Publisher's version (útgefin grein) ; Background Mothers' smoking during pregnancy increases asthma risk in their offspring. There is some evidence that grandmothers' smoking may have a similar effect, and biological plausibility that fathers' smoking during adolescence may influence offspring's health through transmittable epigenetic changes in sperm precursor cells. We evaluated the three-generation associations of tobacco smoking with asthma. Methods Between 2010 and 2013, at the European Community Respiratory Health Survey III clinical interview, 2233 mothers and 1964 fathers from 26 centres reported whether their offspring (aged ≤51 years) had ever had asthma and whether it had coexisted with nasal allergies or not. Mothers and fathers also provided information on their parents' (grandparents) and their own asthma, education and smoking history. Multilevel mediation models within a multicentre three-generation framework were fitted separately within the maternal (4666 offspring) and paternal (4192 offspring) lines. Results Fathers' smoking before they were 15 [relative risk ratio (RRR) = 1.43, 95% confidence interval (CI): 1.01–2.01] and mothers' smoking during pregnancy (RRR = 1.27, 95% CI: 1.01–1.59) were associated with asthma without nasal allergies in their offspring. Grandmothers' smoking during pregnancy was associated with asthma in their daughters [odds ratio (OR) = 1.55, 95% CI: 1.17–2.06] and with asthma with nasal allergies in their grandchildren within the maternal line (RRR = 1.25, 95% CI: 1.02–1.55). Conclusions Fathers' smoking during early adolescence and grandmothers' and mothers' smoking during pregnancy may independently increase asthma risk in offspring. Thus, risk factors for asthma should be sought in both parents and before conception. ; The present analyses are part of the Ageing Lungs in European Cohorts (ALEC) Study [www.alecstudy.org], which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 633212. The coordination of the European Community Respiratory Health Survey (ECRHS) was supported by the European Commission (phases 1 and 2) and the Medical Research Council (phase 3). Local funding agencies for the ECRHS are reported in the Supplementary Appendix, available as Supplementary data at IJE online. Conflict of interest: J.W.H. reports grants from the European Union's Horizon 2020 programme (633212), the Medical Research Council UK (MC_PC_15078) and the National Institutes of Health USA (R01 AI091905, R01 AI121226) during the conduct of the study. R.J. reports grants from the Estonian Research Council (personal grant No. 562) during the conduct of the study, grants/grants pending from the Estonian Research Council (personal research grant No. 562), personal fees for consulting and lecturing from GlaxoSmithKline, Boehringer and Novartis and travel/accommodation/meeting expenses paid by GlaxoSmithKline and Boehringer, outside the submitted work. C.R. reports personal fees for consulting and lecturing from ALK, Astra Zeneca, GSK, Boheringer and Novartis, outside the submitted work. A.G.C. reports grants from Chiesi Farmaceutici and GlaxoSmithKline Italy, during the conduct of the study. P.D. reports personal fees for consulting and lecturing from ALK and Stallergenes Greer and personal fees for consulting from Circassia, Chiesi Farmaceutici, ThermofisherScientific and Menarini, outside the submitted work. D.J. reports grants from the Medical Research Council and the European Union's Horizon 2020 programme, during the conduct of the study. All other authors declare no competing interests. ; Peer Reviewed
INTRODUCTION: The objective of the Health Population Africa (HPAfrica) study is to determine health behaviour and population-based factors, including socioeconomic, ethnographic, hygiene and sanitation factors, at sites of the Severe Typhoid Fever in Africa (SETA) programme. SETA aims to investigate healthcare facility-based fever surveillance in Burkina Faso, the Democratic Republic of the Congo, Ethiopia, Ghana, Madagascar and Nigeria. Meaningful disease burden estimates require adjustment for health behaviour patterns, which are assumed to vary among a study population. METHODS AND ANALYSIS: For the minimum sample size of household interviews required, the assumptions of an infinite population, a design effect and age-stratification and sex-stratification are considered. In the absence of a population sampling frame or household list, a spatial approach will be used to generate geographic random points with an Aeronautical Reconnaissance Coverage Geographic Information System tool. Printouts of Google Earth Pro satellite imagery visualise these points. Data of interest will be assessed in different seasons by applying population-weighted stratified sampling. An Android-based application and a web service will be developed for electronic data capturing and synchronisation with the database server in real time. Sampling weights will be computed to adjust for possible differences in selection probabilities. Descriptive data analyses will be performed in order to assess baseline information of each study population and age-stratified and sex-stratified health behaviour. This will allow adjusting disease burden estimates. In addition, multivariate analyses will be applied to look into associations between health behaviour, population-based factors and the disease burden as determined in the SETA study. ETHICS AND DISSEMINATION: Ethic approvals for this protocol were obtained by the Institutional Review Board of the International Vaccine Institute (No. 2016-0003) and by all collaborating institutions of participating countries. It is anticipated to disseminate findings from this study through publication on a peer-reviewed journal.
