2965 results for "愛の戦士完全破壊2-re-imagination-version-鞭首絞め・電撃・ヘソか"

in 12.94 milliseconds.

• MutWP5__CRUK_Mutographs_of_Cancer__BRCA_Carriers___WG__Novaseq_

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from breast tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will sample from a wide age range of individuals (<20 to >60 years old) to determine whether these processes differ in pre- and post-menopausal women. We will also be comparing the tissue from healthy individuals (samples from breast reduction surgery) to those at elevated risk of breast cancer (mastectomy from BRCA1/2 patients) and those who have breast cancer (adjacent normal, distal normal, and tumour tissue from mastectomy). This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumor development.

  Study EGAS00001003523

• Combined gene expression and digital pathology identifies molecular mediators of T cell exclusion and immune suppression in ovarian cancer

  Close proximity between cytotoxic T lymphocytes and tumour cells is required for effective immunotherapy. However, what determines the spatial distribution of T cells in the tumour microenvironment is not well understood. Coupling digital pathology and transcriptome analysis on a large ovarian tumour cohort, we develop a machine learning approach to molecularly classify and characterize tumour-immune phenotypes. Our study identifies two important hallmarks characterizing T cell excluded tumours: 1) loss of antigen presentation on tumour cells and 2) upregulation of TGFb and activated stroma. Furthermore, we identify TGFb as a key mediator of T cell exclusion. TGFb reduces MHC-I expression in ovarian cancer cells in vitro; TGFb also activates fibroblasts and induced extracellular matrix (ECM) production as a potential physical barrier to hinder T cell infiltration. Our findings indicate that targeting TGFb may represent a promising strategy to overcome T cell exclusion and improve clinical benefits of cancer immunotherapy.

  Study EGAS00001003487

• Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence

  Patients with end-stage kidney disease (ESKD) are at high risk of severe COVID-19. Here, we perform longitudinal blood sampling of ESKD haemodialysis patients with COVID-19, collecting samples pre-infection, serially during infection, and after clinical recovery. Using plasma proteomics, and RNA-sequencing and flow cytometry of immune cells, we identify transcriptomic and proteomic signatures of COVID-19 severity, and find distinct temporal molecular profiles in patients with severe disease. Supervised learning reveals that the plasma proteome is a superior indicator of clinical severity than the PBMC transcriptome. We show that a decreasing trajectory of plasma LRRC15, a proposed co-receptor for SARS-CoV-2, is associated with a more severe clinical course. Strikingly, we observe that two months after the acute infection, patients still display dysregulated gene expression related to vascular, platelet and coagulation pathways, including PF4 (platelet factor 4), which may explain the prolonged thrombotic risk following COVID-19.

  Study EGAS00001006778

• MutWP5__CRUK_Mutographs_of_Cancer__Cancer_Mastectomy__Exome__Novaseq_

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from breast tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will sample from a wide age range of individuals (<20 to >60 years old) to determine whether these processes differ in pre- and post-menopausal women. We will also be comparing the tissue from healthy individuals (samples from breast reduction surgery) to those at elevated risk of breast cancer (mastectomy from BRCA1/2 patients) and those who have breast cancer (adjacent normal, distal normal, and tumour tissue from mastectomy). This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumor development.

  Study EGAS00001003528

• Epigenomic alterations define lethal CIMP-positive ependymomas of infancy

  Ependymomas are common childhood brain tumours that occur throughout the nervous system, but are most common in the paediatric hindbrain. Current standard therapy comprises surgery and radiation, but not cytotoxic chemotherapy as it does not further increase survival. Whole-genome and whole-exome sequencing of 47 hindbrain ependymomas reveals an extremely low mutation rate, and zero significant recurrent somatic single nucleotide variants. Although devoid of recurrent single nucleotide variants and focal copy number aberrations, poor-prognosis hindbrain ependymomas exhibit a CpG island methylator phenotype. Transcriptional silencing driven by CpG methylation converges exclusively on targets of the Polycomb repressive complex 2 which represses expression of differentiation genes through trimethylation of H3K27. CpG island methylator phenotype-positive hindbrain ependymomas are responsive to clinical drugs that target either DNA or H3K27 methylation both in vitro and in vivo. We conclude that epigenetic modifiers are the first rational therapeutic candidates for this deadly malignancy, which is epigenetically deregulated but genetically bland.

