | Google Scholar
  72. S. Moyo, E. Wilkinson, A. Vandormael et al., “Pairwise diversity and tMRCA as potential markers for HIV infection recency,” Medicine, vol. 96, no. 6, p. e6041, 2017. View at: Publisher Site | Google Scholar
  73. J. Trowsdale and P. Parham, “Mini-review: defense strategies and immunity-related genes,” European Journal of Immunology, vol. 34, no. 1, pp. 7–17, 2004. View at: Publisher Site | Google Scholar
  74. J. S. Haldane, “The rate of mutation of human genes,” Hereditas, vol. 35, no. 1, pp. 267–273, 1949. View at: Publisher Site | Google Scholar
  75. A. Allison, “The sickle-cell and haemoglobin c genes in some African populations,” Annals of Human Genetics, vol. 21, no. 1, pp. 67–89, 1956. View at: Publisher Site | Google Scholar
  76. E. T. Wood, D. A. Stover, M. Slatkin, M. W. Nachman, and M. F. Hammer, “The β-globin recombinational hotspot reduces the effects of strong selection around HbC, a recently arisen mutation providing resistance to malaria,” American Journal of Human Genetics, vol. 77, no. 4, pp. 637–642, 2005. View at: Publisher Site | Google Scholar
  77. P. W. Hedrick, “Resistance to malaria in humans: the impact of strong, recent selection,” Malaria Journal, vol. 11, no. 1, p. 349, 2012. View at: Publisher Site | Google Scholar
  78. V. R. De Mendonça, M. S. Goncalves, and M. Barral-Netto, “The host genetic diversity in malaria infection,” Journal of Tropical Medicine, vol. 2012, Article ID 940616, 17 pages, 2012. View at: Publisher Site | Google Scholar
  79. J. B. S. Haldane, “Suggestions as to quantitative measurement of rates of evolution,” Evolution, vol. 3, no. 1, pp. 51–56, 1949. View at: Publisher Site | Google Scholar
  80. J. Novembre, A. P. Galvani, and M. Slatkin, “The geographic spread of the CCR5 Δ32 HIV-resistance allele,” PLoS biology, vol. 3, no. 11, p. e339, 2005. View at: Publisher Site | Google Scholar
  81. J. C. Stephens, D. E. Reich, D. B. Goldstein et al., “Dating the origin of the CCR5-Δ32 AIDS-resistance allele by the coalescence of haplotypes,” American Journal of Human Genetics, vol. 62, no. 6, pp. 1507–1515, 1998. View at: Publisher Site | Google Scholar
  82. F. Libert, P. Cochaux, G. Beckman et al., “The Δ CCR5 mutation conferring protection against HIV-1 in Caucasian populations has a single and recent origin in Northeastern Europe,” Human Molecular Genetics, vol. 7, no. 3, pp. 399–406, 1998. View at: Publisher Site | Google Scholar
  83. G. Lucotte and G. Mercier, “Distribution of the CCR5 gene 32-bp deletion in Europe,” JAIDS Journal of Acquired Immune Deficiency Syndromes, vol. 19, no. 2, pp. 174–177, 1998. View at: Publisher Site | Google Scholar
  84. J. J. Martinson, N. H. Chapman, D. C. Rees, Y.-T. Liu, and J. B. Clegg, “Global distribution of the CCR5 gene 32-basepair deletion,” Nature Genetics, vol. 16, no. 1, pp. 100–103, 1997. View at: Publisher Site | Google Scholar
  85. G. Lucotte, “Distribution of the CCR5 gene 32-basepair deletion in West Europe. A hypothesis about the possible dispersion of the mutation by the Vikings in historical times,” Human Immunology, vol. 62, no. 9, pp. 933–936, 2001. View at: Publisher Site | Google Scholar
  86. G. Lucotte, “Frequencies of 32 base pair deletion of the (Δ32) allele of the CCR5 HIV-1 co-receptor gene in Caucasians: a comparative analysis,” Infection, Genetics and Evolution, vol. 1, no. 3, pp. 201–205, 2002. View at: Publisher Site | Google Scholar