SARS-CoV-2 RNA detection in wastewater is being rapidly developed and adopted as a public health monitoring tool worldwide. With wastewater surveillance programs being implemented across many different scales and by many different stakeholders, it is critical that data collected and shared are accompanied by an appropriate minimal amount of meta-information to enable meaningful interpretation and use of this new information source and intercomparison across datasets. While some databases are being developed for specific surveillance programs locally, regionally, nationally, and internationally, common globally-adopted data standards have not yet been established within the research community. Establishing such standards will require national and international consensus on what meta-information should accompany SARS-CoV-2 wastewater measurements. To establish a recommendation on minimum information to accompany reporting of SARS-CoV-2 occurrence in wastewater for the research community, the United States National Science Foundation (NSF) Research Coordination Network on Wastewater Surveillance for SARS-CoV-2 hosted a workshop in February 2021 with participants from academia, government agencies, private companies, wastewater utilities, public health laboratories, and research institutes. This report presents the primary two outcomes of the workshop: (i) a recommendation on the set of minimum meta-information that is needed to confidently interpret wastewater SARS-CoV-2 data, and (ii) insights from workshop discussions on how to improve standardization of data reporting.
Publisher's version (útgefin grein) ; Background: Previous studies have reported an association between weight increase and excess lung function decline in young adults followed for short periods. We aimed to estimate lung function trajectories during adulthood from 20-year weight change profiles using data from the population-based European Community Respiratory Health Survey (ECRHS). Methods: We included 3673 participants recruited at age 20-44 years with repeated measurements of weight and lung function (forced vital capacity (FVC), forced expiratory volume in 1 s (FEV 1)) in three study waves (1991-93, 1999-2003, 2010-14) until they were 39-67 years of age. We classified subjects into weight change profiles according to baseline body mass index (BMI) categories and weight change over 20 years. We estimated trajectories of lung function over time as a function of weight change profiles using population-averaged generalised estimating equations. Results: In individuals with normal BMI, overweight and obesity at baseline, moderate (0.25-1 kg/year) and high weight gain (>1 kg/year) during follow-up were associated with accelerated FVC and FEV 1 declines. Compared with participants with baseline normal BMI and stable weight (±0.25 kg/year), obese individuals with high weight gain during follow-up had -1011 mL (95% CI -1.259 to -763) lower estimated FVC at 65 years despite similar estimated FVC levels at 25 years. Obese individuals at baseline who lost weight (<-0.25 kg/year) exhibited an attenuation of FVC and FEV 1 declines. We found no association between weight change profiles and FEV 1 /FVC decline. Conclusion: Moderate and high weight gain over 20 years was associated with accelerated lung function decline, while weight loss was related to its attenuation. Control of weight gain is important for maintaining good lung function in adult life. ; Funding The present analyses are part of the ageing lungs in european cohorts (alec) study (www.alecstudy.org), which has received funding from the european Union's horizon 2020 research and innovation programme under grant agreement no. 633212. The local investigators and funding agencies for the european community respiratory health survey are reported in the online supplement. isglobal is a member of the cerca Programme, generalitat de catalunya. ; Peer Reviewed
