@article{BoraCottonScherbaumetal.2017,
  author    = {Bora, Sanjay Singh and Cotton, Fabrice Pierre and Scherbaum, Frank and Edwards, Benjamin and Traversa, Paola},
  title     = {Stochastic source, path and site attenuation parameters and associated variabilities for shallow crustal European earthquakes},
  series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  volume    = {15},
  journal   = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1570-761X},
  doi       = {10.1007/s10518-017-0167-x},
  pages     = {4531 -- 4561},
  year      = {2017},
  abstract  = {We have analyzed the recently developed pan-European strong motion database, RESORCE-2012: spectral parameters, such as stress drop (stress parameter, Delta sigma), anelastic attenuation (Q), near surface attenuation (kappa(0)) and site amplification have been estimated from observed strong motion recordings. The selected dataset exhibits a bilinear distance-dependent Q model with average kappa(0) value 0.0308 s. Strong regional variations in inelastic attenuation were also observed: frequency-independent Q(0) of 1462 and 601 were estimated for Turkish and Italian data respectively. Due to the strong coupling between Q and kappa(0), the regional variations in Q have strong impact on the estimation of near surface attenuation kappa(0). kappa(0) was estimated as 0.0457 and 0.0261 s for Turkey and Italy respectively. Furthermore, a detailed analysis of the variability in estimated kappa(0) revealed significant within-station variability. The linear site amplification factors were constrained from residual analysis at each station and site-class type. Using the regional Q(0) model and a site-class specific kappa(0), seismic moments (M-0) and source corner frequencies f (c) were estimated from the site corrected empirical Fourier spectra. Delta sigma did not exhibit magnitude dependence. The median Delta sigma value was obtained as 5.75 and 5.65 MPa from inverted and database magnitudes respectively. A comparison of response spectra from the stochastic model (derived herein) with that from (regional) ground motion prediction equations (GMPEs) suggests that the presented seismological parameters can be used to represent the corresponding seismological attributes of the regional GMPEs in a host-to-target adjustment framework. The analysis presented herein can be considered as an update of that undertaken for the previous Euro-Mediterranean strong motion database presented by Edwards and Fah (Geophys J Int 194(2):1190-1202, 2013a).},
  language  = {en}
}
@article{MarquerGaillardSugitaetal.2017,
  author    = {Marquer, Laurent and Gaillard, Marie-Jose and Sugita, Shinya and Poska, Anneli and Trondman, Anna-Kari and Mazier, Florence and Nielsen, Anne Birgitte and Fyfe, Ralph M. and Jonsson, Anna Maria and Smith, Benjamin and Kaplan, Jed O. and Alenius, Teija and Birks, H. John B. and Bjune, Anne E. and Christiansen, Jorg and Dodson, John and Edwards, Kevin J. and Giesecke, Thomas and Herzschuh, Ulrike and Kangur, Mihkel and Koff, Tiiu and Latalowa, Maligorzata and Lechterbeck, Jutta and Olofsson, Jorgen and Seppa, Heikki},
  title     = {Quantifying the effects of land use and climate on Holocene vegetation in Europe},
  series = {Quaternary science reviews : the international multidisciplinary research and review journal},
  volume    = {171},
  journal   = {Quaternary science reviews : the international multidisciplinary research and review journal},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0277-3791},
  doi       = {10.1016/j.quascirev.2017.07.001},
  pages     = {20 -- 37},
  year      = {2017},
