Genetic Predisposition and the Variable Course of Infectious Diseases
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Background: Contact with a pathogen is followed by variable courses of infectious disease, which are only partly explicable by classical risk factors. The susceptibility to infection is variable, as is the course of disease after infection. In this review, we discuss the extent to which this variation is due to genetic factors of the affected individual (the host).
Methods: Selective review of the literature on host genetics in infectious disease, with special attention to the pathogens SARS-CoV-2, influenza viruses, Mycobacterium tuberculosis, and human immunodeficiency virus (HIV).
Results: Genetic variants of the host contribute to the pathogenesis of infectious diseases. For example, in HIV infection, a relatively common variant leading to a loss of function of the HIV co-receptor CCR5 affects the course of the disease, as do variants in genes of the major histocompatibility complex (MHC) region. Rare monogenic variants of the interferon immune response system contribute to severe disease courses in COVID-19 and influenza (type I interferon in these two cases) and in tuberculosis (type II interferon). An estimated 1.8% of life-threatening courses of COVID-19 in men under age 60 are caused by a deficiency of toll-like receptor 7. The scientific understanding of host genetic factors has already been beneficial to the development of effective drugs. In a small number of cases, genetic information has also been used for individual therapeutic decision-making and for the identification of persons at elevated risk.
Conclusion: A comprehensive understanding of host genetics can improve the care of patients with infectious diseases. Until the present, the clinical utility of host genetics has been limited to rare cases; in the future, polygenic risk scores summarizing the relevant genetic variants in each patient will enable a wider benefit. To make this possible, multicenter studies are needed that will systematically integrate clinical and genetic data.
Due to the SARS-CoV-2 pandemic, infectious diseases have come into public focus. A frequently discussed aspect in this context is the heterogeneity of disease courses, given that the majority of people infected with SARS-CoV-2 develop either no or only mild symptoms, while others become extremely ill. Susceptibility upon contact with SARS-CoV-2 also varies; for example, some individuals rapidly become infected, others not at all (1).
This interindividual variability following contact with a pathogen can also be seen in a similar form in other infectious diseases. Severe disease courses can be explained, at least in part, by acquired factors, such as advanced age, unhealthy lifestyle (for example, smoking, overweight, alcohol consumption), or certain pre-existing diseases. However, there are disease courses that are more severe or milder than acquired factors would lead one to expect.
As early as the mid-20th century, genetic factors of the host were already being postulated as modulators of disease severity (2). A groundbreaking study conducted in 1988 on the contribution of genetics in infectious diseases showed that adoptees have an approximately five-fold higher risk of dying of an infectious disease if one biological parent died of an infectious disease at an early stage in life (3). These broad findings have also been supported by entity-specific studies. For example, approximately 30% of the clinical variability in COVID-19 is attributable to genetic factors of the host (4).
Using four infectious diseases as examples, we describe in this review article how host genetics can influence their course and how genetic insights can be used for treatment or prevention.
Basic genetic principles
Infectious diseases are multifactorial entities, the etiology of which is contributed to not only by the pathogen but also by genetic as well as non-genetic factors (Figure 1). In this context, the individual genetic contribution is often based on a combination of low-penetrance variants and, more rarely, on isolated high-penetrance variants (mutations). Accordingly, the quantitative contribution of individual variants, the effect size, is usually negatively correlated with the frequency of the variant in the population. Whether predominantly rare or indeed more frequent variants contribute to an infectious disease depends to a crucial extent on three factors:
- The biological complexity of the disease
- The (mostly unknown) extent of selection pressure exerted by the pathogens
- The interaction of the pathogen with the host.
A classic example of this would be mutations that cause hemoglobinopathy: since these simultaneously confer resistance to malaria, carrierships predisposing to hemoglobinopathies are relatively common in regions with high malaria prevalence compared to non-malarial areas (5).
