A Comparative Analysis of Transcribed Genes in the Mouse Hypothalamus and Neocortex Reveals Chromosomal Clustering
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A Regulator of Aldosterone Synthesis in Human Adrenocortical Tissues
S J A FELIZOLA and others PCP4: a regulator of aldosterone 52:2 159–167 Research synthesis PCP4: a regulator of aldosterone synthesis in human adrenocortical tissues Saulo J A Felizola, Yasuhiro Nakamura, Yoshikiyo Ono1, Kanako Kitamura, Kumi Kikuchi1, Yoshiaki Onodera, Kazue Ise, Kei Takase2, Akira Sugawara3, Namita Hattangady4, William E Rainey4, Fumitoshi Satoh1 and Hironobu Sasano Department of Pathology, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Correspondence Miyagi 980-8575, Japan should be addressed 1Division of Nephrology, Endocrinology and Vascular Medicine, Tohoku University Hospital, Sendai, Japan to Y Nakamura Departments of 2Diagnostic Radiology 3Molecular Endocrinology, Tohoku University Graduate School of Medicine, Email 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8575, Japan yasu-naka@ 4Department of Physiology and Medicine, University of Michigan, Ann Arbor, Michigan, USA patholo2.med.tohoku.ac.jp Abstract Purkinje cell protein 4 (PCP4) is a calmodulin (CaM)-binding protein that accelerates calcium Key Words association and dissociation with CaM. It has been previously detected in aldosterone- " Purkinje cell protein 4 (PCP4) producing adenomas (APA), but details on its expression and function in adrenocortical " adrenal cortex tissues have remained unknown. Therefore, we performed the immunohistochemical " aldosterone analysis of PCP4 in the following tissues: normal adrenal (NA; nZ15), APA (nZ15), cortisol- " calmodulin (CaM) producing adenomas (nZ15), and idiopathic hyperaldosteronism cases (IHA; nZ5). APA " CYP11B2 samples (nZ45) were also submitted to quantitative RT-PCR of PCP4, CYP11B1, and CYP11B2, Journal of Molecular Endocrinology as well as DNA sequencing for KCNJ5 mutations. Transient transfection analysis using PCP4 siRNA was also performed in H295R adrenocortical carcinoma cells, following ELISA analysis, and CYP11B2 luciferase assays were also performed after PCP4 vector transfection in order to study the regulation of PCP4 protein expression. -
Genome Wide Association Study of Response to Interval and Continuous Exercise Training: the Predict‑HIIT Study Camilla J
Williams et al. J Biomed Sci (2021) 28:37 https://doi.org/10.1186/s12929-021-00733-7 RESEARCH Open Access Genome wide association study of response to interval and continuous exercise training: the Predict-HIIT study Camilla J. Williams1†, Zhixiu Li2†, Nicholas Harvey3,4†, Rodney A. Lea4, Brendon J. Gurd5, Jacob T. Bonafglia5, Ioannis Papadimitriou6, Macsue Jacques6, Ilaria Croci1,7,20, Dorthe Stensvold7, Ulrik Wislof1,7, Jenna L. Taylor1, Trishan Gajanand1, Emily R. Cox1, Joyce S. Ramos1,8, Robert G. Fassett1, Jonathan P. Little9, Monique E. Francois9, Christopher M. Hearon Jr10, Satyam Sarma10, Sylvan L. J. E. Janssen10,11, Emeline M. Van Craenenbroeck12, Paul Beckers12, Véronique A. Cornelissen13, Erin J. Howden14, Shelley E. Keating1, Xu Yan6,15, David J. Bishop6,16, Anja Bye7,17, Larisa M. Haupt4, Lyn R. Grifths4, Kevin J. Ashton3, Matthew A. Brown18, Luciana Torquati19, Nir Eynon6 and Jef S. Coombes1* Abstract Background: Low cardiorespiratory ftness (V̇O2peak) is highly associated with chronic disease and mortality from all causes. Whilst exercise training is recommended in health guidelines to improve V̇O2peak, there is considerable inter-individual variability in the V̇O2peak response to the same dose of exercise. Understanding how genetic factors contribute to V̇O2peak training response may improve personalisation of exercise programs. The aim of this study was to identify genetic variants that are associated with the magnitude of V̇O2peak response following exercise training. Methods: Participant change in objectively measured V̇O2peak from 18 diferent interventions was obtained from a multi-centre study (Predict-HIIT). A genome-wide association study was completed (n 507), and a polygenic predictor score (PPS) was developed using alleles from single nucleotide polymorphisms= (SNPs) signifcantly associ- –5 ated (P < 1 10 ) with the magnitude of V̇O2peak response. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Proteomic Analysis Uncovers Measles Virus Protein C Interaction with P65