  abstract  = {Early agriculture can be detected in palaeovegetation records, but quantification of the relative importance of climate and land use in influencing regional vegetation composition since the onset of agriculture is a topic that is rarely addressed. We present a novel approach that combines pollen-based REVEALS estimates of plant cover with climate, anthropogenic land-cover and dynamic vegetation modelling results. This is used to quantify the relative impacts of land use and climate on Holocene vegetation at a sub-continental scale, i.e. northern and western Europe north of the Alps. We use redundancy analysis and variation partitioning to quantify the percentage of variation in vegetation composition explained by the climate and land-use variables, and Monte Carlo permutation tests to assess the statistical significance of each variable. We further use a similarity index to combine pollen based REVEALS estimates with climate-driven dynamic vegetation modelling results. The overall results indicate that climate is the major driver of vegetation when the Holocene is considered as a whole and at the sub-continental scale, although land use is important regionally. Four critical phases of land-use effects on vegetation are identified. The first phase (from 7000 to 6500 BP) corresponds to the early impacts on vegetation of farming and Neolithic forest clearance and to the dominance of climate as a driver of vegetation change. During the second phase (from 4500 to 4000 BP), land use becomes a major control of vegetation. Climate is still the principal driver, although its influence decreases gradually. The third phase (from 2000 to 1500 BP) is characterised by the continued role of climate on vegetation as a consequence of late-Holocene climate shifts and specific climate events that influence vegetation as well as land use. The last phase (from 500 to 350 BP) shows an acceleration of vegetation changes, in particular during the last century, caused by new farming practices and forestry in response to population growth and industrialization. This is a unique signature of anthropogenic impact within the Holocene but European vegetation remains climatically sensitive and thus may continue to respond to ongoing climate change. (C) 2017 Elsevier Ltd. All rights reserved.},
  language  = {en}
}
@article{BoraScherbaumKuehnetal.2015,
  author    = {Bora, Sanjay Singh and Scherbaum, Frank and K{\"u}hn, Nicolas and Stafford, Peter and Edwards, Benjamin},
  title     = {Development of a Response Spectral Ground-Motion Prediction Equation (GMPE) for Seismic-Hazard Analysis from Empirical Fourier Spectral and Duration Models},
  series = {Bulletin of the Seismological Society of America},
  volume    = {105},
  journal   = {Bulletin of the Seismological Society of America},
  number    = {4},
  publisher = {Seismological Society of America},
  address   = {Albany},
  issn      = {0037-1106},
  doi       = {10.1785/0120140297},
  pages     = {2192 -- 2218},
  year      = {2015},
  abstract  = {Empirical ground-motion prediction equations (GMPEs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This article presents a holistic framework for the development of a response spectral GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain. The approach for developing a response spectral GMPE is unique, because it combines the predictions of empirical models for the two model components that characterize the spectral and temporal behavior of the ground motion. Essentially, as described in its initial form by Bora et al. (2014), the approach consists of an empirical model for the Fourier amplitude spectrum (FAS) and a model for the ground-motion duration. These two components are combined within the random vibration theory framework to obtain predictions of response spectral ordinates. In addition, FAS corresponding to individual acceleration records are extrapolated beyond the useable frequencies using the stochastic FAS model, obtained by inversion as described in Edwards and Fah (2013a). To that end, a (oscillator) frequency-dependent duration model, consistent with the empirical FAS model, is also derived. This makes it possible to generate a response spectral model that is easily adjustable to different sets of seismological parameters, such as the stress parameter Delta sigma, quality factor Q, and kappa kappa(0). The dataset used in Bora et al. (2014), a subset of the RESORCE-2012 database, is considered for the present analysis. Based upon the range of the predictor variables in the selected dataset, the present response spectral GMPE should be considered applicable over the magnitude range of 4 <= M-w <= 7.6 at distances <= 200 km.},
