Publisher Correction: Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors.

In the version of this article initially published, the name of one author was incorrect (James P. Santo). The correct name is James P. Di Santo. The error has been corrected in the HTML and PDF versions of the article.

Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors.

The quantification and characterization of circulating immune cells provide key indicators of human health and disease. To identify the relative effects of environmental and genetic factors on variation in the parameters of innate and adaptive immune cells in homeostatic conditions, we combined standardized flow cytometry of blood leukocytes and genome-wide DNA genotyping of 1,000 healthy, unrelated people of Western European ancestry. We found that smoking, together with age, sex and latent infection with cytomegalovirus, were the main non-genetic factors that affected variation in parameters of human immune cells. Genome-wide association studies of 166 immunophenotypes identified 15 loci that showed enrichment for disease-associated variants. Finally, we demonstrated that the parameters of innate cells were more strongly controlled by genetic variation than were those of adaptive cells, which were driven by mainly environmental exposure. Our data establish a resource that will generate new hypotheses in immunology and highlight the role of innate immunity in susceptibility to common autoimmune diseases.

Smoking changes adaptive immunity with persistent effects.

Individuals differ widely in their immune responses, with age, sex and genetic factors having major roles in this inherent variability1-6. However, the variables that drive such differences in cytokine secretion-a crucial component of the host response to immune challenges-remain poorly defined. Here we investigated 136 variables and identified smoking, cytomegalovirus latent infection and body mass index as major contributors to variability in cytokine response, with effects of comparable magnitudes with age, sex and genetics. We find that smoking influences both innate and adaptive immune responses. Notably, its effect on innate responses is quickly lost after smoking cessation and is specifically associated with plasma levels of CEACAM6, whereas its effect on adaptive responses persists long after individuals quit smoking and is associated with epigenetic memory. This is supported by the association of the past smoking effect on cytokine responses with DNA methylation at specific signal trans-activators and regulators of metabolism. Our findings identify three novel variables associated with cytokine secretion variability and reveal roles for smoking in the short- and long-term regulation of immune responses. These results have potential clinical implications for the risk of developing infections, cancers or autoimmune diseases.

Neuroendocrine pathways and breast cancer progression: a pooled analysis of somatic mutations and gene expression from two large breast cancer cohorts.

BACKGROUND: Experimental studies indicate that neuroendocrine pathways might play a role in progression of breast cancer. We aim to test the hypothesis that somatic mutations in the genes of neuroendocrine pathways influence breast cancer prognosis, through dysregulated gene expression in tumor tissue. METHODS: We conducted an extreme case-control study including 208 breast cancer patients with poor invasive disease-free survival (iDFS) and 208 patients with favorable iDFS who were individually matched on molecular subtype from the Breast Cancer Cohort at West China Hospital (WCH; N = 192) and The Cancer Genome Atlas (TCGA; N = 224). Whole exome sequencing and RNA sequencing of tumor and paired normal breast tissues were performed. Adrenergic, glucocorticoid, dopaminergic, serotonergic, and cholinergic pathways were assessed for differences in mutation burden and gene expression in relation to breast cancer iDFS using the logistic regression and global test, respectively. RESULTS: In the pooled analysis, presence of any somatic mutation (odds ratio = 1.66, 95% CI: 1.07-2.58) of the glucocorticoid pathway was associated with poor iDFS and a two-fold increase of tumor mutation burden was associated with 17% elevated odds (95% CI: 2-35%), after adjustment for cohort membership, age, menopausal status, molecular subtype, and tumor stage. Differential expression of genes in the glucocorticoid pathway in tumor tissue (P = 0.028), but not normal tissue (P = 0.701), was associated with poor iDFS. Somatic mutation of the adrenergic and cholinergic pathways was significantly associated with iDFS in WCH, but not in TCGA. CONCLUSION: Glucocorticoid pathway may play a role in breast cancer prognosis through differential mutations and expression. Further characterization of its functional role may open new avenues for the development of novel therapeutic targets for breast cancer.

Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges.

