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315 results on '"Zhou, Yi-Hui"'

3. Genetic modifiers of body mass index in individuals with cystic fibrosis

Author

Aksit, Melis A., Bamshad, Michael J., Blackman, Scott M., Blue, Elizabeth, Buckingham, Kati, Chong, Jessica X., Collaco, J. Michael, Cutting, Garry R., Dang, Hong, Eastman, Alice, Faino, Anna, Gallins, Paul J., Gibson, Ronald, Godwin, Beth, Gordon, William W., Hetrick, Kurt, Huang, Le, Knowles, Michael R., Lam, Anh-Thu N., Ling, Hua, Liu, Weifang, Li, Yun, Onchiri, Frankline, O'Neal, Wanda K., Pace, Rhonda G., Pagel, Kymberleigh, Porter, Mark, Pugh, Elizabeth, Raraigh, Karen S., Mikeasky, Rebekah, Rosenfeld, Margaret, Rosen, Jonathan, Stilp, Adrienne, Stonebraker, Jaclyn R., Sun, Quan, Wen, Jia, Wright, Fred A., Yang, Yingxi, Zhang, Peng, Zhou, Yi-Hui, Pugh, Elizabeth W., Faino, Anna V., Gibson, Ronald L., and O’Neal, Wanda

Published
2024
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6. Reduced Rank Multivariate Kernel Ridge Regression

Author

Wang, Wenjia and Zhou, Yi-Hui

Subjects
Mathematics - Statistics Theory, Statistics - Methodology
Abstract

In the multivariate regression, also referred to as multi-task learning in machine learning, the goal is to recover a vector-valued function based on noisy observations. The vector-valued function is often assumed to be of low rank. Although the multivariate linear regression is extensively studied in the literature, a theoretical study on the multivariate nonlinear regression is lacking. In this paper, we study reduced rank multivariate kernel ridge regression, proposed by \cite{mukherjee2011reduced}. We prove the consistency of the function predictor and provide the convergence rate. An algorithm based on nuclear norm relaxation is proposed. A few numerical examples are presented to show the smaller mean squared prediction error comparing with the elementwise univariate kernel ridge regression.

Published
2020

7. Eigenvector-based sparse canonical correlation analysis: Fast computation for estimation of multiple canonical vectors

Author

Wang, Wenjia and Zhou, Yi-Hui

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Classical canonical correlation analysis (CCA) requires matrices to be low dimensional, i.e. the number of features cannot exceed the sample size. Recent developments in CCA have mainly focused on the high-dimensional setting, where the number of features in both matrices under analysis greatly exceeds the sample size. These approaches impose penalties in the optimization problems that are needed to be solve iteratively, and estimate multiple canonical vectors sequentially. In this work, we provide an explicit link between sparse multiple regression with sparse canonical correlation analysis, and an efficient algorithm that can estimate multiple canonical pairs simultaneously rather than sequentially. Furthermore, the algorithm naturally allows parallel computing. These properties make the algorithm much efficient. We provide theoretical results on the consistency of canonical pairs. The algorithm and theoretical development are based on solving an eigenvectors problem, which significantly differentiate our method with existing methods. Simulation results support the improved performance of the proposed approach. We apply eigenvector-based CCA to analysis of the GTEx thyroid histology images, analysis of SNPs and RNA-seq gene expression data, and a microbiome study. The real data analysis also shows improved performance compared to traditional sparse CCA.

Published
2020

8. Fast Multivariate Probit Estimation via a Two-Stage Composite Likelihood

Author

Ting, Bryan W., Wright, Fred A., and Zhou, Yi-Hui

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

The multivariate probit is popular for modeling correlated binary data, with an attractive balance of flexibility and simplicity. However, considerable challenges remain in computation and in devising a clear statistical framework. Interest in the multivariate probit has increased in recent years. Current applications include genomics and precision medicine, where simultaneous modeling of multiple traits may be of interest, and computational efficiency is an important consideration. We propose a fast method for multivariate probit estimation via a two-stage composite likelihood. We explore computational and statistical efficiency, and note that the approach sets the stage for extensions beyond the purely binary setting.

Published
2020

11. A Double Penalty Model for Interpretability

Author

Wang, Wenjia and Zhou, Yi-Hui

Subjects
Statistics - Methodology
Abstract

Modern statistical learning techniques have often emphasized prediction performance over interpretability, giving rise to "black box" models that may be difficult to understand, and to generalize to other settings. We conceptually divide a prediction model into interpretable and non-interpretable portions, as a means to produce models that are highly interpretable with little loss in performance. Implementation of the model is achieved by considering separability of the interpretable and non-interpretable portions, along with a doubly penalized procedure for model fitting. We specify conditions under which convergence of model estimation can be achieved via cyclic coordinate ascent, and the consistency of model estimation holds. We apply the methods to datasets for microbiome host trait prediction and a diabetes trait, and discuss practical tradeoff diagnostics to select models with high interpretability.