  Study EGAS00001000443

• The clinicopathologic spectrum and genomic landscape of de-/trans-differentiated melanoma

  De- and trans-differentiation is a rare and only poorly understood phenomenon in cutaneous melanoma. To study this disease more comprehensively we have retrieved 11 primary cutaneous melanomas from our pathology archives showing biphasic features characterized by a conventional melanoma and additional areas of de-/trans-differentiation as defined by a lack of immunohistochemical expression of all conventional melanocytic markers (S-100 protein, SOX10, Melan-A and HMB-45). The clinical, histologic and immunohistochemical findings were recorded and follow-up was obtained. The patients were mostly elderly (median: 81 years; range: 42-86 years) without significant gender predilection, and the sun-exposed skin of the head and neck area was most commonly affected. The tumors were deeply invasive with a mean tumor thickness of 7 mm (range: 4-80 mm). The dedifferentiated component showed atypical fibroxanthoma-like features in the majority (7), while additional rhabdomyosarcomatous and epithelial transdifferentiation was noted histologically and/or immunohistochemically in two tumors each. The background conventional melanoma component was of desmoplastic (4), superficial spreading (3), nodular (2), lentigo maligna (1) or spindle cell (1) types. For the 7 patients with available follow-up data (median follow-up period of 25 months; range: 8-36 months), 2 died from their disease and 3 developed metastases. Next-generation sequencing of the cohort revealed somatic mutation of established melanoma drivers including mainly NF1 mutations in the conventional component (5 cases), which were also detected in the corresponding de-/trans-differentiated components. In summary, the diagnosis of de-/trans-differentiated melanoma is challenging and depends on the morphologic identification of the conventional melanoma component. Molecular analysis is diagnostically helpful as the mutated gene profile is shared between the conventional and de-/trans-differentiated components. Importantly, de-/trans-differentiation does not appear to confer a more aggressive behavior.

  Dataset EGAD00001007033

• The clinicopathologic spectrum and genomic landscape of de-/trans-differentiated melanoma

  De- and trans-differentiation is a rare and only poorly understood phenomenon in cutaneous melanoma. To study this disease more comprehensively we have retrieved 11 primary cutaneous melanomas from our pathology archives showing biphasic features characterized by a conventional melanoma and additional areas of de-/trans-differentiation as defined by a lack of immunohistochemical expression of all conventional melanocytic markers (S-100 protein, SOX10, Melan-A and HMB-45). The clinical, histologic and immunohistochemical findings were recorded and follow-up was obtained. The patients were mostly elderly (median: 81 years; range: 42-86 years) without significant gender predilection, and the sun-exposed skin of the head and neck area was most commonly affected. The tumors were deeply invasive with a mean tumor thickness of 7 mm (range: 4-80 mm). The dedifferentiated component showed atypical fibroxanthoma-like features in the majority (7), while additional rhabdomyosarcomatous and epithelial transdifferentiation was noted histologically and/or immunohistochemically in two tumors each. The background conventional melanoma component was of desmoplastic (4), superficial spreading (3), nodular (2), lentigo maligna (1) or spindle cell (1) types. For the 7 patients with available follow-up data (median follow-up period of 25 months; range: 8-36 months), 2 died from their disease and 3 developed metastases. Next-generation sequencing of the cohort revealed somatic mutation of established melanoma drivers including mainly NF1 mutations in the conventional component (5 cases), which were also detected in the corresponding de-/trans-differentiated components. In summary, the diagnosis of de-/trans-differentiated melanoma is challenging and depends on the morphologic identification of the conventional melanoma component. Molecular analysis is diagnostically helpful as the mutated gene profile is shared between the conventional and de-/trans-differentiated components. Importantly, de-/trans-differentiation does not appear to confer a more aggressive behavior.

  Dataset EGAD00001007034

• Comprehensive cancer predisposition gene testing in an adult multiple primary tumor series shows a broad range of deleterious variants and atypical tumor phenotypes

