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• SEARCH for Diabetes in Youth Study - Genetic Risk Score

  Genetic risk scores (GRS) quantify polygenic disease risk into a single measure and can aid in disease classification. GRS studies have focused primarily on adult populations of recent European ancestry. We aimed to assess the utility of GRS in classification of diabetes type among racially/ethnically diverse youth in the United States. We used data from SEARCH baseline visits and follow up visits for which key data elements were available. 2260 participants are included. Phenotypic Data to be submitted here include: Self-reported sex, race and ethnicity Diabetes type determined by provider Etiologic diabetes type T1D (antibody positive, or antibody negative/missing and insulin sensitive) T2D (antibody negative and insulin resistant) Diabetes duration (years) Age at diagnosis (years) Measures from baseline, SEARCH 3 (follow-up) and SEARCH 4 (follow-up): Age Height Weight Waist circumference HbA1c Fasting/non-fasting glucose C-peptide Triglycerides HDL LDL Systolic Blood Pressure Diastolic Blood Pressure Measures from SEARCH 3 and 4:Arterial stiffness as characterized by PWVRetinopathy Measures from SEARCH 4 only: LV structure and diastolic function measurements from echocardiogram GAD autoantibody IAD autoantibody ZnT8 autoantibody We performed genotyping using the Illumina Multi-Ethnic Global Array (MEGA) array with 1,697,069 genotyped variants including 748,291 with minor allele frequency <0.01. Genotyping and preliminary quality control checks were performed at the Colorado Center for Personalized Medicine. After additional quality control, 2,238 samples and 900,743 variants remained for analyses. Samples genotyped on the MEGA array were categorized using SEARCH etiologic type and consisted of predominantly type 1 diabetes cases (n=2,051) but also those with other diabetes (n=133 type 2 diabetes, n=52 other diabetes including monogenic diabetes; genetic confirmation with either a genetic clinical test or test performed as an ancillary study to SEARCH). The median reported age at DNA collection was 11.2 years (interquartile range 7.6 – 14.1) with a minimum age of 1.9 years and maximum of 21.9 years. We used additional data genotyped on the Affymetrix 500K imputation scaffold chip with 239,279 genotyped variants. This cohort consisted of predominantly type 2 diabetes cases (n=417) but also those with type 1 (n=104), and other diabetes types (n=16). After additional quality control, 537 samples and 235,967 variants remained. The median reported age at collection was 11.2 years (quartile range 8.1 – 14.2) with a minimum of 2.0 years and a maximum of 21.1 years. About 100 type 2 diabetes cases were also genotyped using the MEGA chip to ensure concordance between the data sets and facilitate strand alignment between the two chips before genotypic imputation. The data sets had n=230,228 genotyped variants in common and concordance between the genotypes was high (mean correlation r2 for SNPs used in GRS=0.95). We used the 1000 Genomes reference panel to impute each data set separately, resulting in a total of 34.5M and 27.8M well-imputed variants (r2>0.8) for the MEGA and Affymetrix data sets respectively. We combined high quality imputed variants for analysis.

  Study phs002703

• Lung Cancer Genetic Study Among Asian Never Smokers

  The Lung Cancer Genetic Study Among Asian Never Smokers includes data from four population-based studies conducted among Asian women: the Shanghai Women's Health Study (SWHS), the Hwasun Cancer Epidemiology Study (HCES1 and HCES2), the Female Lung Cancer Consortium in Asia (FLCCA, including sFLCA), and the Guangzhou Lung Cancer Study (GLCS). Shanghai Women's Health Study (SWHS): The SWHS is a population-based prospective cohort study of approximately 75,000 adult women who were recruited between 1997 and 2000. The cohort has been followed by a combination of record linkage and active follow-ups. Lung cancer patients identified in the SWHS and non-cases were included in the current study.Hwasun Cancer Epidemiology Study-Lung Cancer (HCES1 and HCES2): The HCES is a hospital-based case-control study whose goal is to identify factors of the cancer development and clinical progression in a Korean population. Included in this project were 299 female lung cancer cases from HCES2, who were diagnosed between April 2004 and February 2013 at Chonnam National University Hwasun Hospital, a cancer specified hospital in Jeollanam-do province, South Korea. Controls were randomly selected from women with no previous cancer diagnosis at enrollment in the Namwon Study and the Dong-gu study, ongoing community-based cohort studies in South Korea. A total of 1,103 controls (273 from HCES1 and 830 from HCES2) were genotyped by MEGA. Guangzhou Lung Cancer Study (GLCS): The GLCS is a case-control study including never-smoking women lung cancer cases and controls from Guangzhou, China.In this study, the DNA samples of 1,195 subjects from SWHS and GLCS were genotyped using whole exome sequencing (WES), and the DNA samples of 7,174 subjects from SWHS, HCES1, HCES2, FLCCA, and sFLCA were genotyped using the Multi-Ethnic Global Array (MEGA).

