PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations.

SUMMARY: PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates 'phenome scans', where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner ('PhenoScanner V2'), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants. AVAILABILITY AND IMPLEMENTATION: PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk.This work was supported by the UK Medical Research Council [G0800270; MR/L003120/1], the British Heart Foundation [SP/09/002; RG/13/13/30194; RG/18/13/33946], Pfizer [G73632], the European Research Council [268834], the European Commission Framework Programme 7 [HEALTH-F2-2012-279233], the National Institute for Health Research and Health Data Research UK (*). *The views expressed are those of the authors and not necessarily those of the NHS or the NIHR

PhenoScanner V2:an expanded tool for searching human genotype-phenotype associations

PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates ‘phenome scans’, where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner (‘PhenoScanner V2’), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants. Availability and implementation: PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk

Torsional Force Microscopy of Van der Waals Moir\'es and Atomic Lattices

In a stack of atomically-thin Van der Waals layers, introducing interlayer twist creates a moir\'e superlattice whose period is a function of twist angle. Changes in that twist angle of even hundredths of a degree can dramatically transform the system's electronic properties. Setting a precise and uniform twist angle for a stack remains difficult, hence determining that twist angle and mapping its spatial variation is very important. Techniques have emerged to do this by imaging the moir\'e, but most of these require sophisticated infrastructure, time-consuming sample preparation beyond stack synthesis, or both. In this work, we show that Torsional Force Microscopy (TFM), a scanning probe technique sensitive to dynamic friction, can reveal surface and shallow subsurface structure of Van der Waals stacks on multiple length scales: the moir\'es formed between bilayers of graphene and between graphene and hexagonal boron nitride (hBN), and also the atomic crystal lattices of graphene and hBN. In TFM, torsional motion of an AFM cantilever is monitored as the it is actively driven at a torsional resonance while a feedback loop maintains contact at a set force with the surface of a sample. TFM works at room temperature in air, with no need for an electrical bias between the tip and the sample, making it applicable to a wide array of samples. It should enable determination of precise structural information including twist angles and strain in moir\'e superlattices and crystallographic orientation of VdW flakes to support predictable moir\'e heterostructure fabrication.Comment: 28 pages, 14 figures including supplementary material

PhenoScanner: a database of human genotype-phenotype associations.

UNLABELLED: PhenoScanner is a curated database of publicly available results from large-scale genetic association studies. This tool aims to facilitate 'phenome scans', the cross-referencing of genetic variants with many phenotypes, to help aid understanding of disease pathways and biology. The database currently contains over 350 million association results and over 10 million unique genetic variants, mostly single nucleotide polymorphisms. It is accompanied by a web-based tool that queries the database for associations with user-specified variants, providing results according to the same effect and non-effect alleles for each input variant. The tool provides the option of searching for trait associations with proxies of the input variants, calculated using the European samples from 1000 Genomes and Hapmap. AVAILABILITY AND IMPLEMENTATION: PhenoScanner is available at www.phenoscanner.medschl.cam.ac.uk CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.This work was supported by the UK Medical Research Council [G66840, G0800270], Pfizer [G73632], British Heart Foundation [SP/09/002], UK National Institute for Health Research Cambridge Biomedical Research Centre, European Research Council [268834], and European Commission Framework Programme 7 [HEALTH-F2-2012-279233].This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Oxford University Press

Comparative analysis of algorithms for identifying copy number variation in array CGH data

