Suchergebnisse

1. The Future Makers: Teens in the Age of Diversity -- 2. Doing Away with our Sunday Best: Teenagers and the Remaking of Religion in Australia -- 3. Mind, Body, Spirit: Teenagers and Spirituality -- 4. A Personal Point of View: Discovering Teenage Worldviews 5. 'A Higher Order Out There': Seekers and the Spiritual but not Religious -- 6. Immanent Gods: This Worldly and Indifferent Teens -- 7. Awash but not Adrift in a Sea of Diversity: Teen Attitudes to Religious Diversity -- 8. Taking it to School: Religious Literacy, Religious Instruction and General Religious Education -- 9. Harry Potter, Homophobia and Human Rights: Teens talk about Sexuality Education, Religious Exemptions and Gay Rights -- 10. Conclusion

The Huangtupo landslide is one of the largest in the Three Gorges region, China. The county-seat town of Badong, located on the south shore between the Xiling and Wu gorges of the Yangtze River, was moved to this unstable slope prior to the construction of the Three Gorges Project, since the new Three Gorges reservoir completely submerged the location of the old city. The instability of the slope is affecting the new town by causing residential safety problems. The Huangtupo landslide provides scientists an opportunity to understand landslide response to fluctuating river water level and heavy rainfall episodes, which is essential to decide upon appropriate remediation measures. Interferometric Synthetic Aperture Radar (InSAR) techniques provide a very useful tool for the study of superficial and spatially variable displacement phenomena. In this paper, three sets of radar data have been processed to investigate the Huangtupo landslide. Results show that maximum displacements are affecting the northwest zone of the slope corresponding to Riverside slumping mass I#. The other main landslide bodies (i.e. Riverside slumping mass II#, Substation landslide and Garden Spot landslide) exhibit a stable behaviour in agreement with in situ data, although some active areas have been recognized in the foot of the Substation landslide and Garden Spot landslide. InSAR has allowed us to study the kinematic behaviour of the landslide and to identify its active boundaries. Furthermore, the analysis of the InSAR displacement time-series has helped recognize the different displacement patterns on the slope and their relationships with various triggering factors. For those persistent scatterers, which exhibit long-term displacements, they can be decomposed into a creep model (controlled by geological conditions) and a superimposed recoverable term (dependent on external factors), which appears closely correlated with reservoir water level changes close to the river's edge. These results, combined with in situ data, provide a comprehensive analysis of the Huangtupo landslide, which is essential for its management. ; R. Tomás is supported by a Generalitat Valenciana fellowship BEST-2011/225, P. Liu by a China Scholarship Council (CSC) scholarship and A. Singleton by an EPSRC Industry scholarship. Part of this work is supported by the Natural Environmental Research Council (NERC) through the GAS project (Ref: NE/H001085/1), by a China National Natural Science Foundation of China (NSFC) project (No. 41074005) and by the Spanish Government under project TEC2011-28201-C02.

