Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines

Šćepanović, Petar; Alanio, Cécile; Hammer, Christian; Hodel, Flavia; Bergstedt, Jacob; Patin, Étienne; Thorball, Christian W.; Chaturvedi, Nimisha; Charbit, Bruno; Abel, Laurent; Quintana-Murci, Lluı́s; Duffy, Darragh; Albert, Matthew L.; Fellay, Jacques

doi:10.1186/s13073-018-0568-8

Cited by 138 publications

(152 citation statements)

References 48 publications

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“…The variant call rate was < 97% in 11 donors, which were thus removed from this dataset. We filtered out from both datasets genetic variants based on a set of criteria detailed in [54]. These quality-control filters yielded a total of 661,332 and 87,960 variants for the HumanOmniExpress and HumanExome BeadChips, respectively.…”

Section: Human Dna Genotypingmentioning

confidence: 99%

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A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals

Šćepanović

Hodel

Mondot

et al. 2019

Preprint

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Background. The gut microbiome is an important determinant of human health. Its composition has been shown to be influenced by multiple environmental factors and likely by host genetic variation. In the framework of the Milieu Intérieur Consortium, a total of 1,000 healthy individuals of western European ancestry, with a 1:1 sex ratio and evenly stratified across five decades of life (age 20 -69), were recruited. We generated 16S ribosomal RNA profiles from stool samples for 858 participants. We investigated genetic and non-genetic factors that contribute to individual differences in fecal microbiome composition.Results. Among 110 demographic, clinical and environmental factors, 11 were identified as significantly correlated with α-diversity, ß-diversity or abundance of specific microbial communities in multivariable models. Age and blood alanine aminotransferase levels showed the strongest associations with microbiome diversity. In total, all non-genetic factors explained 16.4% of the variance. We then searched for associations between >5 million single nucleotide polymorphisms and the same indicators of fecal microbiome diversity, including the significant non-genetic factors as covariates. No genome-wide significant associations were identified after correction for multiple testing. A small fraction of previously reported associations between human genetic variants and specific taxa could be replicated in our cohort, while no replication was observed for any of the diversity metrics. Conclusion.In a well-characterized cohort of healthy individuals, we identified several nongenetic variables associated with fecal microbiome diversity. In contrast, host genetics only had a negligible influence. Demographic and environmental factors are thus the main contributors to fecal microbiome composition in healthy individuals.

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Section: Human Dna Genotypingmentioning

confidence: 99%

“…As described previously [54], we used Positional Burrows-Wheeler Transform for genotype imputation, starting with the 661,332 quality-controlled SNPs genotyped on the HumanOmniExpress array. Phasing was performed using EAGLE2 (v2.0.5) [57].…”

Section: Genotype Imputationmentioning

confidence: 99%

A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals

Šćepanović

Hodel

Mondot

et al. 2019

Preprint

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“…Human immune systems vary dramatically across individuals, yet the environmental and genetic determinants of this variability remain poorly characterized. Many studies have identified genetic components and environmental stimuli that alter immune cell composition in peripheral blood [1][2][3][4][5][6] . However, normal tissues and organs also consist of diverse cell types, including infiltrating immune cells.…”

Section: Introductionmentioning

confidence: 99%

“…Identifying the genetic influences on specific patterns of infiltrating immune cells is crucial to understanding disease biology. Beyond further explaining heritable manifestations of infectious diseases and autoimmunity [1][2][3][4][5][6][7][8][9] , such efforts can further uncover the drivers of characteristic immune cell signatures in the tumor microenvironment that are prognostic for cancer progression and predictive of treatment response 10 . For example, response is improved in patients with T cell-inflamed tumors compared to T cell-depleted tumors among patients receiving immune checkpoint inhibitors targeting PD1 and CTLA4 10,11 and among ovarian cancer patients receiving chemotherapy 12 .…”

Section: Introductionmentioning

confidence: 99%

Age, Sex, and Genetics Influence the Abundance of Infiltrating Immune Cells in Human Tissues

