Genome-wide meta-analysis identifies 93 risk loci and enables risk prediction equivalent to monogenic forms of venous thromboembolism.

We report a genome-wide association study of venous thromboembolism (VTE) incorporating 81,190 cases and 1,419,671 controls sampled from six cohorts. We identify 93 risk loci, of which 62 are previously unreported. Many of the identified risk loci are at genes encoding proteins with functions converging on the coagulation cascade or platelet function. A VTE polygenic risk score (PRS) enabled effective identification of both high- and low-risk individuals. Individuals within the top 0.1% of PRS distribution had a VTE risk similar to homozygous or compound heterozygous carriers of the variants G20210A (c.*97 G > A) in F2 and p.R534Q in F5. We also document that F2 and F5 mutation carriers in the bottom 10% of the PRS distribution had a risk similar to that of the general population. We further show that PRS improved individual risk prediction beyond that of genetic and clinical risk factors. We investigated the extent to which venous and arterial thrombosis share clinical risk factors using Mendelian randomization, finding that some risk factors for arterial thrombosis were directionally concordant with VTE risk (for example, body mass index and smoking) whereas others were discordant (for example, systolic blood pressure and triglyceride levels).

Comprehensive characterization of amino acid positions in protein structures reveals molecular effect of missense variants.

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acid-substituting missense variations on protein structure and function being especially challenging. Here we characterize the three-dimensional (3D) amino acid positions affected in pathogenic and population variants from 1,330 disease-associated genes using over 14,000 experimentally solved human protein structures. By measuring the statistical burden of variations (i.e., point mutations) from all genes on 40 3D protein features, accounting for the structural, chemical, and functional context of the variations' positions, we identify features that are generally associated with pathogenic and population missense variants. We then perform the same amino acid-level analysis individually for 24 protein functional classes, which reveals unique characteristics of the positions of the altered amino acids: We observe up to 46% divergence of the class-specific features from the general characteristics obtained by the analysis on all genes, which is consistent with the structural diversity of essential regions across different protein classes. We demonstrate that the function-specific 3D features of the variants match the readouts of mutagenesis experiments for BRCA1 and PTEN, and positively correlate with an independent set of clinically interpreted pathogenic and benign missense variants. Finally, we make our results available through a web server to foster accessibility and downstream research. Our findings represent a crucial step toward translational genetics, from highlighting the impact of mutations on protein structure to rationalizing the variants' pathogenicity in terms of the perturbed molecular mechanisms.

MISCAST: MIssense variant to protein StruCture Analysis web SuiTe.

Human genome sequencing efforts have greatly expanded, and a plethora of missense variants identified both in patients and in the general population is now publicly accessible. Interpretation of the molecular-level effect of missense variants, however, remains challenging and requires a particular investigation of amino acid substitutions in the context of protein structure and function. Answers to questions like 'Is a variant perturbing a site involved in key macromolecular interactions and/or cellular signaling?', or 'Is a variant changing an amino acid located at the protein core or part of a cluster of known pathogenic mutations in 3D?' are crucial. Motivated by these needs, we developed MISCAST (missense variant to protein structure analysis web suite; http://miscast.broadinstitute.org/). MISCAST is an interactive and user-friendly web server to visualize and analyze missense variants in protein sequence and structure space. Additionally, a comprehensive set of protein structural and functional features have been aggregated in MISCAST from multiple databases, and displayed on structures alongside the variants to provide users with the biological context of the variant location in an integrated platform. We further made the annotated data and protein structures readily downloadable from MISCAST to foster advanced offline analysis of missense variants by a wide biological community.

Metallothionein Zn2+- and Cu2+-clusters from first-principles calculations.

Detailed electronic structures of Zn(II) and Cu(II) clusters from metallothioneins (MT) have been obtained using density functional theory (DFT), in order to investigate how oxidative stress-caused Cu(II) intermediates affect Zn-binding to MT and cooperatively lead to Cu(I)MT. The inferred accuracy is ∼0.02-0.03 Å for metal-thiolate bond lengths for the models that are the most realistic MT models so far studied by DFT. We find terminal Zn-S and Cu-S bond lengths of 2.35-2.38 Å and 2.30-2.34 Å, whereas bridging M-S bonds are 0.05-0.11 Å longer. This electronic effect is also reflected in changes in electron density on bridging sulfurs. Various imposed backbone constraints quantify the sensitivity of cluster electronic structure towards protein conformational changes. The large negative charge densities of the clusters are central to MT function, and the smaller ß-clusters are more prone to modification. Oxidative stress-associated Cu(II) binding weakens the Zn-S bonds and is thus likely to impair the Zn(II) transfer function of MTs, providing a mechanism for cooperative Cu(II) binding leading to loss of Zn(II) and dysfunctional Cu(I)MT clusters.