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2. Covariate Selection for Optimizing Balance with Covariate-Adjusted Response-Adaptive Randomization

Author

Guo, Ziqing, Liu, Yang, and Xia, Lucy

Subjects
Statistics - Methodology
Abstract

Balancing influential covariates is crucial for valid treatment comparisons in clinical studies. While covariate-adaptive randomization is commonly used to achieve balance, its performance can be inadequate when the number of baseline covariates is large. It is therefore essential to identify the influential factors associated with the outcome and ensure balance among these critical covariates. In this article, we propose a novel covariate-adjusted response-adaptive randomization that integrates the patients' responses and covariates information to select sequentially significant covariates and maintain their balance. We establish theoretically the consistency of our covariate selection method and demonstrate that the improved covariate balancing, as evidenced by a faster convergence rate of the imbalance measure, leads to higher efficiency in estimating treatment effects. Furthermore, we provide extensive numerical and empirical studies to illustrate the benefits of our proposed method across various settings., Comment: 54 pages, 4 figures

Published
2024

5. 'All fake'

Author

Kilgallon, Steve and Xia, Lucy

Published
2021

7. Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters

Author

Xia, Lucy, Lee, Christy, and Li, Jingyi Jessica

Subjects
Information and Computing Sciences, Graphics, Augmented Reality and Games, Algorithms, Reproducibility of Results
Abstract

Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge, we present a statistical method, scDEED, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell's 2D-embedding neighbors and pre-embedding neighbors, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.

Published
2024

8. Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models

Author

Tian, Ye, Weng, Haolei, Xia, Lucy, and Feng, Yang

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Statistics Theory, Statistics - Methodology
Abstract

Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the EM algorithm that effectively utilizes unknown similarities between related tasks and is robust against a fraction of outlier tasks from arbitrary distributions. The proposed procedure is shown to achieve the minimax optimal rate of convergence for both parameter estimation error and the excess mis-clustering error, in a wide range of regimes. Moreover, we generalize our approach to tackle the problem of transfer learning for GMMs, where similar theoretical results are derived. Additionally, iterative unsupervised multi-task and transfer learning methods may suffer from an initialization alignment problem, and two alignment algorithms are proposed to resolve the issue. Finally, we demonstrate the effectiveness of our methods through simulations and real data examples. To the best of our knowledge, this is the first work studying multi-task and transfer learning on GMMs with theoretical guarantees., Comment: 162 pages, 15 figures, 2 tables

Published
2022

9. Non-splitting Neyman-Pearson Classifiers

Author

Wang, Jingming, Xia, Lucy, Bao, Zhigang, and Tong, Xin

Subjects
Statistics - Methodology, Mathematics - Statistics Theory
Abstract

The Neyman-Pearson (NP) binary classification paradigm constrains the more severe type of error (e.g., the type I error) under a preferred level while minimizing the other (e.g., the type II error). This paradigm is suitable for applications such as severe disease diagnosis, fraud detection, among others. A series of NP classifiers have been developed to guarantee the type I error control with high probability. However, these existing classifiers involve a sample splitting step: a mixture of class 0 and class 1 observations to construct a scoring function and some left-out class 0 observations to construct a threshold. This splitting enables classifier construction built upon independence, but it amounts to insufficient use of data for training and a potentially higher type II error. Leveraging a canonical linear discriminant analysis model, we derive a quantitative CLT for a certain functional of quadratic forms of the inverse of sample and population covariance matrices, and based on this result, develop for the first time NP classifiers without splitting the training sample. Numerical experiments have confirmed the advantages of our new non-splitting parametric strategy.

Published
2021

12. Intentional Control of Type I Error over Unconscious Data Distortion: a Neyman-Pearson Approach to Text Classification

Author

Xia, Lucy, Zhao, Richard, Wu, Yanhui, and Tong, Xin

Subjects
Statistics - Methodology, Computer Science - Machine Learning, Statistics - Applications, Statistics - Machine Learning
Abstract

This paper addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimize the overall classification error and may yield an undesirably large type I error (relevant textual messages are classified as irrelevant), particularly when available data exhibit an asymmetry between relevant and irrelevant information. Data distortion exacerbates this situation and often leads to fallacious prediction. To deal with inestimable data distortion, we propose the use of the Neyman-Pearson (NP) classification paradigm, which minimizes type II error under a user-specified type I error constraint. Theoretically, we show that the NP oracle is unaffected by data distortion when the class conditional distributions remain the same. Empirically, we study a case of classifying posts about worker strikes obtained from a leading Chinese microblogging platform, which are frequently prone to extensive, unpredictable and inestimable censorship. We demonstrate that, even though the training and test data are susceptible to different distortion and therefore potentially follow different distributions, our proposed NP methods control the type I error on test data at the targeted level. The methods and implementation pipeline proposed in our case study are applicable to many other problems involving data distortion.

Published
2018
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13. Neyman-Pearson classification: parametrics and sample size requirement

Author

Tong, Xin, Xia, Lucy, Wang, Jiacheng, and Feng, Yang

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract

The Neyman-Pearson (NP) paradigm in binary classification seeks classifiers that achieve a minimal type II error while enforcing the prioritized type I error controlled under some user-specified level $\alpha$. This paradigm serves naturally in applications such as severe disease diagnosis and spam detection, where people have clear priorities among the two error types. Recently, Tong, Feng and Li (2018) proposed a nonparametric umbrella algorithm that adapts all scoring-type classification methods (e.g., logistic regression, support vector machines, random forest) to respect the given type I error upper bound $\alpha$ with high probability, without specific distributional assumptions on the features and the responses. Universal the umbrella algorithm is, it demands an explicit minimum sample size requirement on class $0$, which is often the more scarce class, such as in rare disease diagnosis applications. In this work, we employ the parametric linear discriminant analysis (LDA) model and propose a new parametric thresholding algorithm, which does not need the minimum sample size requirements on class $0$ observations and thus is suitable for small sample applications such as rare disease diagnosis. Leveraging both the existing nonparametric and the newly proposed parametric thresholding rules, we propose four LDA-based NP classifiers, for both low- and high-dimensional settings. On the theoretical front, we prove NP oracle inequalities for one proposed classifier, where the rate for excess type II error benefits from the explicit parametric model assumption. Furthermore, as NP classifiers involve a sample splitting step of class $0$ observations, we construct a new adaptive sample splitting scheme that can be applied universally to NP classifiers, and this adaptive strategy reduces the type II error of these classifiers., Comment: 44 pages

Published
2018

14. Inferactive data analysis

Author

Bi, Nan, Markovic, Jelena, Xia, Lucy, and Taylor, Jonathan

Subjects
Mathematics - Statistics Theory
Abstract

We describe inferactive data analysis, so-named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise between Tukey's exploratory (roughly speaking "model free") and confirmatory data analysis (roughly speaking classical and "model based"), also allowing for Bayesian data analysis. We view this approach as close in spirit to current practice of applied statisticians and data scientists while allowing frequentist guarantees for results to be reported in the scientific literature, or Bayesian results where the data scientist may choose the statistical model (and hence the prior) after some initial exploratory analysis. While this approach to data analysis does not cover every scenario, and every possible algorithm data scientists may use, we see this as a useful step in concrete providing tools (with frequentist statistical guarantees) for current data scientists. The basis of inference we use is selective inference [Lee et al., 2016, Fithian et al., 2014], in particular its randomized form [Tian and Taylor, 2015a]. The randomized framework, besides providing additional power and shorter confidence intervals, also provides explicit forms for relevant reference distributions (up to normalization) through the {\em selective sampler} of Tian et al. [2016]. The reference distributions are constructed from a particular conditional distribution formed from what we call a DAG-DAG -- a Data Analysis Generative DAG. As sampling conditional distributions in DAGs is generally complex, the selective sampler is crucial to any practical implementation of inferactive data analysis. Our principal goal is in reviewing the recent developments in selective inference as well as describing the general philosophy of selective inference., Comment: 43 pages