bioRxiv preprint doi: https://doi.org/10.1101/2020.05.08.084418; this version posted May 9, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Proteomic Analysis Uncovers Measles Virus Protein C Interaction with p65/iASPP/p53 Protein Complex Alice Meignié1,2*, Chantal Combredet1*, Marc Santolini 3,4, István A. Kovács4,5,6, Thibaut Douché7, Quentin Giai Gianetto 7,8, Hyeju Eun9, Mariette Matondo7, Yves Jacob10, Regis Grailhe9, Frédéric Tangy1**, and Anastassia V. Komarova1, 10** 1 Viral Genomics and Vaccination Unit, Department of Virology, Institut Pasteur, CNRS UMR-3569, 75015 Paris, France 2 Université Paris Diderot, Sorbonne Paris Cité, Paris, France 3 Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284 4 Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115, USA 5 Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208-3109, USA 6 Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary 7 Proteomics platform, Mass Spectrometry for Biology Unit (MSBio), Institut Pasteur, CNRS USR 2000, Paris, France. 8 Bioinformatics and Biostatistics Hub, Computational Biology Department, Institut Pasteur, CNRS USR3756, Paris, France 9 Technology Development Platform, Institut Pasteur Korea, Seongnam-si, Republic of Korea 10 Laboratory of Molecular Genetics of RNA Viruses, Institut Pasteur, CNRS UMR-3569, -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
Supplementary Table 1. Pain and PTSS Associated Genes (N = 604
Supplementary Table 1. Pain and PTSS associated genes (n = 604) compiled from three established pain gene databases (PainNetworks,[61] Algynomics,[52] and PainGenes[42]) and one PTSS gene database (PTSDgene[88]). These genes were used in in silico analyses aimed at identifying miRNA that are predicted to preferentially target this list genes vs. a random set of genes (of the same length). ABCC4 ACE2 ACHE ACPP ACSL1 ADAM11 ADAMTS5 ADCY5 ADCYAP1 ADCYAP1R1 ADM ADORA2A ADORA2B ADRA1A ADRA1B ADRA1D ADRA2A ADRA2C ADRB1 ADRB2 ADRB3 ADRBK1 ADRBK2 AGTR2 ALOX12 ANO1 ANO3 APOE APP AQP1 AQP4 ARL5B ARRB1 ARRB2 ASIC1 ASIC2 ATF1 ATF3 ATF6B ATP1A1 ATP1B3 ATP2B1 ATP6V1A ATP6V1B2 ATP6V1G2 AVPR1A AVPR2 BACE1 BAMBI BDKRB2 BDNF BHLHE22 BTG2 CA8 CACNA1A CACNA1B CACNA1C CACNA1E CACNA1G CACNA1H CACNA2D1 CACNA2D2 CACNA2D3 CACNB3 CACNG2 CALB1 CALCRL CALM2 CAMK2A CAMK2B CAMK4 CAT CCK CCKAR CCKBR CCL2 CCL3 CCL4 CCR1 CCR7 CD274 CD38 CD4 CD40 CDH11 CDK5 CDK5R1 CDKN1A CHRM1 CHRM2 CHRM3 CHRM5 CHRNA5 CHRNA7 CHRNB2 CHRNB4 CHUK CLCN6 CLOCK CNGA3 CNR1 COL11A2 COL9A1 COMT COQ10A CPN1 CPS1 CREB1 CRH CRHBP CRHR1 CRHR2 CRIP2 CRYAA CSF2 CSF2RB CSK CSMD1 CSNK1A1 CSNK1E CTSB CTSS CX3CL1 CXCL5 CXCR3 CXCR4 CYBB CYP19A1 CYP2D6 CYP3A4 DAB1 DAO DBH DBI DICER1 DISC1 DLG2 DLG4 DPCR1 DPP4 DRD1 DRD2 DRD3 DRD4 DRGX DTNBP1 DUSP6 ECE2 EDN1 EDNRA EDNRB EFNB1 EFNB2 EGF EGFR EGR1 EGR3 ENPP2 EPB41L2 EPHB1 EPHB2 EPHB3 EPHB4 EPHB6 EPHX2 ERBB2 ERBB4 EREG ESR1 ESR2 ETV1 EZR F2R F2RL1 F2RL2 FAAH FAM19A4 FGF2 FKBP5 FLOT1 FMR1 FOS FOSB FOSL2 FOXN1 FRMPD4 FSTL1 FYN GABARAPL1 GABBR1 GABBR2 GABRA2 GABRA4 -
Identification of Differentially Expressed Genes in Human Bladder Cancer Through Genome-Wide Gene Expression Profiling