  87. G. Lucotte and F. Dieterlen, “More about the Viking hypothesis of origin of the Δ32 mutation in the CCR5 gene conferring resistance to HIV-1 infection,” Infection, Genetics and Evolution, vol. 3, no. 4, pp. 293–295, 2003. View at: Publisher Site | Google Scholar
  88. H. Rong, Q. Zhang, and Z. Zhang, “Host genetic effect on tuberculosis susceptibility in Chinese Uyghur,” Frontiers in Laboratory Medicine, vol. 1, no. 1, pp. 5–10, 2017. View at: Publisher Site | Google Scholar
  89. F. Wu, W. Zhang, L. Zhang et al., “NRAMP1, VDR, HLA-DRB1, and HLA-DQB1 gene polymorphisms in susceptibility to tuberculosis among the Chinese Kazakh population: a case-control study,” BioMed Research International, vol. 2013, Article ID 484535, 8 pages, 2013. View at: Publisher Site | Google Scholar
  90. A. K. Azad, W. Sadee, and L. S. Schlesinger, “Innate immune gene polymorphisms in tuberculosis,” Infection and Immunity, vol. 80, no. 10, pp. 3343–3359, 2012. View at: Publisher Site | Google Scholar
  91. S. A. Khalilullah, H. Harapan, N. A. Hasan, W. Winardi, I. Ichsan, and M. Mulyadi, “Host genome polymorphisms and tuberculosis infection: what we have to say?” Egyptian Journal of Chest Diseases and Tuberculosis, vol. 63, no. 1, pp. 173–185, 2014. View at: Publisher Site | Google Scholar
  92. H. Li, T. T. Zhang, Y. Q. Zhou, Q. H. Huang, and J. Huang, “SLC11A1 (formerly NRAMP1) gene polymorphisms and tuberculosis susceptibility: a meta-analysis,” International Journal of Tuberculosis and Lung Disease, vol. 10, no. 1, pp. 3–12, 2006. View at: Google Scholar
  93. L. Abel, J. El-Baghdadi, A. A. Bousfiha, J.-L. Casanova, and E. Schurr, “Human genetics of tuberculosis: a long and winding road,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 369, no. 1645, p. 20130428, 2014. View at: Publisher Site | Google Scholar
  94. A. L. Gloyn and M. I. McCarthy, “Variation across the allele frequency spectrum,” Nature Genetics, vol. 42, no. 8, pp. 648–650, 2010. View at: Publisher Site | Google Scholar
  95. J. McClellan and M.-C. King, “Genetic heterogeneity in human disease,” Cell, vol. 141, no. 2, pp. 210–217, 2010. View at: Publisher Site | Google Scholar
  96. E. T. Cirulli and D. B. Goldstein, “Uncovering the roles of rare variants in common disease through whole-genome sequencing,” Nature Reviews Genetics, vol. 11, no. 6, pp. 415–425, 2010. View at: Publisher Site | Google Scholar
  97. P. Garred, J. Eugen-Olsen, A. K. N. Iversen, T. L. Bennfield, A. Svejgaard, and B. Hofmann, “Dual effect of CCR5 Δ32 gene deletion in HIV-1-infected patients,” The Lancet, vol. 349, no. 9069, p. 1884, 1997. View at: Publisher Site | Google Scholar
  98. Y. Omae, L. Toyo-oka, H. Yanai et al., “Pathogen lineage-based genome-wide association study identified CD53 as susceptible locus in tuberculosis,” Journal of Human Genetics, vol. 62, no. 12, pp. 1015–1022, 2017. View at: Publisher Site | Google Scholar
  99. H.-Q. Qu, Q. Li, J. B. McCormick, and S. P. Fisher-Hoch, “What did we learn from the genome-wide association study for tuberculosis susceptibility?” Journal of Medical Genetics, vol. 48, no. 4, pp. 217-218, 2011. View at: Publisher Site | Google Scholar

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