Published
2017

15. Unifying approach to selective inference with applications to cross-validation

Author

Markovic, Jelena, Xia, Lucy, and Taylor, Jonathan

Subjects
Statistics - Methodology
Abstract

We develop tools to do valid post-selective inference for a family of model selection procedures, including choosing a model via cross-validated Lasso. The tools apply universally when the following random vectors are jointly asymptotically multivariate Gaussian: 1. the vector composed of each model's quality value evaluated under certain model selection criteria (e.g. cross-validation errors across folds, AIC, prediction errors etc.) 2. the test statistics from which we make inference on the parameters; it is worth noting that the parameters here are chosen after model selection methods are performed. Under these assumptions, we derive a pivotal quantity that has an asymptotically Unif(0,1) distribution which can be used to perform tests and construct confidence intervals. Both the tests and confidence intervals are selectively valid for the chosen parameter. While the above assumptions may not be satisfied in some applications, we propose a novel variation to these model selection procedures by adding Gaussian randomizations to either one of the two vectors. As a result, the joint distribution of the above random vectors is multivariate Gaussian and our general tools apply. We illustrate our method by applying it to four important procedures for which very few selective inference results have been developed: cross-validated Lasso, cross-validated randomized Lasso, AIC-based model selection among a fixed set of models and inference for a newly introduced novel marginal LOCO parameter, inspired by the LOCO parameter of Rinaldo et al (2016); and we provide complete results for these cases. For randomized model selection procedures, we develop Markov chain Monte Carlo sampling scheme to construct valid post-selective confidence intervals empirically.

Published
2017

17. Two Novel Susceptibility Loci for Prostate Cancer in Men of African Ancestry

Author

Conti, David V, Wang, Kan, Sheng, Xin, Bensen, Jeannette T, Hazelett, Dennis J, Cook, Michael B, Ingles, Sue A, Kittles, Rick A, Strom, Sara S, Rybicki, Benjamin A, Nemesure, Barbara, Isaacs, William B, Stanford, Janet L, Zheng, Wei, Sanderson, Maureen, John, Esther M, Park, Jong Y, Xu, Jianfeng, Stevens, Victoria L, Berndt, Sonja I, Huff, Chad D, Wang, Zhaoming, Yeboah, Edward D, Tettey, Yao, Biritwum, Richard B, Adjei, Andrew A, Tay, Evelyn, Truelove, Ann, Niwa, Shelley, Sellers, Thomas A, Yamoah, Kosj, Murphy, Adam B, Crawford, Dana C, Gapstur, Susan M, Bush, William S, Aldrich, Melinda C, Cussenot, Olivier, Petrovics, Gyorgy, Cullen, Jennifer, Neslund-Dudas, Christine, Stern, Mariana C, Jarai, Zsofia-Kote, Govindasami, Koveela, Chokkalingam, Anand P, Hsing, Ann W, Goodman, Phyllis J, Hoffmann, Thomas, Drake, Bettina F, Hu, Jennifer J, Clark, Peter E, Van Den Eeden, Stephen K, Blanchet, Pascal, Fowke, Jay H, Casey, Graham, Hennis, Anselm JM, Han, Ying, Lubwama, Alexander, Thompson, Ian M, Leach, Robin, Easton, Douglas F, Schumacher, Fredrick, Van den Berg, David J, Gundell, Susan M, Stram, Alex, Wan, Peggy, Xia, Lucy, Pooler, Loreall C, Mohler, James L, Fontham, Elizabeth TH, Smith, Gary J, Taylor, Jack A, Srivastava, Shiv, Eeles, Rosalind A, Carpten, John, Kibel, Adam S, Multigner, Luc, Parent, Marie-Elise, Menegaux, Florence, Cancel-Tassin, Geraldine, Klein, Eric A, Brureau, Laurent, Stram, Daniel O, Watya, Stephen, Chanock, Stephen J, Witte, John S, Blot, William J, Henderson, Brian E, and Haiman, Christopher A

Subjects
Aging, Human Genome, Cancer, Prevention, Prostate Cancer, Biotechnology, Clinical Research, Genetics, Urologic Diseases, 2.1 Biological and endogenous factors, Aetiology, Blacks, Case-Control Studies, Checkpoint Kinase 2, Chromosomes, Human, Pair 13, Chromosomes, Human, Pair 22, Gene Frequency, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Insulin Receptor Substrate Proteins, Male, Polymorphism, Single Nucleotide, Prostatic Neoplasms, PRACTICAL/ELLIPSE Consortium, Black People, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Prostate cancer incidence is 1.6-fold higher in African Americans than in other populations. The risk factors that drive this disparity are unknown and potentially consist of social, environmental, and genetic influences. To investigate the genetic basis of prostate cancer in men of African ancestry, we performed a genome-wide association meta-analysis using two-sided statistical tests in 10 202 case subjects and 10 810 control subjects. We identified novel signals on chromosomes 13q34 and 22q12, with the risk-associated alleles found only in men of African ancestry (13q34: rs75823044, risk allele frequency = 2.2%, odds ratio [OR] = 1.55, 95% confidence interval [CI] = 1.37 to 1.76, P = 6.10 × 10-12; 22q12.1: rs78554043, risk allele frequency = 1.5%, OR = 1.62, 95% CI = 1.39 to 1.89, P = 7.50 × 10-10). At 13q34, the signal is located 5' of the gene IRS2 and 3' of a long noncoding RNA, while at 22q12 the candidate functional allele is a missense variant in the CHEK2 gene. These findings provide further support for the role of ancestry-specific germline variation in contributing to population differences in prostate cancer risk.

Published
2017

18. A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models

Author

Fan, Jianqing, Feng, Yang, and Xia, Lucy

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Applications, Statistics - Machine Learning
Abstract

Measuring conditional dependence is an important topic in statistics with broad applications including graphical models. Under a factor model setting, a new conditional dependence measure based on projection is proposed. The corresponding conditional independence test is developed with the asymptotic null distribution unveiled where the number of factors could be high-dimensional. It is also shown that the new test has control over the asymptotic significance level and can be calculated efficiently. A generic method for building dependency graphs without Gaussian assumption using the new test is elaborated. Numerical results and real data analysis show the superiority of the new method., Comment: 39 pages, 5 figures

Published
2015

20. Supplementary Figure 1 from Polygenic Risk Score Modifies Prostate Cancer Risk of Pathogenic Variants in Men of African Ancestry

Author

Hughley, Raymond W., primary, Matejcic, Marco, primary, Song, Ziwei, primary, Sheng, Xin, primary, Wan, Peggy, primary, Xia, Lucy, primary, Hart, Steven N., primary, Hu, Chunling, primary, Yadav, Siddhartha, primary, Lubmawa, Alexander, primary, Kiddu, Vicky, primary, Asiimwe, Frank, primary, Amanya, Colline, primary, Mutema, George, primary, Job, Kuteesa, primary, Ssebakumba, Mbaaga K., primary, Ingles, Sue A., primary, Hamilton, Ann S., primary, Couch, Fergus J., primary, Watya, Stephen, primary, Conti, David V., primary, Darst, Burcu F., primary, and Haiman, Christopher A., primary

Published
2023
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21. Characterization of Large Structural Genetic Mosaicism in Human Autosomes

