T32 Journal Club

Fall 2020

1. 12/14: Quinton
   Elliott, L.T., Sharp, K., Alfaro-Almagro, F. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210-216 (2018). download
2. 11/30: Nirali
   Sharon et al. Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice. Cell. 2019 May 30; 177(6): 1600-1618.e17. download
3. 11/16: Mykhaylo
   George Davey Smith, Gibran Hemani. Mendelian randomization: genetic anchors for causal inference in epidemiological studies, Human Molecular Genetics, Volume 23, Issue R1, 15 September 2014, Pages R89-R98. download.
4. 11/2: Wendy
   Geelhoed1 et al. Assessment of causality of natriuretic peptides and atrial fibrillation and heart failure: a Mendelian randomization study in the FINRISK cohort. Europace (2020) 22, 1463-1469. download.
5. 10/19: Haoran.
   Pingault et al. (2018). Using genetic data to strengthen causal inference in observational research. Nat Rev Genet. 19: 566-580. download.
6. 10/5: Rachel.
   Hughes et al. (2020). Genome-wide associations of human gut microbiome variation and implications for causal inference analyses. Nat Microbiol 5(9):1079-1087. download.
7. 9/21: Alex Knutson.
   Boyle EA, Li YI, Pritchard JK. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell. 2017;169(7):1177-1186. doi:10.1016/j.cell.2017.05.038 download, a discussion paper, an update.
8. 9/14: organizational

Spring 2019

Fall 2018

Spring 2018

1. April 27: joint with NHLBI T32 at 3:15pm in CCBR.
   "HLA-B*5701 Screening for Hypersensitivity to Abacavir", Mallal S et al. N Engl J Med, 2008 Feb 7;358(6)
2. April 5, replacing the one on April 13:
   IRSA Conference: Statistics, Monte Carlo, and So Much More:A Conference in Honor of Charlie Geyer.
   Note that 1) Friday morning's sessions include several top researchers in genetics/genomics, e.g., E Thompson, Jun Liu and M Newton.. 2) your registration fee will be reimbursed by the Division. 3) Location: 4th floor, Walter Library.
3. March 30: Adam
   Kendall and Yarin Gal. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? online.
4. March 16: NO meeting; enjoy your Spring Break!
5. March 2: Jennifer
   Yifei Chen, Yi Li, Rajiv Narayan, Aravind Subramanian, Xiaohui Xie; Gene expression inference with deep learning, Bioinformatics, 32, 1832-1839. online.
6. Feb 16: Maria, location change: Mayo A110
   Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 12: 931-934. online.
7. Feb 2: Mengli
   Yann LeCun, Yoshua Bengio, Geoffrey Hinton. 2015. Deep learning. Nature 521, 436-444. online.
8. Jan 19: organizational
   Wei Pan's PUBH 7475/8475 notes on ANN/CNN.
   Christof Angermueller, Tanel Parnamaa, Leopold Parts, Oliver Stegle. 2016. Deep learning for computational biology. Molecular Systems Biology (2016) 12, 878. DOI 10.15252/msb.20156651. online.
   William Jones, Kaur Alasoo, Dmytro Fishman, Leopold Parts. 2017. Computational biology: deep learning. Emerging Topics in Life Sciences 1 (3) 257-274; DOI: 10.1042/ETLS20160025 online.
   Ravi D et al. 2017. Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics 21 4-21. online.