Supplementary data are available at IJE online. https://academic.oup.com/ije/article/48/4/1275/5363232#supplementary-data ; BACKGROUND: Earlier age at menopause has been associated with increased risk of coronary heart disease (CHD), but the shape of association and role of established cardiovascular risk factors remain unclear. Therefore, we examined the associations between menopausal characteristics and CHD risk; the shape of the association between age at menopause and CHD risk; and the extent to which these associations are explained by established cardiovascular risk factors. METHODS: We used data from EPIC-CVD, a case-cohort study, which includes data from 23 centres from 10 European countries. We included only women, of whom 10 880 comprise the randomly selected sub-cohort, supplemented with 4522 cases outside the sub-cohort. We conducted Prentice-weighted Cox proportional hazards regressions with age as the underlying time scale, stratified by country and adjusted for relevant confounders. RESULTS: After confounder and intermediate adjustment, post-menopausal women were not at higher CHD risk compared with pre-menopausal women. Among post-menopausal women, earlier menopause was linearly associated with higher CHD risk [HRconfounder and intermediate adjusted per-year decrease = 1.02, 95% confidence interval (CI) = 1.01-1.03, p = 0.001]. Women with a surgical menopause were at higher risk of CHD compared with those with natural menopause (HRconfounder-adjusted = 1.25, 95% CI = 1.10-1.42, p < 0.001), but this attenuated after additional adjustment for age at menopause and intermediates (HR = 1.12, 95% CI = 0.96-1.29, p = 0.15). A proportion of the association was explained by cardiovascular risk factors. CONCLUSIONS: Earlier and surgical menopause were associated with higher CHD risk. These associations could partially be explained by differences in conventional cardiovascular risk factors. These women might benefit from close monitoring of cardiovascular risk factors and disease. ; This work was supported by the European Union Framework 7 (HEALTH-F2-2012–279233), the European Research Council (268834), the UK Medical Research Council (G0800270, MR/L003120/1), the British Heart Foundation (SP/09/002, RG/08/014, RG13/13/30194) and the UK National Institute of Health Research (to EPIC-CVD). The national cohorts are supported by the Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); Ministry of Health and Social Solidarity, Stavros Niarchos Foundation and Hellenic Health Foundation (Greece); Italian Association for Research on Cancer (AIRC) and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Scientific Council and Regional Government of Skåne and Västerbotten (Sweden); Cancer Research UK, Medical Research Council (UK). This work is supported by the Dutch Heart Foundation (2013T083 to V.D.). This work was supported by a UK Medical Research Council Skills Development Fellowship (MR/P014550/1 to S.A.E.P.). None of the funding sources had a role in the collection, analysis and interpretation of the data, nor in the decision to submit the article for publication. ; Peer-reviewed ; Publisher Version
Themen: Wahrgenommene Veränderung in der Straßenkriminalität, Bestechung, Ehebruch und Steuerhinterziehung in den letzten zehn Jahren; Rechtfertigungsgründe für Steuerhinterziehung (Skala); Rechtfertigungsgründe für die Todesstrafe (Skala); Einstellung zu: Adoption durch homosexuelle Paare, Recht auf Suizid, Vorrecht von Männern vor Frauen auf einen Arbeitsplatz bei Arbeitsplatzmangel; Einstellung zum Recht auf Abtreibung in ausgewählten Fällen (Skala: Behinderung des Babys, keine weiteren Kinder gewünscht, Gesundheitsgefährdung der Mutter, ungünstiger Zeitpunkt für ein Kind, finanzielle Schwierigkeiten der Familie, Schwangerschaft infolge Vergewaltigung, alleinstehende Mutter); individuelle oder staatliche Verantwortlichkeit für Renten; Einstellung zum Verlust der Ansprüche auf Arbeitslosengeld bei verweigerter Jobannahme; Rauchen in öffentlichen Gebäuden; Raucherstatus; Wichtigkeit der Erziehungsziele Unabhängigkeit, Gehorsam und Kreativität; Rechtfertigung von Einkommensunterschieden; Einstellung zu einem Obdachlosenheim in der Nachbarschaft; Einstellung zur Bevorzugung von Verwandten bei der Jobvergabe trotz geringerer Qualifikation; Gründe für die Beschäftigung bzw. die Nichtbeschäftigung eines geringer qualifizierten Verwandten (Stufenmodell nach Kohlberg); Kriterien für die