T32 Journal Club

Topic (Spring 2024): Analysis of spatial and single-cell transcriptomic data

Location/Time: Fridays at 10am biweekly (starting 1/19) in UOP 351,  with a Zoom option

Contact Information:

Eric Lock: elock@umn.edu

Saonli Basu: saonli@umn.edu

Schedule:

1/19: Introduction and organizational meeting [UOP 351 & Zoom]

Introduction to single-cell transcriptomics (RNASeq): https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-017-0467-4

Introduction to spatial transcriptomics: https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-022-01075-1

Collection of software for single-cell analysis: https://github.com/seandavi/awesome-single-cell

Additional references: 

Single cell RNA-Seq slides: https://hbctraining.github.io/scRNA-seq/slides/Single_Cell_2_27_20.pdf 

Method of the year: spatial transcriptomics: https://www.nature.com/articles/s41592-020-01033-y

Another review of spatial transcriptomics: https://www.nature.com/articles/s41586-021-03634-9

2/2:  Multiresolution categorical regression for interpretable cell-type annotation (Aaron  Molstad) [UOP 351 & Zoom] 

References:

-https://onlinelibrary.wiley.com/doi/full/10.1111/biom.13926

-https://projecteuclid.org/journals/annals-of-applied-statistics/volume-17/issue-4/Binned-multinomial-logistic-regression-for-integrative-cell-type-annotation/10.1214/23-AOAS1769.short

2/16: Inference after latent variable estimation for single-cell RNA sequencing data

Article: https://academic.oup.com/biostatistics/article/25/1/270/6893953

(Aparna Srinivasan and Nidhi Pai to introduce the paper) 

3/1: Deciphering High-order Structures in Spatial Transcriptomes with Graph-guided Tucker Decomposition (Charlie Broadbent) [UOP 351 & Zoom]

Reference: https://www.nature.com/articles/s41467-023-44017-0

3/15:  Review of spatial transcriptomics and analysis methods (Lin Zhang) [UOP 351 & Zoom]

3/29: CMI-PB challenge: presentations and discussion [UOP 351 & Zoom]

4/12: Review and discussion of differential expression methods for single-cell RNA-Seq [UOP 351 & Zoom]

Confronting false discoveries in single-cell RNA-Seq: https://www.nature.com/articles/s41467-021-25960-2

IDEAS method: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02605-1

iDESC method: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05432-8

4/26: Christy Henzler (MSI) [UOP & Zoom]

Topic (Fall 2023): Multi-"Omics" Data Integration

Location/Time: Fridays 10am in UOP, with a Zoom option (check calendar invite for details)

Contact Information:

Eric Lock: elock@umn.edu

Saonli Basu: saonli@umn.edu

Overview: The main purpose of this journal club is to introduce and discuss applications of genomics/omics in different scientific domains. This semester, we will focus on methodology and applications involving the integration of multiple "omics" datasets, e.g., gene expression (transcriptomics), proteomics, metabolomics, etc.  See the tentative schedule below.

Schedule:

9/15: Introduction and organizational meeting

Overview of multi-omics data and its importance: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1215-1

Collection of multi-omic software & methods: https://github.com/mikelove/awesome-multi-omics

Contest on prediction from multi-omics in immunology: https://www.cmi-pb.org/blog/prediction-challenge-overview/

9/22: Dimension reduction for multi-omic data (Eric Lock) [room: UOP 351]

-JIVE: https://arxiv.org/pdf/1102.4110.pdf

-BIDIFAC: https://arxiv.org/pdf/2002.02601.pdf

-SLIDE: https://onlinelibrary.wiley.com/doi/full/10.1111/biom.13108

-MOFA: https://www.embopress.org/doi/full/10.15252/msb.20178124

10/6: Multiple Augmented Reduced Rank Regression for Pan-Cancer Analysis (Jiuzhou Wang) [room: UOP351]

-maRRR: https://arxiv.org/pdf/2308.16333.pdf

CMI multi-omics challenge information session  on 10/6 (noon over Zoom)

10/20: Multi-omics prediction challenge: form groups and discuss [room: UOP 116]