BACKGROUND: Parkinson's disease (PD) is a genetically complex neurodegenerative disease with ~20 genes known to contain mutations that cause PD or atypical parkinsonism. Large‐scale next‐generation sequencing projects have revolutionized genomics research. Applying these data to PD, many genes have been reported to contain putative disease‐causing mutations. In most instances, however, the results remain quite limited and rather preliminary. Our aim was to assist researchers on their search for PD‐risk genes and variant candidates with an easily accessible and open summary‐level genomic data browser for the PD research community. METHODS: Sequencing and imputed genotype data were obtained from multiple sources and harmonized and aggregated. RESULTS: In total we included a total of 102,127 participants, including 28,453 PD cases, 1650 proxy cases, and 72,024 controls. CONCLUSIONS: We present here the Parkinson's Disease Sequencing Browser: a Shiny‐based web application that presents comprehensive summary‐level frequency data from multiple large‐scale genotyping and sequencing projects https://pdgenetics.shinyapps.io/VariantBrowser/. Published © 2021 This article is a U.S. Government work and is in the public domain in the USA. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

BACKGROUND: Whole‐genome sequencing data are available from several large studies across a variety of diseases and traits. However, massive storage and computation resources are required to use these data, and to achieve sufficient power for discoveries, harmonization of multiple cohorts is critical. OBJECTIVES: The Accelerating Medicines Partnership Parkinson's Disease program has developed a research platform for Parkinson's disease (PD) that integrates the storage and analysis of whole‐genome sequencing data, RNA expression data, and clinical data, harmonized across multiple cohort studies. METHODS: The version 1 release contains whole‐genome sequencing data derived from 3941 participants from 4 cohorts. Samples underwent joint genotyping by the TOPMed Freeze 9 Variant Calling Pipeline. We performed descriptive analyses of these whole‐genome sequencing data using the Accelerating Medicines Partnership Parkinson's Disease platform. RESULTS: The clinical diagnosis of participants in version 1 release includes 2005 idiopathic PD patients, 963 healthy controls, 64 prodromal subjects, 62 clinically diagnosed PD subjects without evidence of dopamine deficit, and 705 participants of genetically enriched cohorts carrying PD risk‐associated GBA variants or LRRK2 variants, of whom 304 were affected. We did not observe significant enrichment of pathogenic variants in the idiopathic PD group, but the polygenic risk score was higher in PD both in nongenetically enriched cohorts and genetically enriched cohorts. The population analysis showed a correlation between genetically enriched cohorts and Ashkenazi Jewish ancestry. CONCLUSIONS: We describe the genetic component of the Accelerating Medicines Partnership Parkinson's Disease platform, a solution to democratize data access and analysis for the PD research community. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article is a U.S. Government work and is in the public ...

International Parkinson's Disease Genomics Consortium (IPDGC). ; [Background] Parkinson's disease (PD) is a neurodegenerative disease with an often complex component identifiable by genome‐wide association studies. The most recent large‐scale PD genome‐wide association studies have identified more than 90 independent risk variants for PD risk and progression across more than 80 genomic regions. One major challenge in current genomics is the identification of the causal gene(s) and variant(s) at each genome‐wide association study locus. The objective of the current study was to create a tool that would display data for relevant PD risk loci and provide guidance with the prioritization of causal genes and potential mechanisms at each locus. ; [Methods] We included all significant genome‐wide signals from multiple recent PD genome‐wide association studies including themost recent PD risk genome‐wide association study, age‐at‐onset genome‐wide association study, progression genome‐wide association study, and Asian population PD risk genome‐wide association study. We gathered data for all genes 1 Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self‐ranked criteria. Multiple databases were queried for each gene to collect additional causal data. ; [Results] We created a PD genome‐wide association study browser tool (https://pdgenetics.shinyapps.io/GWASBrowser/) to assist the PD research community with the prioritization of genes for follow‐up functional studies to identify potential therapeutic targets. ; [Conclusions] Our PD genome‐wide association study browser tool provides users with a useful method of identifying potential causal genes at all known PD risk loci from large‐scale PD genome‐wide association studies. We plan to update this tool with new relevant data as sample sizes increase and new PD risk loci are discovered. