Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association (GWA) meta-analysis based in 135,458 cases and 344,901 control, We identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression, and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relations of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine and define the basis of major depression and imply a continuous measure of risk underlies the clinical phenotype.

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome.

Summary Piwi-interacting RNAs (piRNAs) silence transposons in the germ line of animals. They are thought to derive from long primary transcripts spanning transposon-rich genomic loci, “piRNA clusters.” piRNAs are proposed to direct an auto-amplification loop in which an antisense piRNA, bound to Aubergine or Piwi protein, directs the cleavage of sense RNA, triggering production of a sense piRNA bound to the PIWI protein Argonaute3 (Ago3). In turn, the new piRNA is envisioned to direct cleavage of a cluster transcript, initiating production of a second antisense piRNA. Here, we describe strong loss-of-function mutations in ago3, allowing a direct genetic test of this model. We find that Ago3 acts to amplify piRNA pools and to enforce on them an antisense bias, increasing the number of piRNAs that can act to silence transposons. We also detect a second piRNA pathway centered on Piwi and functioning without benefit of Ago3-catalyzed amplification. Transposons targeted by this second pathway often reside in the flamenco locus, which is expressed in somatic ovarian follicle cells, suggesting a role for piRNAs beyond the germ line.

Summary piRNAs silence transposons and maintain genome integrity during germ-line development. In Drosophila, transposon-rich heterochromatic clusters encode piRNAs either on both genomic strands (dual-strand clusters) or predominantly one genomic strand (uni-strand clusters). Primary piRNAs derived from these clusters are proposed to drive a ping-pong amplification cycle catalyzed by proteins that localize to the perinuclear nuage. We show that the HP1 homologue Rhino is required for nuage organization, transposon silencing, and ping-pong amplification of piRNAs. rhi mutations virtually eliminate piRNAs from the dual-strand clusters and block production of putative precursor RNAs from both strands of the major 42AB dual-strand cluster, but do not block production of transcripts or piRNAs from the uni-strand clusters. Furthermore, Rhino protein associates with the 42AB dual-strand cluster, but does not bind to uni-strand cluster 2 or flamenco. Rhino thus appears to promote transcription of dual-strand clusters, leading to production of piRNAs that drive the ping-pong amplification cycle.

We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.

Genome-wide association studies have identified 108 schizophrenia risk loci, but biological mechanisms for individual loci are largely unknown. Using developmental, genetic and illness-based RNA sequencing expression analysis in human brain, we characterized the human brain transcriptome around these loci and found enrichment for developmentally regulated genes with novel examples of shifting isoform usage across pre- and postnatal life. We found widespread expression quantitative trait loci (eQTLs), including many with transcript specificity and previously unannotated sequence that were independently replicated. We leveraged this general eQTL database to show that 48.1% of risk variants for schizophrenia associate with nearby expression. We lastly found 237 genes significantly differentially expressed between patients and controls, which replicated in an independent dataset, implicated synaptic processes, and were strongly regulated in early development. These findings together offer genetics- and diagnosis-related targets for better modeling of schizophrenia risk. This resource is publicly available at http://eqtl.brainseq.org/phase1 .

The identification of regulatory elements from different cell types is necessary for understanding the mechanisms controlling cell type–specific and housekeeping gene expression. Mapping DNaseI hypersensitive (HS) sites is an accurate method for identifying the location of functional regulatory elements. We used a high throughput method called DNase-chip to identify 3,904 DNaseI HS sites from six cell types across 1% of the human genome. A significant number (22%) of DNaseI HS sites from each cell type are ubiquitously present among all cell types studied. Surprisingly, nearly all of these ubiquitous DNaseI HS sites correspond to either promoters or insulator elements: 86% of them are located near annotated transcription start sites and 10% are bound by CTCF, a protein with known enhancer-blocking insulator activity. We also identified a large number of DNaseI HS sites that are cell type specific (only present in one cell type); these regions are enriched for enhancer elements and correlate with cell type–specific gene expression as well as cell type–specific histone modifications. Finally, we found that approximately 8% of the genome overlaps a DNaseI HS site in at least one the six cell lines studied, indicating that a significant percentage of the genome is potentially functional.