  Study phs002366

• International Genetics of Parkinson Disease Progression (IGPP) Consortium: EPIPARK and HBS2 Cohorts

  EPIPARK and HBS2 cohorts were analyzed as part of the International Genetics of Parkinson Disease Progression (IGPP) Consortium. EPIPARK is a population-based cohort to study non-motor symptoms in parkinsonism in the city of Lübeck, Germany led by Drs. Meike Kasten and Christine Klein. HBS2 is a recently enrolled subset of participants in the Harvard Biomarkers Study led by Dr. Clemens Scherzer (www.bwhparkinsoncenter.org/biobank) of 89 participants with a diagnosis of Parkinson Disease (PD). Genotyping with Illumina Infinium Multi-Ethnic Genotyping Arrays (MEGA chip) was performed by Dr. Scherzer, Harvard Medical School and Brigham and Women’s Hospital. 179 participants with a diagnosis of PD from EPIPARK and 89 participants with a diagnosis of PD from HBS2 met Q/C criteria were analyzed.

  Study phs002328

• Perturb-seq dataset

  We performed a systematic, genome-wide investigation of enhancer regions in colorectal cancer (CRC). We identified 12,117 putative enhancer regions using H3K27ac and H3Kme1 ChIP-seq and ATAC-seq. We performed scRNA-seq in HT29 and SW480 (MSS CRC cell lines) using the Parse Biosciences WT-mega kit with CRISPRi/dCas9 inhibition of these regions (Perturb-seq). The Parse split-pipe pipeline was used to demultiplex the raw fastq files into the processed files (mtx files for genes and gRNA) for each cell line.

  Dataset EGAD50000000375

• Genetic architecture of disease in Greenland is shaped by demographic history, fine-structure, and selection - MEGA chip data

  This data was generated as part of the project titled "Genetic architecture of disease in Greenland is shaped by demographic history, fine-structure, and selection." The study was carried out to investigate the genetic architecture of the Greenlandic population and the consequences it has for disease mapping, diagnosis and prediction.

  Study EGAS50000000658

• Reasons for Geographic and Racial Differences in Stroke Cardiorenal GWAS

  REGARDS is a national, population-based, longitudinal study of incident stroke and associated risk factors including over 30,000 Black and White adults aged 45 years or older from all 48 contiguous U.S. states and the District of Columbia. The study was designed to investigate reasons underlying the higher rate of stroke mortality among Black participants compared to White participants and among residents of the Southeastern U.S. compared to other U.S. regions. By design, Black adults and residents of the deep south were oversampled. Between 2003 and 2007 (baseline visit) participants completed a computer-assisted telephone interview to collect demographic information and medication adherence, and an in-home visit for blood pressure measurements and collection of blood and urine samples. Following the baseline visit, participants have been contacted by phone at six-month intervals to obtain information on incident stroke or secondary outcomes. Additionally, samples and data were collected on about 50% of the original cohort during a second study visit an average of 10 years after the baseline visit. Genotyping was performed as part of an ancillary study on 10,788 (84% Black) participants using Illumina MEGA arrays.

  Study phs002719

• Whole Genome Sequencing for Metastatic Mutational Burden in Extraskeletal Myxoid Chondrosarcoma

  Extraskeletal myxoid chondrosarcoma (EMC) is an ultra-rare cancer. Though it has a favorable prognosis and an indolent course, it has high rates of local recurrence and metastasis to the lungs. EMC is most often characterized by a translocation involving the NR4A3 gene most often fusing with EWSR1, leading to constitutive expression of NR4A3, the biological significance of which is unknown. Our study presents a case of EMC with case-matched lung and advanced pelvic metastases. We conducted Whole Genome Sequencing to examine differences in the mutational landscape at two different stages of metastasis. This is the first study to our knowledge to analyze mega-base scale structural variants (SVs) in EMC. Our data validates copy number variants (CNVs) found in previous studies. While the primary tumor and lung metastasis had similar somatic variations and CNVs, the pelvic metastasis had more SVs with especially increased mutational burden of SVs in chromosome 2. This suggests that different molecular drivers appear in more advanced, relapsing EMC compared with the primary tumor and early lung metastasis. The sequencing data is available through dbGaP.

  Study phs003305

• Population Architecture using Genomics and Epidemiology (PAGE)