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA. ; This work was supported in part by the Intramural Research Programs of the National Institute of Neurological Disorders and Stroke (NINDS), the National Institute on Aging (NIA), and the National Institute of Environmental Health Sciences, both part of the National Institutes of Health, Department of Health and Human Services (project numbers 1ZIA‐NS003154, Z01‐AG000949‐02, and Z01‐ES101986). We thank the research participants and employees of 23andMe for making this work possible. C.W. is supported by the UK Dementia Research Institute funded by the Medical Research Council (MRC), Alzheimer's Society and Alzheimer's Research UK. C.S. is supported by the Ser Cymru II program, which is partly funded by Cardiff University and the European Regional Development Fund through the Welsh Government. Data were generated as part of the PsychENCODE Consortium supported by: U01MH103339, U01MH103365, U01MH103392, U01MH103340, U01MH103346, R01MH105472, R01MH094714, R01MH105898, R21MH102791, R21MH105881, R21MH103877, and P50MH106934 awarded to Schahram Akbarian (Icahn School of Medicine at Mount Sinai), Gregory Crawford (Duke), Stella Dracheva (Icahn School of Medicine at Mount Sinai), Peggy Farnham (USC), Mark Gerstein (Yale), Daniel Geschwind (UCLA), Thomas M. Hyde (LIBD), Andrew Jaffe (LIBD), James A. Knowles (USC), Chunyu Liu (UIC), Dalila Pinto (Icahn School of Medicine at Mount Sinai), Nenad Sestan (Yale), Pamela Sklar (Icahn School of Medicine at Mount Sinai), Matthew State (UCSF), Patrick Sullivan (UNC), Flora Vaccarino (Yale), Sherman Weissman (Yale), Kevin White (UChicago), and Peter Zandi (JHU). The Genotype‐Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this article were obtained from the GTEx Portal on February 12, 2020. Molecular data for the Trans‐Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI). Genome sequencing for "NHLBI TOPMed: Atherosclerosis Risk in Communities (ARIC)" (phs001211.v2.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1)and at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for the "NHLBI TOPMed: Cleveland Clinic Atrial Fibrillation (CCAF) Study" (phs001189.v1.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: Trans‐Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Project: Cardiovascular Health Study (phs001368.v1.p1) was performed at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for "NHLBI TOPMed: Partners HealthCare Biobank" (phs001024.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: Whole Genome Sequencing of Venous Thromboembolism (WGS of VTE)" (phs001402.v1.p1) was performed at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for "NHLBI TOPMed: Novel Risk Factors for the Development of Atrial Fibrillation in Women" (phs001040.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: The Genetics and Epidemiology of Asthma in Barbados" (phs001143.v2.p1) was performed by Illumina Genomic Services (3R01HL104608‐04S1). Genome sequencing for "NHLBI TOPMed: The Vanderbilt Genetic Basis of Atrial Fibrillation" (phs001032.v4.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: Heart and Vascular Health Study (HVH)" (phs000993.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1) and at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for "NHLBI TOPMed: Genetic Epidemiology of COPD (COPDGene)" (phs000951.v3.p3) was performed at the University of Washington Northwest Genomics Center (3R01HL089856‐08S1) and at the Broad Institute of MIT and Harvard (HHSN268201500014C). Genome sequencing for "NHLBI TOPMed: The Vanderbilt Atrial Fibrillation Ablation Registry" (phs000997.v3.p2) was performed at the Broad Institute of MIT and Harvard (3U54HG003067‐12S2, 3U54HG003067‐13S1). Genome sequencing for "NHLBI TOPMed: The Jackson Heart Study" (phs000964.v3.p1) was performed at the University of Washington Northwest Genomics Center (HHSN268201100037C). Genome sequencing for "NHLBI TOPMed: Genetics of Cardiometabolic Health in the Amish" (phs000956.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL121007‐01S1). Genome sequencing for "NHLBI TOPMed: Massachusetts General Hospital Atrial Fibrillation (MGH AF) Study" (phs001062.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1, 3U54HG003067‐12S2, 3U54HG003067‐13S1, 3UM1HG008895‐01S2). Genome sequencing for "NHLBI TOPMed: The Framingham Heart Study" (phs000974.v3.p2) was performed at the Broad Institute of MIT and Harvard (3U54HG003067‐12S2). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering, were provided by the TOPMed Informatics Research Center (3R01HL‐117626‐02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample‐identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL‐120393; U01HL‐120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. The Atherosclerosis Risk in Communities study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institute of Health, Department of Health and Human Services, under contract numbers (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, and HHSN268201700005I). The authors thank the staff and participants of the ARIC study for their important contributions. The research reported in this article was supported by grants from the National Institutes of Health (NIH) National Heart, Lung, and Blood Institute grants R01 HL090620 and R01 HL111314, the NIH National Center for Research Resources for Case Western Reserve University and Cleveland Clinic Clinical and Translational Science Award (CTSA) UL1‐RR024989, the Department of Cardiovascular Medicine philanthropic research