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5 results on '"R. Wolfinger"'

1. How Well do Polygenic Risk Scores Identify Men at High Risk for Prostate Cancer? Systematic Review and Meta-Analysis

Author

Aino Siltari, Ragnar Lönnerbro, Karl Pang, Kirill Shiranov, Alex Asiimwe, Susan Evans-Axelsson, Billy Franks, Amit Kiran, Teemu J. Murtola, Jack Schalken, Carl Steinbeisser, Anders Bjartell, Anssi Auvinen, J. N’Dow, E.J. Smith, R. Shepherd, M. Ribal, N. Mottet, L. Moris, M. Lardas, P-P. Willemse, G. Gandaglia, R. Campi, Rossella Nicoletti, M. Gacci, A. Briganti, M.M. Ratti, E. Alleva, L. Leardini, E.S. Sisca, R. Bangma, M. Roobol, S. Remmers, D. Tilki, T. Visakorpi, K. Talala, T. Tammela, M. van Hemelrijck, K. Bayer, S. Lejeune, S. Byrne, L. Fialho, P. Palaiologou B. De Meulder, C. Auffray, A. Hijazy, S. Power, N. Zounemat Kermani, K. van Bochove, M. Kalafati, M. Moinat, E. Voss, D. Horgan, L. Fullwood, M. Holtorf, D. Lancet, G. Bernstein, I. Omar, S. MacLennan, S. Maclennan, S. Tripathee, M. Wirth, M. Froehner, B. Brenner, A. Borkowetz, C. Thomas, F. Horn, K. Reiche, M. Kreux, A. Josefsson, D. Gasi Tandefekt, J. Hugosson, H. Huisman, J. Schalken, T. Hofmacher, P. Lindgren, E. Andersson, A. Fridhammar, J. Zong, J-E. Butler-Ransohoff, R. Herrera, M. Maass, P. Torremante, M.D. Voss, Z. Devecseri, T. Abbott, C. Dau, K. Papineni, R. Snijder, M. Lambrecht, R. Wolfinger, S. Rogiers, A. Servan, L. Antoni, K. Pacoe, P. Robinson, B. Jaton, D. Bakkard, H. Turunen, O. Kilkku, P. Pohjanjousi, O. Voima, L. Nevalaita, C. Reich, S. Araujo, E. Longden-Chapman, D. Burke, P. Agapow, S. Derkits, M. Licour, C. McCrea, S. Payne, A. Yong, L. Thompson, S. Le Mare, M Bussmann, and D. Kotik

Subjects
All institutes and research themes of the Radboud University Medical Center, Oncology, Urology, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], Urological cancers Radboud Institute for Molecular Life Sciences [Radboudumc 15]
Abstract

Contains fulltext : 291547.pdf (Publisher’s version ) (Open Access) OBJECTIVES: Genome-wide association studies have revealed over 200 genetic susceptibility loci for prostate cancer (PCa). By combining them, polygenic risk scores (PRS) can be generated to predict risk of PCa. We summarize the published evidence and conduct meta-analyses of PRS as a predictor of PCa risk in Caucasian men. PATIENTS AND METHODS: Data were extracted from 59 studies, with 16 studies including 17 separate analyses used in the main meta-analysis with a total of 20,786 cases and 69,106 controls identified through a systematic search of ten databases. Random effects meta-analysis was used to obtain pooled estimates of area under the receiver-operating characteristic curve (AUC). Meta-regression was used to assess the impact of number of single-nucleotide polymorphisms (SNPs) incorporated in PRS on AUC. Heterogeneity is expressed as I(2) scores. Publication bias was evaluated using funnel plots and Egger tests. RESULTS: The ability of PRS to identify men with PCa was modest (pooled AUC 0.63, 95% CI 0.62-0.64) with moderate consistency (I(2) 64%). Combining PRS with clinical variables increased the pooled AUC to 0.74 (0.68-0.81). Meta-regression showed only negligible increase in AUC for adding incremental SNPs. Despite moderate heterogeneity, publication bias was not evident. CONCLUSION: Typically, PRS accuracy is comparable to PSA or family history with a pooled AUC value 0.63 indicating mediocre performance for PRS alone. 01 april 2023