The contribution of host genetic and nongenetic factors to immunological differences in humans remains largely undefined. Here, we generated bacterial-, fungal-, and viral-induced immune transcriptional profiles in an age- and sex-balanced cohort of 1,000 healthy individuals and searched for the determinants of immune response variation. We found that age and sex affected the transcriptional response of most immune-related genes, with age effects being more stimulus-specific relative to sex effects, which were largely shared across conditions. Although specific cell populations mediated the effects of age and sex on gene expression, including CD8+ T cells for age and CD4+ T cells and monocytes for sex, we detected a direct effect of these intrinsic factors for the majority of immune genes. The mapping of expression quantitative trait loci (eQTLs) revealed that genetic factors had a stronger effect on immune gene regulation than age and sex, yet they affected a smaller number of genes. Importantly, we identified numerous genetic variants that manifested their regulatory effects exclusively on immune stimulation, including a Candida albicans-specific master regulator at the CR1 locus. These response eQTLs were enriched in disease-associated variants, particularly for autoimmune and inflammatory disorders, indicating that differences in disease risk may result from regulatory variants exerting their effects only in the presence of immune stress. Together, this study quantifies the respective effects of age, sex, genetics, and cellular heterogeneity on the interindividual variability of immune responses and constitutes a valuable resource for further exploration in the context of different infection risks or disease outcomes.

Causal inference in the field of arrhythmia: An introduction to mendelian randomization.

Mendelian randomization (MR) uses genetic variants associated with an exposure (eg, high blood pressure) as instrumental variables to test causal effects on an outcome (eg, atrial fibrillation [AF]). By leveraging the random assortment of genetic variants during gamete formation, MR reduces biases like confounding and reverse causation. We screened 391 papers, examining 277 that applied MR to investigate arrhythmia and, in others, cardiovascular traits, lifestyle, behavioral traits, and body composition. Our analysis focused on MR studies of arrhythmia and cardiovascular traits. Key findings highlight high systolic blood pressure, low resting heart rate, elevated cardiac troponin I levels, coronary artery disease, and heart failure as risk factors for AF, whereas AF itself increases heart failure risk. As genetic data become more accessible, MR's relevance grows. Sensitivity analyses and integrating MR with other methodologies in a triangulation framework enhance the robustness of causal inferences by navigating different biases.

Adverse Childhood Experiences and Adult Mental Health Outcomes.

Importance: Exposure to adverse childhood experiences (ACEs) has consistently been associated with multiple negative mental health outcomes extending into adulthood. However, given that ACEs and psychiatric disorders cluster within families, it remains to be comprehensively assessed to what extent familial confounding contributes to associations between ACEs and clinically confirmed adult psychiatric disorders. Objective: To investigate whether associations between ACEs and adult mental health outcomes remain after adjusting for familial (genetic and environmental) confounding. Design, Setting, and Participants: This Swedish twin cohort study used a discordant twin pair design based on monozygotic (MZ) and dizygotic (DZ) twins. A total of 25 252 adult twins (aged 18-47 years) from the Swedish Twin Registry born between 1959 and 1998 were followed up from age 19 years until 2016, with a maximum follow-up time of 39 years. Data were analyzed from April 2022 to November 2023. Exposures: A total of 7 ACEs, including family violence, emotional abuse or neglect, physical neglect, physical abuse, sexual abuse, rape, and hate crime, were assessed with items from the Life Stressor Checklist-Revised in a web-based survey. Main Outcomes and Measures: Adult (ages >18 years) clinical diagnosis of psychiatric disorders (ie, depressive, anxiety, alcohol or drug misuse, or stress-related disorders) were obtained from the Swedish National Patient Register. Results: Of 25 252 twins included in the study (15 038 female [59.6%]; mean [SD] age at ACE assessment, 29.9 [8.7] years), 9751 individuals (38.6%) reported exposure to at least 1 ACE. A greater number of ACEs was associated with increased odds of any psychiatric disorder in the full cohort (odds ratio [OR] per additional ACE, 1.52; 95% CI, 1.48-1.57). The association remained but ORs per additional ACE were attenuated in DZ (1.29; 95% CI, 1.14-1.47) and MZ (1.20; 95% CI, 1.02-1.40) twin pairs. Individuals who were exposed to sexual abuse compared with those who were not exposed had increased odds of any clinically confirmed psychiatric disorder in all comparisons: full cohort (OR, 3.09; 95% CI, 2.68-3.56), DZ twin pairs (OR, 2.10; 95% CI, 1.33-3.32), and MZ twin pairs (1.80; 95% CI, 1.04-3.11). Conclusions and relevance: This study found that associations between ACEs and adult mental health outcomes remained after controlling for shared genetic and environmental factors, which was particularly evident after multiple ACEs or sexual abuse. These findings suggest that targeted interventions may be associated with reduced risks of future psychopathology.