  language  = {en}
}
@article{WuttkeLiLietal.2019,
  author    = {Wuttke, Matthias and Li, Yong and Li, Man and Sieber, Karsten B. and Feitosa, Mary F. and Gorski, Mathias and Tin, Adrienne and Wang, Lihua and Chu, Audrey Y. and Hoppmann, Anselm and Kirsten, Holger and Giri, Ayush and Chai, Jin-Fang and Sveinbjornsson, Gardar and Tayo, Bamidele O. and Nutile, Teresa and Fuchsberger, Christian and Marten, Jonathan and Cocca, Massimiliano and Ghasemi, Sahar and Xu, Yizhe and Horn, Katrin and Noce, Damia and Van der Most, Peter J. and Sedaghat, Sanaz and Yu, Zhi and Akiyama, Masato and Afaq, Saima and Ahluwalia, Tarunveer Singh and Almgren, Peter and Amin, Najaf and Arnlov, Johan and Bakker, Stephan J. L. and Bansal, Nisha and Baptista, Daniela and Bergmann, Sven and Biggs, Mary L. and Biino, Ginevra and Boehnke, Michael and Boerwinkle, Eric and Boissel, Mathilde and B{\"o}ttinger, Erwin and Boutin, Thibaud S. and Brenner, Hermann and Brumat, Marco and Burkhardt, Ralph and Butterworth, Adam S. and Campana, Eric and Campbell, Archie and Campbell, Harry and Canouil, Mickael and Carroll, Robert J. and Catamo, Eulalia and Chambers, John C. and Chee, Miao-Ling and Chee, Miao-Li and Chen, Xu and Cheng, Ching-Yu and Cheng, Yurong and Christensen, Kaare and Cifkova, Renata and Ciullo, Marina and Concas, Maria Pina and Cook, James P. and Coresh, Josef and Corre, Tanguy and Sala, Cinzia Felicita and Cusi, Daniele and Danesh, John and Daw, E. Warwick and De Borst, Martin H. and De Grandi, Alessandro and De Mutsert, Renee and De Vries, Aiko P. J. and Degenhardt, Frauke and Delgado, Graciela and Demirkan, Ayse and Di Angelantonio, Emanuele and Dittrich, Katalin and Divers, Jasmin and Dorajoo, Rajkumar and Eckardt, Kai-Uwe and Ehret, Georg and Elliott, Paul and Endlich, Karlhans and Evans, Michele K. and Felix, Janine F. and Foo, Valencia Hui Xian and Franco, Oscar H. and Franke, Andre and Freedman, Barry I. and Freitag-Wolf, Sandra and Friedlander, Yechiel and Froguel, Philippe and Gansevoort, Ron T. and Gao, He and Gasparini, Paolo and Gaziano, J. Michael and Giedraitis, Vilmantas and Gieger, Christian and Girotto, Giorgia and Giulianini, Franco and Gogele, Martin and Gordon, Scott D. and Gudbjartsson, Daniel F. and Gudnason, Vilmundur and Haller, Toomas and Hamet, Pavel and Harris, Tamara B. and Hartman, Catharina A. and Hayward, Caroline and Hellwege, Jacklyn N. and Heng, Chew-Kiat and Hicks, Andrew A. and Hofer, Edith and Huang, Wei and Hutri-Kahonen, Nina and Hwang, Shih-Jen and Ikram, M. Arfan and Indridason, Olafur S. and Ingelsson, Erik and Ising, Marcus and Jaddoe, Vincent W. V. and Jakobsdottir, Johanna and Jonas, Jost B. and Joshi, Peter K. and Josyula, Navya Shilpa and Jung, Bettina and Kahonen, Mika and Kamatani, Yoichiro and Kammerer, Candace M. and Kanai, Masahiro and Kastarinen, Mika and Kerr, Shona M. and Khor, Chiea-Chuen and Kiess, Wieland and Kleber, Marcus E. and Koenig, Wolfgang and Kooner, Jaspal S. and Korner, Antje and Kovacs, Peter and Kraja, Aldi T. and Krajcoviechova, Alena and Kramer, Holly and Kramer, Bernhard K. and Kronenberg, Florian and Kubo, Michiaki and Kuhnel, Brigitte and Kuokkanen, Mikko and Kuusisto, Johanna and La Bianca, Martina and Laakso, Markku and Lange, Leslie A. and Langefeld, Carl D. and Lee, Jeannette Jen-Mai and Lehne, Benjamin and Lehtimaki, Terho and Lieb, Wolfgang and Lim, Su-Chi and Lind, Lars and Lindgren, Cecilia M. and Liu, Jun and Liu, Jianjun and Loeffler, Markus and Loos, Ruth J. F. and Lucae, Susanne and Lukas, Mary