Bevorzugung von Patienten für eine lebenswichtige Operation; Einstellung zur Sterbehilfe; Abhängigkeit der Regeln für Gut und Böse von den jeweiligen Umständen; Kriterien für die Festlegung der eigenen Moralvorstellungen (Skala); interne Kontrolle über das eigene Leben; Präferenz für eine Problemlösung auf individueller Ebene oder durch Änderung der gesellschaftlichen Umstände; soziale Orientierung; regelmäßige ehrenamtliche Arbeit in religiösen bzw. nicht-religiösen Organisationen; regionale Mobilität; Wohnort der besten Freunde; Staatsangehörigkeit des Befragten; Staatsangehörigkeit von Mutter und Vater bei deren Geburt; Einstellung zu Ausländern, Immigranten und Ethnozentrismus-Skala; Politikinteresse; Parteipräferenz (Parteineigung); am wenigsten bevorzugte Partei; Rezeption politischer Nachrichten in den Medien; eigene Vorstellung von Gott; Vorstellungen von einem Leben nach dem Tod; Glaube an Erlösung für alle oder für Auserwählte; Voraussetzungen für eine Erlösung; Ursachen für menschliches Leid (Theodizee-Skala); Selbsteinschätzung der Religiosität; Einfluss der persönlichen religiösen Überzeugungen auf das eigene Leben und die eigenen Entscheidungen; spirituelles Leben; Kirchgangshäufigkeit; Häufigkeit von Beten; subjektive Bedeutung religiöser Feiern bei Geburt, Hochzeit und Tod; Vorstellung von Jesus (Skala); Vorstellung von der Bibel; Glaube an religiös begründete Vorhersagen über eine dramatische Veränderung zur Jahrtausendwende; Zugehörigkeit zu einer Glaubensgemeinschaft; Konfession; Kirchenverbundenheit; Einstellung zum Priesteramt für Frauen; frühere Zugehörigkeit zu einer anderen Kirche bzw. generelle frühere Zugehörigkeit zu einer Kirche; frühere Konfession; Kirchgangshäufigkeit im Jugendalter; transzendente Erfahrung; Besitz religiöser Symbole (z.B. Kreuz oder Christopherusmedaille); Glaube an die Kraft eines religiösen Symbols; Besitz eines Talismans oder Glücksbringers; Glaube an die Kraft eines Glücksbringers; Häufigkeit der Konsultation von Horoskopen; Häufigkeit der Berücksichtigung von Horoskopen im täglichen Leben; Anteil der Freunde mit anderer Religion als der eigenen; subjektiv perzipierte Entwicklung des Anteils religiöser Personen im Land während der letzten 20 Jahre; Einstellung zum Tragen von Kopftüchern in der Schule als Zeichen religiöser Tradition, zum Konsum weicher Drogen als Teil religiöser Riten, zur religiös motivierten Verweigerung von Bluttransfusionen für Kinder durch ein Verbot durch deren Eltern und zum religiös motivierten Suizid (Skala); Einstellung zu religiösen Gruppen (kulturelle Bereicherung, Konfliktursache, Freiheit sich auf andere religiöse Traditionen zu beziehen); Einstellung zur Zulassung der Zeugen Jehovas und der Scientology im eigenen Land; Existenz einer einzig wahren Religion; Einstellung zum Verbot religiöser Symbole in staatlichen Schulen; Einstellung zur finanziellen Unterstützung religiöser Schulen durch den Staat; staatlich unterstützenswerte Religion; Einstellung zum Eid vor Gericht mit Bezug auf Gott; Einstellung zur Konsultation von Vertretern der Hauptreligionen bei Gesetzesfragen mit moralischem Hintergrund (z.B. Abtreibung oder Sterbehilfe); Einstellung zu einem religiös motivierten Verweigerungsrecht bei der Berufsausübung; Wissenschaftsgläubigkeit (gibt dem Leben einen Sinn, wissenschaftlicher Fortschritt erschwert den Glauben an Gott); gewünschter Einfluss der Kirchen auf die Politik und tatsächlicher Einfluss auf die Politik des Landes (Skalometer).
Demographie: Geschlecht; Alter (Geburtsjahr); höchster Schulabschluss; Beschäftigtenstatus; Vollzeitbeschäftigung oder Teilzeitbeschäftigung; Zweitjob; Zusammenleben mit einem Partner; rechtskräftig verheiratet; Familienstand; höchster Schulabschluss des Partners; Konfession des Partners, Kirchgangshäufigkeit des Partners; Kinderzahl; Haushaltsgröße; Haushaltseinkommen; Bereitschaft zu einem Einkommensverzicht zugunsten einer Senkung der Arbeitslosigkeit bzw. als Entwicklungshilfe für die ärmsten Länder; Prozentanteil vom Haushaltseinkommen (1 bis über 2 Prozent), auf den der Befragte verzichten würde (individuell und im Falle einer angenommenen Einkommenskürzung für alle); Ortsgröße; Urbanisierungsgrad.
