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• UK10K NEURO IOP COLLIER

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The Genetics and Psychosis (GAP) set consists of samples from subjects with schizophrenia, ascertained as a new-onset sample. This set is of UK origin, with data on cognition, brain imaging and other endophenotypes. The Maudsley twin series consists of probands ascertained from the Maudsley Twin Register, defined as patients of multiple birth who had suffered psychotic symptoms. This set is of UK origin, with data on cognition, brain imaging and other endophenotypes, with DNA available from an MZ or DZ affected or unaffected co-twin. The Maudsley family study (MFS) consists of over 250 families who have a history of schizophrenia or bipolar disorder Within the Maudsley Family Study, biological markers of psychosis include neuropsychological tests, Evoked Response Potentials Tests (ERPs), MRI scans, dermatoglyphics and eye tracking. Early risk factors for psychosis and clinical symptoms are also investigated. This set is of UK origin, with DNA available from both affected and unaffected relatives in many of the probands. For further information on this study please contact David Collier (david.collier@kcl.ac.uk).

  Study EGAS00001000121

• Germline elongator mutations in sonic hedgehog medulloblastoma

  Cancer genomics has illuminated a wide spectrum of genes and core molecular processes contributing to human malignancy. Still, the genetic and molecular basis of many cancers remains only partially explained. Genetic predisposition accounts for 5-10% of cancer diagnoses and genetic events cooperating with known somatic driver events are poorly understood. Analyzing established cancer predisposition genes in medulloblastoma (MB), a malignant childhood brain tumor, we recently identified pathogenic germline variants that account for 5% of all MB patients. Here, by extending our previous analysis to include all protein-coding genes, we discovered and replicated rare germline loss-of-function (LoF) variants across Elongator Complex Protein 1 (ELP1) on 9q31.3 in 15% of pediatric MBSHH cases, thus implicating ELP1 as the most common MB predisposition gene and increasing genetic predisposition to 40% for pediatric MBSHH. Inheritance was verified based on parent-offspring and pedigree analysis, which identified two families with a history of pediatric MB. ELP1-associated MBs were restricted to the molecular SHHa subtype and were characterized by universal biallelic inactivation of ELP1 due to somatic loss of chromosome 9q. The majority of ELP1-associated MBs exhibited co-occurring somatic PTCH1 (9q22.32) alterations, suggesting that ELP1-deficiency predisposes to tumor development in combination with constitutive activation of SHH signaling. ELP1 is an essential subunit of the evolutionary conserved Elongator complex, whose primary function is to enable efficient translational elongation through tRNAs modifications at the wobble (U34) position. Biochemical, transcriptional, and proteomic analyses revealed that ELP1-associated MBSHH are characterized by a destabilized core Elongator complex, loss of Elongator-dependent tRNA modifications, codon-dependent translational reprogramming, and induction of the unfolded protein response (UPR), consistent with deregulation of protein homeostasis due to Elongator-deficiency in model systems. Our findings suggest that genetic predisposition to proteome instability is a previously underappreciated determinant in the pathogenesis of pediatric brain cancer. These results provide strong rationale for further investigating the role of protein homeostasis in other pediatric and adult cancer types and potential opportunities for novel therapeutic interference.

  Study EGAS00001004126

• APOBEC mutagenesis is a common process in normal human small intestine

  The single base substitution mutational signatures SBS2 and SBS13, likely caused by APOBEC cytosine deaminases, are common in many human cancer types. However, the stimulus activating APOBEC mutagenesis is unknown and understanding of when it occurs in the progression from normal to cancer cell is limited. Here, as part of a wider survey of human tissues, we whole genome sequenced 342 microdissected normal epithelial crypts from the small intestines of 39 individuals. SBS2/13 mutations were present in 17% normal small intestine crypts and were likely due to APOBEC3A activity. Localised clusters of SBS2/13 mutations (kataegis) were also commonly found. APOBEC mutation burdens were variable between individuals and between crypts from the same individual. Crypts with SBS2/13 often had immediate crypt neighbours without SBS2/13, suggesting that the underlying cause of SBS2/13 is cell-intrinsic rather than a widely distributed microenvironmental exposure, or needs to be permitted by cell-intrinsic conditions. APOBEC mutagenesis occurred throughout the human lifespan, including in young children, and was episodic with a small number of episodes occurring during the life history of a single cell. The results indicate that APOBEC mutagenesis is more common in the small intestine epithelium than in many other cell types, and is an episodic process in vivo initiated or permitted by cell intrinsic factors.

