Dysregulation of Post-Transcriptional Modification by Copy Number
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Cytogenomic SNP Microarray - Fetal ARUP Test Code 2002366 Maternal Contamination Study Fetal Spec Fetal Cells
Patient Report |FINAL Client: Example Client ABC123 Patient: Patient, Example 123 Test Drive Salt Lake City, UT 84108 DOB 2/13/1987 UNITED STATES Gender: Female Patient Identifiers: 01234567890ABCD, 012345 Physician: Doctor, Example Visit Number (FIN): 01234567890ABCD Collection Date: 00/00/0000 00:00 Cytogenomic SNP Microarray - Fetal ARUP test code 2002366 Maternal Contamination Study Fetal Spec Fetal Cells Single fetal genotype present; no maternal cells present. Fetal and maternal samples were tested using STR markers to rule out maternal cell contamination. This result has been reviewed and approved by Maternal Specimen Yes Cytogenomic SNP Microarray - Fetal Abnormal * (Ref Interval: Normal) Test Performed: Cytogenomic SNP Microarray- Fetal (ARRAY FE) Specimen Type: Direct (uncultured) villi Indication for Testing: Patient with 46,XX,t(4;13)(p16.3;q12) (Quest: EN935475D) ----------------------------------------------------------------- ----- RESULT SUMMARY Abnormal Microarray Result (Male) Unbalanced Translocation Involving Chromosomes 4 and 13 Classification: Pathogenic 4p Terminal Deletion (Wolf-Hirschhorn syndrome) Copy number change: 4p16.3p16.2 loss Size: 5.1 Mb 13q Proximal Region Deletion Copy number change: 13q11q12.12 loss Size: 6.1 Mb ----------------------------------------------------------------- ----- RESULT DESCRIPTION This analysis showed a terminal deletion (1 copy present) involving chromosome 4 within 4p16.3p16.2 and a proximal interstitial deletion (1 copy present) involving chromosome 13 within 13q11q12.12. This -
Prioritization and Evaluation of Depression Candidate Genes by Combining Multidimensional Data Resources
Prioritization and Evaluation of Depression Candidate Genes by Combining Multidimensional Data Resources Chung-Feng Kao1, Yu-Sheng Fang2, Zhongming Zhao3, Po-Hsiu Kuo1,2,4* 1 Department of Public Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan, 2 Institute of Clinical Medicine, School of Medicine, National Cheng-Kung University, Tainan, Taiwan, 3 Departments of Biomedical Informatics and Psychiatry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America, 4 Research Center for Genes, Environment and Human Health, National Taiwan University, Taipei, Taiwan Abstract Background: Large scale and individual genetic studies have suggested numerous susceptible genes for depression in the past decade without conclusive results. There is a strong need to review and integrate multi-dimensional data for follow up validation. The present study aimed to apply prioritization procedures to build-up an evidence-based candidate genes dataset for depression. Methods: Depression candidate genes were collected in human and animal studies across various data resources. Each gene was scored according to its magnitude of evidence related to depression and was multiplied by a source-specific weight to form a combined score measure. All genes were evaluated through a prioritization system to obtain an optimal weight matrix to rank their relative importance with depression using the combined scores. The resulting candidate gene list for depression (DEPgenes) was further evaluated by a genome-wide association (GWA) dataset and microarray gene expression in human tissues. Results: A total of 5,055 candidate genes (4,850 genes from human and 387 genes from animal studies with 182 being overlapped) were included from seven data sources. -
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. -
Prediction of Meiosis-Essential Genes Based Upon the Dynamic Proteomes Responsive to Spermatogenesis