Optionale Fragen (wurden nicht in allen Ländern gestellt): persönliche Wichtigkeit der Werte Freiheit und Gleichheit; Einstellung zu einer höheren Wirklichkeit und zum Sinn des Lebens (Gott befasst sich mit jedem Menschen persönlich, Gott verleiht dem Leben Bedeutung, Gott als Wert für die Menschheit; das Leid ist nur verständlich durch den Glauben an einen Gott, das Leid ist Teil des Lebens, dem man selbst einen Sinn geben muss, Tod als natürliches Ende, Tod als Übergang zu einem anderen Leben, Leben hat nur dann einen Sinn, wenn man ihm selbst einen Sinn gibt; Glaube an die körperliche Auferstehung der Jungfrau Maria in den Himmel, Glaube an Hilfe von den Heiligen; Religion und Kirchgangshäufigkeit des Vaters und der Mutter als der Befragte 12 Jahre alt war; Schulbildung in einer konfessionellen Schule; Beruf des Befragten und seines Partners (ISCO); Papsttum als Hindernis der christlichen Einheit; Gebetsräume sollten keine dekorative Ausgestaltung haben; Gottesdienste sollten mit Musik und zeremoniellen Gewändern der Priester sein; Einstellung zum Papst; Einstellung zur Mitwirkung von Laien; Bedeutung von Weihnachten; höchster Schulabschluss und Nationalität der Eltern; Nationalität des Befragten.
Aktuell überschlagen sich Vorschläge, wie auf den Krieg in der Ukraine energiepolitisch zu reagieren ist. Ein Schlüsselprinzip rückt dabei erst langsam ins öffentliche Bewusstsein: Energiesuffizienz. Das bedeutet, den Bedarf an Energie zu senken. Energiesuffizienz senkt Kosten, reduziert den Bedarf an Zukäufen, macht energiepolitisch unabhängiger und ist klimapolitisch hilfreich. Sie muss jetzt zu einem zentralen Prinzip politischen Handelns werden. Eine ergänzende Veröffentlichung zu einer Sammlung verschiedener Materialien zu Maßnahmenvorschlägen und Potenzialabschätzungen zu den Themen Energiesuffizienz, Energieeffizienz und Energieunabhängigkeit ist unter https://doi.org/10.5281/zenodo.6405817 abrufbar. Bitte zitieren als: "Autor:innengruppe Energiesuffizienz (2022): Energiesparen als Schlüssel zur Energiesicherheit - Suffizienz als Strategie. https://doi.org/10.5281/zenodo.6419202" Kurzbezeichnung: "Thesenpapier Energiesuffizienz" Kontakt: info@energysufficiency.de
Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes. ; RD-Connect (RD-Connect, an integrated platform connecting registries, biobanks, and clinical bioinformatics) received funding from the Seventh Framework (FP7) Programme of the European Union under grant agreement No 305444. Data were analyzed using the RD-Connect GPAP, which received funding from EU projects Solve-RD, EJP-RD (grant numbers H2020 779257, H2020 825575), Instituto de Salud Carlos III (Grant numbers PT13/0001/0044, PT17/0009/0019; Instituto Nacional de Bioinformática, INB), ELIXIR-EXCELERATE (Grant number EU H2020 #676559) and ELIXIR Implementation Studies (Remote real-time visualization of human rare disease genomics ...
peer-reviewed ; H.D.D., A.J.C., P.J.B. and B.J.H. would like to acknowledge the Dairy Futures Cooperative Research Centre for funding. H.P. and R.F. acknowledge funding from the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr 'Synbreed—Synergistic Plant and Animal Breeding' (grant 0315527B). H.P., R.F., R.E. and K.-U.G. acknowledge the Arbeitsgemeinschaft Süddeutscher Rinderzüchter, the Arbeitsgemeinschaft Österreichischer Fleckviehzüchter and ZuchtData EDV Dienstleistungen for providing genotype data. A. Bagnato acknowledges the European Union (EU) Collaborative Project LowInputBreeds (grant agreement 222623) for providing Brown Swiss genotypes. Braunvieh Schweiz is acknowledged for providing Brown Swiss phenotypes. H.P. and R.F. acknowledge the German Holstein Association (DHV) and the Confederación de Asociaciones de Frisona Española (CONCAFE) for sharing genotype data. H.P. was financially supported by a postdoctoral fellowship from the Deutsche Forschungsgemeinschaft (DFG) (grant PA 2789/1-1). D.B. and D.C.P. acknowledge funding from the Research Stimulus Fund (11/S/112) and Science Foundation Ireland (14/IA/2576). M.S. and F.S.S. acknowledge the Canadian Dairy Network (CDN) for providing the Holstein genotypes. P.S. acknowledges funding from the Genome Canada project entitled 'Whole Genome Selection through Genome Wide Imputation in Beef Cattle' and acknowledges WestGrid and Compute/Calcul Canada for providing computing resources. J.F.T. was supported by the National Institute of Food and Agriculture, US Department of Agriculture, under awards 2013-68004-20364 and 2015-67015-23183. A. Bagnato, F.P., M.D. and J.W. acknowledge EU Collaborative Project Quantomics (grant 516 agreement 222664) for providing Brown Swiss and Finnish Ayrshire sequences and genotypes. A.C.B. and R.F.V. acknowledge funding from the public–private partnership 'Breed4Food' (code BO-22.04-011- 001-ASG-LR) and EU FP7 IRSES SEQSEL (grant 317697). A.C.B. and R.F.V. acknowledge CRV (Arnhem, the Netherlands) for providing data on Dutch and New Zealand Holstein and Jersey bulls. ; Stature is affected by many polymorphisms of small effect in humans1. In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes2,3. Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 × 10−8) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP–seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals.