  PAGE II (2013-2017) seeks to expand understanding gained during PAGE I and similar studies of how ancestry-specific differences in allele frequencies and LD may explain differences in risks of common traits and conditions. Recent studies have identified rare genetic variants that are likely to contribute to common diseases and traits and observed that rare variants likely to be functional, such as those in coding and regulatory regions, tend to be population-specific. PAGE II has genotyped over 50,000 samples using MEGA, an Illumina high density custom exomechip array. The MEGA data is being imputed in PAGE to the 1000 Genomes panel. PAGE also sequenced 1,000 samples representative of 21 populations from the Americas. PAGE has harmonized phenotype data for ~300 trait variables. These datasets will be analyzed to continue emphasis on characterizing population-level disease risks in non-European-descent individuals. Cohorts in PAGE II are: CALiCo (Causal Variants Across the Life Course, a consortium of ARIC, CARDIA, HCHS/SOL, Strong Heart Studies), ISMMS (Mount Sinai BioMe Biobank), MEC (Multiethnic Cohort), WHI (Women's Health Initiative), and Stanford University (PAGE Global Reference Panel). Genotyping services were provided by the Center for Inherited Disease Research (CIDR) and sequence data were provided by the McDonnell Genome Institute at Washington University School of Medicine. PAGE I (2008-2013): The first phase of PAGE examined putative causal genetic variants across approximately 100,000 African Americans, Asian Americans, American Indians, European Americans, Hispanic Americans, and Native Hawaiians from four groups representing nine large U.S.-based cohorts. Two genotyping approaches were employed - targeted genotyping of selected SNPs identified in genome-wide association studies of common disease, and a large-scale effort focused on the Metabochip array, which facilitated trans-ethnic fine mapping of several diseases of public health importance. Cohorts in PAGE I were: CALiCo (Causal Variants Across the Life Course, a consortium of ARIC, CARDIA, CHS, HCHS/SOL, Strong Heart Cohort Study, Strong Heart Family Study), EAGLE (Epidemiologic Architecture for Genes Linked to Environment, based on 3 National Health and Nutrition Examination Surveys (NHANES)), MEC (Multiethnic Cohort) and WHI (Women's Health Initiative). Logistical and scientific support is provided by the PAGE Coordinating Center and the NHGRI Division of Genomic Medicine. PAGE II is funded by the NHGRI and the NIMHD. To access PAGE studies currently available in dbGaP, please click on the links below.Please note that some PAGE studies belong to larger cohorts and have been included as PAGE substudies. For those studies, there is an additional link to the parent study. phs000223 PAGE-ARIC and phs000280 ARIC Cohort phs000236 PAGE-CARDIA and phs000285 CARDIA Cohort phs000301 PAGE-CHS and phs000287 CHS Cohort phs000559 PAGE-EAGLE-BioVu phs000208 PAGE-EAGLE-NHANES phs000555 PAGE-HCHS/SOL and phs000810 HCHS/SOL Cohort phs000220 PAGE-MEC phs000580 PAGE-SHS and SHFS phs001033 PAGE Global Reference Panel phs000227 PAGE-WHI and phs000200 WHI Cohort

  Study phs000356

• Large Scale Genotyping of Psychiatric Disorders

  Psychiatric disorders cause enormous human suffering and cost to society. The goal of this project is to elucidate the genetics of psychiatric disorders through a large consortium effort and genome-wide genotyping. The Psychiatric Genomics Consortium (PGC) (https://www.med.unc.edu/pgc) is an international consortium to pursue genome-wide analyses of common psychiatric disease. The PGC was founded in 2007 to unite investigators from around the world to conduct mega-analyses of individual genome-wide genotype data for schizophrenia (SCZ), bipolar disorder (BIP), autism (AUT), attention-deficit hyperactivity disorder (ADHD), and major depressive disorder (MDD). Subsequently, the PGC has expanded to include Tourette syndrome (TS), obsessive-compulsive disorder (OCD), eating disorders (ED), post-traumatic stress disorder (PTSD), and substance abuse (SUD). The PGC partnered with Illumina to develop the PsychChip, a custom GWAS array aimed at capturing common variation genome-wide for imputation, rare coding variation drawn from the exome chip and dense genotypes for loci with suggestive evidence for association across the initial PGC analyses of SCZ, MDD, BPD, ASD and ADHD. Samples (cases, controls, and families) were collected from Institutional Review Board-approved protocols that aim to study the genetics of psychiatric disorders. Genome-wide genotyping was performed at the Broad Institute (Cambridge, MA, USA) and the Mount Sinai School of Medicine Friedman Brain Institute (New York, NY, USA) using the PsychChip or Exome Chip.

  Study phs001413

• Genetics of Mammographic Density in Ashkenazi Jews

  Mammographic density (MD) is a strong risk factor for breast cancer and is also a highly heritable trait with ~60-70% of the variance due to genetic factors, based on twin studies. MD is also higher in families with a strong history of breast cancer. Genome-wide association studies (GWAS), which focus on common genetic variants, have identified several single nucleotide polymorphisms (SNPs) associated with MD. However, these SNPs explain a very small fraction of the variance of MD, suggesting many other genes are involved. Thus, the vast majority of the heritability of mammographic density remains unexplained and may be explained, at least in part, by rare variants. Ashkenazi Jewish women are a founder population; founder populations frequently have alleles affecting phenotypes which may be unique and/or extremely rare in other populations. Prior reports have identified an association between higher mammographic density and Ashkenazi Jewish ancestry. Therefore, we developed a study of mammographic density in AJ women. We combined datasets from several different cohorts including (a) the California Pacific Medical Center Research Institute (b) the Athena Breast Health Cohort and (c) the Marin Women's study. In each study, we identified women who reported AJ ancestry (determined by self-report or by genetic analysis). We identified mammograms by linking the women to the San Francisco Mammography Registry. We retrieved digital mammographic results and used software to infer volumetric density and percent volumetric density for each woman. We then performed genome-wide genotyping of the samples using the Illumina MEGA array.

  Study phs001857