The aim of the study is comparative analysis of algorithms for identifying copy number variation in array CGH data. Circular Binary segmentation (CBS), CGH segmentation, Price —Smith—Waterman algorithm (the three algorithms are a part of ADaCGH tool) and Cluster Along Chromosome (CLAC) which is not publicly available were studied and used for the analysis of array CGH data. Simulated data set was generated by adding signals of various intensities such as I unit, 11 units and III units representing gains in the copy number (Copy Number Variation, CNV) at random positions corresponding to real probes covering human genome and by adding white Gaussian noise. The white Gaussian noise was generated with the help of program provided by Dr. Nadia Chuzhanova. Two studies of simulated data are carried out in this project (1) The aim of the first study was to investigate the minimum width of CNV (in probes / BAC clones) that can be detected by the all the algorithms for signal intensity of I, II and III units. (2) The aim of the second study was to detect the minimum gap (in probes/BAC clones) between two CNVs of width 2-12 probes /BAC clones by each algorithm for a signal intensity of I, II and 111 units. The results of the study showed that CGH segmentation and CBS can detect a CNV of minimum width 2 BAC clones/probes in simulated data set. However Price-Smith-Waterman algorithm failed to detect any generated CNV. Studies showed that the CNV detection by both algorithms is influenced by noise as well as the width of CNV and the intensity of CNV. The studies also showed that though CBS was able to detect most of the generated CNVs it is more efficient for detection of CNV having higher width. CGH segmentation was more efficient in detection of CNVs that have lower width. Thus CBS and CGH segmentation efficiently can be used for the analysis of array CGH data. The real data set was obtained from 24 malignant peripheral nerve sheath tumour samples and 3 neurofibromas samples (from 27 patients). This data was then analysed with the help of CBS and CGH segmentation on the basis of the results obtained from simulated data set. In general, the ADaCGH is a very good tool for the analysis of array CGH data

Additional file 1: of Remotely acting SMCHD1 gene regulatory elements: in silico prediction and identification of potential regulatory variants in patients with FSHD

Summary of experimental procedures used. A brief description of DNA sequencing and methylation analysis used in this study

87 rare variants associated with blood pressure regulation in meta-analysis of ~1.3 million individuals

Genetic studies of blood pressure (BP) to date have mainly analysed common variants (minor allele frequency, MAF>0.05). In a meta-analysis of up to >1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (MAF≤0.01) variant-BP associations (P<5x10-8) of which, 32 were in new BP-associated loci and 55 were independent BP-associated SNVs within known BP-associated regions. Rare variants, 44% of which were coding, on average had effects ~8 times larger than the mean effects of common variants and indicate potential candidate causal genes at new and known loci e.g. GATA5, PLCB3. BP-associated variants (including rare and common) were enriched in regions of active chromatin in foetal tissues, potentially linking foetal development with BP regulation in later life. Multivariable Mendelian randomisation highlighted inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.Praveen Surendran is supported by a Rutherford Fund Fellowship from the Medical Research Council grant MR/S003746/1. Najim Lahrouchi is supported by the Foundation “De Drie Lichten” in The Netherlands and the Netherlands Cardiovascular Research Initiative, an initiative supported by the Dutch Heart Foundation (CVON2012-10 PREDICT and CVON2018-30 PREDICT2). Jacklyn N. Hellwege was supported by the Vanderbilt Molecular and Genetic Epidemiology of Cancer (MAGEC) Training Program (T32CA160056, PI X.-O. Shu). Nora Franceschini is supported by the National Institute of Health awards HL140385, MD012765 and DK117445. Folkert W. Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre. Panos Deloukas’s work was supported by the British Heart Foundation (BHF) grant RG/14/5/30893. Ruth J.F. Loos is funded by R01DK110113, U01HG007417, R01DK101855, R01DK107786. Caroline Hayward is supported by an MRC University Unit Programme Grant MC_UU_00007/10  (QTL in Health and Disease) and MRC University Unit Programme Grant MC_PC_U127592696. Mark I. McCarthy* is a Wellcome Senior Investigator (098381; 212259) and an NIHR Senior Investigator (NF-SI-0617-10090). The research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), and by the Wellcome (090532, 106130, 098381, 203141, 212259). Teresa Ferreira* is supported by the NIHR Biomedical Research Centre, Oxford. Maciej Tomaszewski is supported by British Heart Foundation (PG/17/35/33001 and PG/19/16/34270) and Kidney Research UK (RP_017_20180302).  John Danesh* is funded by the National Institute for Health Research [Senior Investigator Award]. Cecilia M. Lindgren* is supported by the Li Ka Shing Foundation, WT-SSI/John Fell funds and by the NIHR Biomedical Research Centre, Oxford, by Widenlife and NIH (5P50HD028138-27). Joanna M. M. Howson* was funded by the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust]. *The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Full acknowledgements are provided in the supplementary information