Contest website: https://www.cmi-pb.org/blog/prediction-challenge-overview/

Preprint that describes methods used in an earlier challenge: https://www.biorxiv.org/content/10.1101/2023.08.28.555193v1.full.pdf

Informational meeting recording: https://discuss.cmi-pb.org/t/1st-informational-zoom-session-recording-and-meeting-slides-10-6-2023/209

11/3: Sparse Linear Discriminant Analysis for Multiview Data, and mvLearnR (Sandra Safo)  [Zoom]

SIDA: https://onlinelibrary.wiley.com/doi/full/10.1111/biom.13458 

mvLearnR: https://github.com/lasandrall/mvlearnR/blob/main/mvlearnR_Overview.pdf

11/17: Interpretable integrative Bayesian methods for Multi-Omics Data (Thierry Chekouo) [UOP 240]

Links to references:

1.) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4499566/

2.) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9960952/

3.) https://journals.sagepub.com/doi/abs/10.1177/09622802231181231

12/1: Integrated multi-omics approach to predict dementia: Using an Explainable Variational Autoencoder (E-VAE) classifier model (Sithara Vivek) [Zoom]

Link to reference: https://pubmed.ncbi.nlm.nih.gov/37926392/

Second CMI multi-omics information session on 12/1 at noon over Zoom 

12/15: Multi-omics prediction challenge: present and discuss strategies of the different groups [Zoom]

Topic: Imaging Genetics and Scientific Data Analysis Pipelines (Spring 2022)

Location/ Time: Virtual/ Fridays (3-4PM)

Contact Information:

Saonli Basu: saonli@umn.edu

Eric Feczko: feczk001@umn.edu

Oscar Miranda-Dominguez: miran045@umn.edu

Mark Fiecas: mfiecas@umn.edu

Overview: The main purpose of this journal club is to introduce you to applications of genomics/omics in different scientific domains. This semester, we will focus on reproducibility, best coding practices and developing analysis pipelines. See the tentative schedule below.

Presentations/Tutorial  Schedule:

Week 1 (21 Jan): Organizational Meeting

Week 2 (28 Jan): Reproducibility

Nuzzo, Regina. "Scientific method: statistical errors." Nature News 506.7487 (2014): 150.

Bissell, Mina. "Reproducibility: The risks of the replication drive." Nature News 503.7476 (2013): 333.

Poldrack, Russell A., et al. "Scanning the horizon: towards transparent and reproducible neuroimaging research." Nature reviews neuroscience 18.2 (2017): 115-126.

Peng, Roger D., and Stephanie C. Hicks. "Reproducible Research: A Retrospective." Annual Review of Public Health 42 (2021): 79-93.

Best Practices in Data Analysis and Sharing in Neuroimaging using MRI

https://towardsdatascience.com/scientific-data-analysis-pipelines-and-reproducibility-75ff9df5b4c5

Week 3 (11 Feb): Best coding practices (Oscar + Eric) and Git/github, version control, Intro (Oscar + Eric) and Evaluation and critical discussion of existing code (Oscar + Eric)

Week 4 (25 Feb): Extracting brain network imaging phenotypes via community detection (Oscar + Eric) 

Gates KM, Henry T, Steinley D, Fair DA. A Monte Carlo Evaluation of Weighted Community Detection Algorithms. Front Neuroinform. 2016;10:45. Published 2016 Nov 10. doi:10.3389/fninf.2016.00045

Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ, Berg JJ, Ortega M, Hoyt-Drazen C, Gratton C, Sun H, Hampton JM, Coalson RS, Nguyen AL, McDermott KB, Shimony JS, Snyder AZ, Schlaggar BL, Petersen SE, Nelson SM, Dosenbach NUF. Precision Functional Mapping of Individual Human Brains. Neuron. 2017 Aug 16;95(4):791-807.e7. doi: 10.1016/j.neuron.2017.07.011. Epub 2017 Jul 27. PMID: 28757305; PMCID: PMC5576360.

Wig GS, Schlaggar BL, Petersen SE. Concepts and principles in the analysis of brain networks. Ann N Y Acad Sci. 2011 Apr;1224:126-146. doi: 10.1111/j.1749-6632.2010.05947.x. Erratum in: Ann N Y Acad Sci. 2011 May;1226(1):51. PMID: 21486299.

