TY  - GEN
A1  - Gorski, Mathias
A1  - Jung, Bettina
A1  - Li, Yong
A1  - Matias-Garcia, Pamela R.
A1  - Wuttke, Matthias
A1  - Coassin, Stefan
A1  - Thio, Chris H. L.
A1  - Kleber, Marcus E.
A1  - Winkler, Thomas W.
A1  - Wanner, Veronika
A1  - Chai, Jin-Fang
A1  - Chu, Audrey Y.
A1  - Cocca, Massimiliano
A1  - Feitosa, Mary F.
A1  - Ghasemi, Sahar
A1  - Hoppmann, Anselm
A1  - Horn, Katrin
A1  - Li, Man
A1  - Nutile, Teresa
A1  - Scholz, Markus
A1  - Sieber, Karsten B.
A1  - Teumer, Alexander
A1  - Tin, Adrienne
A1  - Wang, Judy
A1  - Tayo, Bamidele O.
A1  - Ahluwalia, Tarunveer S.
A1  - Almgren, Peter
A1  - Bakker, Stephan J. L.
A1  - Banas, Bernhard
A1  - Bansal, Nisha
A1  - Biggs, Mary L.
A1  - Boerwinkle, Eric
A1  - Böttinger, Erwin
A1  - Brenner, Hermann
A1  - Carroll, Robert J.
A1  - Chalmers, John
A1  - Chee, Miao-Li
A1  - Chee, Miao-Ling
A1  - Cheng, Ching-Yu
A1  - Coresh, Josef
A1  - de Borst, Martin H.
A1  - Degenhardt, Frauke
A1  - Eckardt, Kai-Uwe
A1  - Endlich, Karlhans
A1  - Franke, Andre
A1  - Freitag-Wolf, Sandra
A1  - Gampawar, Piyush
A1  - Gansevoort, Ron T.
A1  - Ghanbari, Mohsen
A1  - Gieger, Christian
A1  - Hamet, Pavel
A1  - Ho, Kevin
A1  - Hofer, Edith
A1  - Holleczek, Bernd
A1  - Foo, Valencia Hui Xian
A1  - Hutri-Kahonen, Nina
A1  - Hwang, Shih-Jen
A1  - Ikram, M. Arfan
A1  - Josyula, Navya Shilpa
A1  - Kahonen, Mika
A1  - Khor, Chiea-Chuen
A1  - Koenig, Wolfgang
A1  - Kramer, Holly
A1  - Kraemer, Bernhard K.
A1  - Kuehnel, Brigitte
A1  - Lange, Leslie A.
A1  - Lehtimaki, Terho
A1  - Lieb, Wolfgang
A1  - Loos, Ruth J. F.
A1  - Lukas, Mary Ann
A1  - Lyytikainen, Leo-Pekka
A1  - Meisinger, Christa
A1  - Meitinger, Thomas
A1  - Melander, Olle
A1  - Milaneschi, Yuri
A1  - Mishra, Pashupati P.
A1  - Mononen, Nina
A1  - Mychaleckyj, Josyf C.
A1  - Nadkarni, Girish N.
A1  - Nauck, Matthias
A1  - Nikus, Kjell
A1  - Ning, Boting
A1  - Nolte, Ilja M.
A1  - O'Donoghue, Michelle L.
A1  - Orho-Melander, Marju
A1  - Pendergrass, Sarah A.
A1  - Penninx, Brenda W. J. H.
A1  - Preuss, Michael H.
A1  - Psaty, Bruce M.
A1  - Raffield, Laura M.
A1  - Raitakari, Olli T.
A1  - Rettig, Rainer
A1  - Rheinberger, Myriam
A1  - Rice, Kenneth M.
A1  - Rosenkranz, Alexander R.
A1  - Rossing, Peter
A1  - Rotter, Jerome
A1  - Sabanayagam, Charumathi
A1  - Schmidt, Helena
A1  - Schmidt, Reinhold
A1  - Schoettker, Ben
A1  - Schulz, Christina-Alexandra
A1  - Sedaghat, Sanaz
A1  - Shaffer, Christian M.
A1  - Strauch, Konstantin
A1  - Szymczak, Silke
A1  - Taylor, Kent D.
A1  - Tremblay, Johanne
A1  - Chaker, Layal
A1  - van der Harst, Pim
A1  - van der Most, Peter J.
A1  - Verweij, Niek
A1  - Voelker, Uwe
A1  - Waldenberger, Melanie
A1  - Wallentin, Lars
A1  - Waterworth, Dawn M.
A1  - White, Harvey D.
A1  - Wilson, James G.
A1  - Wong, Tien-Yin
A1  - Woodward, Mark
A1  - Yang, Qiong
A1  - Yasuda, Masayuki
A1  - Yerges-Armstrong, Laura M.