  Dataset EGAD00001008764

• GAW16 Framingham and Simulated Data

  Important links to apply for individual-level data Genetic Analysis Workshop Instructions to Request Authorized Access Data Use Certification Requirements (DUC) Apply here for controlled access to individual level data Research Use Statement Questions regarding GAW16 should be directed to Vanessa Olmo at vanessa@business-endeavors.com. Problem 2: Description of the Framingham Heart Study In GAW16, we use data drawn from the Framingham Heart Study. The Framingham Heart Study — under the direction of National Heart, Lung, and Blood Institute; NHLBI — began in 1948 with the recruitment of adults from the town of Framingham, Massachusetts. At the time, little was known about the general causes of heart disease and stroke, but the death rates for cardiovascular disease (CVD) had been increasing steadily since the beginning of the 20th century and had become an American epidemic. The Framingham Heart Study is now conducted in collaboration with Boston University. The objective of the Framingham Heart Study was to identify the common factors or characteristics that contribute to CVD by following its development over a long period of time in a large group of participants who had not yet developed overt symptoms of CVD or suffered a heart attack or stroke. Between 1948 and 1953 the researchers recruited 5,209 subjects (2,336 men and 2,873 women) between the ages of 29 and 62 from the town of Framingham, Massachusetts and began the first round of extensive physical examinations and lifestyle interviews that they would later analyze for common patterns related to CVD development. Subjects were recruited from lists of addresses recorded for the town. Two out of every three households were approached for participation in the study. While there was no intention to recruit families for family studies, the plan was to recruit all household members in the ages 30-60 within each house that was selected for study. Hence, many biologically related individuals were recruited, including 1644 spouse pairs. Since 1948, these participants have returned to the study every two years for a detailed medical history, physical examination, and laboratory tests. Now in 2008 at 60 years of follow up, there remain about 500 participants from this cohort. Between 1971 and 1975 the study enrolled a second-generation group — 5,124 of the original participants' children and the spouses of these children — to participate in similar examinations. 2,616 subjects are offspring of the original spouse pairs and 34 are stepchildren. A total of 898 offspring are children of cohort members where only one parent was a study participant and 1,576 are spouses of the offspring. The Offspring Cohort has been followed every four years through 2001 (except between Exams 1 and 2 with an intervening 8 years) using protocols similar to those used for study of the Original Cohort. Between 2002 and 2005 the study enrolled the third generation (Gen3) of the Framingham Heart Study - 4095 offspring of the second generation. None of their spouses were recruited. An additional 103 parents of this third generation, who were not recruited between 1971 and 1975, were also recruited at this time. The latter group is not included in the GAW16 data. With the recruitment of this third generation, the study has increasingly focused on genetic factors associated with the development of cardiovascular disease and its associated risk factors. To date, there is only one examination of this generation of participants. A description of the recruitment of this third generation and comparison with the earlier generations at their initial recruitment is presented in Splansky GL et al., 2007. Further information on the Study can be found at http://www.nhlbi.nih.gov/about/framingham/index.html. Genome-wide Dense SNP Scan in Framingham Heart Study Genetic studies did not begin in the FHS until the 1990s. In the late 1980s and through the 1990s DNA was extracted from blood samples of surviving FHS participants. In 2007, the FHS entered a new phase with the conduct of genotyping for the FHS SHARe (SNP Health Association Resource) project, for which dense SNP genotyping was performed using approximately 550,000 SNPs (GeneChip® Human Mapping 500K Array Set and the 50K Human Gene Focused Panel) in 10,775 samples (some duplicates) from the three generations of subjects (including over 900 pedigrees). Affymetrix conducted all