Single common variants generally contribute in only a minor way to a person’s individual risk. Overall, however, common variants can, in combination with non-genetic factors, significantly influence individual risk (6). To identify associated variants, DNA array-based genotyping of large cohorts is used (Box). In this process, the allelic expression of up to one million variable positions in each genome are recorded and the allele frequencies then compared between affected and unaffected individuals. This approach, which is referred to as a genome-wide association study (GWAS), is extremely cost-effective and has already been successfully used for over 15 years to decipher the genetics of multifactorial diseases (7). The associated variants are mostly found in the non-coding regions of the genome and, as such, have no immediate effect on protein function. Although an effect on gene regulation is assumed, the causal relationships are not directly evident from the GWAS. Only comparatively few GWAS in infectious diseases have been conducted to date (with the exception of COVID-19). In the most extensive GWAS to date, which was carried out in 2017, numerous infectious diseases were investigated and 59 risk factors identified (8). The effect sizes of the individual risk variants (effect size 1.05–1.78) are, as expected, low and therefore are of only limited clinical informative value. It is only by considering individual variants in a combinatorial manner that significantly stronger effects can be predicted (compare polygenic risk scores). Although this approach offers great potential, it has not yet found its way into clinical evaluation.
In the case of individuals whose phenotype is significantly more strongly pronounced than one would expect based on non-genetic risk factors, a substantial genetic influence is assumed. In extreme cases, even monogenic forms of the disease may be present, that is to say, these patients carry a single genetic variant that, after pathogen contact, is in effect solely responsible for the course of disease. These are mostly coding variants that cause major functional changes to the protein and are rare in the population (usually < 1 %). In the case of infectious diseases, these monogenic predispositions to certain infections are attributed to congenital immunodeficiencies and can be inherited in, for example, an autosomal-dominant or autosomal-recessive manner (Table). These predispositions cover a range of infectious phenotypes: on the one hand, there are high-penetrance forms that tend towards earlier, more severe infection and a uniform phenotype (for example, autosomal-recessive IFNGR1 deficiency) and, on the other, low-penetrance forms (for example, autosomal-dominant IFNGR2 deficiency) (9).
In clinical routine, an immune defect of this kind should be considered if patients exhibit unusual disease courses, infections with atypical pathogens, or a remarkable frequency of infections. Guidelines on this are available (10, 11). In order to elucidate the genetic cause of immunodeficiencies, next-generation sequencing (NGS), that is to say, comprehensive sequencing of either the entire protein-coding sequence (exome sequencing) or even the entire genome (whole-genome sequencing), is increasingly used, not least in diagnostics. In contrast to DNA array-based genotyping, sequencing is also able to identify rare variants, some of which have not yet been previously described.
Cell biological mechanisms in pathogen contact
The basis of defense against pathogens lies in the host-specific immune response, which comprises elements of the innate and the adaptive immune response. The former responds rapidly and non-specifically to pathogens, whereas the latter is mounted in a delayed manner but establishes a long-lasting immunological memory (12).
Mechanisms relevant to the host-specific response of innate immunity include the barrier function of the (mucosal) skin, a variety of cells in the immune system (for example, granulocytes, macrophages, and natural killer cells), as well as humoral components, to which the complement system and cytokines belong. The latter encompass a group of immunomodulatory proteins that also include the interferons (Table).
To differentiate between foreign and endogenous components, the innate immune system uses receptors that recognize molecular patterns of pathogens (pattern recognition receptors), such as toll-like receptors (TLR) and receptors for proteins coded in the major histocompatibility complex (MHC) region. Once the pathogen has been recognized and classified, a proinflammatory cascade that protects the host during the initial days of infection is activated. In addition, cytokines and co-stimulatory molecules are produced in the further course for the activation of the adaptive immune system(13).
The adaptive immune response itself is triggered by the presentation of foreign antigens by MHC complexes (coded by human leukocyte antigen [HLA] genes). It has a humoral component through the formation of specific antibodies in plasma cells, as well as a cellular component through the specific activation of cytotoxic T cells and T helper cells.
Host genetics in selected infectious diseases
For the period 2009–2013 in Europe, influenza, tuberculosis, and AIDS were the infectious diseases that caused the greatest number of lost healthy life years (e1) (Figure 2). COVID-19 disease caused by SARS-CoV-2 has also already been held responsible for a considerable disease burden (e2).