A1  - Zhang, Yan
A1  - Snieder, Harold
A1  - Wanner, Christoph
A1  - Boger, Carsten A.
A1  - Kottgen, Anna
A1  - Kronenberg, Florian
A1  - Pattaro, Cristian
A1  - Heid, Iris M.
T1  - Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline
T2  - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät
N2  - Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m(2)/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m(2) at follow-up among those with eGFRcrea 60 mL/min/1.73m(2) or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or (LARP4B). Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.
T3  - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 19 
KW  - acute kidney injury
KW  - end-stage kidney disease
KW  - genome-wide association
KW  - study
KW  - rapid eGFRcrea decline
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-565379
IS  - 19
ER  - 
TY  - JOUR
A1  - Gorski, Mathias
A1  - Jung, Bettina
A1  - Li, Yong
A1  - Matias-Garcia, Pamela R.
A1  - Wuttke, Matthias
A1  - Coassin, Stefan
A1  - Thio, Chris H. L.
A1  - Kleber, Marcus E.
A1  - Winkler, Thomas W.
A1  - Wanner, Veronika
A1  - Chai, Jin-Fang
A1  - Chu, Audrey Y.
A1  - Cocca, Massimiliano
A1  - Feitosa, Mary F.
A1  - Ghasemi, Sahar
A1  - Hoppmann, Anselm
A1  - Horn, Katrin
A1  - Li, Man
A1  - Nutile, Teresa
A1  - Scholz, Markus
A1  - Sieber, Karsten B.
A1  - Teumer, Alexander
A1  - Tin, Adrienne
A1  - Wang, Judy
A1  - Tayo, Bamidele O.
A1  - Ahluwalia, Tarunveer S.
A1  - Almgren, Peter
A1  - Bakker, Stephan J. L.
A1  - Banas, Bernhard
A1  - Bansal, Nisha
A1  - Biggs, Mary L.
A1  - Boerwinkle, Eric
A1  - Böttinger, Erwin
A1  - Brenner, Hermann
A1  - Carroll, Robert J.
A1  - Chalmers, John
A1  - Chee, Miao-Li
A1  - Chee, Miao-Ling
A1  - Cheng, Ching-Yu
A1  - Coresh, Josef
A1  - de Borst, Martin H.
A1  - Degenhardt, Frauke
A1  - Eckardt, Kai-Uwe
A1  - Endlich, Karlhans
A1  - Franke, Andre
A1  - Freitag-Wolf, Sandra
A1  - Gampawar, Piyush
A1  - Gansevoort, Ron T.
A1  - Ghanbari, Mohsen
A1  - Gieger, Christian
A1  - Hamet, Pavel
A1  - Ho, Kevin
A1  - Hofer, Edith
A1  - Holleczek, Bernd
A1  - Foo, Valencia Hui Xian
A1  - Hutri-Kahonen, Nina
A1  - Hwang, Shih-Jen
A1  - Ikram, M. Arfan
A1  - Josyula, Navya Shilpa
A1  - Kahonen, Mika
A1  - Khor, Chiea-Chuen
A1  - Koenig, Wolfgang
A1  - Kramer, Holly
A1  - Kraemer, Bernhard K.
A1  - Kuehnel, Brigitte
A1  - Lange, Leslie A.
A1  - Lehtimaki, Terho
A1  - Lieb, Wolfgang
A1  - Loos, Ruth J. F.
A1  - Lukas, Mary Ann
A1  - Lyytikainen, Leo-Pekka
A1  - Meisinger, Christa
A1  - Meitinger, Thomas
A1  - Melander, Olle
A1  - Milaneschi, Yuri
A1  - Mishra, Pashupati P.
A1  - Mononen, Nina
A1  - Mychaleckyj, Josyf C.
A1  - Nadkarni, Girish N.
A1  - Nauck, Matthias
A1  - Nikus, Kjell
A1  - Ning, Boting
A1  - Nolte, Ilja M.
A1  - O'Donoghue, Michelle L.
A1  - Orho-Melander, Marju
A1  - Pendergrass, Sarah A.
A1  - Penninx, Brenda W. J. H.
A1  - Preuss, Michael H.
A1  - Psaty, Bruce M.
A1  - Raffield, Laura M.
A1  - Raitakari, Olli T.
A1  - Rettig, Rainer
A1  - Rheinberger, Myriam
A1  - Rice, Kenneth M.
A1  - Rosenkranz, Alexander R.
A1  - Rossing, Peter
A1  - Rotter, Jerome
A1  - Sabanayagam, Charumathi
A1  - Schmidt, Helena
A1  - Schmidt, Reinhold
A1  - Schoettker, Ben
A1  - Schulz, Christina-Alexandra
A1  - Sedaghat, Sanaz
A1  - Shaffer, Christian M.
