The genetics of infectious disease susceptibility: has the evidence for epistasis been overestimated?

Hall, Matthew D.; Ebert, Dieter

doi:10.1186/1741-7007-11-79

Cited by 11 publications

(10 citation statements)

References 45 publications

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“…The existence and role of epistasis in human disease has been difficult to demonstrate and remains highly controversial since it was first proposed over one hundred years ago. 14, 31, 32 The predominant difficulty has been the inability to undertake relevant clinical and in vitro functional studies to quantify the consequences of multiple genetic mutations.…”

Section: Discussionmentioning

confidence: 99%

“…For some experiments, B cells were labelled with division-tracking dye cell trace violet (CTV, Invitrogen). 32 For phenotypic and functional analysis, cells were cultured in 96-well plates for 5 or 6 days, collected, stained with CD20, CD27, CD38, IgG, IgM, IgA and the proportion of isotype switched and differentiated antibody secreting cells determined as previously described. 6 Secreted IgM, IgG and IgA levels were determined by Ig Heavy chain-specific immunoassays as previously described.…”

Section: Methodsmentioning

confidence: 99%

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Epistatic interactions between mutations of TACI (TNFRSF13B) and TCF3 result in a severe primary immunodeficiency disorder and systemic lupus erythematosus

et al. 2017

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Common variable immunodeficiency disorders (CVID) are a group of primary immunodeficiencies where monogenetic causes account for only a fraction of cases. On this evidence, CVID is potentially polygenic and epistatic although there are, as yet, no examples to support this hypothesis. We have identified a non-consanguineous family, who carry the C104R (c.310T>C) mutation of the Transmembrane Activator Calcium-modulator and cyclophilin ligand Interactor (TACI, TNFRSF13B) gene. Variants in TNFRSF13B/TACI are identified in up to 10% of CVID patients, and are associated with, but not solely causative of CVID. The proband is heterozygous for the TNFRSF13B/TACI C104R mutation and meets the Ameratunga et al. diagnostic criteria for CVID and the American College of Rheumatology criteria for systemic lupus erythematosus (SLE). Her son has type 1 diabetes, arthritis, reduced IgG levels and IgA deficiency, but has not inherited the TNFRSF13B/TACI mutation. Her brother, homozygous for the TNFRSF13B/TACI mutation, is in good health despite profound hypogammaglobulinemia and mild cytopenias. We hypothesised that a second unidentified mutation contributed to the symptomatic phenotype of the proband and her son. Whole-exome sequencing of the family revealed a de novo nonsense mutation (T168fsX191) in the Transcription Factor 3 (TCF3) gene encoding the E2A transcription factors, present only in the proband and her son. We demonstrate mutations of TNFRSF13B/TACI impair immunoglobulin isotype switching and antibody production predominantly via T-cell-independent signalling, while mutations of TCF3 impair both T-cell-dependent and -independent pathways of B-cell activation and differentiation. We conclude that epistatic interactions between mutations of the TNFRSF13B/TACI and TCF3 signalling networks lead to the severe CVID-like disorder and SLE in the proband.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Epistatic interactions between mutations of TACI (TNFRSF13B) and TCF3 result in a severe primary immunodeficiency disorder and systemic lupus erythematosus

et al. 2017

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“…For each of these traits, there was little evidence for QTL hotspots (Breitling et al, ), arising from either tight linkage or pleiotropy, except for QTL at the end of linkage group 10 that contributed to body size, castration, fecundity and lifespan (Figure ). Epistasis effect sizes were also comparatively low, further reinforcing the largely independent effects of the qualitative and qualitative alleles underlying each metric of susceptibility—but see Hall and Ebert () for why this is more a reflection of the genotypes used for the QTL panel, rather than a true assessment of additive versus epistatic variance in a population; and see also Metzger et al () and Bento et al ().…”