Author

Machiela, Mitchell J, Zhou, Weiyin, Sampson, Joshua N, Dean, Michael C, Jacobs, Kevin B, Black, Amanda, Brinton, Louise A, Chang, I-Shou, Chen, Chu, Chen, Constance, Chen, Kexin, Cook, Linda S, Bou, Marta Crous, De Vivo, Immaculata, Doherty, Jennifer, Friedenreich, Christine M, Gaudet, Mia M, Haiman, Christopher A, Hankinson, Susan E, Hartge, Patricia, Henderson, Brian E, Hong, Yun-Chul, Hosgood, H Dean, Hsiung, Chao A, Hu, Wei, Hunter, David J, Jessop, Lea, Kim, Hee Nam, Kim, Yeul Hong, Kim, Young Tae, Klein, Robert, Kraft, Peter, Lan, Qing, Lin, Dongxin, Liu, Jianjun, Le Marchand, Loic, Liang, Xiaolin, Lissowska, Jolanta, Lu, Lingeng, Magliocco, Anthony M, Matsuo, Keitaro, Olson, Sara H, Orlow, Irene, Park, Jae Yong, Pooler, Loreall, Prescott, Jennifer, Rastogi, Radhai, Risch, Harvey A, Schumacher, Fredrick, Seow, Adeline, Setiawan, Veronica Wendy, Shen, Hongbing, Sheng, Xin, Shin, Min-Ho, Shu, Xiao-Ou, Berg, David VanDen, Wang, Jiu-Cun, Wentzensen, Nicolas, Wong, Maria Pik, Wu, Chen, Wu, Tangchun, Wu, Yi-Long, Xia, Lucy, Yang, Hannah P, Yang, Pan-Chyr, Zheng, Wei, Zhou, Baosen, Abnet, Christian C, Albanes, Demetrius, Aldrich, Melinda C, Amos, Christopher, Amundadottir, Laufey T, Berndt, Sonja I, Blot, William J, Bock, Cathryn H, Bracci, Paige M, Burdett, Laurie, Buring, Julie E, Butler, Mary A, Carreón, Tania, Chatterjee, Nilanjan, Chung, Charles C, Cook, Michael B, Cullen, Michael, Davis, Faith G, Ding, Ti, Duell, Eric J, Epstein, Caroline G, Fan, Jin-Hu, Figueroa, Jonine D, Fraumeni, Joseph F, Freedman, Neal D, Fuchs, Charles S, Gao, Yu-Tang, Gapstur, Susan M, Patiño-Garcia, Ana, Garcia-Closas, Montserrat, Gaziano, J Michael, Giles, Graham G, and Gillanders, Elizabeth M

Subjects
Biological Sciences, Genetics, Human Genome, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Aged, Chromosome Aberrations, Female, Genome, Human, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Mosaicism, Neoplasms, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Analyses of genome-wide association study (GWAS) data have revealed that detectable genetic mosaicism involving large (>2 Mb) structural autosomal alterations occurs in a fraction of individuals. We present results for a set of 24,849 genotyped individuals (total GWAS set II [TGSII]) in whom 341 large autosomal abnormalities were observed in 168 (0.68%) individuals. Merging data from the new TGSII set with data from two prior reports (the Gene-Environment Association Studies and the total GWAS set I) generated a large dataset of 127,179 individuals; we then conducted a meta-analysis to investigate the patterns of detectable autosomal mosaicism (n = 1,315 events in 925 [0.73%] individuals). Restricting to events >2 Mb in size, we observed an increase in event frequency as event size decreased. The combined results underscore that the rate of detectable mosaicism increases with age (p value = 5.5 × 10(-31)) and is higher in men (p value = 0.002) but lower in participants of African ancestry (p value = 0.003). In a subset of 47 individuals from whom serial samples were collected up to 6 years apart, complex changes were noted over time and showed an overall increase in the proportion of mosaic cells as age increased. Our large combined sample allowed for a unique ability to characterize detectable genetic mosaicism involving large structural events and strengthens the emerging evidence of non-random erosion of the genome in the aging population.

Published
2015

22. QUADRO: A supervised dimension reduction method via Rayleigh quotient optimization

Author

Fan, Jianqing, Ke, Zheng Tracy, Liu, Han, and Xia, Lucy

Subjects
Statistics - Methodology
Abstract

We propose a novel Rayleigh quotient based sparse quadratic dimension reduction method - named QUADRO (Quadratic Dimension Reduction via Rayleigh Optimization) - for analyzing high- dimensional data. Unlike in the linear setting where Rayleigh quotient optimization coincides with classification, these two problems are very different under nonlinear settings. In this paper, we clarify this difference and show that Rayleigh quotient optimization may be of independent scientific interests. One major challenge of Rayleigh quotient optimization is that the variance of quadratic statistics involves all fourth cross-moments of predictors, which are infeasible to compute for high-dimensional applications and may accumulate too many stochastic errors. This issue is resolved by considering a family of elliptical models. Moreover, for heavy-tail distributions, robust estimates of mean vectors and covariance matrices are employed to guarantee uniform convergence in estimating nonpolynomially many parameters, even though only the fourth moments are assumed. Methodologically, QUADRO is based on elliptical models which allow us to formulate the Rayleigh quotient maximization as a convex optimization problem. Computationally, we propose an efficient linearized augmented Lagrangian method to solve the constrained optimization problem. Theoretically, we provide explicit rates of convergence in terms of Rayleigh quotient under both Gaussian and general elliptical models. Thorough numerical results on both synthetic and real datasets are also provided to back up our theoretical results., Comment: Published at http://dx.doi.org/10.1214/14-AOS1307 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2013
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23. Aggregation of Affine Estimators

Author

Dai, Dong, Rigollet, Philippe, Xia, Lucy, and Zhang, Tong

Subjects
Mathematics - Statistics Theory, Computer Science - Learning, 62G08
Abstract

We consider the problem of aggregating a general collection of affine estimators for fixed design regression. Relevant examples include some commonly used statistical estimators such as least squares, ridge and robust least squares estimators. Dalalyan and Salmon (2012) have established that, for this problem, exponentially weighted (EW) model selection aggregation leads to sharp oracle inequalities in expectation, but similar bounds in deviation were not previously known. While results indicate that the same aggregation scheme may not satisfy sharp oracle inequalities with high probability, we prove that a weaker notion of oracle inequality for EW that holds with high probability. Moreover, using a generalization of the newly introduced $Q$-aggregation scheme we also prove sharp oracle inequalities that hold with high probability. Finally, we apply our results to universal aggregation and show that our proposed estimator leads simultaneously to all the best known bounds for aggregation, including $\ell_q$-aggregation, $q \in (0,1)$, with high probability.

Published
2013

24. Polygenic Risk Score Modifies Prostate Cancer Risk of Pathogenic Variants in Men of African Ancestry

Author

Hughley, Raymond W., primary, Matejcic, Marco, additional, Song, Ziwei, additional, Sheng, Xin, additional, Wan, Peggy, additional, Xia, Lucy, additional, Hart, Steven N., additional, Hu, Chunling, additional, Yadav, Siddhartha, additional, Lubwama, Alexander, additional, Kiddu, Vicky, additional, Asiimwe, Frank, additional, Amanya, Colline, additional, Mutema, George, additional, Job, Kuteesa, additional, Ssebakumba, Mbaaga Kigongo, additional, Ingles, Sue Ann, additional, Hamilton, Ann S, additional, Couch, Fergus J, additional, Watya, Stephen, additional, Conti, David V., additional, Darst, Burcu F, additional, and Haiman, Christopher A, additional

Published
2023
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25. Genome-wide association study of prostate cancer in men of African ancestry identifies a susceptibility locus at 17q21

Author

Haiman, Christopher A, Chen, Gary K, Blot, William J, Strom, Sara S, Berndt, Sonja I, Kittles, Rick A, Rybicki, Benjamin A, Isaacs, William B, Ingles, Sue A, Stanford, Janet L, Diver, W Ryan, Witte, John S, Hsing, Ann W, Nemesure, Barbara, Rebbeck, Timothy R, Cooney, Kathleen A, Xu, Jianfeng, Kibel, Adam S, Hu, Jennifer J, John, Esther M, Gueye, Serigne M, Watya, Stephen, Signorello, Lisa B, Hayes, Richard B, Wang, Zhaoming, Yeboah, Edward, Tettey, Yao, Cai, Qiuyin, Kolb, Suzanne, Ostrander, Elaine A, Zeigler-Johnson, Charnita, Yamamura, Yuko, Neslund-Dudas, Christine, Haslag-Minoff, Jennifer, Wu, William, Thomas, Venetta, Allen, Glenn O, Murphy, Adam, Chang, Bao-Li, Zheng, S Lilly, Leske, M Cristina, Wu, Suh-Yuh, Ray, Anna M, Hennis, Anselm JM, Thun, Michael J, Carpten, John, Casey, Graham, Carter, Erin N, Duarte, Edder R, Xia, Lucy Y, Sheng, Xin, Wan, Peggy, Pooler, Loreall C, Cheng, Iona, Monroe, Kristine R, Schumacher, Fredrick, Le Marchand, Loic, Kolonel, Laurence N, Chanock, Stephen J, Berg, David Van Den, Stram, Daniel O, and Henderson, Brian E