A1  - Strauch, Konstantin
A1  - Szymczak, Silke
A1  - Taylor, Kent D.
A1  - Tremblay, Johanne
A1  - Chaker, Layal
A1  - van der Harst, Pim
A1  - van der Most, Peter J.
A1  - Verweij, Niek
A1  - Voelker, Uwe
A1  - Waldenberger, Melanie
A1  - Wallentin, Lars
A1  - Waterworth, Dawn M.
A1  - White, Harvey D.
A1  - Wilson, James G.
A1  - Wong, Tien-Yin
A1  - Woodward, Mark
A1  - Yang, Qiong
A1  - Yasuda, Masayuki
A1  - Yerges-Armstrong, Laura M.
A1  - Zhang, Yan
A1  - Snieder, Harold
A1  - Wanner, Christoph
A1  - Boger, Carsten A.
A1  - Kottgen, Anna
A1  - Kronenberg, Florian
A1  - Pattaro, Cristian
A1  - Heid, Iris M.
T1  - Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline
JF  - Kidney international : official journal of the International Society of Nephrology
N2  - Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m(2)/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m(2) at follow-up among those with eGFRcrea 60 mL/min/1.73m(2) or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or (LARP4B). Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.
KW  - acute kidney injury
KW  - end-stage kidney disease
KW  - genome-wide association
KW  - study
KW  - rapid eGFRcrea decline
Y1  - 2020
U6  - https://doi.org/10.1016/j.kint.2020.09.030
SN  - 0085-2538
SN  - 1523-1755
VL  - 99
IS  - 4
SP  - 926
EP  - 939
PB  - Elsevier
CY  - New York
ER  - 
TY  - JOUR
A1  - Khider, D.
A1  - Emile-Geay, J.
A1  - McKay, N. P.
A1  - Gil, Y.
A1  - Garijo, D.
A1  - Ratnakar, V
A1  - Alonso-Garcia, M.
A1  - Bertrand, S.
A1  - Bothe, O.
A1  - Brewer, P.
A1  - Bunn, A.
A1  - Chevalier, M.
A1  - Comas-Bru, L.
A1  - Csank, A.
A1  - Dassie, E.
A1  - DeLong, K.
A1  - Felis, T.
A1  - Francus, P.
A1  - Frappier, A.
A1  - Gray, W.
A1  - Goring, S.
A1  - Jonkers, L.
A1  - Kahle, M.
A1  - Kaufman, D.
A1  - Kehrwald, N. M.
A1  - Martrat, B.
A1  - McGregor, H.
A1  - Richey, J.
A1  - Schmittner, A.
A1  - Scroxton, N.
A1  - Sutherland, E.
A1  - Thirumalai, Kaustubh
A1  - Allen, K.
A1  - Arnaud, F.
A1  - Axford, Y.
A1  - Barrows, T.
A1  - Bazin, L.
A1  - Birch, S. E. Pilaar
A1  - Bradley, E.
A1  - Bregy, J.
A1  - Capron, E.
A1  - Cartapanis, O.
A1  - Chiang, H-W
A1  - Cobb, K. M.
A1  - Debret, M.
A1  - Dommain, Réne
A1  - Du, J.
A1  - Dyez, K.
A1  - Emerick, S.
A1  - Erb, M. P.
A1  - Falster, G.
A1  - Finsinger, W.
A1  - Fortier, D.
A1  - Gauthier, Nicolas
A1  - George, S.
A1  - Grimm, E.
A1  - Hertzberg, J.
A1  - Hibbert, F.
A1  - Hillman, A.
A1  - Hobbs, W.
A1  - Huber, M.
A1  - Hughes, A. L. C.
A1  - Jaccard, S.
A1  - Ruan, J.
A1  - Kienast, M.
A1  - Konecky, B.
A1  - Le Roux, G.
A1  - Lyubchich, V
A1  - Novello, V. F.
A1  - Olaka, L.
A1  - Partin, J. W.
A1  - Pearce, C.
A1  - Phipps, S. J.
A1  - Pignol, C.
A1  - Piotrowska, N.
A1  - Poli, M-S
A1  - Prokopenko, A.
A1  - Schwanck, F.
A1  - Stepanek, C.
A1  - Swann, G. E. A.
A1  - Telford, R.
A1  - Thomas, E.
A1  - Thomas, Z.
A1  - Truebe, S.
A1  - von Gunten, L.
A1  - Waite, A.
A1  - Weitzel, N.
A1  - Wilhelm, B.
A1  - Williams, J.
A1  - Winstrup, M.
A1  - Zhao, N.
A1  - Zhou, Y.