Publisher's version (útgefin grein). ; Background: Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear. Results: Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction. Conclusions: Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes. ; Funding This work was funded by a grant to YJ from the British Heart Foundation (PG/12/38/29615). AGES: This study has been funded by NIH contracts N01-AG-1-2100 and 271201200022C, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The study is approved by the Icelandic National Bioethics Committee, VSN: 00–063. The researchers are indebted to the participants for their willingness to participate in the study. ARIC: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. Funding support for "Building on GWAS for NHLBI-diseases: the U.S. CHARGE consortium" was provided by the NIH through the American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419). BRIGHT: The Exome Chip genotyping was funded by Wellcome Trust Strategic Awards (083948 and 085475). This work was also supported by the Medical Research Council of Great Britain (Grant no. G9521010D); and by the British Heart Foundation (Grant no. PG/02/128). AFD was supported by the British Heart Foundation (Grant nos. RG/07/005/23633 and SP/08/005/25115); and by the European Union Ingenious HyperCare Consortium: Integrated Genomics, Clinical Research, and Care in Hypertension (grant no. LSHM-C7–2006-037093). The BRIGHT study is extremely grateful to all the patients who participated in the study and the BRIGHT nursing team. We would also like to thank the Barts Genome Centre staff for their assistance with this project. CHS: This Cardiovascular Health Study (CHS) research was supported by NHLBI contracts HHSN268201800001C, HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI grants R01HL068986, U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ERF: The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007–2013)/grant agreement HEALTH-F4–2007-201413 by the European Commission under the programme "Quality of Life and Management of the Living Resources" of 5th Framework Programme (no. QLG2-CT-2002-01254). The ERF study was further supported by ENGAGE consortium and CMSB. High-throughput analysis of the ERF data was supported by joint grant from Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). We are grateful to all study participants and their relatives, general practitioners, and neurologists for their contributions to the ERF study and to P Veraart for her help in genealogy, J Vergeer for the supervision of the laboratory work, and P Snijders for his help in data collection. FHS: The Framingham Heart Study (FHS) research reported in this article was supported by a grant from the National Heart, Lung, and Blood Institute (NHLBI), HL120393. Generation Scotland: Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping of the Generation Scotland and Scottish Family Health Study samples was carried out by the Genetics Core Laboratory at the Clinical Research Facility, Edinburgh, Scotland and was funded by the UK's Medical Research Council. GOCHA: The Genetics of Cerebral Hemorrhage with Anticoagulation was carried out as a collaborative study supported by grants R01NS073344, R01NS059727, and 5K23NS059774 from the NIH–National Institute of Neurological Disorders and Stroke (NIH-NINDS). GRAPHIC: The GRAPHIC Study was funded by the British Heart Foundation (BHF/RG/2000004). NJS and CPN are supported by the British Heart Foundation and is a NIHR Senior Investigator. This work falls under the portfolio of research supported by the NIHR Leicester Cardiovascular Biomedical Research. INGI-FVG: This study has been funded by Regione FVG (L.26.2008). INTER99: The Inter99 was initiated by Torben Jørgensen (PI), Knut Borch-Johnsen (co-PI), Hans Ibsen and Troels F. Thomsen. The steering committee comprises the former two and Charlotta Pisinger. The study was financially supported by research grants from the Danish Research Council, the Danish Centre for Health Technology Assessment, Novo Nordisk Inc., Research Foundation of Copenhagen County, Ministry of Internal Affairs and Health, the Danish Heart Foundation, the Danish Pharmaceutical Association, the Augustinus Foundation, the Ib Henriksen Foundation, the Becket Foundation, and the Danish Diabetes Association. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). JHS: We thank the Jackson Heart Study (JHS) participants and staff for their contributions to this work. The JHS is supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities. Dr. Wilson is supported by U54GM115428 from the National Institute of General Medical Sciences. KORA: The KORA study was initiated and financed by the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. Korcula: This work was funded by the Medical Research Council UK, The Croatian Ministry of Science, Education and Sports (grant 216–1080315-0302), the Croatian Science Foundation (grant 8875), the Centre of Excellence in Personalized health care, and the Centre of Competencies for Integrative Treatment, Prevention and Rehabilitation using TMS. LifeLines: The LifeLines Cohort Study and generation and management of GWAS genotype data for the LifeLines Cohort Study are supported by The Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the Economic Structure Enhancing Fund (FES) of the Dutch government, the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation, and Dutch Diabetes Research Foundation. Niek Verweij is supported by NWO-VENI (016.186.125) and Marie Sklodowska-Curie GF (call: H2020-MSCA-IF-2014, Project ID: 661395). UHP: Folkert W. Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre. Ilonca Vaartjes is supported by a Dutch Heart Foundation grant DHF project "Facts and Figures." MGH-CAMP: Dr. Patrick Ellinor is funded by NIH grants (2R01HL092577, 1R01HL128914, R01HL104156, and K24HL105780) and American Heart Association Established Investigator Award 13EIA14220013 (Ellinor). Dr. Steve Lubitz is funded by NIH grants K23HL114724 and a Doris Duke Charitable Foundation Clinical Scientist Development Award 2014105. NEO: The authors of the NEO study thank all individuals who participated in the Netherlands Epidemiology in Obesity study, all participating general practitioners for inviting eligible participants, and all research nurses for collection of the data. We thank the NEO study group, Pat van Beelen, Petra Noordijk, and Ingeborg de Jonge for the coordination, lab, and data management of the NEO study. We also thank Arie Maan for the analyses of the electrocardiograms. The genotyping in the NEO study was supported by the Centre National de Génotypage (Paris, France), headed by Jean-Francois Deleuze. The NEO study is supported by the participating Departments, the Division and the Board of Directors of the Leiden University Medical Center, and by the Leiden University, Research Profile Area Vascular and Regenerative Medicine. Dennis Mook-Kanamori is supported by Dutch Science Organization (ZonMW-VENI Grant 916.14.023). RS-I: The generation and management of the Illumina Exome Chip v1.0 array data for the Rotterdam Study (RS-I) was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The Exome chip array dataset was funded by the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, from the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO)-sponsored Netherlands Consortium for Healthy Aging (NCHA; project nr. 050–060-810); the Netherlands Organization for Scientific Research (NWO; project number 184021007); and by the Rainbow Project (RP10; Netherlands Exome Chip Project) of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL; www.bbmri.nl). We thank Ms. Mila Jhamai, Ms. Sarah Higgins, and Mr. Marijn Verkerk for their help in creating the exome chip database, and Carolina Medina-Gomez, MSc, Lennard Karsten, MSc, and Linda Broer PhD for QC and variant calling. Variants were called using the best practice protocol developed by Grove et al. as part of the CHARGE consortium exome chip central calling effort. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study, and the participating general practitioners and pharmacists. The work of Bruno H. Stricker is supported by grants from the Netherlands Organization for Health Research and Development (ZonMw) (Priority Medicines Elderly 113102005 to ME and DoelmatigheidsOnderzoek 80–82500–98-10208 to BHS). The work of Mark Eijgelsheim is supported by grants from the Netherlands Organization for Health Research and Development (ZonMw) (Priority Medicines Elderly 113102005 to ME and DoelmatigheidsOnderzoek 80–82500–98-10208 to BHS). SHIP: SHIP is supported by the BMBF (grants 01ZZ9603, 01ZZ0103, and 01ZZ0403) and the German Research Foundation (Deutsche Forschungsgemeinschaft [DFG]; grant GR 1912/5–1). SHIP and SHIP-TREND are part of the Community Medicine Research net (CMR) of the Ernst-Moritz-Arndt University Greifswald (EMAU) which is funded by the BMBF as well as the Ministry for Education, Science and Culture and the Ministry of Labor, Equal Opportunities, and Social Affairs of the Federal State of Mecklenburg-West Pomerania. The CMR encompasses several research projects that share data from SHIP. The EMAU is a member of the Center of Knowledge Interchange (CKI) program of the Siemens AG. SNP typing of SHIP and SHIP-TREND using the Illumina Infinium HumanExome BeadChip (version v1.0) was supported by the BMBF (grant 03Z1CN22). We thank all SHIP and SHIP-TREND participants and staff members as well as the genotyping staff involved in the generation of the SNP data. TWINSUK: TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. UKBB: This research has been conducted using the UK Biobank Resource (application 8256 - Understanding genetic influences in the response of the cardiac electrical system to exercise) and is supported by Medical Research Council grant MR/N025083/1. We also wish to acknowledge the support of the NIHR Cardiovascular Biomedical Research Unit at Barts and Queen Mary University of London, UK. PD Lambiase acknowledges support from the UCLH Biomedicine NIHR. MO is supported