In addition to viral subtype, monogenic defects that reduce the effect or production of type I/type III interferons have been identified as causal for severe influenza. The affected genes influence this defense pathway (14), for example, by impairing detection of viral RNA (TLR3), restricting the induction of interferon I/III expression (IRF7), or affecting the action of interferon I/III (IRF9). Inexplicably severe courses of influenza have also been observed in individuals with mutations in GATA2 (e3) and DBR1 (e4); in both mutations, the pathomechanism is independent of the interferon defense system (Table). Estimates on how many cases of severe influenza are caused by monogenic defects are currently not available. However, it is assumed that they account for only a small portion of the variance. Despite the comparatively high incidence of infections with influenza viruses, only a handful of GWAS have been published to date, with inconclusive (equivocal) results. The strongest evidence has been identified for the non-coding variants near the CD55 and IFITM3 genes; however, this has not been independently confirmed as yet, nor has the causal role of these genes been demonstrated (15).
As early on as in 1996, a homozygous loss-of-function variant in the CCR5 gene was identified as a protective cause (16). The CCR5 protein, together with the surface antigen CD4, serves as a crucial receptor for HIV adsorption—therefore, the absence of CCR5 prevents the virus from entering the host cell. These insights formed the basis for the development of the CCR5 antagonist maraviroc as an HIV drug. Furthermore, in patients with hemato-oncologic disease receiving stem cell transplantation, it was possible to treat HIV infection by selecting donors with a homozygous CCR5 deletion (17, e5). Using GWAS, frequent low-penetrance variants associated with the progression of HIV infection were also identified (18). For example, there are other independent associations at the CCR5 locus that control the expression of CCR5 in CD4+ T cells. Thus, a smaller amount of CCR5 is associated with a lower potential for in vitro HIV infection (19).
Consistent associations with alleles in the MHC Class I region have also been described (20). Here, the biological mechanisms at the affected genes (HLA-A, HLA-B, HLA-C) differ: at the HLA-A and HLA-B loci, the mechanism is characterized by the expression of two and three amino acids, respectively, whereby various alleles here can influence the course of HIV infection both positively and negatively (20). In contrast, the effect at the HLA-C locus is mediated by the amount of expression, that is to say, in a regulatory manner. High HLA-C expression is associated with milder disease (21). Milder disease has also been reported in individuals with different HLA alleles (the so-called heterozygote advantage) (22). Genetic findings on HIV are already being applied in treatment: for example, HIV-infected patients are only treated with abacavir if they do not carry the HLA-B*57:01 allele, since approximately 50% of HLA-B*57:01-allele carriers develop severe adverse effects (23). From a mechanistic perspective, abacavir binds to HLA-B*57:01 and alters the endogenous peptide repertoire presented on the surface. As a result, T cells react to the newly presented peptides and trigger a hypersensitivity reaction (24).
Two independent GWAS (8, e6) have been conducted to date for infections with the Mycobacterium tuberculosis bacterium. These studies identified associations in genes in MHC class II, notably two amino acid exchanges in HLA-DR and HLA-DQ, as well as one non-coding variant. Associations in the HLA-DR region have also been demonstrated in infections with the related strain, Mycobacterium leprae (e7). It is becoming increasingly evident that in the presence of associations in the MHC region, these primarily involve class I molecules in viral infections and class II molecules in bacterial infections. This is supported by the biological mechanisms of antigen presentation, which differ between bacteria and viruses (25).
In addition, over 20 monogenic defects that reduce the effect or production of type II interferon are known; these result in increased susceptibility to mycobacterial infections (for example, mutations in IFNGR1, IL12RB2, IL23R, IRF8, STAT1) (Table) (26). In particular, defects that cause reduced interferon production (for example, in the IL12B gene) can be treated adjunctively with recombinant interferons (9). The penetrance of the individual immune defects differs—it can be complete, but it can also be significantly reduced. A prominent example would be autosomal recessive IFNGR1 deficiency, which is associated with complete penetrance and an overall poor prognosis. Although hematopoietic stem cell transplantation can be performed as a curative treatment, complications are common (9).
Since mid-2020, GWAS have been used to investigate common risk variants for COVID-19. One of the largest GWAS was published in July 2021 by an international consortium (COVID-19 Host Genetics Initiative): by investigating just under 50,000 affected individuals, it was possible to identify 13 loci, some of which are associated with susceptibility to, and others with severity of, COVID-19 (27) (eTable). Some variants could only be identified by including non-European populations, since those particular variants are too rare in the European population to reach statistical significance. What is also interesting is the absence (to date) of genetic findings in the MHC region. Not surprisingly, the associations in COVID-19 also lie primarily in the non-coding region of the genome and are still not understood in terms of their function. However, the regions cover a multitude of genes whose involvement in the pathogenesis of COVID-19 seems plausible on the basis of biological insights (for example, OAS1 and IFNAR2). The strongest association to date has been reported for variants on chromosome 3 (1, e8, 28). A correlation between the risk allele and extensive clinical findings showed a particularly strong association with respiratory failure in under 60-year-olds (effect size 2.7), which is in line with the effect size of established clinical risk factors (29).