bioRxiv preprint doi: https://doi.org/10.1101/2020.02.05.936435; this version posted February 6, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Prediction of meiosis-essential genes based upon the dynamic proteomes responsive to spermatogenesis Kailun Fang1,2,3,8, Qidan Li3,4,5,8, Yu Wei2,3,8, Jiaqi Shen6,8, Wenhui Guo3,4,5,7,8, Changyang Zhou2,3,8, Ruoxi Wu1, Wenqin Ying2, Lu Yu1,2, Jin Zi5, Yuxing Zhang3,4,5, Hui Yang2,3,9*, Siqi Liu3,4,5,9*, Charlie Degui Chen1,3,9* 1. State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China. 2. State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Institute of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. 3. University of the Chinese Academy of Sciences, Beijing 100049, China. 4. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China. 5. BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China. 6. United World College Changshu China, Jiangsu 215500, China. 7. Malvern College Qingdao, Shandong, 266109, China 8. -
Supplemental Table 1. Complete Gene Lists and GO Terms from Figure 3C
Supplemental Table 1. Complete gene lists and GO terms from Figure 3C. Path 1 Genes: RP11-34P13.15, RP4-758J18.10, VWA1, CHD5, AZIN2, FOXO6, RP11-403I13.8, ARHGAP30, RGS4, LRRN2, RASSF5, SERTAD4, GJC2, RHOU, REEP1, FOXI3, SH3RF3, COL4A4, ZDHHC23, FGFR3, PPP2R2C, CTD-2031P19.4, RNF182, GRM4, PRR15, DGKI, CHMP4C, CALB1, SPAG1, KLF4, ENG, RET, GDF10, ADAMTS14, SPOCK2, MBL1P, ADAM8, LRP4-AS1, CARNS1, DGAT2, CRYAB, AP000783.1, OPCML, PLEKHG6, GDF3, EMP1, RASSF9, FAM101A, STON2, GREM1, ACTC1, CORO2B, FURIN, WFIKKN1, BAIAP3, TMC5, HS3ST4, ZFHX3, NLRP1, RASD1, CACNG4, EMILIN2, L3MBTL4, KLHL14, HMSD, RP11-849I19.1, SALL3, GADD45B, KANK3, CTC- 526N19.1, ZNF888, MMP9, BMP7, PIK3IP1, MCHR1, SYTL5, CAMK2N1, PINK1, ID3, PTPRU, MANEAL, MCOLN3, LRRC8C, NTNG1, KCNC4, RP11, 430C7.5, C1orf95, ID2-AS1, ID2, GDF7, KCNG3, RGPD8, PSD4, CCDC74B, BMPR2, KAT2B, LINC00693, ZNF654, FILIP1L, SH3TC1, CPEB2, NPFFR2, TRPC3, RP11-752L20.3, FAM198B, TLL1, CDH9, PDZD2, CHSY3, GALNT10, FOXQ1, ATXN1, ID4, COL11A2, CNR1, GTF2IP4, FZD1, PAX5, RP11-35N6.1, UNC5B, NKX1-2, FAM196A, EBF3, PRRG4, LRP4, SYT7, PLBD1, GRASP, ALX1, HIP1R, LPAR6, SLITRK6, C16orf89, RP11-491F9.1, MMP2, B3GNT9, NXPH3, TNRC6C-AS1, LDLRAD4, NOL4, SMAD7, HCN2, PDE4A, KANK2, SAMD1, EXOC3L2, IL11, EMILIN3, KCNB1, DOK5, EEF1A2, A4GALT, ADGRG2, ELF4, ABCD1 Term Count % PValue Genes regulation of pathway-restricted GDF3, SMAD7, GDF7, BMPR2, GDF10, GREM1, BMP7, LDLRAD4, SMAD protein phosphorylation 9 6.34 1.31E-08 ENG pathway-restricted SMAD protein GDF3, SMAD7, GDF7, BMPR2, GDF10, GREM1, BMP7, LDLRAD4, phosphorylation -
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, -
Gene Expression Profiling of Corpus Luteum Reveals the Importance Of
bioRxiv preprint doi: https://doi.org/10.1101/673558; this version posted February 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Gene expression profiling of corpus luteum reveals the 2 importance of immune system during early pregnancy in 3 domestic sheep. 4 Kisun Pokharel1, Jaana Peippo2 Melak Weldenegodguad1, Mervi Honkatukia2, Meng-Hua Li3*, Juha 5 Kantanen1* 6 1 Natural Resources Institute Finland (Luke), Jokioinen, Finland 7 2 Nordgen – The Nordic Genetic Resources Center, Ås, Norway 8 3 College of Animal Science and Technology, China Agriculture University, Beijing, China 9 * Correspondence: MHL, [email protected]; JK, [email protected] 10 Abstract: The majority of pregnancy loss in ruminants occurs during the preimplantation stage, which is thus 11 the most critical period determining reproductive success. While ovulation rate is the major determinant of 12 litter size in sheep, interactions among the conceptus, corpus luteum and endometrium are essential for 13 pregnancy success. Here, we performed a comparative transcriptome study by sequencing total mRNA from 14 corpus luteum (CL) collected during the preimplantation stage of pregnancy in Finnsheep, Texel and F1 15 crosses, and mapping the RNA-Seq reads to the latest Rambouillet reference genome. A total of 21,287 genes 16 were expressed in our dataset. Highly expressed autosomal genes in the CL were associated with biological 17 processes such as progesterone formation (STAR, CYP11A1, and HSD3B1) and embryo implantation (eg. -
Complexin-1 Is a Biomarker of Synucleinopathy