Fall 2017

1. Dec 8: Chong Wu; Location change: Mayo D199
   Adaptive testing on a high-dimensional parameter in the presence of a low- or high-dimensional nuisance parameter in GLMs.
2. Nov 24: Thanksgiving Holiday
3. Nov 10: Isabelle
   References:
   Kaushal, A., Zhang, H., Karmus, W., Ray, M., Torres, M., Smith, A., Wang, S. Comparison of different cell correction methods for genome-scale epigenetics studies. BMC Bioinformatics. 2017; 18:216. doi: 10.1186/s12859-017-1611-2. 
   Rahmani, E., Zaitlen, N., Baran, Y., Eng, C., Hu, D., Galanter, J., Oh, S., Burchard, E., Eskin, E., Zou, J., Halperin, E. Sparse PCA corrects for cell-type heterogeneity in epigenome-wide association studies. Nat Methods. 2016; 13:5. doi: 10.1038/nmeth.3809
4. Oct 27: Yangqing Deng
   References:
   Cai X, Bazerque JA, Giannakis GB. Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.PLoS Comp Biol. 2013;9(5):e1003068.
   Wang P, Rahman M, Jin L, Xiong M. A new statistical framework for genetic pleiotropic analysis of high dimensional phenotype data. BMC Genomics. 2016;17:881. doi:10.1186/s12864-016-3169-1.
5. Oct 13: Jack Pattee
   References:
   "Genotype imputation for genome-wide association studies", J Marchini and B Howie, Nature Genetics Reviews (2010)
   "Haplotype reference consortium panel: Practical implications of imputations with large reference panels", A Iglesias et al, Human Mutation (2017)
6. Sept 29: Adam Kaplan
   References:
   Kaplan A, Lock EF. Prediction with Dimension Reduction of Multiple Molecular Data Sources for Patient Survival. Cancer Inform 2017; 16: 1176935117718517
   Burgess, J.K., Karlsson, J.C., Mauad, T., Tjin, G., & Westergrenâ~@~PThorsson, G. (2016). The extracellular matrix â~@~S the underâ~@~Precognized element in lung disease? The Journal of pathology.
7. Sept 15: at 3pm, organizational; Chong Wu.
   References:
   Xu Z, Wu C, Pan W; Alzheimer's Disease Neuroimaging Initiative (2017). Imaging-wide association study: Integrating imaging endophenotypes in GWAS. Neuroimage. 2017 Jul 20;159:159-169. doi: 10.1016/j.neuroimage.2017.07.036. Download

Spring 2017

1. April 21 (time/location change: Friday, 10am, Mayo A110): Dr. Kelly Zou, Pfizer Inc.
2. May 5: students lunch together.
   Real-World Evidence in the Era of Big Data.
3. April 3 (time/location change: Monday, 3:30pm, Moos 2-620): Dr. Yuehua Cui, Michigan State U.
   Statistical Analysis of Gene-environment Interactions: A Semi-parametric Perspective.
4. March 22 (time/location change: Monday, 3:30pm, Moos 2-620): Dr. Michael Epstein, Emory U.
   Genetic Analysis of Multivariate Phenotypes.
5. March 3: Jack Pattee
   References:
   Dudbridge and Gusnanto (2008). "Estimation of Significance Thresholds for Genomewide Association Scans", Genetic Epidemiology. Download
   Pulit et al (2016). "Resetting the bar: Statistical Significance in whole-genome sequencing-based association studies of global populations", Genetic Epidemiology. Download
6. Feb 17: Chong Wu
   GAW20 Datasets: Epigenetic and Pharmacogenomic Data
7. Feb 3: Location: Moos Tower Room 5-125; Time: 10am-11am
   Division of Biostatistics faculty candidate, Sha Cao, currently a doctoral candidate in the Department of Statistics at the University of Georgia, will present: Sparse Dictionary Learning with Prior Gene Network Knowledge for Tumor Tissue Deconvolution.
8. Jan 20: organizational

Fall 2016

1. Dec 9: No JC on the day! The orginally scheduled one (joint with Imaging Working Group) by Drs. Greg Metzger (CMRR) and Joe Koopmeiners, Mayo A301, has been postponed to a future date TBA
2. Nov 11: Joint with NHLBI T32; Location and time changes: CCBR, 3-4pm
   References:
   Pirmohamed et al. (2013). A Randomized Trial of Genotype-Guided Dosing of Warfarin. NEJM 369:2294-2303. online.
   Schork NJ (2015). Personalized medicine: Time for one-person trials. Nature 520:609-611. online.
   Lillie EO, et al (2011). The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per Med. 8(2): 161-173. online.
   Lipkovich I, et al (2016). Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials. Stat Med (in press). online.
   WP's slides.
3. Oct 28: Dr. Wuming Gong, LHI, UofM. Molecular signature of early cardiovascular lineages revealed by single cell transcriptomics.
   References:
   Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet (2015). doi:10.1038/nrg3833
   Ning, L. et al. Current Challenges in the Bioinformatics of Single Cell Genomics. Front Oncol 4, 7 (2014).
   Liu, S. & Trapnell, C. Single-cell transcriptome sequencing: recent advances and remaining challenges. F1000Res 5, (2016).
4. Oct 14: Jaron Arbet References:
   He Q, Avery CL, and Lin DY. (2013). A General Framework for Association Tests With Multivariate Traits in Large Scale Genomics Studies. Genetic epidemiology 37: 759-767.
   
