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• Method to Assess Lung Water Accumulation During Exercise

  The goal of this non-significant risk medical device study is to test the initial technical feasibility of a 0.55T MRI in healthy volunteers by technical optimization of scanner protocols, and to test accuracy of standard MR measurements in adult patients with known stable disease. Exercise-induced lung water is a key feature in heart failure. Dynamic quantitative lung water imaging during exercise stress is therefore of interest to unmask latent heart failure. We developed a novel magnetic resonance imaging (MRI) method using a proton density weighted ultrashort echo time MRI sequence along with a time-resolved and respiratory motion compensated image reconstruction for exercise lung water imaging. We validated our method in a porcine model of dynamic lung water accumulation (n=5), and tested its feasibility for use in humans using 12 healthy subjects, 1 patient with heart failure with preserved ejection fraction, and 1 patient with heart failure with reduced ejection fraction. Data suggest the novel imaging method is capable of dynamically imaging lung water accumulation during exercise stress. Reference 1 (PMID: 37288601) contains the main results for this study.

  Study phs003346

• Youth-GEMs, the project using data to define the mental health trajectories of young people

  Mental health trajectories are shaped by genetic, cognitive, social, and environmental factors that are particularly influential during human development. How exactly the interplay between these factors is impacting mental health, however, is yet unknown. The project “Gene Environment interactions in Mental health trajectories of Youth (Youth-GEMs)”, funded by the European Commission under the Horizon Europe programme, aims to help reducing mental suffering among European youth. How do Youth-GEMs contribute to bettering mental health in young people? Youth-GEMs aims to identify specific neurological, (epi)genetic and environmental drivers on mental health trajectories of young people. To do so, the project is working on a public knowledge base on functional genomics of the developing human brain, between birth and 24 years old. Reliable predictive models for trajectories of onset and course of mental illness in young people of the general population in Europe will be developed. Additionally, a secure and FAIR data infrastructure in mental health is being produced. Engaging and empowering young people Youth-GEMs is also working on the engagement of young people in the scientific field of Psychiatry. For that, the project is developing initiatives such as Science Café, intended to create a learning community where ideas and stories are shared. The 10th Science Café will take place today, with the participation of Lauren Fromont from the EGA. How do the EGA contribute to Youth-GEMs? The contributions require careful data management and harmonisation: we are dealing with an extraordinary amount of data types, sources, and time points (since the studies are longitudinal). To analyse them together and to determine which factors and interactions are truly key to account for mental health trajectories, the variables need to be transformed in a common “language”, i.e., harmonised under a common data model. Further, to guarantee FAIRness of data storage and sharing for future projects that will further advance our knowledge in the mental health field, we are ensuring that data are processed securely but can also be discovered easily. Find out more information about the project in the Youth-GEMs website.

  Blog youth-gems-mental-health

• INCLUDE: Down Syndrome Early Post-Natal Brain Multiome

  Neurodevelopmental deficiencies and neuronal dysfunction contribute to cognitive impairment in individuals with Down syndrome, but what molecular pathways underlie the defects that lead to intellectual disability remain unclear. Human brain development is precisely choreographed through tightly regulated transcriptional programs. Proper regulation of these transcriptional programs is necessary for developing the numerous functionally distinct regions, cell types, and connections of the brain, and forming the basis for human-specific social, cognitive, and motor abilities. These data establish a reference of molecular features of the DS developing brain. Such foundational information will provide not only a reference for any existing and future models, but also a basis for understanding disrupted neural development and impaired cognition in DS.Dataset 1 contains single-nucleus RNAseq and ATACseq from frozen postmortem human basal forebrain tissue. Dataset 2 contains single-nucleus RNAseq and ATACseq from frozen postmortem human dorsolateral prefrontal tissue. Dataset 3 contains single-nucleus RNAseq and ATACseq from frozen postmortem human caudate nucleus tissue.

  Study phs004098

• Whole Exome Sequencing for Colorectal Cancer

  This project was designed to use next generation sequencing technology to screen the protein coding regions of the genome for low frequency variants in a panel of high-risk colorectal adenocarcinoma cases. Blood and cell-line DNA for colorectal cancer patients and a subset of quality control samples that had existing whole exome sequence data were analyzed using Illumina HiSeq sequencers. Samples from this project were from participants in the Women's Health Initiative (WHI) and the Diet, Activity, and Lifestyle Study (DALS).