521-531 24/7/06 18:28 Page 521 ONCOLOGY REPORTS 16: 521-531, 2006 521 Identification of differentially expressed genes in human bladder cancer through genome-wide gene expression profiling KAZUMORI KAWAKAMI1,3, HIDEKI ENOKIDA1, TOKUSHI TACHIWADA1, TAKENARI GOTANDA1, KENGO TSUNEYOSHI1, HIROYUKI KUBO1, KENRYU NISHIYAMA1, MASAKI TAKIGUCHI2, MASAYUKI NAKAGAWA1 and NAOHIKO SEKI3 1Department of Urology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima 890-8520; Departments of 2Biochemistry and Genetics, and 3Functional Genomics, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8670, Japan Received February 15, 2006; Accepted April 27, 2006 Abstract. Large-scale gene expression profiling is an effective CKS2 gene not only as a potential biomarker for diagnosing, strategy for understanding the progression of bladder cancer but also for staging human BC. This is the first report (BC). The aim of this study was to identify genes that are demonstrating that CKS2 expression is strongly correlated expressed differently in the course of BC progression and to with the progression of human BC. establish new biomarkers for BC. Specimens from 21 patients with pathologically confirmed superficial (n=10) or Introduction invasive (n=11) BC and 4 normal bladder samples were studied; samples from 14 of the 21 BC samples were subjected Bladder cancer (BC) is among the 5 most common to microarray analysis. The validity of the microarray results malignancies worldwide, and the 2nd most common tumor of was verified by real-time RT-PCR. Of the 136 up-regulated the genitourinary tract and the 2nd most common cause of genes we detected, 21 were present in all 14 BCs examined death in patients with cancer of the urinary tract (1-7). -
Viewed and Published Immediately Upon Acceptance Cited in Pubmed and Archived on Pubmed Central Yours — You Keep the Copyright
BMC Genomics BioMed Central Research article Open Access Differential gene expression in ADAM10 and mutant ADAM10 transgenic mice Claudia Prinzen1, Dietrich Trümbach2, Wolfgang Wurst2, Kristina Endres1, Rolf Postina1 and Falk Fahrenholz*1 Address: 1Johannes Gutenberg-University, Institute of Biochemistry, Mainz, Johann-Joachim-Becherweg 30, 55128 Mainz, Germany and 2Helmholtz Zentrum München – German Research Center for Environmental Health, Institute for Developmental Genetics, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany Email: Claudia Prinzen - [email protected]; Dietrich Trümbach - [email protected]; Wolfgang Wurst - [email protected]; Kristina Endres - [email protected]; Rolf Postina - [email protected]; Falk Fahrenholz* - [email protected] * Corresponding author Published: 5 February 2009 Received: 19 June 2008 Accepted: 5 February 2009 BMC Genomics 2009, 10:66 doi:10.1186/1471-2164-10-66 This article is available from: http://www.biomedcentral.com/1471-2164/10/66 © 2009 Prinzen et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: In a transgenic mouse model of Alzheimer disease (AD), cleavage of the amyloid precursor protein (APP) by the α-secretase ADAM10 prevented amyloid plaque formation, and alleviated cognitive deficits. Furthermore, ADAM10 overexpression increased the cortical synaptogenesis. These results suggest that upregulation of ADAM10 in the brain has beneficial effects on AD pathology. Results: To assess the influence of ADAM10 on the gene expression profile in the brain, we performed a microarray analysis using RNA isolated from brains of five months old mice overexpressing either the α-secretase ADAM10, or a dominant-negative mutant (dn) of this enzyme. -
Structural Basis for Isoform-Specific Kinesin-1 Recognition of Y-Acidic
bioRxiv preprint doi: https://doi.org/10.1101/322057; this version posted May 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Structural basis for isoform-specific kinesin-1 recognition of Y-acidic cargo adaptors Stefano Pernigo1, Magda Chegkazi1, Yan Y. Yip1, Conor Treacy1, Giulia Glorani1, Kjetil Hansen2, Argyris Politis2, Mark P. Dodding1,3,*, Roberto A. Steiner1,* 1Randall Centre of Cell and Molecular Biophysics, Faculty of Life Sciences and Medicine, King’s College London, London SE1 1UL, UK 2Department of Chemistry, King’s College London Britannia House 7 Trinity Street, London, SE1 1DB (UK) 3current address School of Biochemistry, Faculty of Biomedical Sciences, University of Bristol, University Walk, Bristol BS8 1TD *Correspondence email: [email protected] or [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/322057; this version posted May 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The light chains (KLCs) of the heterotetrameric microtubule motor kinesin-1, that bind to cargo adaptor proteins and regulate its activity, have a capacity to recognize short peptides via their tetratricopeptide repeat domains (KLCTPR). Here, using X-ray crystallography, we show how kinesin-1 recognizes a novel class of adaptor motifs that we call ‘Y-acidic’ (tyrosine flanked by acidic residues), in a KLC-isoform specific manner. Binding specificities of Y-acidic motifs (present in JIP1 and in TorsinA) to KLC1TPR are distinct from those utilized for the recognition of W-acidic motifs found in adaptors that are KLC- isoform non-selective. -