Subjects
Biological Sciences, Genetics, Human Genome, Aging, Urologic Diseases, Prevention, Prostate Cancer, Cancer, 2.1 Biological and endogenous factors, Aetiology, Black or African American, Chromosomes, Human, Pair 17, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

In search of common risk alleles for prostate cancer that could contribute to high rates of the disease in men of African ancestry, we conducted a genome-wide association study, with 1,047,986 SNP markers examined in 3,425 African-Americans with prostate cancer (cases) and 3,290 African-American male controls. We followed up the most significant 17 new associations from stage 1 in 1,844 cases and 3,269 controls of African ancestry. We identified a new risk variant on chromosome 17q21 (rs7210100, odds ratio per allele = 1.51, P = 3.4 × 10(-13)). The frequency of the risk allele is ∼5% in men of African descent, whereas it is rare in other populations (

Published
2011

26. A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer

Author

Wu, Lang, Shi, Wei, Long, Jirong, Guo, Xingyi, Michailidou, Kyriaki, Beesley, Jonathan, Bolla, Manjeet K., Shu, Xiao-Ou, Lu, Yingchang, Cai, Qiuyin, Al-Ejeh, Fares, Rozali, Esdy, Wang, Qin, Dennis, Joe, Li, Bingshan, Zeng, Chenjie, Feng, Helian, Gusev, Alexander, Barfield, Richard T., Andrulis, Irene L., Anton-Culver, Hoda, Arndt, Volker, Aronson, Kristan J., Auer, Paul L., Barrdahl, Myrto, Baynes, Caroline, Beckmann, Matthias W., Benitez, Javier, Bermisheva, Marina, Blomqvist, Carl, Bogdanova, Natalia V., Bojesen, Stig E., Brauch, Hiltrud, Brenner, Hermann, Brinton, Louise, Broberg, Per, Brucker, Sara Y., Burwinkel, Barbara, Caldés, Trinidad, Canzian, Federico, Carter, Brian D., Castelao, J. Esteban, Chang-Claude, Jenny, Chen, Xiaoqing, Cheng, Ting-Yuan David, Christiansen, Hans, Clarke, Christine L., NBCS Collaborators, Collée, Margriet, Cornelissen, Sten, Couch, Fergus J., Cox, David, Cox, Angela, Cross, Simon S., Cunningham, Julie M., Czene, Kamila, Daly, Mary B., Devilee, Peter, Doheny, Kimberly F., Dörk, Thilo, dos-Santos-Silva, Isabel, Dumont, Martine, Dwek, Miriam, Eccles, Diana M., Eilber, Ursula, Eliassen, A. Heather, Engel, Christoph, Eriksson, Mikael, Fachal, Laura, Fasching, Peter A., Figueroa, Jonine, Flesch-Janys, Dieter, Fletcher, Olivia, Flyger, Henrik, Fritschi, Lin, Gabrielson, Marike, Gago-Dominguez, Manuela, Gapstur, Susan M., García-Closas, Montserrat, Gaudet, Mia M., Ghoussaini, Maya, Giles, Graham G., Goldberg, Mark S., Goldgar, David E., González-Neira, Anna, Guénel, Pascal, Hahnen, Eric, Haiman, Christopher A., Håkansson, Niclas, Hall, Per, Hallberg, Emily, Hamann, Ute, Harrington, Patricia, Hein, Alexander, Hicks, Belynda, Hillemanns, Peter, Hollestelle, Antoinette, Hoover, Robert N., Hopper, John L., Huang, Guanmengqian, Humphreys, Keith, Hunter, David J., Jakubowska, Anna, Janni, Wolfgang, John, Esther M., Johnson, Nichola, Jones, Kristine, Jones, Michael E., Jung, Audrey, Kaaks, Rudolf, Kerin, Michael J., Khusnutdinova, Elza, Kosma, Veli-Matti, Kristensen, Vessela N., Lambrechts, Diether, Le Marchand, Loic, Li, Jingmei, Lindström, Sara, Lissowska, Jolanta, Lo, Wing-Yee, Loibl, Sibylle, Lubinski, Jan, Luccarini, Craig, Lux, Michael P., MacInnis, Robert J., Maishman, Tom, Kostovska, Ivana Maleva, Mannermaa, Arto, Manson, JoAnn E., Margolin, Sara, Mavroudis, Dimitrios, Meijers-Heijboer, Hanne, Meindl, Alfons, Menon, Usha, Meyer, Jeffery, Mulligan, Anna Marie, Neuhausen, Susan L., Nevanlinna, Heli, Neven, Patrick, Nielsen, Sune F., Nordestgaard, Børge G., Olopade, Olufunmilayo I., Olson, Janet E., Olsson, Håkan, Peterlongo, Paolo, Peto, Julian, Plaseska-Karanfilska, Dijana, Prentice, Ross, Presneau, Nadege, Pylkäs, Katri, Rack, Brigitte, Radice, Paolo, Rahman, Nazneen, Rennert, Gad, Rennert, Hedy S., Rhenius, Valerie, Romero, Atocha, Romm, Jane, Rudolph, Anja, Saloustros, Emmanouil, Sandler, Dale P., Sawyer, Elinor J., Schmidt, Marjanka K., Schmutzler, Rita K., Schneeweiss, Andreas, Scott, Rodney J., Scott, Christopher G., Seal, Sheila, Shah, Mitul, Shrubsole, Martha J., Smeets, Ann, Southey, Melissa C., Spinelli, John J., Stone, Jennifer, Surowy, Harald, Swerdlow, Anthony J., Tamimi, Rulla M., Tapper, William, Taylor, Jack A., Terry, Mary Beth, Tessier, Daniel C., Thomas, Abigail, Thöne, Kathrin, Tollenaar, Rob A. E. M., Torres, Diana, Truong, Thérèse, Untch, Michael, Vachon, Celine, Van Den Berg, David, Vincent, Daniel, Waisfisz, Quinten, Weinberg, Clarice R., Wendt, Camilla, Whittemore, Alice S., Wildiers, Hans, Willett, Walter C., Winqvist, Robert, Wolk, Alicja, Xia, Lucy, Yang, Xiaohong R., Ziogas, Argyrios, Ziv, Elad, kConFab/AOCS Investigators, Dunning, Alison M., Pharoah, Paul D. P., Simard, Jacques, Milne, Roger L., Edwards, Stacey L., Kraft, Peter, Easton, Douglas F., Chenevix-Trench, Georgia, and Zheng, Wei

Published
2018
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29. Genome-wide association study of abdominal MRI-measured visceral fat: The multiethnic cohort adiposity phenotype study

Author

Streicher, Samantha A., primary, Lim, Unhee, additional, Park, S. Lani, additional, Li, Yuqing, additional, Sheng, Xin, additional, Hom, Victor, additional, Xia, Lucy, additional, Pooler, Loreall, additional, Shepherd, John, additional, Loo, Lenora W. M., additional, Ernst, Thomas, additional, Buchthal, Steven, additional, Franke, Adrian A., additional, Tiirikainen, Maarit, additional, Wilkens, Lynne R., additional, Haiman, Christopher A., additional, Stram, Daniel O., additional, Cheng, Iona, additional, and Le Marchand, Loïc, additional

Published
2023
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32. A novel scale for the evaluation of physician drawn medical illustrations.

Author

Siegel, Noah, Lee, Cassandra, Oddo, Brandon, Robinson, Anthony, Xia, Lucy, Grimes, Jill, and Wisco, Jonathan J.