Published
2019

13. Set-based differential covariance testing for high-throughput data

Author

Zhou, Yi-Hui

Subjects
Statistics - Methodology
Abstract

The problem of detecting changes in covariance for a single pair of features has been studied in some detail, but may be limited in importance or general applicability. In contrast, testing equality of covariance matrices of a {\it set} of features may offer increased power and interpretability. Such approaches have received increasing attention in recent years, especially in the context of high-dimensional testing. These approaches have been limited to the two-sample problem and involve varying assumptions on the number of features $p$ vs. the sample size $n$. In addition, there has been little discussion of the motivating principles underlying various choices of statistic, and no general approaches to test association of covariances with a continuous outcome. We propose a uniform framework to test association of covariance matrices with an experimental variable, whether discrete or continuous. We describe four different summary statistics, to ensure power and flexibility under various settings, including a new "connectivity" statistic that is sensitive to changes in overall covariance magnitude. The approach is not limited by the data dimensions, and is applicable to situations where $p >> n$. For several statistics we obtain asymptotic $p$-values under relatively mild conditions. For the two-sample special case, we show that the proposed statistics are permutationally equivalent or similar to existing proposed statistics. We demonstrate the power and utility of our approaches via simulation and analysis of real data., Comment: arXiv admin note: substantial text overlap with arXiv:1609.00736

Published
2017

14. Pleiotropic modifiers of age-related diabetes and neonatal intestinal obstruction in cystic fibrosis

Author

Aksit, Melis A., Bamshad, Michael J., Blackman, Scott M., Blue, Elizabeth, Buckingham, Kati, Chong, Jessica X., Collaco, J. Michael, Cutting, Garry R., Dang, Hong, Eastman, Alice, Faino, Anna, Gallins, Paul J., Gibson, Ronald, Godwin, Beth, Gordon, William W., Hetrick, Kurt, Huang, Le, Knowles, Michael R., Lam, Anh-Thu N., Ling, Hua, Liu, Weifang, Li, Yun, Onchiri, Frankline, O'Neal, Wanda K., Pace, Rhonda G., Pagel, Kymberleigh, Porter, Mark, Pugh, Elizabeth, Raraigh, Karen S., Mikeasky, Rebekah, Rosenfeld, Margaret, Rosen, Jonathan, Stilp, Adrienne, Stonebraker, Jaclyn R., Sun, Quan, Wen, Jia, Wright, Fred A., Yang, Yingxi, Zhang, Peng, Zhang, Yan, Zhou, Yi-Hui, Faino, Anna V., Stilp, Adrienne M., Blue, Elizabeth E., and Gibson, Ronald L.

Published
2022
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17. Genetic variation in severe cystic fibrosis liver disease is associated with novel mechanisms for disease pathogenesis

Author

Stonebraker, Jaclyn R., primary, Pace, Rhonda G., additional, Gallins, Paul J., additional, Dang, Hong, additional, Aksit, Melis A., additional, Faino, Anna V., additional, Gordon, William W., additional, MacParland, Sonya, additional, Bamshad, Michael J., additional, Gibson, Ronald L., additional, Cutting, Garry R., additional, Durie, Peter R., additional, Wright, Fred A., additional, Zhou, Yi-Hui, additional, Blackman, Scott M., additional, O’Neal, Wanda K., additional, Ling, Simon C., additional, and Knowles, Michael R., additional

Published
2024
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19. A robust covariance testing approach for high-throughput data

Author

Zhou, Yi-Hui

Subjects
Statistics - Methodology
Abstract

The problem of testing changes in covariance has received increasing attention in recent years, especially in the context of high-dimensional testing. A number of approaches have been proposed, all limited to the two-sample problem and involving varying statistics and assumptions on the number of features $p$ vs. the sample size $n$. There are no general approaches to test association of covariances with a continuous outcome. We propose a uniform framework for testing association of covariances with an experimental variable, whether discrete or continuous. The approach is not limited by the data dimensions. Our test procedure (i) does not rely on parametric assumptions, (ii) works well for a range of $p$ and $n$ (e.g., does not require $n > p$), (iii) provides correct type I error control, and (iv) includes four different statistics, to ensure power and flexibility under various settings, including a new "connectivity" statistic that is sensitive to changes in overall covariance magnitude. We demonstrate that, for the two-sample special case, the proposed statistics are permutationally equivalent or similar to existing proposed statistics. We demonstrate the power and utility of our approaches via simulation and analysis of real data. The approach is implemented in an $R$ package.

Published
2016

20. Computation of ancestry scores with mixed families and unrelated individuals

Author

Zhou, Yi-Hui, Marron, J. S., and Wright, Fred

Subjects
Statistics - Methodology
Abstract

The issue of robustness to family relationships in computing genotype ancestry scores such as eigenvector projections has received increased attention in genetic association, as the scores are widely used to control spurious association. We use a motivational example from the North American Cystic Fibrosis (CF) Consortium genetic association study with 3444 individuals and 898 family members to illustrate the challenge of computing ancestry scores when sets of both unrelated individuals and closely-related family members are included. We propose novel methods to obtain ancestry scores and demonstrate that the proposed methods outperform existing methods. The current standard is to compute loadings (left singular vectors) using unrelated individuals and to compute projected scores for remaining family members. However, projected ancestry scores from this approach suffer from shrinkage toward zero. We consider in turn alternate strategies: (i) within-family data orthogonalization, (ii) matrix substitution based on decomposition of a target family-orthogonalized covariance matrix, (iii) covariance-preserving whitening, retaining covariances between unrelated pairs while orthogonalizing family members, and (iv) using family-averaged data to obtain loadings. Except for within-family orthogonalization, our proposed approaches offer similar performance and are superior to the standard approaches. We illustrate the performance via simulation and analysis of the CF dataset.