An increased susceptibility to severe COVID-19 due to monogenic defects that reduce the effect or production of type I interferon has also been described: exome sequencing identified hemizygous mutations in the TLR7 gene as causal for extremely severe disease in two pairs of brothers (aged 20–35 years) (30). A number of studies have confirmed this finding (31, 32, e9). It is estimated that approximately 1–2% of life-threatening COVID-19 infections in males aged under 60 years can be attributed to TLR7 deficiency (point estimate, 1.8%) (31). Awareness of TLR7 mutation status has already been used for prevention by fast-tracking male mutation carriers for vaccination (32). Involvement of rarer variants has also been demonstrated in other genes in the interferon-1 defense system, including a homozygous TBKI mutation in a child with lethal COVID-19 (33). Synthetically produced type I interferon has already been used to treat isolated cases of individuals with defects in interferon-1 production (34).
Translation of genetic findings in infectiology
Although maraviroc is not currently used as the first-line drug in HIV treatment, this example illustrates the clinical benefit of genetic findings. At present, less than 10% of approved drugs are based on genetic insights. However, active substances selected for clinical trials on the basis of genetic evidence are approximately two- to four-fold more likely to be approved as drugs compared to active substances that were selected without considering genetic data. Thus, the success rate for drug development is significantly higher if the selection of drug targets is supported by genetic evidence (35).
In addition to the abovementioned diseases, there are a number of other translational examples: for example, homozygosity for certain variants on the IL28B locus is the strongest predictor for the efficacy of combination therapy with interferon and ribavirin in hepatitis C infection (36), and successful treatment with ruxolitinib has been reported in patients with chronic mucocutaneous candidiasis caused by gain-of-function mutations in STAT1 (37). To date, only a handful of genetic variants have been included in pharmacogenetic assessments of this kind. However, diagnostics in the future will incorporate a greater number of different variants together with non-genetic factors, thereby enabling more precise prediction.
Infectious diseases will continue to pose a challenge for health care systems in the future, due, among other reasons, to the appearance of new pathogens, increasing resistance to treatment strategies for known pathogens, and the worldwide disparity in access to vaccines and drugs (38). In addition to the significance of the infection itself, etiological links between an infection and other, mostly multifactorial diseases are increasingly coming to light (for example, Epstein-Barr virus infection was recently identified as the likely main cause of multiple sclerosis [e10]). Greater understanding of host genetics, both at the individual and at the population level, will substantially contribute to personalized medicine and, as such, is of central clinical importance. In order to achieve this goal, inter-site studies that combine clinical, infectious disease, and genetic information are needed.
Dr. Schmidt received a Gerok grant from the BONFOR program of the Medical Faculty of the University of Bonn (account number O-149.0134). Dr. Ludwig is supported by the Emmy Noether program of the German Research Foundation (DFG) (LU 1944/3–1). Both authors are members of the German COVID-19 Multi-Omics Initiative (DeCOI-Host Genetics). Dr. Ludwig also receives funding from the German Federal Ministry of Education and Research (BMBF) for the COVIMMUNE project.
We would like to thank Professor Markus Nöthen for discussing the manuscript with us, Dr. Anna Vyvers for her critical reading of the manuscript, as well as Mrs. Christine Fischer for her support in designing the figures.
Conflict of interest statement
Dr. Schmidt holds shares in Twist Bioscience.
Prof. Frick received study support (third-party funding) from the DFG.
Dr. Ludwig holds shares in LAMPseq Diagnostics GmbH. She has received lecture fees from the Dr. Hans Riegel-Stiftung
The remaining authors declare that no conflict of interests exists.
Manuscript received on 31 August 2021, revised version accepted on 11 January 2022.
Translated from the original German by Christine Rye.