Subjects
MEDICAL illustration, HEALTH literacy, CLINICAL medical education, CRONBACH'S alpha, ASSESSMENT of education
Abstract

Effective communication is a crucial component of patient-centered care and individuals with low health literacy face significant challenges in managing their health, leading to longer hospital stays and worse outcomes. Visual aids, such as medical illustrations and pictograms, can enhance patient understanding and memory retention; however, there is a lack in the medical field of tools for evaluating and improving a physician's ability to draw clinical illustrations for their patient. This article explores an aesthetic scale created in collaboration between Boston University Medical School and the Boston University Fine-Arts department. The scale scores basic design elements that could reasonably be improved in a clinical setting. A pilot study demonstrated interrater reliability between trained artists scoring images of varying concepts and visual quality with a Cronbach's alpha of 0.95. This scale has potential use in medical visual education and clinical evaluation. [ABSTRACT FROM AUTHOR]

Published
2023
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33. Association analysis identifies 65 new breast cancer risk loci

Author

Michailidou, Kyriaki, Lindström, Sara, Dennis, Joe, Beesley, Jonathan, Hui, Shirley, Kar, Siddhartha, Lemaçon, Audrey, Soucy, Penny, Glubb, Dylan, Rostamianfar, Asha, Bolla, Manjeet K., Wang, Qin, Tyrer, Jonathan, Dicks, Ed, Lee, Andrew, Wang, Zhaoming, Allen, Jamie, Keeman, Renske, Eilber, Ursula, French, Juliet D., Qing Chen, Xiao, Fachal, Laura, McCue, Karen, McCart Reed, Amy E., Ghoussaini, Maya, Carroll, Jason S., Jiang, Xia, Finucane, Hilary, Adams, Marcia, Adank, Muriel A., Ahsan, Habibul, Aittomäki, Kristiina, Anton-Culver, Hoda, Antonenkova, Natalia N., Arndt, Volker, Aronson, Kristan J., Arun, Banu, Auer, Paul L., Bacot, François, Barrdahl, Myrto, Baynes, Caroline, Beckmann, Matthias W., Behrens, Sabine, Benitez, Javier, Bermisheva, Marina, Bernstein, Leslie, Blomqvist, Carl, Bogdanova, Natalia V., Bojesen, Stig E., Bonanni, Bernardo, Børresen-Dale, Anne-Lise, Brand, Judith S., Brauch, Hiltrud, Brennan, Paul, Brenner, Hermann, Brinton, Louise, Broberg, Per, Brock, Ian W., Broeks, Annegien, Brooks-Wilson, Angela, Brucker, Sara Y., Brüning, Thomas, Burwinkel, Barbara, Butterbach, Katja, Cai, Qiuyin, Cai, Hui, Caldés, Trinidad, Canzian, Federico, Carracedo, Angel, Carter, Brian D., Castelao, Jose E., Chan, Tsun L., David Cheng, Ting-Yuan, Seng Chia, Kee, Choi, Ji-Yeob, Christiansen, Hans, Clarke, Christine L., Collée, Margriet, Conroy, Don M., Cordina-Duverger, Emilie, Cornelissen, Sten, Cox, David G., Cox, Angela, Cross, Simon S., Cunningham, Julie M., Czene, Kamila, Daly, Mary B., Devilee, Peter, Doheny, Kimberly F., Dörk, Thilo, dos-Santos-Silva, Isabel, Dumont, Martine, Durcan, Lorraine, Dwek, Miriam, Eccles, Diana M., Ekici, Arif B., Eliassen, A. Heather, Ellberg, Carolina, Elvira, Mingajeva, Engel, Christoph, Eriksson, Mikael, Fasching, Peter A., Figueroa, Jonine, Flesch-Janys, Dieter, Fletcher, Olivia, Flyger, Henrik, Fritschi, Lin, Gaborieau, Valerie, Gabrielson, Marike, Gago-Dominguez, Manuela, Gao, Yu-Tang, Gapstur, Susan M., García-Sáenz, José A., Gaudet, Mia M., Georgoulias, Vassilios, Giles, Graham G., Glendon, Gord, Goldberg, Mark S., Goldgar, David E., González-Neira, Anna, Grenaker Alnæs, Grethe I., Grip, Mervi, Gronwald, Jacek, Grundy, Anne, Guénel, Pascal, Haeberle, Lothar, Hahnen, Eric, Haiman, Christopher A., Håkansson, Niclas, Hamann, Ute, Hamel, Nathalie, Hankinson, Susan, Harrington, Patricia, Hart, Steven N., Hartikainen, Jaana M., Hartman, Mikael, Hein, Alexander, Heyworth, Jane, Hicks, Belynda, Hillemanns, Peter, Ho, Dona N., Hollestelle, Antoinette, Hooning, Maartje J., Hoover, Robert N., Hopper, John L., Hou, Ming-Feng, Hsiung, Chia-Ni, Huang, Guanmengqian, Humphreys, Keith, Ishiguro, Junko, Ito, Hidemi, Iwasaki, Motoki, Iwata, Hiroji, Jakubowska, Anna, Janni, Wolfgang, John, Esther M., Johnson, Nichola, Jones, Kristine, Jones, Michael, Jukkola-Vuorinen, Arja, Kaaks, Rudolf, Kabisch, Maria, Kaczmarek, Katarzyna, Kang, Daehee, Kasuga, Yoshio, Kerin, Michael J., Khan, Sofia, Khusnutdinova, Elza, Kiiski, Johanna I., Kim, Sung-Won, Knight, Julia A., Kosma, Veli-Matti, Kristensen, Vessela N., Krüger, Ute, Kwong, Ava, Lambrechts, Diether, Le Marchand, Loic, Lee, Eunjung, Lee, Min Hyuk, Lee, Jong Won, Neng Lee, Chuen, Lejbkowicz, Flavio, Li, Jingmei, Lilyquist, Jenna, Lindblom, Annika, Lissowska, Jolanta, Lo, Wing-Yee, Loibl, Sibylle, Long, Jirong, Lophatananon, Artitaya, Lubinski, Jan, Luccarini, Craig, Lux, Michael P., Ma, Edmond S. K., MacInnis, Robert J., Maishman, Tom, Makalic, Enes, Malone, Kathleen E., Kostovska, Ivana Maleva, Mannermaa, Arto, Manoukian, Siranoush, Manson, JoAnn E., Margolin, Sara, Mariapun, Shivaani, Martinez, Maria Elena, Matsuo, Keitaro, Mavroudis, Dimitrios, McKay, James, McLean, Catriona, Meijers-Heijboer, Hanne, Meindl, Alfons, Menéndez, Primitiva, Menon, Usha, Meyer, Jeffery, Miao, Hui, Miller, Nicola, Taib, Nur Aishah Mohd, Muir, Kenneth, Mulligan, Anna Marie, Mulot, Claire, Neuhausen, Susan L., Nevanlinna, Heli, Neven, Patrick, Nielsen, Sune F., Noh, Dong-Young, Nordestgaard, Børge G., Norman, Aaron, Olopade, Olufunmilayo I., Olson, Janet E., Olsson, Håkan, Olswold, Curtis, Orr, Nick, Pankratz, V. Shane, Park, Sue K., Park-Simon, Tjoung-Won, Lloyd, Rachel, Perez, Jose I. A., Peterlongo, Paolo, Peto, Julian, Phillips, Kelly-Anne, Pinchev, Mila, Plaseska-Karanfilska, Dijana, Prentice, Ross, Presneau, Nadege, Prokofyeva, Darya, Pugh, Elizabeth, Pylkäs, Katri, Rack, Brigitte, Radice, Paolo, Rahman, Nazneen, Rennert, Gadi, Rennert, Hedy S., Rhenius, Valerie, Romero, Atocha, Romm, Jane, Ruddy, Kathryn J., Rüdiger, Thomas, Rudolph, Anja, Ruebner, Matthias, Rutgers, Emiel J. T., Saloustros, Emmanouil, Sandler, Dale P., Sangrajrang, Suleeporn, Sawyer, Elinor J., Schmidt, Daniel F., Schmutzler, Rita K., Schneeweiss, Andreas, Schoemaker, Minouk J., Schumacher, Fredrick, Schürmann, Peter, Scott, Rodney J., Scott, Christopher, Seal, Sheila, Seynaeve, Caroline, Shah, Mitul, Sharma, Priyanka, Shen, Chen-Yang, Sheng, Grace, Sherman, Mark E., Shrubsole, Martha J., Shu, Xiao-Ou, Smeets, Ann, Sohn, Christof, Southey, Melissa C., Spinelli, John J., Stegmaier, Christa, Stewart-Brown, Sarah, Stone, Jennifer, Stram, Daniel O., Surowy, Harald, Swerdlow, Anthony, Tamimi, Rulla, Taylor, Jack A., Tengström, Maria, Teo, Soo H., Beth Terry, Mary, Tessier, Daniel C., Thanasitthichai, Somchai, Thöne, Kathrin, Tollenaar, Rob A. E. M., Tomlinson, Ian, Tong, Ling, Torres, Diana, Truong, Thérèse, Tseng, Chiu-Chen, Tsugane, Shoichiro, Ulmer, Hans-Ulrich, Ursin, Giske, Untch, Michael, Vachon, Celine, van Asperen, Christi J., Van Den Berg, David, van den Ouweland, Ans M. W., van der Kolk, Lizet, van der Luijt, Rob B., Vincent, Daniel, Vollenweider, Jason, Waisfisz, Quinten, Wang-Gohrke, Shan, Weinberg, Clarice R., Wendt, Camilla, Whittemore, Alice S., Wildiers, Hans, Willett, Walter, Winqvist, Robert, Wolk, Alicja, Wu, Anna H., Xia, Lucy, Yamaji, Taiki, Yang, Xiaohong R., Har Yip, Cheng, Yoo, Keun-Young, Yu, Jyh-Cherng, Zheng, Wei, Zheng, Ying, Zhu, Bin, Ziogas, Argyrios, Ziv, Elad, Lakhani, Sunil R., Antoniou, Antonis C., Droit, Arnaud, Andrulis, Irene L., Amos, Christopher I., Couch, Fergus J., Pharoah, Paul D. P., Chang-Claude, Jenny, Hall, Per, Hunter, David J., Milne, Roger L., García-Closas, Montserrat, Schmidt, Marjanka K., Chanock, Stephen J., Dunning, Alison M., Edwards, Stacey L., Bader, Gary D., Chenevix-Trench, Georgia, Simard, Jacques, Kraft, Peter, and Easton, Douglas F.

