Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders, bioRxiv, 2019-01-27

SummaryGenetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed a meta-analysis of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficithyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders identifying three groups of inter-related disorders. We detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning in the second trimester prenatally, and play prominent roles in a suite of neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

biorxiv genomics 100-200-users 2019

Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Supplemental Tables 1 - 18, bioRxiv, 2019-01-27

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed a meta-analysis of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficithyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders identifying three groups of inter-related disorders. We detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning in the second trimester prenatally, and play prominent roles in a suite of neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

biorxiv genomics 100-200-users 2019

Global phylogeography and ancient evolution of the widespread human gut virus crAssphage, bioRxiv, 2019-01-27

Microbiomes are vast communities of microbes and viruses that populate all natural ecosystems. Viruses have been considered the most variable component of microbiomes, as supported by virome surveys and examples of high genomic mosaicism. However, recent evidence suggests that the human gut virome is remarkably stable compared to other environments. Here we investigate the origin, evolution, and epidemiology of crAssphage, a widespread human gut virus. Through a global collaboratory, we obtained DNA sequences of crAssphage from over one-third of the world's countries, and showed that its phylogeography is locally clustered within countries, cities, and individuals. We also found colinear crAssphage-like genomes in both Old-World and New-World primates, challenging genomic mosaicism and suggesting that the association of crAssphage with primates may be millions of years old. We conclude that crAssphage is a benign globetrotter virus that may have co-evolved with the human lineage and an integral part of the normal human gut virome.

biorxiv microbiology 0-100-users 2019

High-pass filtering artifacts in multivariate classification of neural time series data, bioRxiv, 2019-01-27

The application of time-resolved multivariate pattern classification analyses (MVPA) to EEG and MEG data has become increasingly popular. Traditionally, such time series data are high-pass filtered before analyses, in order to remove slow drifts. Here we show that high-pass filtering should be applied with extreme caution in MVPA, as it may easily create artifacts that result in displacement of decoding accuracy, leading to statistically significant above-chance classification during time periods in which the source is clearly not in brain activity. In both real and simulated EEG data, we show that spurious decoding may emerge with filter cut-off settings from as modest as 0.1 Hz. We provide an alternative method of removing slow drift noise, referred to as robust detrending (de Cheveigne & Arzounian, 2018), which, when applied in concert with masking of cortical events does not result in the temporal displacement of information. We show that temporal generalization may benefit from robust detrending, without any of the unwanted side effects introduced by filtering. However, we conclude that for sufficiently clean data sets, no filtering or detrending at all may work sufficiently well. Implications for other types of data are discussed, followed by a number of recommendations.

biorxiv neuroscience 100-200-users 2019

Short-range interactions govern cellular dynamics in microbial multi-genotype systems, bioRxiv, 2019-01-27

Ecosystem processes result from interaction between organisms. When interactions are local, the spatial organization of organisms defines their network of interactions, and thus influences the system's functioning. This can be especially relevant for microbial systems, which often consist of spatially structured communities of cells connected by a dense interaction network. Here we measured the spatial interaction network between cells in microbial systems and identify the factors that determine it. Combining quantitative single-cell analysis of synthetic bacterial communities with mathematical modeling, we find that cells only interact with other cells in their immediate neighbourhood. This short interaction range impacts the functioning of the whole system by reducing its ability to perform metabolic processes collectively. Our experiments and models demonstrate that the spatial scale of cell-to-cell interaction plays a fundamental role in understanding and controlling natural communities, and in engineering microbial systems for specific purposes.

biorxiv systems-biology 100-200-users 2019

Causal relationships between obesity and the leading causes of death in women and men, bioRxiv, 2019-01-26

AbstractObesity traits are causally implicated with risk of cardiometabolic diseases. It remains unclear whether there are similar causal effects of obesity traits on other non-communicable diseases. Also, it is largely unexplored whether there are any sex-specific differences in the causal effects of obesity traits on cardiometabolic diseases and other leading causes of death.We constructed sex-specific genetic risk scores (GRS) for three obesity traits; body mass index (BMI), waist-hip ratio (WHR), and WHR adjusted for BMI, including 565, 324, and 337 genetic variants, respectively. These GRSs were then used as instrumental variables to assess associations between the obesity traits and leading causes of mortality in the UK Biobank using Mendelian randomization. We also investigated associations with potential mediators, including smoking, glycemic and blood pressure traits. Sex-differences were subsequently assessed by Cochran’s Q-test (Phet).A Mendelian randomization analysis of 228,466 women and 195,041 men showed that obesity causes coronary artery disease, stroke (particularly ischemic), chronic obstructive pulmonary disease, lung cancer, type 2 and 1 diabetes mellitus, non-alcoholic fatty liver disease, chronic liver disease, and acute and chronic renal failure. Higher BMI led to higher risk of type 2 diabetes in women than in men (Phet=1.4×10-5). Waist-hip-ratio led to a higher risk of chronic obstructive pulmonary disease (Phet=3.7×10-6) and higher risk of chronic renal failure (Phet=1.0×10-4) in men than women.Obesity traits have an etiological role in the majority of the leading global causes of death. Sex differences exist in the effects of obesity traits on risk of type 2 diabetes, chronic obstructive pulmonary disease, and renal failure, which may have downstream implications for public health.Author summaryObesity is increasing globally and has been linked to major causes of death, such as diabetes and heart disease. Still, the causal effects of obesity on other leading causes of death is relatively unexplored. It is also unclear if any such effects differ between men and women.Mendelian randomization is a method that explores causal relationships between traits using genetic data. Using Mendelian randomization, we investigated the effects of obesity traits on leading causes of death and assessed if any such effects differ between men and women.We found that obesity increases the risks of heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, diabetes, kidney disease, non-alcoholic fatty liver disease and chronic liver disease. Higher body mass index led to a higher risk of type 2 diabetes in women than in men, whereas a higher waist-hip ratio increased risks of chronic obstructive pulmonary disease and chronic kidney disease more in men than in women.In summary, obesity traits are causally involved in the majority of the leading causes of death, and some obesity traits affect disease risk differently in men and women. This has potential implications for public health strategies and indicates that sex-specific preventative measure may be needed.AbbreviationsBMI, Body mass index; CAD coronary artery disease; CLD, chronic liver disease; COPD, chronic obstructive pulmonary disease; DBP, diastolic blood pressure; FG, fasting glucose; FI, fasting insulin; GIANT, Genetic Investigation of ANthropometric Traits; GRS, genetic risk score; GWAS, genomewide association study; MAGIC, the Meta-Analyses of Glucose and Insulin-related traits Consortium; MR, Mendelian randomization; NAFLD, non-alcoholic fatty liver disease; OR, odds ratio; T1D, type 1 diabetes; T2D, type 2 diabetes; SBP, systolic blood pressure; SD, standard deviation; SNP, single nucleotide polymorphism; WHO, the World Health Organization; WHR, waist-hip-ratio; WHRadjBMI, waist-hip-ratio adjusted for body mass index.

biorxiv epidemiology 0-100-users 2019