Published
2016

21. Genetic effects on gene expression across human tissues

Author

Aguet, François, Brown, Andrew A, Castel, Stephane E, Davis, Joe R, He, Yuan, Jo, Brian, Mohammadi, Pejman, Park, YoSon, Parsana, Princy, Segrè, Ayellet V, Strober, Benjamin J, Zappala, Zachary, Cummings, Beryl B, Gelfand, Ellen T, Hadley, Kane, Huang, Katherine H, Lek, Monkol, Li, Xiao, Nedzel, Jared L, Nguyen, Duyen Y, Noble, Michael S, Sullivan, Timothy J, Tukiainen, Taru, MacArthur, Daniel G, Getz, Gad, Addington, Anjene, Guan, Ping, Koester, Susan, Little, A Roger, Lockhart, Nicole C, Moore, Helen M, Rao, Abhi, Struewing, Jeffery P, Volpi, Simona, Brigham, Lori E, Hasz, Richard, Hunter, Marcus, Johns, Christopher, Johnson, Mark, Kopen, Gene, Leinweber, William F, Lonsdale, John T, McDonald, Alisa, Mestichelli, Bernadette, Myer, Kevin, Roe, Bryan, Salvatore, Michael, Shad, Saboor, Thomas, Jeffrey A, Walters, Gary, Washington, Michael, Wheeler, Joseph, Bridge, Jason, Foster, Barbara A, Gillard, Bryan M, Karasik, Ellen, Kumar, Rachna, Miklos, Mark, Moser, Michael T, Jewell, Scott D, Montroy, Robert G, Rohrer, Daniel C, Valley, Dana, Mash, Deborah C, Davis, David A, Sobin, Leslie, Barcus, Mary E, Branton, Philip A, Abell, Nathan S, Balliu, Brunilda, Delaneau, Olivier, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Han, Buhm, He, Amy Z, Hormozdiari, Farhad, Li, Xin, Liu, Boxiang, Kang, Eun Yong, McDowell, Ian C, Ongen, Halit, Palowitch, John J, Peterson, Christine B, Quon, Gerald, Ripke, Stephan, Saha, Ashis, Shabalin, Andrey A, Shimko, Tyler C, Sul, Jae Hoon, Teran, Nicole A, Tsang, Emily K, Zhang, Hailei, Zhou, Yi-Hui, Bustamante, Carlos D, Cox, Nancy J, and Guigó, Roderic

Subjects
Human Genome, Genetics, Biotechnology, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Aetiology, Underpinning research, Generic health relevance, Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Lead analysts:, Laboratory, Data Analysis &Coordinating Center (LDACC):, NIH program management:, Biospecimen collection:, Pathology:, eQTL manuscript working group:, General Science & Technology
Abstract

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

Published
2017

24. Hypothesis testing at the extremes: fast and robust association for high-throughput data

Author

Zhou, Yi-Hui and Wright, Fred

Subjects
Statistics - Methodology
Abstract

A number of biomedical problems require performing many hypothesis tests, with an attendant need to apply stringent thresholds. Often the data take the form of a series of predictor vectors, each of which must be compared with a single response vector, perhaps with nuisance covariates. Parametric tests of association are often used, but can result in inaccurate type I error at the extreme thresholds, even for large sample sizes. Furthermore, standard two-sided testing can reduce power compared to the doubled $p$-value, due to asymmetry in the null distribution. Exact (permutation) testing is attractive, but can be computationally intensive and cumbersome. We present an approximation to exact association tests of trend that is accurate and fast enough for standard use in high-throughput settings, and can easily provide standard two-sided or doubled $p$-values. The approach is shown to be equivalent under permutation to likelihood ratio tests for the most commonly used generalized linear models. For linear regression, covariates are handled by working with covariate-residualized responses and predictors. For generalized linear models, stratified covariates can be handled in a manner similar to exact conditional testing. Simulations and examples illustrate the wide applicability of the approach.

Published
2014

25. Heritability and genomics of gene expression in peripheral blood

Author

Wright, Fred A, Sullivan, Patrick F, Brooks, Andrew I, Zou, Fei, Sun, Wei, Xia, Kai, Madar, Vered, Jansen, Rick, Chung, Wonil, Zhou, Yi-Hui, Abdellaoui, Abdel, Batista, Sandra, Butler, Casey, Chen, Guanhua, Chen, Ting-Huei, D'Ambrosio, David, Gallins, Paul, Ha, Min Jin, Hottenga, Jouke Jan, Huang, Shunping, Kattenberg, Mathijs, Kochar, Jaspreet, Middeldorp, Christel M, Qu, Ani, Shabalin, Andrey, Tischfield, Jay, Todd, Laura, Tzeng, Jung-Ying, van Grootheest, Gerard, Vink, Jacqueline M, Wang, Qi, Wang, Wei, Wang, Weibo, Willemsen, Gonneke, Smit, Johannes H, de Geus, Eco J, Yin, Zhaoyu, Penninx, Brenda WJH, and Boomsma, Dorret I

Subjects
Biotechnology, Human Genome, Genetics, Blood, Gene Expression Profiling, Gene Expression Regulation, Genotype, Humans, Inheritance Patterns, Likelihood Functions, Netherlands, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

