Yu-Ru Su, PhD, specializes in statistical genetics, survival analysis, and functional/longitudinal data analysis. Her research interests cover a wide spectrum of statistical methods for modern biomedical studies, especially in cancer prevention and precision medicine. Her current research focuses on integrating information in genetics, environmental, and clinical data to develop precise risk models of cancers with a goal of promoting personalized prevention/surveillance strategies.
Before joining Kaiser Permanente Washington Health Research Institute, Dr. Su received her postdoctoral research training at Fred Hutchinson Cancer Research Center, where she was promoted to a staff scientist position. During her time at Fred Hutch, she was part of the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), one of the world’s leading collaborations in colorectal cancer research. At GECCO, she conducted complex analyses aiming to discover genetic risk factors and interactions between genetics and environmental factors for colorectal cancer. These findings are essential for developing risk prediction models. She also developed novel and computationally feasible statistical methods via the kernel machine framework for detecting novel genetic associations with complex diseases by bringing in functional information from multi-omics data. Another field of her methods research focuses on statistical approaches for functional association between functional exposures and a scalar outcome. Dr. Su developed a new dimension reduction technique and a testing approach for inferences on the infinite-dimensional association. The application of these methods in modern genetic and aging studies is leading to a better understanding of underlying mechanism of complex diseases, including cancer and dementia.
Dr. Su received her PhD in biostatistics from the University of California, Davis. Her dissertation focused on statistical estimating procedures used to infer associations of survival outcomes and complex exposures. An example is time-varying covariates, based on incomplete data such as intermittently measured longitudinal covariates and left-truncation or doubly-censored survival outcomes. She investigated asymptotic properties of the proposed methods via modern semiparametric theory and proposed complex algorithms for handling incompleteness in data.
At Kaiser Permanente Washington Health Research Institute, Dr. Su collaborates with scientists from multiple disciplines to pursue answers and solutions to scientific questions related to breast cancer, Alzheimer’s disease and dementia, and opioid use disorders. She actively collaborates with the Breast Cancer Surveillance Consortium to investigate the screening performance of multiple screening modalitiesin women with and without breast cancer history, to build reliable risk prediction models and personalized strategies for screening and surveillance strategies. She also closely works with the Adult Change in Thought (ACT) study to understand the connection between dementia and other clinical and health conditions.
Su YR, Buist DS, Lee JM, Ichikawa L, Miglioretti DL, Aiello Bowles EJ, Wernli KJ, Kerlikowske K, Tosteson A, Lowry KP, Henderson LM, Sprague BL, Hubbard RA. Performance of statistical and machine learning risk prediction models for surveillance benefits and failures in breast cancer survivors. Cancer Epidemiol Biomarkers Prev. 2023 Jan 25:EPI-22-0677. doi: 10.1158/1055-9965.EPI-22-0677. [Epub ahead of print]. PubMed
Su YR, Sakoda LC, Jeon J, Thomas M, Lin Y, Schneider JL, Udaltsova N, Lee JK, Lansdorp-Vogelaar I, Peterse EFP, Zauber AG, Zheng J, Zheng Y, Hauser E, Baron JA, Barry EL, Bishop DT, Brenner H, Buchanan DD, Burnett-Hartman A, Campbell PT, Casey G, Castellví-Bel S, Chan AT, Chang-Claude J, Figueiredo JC, Gallinger SJ, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampe J, Hampel H, Harrison TA, Hoffmeister M, Hua X, Huyghe JR, Jenkins MA, Keku TO, Le Marchand L, Li L, Lindblom A, Moreno V, Newcomb PA, Pharoah PDP, Platz EA, Potter JD, Qu C, Rennert G, Schoen RE, Slattery ML, Song M, van Duijnhoven FJB, Van Guelpen B, Vodicka P, Wolk A, Woods MO, Wu AH, Hayes RB, Peters U, Corley DA, Hsu L. Validation of a genetic-enhanced risk prediction model for colorectal cancer in a large community-based cohort. Cancer Epidemiol Biomarkers Prev. 2023 Jan 9;EPI-22-0817. doi: 10.1158/1055-9965.EPI-22-0817. Online ahead of print. PubMed
Carreras-Torres R, Kim AE, Lin Y, Díez-Obrero V, Bien SA, Qu C, Wang J, Dimou N, Aglago EK, Albanes D, Arndt V, Baurley JW, Berndt SI, Bézieau S, Bishop DT, Bouras E, Brenner H, Budiarto A, Campbell PT, Casey G, Chan AT, Chang-Claude J, Chen X, Conti DV, Dampier CH, Devall MA, Drew DA, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Harrison TA, Hidaka A, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl KM, Kawaguchi E, Keku TO, Kundaje A, Le Marchand L, Lewinger JP, Li L, Mahesworo B, Morrison JL, Murphy N, Nan H, Nassir R, Newcomb PA, Obón-Santacana M, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Pharoah PDP, Platz EA, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su YR, Tangen CM, Thomas DC, Tian Y, Tsilidis KK, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Cenggoro TW, Weinstein SJ, White E, Wolk A, Woods MO, Hsu L, Peters U, Moreno V, Gauderman WJ. Genome-wide interaction study with smoking for colorectal cancer risk identifies novel genetic loci related to tumor suppression, inflammation and immune response. Cancer Epidemiol Biomarkers Prev. 2022 Dec 28;EPI-22-0763. doi: 10.1158/1055-9965.EPI-22-0763. PubMed
Lee CS, Krakauer C, Su YR, Walker R, Blazes M, McCurry SM, Bowen JD, McCormick WC, Lee AY, Boyko E, O'Hare A, Larson EB, Crane PK. Diabetic retinopathy and dementia association, beyond diabetes severity. Am J Ophthalmol. 2022 Dec 10:S0002-9394(22)00486-X. doi: 10.1016/j.ajo.2022.12.003. [Epub ahead of print]. PubMed
Tian Y, Kim AE, Bien SA, Lin Y, Qu C, Harrison TA, Carreras-Torres R, Díez-Obrero V, Dimou N, Drew DA, Hidaka A, Huyghe JR, Jordahl KM, Morrison J, Murphy N, Obón-Santacana M, Ulrich CM, Ose J, Peoples AR, Ruiz-Narvaez EA, Shcherbina A, Stern MC, Su YR, van Duijnhoven FJB, Arndt V, Baurley JW, Berndt SI, Bishop DT, Brenner H, Buchanan DD, Chan AT, Figueiredo JC, Gallinger S, Gruber SB, Harlid S, Hoffmeister M, Jenkins MA, Joshi AD, Keku TO, Larsson SC, Le Marchand L, Li L, Giles GG, Milne RL, Nan H, Nassir R, Ogino S, Budiarto A, Platz EA, Potter JD, Prentice RL, Rennert G, Sakoda LC, Schoen RE, Slattery ML, Thibodeau SN, Van Guelpen B, Visvanathan K, White E, Wolk A, Woods MO, Wu AH, Campbell PT, Casey G, Conti DV, Gunter MJ, Kundaje A, Lewinger JP, Moreno V, Newcomb PA, Pardamean B, Thomas DC, Tsilidis KK, Peters U, Gauderman WJ, Hsu L, Chang-Claude J. Genome-wide interaction analysis of genetic variants with menopausal hormone therapy for colorectal cancer risk. J Natl Cancer Inst. 2022 Aug 8;114(8):1135-1148. doi: 10.1093/jnci/djac094. PubMed
Kaiser Permanente Washington has been part of the national Breast Cancer Surveillance Consortium since 1994. Learn about the Kaiser Permanente Washington Breast Cancer Surveillance Registry here.
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