by an IEF 2013 Marie Curie fellowship. JR acknowledges support from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007–2013) under REA grant agreement no. 608765. YFS: The Young Finns Study has been financially supported by the Academy of Finland: grants 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi); the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; and Diabetes Research Foundation of Finnish Diabetes Association. The expert technical assistance in the statistical analyses by Irina Lisinen is gratefully acknowledged. Cell culture and biochemistry: Funding was provided by the National Institutes of Health (Program of Excellence in Glycoscience award HL107147 to SSA and F32AR063548 to TJM) and the David and Lindsay Morgenthaler Postdoctoral Fellowship (to TJM) and by the Allen Distinguished Investigator Program, through support made by The Paul G. Allen Frontiers Group and the American Heart Association (to SSA). Mutant mouse model: Adamts6 mutant mice were generated and further propagated and analyzed by funding provided by NIH grants HL098180 and HL132024 (to CWL) and by the Allen Distinguished Investigator Program, through support made by The Paul G. Allen Frontiers Group and the American Heart Association (to SSA). ; Peer Reviewed
Background High plasma HDL cholesterol is associated with reduced risk of myocardial infarction, but whether this association is causal is unclear. Exploiting the fact that genotypes are randomly assigned at meiosis, are independent of non-genetic confounding, and are unmodifi ed by disease processes, mendelian random isation can be used to test the hypothesis that the association of a plasma biomarker with disease is causal. Methods We performed two mendelian randomisation analyses. First, we used as an instrument a single nucleotide polymorphism (SNP) in the endothelial lipase gene (LIPG Asn396Ser) and tested this SNP in 20 studies (20 913 myocardial infarction cases, 95 407 controls). Second, we used as an instrument a genetic score consisting of 14 common SNPs that exclusively associate with HDL cholesterol and tested this score in up to 12 482 cases of myocardial infarction and 41 331 controls. As a positive control, we also tested a genetic score of 13 common SNPs exclusively associated with LDL cholesterol. Findings Carriers of the LIPG 396Ser allele (2·6% frequency) had higher HDL cholesterol (0·14 mmol/L higher, p=8×10– ¹³) but similar levels of other lipid and non-lipid risk factors for myocardial infarction compared with noncarriers. This diff erence in HDL cholesterol is expected to decrease risk of myocardial infarction by 13% (odds ratio [OR] 0·87, 95% CI 0·84–0·91). However, we noted that the 396Ser allele was not associated with risk of myocardial infarction (OR 0·99, 95% CI 0·88–1·11, p=0·85). From observational epidemiology, an increase of 1 SD in HDL cholesterol was associated with reduced risk of myocardial infarction (OR 0·62, 95% CI 0·58–0·66). However, a 1 SD increase in HDL cholesterol due to genetic score was not associated with risk of myocardial infarction (OR 0·93, 95% CI 0·68–1·26, p=0·63). For LDL cholesterol, the estimate from observational epidemiology (a 1 SD increase in LDL cholesterol associated with OR 1·54, 95% CI 1·45–1·63) was concordant with that from genetic score (OR 2·13, 95% CI 1·69–2·69, p=2×10– ¹⁰). Interpretation Some genetic mechanisms that raise plasma HDL cholesterol do not seem to lower risk of myocardial infarction. These data challenge the concept that raising of plasma HDL cholesterol will uniformly translate into reductions in risk of myocardial infarction. Funding US National Institutes of Health, The Wellcome Trust, European Union, British Heart Foundation, and the German Federal Ministry of Education and Research. ; 115770
BACKGROUND: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. RESULTS: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. CONCLUSION: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. ; The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnología Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto käyttöön grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC – IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124. The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660 /2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI / ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Università degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443 ; Sí
BACKGROUND: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. RESULTS: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. CONCLUSION: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. ; The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto kayttoon grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC -IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124. The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660/2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Universita degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443. ; Sí