Dr. rer. nat. Kerstin U. Ludwig
Institut für Humangenetik, Department of Genomics
Venusberg-Campus 1, Gebäude 76
53127 Bonn, Germany
Cite this as
Schmidt A, Groh AM, Frick JS, Vehreschild MJGT, Ludwig KU:
Genetic predisposition and the variable course of infectious diseases.
Dtsch Arztebl Int 2022; 119: 117–23. DOI: 10.3238/arztebl.m2022.0105
Medical Department II, Infectiology, University HospitalFrankfurt, Goethe University Frankfurt: Ana M. Groh, Prof. Dr. med. Maria J. G. T. Vehreschild
Interfaculty Institute for Microbiology and Infection Medicine, University Hospital and Faculty of Medicine Tübingen: Prof. Dr. med. Julia S. Frick
MVZ Laboratory Ludwigsburg GbR: Prof. Dr. med. Julia S. Frick
|1.||Shelton JF, Shastri AJ, Ye C, et al.: Trans-ancestry analysis reveals genetic and nongenetic associations with COVID-19 susceptibility and severity. Nat Genet 2021; 53: 801–8 CrossRefMEDLINE|
|2.||Barua D, Paguio AS: ABO blood groups and cholera. Ann Hum Biol 1977; 4: 489–92 CrossRef MEDLINE|
|3.||Sørensen TI, Nielsen GG, Andersen PK, Teasdale TW: Genetic and environmental influences on premature death in adult adoptees. N Engl J Med 1988; 318: 727–32 CrossRef|
|4.||Williams FMK, Freidin MB, Mangino M, et al.: Self-reported symptoms of COVID-19, including symptoms most predictive of SARS-CoV-2 infection, are heritable. Twin Res Hum Genet 2020; 23: 316–21 CrossRef MEDLINE|
|5.||Hedrick PW: Population genetics of malaria resistance in humans. Heredity (Edinb) 2011; 107: 283–304 CrossRef MEDLINE PubMed Central|
|6.||Khera AV, Chaffin M, Aragam KG, et al.: Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 2018; 50: 1219–24 CrossRef MEDLINE PubMed Central|
|7.||Loos RJF: 15 years of genome-wide association studies and no signs of slowing down. Nat Commun 2020; 11: 5900 CrossRef MEDLINE PubMed Central|
|8.||Tian C, Hromatka BS, Kiefer AK, et al.: Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections. Nat Commun 2017; 8: 599 CrossRef MEDLINE PubMed Central|
|9.||Bustamante J, Boisson-Dupuis S, Abel L, Casanova JL: Mendelian susceptibility to mycobacterial disease: genetic, immunological, and clinical features of inborn errors of IFN-gamma immunity. Semin Immunol 2014; 26: 454–70 CrossRef MEDLINE PubMed Central|
|10.||Farmand S, Baumann U, von Bernuth H, et al.: [Interdisciplinary AWMF guideline for the diagnostics of primary immunodeficiency]. Klin Padiatr 2011; 223: 378–85 CrossRef MEDLINE|
|11.||Hanitsch L, Baumann U, Boztug K, et al.: Treatment and management of primary antibody deficiency: German interdisciplinary evidence-based consensus guideline. Eur J Immunol 2020; 50: 1432–46 CrossRef MEDLINE|
|12.||Netea MG, Schlitzer A, Placek K, Joosten LAB, Schultze JL: Innate and adaptive immune memory: an evolutionary continuum in the host’s response to pathogens. Cell Host Microbe 2019; 25: 13–26 CrossRef MEDLINE|
|13.||Takeda K, Kaisho T, Akira S: Toll-like receptors. Annu Rev Immunol 2003; 21: 335–76 CrossRef MEDLINE PubMed Central|
|14.||Lim HK, Huang SXL, Chen J, et al.: Severe influenza pneumonitis in children with inherited TLR3 deficiency. J Exp Med 2019; 216: 2038–56 CrossRef MEDLINE|
|15.||Garcia-Etxebarria K, Bracho MA, Galán JC, et al.: No major host genetic risk factor contributed to A(H1N1)2009 influenza severity. PloS One 2015; 10: e0135983 CrossRef MEDLINEPubMed Central|
|16.||Liu R, Paxton WA, Choe S, et al.: Homozygous defect in HIV-1 coreceptor accounts for resistance of some multiply-exposed individuals to HIV-1 infection. Cell 1996; 86: 367–77 CrossRef|
