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Genotype data and related phenotype data for Ovarian Cancer Association Consortium project investigating common variantion in GTPAse genes and ovarian cancer risk

Table S3. Cis acting methylation quantitative trait loci in the 40 genes highlighted in the EWAS. (XLS 31 kb)

Table S5. Mediation analysis omnibus P values for the 19 meQTLs associated with serum cotinine levels. (XLS 33 kb)

Table S7. CpG sites showing significant (FDR pâ

Table S9. Literature search results for smoking EWAS. (XLS 9 kb)

Additional file 1. Cited structures.

Additional file 3: Summary of Zenodo view and download statistics, plus citations (CSV format) as of 28 April 2022 [235].

Related Article: Zdeněk Dvořák, Felix Kopp, Cait M. Costello, Jazmin S. Kemp, Hao Li, Aneta Vrzalová, Martina Štěpánková, Iveta Bartoňková, Eva Jiskrová, Karolína Poulíková, Barbora Vyhlídalová, Lars U. Nordstroem, Chamini V. Karunaratne, Harmit S. Ranhotra, Kyu Shik Mun, Anjaparavanda P. Naren, Iain A. Murray, Gary H. Perdew, Julius Brtko, Lucia Toporova, Arne Schön, William G. Wallace, William G. Walton, Matthew R. Redinbo, Katherine Sun, Amanda Beck, Sandhya Kortagere, Michelle C. Neary, Aneesh Chandran, Saraswathi Vishveshwara, Maria M. Cavalluzzi, Giovanni Lentini, Julia Yue Cui, Haiwei Gu, John C. March, Shirshendu Chatterjee, Adam Matson, Dennis Wright, Kyle L. Flannigan, Simon A. Hirota, Ryan Balfour Sartor, Sridhar Mani|2020|EMBO Mol. Med.|12||doi:10.15252/emmm.201911621 An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Related Article: Zdeněk Dvořák, Felix Kopp, Cait M. Costello, Jazmin S. Kemp, Hao Li, Aneta Vrzalová, Martina Štěpánková, Iveta Bartoňková, Eva Jiskrová, Karolína Poulíková, Barbora Vyhlídalová, Lars U. Nordstroem, Chamini V. Karunaratne, Harmit S. Ranhotra, Kyu Shik Mun, Anjaparavanda P. Naren, Iain A. Murray, Gary H. Perdew, Julius Brtko, Lucia Toporova, Arne Schön, William G. Wallace, William G. Walton, Matthew R. Redinbo, Katherine Sun, Amanda Beck, Sandhya Kortagere, Michelle C. Neary, Aneesh Chandran, Saraswathi Vishveshwara, Maria M. Cavalluzzi, Giovanni Lentini, Julia Yue Cui, Haiwei Gu, John C. March, Shirshendu Chatterjee, Adam Matson, Dennis Wright, Kyle L. Flannigan, Simon A. Hirota, Ryan Balfour Sartor, Sridhar Mani|2020|EMBO Mol. Med.|12||doi:10.15252/emmm.201911621 An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Presence of Common Scales: country: name of the country or the territory; geodi: two-letters country code; iso: three-letters country code; d: date of the observation; cases: number of cases reported on the given day by the European Centre for Disease Prevention and Control; deaths: number of deaths reported on the given day by the European Centre for Disease Prevention and Control; school: binary variable equal to1 if schools were closed and 0 otherwise; school_local: binary flag to distinguish localized school closures from other cases. 1 denotes that school closures were implemented at the local level and 0 denotes that school closures were not implemented at the local level (either at the national level or no school closures). The data on the scale of school closures is imported from the UNESCO. The interaction of school and school_local allows researchers to create three levels of measures: no school closures (school=0 and school_local=0), localized school closures (school=1 and school_local==1) or national school closures (school=1 and school_local=0); domestic: binary variable equal to 1 if there was a domestic lockdown and 0 otherwise; domestic_local: binary variable to distinguish localized domestic lockdowns from other cases. 1 denotes that domestic lockdowns were implemented at the local level and 0 means that domestic lockdowns were not implemented at the local-level (either at the national level or not implemented). The nature of the domestic lockdown is based on our reading of the measures reported by the ACAPS. The interaction of domestic and domestic_local allows researchers to create three levels of measures: no domestic lockdown (domestic=0 and domestic_local=0), localized domestic lockdowns (domestic=1 and domestic_local=1) or national domestic lockdowns (domestic=1 and domestic_local=0); travel: binary variable equal to1 if travel restrictions were implemented and 0 otherwise; travel_partial: binary flag to differentiate partial travel restrictions from other cases. 