Association between inflammatory biomarkers before pregnancy and risk of perinatal depression: A prospective cohort study of 4483 women in Sweden.

AIM: Perinatal depression (PND) is a global health concern, affecting millions of childbearing women. Emerging data suggest that inflammation may play a role in the development of PND. Peripheral blood inflammatory biomarkers before pregnancy are widely tested in clinical practice at minimum cost, yet their potential role in PND risk remains unknown. METHODS: We conducted a prospective cohort study of 4483 birthing women during 2009-2021 within the LifeGene study with linkage to Swedish registers. Peripheral blood inflammatory biomarkers were profiled at baseline. Cases of PND were identified using validated tools or clinical diagnosis from subsequent pregnancies and postpartum periods. Logistic regression models were employed to assess the associations of each inflammatory biomarker (z scored) with PND. RESULTS: We identified 495 (11.0 %) PND cases with an average age of 29.2 years. Pre-pregnancy platelet-to-lymphocyte ratio (PLR) was positively associated [OR, 95 % CI:1.14(1.01,1.27)], while lymphocyte count was inversely associated [OR, 95 % CI: 0.89(0.80,0.98)] with PND. A dose-response relationship was indicated for both PLR and lymphocytes when analyzed in categories based on tertile distribution. These associations appeared more pronounced for postpartum depression than antepartum depression and were independent of psychiatric comorbidities. CONCLUSION: With implications for future mechanistic research, these findings suggest that blood levels of lymphocytes and PLR before pregnancy are associated with subsequent risk of PND in a dose-response manner.

Causes and consequences of major depressive disorder: An encompassing Mendelian randomization study.

Background: Major depressive disorder (MDD) is a prevalent and debilitating disorder that has been associated with a range of risk factors and outcomes. Causal pathways between MDD and other traits can be studied using genetic variants as instrumental variables. Methods: A literature review was conducted to identify 201 MDD-associated traits. For 115 traits, there were well-powered genome-wide association study (GWAS) results available that could be used to assess the genetic correlation with MDD. Of these, there were 89 meeting criteria for investigating causal associations in both directions using two-sample Mendelian randomization (TSMR). Of the traits that were not captured by GWAS, 43 could be included as outcomes of MDD using one-sample MR (OSMR). A range of methods and sensitivity tests was applied to gauge robustness of results, together with statistical power analyses to aid interpretation. Outcomes: Moderate to strong genetic overlap was found between MDD and most traits. Support for causal effects of MDD liability were found for circadian, cognitive, diet, medical disease, endocrine, functional, inflammatory, metabolic, mortality, physical activity, reproduction, risk behavior, social, socioeconomic, and suicide outcomes. Most associations were bidirectional, although there was less evidence for diet, disease, and endocrine traits causing MDD risk. Results were robust across sensitivity analyses. Interpretation: This study provides a systematic overview of traits putatively causally related to MDD, confirming previous findings as well as identifying new associations. Our results highlight the importance of MDD as a risk factor cross-cutting across medical, functional, and psychosocial domains and emphasize the need for concerted efforts at reducing this highly prevalent disorder.

COSGAP: COntainerized Statistical Genetics Analysis Pipelines.