  Multiple primary tumors (MPT) affect a substantial proportion of cancer survivors and may result from various causes including inherited predisposition. Currently, germline genetic testing of MPT cases for cancer predisposition gene (CPG) variants is mostly targeted by tumor type. We ascertained pre-assessed MPT cases from genetics centers (defined as ≥2 primaries by age 60 years or ≥3 by 70) and performed whole genome sequencing (WGS) on 460 individuals from 440 families. Despite previous negative genetic assessment/molecular investigations, pathogenic variants in moderate and high-risk CPGs were detected in 67/440 (15.2%) of probands. WGS detected variants that would not be (or were not) detected by targeted resequencing strategies including structural variants at low frequency (6/440 (1.4%) of probands). In most individuals with a germline variant assessed as pathogenic or likely pathogenic (P/LP), at least one of their tumor types was characteristic of variants in the relevant CPG. However, in 29 probands (42.2% of those with a P/LP variant) the tumor phenotype appeared discordant. The frequency of individuals with truncating or splice site CPG variants and at least one discordant tumor type was significantly higher than a control population (χ2=43.642 P=<0.0001). 2/67 (3%) of probands with P/LP variants had evidence of multiple inherited neoplasia allele syndrome (MINAS) with deleterious variants in two CPGs. Summing together variant detection rates from a similarly ascertained previous MPT case series, the present results suggest that first-line comprehensive CPG analysis in a clinical genetics referral-based MPT cohort would detect a deleterious variant in about a third of cases.

  Dataset EGAD00001004088

• Dataset of methylation data from whole blood DNA

  This dataset includes raw data (.idat) for the Illumina Human450k beadchip and methylation levels (.txt files). Methylation level were treated for normalization and background substraction. We removed probes with at least one of the following characteristics: (1) weak signal (p > 0.01) (2128 CpG sites), (2) SNP-enriched sites (4100 sites), (3) out of a CpG context (not on a CG) (3149 sites), or (4) located on sex chromosomes (11,129 sites). A total of 465,071 CpG sites were analyzed initially. Signal was then normalized, first by scaling to the internal controls using the methylumi R package, then by applying the method of subset-quantile within array normalization (SWAN) implemented in the minfi R package.

  Dataset EGAD00010001593

• InterPregGen-GWAS-KAZ-2

  Preeclampsia (PE) is a syndrome affecting pregnant mothers and fetus/babies characterised by hypertension and proteinuria, and is a leading cause of maternal and fetal death and of premature births worldwide. The InterPregGen Consortium was funded by a European Framework 7 (FP7) grant and grew out of the WTCCC3 GWAS comparing ~2000 UK PE mothers with ~6000 common UK controls. This dataset includes OmniExpress genotyping of maternal and fetal PE cases and controls from Kazakhstan. This study is one component of the InterPregGen FP7 project. DNA samples for this component were collected by InterPregGen Consortium collaborators at the Scientific Center of Obstetrics, Gynecology and Perinatology, Almaty, Kazakhstan (Gulnara Svyatova, Principal Investigator).

  Dataset EGAD00010001947

• Summary Statistics GWAS SSNS

  Column 1 rsid: SNP identifier;Column 2 chromosome: name of chromosome on which the SNP is located;Column 3: position: base pair position on the chromosome;Column 4 minor_test_allele: the base that constitutes the minor allele;Column 5 major_allele: the base that constitutes the major allele;Column 6 maf: the frequency of the minor allele, indicated as a fraction of 1;Column 7 allele_freq_cases: the minor allele frequency in cases;Column 8 allele_freq_controls: the minor allele frequency in controls;Column 9 regression_pvalue: the p-value for the difference in allele frequency between cases and controls;Column 10 odds_ratio: the odds ratio, as calculated using logistic regression under an additive model with adjustment for the first ten principal components of ancestry

  Dataset EGAD00010002316

• Raw scRNA-seq data from B-lineage cells in the blood of celiac disease patients

  Raw scRNA-seq data from 355 IgA+ peripheral blood B-lineage cells of two untreated celiac disease patients. The data was generated with the Smart-seq2 protocol and sequenced on a NextSeq500 instrument (Illumina) with 75 bp paired-end reads in high-output mode. The dataset contains R1 and R2 reads for each single cell (fastq.gz files) for cells passing quality control based on number of detected genes, reads, mitochondrial genes, reads mapping to the reference transcriptome and a productively rearranged immunoglobulin heavy chain IgA isotype reconstructed by the computational tool BraCeR. Metadata for the cells is provided in a csv file.