|17.||Hütter G, Nowak D, Mossner M, et al.: Long-term control of HIV by CCR5 Delta32/Delta32 stem-cell transplantation. N Engl J Med 2009; 360: 692–8 CrossRef MEDLINE|
|18.||McLaren PJ, Pulit SL, Gurdasani D, et al.: Evaluating the impact of functional genetic variation on HIV-1 control. J Infect Dis 2017; 216: 1063–9 CrossRef MEDLINE PubMed Central|
|19.||Kulkarni S, Lied A, Kulkarni V, et al.: CCR5AS lncRNA variation differentially regulates CCR5, influencing HIV disease outcome. Nat Immunol 2019; 20: 824–34 CrossRef CrossRef MEDLINEPubMed Central|
|20.||McLaren PJ, Coulonges C, Bartha I, et al.: Polymorphisms of large effect explain the majority of the host genetic contribution to variation of HIV-1 virus load. Proc Natl Acad Sci U S A 2015; 112: 14658–63 CrossRef MEDLINE PubMed Central|
|21.||Apps R, Qi Y, Carlson JM, et al.: Influence of HLA-C expression level on HIV control. Science 2013; 340: 87–91 CrossRef MEDLINE PubMed Central|
|22.||Carrington M, Nelson GW, Martin MP, et al.: HLA and HIV-1: heterozygote advantage and B*35-Cw*04 disadvantage. Science 1999; 283: 1748–52 CrossRef MEDLINE|
|23.||Mallal S, Nolan D, Witt C, et al.: Association between presence of HLA-B*5701, HLA-DR7, and HLA-DQ3 and hypersensitivity to HIV-1 reverse-transcriptase inhibitor abacavir. Lancet 2002; 359: 727–32 CrossRef|
|24.||Illing PT, Vivian JP, Dudek NL, et al.: Immune self-reactivity triggered by drug-modified HLA-peptide repertoire. Nature 2012; 486: 554–8 CrossRef MEDLINE|
|25.||Blum JS, Wearsch PA, Cresswell P: Pathways of antigen processing. Annu Rev Immunol 2013; 31: 443–73 CrossRef MEDLINE PubMed Central|
|26.||Rosain J, Kong XF, Martinez-Barricarte R, et al.: Mendelian susceptibility to mycobacterial disease: 2014–2018 update. Immunol Cell Biol 2019; 97: 360–7 CrossRef MEDLINE PubMed Central|
|27.||COVID-19 Host Genetics Initiative: Mapping the human genetic architecture of COVID-19. Nature 2021; 600: 472–7 CrossRefMEDLINE PubMed Central|
|28.||Severe Covid GG, Ellinghaus D, Degenhardt F, et al.: Genomewide association study of severe Covid-19 with respiratory failure. N Engl J Med 2020; 383: 1522–34 CrossRef MEDLINE PubMed Central|
|29.||Nakanishi T, Pigazzini S, Degenhardt F, et al.: Age-dependent impact of the major common genetic risk factor for COVID-19 on severity and mortality. J Clin Invest 2021; 131 CrossRef|
|30.||van der Made CI, Simons A, Schuurs-Hoeijmakers J, et al.: Presence of genetic variants among young men with severe COVID-19. JAMA 2020; 324: 663–73 CrossRef MEDLINE PubMed Central|
|31.||Asano T, Boisson B, Onodi F, et al.: X-linked recessive TLR7 deficiency in ~1% of men under 60 years old with life-threatening COVID-19. Sci Immunol 2021; 6.|
|32.||Solanich X, Vargas-Parra G, van der Made CI, et al.: Genetic screening for TLR7 variants in young and previously healthy men with severe COVID-19. Front Immunol 2021; 12: 719115 CrossRef MEDLINE PubMed Central|
|33.||Schmidt A, Peters S, Knaus A, et al.: TBK1 and TNFRSF13B mutations and an autoinflammatory disease in a child with lethal COVID-19. NPJ Genom Med 2021; 6: 55 CrossRef MEDLINE PubMed Central|
|34.||Lévy R, Bastard P, Lanternier F, Lecuit M, Zhang SY, Casanova JL: IFN-α2a therapy in two patients with inborn errors of TLR3 and IRF3 infected with SARS-CoV-2. J Clin Immunol 2021; 41: 26–7 CrossRef MEDLINE PubMed Central|