1 denotes that travel restrictions were partial and 0 denotes that travel restrictions were not partial (either strict or not implemented). The nature of the travel restrictions is based on our reading of the measures reported by the ACAPS. The interaction of travel and travel_partial allows researchers to create three levels of measures: no travel restrictions (travel=0 and travel_partial=0), partial travel restrictions (travel=1 and travel_partial=1) or strict travel restrictions (travel=1 and travel_partial=0); travel_dom: binary variable equal to1 if travel restrictions within the country (e.g. inter-region travels) were implemented and 0 otherwise; travel_dom_partial: binary flag to differentiate partial domestic travel restrictions from other cases. 1 denotes that travel restrictions were partial and 0 denotes that travel restrictions were not partial (either strict or not implemented). The nature of the travel restrictions is based on our reading of the measures reported by the ACAPS. The interaction of travel and travel_partial allows researchers to create three levels of measures: no domestic travel restrictions (travel_dom=0 and travel_dom_partial=0), partial domestic travel restrictions (travel_dom=1 and travel_dom_partial=1) or strict domestic travel restrictions (travel_dom=1 and travel_dom_partial=0); curf: binary variable equal to1 if a curfew was implemented and 0 otherwise; curf_partial: binary flag to differentiate partial curfews from other cases. 1 denotes that the curfew was partial and 0 denotes that the curfew was not partial (either strict or not implemented). The nature of the curfew is based on our reading of the measures reported by the ACAPS. The interaction of curf and curf_partial allows researchers to create three levels of measures: no curfew (curf=0 and curf_partial=0), partial curfew (curf=1 and curf_partial=1) or strict curfew (curf=1 and curf_partial=0); mass: binary variable equal to1 if bans on mass gatherings were implemented and 0 otherwise; mass_partial: binary flag to distinguish localized bans on mass gatherings from other cases. 1 denotes that bans on mass gatherings were partial and 0 denotes that bans on mass gatherings were not partial (either strict or not implemented). The nature of the bans on mass gatherings is based on our reading of the measures reported by the ACAPS. The interaction of mass and mass_partial allows researchers to create three levels of measures: no bans on mass gatherings (mass=0 and mass_partial=0), localized or partial bans (mass=1 and mass_partial=1) or national or strict bans (mass=1 and mass_partial=0); elect: binary variable equal to1 if some elections were postponed and 0 otherwise; elect_partial: binary flag to differentiate countries which postponed only some of the elections from the others. 1 denotes that countries both maintained and postponed elections and 0 denotes that elections were either postponed, maintained or were not scheduled. IDEA lists all maintained and postponed elections since the beginning of 2020. The interaction of elect and elect_partial allows researchers to differentiate three settings: all elections were maintained despite COVID-19 (elect=0 and elect_partial=0), some elections were maintained and others were postponed (elect=1 and elect_partial=1) or all elections were postponed (elect=1 and elect_partial=0); sport: binary variable equal to1 if bans on sporting and large events were implemented and 0 otherwise; sport_partial: binary flag to distinguish partial bans and cancellations of sporting and large events. 1 denotes that bans on sporting and large events were localized, strict or with no spectators, 0 that bans on sporting and large events are not localized or partial (either national or no measures implemented). The nature of the bans on sporting and large events is based on our reading of the measures reported by the ACAPS. The interaction of sport and sport_partial allows researchers to create three levels of measures: no bans (sport=0 and sport_partial=0), partial bans (sport=1 and sport_partial=1) or national bans on mass gatherings (sport=1 and sport_partial=0); rest: binary variable equal to1 if restaurants were closed and 0 otherwise; rest_local: binary flag to distinguish localized and/or partial restaurant and bar closures from other cases. The variable is coded 1 in the three following situations: localized closures, limitations on the number of customers in bars and restaurants, and closures of either bars or restaurants. 0 indicates national closures or no closures at all. The coding is based on our reading of the measures reported by the ACAPS. The interaction of rest and rest_local allows researchers to create three levels of measures: no closures (rest=0 and rest_local=0), localized closures (rest=1 and rest_local=1) or national closures (rest=1 and rest_local=0); testing: binary variable equal to1 if there was a public testing policy and 0 otherwise; testing_narrow: binary flag to distinguish narrow testing policies from large testing policies. 