We assessed gene expression profiles in 2,752 twins, using a classic twin design to quantify expression heritability and quantitative trait loci (eQTLs) in peripheral blood. The most highly heritable genes (∼777) were grouped into distinct expression clusters, enriched in gene-poor regions, associated with specific gene function or ontology classes, and strongly associated with disease designation. The design enabled a comparison of twin-based heritability to estimates based on dizygotic identity-by-descent sharing and distant genetic relatedness. Consideration of sampling variation suggests that previous heritability estimates have been upwardly biased. Genotyping of 2,494 twins enabled powerful identification of eQTLs, which we further examined in a replication set of 1,895 unrelated subjects. A large number of non-redundant local eQTLs (6,756) met replication criteria, whereas a relatively small number of distant eQTLs (165) met quality control and replication standards. Our results provide a new resource toward understanding the genetic control of transcription.

Published
2014

32. Genetic Modifiers of Cystic Fibrosis Lung Disease Severity: Whole Genome Analysis of 7,840 Patients

Author

Zhou, Yi-Hui, primary, Gallins, Paul J., additional, Pace, Rhonda G., additional, Dang, Hong, additional, Aksit, Melis A., additional, Blue, Elizabeth E, additional, Buckingham, Kati J., additional, Collaco, Joseph M., additional, Faino, Anna V, additional, Gordon, William W., additional, Hetrick, Kurt N., additional, Ling, Hua, additional, Liu, Weifang, additional, Onchiri, Frankline M, additional, Pagel, Kymberleigh, additional, Pugh, Elizabeth W., additional, Raraigh, Karen S, additional, Rosenfeld, Margaret, additional, Sun, Quan, additional, Wen, Jia, additional, Li, Yun, additional, Corvol, Harriet, additional, Strug, Lisa J, additional, Bamshad, Michael J, additional, Blackman, Scott M, additional, Cutting, Garry R, additional, Gibson, Ronald L., additional, O'Neal, Wanda K, additional, Wright, Fred A., additional, and Knowles, Michael R, additional

Published
2023
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35. The impact of rare variation on gene expression across tissues

Author

Li, Xin, Kim, Yungil, Tsang, Emily K., Davis, Joe R., Damani, Farhan N., Chiang, Colby, Hess, Gaelen T., Zappala, Zachary, Strober, Benjamin J., Scott, Alexandra J., Li, Amy, Ganna, Andrea, Bassik, Michael C., Merker, Jason D., Aguet, Franois, Ardlie, Kristin G., Cummings, Beryl B., Gelfand, Ellen T., Getz, Gad, Hadley, Kane, Handsaker, Robert E., Huang, Katherine H., Kashin, Seva, Karczewski, Konrad J., Lek, Monkol, Li, Xiao, MacArthur, Daniel G., Nedzel, Jared L., Nguyen, Duyen T., Noble, Michael S., Segr, Ayellet V., Trowbridge, Casandra A., Tukiainen, Taru, Abell, Nathan S., Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K., Brown, Andrew, Brown, Christopher D., Castel, Stephane E., Chen, Lin S., Conrad, Donald F., Cox, Nancy J., Delaneau, Olivier, Dermitzakis, Emmanouil T., Engelhardt, Barbara E., Eskin, Eleazar, Ferreira, Pedro G., Frsard, Laure, Gamazon, Eric R., Garrido-Martn, Diego, Gewirtz, Ariel D.H., Gliner, Genna, Gloudemans, Michael J., Guigo, Roderic, Hall, Ira M., Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I., McDowell, Ian C., Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B., Muoz-Aguirre, Manuel, Ndungu, Anne W., Nicolae, Dan L., Nobel, Andrew B., Oliva, Meritxell, Ongen, Halit, Palowitch, John J., Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J., Peterson, Christine B., Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Shabalin, Andrey A., Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E., Sul, Jae Hoon, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A., Xi, Hualin S., Yeger-Lotem, Esti, Zaugg, Judith B., Zhou, Yi-Hui, Akey, Joshua M., Bates, Daniel, Chan, Joanne, Claussnitzer, Melina, Demanelis, Kathryn, Diegel, Morgan, Doherty, Jennifer A., Feinberg, Andrew P., Fernando, Marian S., Halow, Jessica, Hansen, Kasper D., Haugen, Eric, Hickey, Peter F., Hou, Lei, Jasmine, Farzana, Jian, Ruiqi, Jiang, Lihua, Johnson, Audra, Kaul, Rajinder, Kellis, Manolis, Kibriya, Muhammad G., Lee, Kristen, Billy Li, Jin, Li, Qin, Lin, Jessica, Lin, Shin, Linder, Sandra, Linke, Caroline, Liu, Yaping, Maurano, Matthew T., Molinie, Benoit, Nelson, Jemma, Neri, Fidencio J., Park, Yongjin, Pierce, Brandon L., Rinaldi, Nicola J., Rizzardi, Lindsay F., Sandstrom, Richard, Skol, Andrew, Smith, Kevin S., Snyder, Michael P., Stamatoyannopoulos, John, Tang, Hua, Wang, Li, Wang, Meng, Van Wittenberghe, Nicholas, Wu, Fan, Zhang, Rui, Nierras, Concepcion R., Branton, Philip A., Carithers, Latarsha J., Guan, Ping, Moore, Helen M., Rao, Abhi, Vaught, Jimmie B., Gould, Sarah E., Lockart, Nicole C., Martin, Casey, Struewing, Jeffery P., Volpi, Simona, Addington, Anjene M., Koester, Susan E., Little, A. Roger, Brigham, Lori E., Hasz, Richard, Hunter, Marcus, Johns, Christopher, Johnson, Mark, Kopen, Gene, Leinweber, William F., Lonsdale, John T., McDonald, Alisa, Mestichelli, Bernadette, Myer, Kevin, Roe, Brian, Salvatore, Michael, Shad, Saboor, Thomas, Jeffrey A., Walters, Gary, Washington, Michael, Wheeler, Joseph, Bridge, Jason, Foster, Barbara A., Gillard, Bryan M., Karasik, Ellen, Kumar, Rachna, Miklos, Mark, Moser, Michael T., Jewell, Scott D., Montroy, Robert G., Rohrer, Daniel C., Valley, Dana R., Davis, David A., Mash, Deborah C., Undale, Anita H., Smith, Anna M., Tabor, David E., Roche, Nancy V., McLean, Jeffrey A., Vatanian, Negin, Robinson, Karna L., Sobin, Leslie, Barcus, Mary E., Valentino, Kimberly M., Qi, Liqun, Hunter, Steven, Hariharan, Pushpa, Singh, Shilpi, Um, Ki Sung, Matose, Takunda, Tomaszewski, Maria M., Barker, Laura K., Mosavel, Maghboeba, Siminoff, Laura A., Traino, Heather M., Flicek, Paul, Juettemann, Thomas, Ruffier, Magali, Sheppard, Dan, Taylor, Kieron, Trevanion, Stephen J., Zerbino, Daniel R., Craft, Brian, Goldman, Mary, Haeussler, Maximilian, Kent, W. James, Lee, Christopher M., Paten, Benedict, Rosenbloom, Kate R., Vivian, John, and Zhu, Jingchun