|35.||Nelson MR, Tipney H, Painter JL, et al.: The support of human genetic evidence for approved drug indications. Nat Genet 2015; 47: 856–60 CrossRef MEDLINE|
|36.||Tanaka Y, Nishida N, Sugiyama M, et al.: Genome-wide association of IL28B with response to pegylated interferon-alpha and ribavirin therapy for chronic hepatitis C. Nat Genet 2009; 41: 1105–9 CrossRef MEDLINE|
|37.||Bloomfield M, Kanderova V, Parackova Z, et al.: Utility of Ruxolitinib in a child with chronic mucocutaneous Ccandidiasis caused by a novel STAT1 gain-of-function mutation. J Clin Immunol 2018; 38: 589–601 CrossRef MEDLINE|
|38.||Becker K, Hu Y, Biller-Andorno N: Infectious diseases—a global challenge. Int J Med Microbiol 2006; 296: 179–85 CrossRef MEDLINE PubMed Central|
|39.||Casanova JL, Abel L: Lethal infectious diseases as inborn errors of immunity: toward a synthesis of the germ and genetic theories. Annu Rev Pathol 2021; 16: 23–50 CrossRef MEDLINE PubMed Central|
|40.||Kwok AJ, Mentzer A, Knight JC: Host genetics and infectious disease: new tools, insights and translational opportunities. Nat Rev Genet 2021; 22: 137–53 CrossRef CrossRef MEDLINE PubMed Central|
|e1.||Cassini A, Colzani E, Pini A, et al.: Impact of infectious diseases on population health using incidence-based disability-adjusted life years (DALYs): results from the burden of communicable diseases in Europe study, European Union and European economic area countries, 2009 to 2013. Euro Surveill 2018; 23 CrossRef MEDLINE PubMed Central|
|e2.||Rommel A, Lippe EV, Plass D, et al.: The COVID-19 disease burden in Germany in 2020-years of life lost to death and disease over the course of the pandemic. Dtsch Arztebl Int 2021; 118 VOLLTEXT|
|e3.||Sologuren I, Martínez-Saavedra MT, Solé-Violan J, et al.: Lethal influenza in two related adults with inherited GATA2 deficiency. J Clin Immunol 2018; 38: 513–26 CrossRef MEDLINE PubMed Central|
|e4.||Zhang SY, Clark NE, Freije CA, et al.: Inborn errors of RNA lariat metabolism in humans with brainstem viral infection. Cell 2018; 172: 952–65 e18.|
|e5.||Gupta RK, Abdul-Jawad S, McCoy LE, et al.: HIV-1 remission following CCR5Delta32/Delta32 haematopoietic stem-cell transplantation. Nature 2019; 568: 244–8 CrossRef MEDLINE PubMed Central|
|e6.||Sveinbjornsson G, Gudbjartsson DF, Halldorsson BV, et al.: HLA class II sequence variants influence tuberculosis risk in populations of European ancestry. Nat Genet 2016; 48: 318–22 CrossRef MEDLINE PubMed Central|
|e7.||Wang Z, Sun Y, Fu X, et al.: A large-scale genome-wide association and meta-analysis identified four novel susceptibility loci for leprosy. Nat Commun 2016; 7: 13760 CrossRef MEDLINE PubMed Central|
|e8.||Pairo-Castineira E, Clohisey S, Klaric L, et al.: Genetic mechanisms of critical illness in COVID-19. Nature 2021; 591: 92–8 CrossRef MEDLINE|
|e9.||Fallerini C, Daga S, Mantovani S, et al.: Association of toll-like receptor 7 variants with life-threatening COVID-19 disease in males: findings from a nested case-control study. Elife 2021; 10.|
|e10.||Bjornevik K, Cortese M, Healy BC, et al.: Longitudinal analysis reveals high prevalence of Epstein-Barr virus. Science 2022; 375: 296–301 CrossRef MEDLINE|
|e11.||Zacher B, Haller S, Willrich N, et al.: Application of a new methodology and R package reveals a high burden of healthcare-associated infections (HAI) in Germany compared to the average in the European Union/European Economic Area, 2011 to 2012. Euro Surveill 2019; 24 CrossRef MEDLINE PubMed Central|
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