Summary: The collection and analysis of sensitive data in large-scale consortia for statistical genetics is hampered by multiple challenges, due to their non-shareable nature. Time-consuming issues in installing software frequently arise due to different operating systems, software dependencies, and limited internet access. For federated analysis across sites, it can be challenging to resolve different problems, including format requirements, data wrangling, setting up analysis on high-performance computing (HPC) facilities, etc. Easier, more standardized, automated protocols and pipelines can be solutions to overcome these issues. We have developed one such solution for statistical genetic data analysis using software container technologies. This solution, named COSGAP: "COntainerized Statistical Genetics Analysis Pipelines," consists of already established software tools placed into Singularity containers, alongside corresponding code and instructions on how to perform statistical genetic analyses, such as genome-wide association studies, polygenic scoring, LD score regression, Gaussian Mixture Models, and gene-set analysis. Using provided helper scripts written in Python, users can obtain auto-generated scripts to conduct the desired analysis either on HPC facilities or on a personal computer. COSGAP is actively being applied by users from different countries and projects to conduct genetic data analyses without spending much effort on software installation, converting data formats, and other technical requirements. Availability and implementation: COSGAP is freely available on GitHub (https://github.com/comorment/containers) under the GPLv3 license.

Distinct genomic signatures and modifiable risk factors underly the comorbidity between major depressive disorder and cardiovascular disease.

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Using genomic data, this study elucidates biological mechanisms, key risk factors, and causal pathways underlying their comorbidity. We show that CVDs share a large proportion of their genetic risk factors with MDD. Multivariate genome-wide association analysis of the shared genetic liability between MDD and atherosclerotic CVD (ASCVD) revealed seven novel loci and distinct patterns of tissue and brain cell-type enrichments, suggesting a role for the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic, and psychosocial/lifestyle risk factors. Finally, we found support for causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and demonstrated that the causal effects were partly explained by metabolic and psychosocial/lifestyle factors. The distinct signature of MDD-ASCVD comorbidity aligns with the idea of an immunometabolic sub-type of MDD more strongly associated with CVD than overall MDD. In summary, we identify plausible biological mechanisms underlying MDD-CVD comorbidity, as well as key modifiable risk factors for prevention of CVD in individuals with MDD.

Quantifying the Relative Importance of Genetics and Environment on the Comorbidity between Mental- and Cardiometabolic Disorders: A Comprehensive Analysis of National Register Data from 17 million Scandinavians.

Mental disorders (MDs) are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders (CMDs). Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and complete genealogies of Denmark and Sweden (n=17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six MDs and 14 CMDs. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with CMDs, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with CMDs was mainly or fully driven by environmental factors. These findings provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.

Quantifying the relative importance of genetics and environment on the comorbidity between mental and cardiometabolic disorders using 17 million Scandinavians.

Mental disorders are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders. Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and near-complete genealogies of Denmark and Sweden (n = 17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six mental disorders and 15 cardiometabolic disorders. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with cardiometabolic disorders, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with cardiometabolic disorders was mainly or fully driven by environmental factors. In this work we provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.

Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease.

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.

Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease.

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.

Associations of schizophrenia with arrhythmic disorders and electrocardiogram traits: an in-depth genetic exploration of population samples.

Background: An important contributor to the decreased life expectancy of individuals with schizophrenia is sudden cardiac death. While arrhythmic disorders play an important role in this, the nature of the relation between schizophrenia and arrhythmia is not fully understood. Methods: We leveraged summary-level data of large-scale genome-wide association studies of schizophrenia (53,386 cases 77,258 controls), arrhythmic disorders (atrial fibrillation, 55,114 cases 482,295 controls; Brugada syndrome, 2,820 cases 10,001 controls) and electrocardiogram traits (heart rate (variability), PR interval, QT interval, JT interval, and QRS duration, n=46,952-293,051). First, we examined shared genetic liability by assessing global and local genetic correlations and conducting functional annotation. Next, we explored bidirectional causal relations between schizophrenia and arrhythmic disorders and electrocardiogram traits using Mendelian randomization. Outcomes: There was no evidence for global genetic correlations, except between schizophrenia and Brugada (rg=0·14, p=4·0E-04). In contrast, strong positive and negative local genetic correlations between schizophrenia and all cardiac traits were found across the genome. In the strongest associated regions, genes related to immune system and viral response mechanisms were overrepresented. Mendelian randomization indicated a causal, increasing effect of liability to schizophrenia on Brugada syndrome (OR=1·15, p=0·009) and heart rate during activity (beta=0·25, p=0·015). Interpretation: While there was little evidence for global genetic correlations, specific genomic regions and biological pathways important for both schizophrenia and arrhythmic disorders and electrocardiogram traits emerged. The putative causal effect of liability to schizophrenia on Brugada warrants increased cardiac monitoring and potentially early medical intervention in patients with schizophrenia. Funding: European Research Council Starting Grant.