Subjects
Disease susceptibility -- Genetic aspects, Genetic variation -- Observations, Gene expression -- Observations, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Xin Li [1]; Yungil Kim [2]; Emily K. Tsang [1, 3]; Joe R. Davis [1, 4]; Farhan N. Damani [2]; Colby Chiang [5]; Gaelen T. Hess [4]; Zachary Zappala [...]

Published
2017
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36. Dynamic landscape and regulation of RNA editing in mammals

Author

Tan, Meng How, Li, Qin, Shanmugam, Raghuvaran, Piskol, Robert, Kohler, Jennefer, Young, Amy N., Liu, Kaiwen Ivy, Zhang, Rui, Ramaswami, Gokul, Ariyoshi, Kentaro, Gupte, Ankita, Keegan, Liam P., George, Cyril X., Ramu, Avinash, Huang, Ni, Pollina, Elizabeth A., Leeman, Dena S., Rustighi, Alessandra, Goh, Y. P. Sharon, Aguet, Franois, Ardlie, Kristin G., Cummings, Beryl B., Gelfand, Ellen T., Getz, Gad, Hadley, Kane, Handsaker, Robert E., Huang, Katherine H., Kashin, Seva, Karczewski, Konrad J., Lek, Monkol, Li, Xiao, MacArthur, Daniel G., Nedzel, Jared L., Nguyen, Duyen T., Noble, Michael S., Segr, Ayellet V., Trowbridge, Casandra A., Tukiainen, Taru, Abell, Nathan S., Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K., Brown, Andrew, Brown, Christopher D., Castel, Stephane E., Chen, Lin S., Chiang, Colby, Conrad, Donald F., Cox, Nancy J., Damani, Farhan N., Davis, Joe R., Delaneau, Olivier, Dermitzakis, Emmanouil T., Engelhardt, Barbara E., Eskin, Eleazar, Ferreira, Pedro G., Frsard, Laure, Gamazon, Eric R., Garrido-Martn, Diego, Gewirtz, Ariel D. H., Gliner, Genna, Gloudemans, Michael J., Guigo, Roderic, Hall, Ira M., Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I., McDowell, Ian C., Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B., Muoz-Aguirre, Manuel, Ndungu, Anne W., Nicolae, Dan L., Nobel, Andrew B., Oliva, Meritxell, Ongen, Halit, Palowitch, John J., Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J., Peterson, Christine B., Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J., Shabalin, Andrey A., Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E., Strober, Benjamin J., Sul, Jae Hoon, Tsang, Emily K., Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A., Xi, Hualin S., Yeger-Lotem, Esti, Zappala, Zachary, Zaugg, Judith B., Zhou, Yi-Hui, Akey, Joshua M., Bates, Daniel, Chan, Joanne, Claussnitzer, Melina, Demanelis, Kathryn, Diegel, Morgan, Doherty, Jennifer A., Feinberg, Andrew P., Fernando, Marian S., Halow, Jessica, Hansen, Kasper D., Haugen, Eric, Hickey, Peter F., Hou, Lei, Jasmine, Farzana, Jian, Ruiqi, Jiang, Lihua, Johnson, Audra, Kaul, Rajinder, Kellis, Manolis, Kibriya, Muhammad G., Lee, Kristen, Li, Jin Billy, Lin, Jessica, Lin, Shin, Linder, Sandra, Linke, Caroline, Liu, Yaping, Maurano, Matthew T., Molinie, Benoit, Nelson, Jemma, Neri, Fidencio J., Park, Yongjin, Pierce, Brandon L., Rinaldi, Nicola J., Rizzardi, Lindsay F., Sandstrom, Richard, Skol, Andrew, Smith, Kevin S., Snyder, Michael P., Stamatoyannopoulos, John, Tang, Hua, Wang, Li, Wang, Meng, Van Wittenberghe, Nicholas, Wu, Fan, Nierras, Concepcion R., Branton, Philip A., Carithers, Latarsha J., Guan, Ping, Moore, Helen M., Rao, Abhi, Vaught, Jimmie B., Gould, Sarah E., Lockart, Nicole C., Martin, Casey, Struewing, Jeffery P., Volpi, Simona, Addington, Anjene M., Koester, Susan E., Little, A. Roger, Brigham, Lori E., Hasz, Richard, Hunter, Marcus, Johns, Christopher, Johnson, Mark, Kopen, Gene, Leinweber, William F., Lonsdale, John T., McDonald, Alisa, Mestichelli, Bernadette, Myer, Kevin, Roe, Brian, Salvatore, Michael, Shad, Saboor, Thomas, Jeffrey A., Walters, Gary, Washington, Michael, Wheeler, Joseph, Bridge, Jason, Foster, Barbara A., Gillard, Bryan M., Karasik, Ellen, Kumar, Rachna, Miklos, Mark, Moser, Michael T., Jewell, Scott D., Montroy, Robert G., Rohrer, Daniel C., Valley, Dana R., Davis, David A., Mash, Deborah C., Undale, Anita H., Smith, Anna M., Tabor, David E., Roche, Nancy V., McLean, Jeffrey A., Vatanian, Negin, Robinson, Karna L., Sobin, Leslie, Barcus, Mary E., Valentino, Kimberly M., Qi, Liqun, Hunter, Steven, Hariharan, Pushpa, Singh, Shilpi, Um, Ki Sung, Matose, Takunda, Tomaszewski, Maria M., Barker, Laura K., Mosavel, Maghboeba, Siminoff, Laura A., Traino, Heather M., Flicek, Paul, Juettemann, Thomas, Ruffier, Magali, Sheppard, Dan, Taylor, Kieron, Trevanion, Stephen J., Zerbino, Daniel R., Craft, Brian, Goldman, Mary, Haeussler, Maximilian, Kent, W. James, Lee, Christopher M., Paten, Benedict, Rosenbloom, Kate R., Vivian, John, Zhu, Jingchun, Chawla, Ajay, Del Sal, Giannino, Peltz, Gary, Brunet, Anne, Samuel, Charles E., OConnell, Mary A., Walkley, Carl R., and Nishikura, Kazuko