Week 5 (18 Mar): Dimensionality reduction: PLSR and CCA (Oscar and Eric)

Week 6 (1 Apr): Groups 1+2 (from previous semester)

Week 7 (15 Apr): Groups 3+4  (from previous semester)

Week 8 (29 Apr): Groups 5+6  (from previous semester)

Topic: Imaging Genetics (Fall 2021)

Location: Mayo: D-325  (hybrid)

Overview: The main purpose of this journal club is to introduce you to applications of genomics/omics in different scientific domains. This year, we will be focusing on `imaging genetics’. We will have discussions and a tutorial on statistical analyses of neuroimaging data  (STAND) to familiarize you with statistical methodologies/software in imaging genetics. See more details on STAND below.

Schedule:

17 Sep Organizational Meeting

24 Sep: Overview of Imaging Genetics: 

Ge, Tian, Gunter Schumann, and Jianfeng Feng. "Imaging genetics—towards discovery neuroscience." Quantitative Biology 1, no. 4 (2013): 227-245.

Nathoo, Farouk S., Linglong Kong, Hongtu Zhu, and Alzheimer's Disease Neuroimaging Initiative. "A review of statistical methods in imaging genetics." Canadian Journal of Statistics 47, no. 1 (2019): 108-131.

1 Oct: Discussion: What is MRI Data 

8 Oct: Tutorial: MRI Data+ Data Repositories 

15 Oct Brain-wide association studies:  

Vounou, Maria, et al. "Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach." Neuroimage 53.3 (2010): 1147-1159.

Elliott, Lloyd T., Kevin Sharp, Fidel Alfaro-Almagro, Sinan Shi, Karla L. Miller, Gwenaëlle Douaud, Jonathan Marchini, and Stephen M. Smith. "Genome-wide association studies of brain imaging phenotypes in UK Biobank." Nature 562, no. 7726 (2018): 210-216.

22 Oct Tutorial: GWAS/BWAS

29 Oct: Heritability

Anderson, Kevin M., Tian Ge, Ru Kong, Lauren M. Patrick, R. Nathan Spreng, Mert R. Sabuncu, BT Thomas Yeo, and Avram J. Holmes. "Heritability of individualized cortical network topography." Proceedings of the National Academy of Sciences 118, no. 9 (2021).

Ganjgahi, H., Winkler, A. M., Glahn, D. C., Blangero, J., Kochunov, P., & Nichols, T. E. (2015). Fast and powerful heritability inference for family-based neuroimaging studies. Neuroimage, 115, 256-268.

5 Nov Tutorial: Heritability

12 Nov PRS/PNS

 van der Meer, Dennis, Oleksandr Frei, Tobias Kaufmann, Alexey A. Shadrin, Anna Devor, Olav B. Smeland, Wesley K. Thompson et al. "Understanding the genetic determinants of the brain with MOSTest." Nature communications 11, no. 1 (2020): 1-9.

Zhao, Weiqi, Clare E. Palmer, Wesley Thompson, Terry L. Jernigan, Anders M. Dale, and Chun Chieh Fan. "The Bayesian polyvertex score (PVS-B): a whole-brain phenotypic prediction framework for neuroimaging studies." bioRxiv (2019): 813915.

19 Nov Tutorial: PRS/PNS

3 Dec Presentations

10 Dec Presentations

*****************************************************************

Statistical Tutorial for Analysis of Neuroimaging Data (STAND)

Brief Description

The Statistical Tutorial for Analysis of Neuroimaging Data (STAND) will instruct students in best standards and practices for modern analysis of derived neuroimaging data. Tutorials will cover broad topics from understanding neuroimaging derived data and data access to data analysis. Students will discuss papers concerning best practices and analytic approaches and get experience running neuroimaging data analyses. 