fund, Heart and Vascular Institute, Cleveland Clinic, the Fondation Leducq grant 07‐CVD 03, and the Atrial Fibrillation Innovation Center, state of Ohio. This research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01‐HC85079, N01‐HC‐85080, N01‐HC‐85081, N01‐HC‐85082, N01‐HC‐85083, N01‐HC‐85084, N01‐HC‐85085, N01‐HC‐85086, N01‐HC‐35129, N01‐HC‐15103, N01‐HC‐55222, N01‐HC‐75150, N01‐HC‐45133, and N01‐ HC‐85239; grant numbers U01 HL080295 and U01 HL130014 from the National Heart, Lung, and Blood Institute, and R01 AG023629 from the National Institute on Aging, with additional contribution from the National Institute of Neurological Disorders and Stroke. A full list of principal CHS investigators and institutions can be found at https://chs-nhlbi.org/pi. This article was not prepared in collaboration with CHS investigators and does not necessarily reflect the opinions or views of CHS or the NHLBI. We thank the Broad Institute for generating high‐quality sequence data supported by NHLBI grant 3R01HL092577‐06S1 to Dr. Patrick Ellinor. Funded in part by grants from the National Institutes of Health, National Heart, Lung, and Blood Institute (HL66216 and HL83141), and the National Human Genome Research Institute (HG04735). The Women's Genome Health Study (WGHS) is supported by HL 043851 and HL099355 from the National Heart, Lung, and Blood Institute and CA 047988 from the National Cancer Institute, the Donald W. Reynolds Foundation with collaborative scientific support and funding for genotyping provided by Amgen. AF end‐point confirmation was supported by HL‐093613 and a grant from the Harris Family Foundation and Watkin's Foundation. The Genetics and Epidemiology of Asthma in Barbados is supported by National Institutes of Health (NIH) National Heart, Lung, and Blood Institute TOPMed (R01 HL104608‐S1), and R01 AI20059, K23 HL076322, and RC2 HL101651. The research reported in this article was supported by grants from the American Heart Association to Dr. Darbar (EIA 0940116N), and grants from the National Institutes of Health (NIH) to Dr. Darbar (HL092217), and Dr. Roden (U19 HL65962, and UL1 RR024975). This project was also supported by a CTSA award (UL1TR000445) from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences of the NIH. The research reported in this article was supported by grants HL068986, HL085251, HL095080, and HL073410 from the National Heart, Lung, and Blood Institute. This article was not prepared in collaboration with Heart and Vascular Health (HVH) Study investigators and does not necessarily reflect the opinions or views of the HVH Study or the NHLBI. This research used data generated by the COPDGene study, which was supported by NIH grants U01 HL089856 and U01 HL089897. The COPDGene project is also supported by the COPD Foundation through contributions made by an Industry Advisory Board composed of Pfizer, AstraZeneca, Boehringer Ingelheim, Novartis, and Sunovion. Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL‐117626‐02S1; contract HHSN268201800002I). Phenotype harmonization, data management, sample‐identity QC, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL‐120393‐02S1; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. This study is part of the Centers for Common Disease Genomics (CCDG) program, a large‐scale genome sequencing effort to identify rare risk and protective alleles that contribute to a range of common disease phenotypes. The CCDG program is funded by the National Human Genome Research Institute (NHGRI) and the National Heart, Lung, and Blood Institute (NHLBI). Sequencing was completed at the Human Genome Sequencing Center at Baylor College of Medicine under NHGRI grant UM1 HG008898. The research reported in this article was supported by grants from the American Heart Association to Dr. Shoemaker (11CRP742009) and Dr. Darbar (EIA 0940116N), and grants from the National Institutes of Health (NIH) to Dr. Darbar (R01 HL092217) and Dr. Roden (U19 HL65962 and UL1 RR024975). The project was also supported by a CTSA award (UL1 TR00045) from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the NIH. The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I/HHSN26800001), and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I, and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute for Minority Health and Health Disparities (NIMHD). The authors also thank the staffs and participants of the JHS. The Amish studies on which these data are based were supported by NIH grants R01 AG18728, U01 HL072515, R01 HL088119, R01 HL121007, and P30 DK072488. See publication PMID: 18440328. The research reported in this article was supported by NIH grants K23HL071632, K23HL114724, R21DA027021, R01HL092577, R01HL092577S1, R01HL104156, K24HL105780, and U01HL65962. The research has also been supported by an Established Investigator Award from the American Heart Association (13EIA14220013) and by support from the Fondation Leducq (14CVD01). This article was not prepared in collaboration with MGH AF Study investigators and does not necessarily reflect the opinions or views of the MGH AF Study investigators or the NHLBI. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (contract nos. N01‐HC‐25195, HHSN268201500001I, and 75N92019D00031). This article was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI.