DMM Advance Online Articles. Posted 20 January 2017 as doi: 10.1242/dmm.028035 Access the most recent version at http://dmm.biologists.org/lookup/doi/10.1242/dmm.028035 BLOOD RNA BIOMARKERS IN PRODROMAL PARK4 AND REM SLEEP BEHAVIOR DISORDER SHOW ROLE OF COMPLEXIN-1 LOSS FOR RISK OF PARKINSON’S DISEASE Suna Lahut1,2, Suzana Gispert1, Özgür Ömür1,2, Candan Depboylu3, Kay Seidel4, Jorge Antolio Domínguez-Bautista1, Nadine Brehm1, Hülya Tireli5, Karl Hackmann6, Caroline Pirkevi2, Barbara Leube7, Vincent Ries3, Kerstin Reim8, Nils Brose8, Wilfred F. den Dunnen9, Madrid Johnson10, Zsuzsanna Wolf11, Marc Schindewolf12, Wiebke Schrempf13, Kathrin Reetz14, Peter Young15, David Vadasz3, Achilleas S. Frangakis10, Evelin Schröck6, Helmuth Steinmetz1, Marina Jendrach1, Udo Rüb4, Ayşe Nazlı Başak2¶, Wolfgang Oertel3¶, Georg Auburger1¶ 1Exp. Neurology, Goethe University Medical School, 60590 Frankfurt/Main, Germany; 2NDAL, Boğaziçi University, 34342 Istanbul, Turkey; 3Dept. of Neurology, Philipps University, Baldingerstrasse, 35043 Marburg, Germany; 4Dr. Senckenberg Chronomedical Institute, Goethe University, 60590 Frankfurt/Main, Germany; 5Dept. of Neurology, Haydarpaşa Numune Training and Research Hospital, 34668 Istanbul, Turkey; 6Institute for Clinical Genetics, Faculty of Medicine Carl Gustav Carus, TU Dresden, Fetscherstrasse 74, 01307 Dresden, Germany; 7Institute of Human Genetics, Heinrich Heine University, 40225 Düsseldorf, Germany; 8Dept. of Molecular Neurobiology and Center for the Molecular Physiology of the Brain, Max Planck Institute of Experimental Medicine, 37075 Göttingen, Germany; 9Dept. of Pathology and Medical Biology, Medical Center, University, 9700 RB Groningen, Netherlands; 10Institut für Biophysik and Frankfurt Institute for Molecular Life Sciences, Goethe University, 60438 Frankfurt/Main, Germany; 11Hemophilia Centre, Medical Clinic III, Institute of Transfusion Medicine and Immunhematology Goethe University, 60590 Frankfurt/Main, Germany; 12Dept. -
Single-Cell RNA-Seq of Dopaminergic Neurons Informs Candidate Gene Selection for Sporadic
bioRxiv preprint doi: https://doi.org/10.1101/148049; this version posted August 31, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Single-cell RNA-seq of dopaminergic neurons informs candidate gene selection for sporadic 2 Parkinson's disease 3 4 Paul W. Hook1, Sarah A. McClymont1, Gabrielle H. Cannon1, William D. Law1, A. Jennifer 5 Morton2, Loyal A. Goff1,3*, Andrew S. McCallion1,4,5* 6 7 1McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of 8 Medicine, Baltimore, Maryland, United States of America 9 2Department of Physiology Development and Neuroscience, University of Cambridge, 10 Cambridge, United Kingdom 11 3Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, 12 Maryland, United States of America 13 4Department of Comparative and Molecular Pathobiology, Johns Hopkins University School of 14 Medicine, Baltimore, Maryland, United States of America 15 5Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, 16 United States of America 17 *, To whom correspondence should be addressed: [email protected] and [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/148049; this version posted August 31, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. -
Metastatic Adrenocortical Carcinoma Displays Higher Mutation Rate and Tumor Heterogeneity Than Primary Tumors
ARTICLE DOI: 10.1038/s41467-018-06366-z OPEN Metastatic adrenocortical carcinoma displays higher mutation rate and tumor heterogeneity than primary tumors Sudheer Kumar Gara1, Justin Lack2, Lisa Zhang1, Emerson Harris1, Margaret Cam2 & Electron Kebebew1,3 Adrenocortical cancer (ACC) is a rare cancer with poor prognosis and high mortality due to metastatic disease. All reported genetic alterations have been in primary ACC, and it is 1234567890():,; unknown if there is molecular heterogeneity in ACC. Here, we report the genetic changes associated with metastatic ACC compared to primary ACCs and tumor heterogeneity. We performed whole-exome sequencing of 33 metastatic tumors. The overall mutation rate (per megabase) in metastatic tumors was 2.8-fold higher than primary ACC tumor samples. We found tumor heterogeneity among different metastatic sites in ACC and discovered recurrent mutations in several novel genes. We observed 37–57% overlap in genes that are mutated among different metastatic sites within the same patient. We also identified new therapeutic targets in recurrent and metastatic ACC not previously described in primary ACCs. 1 Endocrine Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 2 Center for Cancer Research, Collaborative Bioinformatics Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 3 Department of Surgery and Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Correspondence and requests for materials should be addressed to E.K. (email: [email protected]) NATURE COMMUNICATIONS | (2018) 9:4172 | DOI: 10.1038/s41467-018-06366-z | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-06366-z drenocortical carcinoma (ACC) is a rare malignancy with types including primary ACC from the TCGA to understand our A0.7–2 cases per million per year1,2. -