5. Sept 30: Chong Wu
   References:
   Johnstone, Iain M. Approximate null distribution of the largest root in multivariate analysis. The annals of applied statistics 3.4 (2009): 1616.
   Johnstone, Iain M. On the distribution of the largest eigenvalue in principal components analysis. Annals of statistics (2001): 295-327.
   Frost, H. Robert, Christopher I. Amos, and Jason H. Moore. A global test for genegene interactions based on random matrix theory. Genetic Epidemiology (2016).
   Patterson, Nick, Alkes L. Price, and David Reich. Population structure and eigenanalysis. PLoS Genetics 2.12 (2006): e190.
6. Sept 16: Jack Pattee
   References:
   Vilhjalmsson, B, et al. "Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores." American Journal of Human Genetics 97 (2015): 576-592.
   Mak, T. et al. "Polygenic Scores Using Summary Statistics Via Penalized Regression." Preprint.
7. Note: The BD2K Guide to the Fundamentals of Data Science Series, Every Friday beginning September 9, 2016, 12pm - 1pm Eastern Time / 9am - 10am Pacific Time. Here

Spring 2016

1. April 22: Jack Pattee
   References:
   D. Speed, D. Balding. MultiBLUP: Improved SNP-based prediction for complex traits.
   R. Maier, G. Moser, G. Chen et al. Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder.
2. April 8: Jaron Arbet
   References:
   Ayers, Kristin L., and Heather J. Cordell. "SNP selection in genomeâ~@~Pwide and candidate gene studies via penalized logistic regression." Genetic epidemiology 34.8 (2010): 879-891.
   Yi, Hui, et al. "Penalized multimarker vs. single-marker regression methods for genome-wide association studies of quantitative traits." Genetics 199.1 (2015): 205-222.
3. March 25: Brandon Coombes
   References:
   R Ottman. An epidemiologic approach to gene-environment interaction. Genetic Epidemiology, 7(3):177. doi:10.1002/gepi.1370070302, 1990.
   JM Satagopan, SH Olson, and RC Elston. Statistical interactions and bayes estimation of log odds in case-control studies. Statistical Methods in Medical Research, 0(0):1-18, 2015.
   X Wang, RC Elston, and X Zhu. The meaning of interaction. Hum. Hered., 70:269-277, 2010.
   CR Weinberg. Commentary: Thoughts on assessing evidence for gene by environment interaction. Int. J. Epidemiol., 41:705-707, 2012.
4. March 11: Yun Bai
   References:
   Jaffe, Andrew E., and Rafael A. Irizarry. 2014. Accounting for Cellular Heterogeneity Is Critical in Epigenome-Wide Association Studies. Genome Biology 15 (2): R31. doi:10.1186/gb-2014-15-2-r31.
   Barfield, Richard T., Lynn M. Almli, Varun Kilaru, Alicia K. Smith, Kristina B. Mercer, Richard Duncan, Torsten Klengel, et al. 2014. Accounting for Population Stratification in DNA Methylation Studies. Genetic Epidemiology 38 (3): 231-41. doi:10.1002/gepi.21789.
   Zou, James, Christoph Lippert, David Heckerman, Martin Aryee, and Jennifer Listgarten. 2014. Epigenome-Wide Association Studies without the Need for Cell-Type Composition. Nature Methods 11 (3): 309-11. doi:10.1038/nmeth.2815.
5. Feb 26: Junghi Kim. Time/location change: 1-1:55pm, Mayo A301.
   References:
   Lee et al (2010). Biclustering via sparse singular value decomposition. Biometrics. 2010 Dec;66(4):1087-95. online.
   Eavani H, Satterthwaite TD, Filipovych R, Gur RE, Gur RC, Davatzikos C. Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI. Neuroimage. 2015 Jan 15;105:286-99. online.
6. Feb 12: Dr. Tao Lu, SUNY-ALbany. Time/location change: 10-11am, Moos 2-530.
7. Jan 29: Dr. Yizhe Zhao, UNC. Time/location change: 10-11am, Moos 2-530.