Amyotrophic lateral sclerosis (ALS) is the most common form of motor neuron disease (MND). It is currently incurable and treatment is largely limited to supportive care. Family history is associated with an increased risk of ALS, and many Mendelian causes have been discovered. However, most forms of the disease are not obviously familial. Recent advances in human genetics have enabled genome-wide analyses of single nucleotide polymorphisms (SNPs) that make it possible to study complex genetic contributions to human disease. Genome-wide SNP analyses require a large sample size and thus depend upon collaborative efforts to collect and manage the biological samples and corresponding data. Public availability of biological samples (such as DNA), phenotypic and genotypic data further enhances research endeavors. Here we discuss a large collaboration among academic investigators, government, and non-government organizations which has created a public repository of human DNA, immortalized cell lines, and clinical data to further gene discovery in ALS. This resource currently maintains samples and associated phenotypic data from 2332 MND subjects and 4692 controls. This resource should facilitate genetic discoveries which we anticipate will ultimately provide a better understanding of the biological mechanisms of neurodegeneration in ALS.

Amyotrophic lateral sclerosis (ALS) is the most common form of motor neuron disease (MND). It is currently incurable and treatment is largely limited to supportive care. Family history is associated with an increased risk of ALS, and many Mendelian causes have been discovered. However, most forms of the disease are not obviously familial. Recent advances in human genetics have enabled genome-wide analyses of single nucleotide polymorphisms (SNPs) that make it possible to study complex genetic contributions to human disease. Genome-wide SNP analyses require a large sample size and thus depend upon collaborative efforts to collect and manage the biological samples and corresponding data. Public availability of biological samples (such as DNA), phenotypic and genotypic data further enhances research endeavors. Here we discuss a large collaboration among academic investigators, government, and non-government organizations which has created a public repository of human DNA, immortalized cell lines, and clinical data to further gene discovery in ALS. This resource currently maintains samples and associated phenotypic data from 2332 MND subjects and 4692 controls. This resource should facilitate genetic discoveries which we anticipate will ultimately provide a better understanding of the biological mechanisms of neurodegeneration in ALS.

Amyotrophic lateral sclerosis (ALS) is the most common form of motor neuron disease (MND). It is currently incurable and treatment is largely limited to supportive care. Family history is associated with an increased risk of ALS, and many Mendelian causes have been discovered. However, most forms of the disease are not obviously familial. Recent advances in human genetics have enabled genome-wide analyses of single nucleotide polymorphisms (SNPs) that make it possible to study complex genetic contributions to human disease. Genome-wide SNP analyses require a large sample size and thus depend upon collaborative efforts to collect and manage the biological samples and corresponding data. Public availability of biological samples (such as DNA), phenotypic and genotypic data further enhances research endeavors. Here we discuss a large collaboration among academic investigators, government, and non-government organizations which has created a public repository of human DNA, immortalized cell lines, and clinical data to further gene discovery in ALS. This resource currently maintains samples and associated phenotypic data from 2332 MND subjects and 4692 controls. This resource should facilitate genetic discoveries which we anticipate will ultimately provide a better understanding of the biological mechanisms of neurodegeneration in ALS.