Section: Discussionmentioning

confidence: 90%

“…Epistasis effect sizes were also comparatively low, further reinforcing the largely independent effects of the qualitative and qualitative alleles underlying each metric of susceptibility-but see Hall and Ebert (2013) for why this is more a reflection of the genotypes used for the QTL panel, rather than a true assessment of additive versus epistatic variance in a population; and see also Metzger et al (2016) and Bento et al (2017).…”

Section: Discussionmentioning

confidence: 95%

Dissecting the genetic architecture of a stepwise infection process

2019

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How a host fights infection depends on an ordered sequence of steps, beginning with attempts to prevent a pathogen from establishing an infection, through to steps that mitigate a pathogen's control of host resources or minimize the damage caused during infection. Yet empirically characterizing the genetic basis of these steps remains challenging. Although each step is likely to have a unique genetic and environmental signature, and may therefore respond to selection in different ways, events that occur earlier in the infection process can mask or overwhelm the contributions of subsequent steps. In this study, we dissect the genetic architecture of a stepwise infection process using a quantitative trait locus (QTL) mapping approach. We control for variation at the first line of defence against a bacterial pathogen and expose downstream genetic variability related to the host's ability to mitigate the damage pathogens cause. In our model, the water‐flea Daphnia magna, we found a single major effect QTL, explaining 64% of the variance, that is linked to the host's ability to completely block pathogen entry by preventing their attachment to the host oesophagus; this is consistent with the detection of this locus in previous studies. In susceptible hosts allowing attachment, however, a further 23 QTLs, explaining between 5% and 16% of the variance, were mapped to traits related to the expression of disease. The general lack of pleiotropy and epistasis for traits related to the different stages of the infection process, together with the wide distribution of QTLs across the genome, highlights the modular nature of a host's defence portfolio, and the potential for each different step to evolve independently. We discuss how isolating the genetic basis of individual steps can help to resolve discussion over the genetic architecture of host resistance.

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“…In parasites, this tendency relates to high mutation rates and population sizes [4,47,48], as well as elevated gene redundancy and ploidy [43]. In hosts and vectors, the effect likely arises from prevalent polygenic, epistatic and pleiotropic control of interaction traits [49,50]. Simulations show how quickly differentiation-based methods lose power to detect adaptive change as selection intensity weakens, reaching complete impotence at levels still easily managed by correlative alternatives [51].…”

Section: What Makes Landscape Genomics Such a Powerful Tool To Study mentioning

confidence: 99%

Prediction and Prevention of Parasitic Diseases Using a Landscape Genomics Framework

Schwabl

Llewellyn

Landguth

et al. 2017

Trends in Parasitology

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Summary Substantial heterogeneity exists in the dispersal, distribution and transmission of parasitic species. Understanding and predicting how such features are governed by the ecological variation of landscape they inhabit is the central goal of spatial epidemiology. Genetic data can further inform functional connectivity among parasite, host and vector populations in a landscape. Gene flow correlates with the spread of epidemiologically relevant phenotypes among parasite and vector populations (e.g., virulence, drug and pesticide resistance), as well as invasion and re-invasion risk where parasite transmission is absent due to current or past intervention measures. However, the formal integration of spatial and genetic data (‘landscape genetics’) is scarcely ever applied to parasites. Here, we discuss the specific challenges and practical prospects for the use of landscape genetics and genomics to understand the biology and control of parasitic disease and present a practical framework for doing so.

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The genetics of infectious disease susceptibility: has the evidence for epistasis been overestimated?

Cited by 11 publications

References 45 publications

Epistatic interactions between mutations of TACI (TNFRSF13B) and TCF3 result in a severe primary immunodeficiency disorder and systemic lupus erythematosus

Epistatic interactions between mutations of TACI (TNFRSF13B) and TCF3 result in a severe primary immunodeficiency disorder and systemic lupus erythematosus

Dissecting the genetic architecture of a stepwise infection process

Prediction and Prevention of Parasitic Diseases Using a Landscape Genomics Framework

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