genotyping for the FHS SHARe project, using the 250K Sty, 250K Nsp, and the supplemental 50K platforms. Eighty-nine percent of the DNA samples were collected during the 1990s. To maximize the power of the study, we also extracted DNA from 1133 blood samples, drawn from subjects who had no DNA, to include in the SHARe project. These samples had been sitting in our refrigerators for some time, a few as far back as the 1970s. We refer to these DNA samples as the legacy samples. These samples had a higher failure rate in the genotyping process (40%) than the other eighty-nine percent (3%). Affymetrix invoked its own criteria for a sample to succeed in genotyping. All non-legacy samples must succeed on all three platforms, while legacy samples needed to pass on at least one platform. When a sample failed, additional attempts were made. Samples that repeatedly failed 2-4 times were called failures. Other samples failed due to issues of genotyped sex identification not matching our records or low SNP concordance among SNPs common across arrays or contamination. Eighty-nine percent of the legacy samples for which genotyping results are available passed all three platforms. The genotyping data from the 10,043 samples from 9354 subjects that passed the Affymetrix criteria were additionally checked for gender consistency and consistency with family structure, resulting in genotyping data for 9,274 participants in FHS SHARe. Genotype calls were made with the BRLMM algorithm. The SHARe database is housed at the National Center for Biotechnology Information database of genotypes and phenotypes (NCBI dbGaP) and contains all ~550,000 SNPs. This genome-wide dense SNP scan and a subset of phenotypes from the Framingham Heart Study are the focus of the Genetic Analysis Workshop 16. Further information on the specific variables in the Problem 2 dataset can be found by clicking on the Documents tab at the top of the page. Problem 3: Description of the Simulated Data Set The focus of this simulation is gene discovery in genome-wide association scans (GWAS). The Framingham Heart Study data set (distributed as "Problem 2") is the basis for the FHS* simulated data. The pedigree structures are derived from the data distributed for Problem 2, and we distribute an accompanying triplet file (triplet_sim) containing person ID, father ID, mother ID, to ensure the identical subjects, pedigrees, and singletons are used in the simulated data analysis. Consistent with standard practice, founders and singletons are designated as subjects with both fshare and mshare equal to zero (missing). The simulated data includes a total of 6,479 subjects with both phenotype and genotype data, in 942 pedigrees distributed among 3 generations and 188 singletons. Data inclusion is consistent with the subjects' consent for use by both for-profit and not-for-profit researchers. The genotypes for all Problem 3 replicates are fixed as measured and distributed for Problem 2 for both the genomewide scan and the additional candidate gene SNPs, for a total of approximately 550,000 SNPs (GeneChip® Human Mapping 500K Array Set and the 50K Human Gene Focused Panel). Thus, to analyze the Problem 3 simulated data you also will need to download the Problem 2 genotypes. Note that there are slight discrepancies in counts between Problems 2 and 3 due to a change in consents between the two datasets. Several phenotypes that contribute to coronary heart disease (CHD) were simulated for all individuals with genotypes across three different time points, 10 years apart. All genotyped individuals have complete data; the effects of missing values can be investigated by user-specified missing value patterns. There are 200 longitudinal datasets created, based on the generating model, and each replication is found in a separate dataset. We suggest that if only one replication is to be analyzed, that it be replication 1 to enable more precise comparisons among analytical approaches. The 'shareid' will allow you to merge the simulated phenotype data with the Problem 2 genotype data, and reconstruction of the pedigrees using the distributed 'triplet_sim' file or for larger families' relationships with the triplet distributed with the Problem 2. The simulated data problem is further described in the associated readme file, and a data dictionary is provided defining all the variables. For disclosure of the generating model for these data, please contact Jean MacCluer at jean@sfbrgenetics.org.