Subjects
Genetic research, Mammals -- Genetic aspects, RNA processing -- Research, Genetic regulation, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Meng How Tan (corresponding author) [1, 2, 3]; Qin Li [1]; Raghuvaran Shanmugam [2, 3]; Robert Piskol [1]; Jennefer Kohler [1]; Amy N. Young [1]; Kaiwen Ivy Liu [3]; [...]

Published
2017
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37. Landscape of X chromosome inactivation across human tissues

Author

Tukiainen, Taru, Villani, Alexandra-Chlo, Yen, Angela, Rivas, Manuel A., Marshall, Jamie L., Satija, Rahul, Aguirre, Matt, Gauthier, Laura, Fleharty, Mark, Kirby, Andrew, Cummings, Beryl B., Castel, Stephane E., Karczewski, Konrad J., Aguet, Franois, Byrnes, Andrea, Ardlie, Kristin G., Gelfand, Ellen T., Getz, Gad, Hadley, Kane, Handsaker, Robert E., Huang, Katherine H., Kashin, Seva, Lek, Monkol, Li, Xiao, MacArthur, Daniel G., Nedzel, Jared L., Nguyen, Duyen T., Noble, Michael S., Segr, Ayellet V., Trowbridge, Casandra A., Abell, Nathan S., Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K., Brown, Andrew, Brown, Christopher D., Chen, Lin S., Chiang, Colby, Conrad, Donald F., Cox, Nancy J., Damani, Farhan N., Davis, Joe R., Delaneau, Olivier, Dermitzakis, Emmanouil T., Engelhardt, Barbara E., Eskin, Eleazar, Ferreira, Pedro G., Frsard, Laure, Gamazon, Eric R., Garrido-Martn, Diego, Gewirtz, Ariel D. H., Gliner, Genna, Gloudemans, Michael J., Guigo, Roderic, Hall, Ira M., Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I., McDowell, Ian C., Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B., Muoz-Aguirre, Manuel, Ndungu, Anne W., Nicolae, Dan L., Nobel, Andrew B., Oliva, Meritxell, Ongen, Halit, Palowitch, John J., Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J., Peterson, Christine B., Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J., Shabalin, Andrey A., Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E., Strober, Benjamin J., Sul, Jae Hoon, Tsang, Emily K., Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A., Xi, Hualin S., Yeger-Lotem, Esti, Zappala, Zachary, Zaugg, Judith B., Zhou, Yi-Hui, Akey, Joshua M., Bates, Daniel, Chan, Joanne, Claussnitzer, Melina, Demanelis, Kathryn, Diegel, Morgan, Doherty, Jennifer A., Feinberg, Andrew P., Fernando, Marian S., Halow, Jessica, Hansen, Kasper D., Haugen, Eric, Hickey, Peter F., Hou, Lei, Jasmine, Farzana, Jian, Ruiqi, Jiang, Lihua, Johnson, Audra, Kaul, Rajinder, Kellis, Manolis, Kibriya, Muhammad G., Lee, Kristen, Li, Jin Billy, Li, Qin, Lin, Jessica, Lin, Shin, Linder, Sandra, Linke, Caroline, Liu, Yaping, Maurano, Matthew T., Molinie, Benoit, Nelson, Jemma, Neri, Fidencio J., Park, Yongjin, Pierce, Brandon L., Rinaldi, Nicola J., Rizzardi, Lindsay F., Sandstrom, Richard, Skol, Andrew, Smith, Kevin S., Snyder, Michael P., Stamatoyannopoulos, John, Tang, Hua, Wang, Li, Wang, Meng, Van Wittenberghe, Nicholas, Wu, Fan, Zhang, Rui, Nierras, Concepcion R., Branton, Philip A., Carithers, Latarsha J., Guan, Ping, Moore, Helen M., Rao, Abhi, Vaught, Jimmie B., Gould, Sarah E., Lockart, Nicole C., Martin, Casey, Struewing, Jeffery P., Volpi, Simona, Addington, Anjene M., Koester, Susan E., Little, A. Roger, Brigham, Lori E., Hasz, Richard, Hunter, Marcus, Johns, Christopher, Johnson, Mark, Kopen, Gene, Leinweber, William F., Lonsdale, John