1 denotes that testing policies were targeted to some individuals, 0 that testing policies were not targeted (either large or not implemented). The nature of the testing policy is based on the information reported in the measures ���mass population testing��� and ���testing policy��� in the ACAPS. When the measure was targeted, testing_narrow was coded 1. On the contrary, when the measure was not targeted, testing_narrow was coded 0. The interaction of testing and testing_narrow allows researchers to create three levels of measures: no testing policy (testing=0 and testing_narrow =0), narrow testing policy (testing=1 and testing_narrow =1) or large testing policy (testing=1 and testing_narrow =0); surveillance: binary variable equal to1 if mobile app or bracelet surveillance was implemented and 0 otherwise; surveillance_partial: binary variable equal to1 if the enhanced surveillance is optional or reserved for a category of person (e.g. certain professions or foreigners) and 0 otherwise, based on information in the ACAPS. When the measure was partial, surveillance_partial was coded 1. On the contrary, when the measure was strict (anybody suspected of having COVID-19), surveillance_partial was coded 0. The interaction of surveillance and surveillance_partial allows researchers to create three levels of measures: no surveillance (surveillance=0 and surveillance_partial =0), partial surveillance (surveillance=1 and surveillance_partial=1) or strict surveillance (surveillance=1 and surveillance_partial =0); masks : binary variable equal to1 if mandates to wear masks in public spaces were implemented and 0 otherwise; masks_partial: binary variable equal to1 if the obligation to wear masks is regional, based on information in the ACAPS. When the measure was regional, masks_partial was coded 1. On the contrary, when the measure was national, masks_partial was coded 0. The interaction of masks and masks_partial allows researchers to create three levels of measures: no obligations to wear masks (masks=0 and masks_partial =0), regional obligations to wear masks (masks=1 and masks_partial=1) or national obligations to wear masks (masks=1 and masks_partial =0); state: binary variable equal to1 if the state of emergency is declared and 0 otherwise; state_partial: binary variable equal to1 if the state of emergency is declared on a local basis and 0 otherwise, based on information in the ACAPS. When the measure was local, state_partial was coded 1. On the contrary, when the measure was not localized, state_partial was coded 0. The interaction of state and state_partial allows researchers to create three levels of measures: no state of emergency (state=0 and state_partial =0), partial state of emergency (state=1 and state_partial=1) or national state of emergency (state=1 and state_partial =0); cash: binary variable equal to1 if cash transfers are implemented and 0 otherwise; wage: binary variable equal to1 if wage support is implemented and 0 otherwise; credit: binary variable equal to1 if credit schemes are implemented and 0 otherwise; taxc: binary variable equal to1 if tax credits are implemented and 0 otherwise; taxd: binary variable equal to1 if tax delays are implemented and 0 otherwise; export: binary variable equal to1 if supports to importers or exporters are implemented and 0 otherwise; rate: binary variable equal to1 if the Central Bank lowered the interest rates and 0 otherwise; Rigidity_Public_Health: average of the ten coded public health measures. Public health measures are valued 0.5 if they are localized or partial and 1 if they are national or strict. 0 indicates no measures; Economic_Measures: average of the coded economic measures. The Response2covid19 dataset tracks governments��� responses to COVID-19 all around the world. The dataset is at the country-level and covers the January-October 2020 period; it is updated on a monthly basis. It tracks 20 measures ��� 13 public health measures and 7 economic measures ��� taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset (���Governments��� Responses to COVID-19 (Response2covid19)���) and the reference: Porcher, Simon "A novel dataset of governments' responses to COVID-19 all around the world", Chaire EPPP 2020-03 discussion paper, 2020. Public health measures include international and domestic travel restrictions, bans on mass gatherings, school closing and domestic lockdown among others. Economic measures include wage support, cash transfers, interest rates cuts, tax cuts and delays, and support to exporters or importers. . Smallest Geographic Unit: Country-level other; Cite data as Porcher, Simon "A novel dataset of governments��� responses to COVID-19 all around the world", chaire EPPP discussions paper 2020-03. Link: https://www.chaire-eppp.org/wp-content/uploads/2020/05/WP202003.pdf All countries with available information (228 countries). Response Rates: 62,700 observations; 228 countries.