Whole-Exome Sequencing Associates Novel CSMD1 Gene Mutations with Familial Parkinson Disease
Whole-exome sequencing associates novel CSMD1 gene mutations with familial Parkinson disease Javier Ruiz-Martínez, ABSTRACT MD, PhD Objective: Despite the enormous advancements made in deciphering the genetic architecture of Luis J. Azcona, BBA Parkinson disease (PD), the majority of PD is idiopathic, with single gene mutations explaining only Alberto Bergareche, MD a small proportion of the cases. Jose F. Martí-Massó, MD, Methods: In this study, we clinically evaluated 2 unrelated Spanish families diagnosed with PD, in PhD which known PD genes were previously excluded, and performed whole-exome sequencing anal- Coro Paisán-Ruiz, PhD yses in affected individuals for disease gene identification. Results: Patients were diagnosed with typical PD without relevant distinctive symptoms. Two dif- Correspondence to ferent novel mutations were identified in the CSMD1 gene. The CSMD1 gene, which encodes Dr. Paisán-Ruiz: a complement control protein that is known to participate in the complement activation and [email protected] inflammation in the developing CNS, was previously shown to be associated with the risk of PD in a genome-wide association study. Conclusions: We conclude that the CSMD1 mutations identified in this study might be responsible for the PD phenotype observed in our examined patients. This, along with previous reported studies, may suggest the complement pathway as an important therapeutic target for PD and other neurodegenerative diseases. Neurol Genet 2017;3:e177; doi: 10.1212/NXG.0000000000000177 GLOSSARY AD 5 Alzheimer disease; CCP 5 complement control protein; fPD 5 familial Parkinson disease; H&Y 5 Hoehn and Yahr; INDEL 5 insertions/deletions; LOPD 5 late-onset PD; PD 5 Parkinson disease; RBD 5 REM sleep behavior disorder; RLS 5 restless legs syndrome; SNV 5 single nucleotide variant; WES 5 whole-exome sequencing. -
Whole Genome Sequencing of Familial Non-Medullary Thyroid Cancer Identifies Germline Alterations in MAPK/ERK and PI3K/AKT Signaling Pathways
biomolecules Article Whole Genome Sequencing of Familial Non-Medullary Thyroid Cancer Identifies Germline Alterations in MAPK/ERK and PI3K/AKT Signaling Pathways Aayushi Srivastava 1,2,3,4 , Abhishek Kumar 1,5,6 , Sara Giangiobbe 1, Elena Bonora 7, Kari Hemminki 1, Asta Försti 1,2,3 and Obul Reddy Bandapalli 1,2,3,* 1 Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany; [email protected] (A.S.); [email protected] (A.K.); [email protected] (S.G.); [email protected] (K.H.); [email protected] (A.F.) 2 Hopp Children’s Cancer Center (KiTZ), D-69120 Heidelberg, Germany 3 Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), D-69120 Heidelberg, Germany 4 Medical Faculty, Heidelberg University, D-69120 Heidelberg, Germany 5 Institute of Bioinformatics, International Technology Park, Bangalore 560066, India 6 Manipal Academy of Higher Education (MAHE), Manipal, Karnataka 576104, India 7 S.Orsola-Malphigi Hospital, Unit of Medical Genetics, 40138 Bologna, Italy; [email protected] * Correspondence: [email protected]; Tel.: +49-6221-42-1709 Received: 29 August 2019; Accepted: 10 October 2019; Published: 13 October 2019 Abstract: Evidence of familial inheritance in non-medullary thyroid cancer (NMTC) has accumulated over the last few decades. However, known variants account for a very small percentage of the genetic burden. Here, we focused on the identification of common pathways and networks enriched in NMTC families to better understand its pathogenesis with the final aim of identifying one novel high/moderate-penetrance germline predisposition variant segregating with the disease in each studied family.