Published
2023

2. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Author

Z. SU, P. LABAJ, S. LI, J. THIERRY-MIEG, D. THIERRY-MIEG, W. SHI, C. WANG, G. SCHROTH, R. SETTERQUIST, J. THOMPSON, W. JONES, W. XIAO, W. XU, R. JENSEN, R. KELLY, J. XU, A. CONESA, C. FURLANELLO, H. GAO, H. HONG, N. JAFARI, S. LETOVSKY, Y. LIAO, F. LU, E. OAKELEY, Z. PENG, C. PRAUL, J. SANTOYO-LOPEZ, A. SCHERER, T. SHI, G. SMYTH, F. STAEDTLER, P. SYKACEK, X. TAN, E. THOMPSON, J. VANDESOMPELE, M. WANG, J. WANG, R. WOLFINGER, J. ZAVADIL, S. AUERBACH, W. BAO, H. BINDER, T. BLOMQUIST, M. BRILLIANT, P. BUSHEL, W. CAIN, J. CATALANO, C. CHANG, T. CHEN, G. CHEN, R. CHEN, M. CHIERICI, T. CHU, D. CLEVERT, Y. DENG, A. DERTI, V. DEVANARAYAN, Z. DONG, J. DOPAZO, T. DU, H. FANG, Y. FANG, M. FASOLD, A. FERNANDEZ, M. FISCHER, P. FURIO-TARI, J. FUSCOE, F. CAIMENT, S. GAJ, J. GANDARA, W. GE, Y. GONDO, B. GONG, M. GONG, Z. GONG, B. GREEN, C. GUO, L. GUO, J. HADFIELD, J. HELLEMANS, S. HOCHREITER, M. JIA, M. JIAN, C. JOHNSON, S. KAY, J. KLEINJANS, S. LABABIDI, S. LEVY, Q. LI, L. LI, P. LI, Y. LI, H. LI, J. LI, S. LIN, F. LOPEZ, X. LU, H. LUO, X. MA, J. MEEHAN, D. MEGHERBI, N. MEI, B. MU, B. NING, A. PANDEY, J. PEREZ-FLORIDO, R. PERKINS, R. PETERS, J. PHAN, M. PIROOZNIA, F. QIAN, T. QING, L. RAINBOW, P. ROCCA-SERRA, L. SAMBOURG, S. SANSONE, S. SCHWARTZ, R. SHAH, J. SHEN, T. SMITH, O. STEGLE, N. STRALIS-PAVESE, E. STUPKA, Y. SUZUKI, L. SZKOTNICKI, M. TINNING, B. TU, J. VAN DEFT, A. VELA-BOZA, E. VENTURINI, S. WALKER, L. WAN, W. WANG, E. WIEBEN, J. WILLEY, P. WU, J. XUAN, Y. YANG, Z. YE, Y. YIN, Y. YU, Y. YUAN, J. ZHANG, K. ZHANG, W. ZHANG, Y. ZHANG, C. ZHAO, Y. ZHENG, Y. ZHOU, P. ZUMBO, W. TONG, D. KREIL, C. MASON, and L. SHI

Abstract

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific-filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

Published
2014

3. The MicroArray Quality Control (MAQC)-IIII study of common practices for the development and validation of microarray-based predictive models