Psychiatric disorders and subsequent risk of cardiovascular disease: a longitudinal matched cohort study across three countries.

Background: Several psychiatric disorders have been associated with increased risk of cardiovascular disease (CVD), however, the role of familial factors and the main disease trajectories remain unknown. Methods: In this longitudinal cohort study, we identified a cohort of 900,240 patients newly diagnosed with psychiatric disorders during January 1, 1987 and December 31, 2016, their 1,002,888 unaffected full siblings, and 1:10 age- and sex-matched reference population from nationwide medical records in Sweden, who had no prior diagnosis of CVD at enrolment. We used flexible parametric models to determine the time-varying association between first-onset psychiatric disorders and incident CVD and CVD death, comparing rates of CVD among patients with psychiatric disorders to the rates of unaffected siblings and matched reference population. We also used disease trajectory analysis to identify main disease trajectories linking psychiatric disorders to CVD. Identified associations and disease trajectories of the Swedish cohort were validated in a similar cohort from nationwide medical records in Denmark (N = 875,634 patients, same criteria during January 1, 1969 and December 31, 2016) and in Estonian cohorts from the Estonian Biobank (N = 30,656 patients, same criteria during January 1, 2006 and December 31, 2020), respectively. Findings: During up to 30 years of follow-up of the Swedish cohort, the crude incidence rate of CVD was 9.7, 7.4 and 7.0 per 1000 person-years among patients with psychiatric disorders, their unaffected siblings, and the matched reference population. Compared with their siblings, patients with psychiatric disorders experienced higher rates of CVD during the first year after diagnosis (hazard ratio [HR], 1.88; 95% confidence interval [CI], 1.79-1.98) and thereafter (1.37; 95% CI, 1.34-1.39). Similar rate increases were noted when comparing with the matched reference population. These results were replicated in the Danish cohort. We identified several disease trajectories linking psychiatric disorders to CVD in the Swedish cohort, with or without mediating medical conditions, including a direct link between psychiatric disorders and hypertensive disorder, ischemic heart disease, venous thromboembolism, angina pectoris, and stroke. These trajectories were validated in the Estonian Biobank cohort. Interpretation: Independent of familial factors, patients with psychiatric disorders are at an elevated risk of subsequent CVD, particularly during first year after diagnosis. Increased surveillance and treatment of CVDs and CVD risk factors should be considered as an integral part of clinical management, in order to reduce risk of CVD among patients with psychiatric disorders. Funding: This research was supported by EU Horizon 2020 Research and Innovation Action Grant, European Research Council Consolidator grant, Icelandic Research fund, Swedish Research Council, US NIMH, the Outstanding Clinical Discipline Project of Shanghai Pudong, the Fundamental Research Funds for the Central Universities, and the European Union through the European Regional Development Fund; the Research Council of Norway; the South-East Regional Health Authority, the Stiftelsen Kristian Gerhard Jebsen, and the EEA-RO-NO-2018-0535.

Epidemiological overview of major depressive disorder in Scandinavia using nationwide registers.

