Biostatistics 866: Main Page

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Objective

Gene mapping studies study the relationship between genetic variation and susceptibility to human disease. These studies are changing rapidly with the availability of techniques for very large scale genetic analysis, whether based on sequencing or on genotyping. Biostatistics 866 is a Ph.D. level course that helps students understand some of the key building blocks of modern genetic analysis tools. It is meant to provide students with a toolkit to facilitate development and implementation of new statistical methods.

For additional information, see also Core Competencies in Biostatistics Program covered by this course.

Target Audience

It is highly recommended that students registering for Biostatistics 830 should have previously completed Biostatistics 666 and Biostatistics 615/815, which are courses introducing methods for genetic analysis and programming principles, respectively.

Scheduling

For Winter 2015, classes are scheduled for Tuesdays and Thursday, 8:30 - 10 am.

Grading

The final grade will take into account your performance in problem sets and worksheets as well as your participation in class.

Class Worksheets

Coming soon!

Standards of Academic Conduct

The following is an extract from the School of Public Health's Student Code of Conduct [1]:

Student academic misconduct includes behavior involving plagiarism, cheating, fabrication, falsification of records or official documents, intentional misuse of equipment or materials, and aiding and abetting the perpetration of such acts. The preparation of reports, papers, and examinations, assigned on an individual basis, must represent each student’s own effort. Reference sources should be indicated clearly. The use of assistance from other students or aids of any kind during a written examination, except when the use of books or notes has been approved by an instructor, is a violation of the standard of academic conduct.

In the context of this course, any work you hand-in should be your own and any material that is a transcript (or interpreted transcript) of work by others must be clearly labeled as such. If you turn in work that is directly copied from another student or from a published or unpublished source without attribution, you risk failing the course.

Planned Reading

  • Coventry A, Bull-Otterson LM, Liu X, Clark AG, Maxwell TJ, Crosby J, Hixson JE, Rea TJ, Muzny DM, Lewis LR, Wheeler DA, Sabo A, Lusk C, Weiss KG, Akbar H, Cree A, Hawes AC, Newsham I, Varghese RT, Villasana D, Gross S, Joshi V, Santibanez J, Morgan M, Chang K, Iv WH, Templeton AR, Boerwinkle E, Gibbs R, Sing CF (2010) Deep resequencing reveals excess rare recent variants consistent with explosive population growth. Nat Commun. 1:131. PMID: 21119644
  • Delaneau O, Zagury JF, Marchini J (2013) Improved whole-chromosome phasing for disease and population genetic studies. Nat Methods. 10:5-6. PMID: 23269371
  • Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet. 44:955-9. PMID: 22820512 [Code Snippets]
  • Iqbal Z, Caccamo M, Turner I, Flicek P, McVean G (2012) De novo assembly and genotyping of variants using colored de Bruijn graphs. Nat Genet. 44:226-32. PMID: 22231483
  • Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, Freimer NB, Sabatti C, Eskin E (2010) Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42:348-354
  • Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46 310–315
  • Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 25:1754-60. PMID: 19451168
  • Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 34:816-34. PMID: 21058334 [Code Snippets]
  • Lin DY, Zeng D (2010) Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data. Genet Epidemiol. 34:60-6. PMID: 19847795
  • Liu DJ, Peloso GM, Zhan X, Holmen OL, Zawistowski M, Feng S, Nikpay M, Auer PL, Goel A, Zhang H, Peters U, Farrall M, Orho-Melander M, Kooperberg C, McPherson R, Watkins H, Willer CJ, Hveem K, Melander O, Kathiresan S, Abecasis GR (2014) Meta-analysis of gene-level tests for rare variant association. Nat Genet. 46:200-4
  • Wang C, Zhan X, Bragg-Gresham J, Kang HM, Stambolian D, Chew EY, Branham KE, Heckenlively J; FUSION Study, Fulton R, Wilson RK, Mardis ER, Lin X, Swaroop A, Zöllner S, Abecasis GR (2014) Ancestry estimation and control of population stratification for sequence-based association studies. Nat Genet. 2014 46:409-15
  • Wen X, Stephens M (2010) Using linear predictors to impute allele frequencies from summary or pooled genotype data. Ann Appl Stat. 4:1158-1182. PMID: 21479081

Course History

This course is offered occasionally. Typically, Mike Boehnke has taught it. Each instructor gives the course a different flavor, so you may find it worthwhile -- even if you have taken the course with Mike previously.