Author

L. SHI, G. CAMPBELL, W. JONES, F. CAMPAGNE, Z. WEN, S. WALKER, Z. SU, T. CHU, F. GOODSAID, L. PUSZTAI, J. SHAUGHNESSY, A. OBERTHUER, R. THOMAS, R. PAULES, M. FIELDEN, B. BARLOGIE, W. CHEN, P. DU, M. FISCHER, C. FURLANELLO, B. GALLAS, X. GE, D. MEGHERBI, W. SYMMANS, M. WANG, J. ZHANG, H. BITTER, B. BRORS, P. BUSHEL, M. BYLESJO, M. CHEN, J. CHENG, J. CHOU, T. DAVISON, M. DELORENZI, Y. DENG, V. DEVANARAYAN, D. DIX, J. DOPAZO, K. DORFF, F. ELLOUMI, J. FAN, S. FAN, X. FAN, H. FANG, N. GONZALUDO, K. HESS, H. HONG, J. HUAN, R. IRIZARRY, R. JUDSON, D. JURAEVA, S. LABABIDI, C. LAMBERT, L. LI, Y. LI, Z. LI, S. LIN, G. LIU, E. LOBENHOFER, J. LUO, W. LUO, M. MCCALL, Y. NIKOLSKY, G. PENNELLO, R. PERKINS, R. PHILIP, V. POPOVICI, N. PRICE, F. QIAN, A. SCHERER, T. SHI, W. SHI, J. SUNG, D. THIERRY-MIEG, J. THIERRY-MIEG, V. THODIMA, J. TRYGG, L. VISHNUVAJJALA, S. WANG, J. WU, Y. WU, Q. XIE, W. YOUSEF, L. ZHANG, X. ZHANG, S. ZHONG, Y. ZHOU, S. ZHU, D. ARASAPPAN, W. BAO, A. LUCAS, F. BERTHOLD, R. BRENNAN, A. BUNESS, J. CATALANO, C. CHANG, R. CHEN, Y. CHENG, J. CUI, W. CZIKA, F. DEMICHELIS, X. DENG, D. DOSYMBEKOV, R. EILS, Y. FENG, J. FOSTEL, S. FULMER-SMENTEK, J. FUSCOE, L. GATTO, W. GE, D. GOLDSTEIN, L. GUO, D. HALBERT, J. HAN, S. HARRIS, C. HATZIS, D. HERMAN, J. HUANG, R. JENSEN, R. JIANG, C. JOHNSON, G. JURMAN, Y. KAHLERT, S. KHUDER, M. KOHL, J. LI, M. LI, Q. LI, S. LI, J. LIU, Y. LIU, Z. LIU, L. MENG, M. MADERA, F. MARTINEZ-MURILLO, I. MEDINA, J. MEEHAN, K. MICLAUS, R. MOFFITT, D. MONTANER, P. MUKHERJEE, G. MULLIGAN, P. NEVILLE, T. NIKOLSKAYA, B. NING, G. PAGE, J. PARKER, R. PARRY, X. PENG, R. PETERSON, J. PHAN, B. QUANZ, Y. REN, S. RICCADONNA, A. ROTER, F. SAMUELSON, M. SCHUMACHER, J. SHAMBAUGH, Q. SHI, R. SHIPPY, S. SI, A. SMALTER, C. SOTIRIOU, M. SOUKUP, F. STAEDTLER, G. STEINER, T. STOKES, Q. SUN, P. TAN, R. TANG, Z. TEZAK, B. THORN, M. TSYGANOVA, Y. TURPAZ, S. VEGA, R. VISINTAINER, J. VON FRESE, C. WANG, E. WANG, J. WANG, W. WANG, F. WESTERMANN, J. WILLEY, M. WOODS, S. WU, N. XIAO, J. XU, L. XU, L. YANG, X. ZENG, M. ZHANG, C. ZHAO, R. PURI, U. SCHERF, W. TONG, R. WOLFINGER, and MAQC Consortium

Abstract

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Published
2010

4. Flexible Response Surface Methods via Spatial Regression and Eblups

Author

M. O’Connell and R. Wolfinger

Subjects
EVOP, Polynomial, Mathematical optimization, Restricted maximum likelihood, Kriging, Context (language use), Covariance, Minimax, Representation (mathematics), Mathematics
Abstract

Spatial regression models provide a complementary alternative to polynomial response surface methods in the context of process optimization. The models enable estimation of design variable effects and via EBLUPS smooth data-faithful approximations to the unknown response function over the design space. The covariance structure of the particular spatial models drives the predicted response surfaces and both isotropic and geometrically anisotropic forms are considered. Estimation of covari­ance parameters is achieved via maximum likelihood or restricted maximum likelihood. A feature of the method is the visually appealing graphical summaries that are produced. These allow rapid identification of process windows on the design space for which the response(s) achieves target performance. The models perform well in association with spatial designs such as the maximin and minimax designs. The EVOP approach is also possible and in this context the models provide a representation of the response over the entire series of designs. An example involving the optimization of assay components in a DNA amplification procedure provides illustration.

Published
1997

5. [Untitled]

Author

E. Satyaraj, R.H. Buckley, R Wolfinger, SA Kingsmore, S. Lezhnin, Dhavalkumar D. Patel, and V. Tchernev

Subjects
medicine.medical_specialty, Severe combined immunodeficiency, Rheumatology, business.industry, Cytokine Network, Internal medicine, Immunology, Medicine, business, medicine.disease, Bioinformatics, T-cell homeostasis
Published
2004

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