  Dataset EGAD50000000340

• Bulk RNAseq from in vitro generated macrophages and T cells

  Monocyte derived macrophages were polarised into M1 or M2 using IFNg+ ILP or IL-4 respectively (n =4) . Pan T cells were cultured with or without IL-2 for 4 days (n=4 - 6). Pan T cells were co-cultured with THP1 tumour cells for 20 hours in the presence or absence of ImmTAC molecules and in the presence or absence of M2 macrophages. Sorted T cells and macrophage populations prior and post co-culture were analysed by bulk RNA sequencing (60 million reads per sample) using the Illumina NovaSeq system

  Dataset EGAD50000001226

• Single cell RNAseq and TCRseq data from tumor and blood samples from 4 patients with muscle invasive bladder cancer

  For this dataset we performed single cell RNAseq paired with single cell TCR-seq on tumor and blood samples from 4 patients. This dataset contains 4 tumor samples as well as 4 blood samples. Each sample is made up of 2 sets of paired fastq files. The first pair contains reads corresponding to RNA transcripts (_Transcripts in file name), while the second pair contain reads corresponding to TCRs (_VDJ in file name). Sequenced on the Illumina NovaSeq6000 platform in a paired-end run using an SP flow cell (v1.5, 300 cycles).

  Dataset EGAD50000001381

• Sequence data to study genomic CNVs that drive apoptotic resistance and relapses on immune checkpoint inhibitors

  The dataset consists of bulk whole exome sequencing (WES) and whole genome sequencing (WGS) data from baseline and disease-progressed tumors after immune checkpoint inhibitor (ICI) treatment, derived from patients, human cell lines, and mouse models. Whole exome sequencing includes 17 clinical cases and one human cell line model with parental and acquired resistant cell lines (n = 5). Whole genome sequencing encompasses the YUMMER mouse model, including vehicle-treated controls (n = 2) and acquired resistant tumors (n = 3). The sequence reads have been aligned to hg19 and mm9 reference genomes, with mapped files (BAM) made available for analysis.

  Dataset EGAD50000001537

• Summary Statistics GWAS SSNS

  Column 1 “rsid”: SNP identifier Column 2 “chromosome”: name of chromosome on which the SNP is located Column 3: “position”: base pair position on the chromosome Column 4 “minor_test_allele”: the base that constitutes the minor allele Column 5 “major_allele”: the base that constitutes the major allele Column 6 “maf”: the frequency of the minor allele, indicated as a fraction of 1 Column 7 “allele_freq_cases”: the minor allele frequency in cases Column 8 “allele_freq_controls”: the minor allele frequency in controls Column 9 “regression_pvalue”: the p-value for the difference in allele frequency between cases and controls Column 10 “odds_ratio”: the odds ratio, as calculated using logistic regression under an additive model with adjustment for the first ten principal components of ancestry

  Dataset EGAD00001008782

• Nuclease deficiencies alter plasma cell-free DNA methylation profiles (Mouse samples)

  The dataset contains sequencing data in wildtype, Dnase1-deficient and Dnase1l3-deficient mice. We performed 2 x 75bp paired-end whole genome bisulfite sequencing of pooled plasma cell-free DNA (cfDNA) and buffy coat genomic DNA. The effects of DNASE1L3 or DNASE1 deficiency on cfDNA methylation was explored in plasma of mice deficient in these nucleases.

  Dataset EGAD00001007751

• BLUEPRINT Whole-genome fingerprint of the DNA methylome during human B-cell differentiation

  We used whole-genome bisulfite sequencing (WGBS) to generate unbiased DNA methylation maps of six purified B-cell subpopulations: hematopoietic progenitor cells (HPC); pre-B-II cells (preB2C); naive B cells from peripheral blood (naiBC); germinal center B cells (gcBC); memory B cells from peripheral blood (memBC) and plasma cells from bone marrow (bm-PC). WGBS was performed in 2 biological replicates from each subpopulation.

  Dataset EGAD00001001304

• Inter and intra - tumor heterogeneity in Colorectal Cancer

  The dataset consists of two main sample groups. 1) The inter-tumour sample group contains a total of 97 samples from 27 patients. Each patient has a single normal and primary sample as well as one or more metastases. All samples were sequenced using IonTorrent PGM and a custom colorectal cancer (CRC) panel. 2) The intra-tumour sample group contains a total of 68 samples from a single tumour as well as a normal tissue sample. All 68 samples were sequenced using IonTorrent PGM and a custom CRC panel. Shallow whole genome sequencing was additionally applied to 10 of the samples using Illumina HiSeq 4000.

  Dataset EGAD00001003131

• The natural history of clonal haematopoiesis: time-series (phase 1-5) targeted

  Deep targeted sequencing of 56 genes associated with clonal haematopoiesis and haematological malignancy in peripheral blood-derived DNA from 385 older adults, each sampled 2-5 times over ~13 years.