Marderstein

Uppal

Verma

et al. 2019

Preprint

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Despite infiltrating immune cells playing an essential role in human disease and the patient response to treatment, the central mechanisms influencing variability in infiltration patterns are unclear. Using bulk RNA-seq data from 53 GTEx tissues, we applied cell-type deconvolution algorithms to evaluate the immune landscape across the healthy human body. We first performed a differential expression analysis of inflamed versus non-inflamed samples to identify essential pathways and regulators of infiltration. Next, we found 21 of 73 infiltration-related phenotypes to be associated with either age or sex (FDR < 0.1). Through our genetic analysis, we discovered 13 infiltration-related phenotypes have genome-wide significant associations (iQTLs) (P < 5.0 x 10 -8 ), with a significant enrichment of tissue-specific expression quantitative trait loci in suggested iQTLs (P < 10 -5 ). We highlight an association between neutrophil content in lung tissue and a variant near the CUX1 transcription factor gene (P = 9.7 x 10 -11 ), which has been previously linked to neutrophil infiltration, inflammatory mechanisms, and the regulation of several immune response genes. Together, our results identify key factors influencing interindividual variability of specific tissue infiltration patterns, which could provide insights on therapeutic targets for shifting infiltration profiles to a more favorable one. Results Robust estimation of immune cell types in bulk RNA-seq profiles.To describe immune content from bulk RNA-seq samples, we used two central algorithms: xCell 13 and CIBERSORT 14 . xCell relies on a modification of single sample gene-set enrichment analysis to estimate cell type scores, while CIBERSORT employs a linear support vector regression model. The default reference signatures allow deconvolution of 64 immune and stroma cell types for xCell and 22 immune cell types for CIBERSORT. CIBERSORT also calculates a scaling factor that measures the degree of infiltration. We refer to the relative proportions from CIBERSORT as "CIBERSORT-Relative" and the product of the relative proportions with the scaling factor as "CIBERSORT-Absolute". We estimate three scores for each cell type to describe the immune content from the gene expression data for each tissue in each individual: xCell, CIBERSORT-Relative, and CIBERSORT-Absolute scores.We first hypothesized that the relative and absolute scores from CIBERSORT encapsulated different aspects of the single-cell deconvolution. While "CIBERSORT-Absolute" simultaneously quantifies a degree of immune infiltration, "CIBERSORT-Relative" is focused on capturing compositional changes in the immune content (Supplementary Note). We simulated synthetic mixes composed of bulk tissue "spiked" in silico with CD4+ T cells and CD8+ T cells (see Methods). We correlated the known amount of CD4+ and CD8+ T cell infiltration in these mixtures with estimated deconvolution scores under a "tissue" scenario and an "immune cell" scenario. In the "immune cell" scenario, we let the true infiltration be the prop...

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“…We therefore explore the biological functions associated with the stability selected probes, to improve knowledge of the epigenetic changes that characterize differentiated immune cells. A similar two-pronged approach is used to predict other conditions and traits collected within the Milieu Intérieur study, including age, smoking, height, BMI, routine chemical and hematological laboratory tests, and the serological responses to antigens of 13 common pathogens (22). Several of these traits have not previously been modelled using all DNAm probes jointly.…”

Section: Introductionmentioning

confidence: 99%

Accurate prediction of cell composition, age, smoking consumption and infection serostatus based on blood DNA methylation profiles

Bergstedt

Urrutia

Duffy

et al. 2018

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DNA methylation is a stable epigenetic alteration that plays a key role in cellular differentiation and gene regulation, and that has been proposed to mediate environmental effects on disease risk. Epigenome-wide association studies have identified and replicated associations between methylation sites and several disease conditions, which could serve as biomarkers in predictive medicine and forensics. Nevertheless, heterogeneity in cellular proportions between the compared groups could complicate interpretation. Reference-based cell-type deconvolution methods have proven useful in correcting epigenomic studies for cellular heterogeneity, but they rely on reference libraries of sorted cells and only predict a limited number of cell populations. Here we leverage >850,000 methylation sites included in the Methyla-tionEPIC array and use elastic net regularized and stability selected regression models to predict the circulating levels of 70 blood cell subsets, measured by standardized flow cytometry in 962 healthy donors of western European descent. We show that our predictions, based on a hundred of methylation sites or lower, are less error-prone than other existing methods, and extend the number of cell types that can be accurately predicted. Application of the same methods to age, smoking consumption and several serological responses to pathogen antigens also provide accurate estimations. Together, our study substantially improves predictions of blood cell composition based on methylation profiles, which will be critical in the emerging field of medical epigenomics. Methylation | Prediction | Elastic net | Cellular composition | EWAS | AgeCorrespondence: jacob.bergstedt @control.lth.se, epatin @pasteur.fr

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Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines

Cited by 138 publications

References 48 publications

A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals

A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals

Age, Sex, and Genetics Influence the Abundance of Infiltrating Immune Cells in Human Tissues

Accurate prediction of cell composition, age, smoking consumption and infection serostatus based on blood DNA methylation profiles

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