T1  - PaCTS 1.0: A Crowdsourced Reporting Standard for Paleoclimate Data
JF  - Paleoceanography and paleoclimatology
N2  - The progress of science is tied to the standardization of measurements, instruments, and data. This is especially true in the Big Data age, where analyzing large data volumes critically hinges on the data being standardized. Accordingly, the lack of community-sanctioned data standards in paleoclimatology has largely precluded the benefits of Big Data advances in the field. Building upon recent efforts to standardize the format and terminology of paleoclimate data, this article describes the Paleoclimate Community reporTing Standard (PaCTS), a crowdsourced reporting standard for such data. PaCTS captures which information should be included when reporting paleoclimate data, with the goal of maximizing the reuse value of paleoclimate data sets, particularly for synthesis work and comparison to climate model simulations. Initiated by the LinkedEarth project, the process to elicit a reporting standard involved an international workshop in 2016, various forms of digital community engagement over the next few years, and grassroots working groups. Participants in this process identified important properties across paleoclimate archives, in addition to the reporting of uncertainties and chronologies; they also identified archive-specific properties and distinguished reporting standards for new versus legacy data sets. This work shows that at least 135 respondents overwhelmingly support a drastic increase in the amount of metadata accompanying paleoclimate data sets. Since such goals are at odds with present practices, we discuss a transparent path toward implementing or revising these recommendations in the near future, using both bottom-up and top-down approaches.
KW  - standards
KW  - FAIR
KW  - paleoclimate
KW  - paleoceanography
KW  - data
KW  - best practices
Y1  - 2019
U6  - https://doi.org/10.1029/2019PA003632
SN  - 2572-4517
SN  - 2572-4525
VL  - 34
IS  - 10
SP  - 1570
EP  - 1596
PB  - American Geophysical Union
CY  - Washington
ER  - 
TY  - JOUR
A1  - Garcia, Ada
A1  - Ruhl, R.
A1  - Schweigert, Florian J.
T1  - Retinoid concentrations in the mouse during postnatal development and after maternal vitamin a supplementation
N2  - Background: Vitamin A (VA) and its derivates (retinoids) are important nutritional substances, which mediate their biological activity mainly via nuclear retinoid receptors. Maternal VA intake during lactation influences the VA content in milk and the VA status of the progeny. We investigated the effects of maternal supplementation during lactation and direct supplementation to the pups after weaning on the retinoid concentration in serum and liver of neonatal mice using high doses of VA. Methods: Dams were fed a basal (4,500 retinol equivalents/ kg diet) or a VA- supplemented (324,000 retinol equivalents/ kg diet) diet during lactation. Pups kept receiving the same diet after weaning. Serum and liver samples of the pups were collected during lactation at days 1, 3, 5, 7, and 14 and post-weaning at days 21 and 65 after birth. Samples were analysed for retinoids by high-performance liquid chromatography. Results: Maternal VA supplementation resulted in significantly higher concentrations of retinol, retinyl palmitate and retinyl stearate in serum of mice neonates at days 5, 7, 14, 21 and 65 after birth in comparison to the basal diet, whereas significantly higher concentrations were observed in liver at days 5, 14, 21 and 65 after birth. At day 7 after birth, a decrease in the liver retinoid concentrations occurred in the VA-supplemented diet. Conclusion: Our results show for the first time that supplementation with high doses of VA during the lactation period in mice can affect serum retinol concentrations in the neonates and report that day 7 after birth is a critical time in the tissue distribution of retinoids during postnatal development. Copyright (C) 2005 S. Karger AG, Basel
Y1  - 2005
SN  - 0250-6807
ER  - 
TY  - JOUR
A1  - Dormann, Carsten F.
A1  - Elith, Jane
A1  - Bacher, Sven
A1  - Buchmann, Carsten M.
A1  - Carl, Gudrun
A1  - Carre, Gabriel
A1  - Garcia Marquez, Jaime R.
A1  - Gruber, Bernd
A1  - Lafourcade, Bruno
A1  - Leitao, Pedro J.
A1  - Münkemüller, Tamara
A1  - McClean, Colin
A1  - Osborne, Patrick E.
A1  - Reineking, Bjoern
A1  - Schröder-Esselbach, Boris
A1  - Skidmore, Andrew K.
A1  - Zurell, Damaris
A1  - Lautenbach, Sven
T1  - Collinearity a review of methods to deal with it and a simulation study evaluating their performance
JF  - Ecography : pattern and diversity in ecology ; research papers forum
N2  - Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the folk lore'-thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.
Y1  - 2013
U6  - https://doi.org/10.1111/j.1600-0587.2012.07348.x
SN  - 0906-7590
SN  - 1600-0587
VL  - 36
IS  - 1
SP  - 27
EP  - 46
PB  - Wiley-Blackwell
CY  - Hoboken
ER  -