T., McDonald, Alisa, Mestichelli, Bernadette, Myer, Kevin, Roe, Brian, Salvatore, Michael, Shad, Saboor, Thomas, Jeffrey A., Walters, Gary, Washington, Michael, Wheeler, Joseph, Bridge, Jason, Foster, Barbara A., Gillard, Bryan M., Karasik, Ellen, Kumar, Rachna, Miklos, Mark, Moser, Michael T., Jewell, Scott D., Montroy, Robert G., Rohrer, Daniel C., Valley, Dana R., Davis, David A., Mash, Deborah C., Undale, Anita H., Smith, Anna M., Tabor, David E., Roche, Nancy V., McLean, Jeffrey A., Vatanian, Negin, Robinson, Karna L., Sobin, Leslie, Barcus, Mary E., Valentino, Kimberly M., Qi, Liqun, Hunter, Steven, Hariharan, Pushpa, Singh, Shilpi, Um, Ki Sung, Matose, Takunda, Tomaszewski, Maria M., Barker, Laura K., Mosavel, Maghboeba, Siminoff, Laura A., Traino, Heather M., Flicek, Paul, Juettemann, Thomas, Ruffier, Magali, Sheppard, Dan, Taylor, Kieron, Trevanion, Stephen J., Zerbino, Daniel R., Craft, Brian, Goldman, Mary, Haeussler, Maximilian, Kent, W. James, Lee, Christopher M., Paten, Benedict, Rosenbloom, Kate R., Vivian, John, Zhu, Jingchun, Regev, Aviv, and Hacohen, Nir

Subjects
Chromosomes -- Physiological aspects, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Taru Tukiainen (corresponding author) [1, 2]; Alexandra-Chlo Villani [2, 3]; Angela Yen [2, 4]; Manuel A. Rivas [1, 2, 5]; Jamie L. Marshall [1, 2]; Rahul Satija [2, 6, [...]

Published
2017
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39. MagicalRsq: Machine-learning-based genotype imputation quality calibration

Author

Sun, Quan, primary, Yang, Yingxi, additional, Rosen, Jonathan D., additional, Jiang, Min-Zhi, additional, Chen, Jiawen, additional, Liu, Weifang, additional, Wen, Jia, additional, Raffield, Laura M., additional, Pace, Rhonda G., additional, Zhou, Yi-Hui, additional, Wright, Fred A., additional, Blackman, Scott M., additional, Bamshad, Michael J., additional, Gibson, Ronald L., additional, Cutting, Garry R., additional, Knowles, Michael R., additional, Schrider, Daniel R., additional, Fuchsberger, Christian, additional, and Li, Yun, additional

Published
2022
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40. Identification and single-base gene-editing functional validation of a cis-EPO variant as a genetic predictor for EPO-increasing therapies

Author

Harlow, Charli E., primary, Gandawijaya, Josan, additional, Bamford, Rosemary A., additional, Martin, Emily-Rose, additional, Wood, Andrew R., additional, van der Most, Peter J., additional, Tanaka, Toshiko, additional, Leonard, Hampton L., additional, Etheridge, Amy S., additional, Innocenti, Federico, additional, Beaumont, Robin N., additional, Tyrrell, Jessica, additional, Nalls, Mike A., additional, Simonsick, Eleanor M., additional, Garimella, Pranav S., additional, Shiroma, Eric J., additional, Verweij, Niek, additional, van der Meer, Peter, additional, Gansevoort, Ron T., additional, Snieder, Harold, additional, Gallins, Paul J., additional, Jima, Dereje D., additional, Wright, Fred, additional, Zhou, Yi-hui, additional, Ferrucci, Luigi, additional, Bandinelli, Stefania, additional, Hernandez, Dena G., additional, van der Harst, Pim, additional, Patel, Vickas V., additional, Waterworth, Dawn M., additional, Chu, Audrey Y., additional, Oguro-Ando, Asami, additional, and Frayling, Timothy M., additional