  Dataset EGAD00001007682

• LLDeep DAG2+: scRNA-seq in PBMCs

  Single cell transcriptomics of PBMCs of 47 donors from the Lifelines Deep cohort (general population, Northern part of the Netherlands). Cells of five or six different donors were pooled together in one sample pool, resulting in eight different sample pools. In total, 28.855 cells were captured and their transcriptomes were sequenced to an average depth of 74k. Genotype data was available for each donor, which allowed us to use the Demuxlet method that uses variable SNPs between the pooled individuals to determine which cell belongs to which individual. Since genotype information is lacking of 2 individuals, the transcriptome of only 45 individuals could be retrieved.

  Dataset EGAD00001003459

• Multiregion Whole Exome sequencing of pHGG and DIPG

  The failure to develop effective therapies for paediatric glioblastoma (pGBM) and diffuse intrinsic pontine glioma (DIPG) is in part due to their intrinsic heterogeneity. Analysis of 142 sequenced cases revealed multiple tumour subclones, spatially and temporally co-existing in a stable manner as observed by multiple sampling strategies. This dataset provides multi region sequencing of high grade gliomas and diffuse intrinsic pontine gliomas from 15 patients. DNA was extracted from FFPE sections in 2-13 regions of each tumour and sequenced with Agilent SureSelect whole exome sequencing. Germline DNA was also sequenced in 14 cases. Data was aligned to hg19 with bwa and is provided as 79 separate BAM files.

  Dataset EGAD00001004114

• Transcriptomic profiling of RIRCD patient skeletal muscle, comparing muscle during affected phase to healthy control, and affected patient with and without additional EARS2 mutations.

  Illumina Nextseq total RNA sequencing profiles of skeletal muscle biopsies of 5 affected patients (F3/2M, F4/1F, F5/1M, F2/2F, F2/1M) compared to 6 control (Control040500, Control3509, Control3934, Control3949, Control4994, Control5106) and comparing 3 patients with additional EARS2 mutations (F2/2F, F2/1M, F5/1M) to 2 patients without (F3/2M, F4/1F). Illumina MiSeq total RNA sequencing profiles of skeletal muscle biopsies from patient F3/1M during affected disease phase (4 replicates: F3-1M_Affected_1, F3-1M_Affected_2, F3-1M_Affected_3, F3-1M_Affected_4) compared to recovered phase (F3-1M_Recovered_1, F3-1M_Recovered_2, F3-1M_Recovered_3, F3-1M_Recovered_4).

  Dataset EGAD00001006381

• Cell-free DNA sequencing data of healthy control, atrophic gastritis, and gastric cancer patients’ blood

  Sample cohort (n=48) is consisted of healthy, atrophic gastritis and gastric cancer patients. Some of the gastric cancer patients samples are collected at the separate time points: -1 before the operation; -2 after the operation; -3 during the control visit. For the hybridisation capture of the genes unique 15 gastric cancer-related gene panel was developed and very deep sequencing using TruSight Oncology Unique Molecular Identifier (UMI) Reagents (Illumina) was used.

  Dataset EGAD00001011153

• Implementation, Adoption, and Utility of Family History in Diverse Care Settings