Published
2017
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34. Clonal hematopoiesis and risk of prostate cancer in large samples of European ancestry men

Author

Wang, Anqi, primary, Xu, Yili, additional, Yu, Yao, additional, Nead, Kevin T, additional, Kim, TaeBeom, additional, Xu, Keren, additional, Dadaev, Tokhir, additional, Saunders, Ed, additional, Sheng, Xin, additional, Wan, Peggy, additional, Pooler, Loreall, additional, Xia, Lucy Y, additional, Chanock, Stephen, additional, Berndt, Sonja I, additional, Gapstur, Susan M, additional, Stevens, Victoria, additional, Albanes, Demetrius, additional, Weinstein, Stephanie J, additional, Gnanapragasam, Vincent, additional, Giles, Graham G, additional, Nguyen-Dumont, Tu, additional, Milne, Roger L, additional, Pomerantz, Mark M, additional, Schmidt, Julie A, additional, Stopsack, Konrad H, additional, Mucci, Lorelei A, additional, Catalona, William J, additional, Hetrick, Kurt N, additional, Doheny, Kimberly F, additional, MacInnis, Robert J, additional, Southey, Melissa C, additional, Eeles, Rosalind A, additional, Wiklund, Fredrik, additional, Kote-Jarai, Zsofia, additional, de Smith, Adam J, additional, Conti, David V, additional, Huff, Chad, additional, Haiman, Christopher A, additional, and Darst, Burcu F, additional

Published
2022
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35. Abstract 688: Multi-stage exome sequencing study of 17,546 aggressive and non-aggressive prostate cancer cases

Author

Darst, Burcu F., primary, Saunders, Ed, additional, Dadaev, Tokhir, additional, Sheng, Xin, additional, Wan, Peggy, additional, Pooler, Loreall, additional, Xia, Lucy Y., additional, Chanock, Stephen, additional, Berndt, Sonja I., additional, Gapstur, Susan M., additional, Stevens, Victoria, additional, Albanes, Demetrius, additional, Weinstein, Stephanie J., additional, Gnanapragasam, Vincent, additional, Giles, Graham G., additional, Nguyen-Dumont, Tu, additional, Milne, Roger L., additional, Pomerantz, Mark M., additional, Schmidt, Julie A., additional, Travis, Ruth C., additional, Key, Timothy J., additional, Stopsack, Konrad H., additional, Mucci, Lorelei A., additional, Catalona, William J., additional, Marosy, Beth, additional, Hetrick, Kurt N., additional, Doheny, Kimberly F., additional, MacInnis, Robert J., additional, Southey, Melissa C., additional, Eeles, Rosalind A., additional, Wiklund, Fredrik, additional, Kote-Jarai, Zsofia, additional, Conti, David V., additional, and Haiman, Christopher A., additional

Published
2022
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36. Testing specification of distribution in stochastic frontier analysis

Author

Cheng, Ming-yen, Wang, Shouxia, Xia, Lucy, Zhang, Xibin, Cheng, Ming-yen, Wang, Shouxia, Xia, Lucy, and Zhang, Xibin

Abstract

Stochastic frontier analysis is regularly used in empirical studies to evaluate the productivity and efficiency of companies. A typical stochastic frontier model involves a parametric frontier subject to a composite error term consisting of an inefficiency and a random error. We develop new tests for specification of distribution of the inefficiency. We focus on simultaneous relaxation of two common assumptions: (1) parametric frontier which may lead to false conclusions when misspecified, and (2) homoscedasticity which can be easily violated when working with real data. While these two issues have been extensively studied in prior research exploring the estimation of a stochastic frontier and inefficiencies, they have not been properly addressed in the considered testing problem. We propose novel bootstrap and asymptotic distribution-free tests with neither parametric frontier nor homoscedasticity assumptions, in both cross-sectional and panel settings. Our tests are asymptotically consistent, simple to implement and widely applicable. Their powers against general fixed alternatives tend to one as sample size increases, and they can detect root- order local alternatives. We demonstrate their efficacies through extensive simulation studies. When applied to a banking panel dataset, our tests provide sound justification for the commonly used exponential specification for banking data. The findings also show that a new parametric frontier model is more plausible than the conventional translog frontier.