  Study phs000128

• dbGaP Collection: NHLBI Heart Failure Related dbGaP Data (No IRB requirement)

  The National Heart, Lung, and Blood Institute (NHLBI) Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research has concluded. The NHLBI will continue to make this collection of data available to the research community.Past information regarding the Data Collection for the NHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research is below.The National Heart, Lung, and Blood Institute (NHLBI), part of the National Institutes of Health (NIH), is inviting novel Solutions for the NHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research. The goal of the challenge is to foster innovation in computational analysis and machine learning approaches utilizing large-scale NHLBI-funded datasets to identify new paradigms in heart failure research. The challenge aims to address the need for new open source disease models that can define sub-categorizations of adult heart failure to serve as a springboard for new research hypotheses and tool development in areas of heart failure research from basic to clinical settings. NHLBI has a history of making considerable investments in the creation of deep data resources including: long-standing, deeply-phenotyped epidemiological cohorts, innovative clinical trials, and large-scale precision medicine efforts that have generated whole genome sequencing and "other omics" data for more than one-hundred thousand individuals. To provide challenge participants with streamlined access to data across NHLBI's numerous studies containing heart failure data, the NHLBI has created this dbGaP study collection. Data access for this collection is controlled by NHLBI's Data Access Committee. Challenge participants are required to follow the use restrictions and acknowledgement instructions from the original dataset(s). Please note that the data in this collection are not harmonized across studies or otherwise altered from the original study. The NHLBI Big Data Analysis Challenge: Heart Failure Data Collection contains all NHLBI studies currently in dbGaP that contain data that may be relevant to research on heart failure. The studies in this collection are approved for General Research Use (GRU), Health/Medical/Biomedical Use (HMB), or Disease-Specific Use (DS) that permits research on heart failure. These studies span a variety of study designs, inclusion and exclusion criteria, sample sizes, and provide a wide breadth and depth of phenotype data on study participants. The available genomic data in these studies also varies, including genotyping arrays, sequencing (targeted, exome, whole genome), and additional -omic data (e.g., RNA or metabolite profiles). Please refer to each study's individual accession page to learn more about how study data were collected. Challenge participants are reminded that in addition to dbGaP, NHLBI's Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) contains other studies that have collected data relevant to heart failure and may wish to utilize BioLINCC data in the development of their Solution. Please refer to the individual accession page for each study in this collection to learn more about the history of each study.

  Study phs001991

• 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

• Understanding the genetic risk underlying racial disparities in uterine fibroids

  Fibroids affect 77% of women by onset of menopause in the U.S. and account for $2.1 billion in healthcare costs each year. Fibroids negatively impact reproductive health causing heavy and painful menses, pelvic pain and pressure, pregnancy complications, and interventions including myomectomy and hysterectomy. Until recently, tumor tissue and cell culture studies investigating fibroid growth have been the primary sources for understanding fibroid pathophysiology. Genetic analysis can provide a powerful and cost effective tool to identify etiological and causal factors, especially since a genetic predisposition to fibroids has already been documented from twin studies. As much as 69% of risk is explained by genetic factors. Racial disparities also support a role for genetics with fibroid risk. African American women have earlier age of onset, more numerous and larger fibroids with a greater lifetime incidence compared to Caucasians. We propose to identify genetic markers for risk of fibroids through a genome-wide association study (GWAS) of African American and Caucasian participants, leveraging ancestral differences to narrow down genomic regions for targeted follow- up analyses. To accomplish this we will take advantage of a unique Vanderbilt resource, the BioVU DNA databank. BioVU currently has over 122,470 adults linked to electronic medical records. From BioVU we have already identified 2,902 African American and Caucasian subjects who meet our stringent inclusion criteria to conduct a GWAS of fibroids, including pelvic imaging. Available imaging is critical, because many women with fibroids are asymptomatic and without imaging, studies may misclassify as many as 51% of women. We have also defined definitive controls who reached menopause without fibroids. We have a strong group of nationally known fibroid researchers who will provide over 10,000 samples for replication. Our first Specific Aim is to conduct a GWAS for association between common single nucleotide polymorphisms (SNPs) and fibroid risk. Using a case-control design we will perform a GWAS in 2,902 (1,451 fibroid cases and 1,451 controls) women from BioVU stratified by African American and Caucasian race. Secondary admixture mapping (AM) analyses will also be performed to identify chromosomal regions of interest to prioritize for replication. Our second Aim is to resequence chromosome regions identified from GWAS and AM to discover rare variants. Finally, in Aim 3 we will replicate SNPs selected from Aim 1 and 2 in independent samples of at least 3,230 fibroid cases and 7,097 controls. We propose an efficient and cost-effective approach to identify genetic risk factors for fibroids, by taking advantage of imaging information and DNA available through BioVU. This study represents the largest GWAS of uterine fibroids and the first among African Americans leveraging emerging technologies and new statistical approaches to conduct this study. Our proposed study will fundamentally change knowledge about fibroids and lay the ground work for breakthroughs in understanding mechanisms of fibroid formation and in identifying novel therapeutic approaches.

