Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture, we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD.

Highlights d 102 genes implicated in risk for autism spectrum disorder (ASD genes, FDR % 0.1) d Most are expressed and enriched early in excitatory and inhibitory neuronal lineages d Most affect synapses or regulate other genes; how these roles dovetail is unknown d Some ASD genes alter early development broadly, others appear more specific to ASD

At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation” that won the conference’s best paper award. In this technical correspondence, we want to point out some inaccuracies in the way DBSCAN was represented, and why the criticism should have been directed at the assumption about the performance of spatial index structures such as R-trees and not at an algorithm that can use such indexes. We will also discuss the relationship of DBSCAN performance and the indexability of the dataset, and discuss some heuristics for choosing appropriate DBSCAN parameters. Some indicators of bad parameters will be proposed to help guide future users of this algorithm in choosing parameters such as to obtain both meaningful results and good performance. In new experiments, we show that the new SIGMOD 2015 methods do not appear to offer practical benefits if the DBSCAN parameters are well chosen and thus they are primarily of theoretical interest. In conclusion, the original DBSCAN algorithm with effective indexes and reasonably chosen parameter values performs competitively compared to the method proposed by Gan and Tao.

Networks such as the World Wide Web, social networks, and metabolic networks, which contain valuable information, draw increasing attention in scientific communities. Network clustering (or graph partitioning) is the discovery of underlying clusters of related vertices in networks. But, beyond organizing vertices into clusters of peers is the question of what role each vertex plays in the network. This article presents some new ways of uncovering underlying structures, including the roles that vertices play in the network. Identifying vertex roles is useful for applications such as viral marketing and epidemiology. For example, hubs are responsible for spreading ideas or disease. We applied our algorithm to analyze some real networks. The results demonstrate a superior performance over other methods such as modularity‐based algorithms.

Aerobic glycolysis (the Warburg effect) facilitates tumor growth, and drugs targeting aerobic glycolysis are being developed. However, how the Warburg effect is directly regulated is largely unknown. Here we show that transcription factor SIX1 directly increases the expression of many glycolytic genes, promoting the Warburg effect and tumor growth in vitro and in vivo. SIX1 regulates glycolysis through HBO1 and AIB1 histone acetyltransferases. Cancer-related SIX1 mutation increases its ability to promote aerobic glycolysis and tumor growth. SIX1 glycolytic function is directly repressed by microRNA-548a-3p, which is downregulated, inversely correlates with SIX1, and is a good predictor of prognosis in breast cancer patients. Thus, the microRNA-548a-3p/SIX1 axis strongly links aerobic glycolysis to carcinogenesis and may become a promising cancer therapeutic target.