Fall 2015

1. Sept 25: Note different time and location: 10--11am, Mayo 3-100, Biostat seminar by Dr. Saurabh Ghosh.
2. Sept 30: organizational meeting; coming over for information and/or signing up for a presentation.
3. Oct 14: "Meta analysis for association with rare variants", Dr. Il-youp Kwak, U of M. Note different location: Moos 2-120
4. Oct 28: "Microbiome", Dr. Jun Chen, Mayo Clinic.
5. Nov 11: "GWAS", Dr. Weihong Tang, Division of Epidemiology and Community Health, SPH, UofM.
6. Nov 25 (too close to the Thanksgiving?)
7. Dec 9: "PheWAS", Dr. Erin Austin, Mayo Clinic.

Spring 2015

1. April 24: Professor Julian Wolfson. A301 Mayo; please note the place different from the usual one.
   Title: Machine learning methods for risk prediction with censored EHR data.
2. April 10: 12:15-1:30 p.m., 2-470 PWB, The challenge of creating rules for translational science: Return of results and incidental findings in genomics. Susan M. Wolf, J.D., McKnight Presidential Professor of Law, Medicine and Public Policy, Faegre Baker Daniels Professor of Law, Faculty member, Center for Bioethics, University of Minnesota
3. March 27: 1-2pm, Mayo 1250, joint with the Imaging Working Group.
   Presenter: Professor Eric Lock
   References: Floch et al (2012). Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares. Neuroimage, 63:11-24. online.
   
4. March 13: No meeting--Spring break.
5. Feb 27: 1:30-2:30pm (or 3pm if needed), Wei Pan, An introduction to GWAS: Rare Variants.
6. Feb 13: 1:25-2:25pm, Wei Pan, An introduction to GWAS: Common Variants.
7. Feb 9: 3:30-4:30pm, Professor Ali Shojaie, Dept of Biostat, U of Washington.

Fall 2014

1. Nov 28, 2014: no meeting due to holiday.
2. Nov 14, 2014
   Presenter: Debashree Ray
   References:
   1. Chen H, Meigs JB, and Dupuis J (2013). Sequence Kernel Association Test for Quantitative Traits in Family Samples. online.
   2.Jiang Y, Conneely KN, andEpstein MP (2014). Flexible and Robust Methods for Rare-Variant Testing of Quantitative Traits in Trios and Nuclear Families. online.
3. No meeting on Oct 31, 2014; next meeting on Nov 14, 2014
4. Oct 17, 2014
   Presenter: Il-Youp Kwak
   References:
   1. Review of statistical methods for QTL mapping in experimental crosses. Lab Anim (NY). 2001 Jul-Aug;30(7):44-52 ( https://www.biostat.wisc.edu/~kbroman/publications/labanimal.pdf )
   2. A Guide to QTL Mapping with R/qtl, by Karl W. Broman and Saunak Sen.
5. Oct 3, 2014
   Presenter: Brandon Coombes
   Papers: 1. Dudbridge F, Fletcher O. (2014). Gene-environment dependence creates spurious gene-environment interaction. online.
   2. Hunter DJ (2005). Gene-environment interactions in human diseases. online.
   3. Lin X, Lee S, et al. (2013). Test for interactions between a genetic marker set and environment in generalized linear models. online.
   4. Zhu R, Zhao H, Ma S. (2014). Identifying gene-environment and gene-gene interactions using a progressive penalization approach. online.
   
6. Sept 19, 2014
   Presenter: Yun Bai
   Papers:
   1. Teslovich et al (2010). Biological, clinical and population relevance of 95 loci for blood lipids. online.
   2. Stephens (2013). A Unified Framework for Association Analysis with Multiple Related Phenotypes. online.
   3. Galesloot et al (2014). A Comparison of Multivariate Genome-Wide Association Methods. online.
   4. Zhang Y, Xu Z, Shen X, Pan W; Alzheimer's Disease Neuroimaging Initiative. Testing for association with multiple traits in generalized estimation equations, with application to neuroimaging data. online.
7. Sept 5, 2014
   Presenter: Chen Gao
   Papers:
   1. Zhou, Pan and Shen (2009). Penalized model-based clustering with unconstrained covariance matrices. online.
   2. Danaher, Wang and Witten (2014). The joint graphical lasso for inverse covariance estimation across multiple classes. online.
   3. Zhu, Shen and Pan (2014). Structural pursuit over multiple undirected graphs. online.