It is well established that autism spectrum disorders (ASD) have a strong genetic component. However, for at least 70% of cases, the underlying genetic cause is unknown1. Under the hypothesis that de novo mutations underlie a substantial fraction of the risk for developing ASD in families with no previous history of ASD or related phenotypes—so-called sporadic or simplex families2,3, we sequenced all coding regions of the genome, i.e. the exome, for parent-child trios exhibiting sporadic ASD, including 189 new trios and 20 previously reported4. Additionally, we also sequenced the exomes of 50 unaffected siblings corresponding to these new (n = 31) and previously reported trios (n = 19)4, for a total of 677 individual exomes from 209 families. Here we show de novo point mutations are overwhelmingly paternal in origin (4:1 bias) and positively correlated with paternal age, consistent with the modest increased risk for children of older fathers to develop ASD5. Moreover, 39% (49/126) of the most severe or disruptive de novo mutations map to a highly interconnected beta-catenin/chromatin remodeling protein network ranked significantly for autism candidate genes. In proband exomes, recurrent protein-altering mutations were observed in two genes, CHD8 and NTNG1. Mutation screening of six candidate genes in 1,703 ASD probands identified additional de novo, protein-altering mutations in GRIN2B, LAMC3, and SCN1A. Combined with copy number variant (CNV) data, these results suggest extreme locus heterogeneity but also provide a target for future discovery, diagnostics, and therapeutics.

Autism Genes, Again and Again Despite recent advances in sequencing technologies and their lowered costs—effective, highly sensitive, and specific sequencing of multiple genes of interest from large cohorts remains expensive. O'Roak et al. (p. 1619 ; published online 15 November) modified molecular inversion probe methods for target-specific capture and sequencing to resequence candidate genes in thousands of patients. The technique was applied to 44 candidate genes to identify de novo mutations in a large cohort of individuals with and without autism spectrum disorder. The analysis revealed several de novo mutations in genes that together contribute to 1% of sporadic autism spectrum disorders, supporting the notion that multiple genes underlie autism-spectrum disorders.

The human microbiome plays a key role in human health and is associated with numerous diseases. Metagenomic-based studies are now generating valuable information about the composition of the microbiome in health and in disease, demonstrating nonneutral assembly processes and complex co-occurrence patterns. However, the underlying ecological forces that structure the microbiome are still unclear. Specifically, compositional studies alone with no information about mechanisms of interaction, potential competition, or syntrophy, cannot clearly distinguish habitat-filtering and species assortment assembly processes. To address this challenge, we introduce a computational framework, integrating metagenomic-based compositional data with genome-scale metabolic modeling of species interaction. We use in silico metabolic network models to predict levels of competition and complementarity among 154 microbiome species and compare predicted interaction measures to species co-occurrence. Applying this approach to two large-scale datasets describing the composition of the gut microbiome, we find that species tend to co-occur across individuals more frequently with species with which they strongly compete, suggesting that microbiome assembly is dominated by habitat filtering. Moreover, species' partners and excluders exhibit distinct metabolic interaction levels. Importantly, we show that these trends cannot be explained by phylogeny alone and hold across multiple taxonomic levels. Interestingly, controlling for host health does not change the observed patterns, indicating that the axes along which species are filtered are not fully defined by macroecological host states. The approach presented here lays the foundation for a reverse-ecology framework for addressing key questions concerning the assembly of host-associated communities and for informing clinical efforts to manipulate the microbiome.T he human body is home to numerous microbial species and several complex microbial ecosystems. Advances in sequencing technologies and metagenomics now allow researchers to characterize the composition of species that inhabit the human body and the variation these communities exhibit in health and in disease (1-3). Specifically, recent studies of the microbiome have found tremendous variation among healthy individuals (1) and demonstrated clear associations between species composition and several host phenotypes including obesity (4, 5), inflammatory bowel disease (IBD) (2), and diabetes (6), as well as with external factors such as diet (7). These studies further demonstrated that, as in many other ecosystems, the composition of species in the microbiome exhibits distinct patterns that clearly deviate from a random distribution. For example, species composition in the human microbiome exhibits a significant checkerboard pattern, indicating pairs of taxa that exclude one another from shared environments (8, 9). These patterns are similar to those seen in macroecological communities, suggesting that similar pressures may act upon such m...

We collected personal, dense, dynamic data for 108 individuals over 9 months, including whole genome sequence; clinical tests, metabolomes, proteomes and microbiomes at three time points; and daily activity tracking. Using these data we generated a correlation network and identified communities of related analytes that were associated with physiology and disease. We demonstrate how connectivity within these communities identified known and candidate biomarkers, e.g. gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease. We calculated polygenic scores from GWAS for 127 traits and diseases, and identified molecular correlates of polygenic risk, e.g. genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine. Finally, behavioral coaching informed by personalized data helped participants improve clinical biomarkers. Personal, dense, dynamic data clouds will improve understanding of health and disease, especially for early transition states. This approach to “scientific wellness” represents an opportunity largely missing in contemporary health care.

No abstract