  Study phs000410

• Systems biology of Colorectal Cancer

  Colorectal cancer (CRC) is one of the most common cancers in both males and females, and it is perhaps the best understood of all epithelial tumors in terms of its molecular origin. Yet, despite large amount of work that has concentrated on understanding of colon tumorigenesis, we still do not know the full complement of molecular lesions that are individually necessary – and together sufficient – to cause colorectal cancer. Neither do we understand why some specific mutations that are relatively rare in other tumors (e.g. loss of the APC tumor suppressor) are extremely common in colorectal cancer. We propose here to use the tools of systems biology to develop a quantitative and comprehensive model of colorectal tumorigenesis. The model will include identification of cell-type specific and oncogenic pathways that contribute to colon tumorigenesis, and explain in molecular detail how a genotype of an individual CRC leads to activation of downstream genes that drive uncontrolled cell growth. This model will subsequently be used to find novel therapeutic targets, to guide genetic screening to identify individuals with elevated risk for developing CRC, and to classify patients into molecular subgroups to select the treatment combination that is optimal for each patient (personalized medicine). The specific objectives of the SYSCOL project are: 1. Identify genetic markers for individual risk using genotyping and sequencing of constitutional DNA from sporadic and familial CRC cases and controls 2. Identify genes and regulatory elements that contribute to colorectal cancer cell growth 3. Use data from Aims 1-2 to develop a quantitative model for colorectal tumorigenesis 4. Apply the model for identification of high-risk individuals, for molecular classification of the disease, and for identification of novel molecular treatment targets

  Study EGAS00001000854

• Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes

  Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide. Only a fraction of NSCLC harbour actionable driver mutations and there is an urgent need for patient-derived model systems that will enable the development of new targeted therapies. NSCLC and other cancers display profound proteome remodelling compared to normal tissue that is not predicted by DNA or RNA analyses. We generated NSCLC patient-derived xenografts (PDXs) that recapitulate the histology and molecular features of primary NSCLC. Here, we completed whole exome sequencing on 122 NSCLC PDXs.

  Study EGAS00001006068

• SweGen whole-genome sequencing from the Northern Sweden Population Health Study

  The dataset contains whole-genome sequencing data (aligned read files) in CRAM-format (lossless compression) for a total of 58 DNA samples originating from the Northern Sweden Population Health Study (NSPHS). For each of the 58 individuals, DNA was extracted from a blood sample and subject to whole genome sequencing (WGS). The WGS was performed using 2x150 bp paired-end chemistry on Illumina HiSeq X Ten instrumentation at the SciLifeLab National Genomics Infrastructure (NGI) in Stockholm and Uppsala. FASTQ files generated by WGS were analyzed using the nf-core pipeline Sarek, which includes pre-processing, alignment to the human GRCh38 reference genome, and germline variant calling. The NSPHS study was approved by the local ethics committee at the University of Uppsala (Regionala Etikprövningsnämnden, Uppsala, 2005:325 and 2016-03-09). All participants gave their written informed consent to the study including the examination of environmental and genetic causes of disease in compliance with the Declaration of Helsinki.

  Dataset EGAD50000001325

• SweGen genetic variation from the Northern Sweden Population Health Study

  The dataset contains files with single nucleotide variants in VCF format for a total of 58 DNA samples originating from the Northern Sweden Population Health Study (NSPHS). For each of the 58 individuals, DNA was extracted from a blood sample and subject to whole genome sequencing (WGS). The WGS was performed using 2x150 bp paired-end chemistry on Illumina HiSeq X Ten instrumentation at the SciLifeLab National Genomics Infrastructure (NGI) in Stockholm and Uppsala. FASTQ files generated by WGS were analyzed using the nf-core pipeline Sarek, which includes pre-processing, alignment to the human GRCh38 reference genome, and germline variant calling. The NSPHS study was approved by the local ethics committee at the University of Uppsala (Regionala Etikprövningsnämnden, Uppsala, 2005:325 and 2016-03-09). All participants gave their written informed consent to the study including the examination of environmental and genetic causes of disease in compliance with the Declaration of Helsinki.