Instructors: Eric (Fez) Feczko, Oscar Miranda-Dominguez ; Department of Pediatrics 

Tutorial sessions will provide students the opportunity to work with MRI data directly and learn best standards and practices for neuroimaging data analysis. A primary instructor will perform a demonstration for the tutorial, while students will be able to follow the steps on their own computers interactively. Other instructors will help students troubleshoot the demonstrations, should obstacles arise. Critically, we do not expect all students to complete all tutorials within the hour; we do expect students to give their best effort towards completion, and hopefully glean insights from the experience. 

List of topics we will cover

1. MRI data: How do we determine MRI data quality?

2. Data repositories: How do we access large datasets? How do we inspect large datasets for quality?

3. Brain Wide Association Studies:  Limitations and considerations for the identification of biomarkers in brain imaging

4. Brain Imaging Heritability:  Is the brain inherited or shaped by the environment, how can this be measured? Approaches to estimating heritability from big data samples

5. Polyneuro Risk Scores:  Are brain/behavior associations distributed or focal? Can we use the brain to characterize an individual’s expected outcomes? Best standards and practices for Polyneuro risk score prediction.

List of software we will learn

Freesurfer

Fsl

Workbench

nipype

MRIQC

PALM

MsrginalModelCifti

MOSTest

PRS

Spring 2021  (Theme: Transethnic Association Studies)

Time: 3:30-4:30 every other Monday; virtual/zoom.

February 1:  Seonkyeong Jang, PhD student, Department of Psychology: “The contribution of rare variants to the heritability of tobacco use: evidence from whole-genome sequence of up to 26,000 individuals”.

February 15: Tianzhong Yang, Assistant Professor, Division of Biostatistics, Childhood cancers and statistical methods. (No need to read any paper)

March 1:  Kelsey Grinde, Assistant Professor, Department of Mathematics, Statistics and Computer Science, Macalester College.

Grinde, Kelsey E., et al. "Genome-wide significance thresholds for admixture mapping studies." The American Journal of Human Genetics 104.3 (2019): 454-465.

March 15: Alex Knutson & Zhaotong Lin,

Kichaev, Gleb, and Bogdan Pasaniuc. "Leveraging functional-annotation data in trans-ethnic fine-mapping studies." The American Journal of Human Genetics 97.2 (2015): 260-271.

March 29: Mykhaylo Malakhov & Michael Anderson,

Márquez‐Luna, Carla, et al. "Multiethnic polygenic risk scores improve risk prediction in diverse populations." Genetic epidemiology 41.8 (2017): 811-823.

April  12 (We will meet the week after the Spring break): Rachel Zilinskas & Wendy Wang

Geoffroy, Elyse, Isabelle Gregga, and Heather E. Wheeler. "Population-Matched Transcriptome Prediction Increases TWAS Discovery and Replication Rate." Iscience 23.12 (2020): 101850.

April 19: Nirali Patel & Quinton Neville

Veturi, Yogasudha, et al. "Modeling heterogeneity in the genetic architecture of ethnically diverse groups using random effect interaction models." Genetics 211.4 (2019): 1395-1407.

May 3:  Saonli Basu will summarize the topics we’ll cover this semester.

Fall 2020

Time: 3:00-4:00 every other Monday; virtual/zoom.

1. 12/14: Quinton

2. 11/30: Nirali

3. 11/16: Mykhaylo

4. 11/2: Wendy

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

Run by Prof Weihua Guan.

Fall 2018

Run by Prof Mark Fiecas.

Spring 2018

Time: 11-12 every other Friday in Mayo D199 (unless specified otherwise).

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

Time: 2:30-3:30 every other Friday in Mayo A434 (unless specified otherwise).

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:Â 10.1186/s12859-017-1611-2.Â
   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:Â 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

Time: 12:10-1:10 every other Friday; Location: Mayo A434 (unless specified otherwise).

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

Time: 1--2pm every other Friday; Location: Mayo A434 (unless specified otherwise).

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

Time: 1--2pm every other Friday; Location: Mayo A434. (unless specified otherwise).

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

Time: 12:30--1:30pm every other Friday; Location: Mayo A301, SPH Conference Room.

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

Time: 1:30--2:30pm every other Friday; Location: Mayo A434, Conference Room.

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

Time: 1:30--2:30pm every other Friday; Location: Mayo A434, Conference Room.

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.