Open Data for Differential Network Analysis in Glioma
International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. -
The Genetics of Bipolar Disorder
Molecular Psychiatry (2008) 13, 742–771 & 2008 Nature Publishing Group All rights reserved 1359-4184/08 $30.00 www.nature.com/mp FEATURE REVIEW The genetics of bipolar disorder: genome ‘hot regions,’ genes, new potential candidates and future directions A Serretti and L Mandelli Institute of Psychiatry, University of Bologna, Bologna, Italy Bipolar disorder (BP) is a complex disorder caused by a number of liability genes interacting with the environment. In recent years, a large number of linkage and association studies have been conducted producing an extremely large number of findings often not replicated or partially replicated. Further, results from linkage and association studies are not always easily comparable. Unfortunately, at present a comprehensive coverage of available evidence is still lacking. In the present paper, we summarized results obtained from both linkage and association studies in BP. Further, we indicated new potential interesting genes, located in genome ‘hot regions’ for BP and being expressed in the brain. We reviewed published studies on the subject till December 2007. We precisely localized regions where positive linkage has been found, by the NCBI Map viewer (http://www.ncbi.nlm.nih.gov/mapview/); further, we identified genes located in interesting areas and expressed in the brain, by the Entrez gene, Unigene databases (http://www.ncbi.nlm.nih.gov/entrez/) and Human Protein Reference Database (http://www.hprd.org); these genes could be of interest in future investigations. The review of association studies gave interesting results, as a number of genes seem to be definitively involved in BP, such as SLC6A4, TPH2, DRD4, SLC6A3, DAOA, DTNBP1, NRG1, DISC1 and BDNF. -
The Genetic Architecture of Down Syndrome Phenotypes Revealed by High-Resolution Analysis of Human Segmental Trisomies
The genetic architecture of Down syndrome phenotypes revealed by high-resolution analysis of human segmental trisomies Jan O. Korbela,b,c,1, Tal Tirosh-Wagnerd,1, Alexander Eckehart Urbane,f,1, Xiao-Ning Chend, Maya Kasowskie, Li Daid, Fabian Grubertf, Chandra Erdmang, Michael C. Gaod, Ken Langeh,i, Eric M. Sobelh, Gillian M. Barlowd, Arthur S. Aylsworthj,k, Nancy J. Carpenterl, Robin Dawn Clarkm, Monika Y. Cohenn, Eric Dorano, Tzipora Falik-Zaccaip, Susan O. Lewinq, Ira T. Lotto, Barbara C. McGillivrayr, John B. Moeschlers, Mark J. Pettenatit, Siegfried M. Pueschelu, Kathleen W. Raoj,k,v, Lisa G. Shafferw, Mordechai Shohatx, Alexander J. Van Ripery, Dorothy Warburtonz,aa, Sherman Weissmanf, Mark B. Gersteina, Michael Snydera,e,2, and Julie R. Korenbergd,h,bb,2 Departments of aMolecular Biophysics and Biochemistry, eMolecular, Cellular, and Developmental Biology, and fGenetics, Yale University School of Medicine, New Haven, CT 06520; bEuropean Molecular Biology Laboratory, 69117 Heidelberg, Germany; cEuropean Molecular Biology Laboratory (EMBL) Outstation Hinxton, EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom; dMedical Genetics Institute, Cedars–Sinai Medical Center, Los Angeles, CA 90048; gDepartment of Statistics, Yale University, New Haven, CT 06520; Departments of hHuman Genetics, and iBiomathematics, University of California, Los Angeles, CA 90095; Departments of jPediatrics and kGenetics, University of North Carolina, Chapel Hill, NC 27599; lCenter for Genetic Testing,