  The purpose of this study is to address the key question of whether and how family health history (FHH) is adopted as a tool to more efficiently manage patients at risk for breast, colon, ovarian, and hereditary cancer syndromes as well as thrombophilia and coronary heart disease (CHD) and to provide evidence supporting clinical utility -- improved health behaviors in patients and physician screening recommendations. Five health care delivery organizations will participate in this demonstration project: Duke University, the Medical College of Wisconsin, the Air Force, Essentia Health, University of North Texas. Duke will serve as a coordinating center for this project (Pro00043372) as well as a site. Healthcare Effectiveness Data and Information Set (HEDIS) measures as intermediate clinical effectiveness measures for Coronary Heart Disease (CHD) and selected cancers as well as survey/formative data and electronic medical record (EMR) data will be used as outcomes measures. The research model is purposely designed to mimic clinical delivery as an important step toward widespread implementation and sustainability. In addition, a cost-effectiveness analysis comparing usual care to the FHH guided preventive health model will be used. The completion of this project will result in an optimal strategy for integration of FHH data collection and clinical decision support (CDS) tools into an EMR and demonstrate the utility of the FHH intervention among diverse primary care patients, their settings, their providers, and the health systems that deliver their care. Specific Aim 1: To optimize the collection of patient entered FHH in diverse clinical environments for coronary heart disease, thrombosis, and selected cancers Specific Aim 2: To export FHH data to an open clinical decision support (open CDS) platform and return CDS results to providers and patients (and to EMRs where relevant). To explore the integration of genetic risk and FHH data at selected sites. Specific Aim 3: To assess the clinical and personal utility of FHH using a pragmatic observational study design to assess reach, adoption, integrity, exposure, and sustainability, and to capture, analyze, and report effectiveness outcomes at each stakeholder level: patient, provider, and clinic/system. Specific Aim 4: To take a leadership role in the dissemination of guidelines for a FHH intervention across in diverse practice settings. AAA Hypertension Lupus Multiple sclerosis Obesity Osteoporosis Thyroid disease Rheumatoid arthritis Blood clotting (6): Blood Clot (high risk features) Factor V PT mutation AT III deficiency Protein S deficiency Protein C deficiency Brain Disorders (5): Dementia Hemorrhagic stroke Ischemic stroke Macrocephaly Seizure Cancer/Adenomas (32): Bone cancer Brain cancer Breast cancer Colon cancer Adrenal cortex tumor Neuroendocrine tumor Paraganglioma Pheochromoctyoma Pituitary adenoma Medullary thyroid cancer Non-medullary (follicular or papillary) thyroid cancer Don't know type of thyroid cancer Thyroid nodule Other type of endocrine cancer Esophageal cancer Kidney cancer Leukemia Lipoma Liver cancer Muscle cancer Ovarian cancer Pancreatic cancer Prostate cancer Rectal cancer Retinoblastoma Melanoma Non-melanoma skin cancer Do not know type of skin cancer Small bowel cancer Stomach cancer Uterine cancer Other type of cancer Cardiovascular/heart/artery disease (5): Atrial fibrillation Carotid stenosis Heart attack/coronary artery disease Peripheral arterial disease Other heart disease Diabetes (3): Gestational diabetes Diabetes type I Diabetes type 2 Digestive Disorders (5): Colon polyp Crohn's disease Irritable bowel syndrome Ulcerative colitis Other digestive disorder Eye Disorder (3): Blindness Glaucoma Macular degeneration Hereditary Cancer Syndromes (22): Birt-Hogg-Dube syndrome Cowden syndrome Familial adenomatous polyposis Hereditary breast and ovarian cancer syndrome Hereditary diffuse gastric cancer Hereditary leiomyomatosis and renal cell carcinoma syndrome Hereditary melenoma Hereditary papillary renal cancer syndrome Hereditary paraganglioma-pheochromocytoma syndrome Hereditary retinoblastoma Juvenile polyposis Li-Fraumeni syndrome Lynch syndrome Mutyh-associated polyposis Malignant hyperthermia susceptibility Multiple endocrine neoplasia type 1 Multiple endocrine neoplasia type 2 Nevoid basal cell carcinoma syndrome Peutz-Jeghers syndrome Tuberous Sclerosis complex Von-Hippel Lindau syndrome Other hereditary cancer Hereditary Cardiovascular syndromes (10): Long qt Brugada Catecholaminergic polymorphic ventricular tachycardia Hypertrophic cardiomyopathy Dilated cardiomyopathy Left ventricular non-compaction syndrome Arrhythmogenic right ventricular dysplasia Ehlers-Danlos syndrome Marfan syndrome Other hereditary cardiovascular syndrome High cholesterol (2): Hyperlipidemia Familial hypercholesterolemia Kidney Disease (6): Cystic kidney disease Diabetic kidney disease Kidney nephrosis Nephritis Nephrotic syndrome Other kidney disease Liver Disease (6): Alpha 1 antitrypsinase deficiency Auto-immune hepatitis Hereditary hemochromatosis Primary biliary cirrhosis Sclerosing cholangitis Wilson's disease Lung Disease (3): Asthma COPD/chronic bronchitis/emphysema Other lung disease Psychological disorder (14): Addiction ADHD Autism Bipolar Depression Eating disorder Intellectual disability Obsessive compulsive disorder Panic disorder Personality disorder PTSD Schizophrenia Social phobia Sickle cell/thalassemia (3): Sickle cell disease Sickle cell trait Thalassemia

  Study phs001641