Biostatistics 830: Main Page

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 830 is a Ph.D. level course that dissects some recently developed methods and the principles behind their implementation. 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 820 should have previously completed Biostatistics 666 and Biostatistics 615/815, which are courses introducing methods for genetic analysis and programming principles, respectively.

Scheduling

For Fall 2013, classes are scheduled for Mondays and Wednesdays, 3:00 - 4:30 pm.

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

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.

Required Reading

• Browning SR, Browning BL (2007) Rapid and accurate haplotype phasing and

missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 81:1084-97. PMID: 17924348

• 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

• 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

• Jun G, Flickinger M, Hetrick KN, Romm JM, Doheny KF, Abecasis GR, Boehnke M,

Kang HM (2012) Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am J Hum Genet. 91:839-48. PMID: 23103226

• Li H, Ruan J, Durbin R (2008) Mapping short DNA sequencing reads and calling

variants using mapping quality scores. Genome Res. 18:1851-8. PMID: 18714091

• Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler

transform. Bioinformatics. 25:1754-60. PMID: 19451168

• Li H, Durbin R (2011) Inference of human population history from individual

whole-genome sequences. Nature. 475:493-6. PMID: 21753753

• 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

• 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

• 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

• Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly

using de Bruijn graphs. Genome Res. 18:821-9. PMID: 18349386

Course History

This course is an ad-hoc course, first taught by Goncalo Abecasis in the Fall of 2013.