Published
2022

37. A Rare Germline HOXB13 Variant Contributes to Risk of Prostate Cancer in Men of African Ancestry

Author

Darst, Burcu F., Hughley, Raymond, Pfennig, Aaron, Hazra, Ujani, Fan, Caoqi, Wan, Peggy, Sheng, Xin, Xia, Lucy, Andrews, Caroline, Chen, Fei, Berndt, Sonja I., Kote-Jarai, Zsofia, Govindasami, Koveela, Bensen, Jeannette T., Ingles, Sue A., Rybicki, Benjamin A., Nemesure, Barbara, John, Esther M., Fowke, Jay H., Huff, Chad D., Strom, Sara S., Isaacs, William B., Park, Jong Y., Zheng, Wei, Ostrander, Elaine A., Walsh, Patrick C., Carpten, John, Sellers, Thomas A., Yamoah, Kosj, Murphy, Adam B., Sanderson, Maureen, Crawford, Dana C., Gapstur, Susan M., Bush, William S., Aldrich, Melinda C., Cussenot, Olivier, Petrovics, Gyorgy, Cullen, Jennifer, Neslund-Dudas, Christine, Kittles, Rick A., Xu, Jianfeng, Stern, Mariana C., Chokkalingam, Anand P., Multigner, Luc, Parent, Marie-Elise, Menegaux, Florence, Cancel-Tassin, Geraldine, Kibel, Adam S., Klein, Eric A., Goodman, Phyllis J., Stanford, Janet L., Drake, Bettina F., Hu, Jennifer J., Clark, Peter E., Blanchet, Pascal, Casey, Graham, Hennis, Anselm J.M., Lubwama, Alexander, Thompson, Ian M., Jr, Leach, Robin J., Gundell, Susan M., Pooler, Loreall, Mohler, James L., Fontham, Elizabeth T.H., Smith, Gary J., Taylor, Jack A., Brureau, Laurent, Blot, William J., Biritwum, Richard, Tay, Evelyn, Truelove, Ann, Niwa, Shelley, Tettey, Yao, Varma, Rohit, McKean-Cowdin, Roberta, Torres, Mina, Jalloh, Mohamed, Magueye Gueye, Serigne, Niang, Lamine, Ogunbiyi, Olufemi, Oladimeji Idowu, Michael, Popoola, Olufemi, Adebiyi, Akindele O., Aisuodionoe-Shadrach, Oseremen I., Nwegbu, Maxwell, Adusei, Ben, Mante, Sunny, Darkwa-Abrahams, Afua, Yeboah, Edward D., Mensah, James E., Anthony Adjei, Andrew, Diop, Halimatou, Cook, Michael B., Chanock, Stephen J., Watya, Stephen, Eeles, Rosalind A., Chiang, Charleston W.K., Lachance, Joseph, Rebbeck, Timothy R., Conti, David V., and Haiman, Christopher A.

Published
2022
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40. Abstract PO-197: Combined effect of a prostate cancer polygenic risk score and germline pathogenic variants in DNA damage repair genes on prostate cancer risk in men of African ancestry

Author

Hughley, Raymond W., primary, Darst, Burcu F., additional, Matejcic, Marco, additional, Patel, Yesha, additional, Lilyquist, Jenna, additional, Hart, Steven N., additional, Polley, Eric C., additional, Xia, Lucy, additional, Sheng, Xin, additional, Lubmawa, Alexander, additional, Ingles, Sue A., additional, Wilkens, Lynne, additional, Marchand, Loïc L., additional, Watya, Stephen, additional, Couch, Fergus J., additional, Conti, David V., additional, and Haiman, Christopher A., additional

Published
2022
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41. Genome-wide association study of endometrial cancer in E2C2

Author

De Vivo, Immaculata, Prescott, Jennifer, Setiawan, Veronica Wendy, Olson, Sara H., Wentzensen, Nicolas, Attia, John, Black, Amanda, Brinton, Louise, Chen, Chu, Chen, Constance, Cook, Linda S., Crous-Bou, Marta, Doherty, Jennifer, Dunning, Alison M., Easton, Douglas F., Friedenreich, Christine M., Garcia-Closas, Montserrat, Gaudet, Mia M., Haiman, Christopher, Hankinson, Susan E., Hartge, Patricia, Henderson, Brian E., Holliday, Elizabeth, Horn-Ross, Pamela L., Hunter, David J., Le Marchand, Loic, Liang, Xiaolin, Lissowska, Jolanta, Long, Jirong, Lu, Lingeng, Magliocco, Anthony M., McEvoy, Mark, O’Mara, Tracy A., Orlow, Irene, Painter, Jodie N., Pooler, Loreall, Rastogi, Radhai, Rebbeck, Timothy R., Risch, Harvey, Sacerdote, Carlotta, Schumacher, Fredrick, Scott, Rodney J., Sheng, Xin, Shu, Xiao-ou, Spurdle, Amanda B., Thompson, Deborah, VanDen Berg, David, Weiss, Noel S., Xia, Lucy, Xiang, Yong-Bing, Yang, Hannah P., Yu, Herbert, Zheng, Wei, Chanock, Stephen, Kraft, Peter, and The Australian National Endometrial Cancer Study Group

Published
2014
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43. Genome-wide association study of pancreatic fat: The Multiethnic Cohort Adiposity Phenotype Study

Author

Streicher, Samantha A., primary, Lim, Unhee, additional, Park, S. Lani, additional, Li, Yuqing, additional, Sheng, Xin, additional, Hom, Victor, additional, Xia, Lucy, additional, Pooler, Loreall, additional, Shepherd, John, additional, Loo, Lenora W. M., additional, Darst, Burcu F., additional, Highland, Heather M., additional, Polfus, Linda M., additional, Bogumil, David, additional, Ernst, Thomas, additional, Buchthal, Steven, additional, Franke, Adrian A., additional, Setiawan, Veronica Wendy, additional, Tiirikainen, Maarit, additional, Wilkens, Lynne R., additional, Haiman, Christopher A., additional, Stram, Daniel O., additional, Cheng, Iona, additional, and Le Marchand, Loïc, additional

Published
2021
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44. Mendelian randomization analyses suggest a role for cholesterol in the development of endometrial cancer

Author

Kho, Pik Fang, Amant, Frederic, Annibali, Daniela, Ashton, Katie, Attia, John, Auer, Paul L., Beckmann, Matthias W., Black, Amanda, Brinton, Louise, Buchanan, Daniel D., Chanock, Stephen J., Chen, Chu, Chen, Maxine M., Cheng, Timothy H.T., Cook, Linda S., Crous-Bous, Marta, Czene, Kamila, De Vivo, Immaculata, Dennis, Joe, Dörk, Thilo, Dowdy, Sean C., Dunning, Alison M., Dürst, Matthias, Easton, Douglas F., Ekici, Arif B., Fasching, Peter A., Fridley, Brooke L., Friedenreich, Christine M., García-Closas, Montserrat, Gaudet, Mia M., Giles, Graham G., Goode, Ellen L., Gorman, Maggie, Haiman, Christopher A., Hall, Per, Hankinson, Susan E., Hein, Alexander, Hillemanns, Peter, Hodgson, Shirley, Hoivik, Erling A., Holliday, Elizabeth G., Hunter, David J., Jones, Angela M., Kraft, Peter, Krakstad, Camilla, Lambrechts, Diether, Le Marchand, Loic, Liang, Xiaolin, Lindblom, Annika, Lissowska, Jolanta, Long, Jirong, Lu, Lingeng, Magliocco, Anthony M., Martin, Lynn, McEvoy, Mark, Milne, Roger L., Mints, Miriam, Nassir, Rami, Otton, Geoffrey, Palles, Claire, Pooler, Loreall, Proietto, Tony, Rebbeck, Timothy R., Renner, Stefan P., Risch, Harvey A., Rübner, Matthias, Runnebaum, Ingo, Sacerdote, Carlotta, Sarto, Gloria E., Schumacher, Fredrick, Scott, Rodney J., Setiawan, V. Wendy, Shah, Mitul, Sheng, Xin, Shu, Xiao Ou, Southey, Melissa C., Tham, Emma, Tomlinson, Ian, Trovik, Jone, Turman, Constance, Tyrer, Jonathan P., Van Den Berg, David, Wang, Zhaoming, Wentzensen, Nicolas, Xia, Lucy, Xiang, Yong Bing, Yang, Hannah P., Yu, Herbert, Zheng, Wei, Webb, Penelope M., Thompson, Deborah J., Spurdle, Amanda B., Glubb, Dylan M., O'Mara, Tracy A., Kho, Pik Fang, Amant, Frederic, Annibali, Daniela, Ashton, Katie, Attia, John, Auer, Paul L., Beckmann, Matthias W., Black, Amanda, Brinton, Louise, Buchanan, Daniel D., Chanock, Stephen J., Chen, Chu, Chen, Maxine M., Cheng, Timothy H.T., Cook, Linda S., Crous-Bous, Marta, Czene, Kamila, De Vivo, Immaculata, Dennis, Joe, Dörk, Thilo, Dowdy, Sean C., Dunning, Alison M., Dürst, Matthias, Easton, Douglas F., Ekici, Arif B., Fasching, Peter A., Fridley, Brooke L., Friedenreich, Christine M., García-Closas, Montserrat, Gaudet, Mia M., Giles, Graham G., Goode, Ellen L., Gorman, Maggie, Haiman, Christopher A., Hall, Per, Hankinson, Susan E., Hein, Alexander, Hillemanns, Peter, Hodgson, Shirley, Hoivik, Erling A., Holliday, Elizabeth G., Hunter, David J., Jones, Angela M., Kraft, Peter, Krakstad, Camilla, Lambrechts, Diether, Le Marchand, Loic, Liang, Xiaolin, Lindblom, Annika, Lissowska, Jolanta, Long, Jirong, Lu, Lingeng, Magliocco, Anthony M., Martin, Lynn, McEvoy, Mark, Milne, Roger L., Mints, Miriam, Nassir, Rami, Otton, Geoffrey, Palles, Claire, Pooler, Loreall, Proietto, Tony, Rebbeck, Timothy R., Renner, Stefan P., Risch, Harvey A., Rübner, Matthias, Runnebaum, Ingo, Sacerdote, Carlotta, Sarto, Gloria E., Schumacher, Fredrick, Scott, Rodney J., Setiawan, V. Wendy, Shah, Mitul, Sheng, Xin, Shu, Xiao Ou, Southey, Melissa C., Tham, Emma, Tomlinson, Ian, Trovik, Jone, Turman, Constance, Tyrer, Jonathan P., Van Den Berg, David, Wang, Zhaoming, Wentzensen, Nicolas, Xia, Lucy, Xiang, Yong Bing, Yang, Hannah P., Yu, Herbert, Zheng, Wei, Webb, Penelope M., Thompson, Deborah J., Spurdle, Amanda B., Glubb, Dylan M., and O'Mara, Tracy A.