Background: Major depressive disorder (MDD) is a common psychiatric disorder associated with a high disease burden. This study gives a comprehensive overview of the prevalence, outcomes, treatment, and genetic epidemiology of MDD within and across the Scandinavian countries. Methods: This study has aimed to assess and compare across Norway, Denmark, and Sweden 1) the prevalence and trajectories of MDD and comorbidity, 2) outcomes and treatment, and 3) heritability (Denmark and Sweden only). The analyses leveraged data on 272,944 MDD cases (and 6.2 million non-cases) from Norway, Sweden, and Denmark in specialist care in national longitudinal health registers covering 1975-2013. Relying on harmonized public data global comparisons of socioeconomic and health metrics were performed to assess to what extent findings are generalizable. Findings: MDD ranked among the most prevalent psychiatric disorders. For many cases, the disorder trajectory was severe, with varying proportions experiencing recurrence, developing comorbid disorders, requiring inpatient treatment, or dying of suicide. Important country differences in specialist care prevalence and treatment were observed. Heritability estimates were moderate (35-48%). In terms of socioeconomic and health indices, the Scandinavian nations were comparable to one another and grouped with other Western nations. Interpretation: The Scandinavian countries were similar with regards to MDD epidemiological measures, but we show that differences in health care organization need to be taken into consideration when comparing countries. This study demonstrates the utility of using comprehensive population-wide registry data, outlining possibilities for other applications. The findings will be of use to policy makers for developing better prevention and intervention strategies. Funding: Swedish Research Council (Vetenskapsrådet, award D0886501 to PFS), US National Institutes of Mental HealthR01 MH123724 (to PFS), European Union's Horizon 2020 Research and Innovation Program (847776 and 964874, to OA) and European Research Council grant (grant agreement ID 101042183, to YL).

The immune factors driving DNA methylation variation in human blood.

Epigenetic changes are required for normal development, yet the nature and respective contribution of factors that drive epigenetic variation in humans remain to be fully characterized. Here, we assessed how the blood DNA methylome of 884 adults is affected by DNA sequence variation, age, sex and 139 factors relating to life habits and immunity. Furthermore, we investigated whether these effects are mediated or not by changes in cellular composition, measured by deep immunophenotyping. We show that DNA methylation differs substantially between naïve and memory T cells, supporting the need for adjustment on these cell-types. By doing so, we find that latent cytomegalovirus infection drives DNA methylation variation and provide further support that the increased dispersion of DNA methylation with aging is due to epigenetic drift. Finally, our results indicate that cellular composition and DNA sequence variation are the strongest predictors of DNA methylation, highlighting critical factors for medical epigenomics studies.

The genomic landscape of contemporary western Remote Oceanians.

The Vanuatu archipelago served as a gateway to Remote Oceania during one of the most extensive human migrations to uninhabited lands ∼3,000 years ago. Ancient DNA studies suggest an initial settlement by East Asian-related peoples that was quickly followed by the arrival of Papuan-related populations, leading to a major population turnover. Yet there is uncertainty over the population processes and the sociocultural factors that have shaped the genomic diversity of ni-Vanuatu, who present nowadays among the world's highest linguistic and cultural diversity. Here, we report new genome-wide data for 1,433 contemporary ni-Vanuatu from 29 different islands, including 287 couples. We find that ni-Vanuatu derive their East Asian- and Papuan-related ancestry from the same source populations and descend from relatively synchronous, sex-biased admixture events that occurred ∼1,700-2,300 years ago, indicating a peopling history common to the whole archipelago. However, East Asian-related ancestry proportions differ markedly across islands, suggesting that the Papuan-related population turnover was geographically uneven. Furthermore, we detect Polynesian ancestry arriving ∼600-1,000 years ago to Central and South Vanuatu in both Polynesian-speaking and non-Polynesian-speaking populations. Last, we provide evidence for a tendency of spouses to carry similar genetic ancestry, when accounting for relatedness avoidance. The signal is not driven by strong genetic effects of specific loci or trait-associated variants, suggesting that it results instead from social assortative mating. Altogether, our findings provide an insight into both the genetic history of ni-Vanuatu populations and how sociocultural processes have shaped the diversity of their genomes.