  Study phs001409

• ZNF703 is a common Luminal B breast cancer oncogene that differentially regulates luminal and basal progenitors in human mammary epithelium.

  The telomeric amplicon at 8p12 is common in oestrogen receptor-positive (ER+) breast cancers. Array-CGH and expression analyses of 1172 primary breast tumours revealed that ZNF703 was the single gene within the minimal amplicon and was amplified predominantly in the Luminal B subtype. Amplification was shown to correlate with increased gene and protein expression and was associated with a distinct expression signature and poor clinical outcome. ZNF703 transformed NIH 3T3 fibroblasts, behaving as a classical oncogene, and regulated proliferation in human luminal breast cancer cell lines and immortalized human mammary epithelial cells. Manipulation of ZNF703 expression in the luminal MCF7 cell line modified the effects of TGFβ on proliferation. Overexpression of ZNF703 in normal human breast epithelial cells enhanced the frequency of in vitro colony-forming cells from luminal progenitors. Taken together, these data strongly point to ZNF703 as a novel oncogene in Luminal B breast cancer.

  Study EGAS00000000082

• A microRNA-signature that correlates with cognition and is a target against cognitive decline

  While some individuals age without pathological memory impairments, others develop age-associated cognitive diseases. Since changes in cognitive function develop slowly over time in these patients, they are often diagnosed at an advanced stage of molecular pathology, a time point when causative treatments fail. Thus, there is great need for the identification of inexpensive and minimal invasive approaches that could be used for screening with the aim to to identify individuals at risk for cognitive decline that can then undergo further diagnostics and eventually stratified therapies. In this study we use an integrative approach combining the analysis of human data and mechanistic studies in model systems to identify a circulating 3-microRNA signature that reflect key processes linked to neural homeostasis and informs about cognitive status. We furthermore provide evidence that expression changes of this signature represent multiple mechanisms de-regulated in the aging and diseased brain and are a suitable target for RNA therapeutics.

  Study EGAS00001005627

• Loss of Epigenetic Barrier is Required for Enhancer Hijacking-Mediated Oncogenic Transcription

  Structural variants (SVs) involving enhancer hijacking can disrupt chromatin topologies to cause oncogene activation in cancer genomes, yet the molecular determinants for the transcriptional output of enhancer hijacking remain largely unknown. We developed a multimodal approach to integrate genome sequencing, chromosome conformation, and sequence-based deep learning for quantitative analysis of transcriptional effects and structural reorganization imposed by SVs in leukemic genomes. We identified candidate pathogenic SVs including recurrent t(5;14) translocations that cause the hijacking of BCL11B enhancers for oncogenic activation of TLX3-dependent transcriptional programs. By engineering patient-associated t(5;14) in isogenic leukemia cells, we uncovered an uncharacterized mechanism whereby DNA methylation serves as an epigenetic barrier to enhancer hijacking and loss of epigenetic barrier is a molecular determinant for the transcriptional output of pathogenic SVs. Hence, leveraging the epigenetic barriers of SV-mediated oncogenic programs may provide new opportunities to reprogram gene regulation as epigenetic therapies in human disease.

  Study EGAS00001006140