  Dataset EGAD50000001324

• Androgen deprivation therapy promotes an inflammatory and obesity-like microenvironment in periprostatic fat

  Prostate cancer is a leading cause of cancer-related death and morbidity worldwide. Androgen deprivation therapy (ADT) is the cornerstone of management for advanced disease. The use of androgen deprivation therapies is associated with multiple side effects, including metabolic syndrome and truncal obesity. At the same time, obesity has been associated with both prostate cancer development and disease progression, linked to its effects on chronic inflammation at a tissue level. The connection between androgen deprivation therapy, obesity, inflammation, and prostate cancer progression is well-established in clinical settings; however, an understanding of the changes in adipose tissue at the molecular level induced by castrating therapies is missing. Here we investigated the transcriptional changes in periprostatic fat tissue induced by profound androgen deprivation therapy in a group of patients with high-risk tumours compared to a matching untreated cohort. We find that androgen deprivation therapy is associated with a pro-inflammatory and obesity-like adipose tissue microenvironment. This study suggests that the beneficial effect of androgen deprivation therapy may be partially counteracted by metabolic and inflammatory side effects in the adipose tissue surrounding the prostate.

  Study EGAS00001003286

• ProHealth: Kaiser Permanente Genome-wide Association Study of Prostate Cancer

  A genome-wide association study (GWAS) of prostate cancer (PCa) was conducted in Kaiser Permanente (KP) Northern California health plan members (7,783 cases, 38,595 controls; 80.3% non-Hispanic white, 4.9% African-American, 7.0% East Asian, and 7.8% Latino) [PMID: 26034056]. The data for these members were drawn from three KP cohort studies: Research Program in Genes, Environment and Health (RPGEH) ProHealth, and California Men's Health Study (CMHS) (described further under Study History). Four custom arrays were designed for genotyping, one for each of the four major race-ethnicity groups in the RPGEH cohort: African Americans, East Asians, Latinos, and Non-Hispanic Whites. The number of SNPs and SNP content varied by array, with SNP content designed to maximize the genome-wide coverage of low frequency and more common variants specific to the different race-ethnicity groups, including newly identified SNPs from sequencing projects, and SNPs with established associations with disease phenotypes and risk factors [PMIDs: 21565264, 21903159]. Within the total study cohort, n=34,736 completed a consent which permitted deposition of data to NIH. Genotyping followed the same general procedure described in [PMIDs: 26092718, plus additional quality control (QC) steps for the additional men, in order to control for potential batch and kit effects, described in [PMID: 26034056. Briefly, we first repeated the filters described in [PMID: 26092718] for all four arrays (EUR, LAT, EAS, AFR). Then, on an array-wise basis, we removed SNPs with MAF<0.01, with a call rate<95%, or with Hardy-Weinberg Equilibrium (HWE) p-value in homogeneous groups<1x10ˆ-5. Furthermore, on the EUR array, to adjust for potential kit effect, we conducted a GWAS of kit, and removed those kit associated SNPs with p<1x10ˆ-6; we also re-genotyped each of the new samples (those not genotyped with the original GERA data) with some of the original GERA data, and removed SNPs with >13/1,268 (1%) mismatches. For the AFR array, to adjust potential plate batch issues, we conducted a GWAS of whether an individual was in the original GERA data vs. in the newly genotyped data and removed those batch-associated SNPs with p<0.05 (we used a stronger threshold than that used for the EUR array because there were fewer individuals on the AFR array); we also re-genotyped each of the new samples with the original GERA data and removed SNPs with >2/78 (2.6%). After the QC described above, imputation was performed as described in [PMID: 26034056]. Imputation was performed on an array-wise basis, pre-phasing with SHAPE-IT v2.5 [PMID: 22138821], and imputing from the 1000 Genomes Project October 2014 release as a cosmopolitan reference panel with IMPUTE2 [PMID: 22384356]. In addition to the GWAS described above, a nested exome-wide association study (EWAS) of PCa was also conducted (7,489 cases, 7,323 controls; 78% non-Hispanic white, 9% African-American, 3% East Asian, 6% Latino, 4% Other). A custom EWAS array primarily focused on rare variants was designed for genotyping that complemented the GWAS arrays [PMID: 26034056]. The EWAS array content included missense and loss-of-function mutations, and rare exonic mutations from The Cancer Genome Atlas (TCGA) and dbGaP prostate cancer tumor exomes [PMID: 26544944; PMID: 26544944]. Much of the EWAS array design content overlapped with the probesets on the UK Biobank Affymetrix Axiom array [PMID: 30305743]. Genotyping and QC steps taken to filter out samples exhibiting low quality and variants with low call rates are described in Emami et al., 2020 [biorXiv]. The resulting EWAS array genotypes are provided here.

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