Ann and Lyytikainen, Leo-Pekka and Magi, Reedik and Magnusson, Patrik K. E. and Mahajan, Anubha and Martin, Nicholas G. and Martins, Jade and Marz, Winfried and Mascalzoni, Deborah and Matsuda, Koichi and Meisinger, Christa and Meitinger, Thomas and Melander, Olle and Metspalu, Andres and Mikaelsdottir, Evgenia K. and Milaneschi, Yuri and Miliku, Kozeta and Mishra, Pashupati P. and Program, V. A. Million Veteran and Mohlke, Karen L. and Mononen, Nina and Montgomery, Grant W. and Mook-Kanamori, Dennis O. and Mychaleckyj, Josyf C. and Nadkarni, Girish N. and Nalls, Mike A. and Nauck, Matthias and Nikus, Kjell and Ning, Boting and Nolte, Ilja M. and Noordam, Raymond and Olafsson, Isleifur and Oldehinkel, Albertine J. and Orho-Melander, Marju and Ouwehand, Willem H. and Padmanabhan, Sandosh and Palmer, Nicholette D. and Palsson, Runolfur and Penninx, Brenda W. J. H. and Perls, Thomas and Perola, Markus and Pirastu, Mario and Pirastu, Nicola and Pistis, Giorgio and Podgornaia, Anna I. and Polasek, Ozren and Ponte, Belen and Porteous, David J. and Poulain, Tanja and Pramstaller, Peter P. and Preuss, Michael H. and Prins, Bram P. and Province, Michael A. and Rabelink, Ton J. and Raffield, Laura M. and Raitakari, Olli T. and Reilly, Dermot F. and Rettig, Rainer and Rheinberger, Myriam and Rice, Kenneth M. and Ridker, Paul M. and Rivadeneira, Fernando and Rizzi, Federica and Roberts, David J. and Robino, Antonietta and Rossing, Peter and Rudan, Igor and Rueedi, Rico and Ruggiero, Daniela and Ryan, Kathleen A. and Saba, Yasaman and Sabanayagam, Charumathi and Salomaa, Veikko and Salvi, Erika and Saum, Kai-Uwe and Schmidt, Helena and Schmidt, Reinhold and Ben Schottker, and Schulz, Christina-Alexandra and Schupf, Nicole and Shaffer, Christian M. and Shi, Yuan and Smith, Albert V. and Smith, Blair H. and Soranzo, Nicole and Spracklen, Cassandra N. and Strauch, Konstantin and Stringham, Heather M. and Stumvoll, Michael and Svensson, Per O. and Szymczak, Silke and Tai, E-Shyong and Tajuddin, Salman M. and Tan, Nicholas Y. Q. and Taylor, Kent D. and Teren, Andrej and Tham, Yih-Chung and Thiery, Joachim and Thio, Chris H. L. and Thomsen, Hauke and Thorleifsson, Gudmar and Toniolo, Daniela and Tonjes, Anke and Tremblay, Johanne and Tzoulaki, Ioanna and Uitterlinden, Andre G. and Vaccargiu, Simona and Van Dam, Rob M. and Van der Harst, Pim and Van Duijn, Cornelia M. and Edward, Digna R. Velez and Verweij, Niek and Vogelezang, Suzanne and Volker, Uwe and Vollenweider, Peter and Waeber, Gerard and Waldenberger, Melanie and Wallentin, Lars and Wang, Ya Xing and Wang, Chaolong and Waterworth, Dawn M. and Bin Wei, Wen and White, Harvey and Whitfield, John B. and Wild, Sarah H. and Wilson, James F. and Wojczynski, Mary K. and Wong, Charlene and Wong, Tien-Yin and Xu, Liang and Yang, Qiong and Yasuda, Masayuki and Yerges-Armstrong, Laura M. and Zhang, Weihua and Zonderman, Alan B. and Rotter, Jerome I. and Bochud, Murielle and Psaty, Bruce M. and Vitart, Veronique and Wilson, James G. and Dehghan, Abbas and Parsa, Afshin and Chasman, Daniel I. and Ho, Kevin and Morris, Andrew P. and Devuyst, Olivier and Akilesh, Shreeram and Pendergrass, Sarah A. and Sim, Xueling and Boger, Carsten A. and Okada, Yukinori and Edwards, Todd L. and Snieder, Harold and Stefansson, Kari and Hung, Adriana M. and Heid, Iris M. and Scholz, Markus and Teumer, Alexander and Kottgen, Anna and Pattaro, Cristian},
  title     = {A catalog of genetic loci associated with kidney function from analyses of a million individuals},
  series = {Nature genetics},
  volume    = {51},
  journal   = {Nature genetics},
  number    = {6},
  publisher = {Nature Publ. Group},
  address   = {New York},
  organization    = {Lifelines COHort Study},
  issn      = {1061-4036},
  doi       = {10.1038/s41588-019-0407-x},
  pages     = {957 -- +},
  year      = {2019},
  abstract  = {Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.},
  language  = {en}
}