Abstract

Blood lipids have been associated with the development of a range of cancers, including breast, lung and colorectal cancer. For endometrial cancer, observational studies have reported inconsistent associations between blood lipids and cancer risk. To reduce biases from unmeasured confounding, we performed a bidirectional, two-sample Mendelian randomization analysis to investigate the relationship between levels of three blood lipids (low-density lipoprotein [LDL] and high-density lipoprotein [HDL] cholesterol, and triglycerides) and endometrial cancer risk. Genetic variants associated with each of these blood lipid levels (P < 5 × 10−8) were identified as instrumental variables, and assessed using genome-wide association study data from the Endometrial Cancer Association Consortium (12 906 cases and 108 979 controls) and the Global Lipids Genetic Consortium (n = 188 578). Mendelian randomization analyses found genetically raised LDL cholesterol levels to be associated with lower risks of endometrial cancer of all histologies combined, and of endometrioid and non-endometrioid subtypes. Conversely, higher genetically predicted HDL cholesterol levels were associated with increased risk of non-endometrioid endometrial cancer. After accounting for the potential confounding role of obesity (as measured by genetic variants associated with body mass index), the association between genetically predicted increased LDL cholesterol levels and lower endometrial cancer risk remained significant, especially for non-endometrioid endometrial cancer. There was no evidence to support a role for triglycerides in endometrial cancer development. Our study supports a role for LDL and HDL cholesterol in the development of non-endometrioid endometrial cancer. Further studies are required to understand the mechanisms underlying these findings.

Published
2021

45. Genome-wide Association Study of Pancreatic Fat: The Multiethnic Cohort Adiposity Phenotype Study

Author

Streicher, Samantha A, primary, Lim, Unhee, additional, Park, S. Lani, additional, Li, Yuqing, additional, Sheng, Xin, additional, Hom, Victor, additional, Xia, Lucy, additional, Pooler, Loreall, additional, Shepherd, John, additional, Loo, Lenora WM, additional, Darst, Burcu F, additional, Highland, Heather M, additional, Polfus, Linda M, additional, Bogumil, David, additional, Ernst, Thomas, additional, Buchthal, Steven, additional, Franke, Adrian A, additional, Setiawan, Veronica Wendy, additional, Tiirikainen, Maarit, additional, Wilkens, Lynne R, additional, Haiman, Christopher A, additional, Stram, Daniel O, additional, Cheng, Iona, additional, and Marchand, Loïc Le, additional

Published
2021
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46. Neyman-Pearson classification: parametrics and sample size requirement

Author

Tong, Xin, Xia, Lucy, Wang, Jiacheng, Feng, Yang, Tong, Xin, Xia, Lucy, Wang, Jiacheng, and Feng, Yang

Abstract

The Neyman-Pearson (NP) paradigm in binary classification seeks classifiers that achieve a minimal type II error while enforcing the prioritized type I error controlled under some user-specified level α. This paradigm serves naturally in applications such as severe disease diagnosis and spam detection, where people have clear priorities among the two error types. Recently, Tong, Feng, and Li (2018) proposed a nonparametric umbrella algorithm that adapts all scoring-type classification methods (e.g., logistic regression, support vector machines, random forest) to respect the given type I error (i.e., conditional probability of classifying a class 0 observation as class 1 under the 0-1 coding) upper bound α with high probability, without specific distributional assumptions on the features and the responses. Universal the umbrella algorithm is, it demands an explicit minimum sample size requirement on class 0, which is often the more scarce class, such as in rare disease diagnosis applications. In this work, we employ the parametric linear discriminant analysis (LDA) model and propose a new parametric thresholding algorithm, which does not need the minimum sample size requirements on class 0 observations and thus is suitable for small sample applications such as rare disease diagnosis. Leveraging both the existing nonparametric and the newly proposed parametric thresholding rules, we propose four LDA-based NP classifiers, for both low- and high-dimensional settings. On the theoretical front, we prove NP oracle inequalities for one proposed classifier, where the rate for excess type II error benefits from the explicit parametric model assumption. Furthermore, as NP classifiers involve a sample splitting step of class 0 observations, we construct a new adaptive sample splitting scheme that can be applied universally to NP classifiers, and this adaptive strategy reduces the type II error of these classifiers. The proposed NP classifiers are implemented in the R package np

Published
2020

47. Intentional Control of Type I Error over Unconscious Data Distortion: a Neyman-Pearson Approach to Text Classification

Author

Xia, Lucy, Zhao, Richard, Wu, Yanhui, Tong, Xin, Xia, Lucy, Zhao, Richard, Wu, Yanhui, and Tong, Xin

Abstract

This article addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimize the overall classification error and may yield an undesirably large Type I error (relevant textual messages are classified as irrelevant), particularly when available data exhibit an asymmetry between relevant and irrelevant information. Data distortion exacerbates this situation and often leads to fallacious prediction. To deal with inestimable data distortion, we propose the use of the Neyman-Pearson (NP) classification paradigm, which minimizes Type II error under a user-specified Type I error constraint. Theoretically, we show that the NP oracle is unaffected by data distortion when the class conditional distributions remain the same. Empirically, we study a case of classifying posts about worker strikes obtained from a leading Chinese microblogging platform, which are frequently prone to extensive, unpredictable and inestimable censorship. We demonstrate that, even though the training and test data are susceptible to different distortion and therefore potentially follow different distributions, our proposed NP methods control the Type I error on test data at the targeted level. The methods and implementation pipeline proposed in our case study are applicable to many other problems involving data distortion. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Published
2020

50. Pathogenic Variants in Cancer Predisposition Genes and Prostate Cancer Risk in Men of African Ancestry

Author

Matejcic, Marco, primary, Patel, Yesha, additional, Lilyquist, Jenna, additional, Hu, Chunling, additional, Lee, Kun Y., additional, Gnanaolivu, Rohan D., additional, Hart, Steven N., additional, Polley, Eric C., additional, Yadav, Siddhartha, additional, Boddicker, Nicholas J., additional, Samara, Raed, additional, Xia, Lucy, additional, Sheng, Xin, additional, Lubmawa, Alexander, additional, Kiddu, Vicky, additional, Masaba, Benon, additional, Namuguzi, Dan, additional, Mutema, George, additional, Job, Kuteesa, additional, Dabanja, Henry M., additional, Ingles, Sue A., additional, Wilkens, Lynne, additional, Le Marchand, Loic, additional, Watya, Stephen, additional, Couch, Fergus J., additional, Conti, David V., additional, and Haiman, Christopher A., additional

Published
2020
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