Published
2022
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41. Airway Mucosal Host Defense Is Key to Genomic Regulation of Cystic Fibrosis Lung Disease Severity

Author

Polineni, Deepika, Dang, Hong, Gallins, Paul J., Jones, Lisa C., Pace, Rhonda G., Stonebraker, Jaclyn R., Commander, Leah A., Krenicky, Jeanne E., Zhou, Yi-Hui, Corvol, Harriet, Cutting, Garry R., Drumm, Mitchell L., Strug, Lisa J., Boyle, Michael P., Durie, Peter R., Chmiel, James F., Zou, Fei, Wright, Fred A., OʼNeal, Wanda K., and Knowles, Michael R.

Published
2018
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46. Accounting for population structure in genetic studies of cystic fibrosis

Author

Kingston, Hanley, primary, Stilp, Adrienne M., additional, Gordon, William, additional, Broome, Jai, additional, Gogarten, Stephanie M., additional, Ling, Hua, additional, Barnard, John, additional, Dugan-Perez, Shannon, additional, Ellinor, Patrick T., additional, Gabriel, Stacey, additional, Germer, Soren, additional, Gibbs, Richard A., additional, Gupta, Namrata, additional, Rice, Kenneth, additional, Smith, Albert V., additional, Zody, Michael C., additional, Blackman, Scott M., additional, Cutting, Garry, additional, Knowles, Michael R., additional, Zhou, Yi-Hui, additional, Rosenfeld, Margaret, additional, Gibson, Ronald L., additional, Bamshad, Michael, additional, Fohner, Alison, additional, and Blue, Elizabeth E., additional

Published
2022
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47. A Double Penalty Model for Ensemble Learning

Author

Wang, Wenjia, Zhou, Yi-Hui, Wang, Wenjia, and Zhou, Yi-Hui

Abstract

Modern statistical learning techniques often include learning ensembles, for which the combination of multiple separate prediction procedures (ensemble components) can improve prediction accuracy. Although ensemble approaches are widely used, work remains to improve our understanding of the theoretical underpinnings of aspects such as identifiability and relative convergence rates of the ensemble components. By considering ensemble learning for two learning ensemble components as a double penalty model, we provide a framework to better understand the relative convergence and identifiability of the two components. In addition, with appropriate conditions the framework provides convergence guarantees for a form of residual stacking when iterating between the two components as a cyclic coordinate ascent procedure. We conduct numerical experiments on three synthetic simulations and two real world datasets to illustrate the performance of our approach, and justify our theory. © 2022 by the authors.

Published
2022

48. Identification and single-base gene-editing functional validation of a cis-EPO variant as a genetic predictor for EPO-increasing therapies

Author

Gezonde Vaten, Circulatory Health, Team Medisch, Harlow, Charli E, Gandawijaya, Josan, Bamford, Rosemary A, Martin, Emily-Rose, Wood, Andrew R, van der Most, Peter J, Tanaka, Toshiko, Leonard, Hampton L, Etheridge, Amy S, Innocenti, Federico, Beaumont, Robin N, Tyrrell, Jessica, Nalls, Mike A, Simonsick, Eleanor M, Garimella, Pranav S, Shiroma, Eric J, Verweij, Niek, van der Meer, Peter, Gansevoort, Ron T, Snieder, Harold, Gallins, Paul J, Jima, Dereje D, Wright, Fred, Zhou, Yi-Hui, Ferrucci, Luigi, Bandinelli, Stefania, Hernandez, Dena G, van der Harst, Pim, Patel, Vickas V, Waterworth, Dawn M, Chu, Audrey Y, Oguro-Ando, Asami, Frayling, Timothy M, Gezonde Vaten, Circulatory Health, Team Medisch, Harlow, Charli E, Gandawijaya, Josan, Bamford, Rosemary A, Martin, Emily-Rose, Wood, Andrew R, van der Most, Peter J, Tanaka, Toshiko, Leonard, Hampton L, Etheridge, Amy S, Innocenti, Federico, Beaumont, Robin N, Tyrrell, Jessica, Nalls, Mike A, Simonsick, Eleanor M, Garimella, Pranav S, Shiroma, Eric J, Verweij, Niek, van der Meer, Peter, Gansevoort, Ron T, Snieder, Harold, Gallins, Paul J, Jima, Dereje D, Wright, Fred, Zhou, Yi-Hui, Ferrucci, Luigi, Bandinelli, Stefania, Hernandez, Dena G, van der Harst, Pim, Patel, Vickas V, Waterworth, Dawn M, Chu, Audrey Y, Oguro-Ando, Asami, and Frayling, Timothy M

Published
2022

50. Complete CFTR gene sequencing in 5,058 individuals with cystic fibrosis informs variant-specific treatment

Author

Raraigh, Karen S, primary, Aksit, Melis A, additional, Hetrick, Kurt, additional, Pace, Rhonda G, additional, Ling, Hua, additional, O'Neal, Wanda, additional, Blue, Elizabeth, additional, Zhou, Yi-Hui, additional, Bamshad, Michael J, additional, Blackman, Scott M, additional, Gibson, Ronald L, additional, Knowles, Michael R, additional, and Cutting, Garry R, additional

Published
2022
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