BIOM 262: Quantitative Methods in Genetics

UCSD Genetics Training Program

Spring, 2009

BIOM 262: Quantitative Methods in Genetics
Course description

Unit Dates Faculty and Materials
1
 March 30 - April 3
MWF 1-3
CMME 2001

note different time and location first week only!

 John Kelsoe: Association studies, part A
Case-control, population extremes, and other study designs.  Phenotyping depth vs. study size.  Ascertainment bias.  Common vs. rare polymorphisms.  Candidate gene selection.  Genotyping methods.  SNP, haplotype and diplotype methods. Analytical approaches.  Corrections for multiple hypotheses.  The transmission disequilibrium test.  Replication and biological correlates.  Sample analysis.
Monday: Introduction and hands-on analysis: Download plink and documentation. Erin Smith's plink tutorial: [doc] [pdf].

Wednesday: Download R and Haploview
Browse An Introduction to R for basic syntax and capabilities if you are not yet familiar with this language.

Friday: Papers
The Wellcome Trust Case Control Consortium (2007) Nature 447: 661-678.
Commentary: Donnelly (2008) Nature 456: 728-731.
2
 April 6 - April 10
TTh 1-4
LBR205		 Kang Zhang: Association studies, part B
Considerations for Genome-wide association studies.  Populations and ascertainment issues.  Pooled vs. replicate samples.  Analytical methods.  Corrections and thresholds for large scale of hypotheses.  Sample analysis.
Download presentation: [.ppt]
3
 April 13 - April 17
TTh 1-4
LBR205		 Raffi Aroian: Analysis of mutants and epistasis
How to compare data sets relevant to genetic analyses and medically related experiments.  Statistical analyses in studies of host–pathogen interactions and curative experiments to find therapies for human infectious diseases. 
Assigned readings - theory:
• Cumming et al. (2007) Error bars in experimental biology. J. Cell Biol. 177: 7-11.
• Gaddis and Gaddis (1990) Introduction to Biostatistics
Part 1 Part 2 Part 3 Part 4 Part 5

Assigned papers - application:
• Bischof et al,. 2008: How does mutant xyz statistically compare to wild-type animals or other mutants in their ability to mount an immune defense against various toxins and pathogens using quantitative dose-dependent lethality assays and timed-killing assays.
• Cappello et al., 2006: How do two different treatments (one standard, the other experimental) compare in their ability to cure a hookworm infection in a rodent model for human hookworm infection.  Is the experimental treatment therapeutic relative to no treatment?  Is it as good as the standard treatment?
• Li et al., 2007: How do different genetically engineered lines of plants compare in their ability to be infected by a plant parasite?  Can we genetically engineer a plant to be resistant to plant parasites?  How do different lines statistically compare to each other and to controls?
4
 April 20 - April 24
TTh 1-4
LBR205		 Bruce Hamilton: Linkage studies in experimental crosses
Simple vs. multilocus inheritance.  Design considerations for experimental crosses.  Ethical considerations for vertebrate animals.  LOD scores and p-values.  Analytical approaches and their assumptions.  Permutations and empirical p-values. 
Papers for Tue., April 21:
Lander and Kruglyak (1995) Nature Genetics 11: 241-247.
Churchill and Doerge (1994) Genetics 138: 963-971.
Broman et al. (2003) Bioinformatics 19: 889-890.

Analysis with sample data: R and R/QTL
Browse An Introduction to R for basic syntax and capabilities.
5
 April 27 - May 1
TTh 1-4
LBR205		 Joe Gleeson: Linkage studies in humans subjects
Considerations for linkage in human subjects.  Considerations in collecting human subject material: statistical power, likely availability, informed consent and ethics.  Analytical approaches and selected software.  Walk through analysis of published data.
6
 May 4 - May 8
TTh 1-4
LBR205		 X.-D. Fu and Gene Yeo: Analysis of high-throughput sequencing data, part A
Introduction to high throughput sequencing platforms. Library construction, cluster formation, single vs pair-end sequencing,image analysis and base calling. Data analysis: alignment, Karlin-Altschul statistics, gene annotation, coverage statistics, sampling statistics.
Papers for Tue., May 5:
Mortazavi et al. (2008) Nature Methods 5:621-628.
Core et al. (2008) Science 322:1854-1848.

Papers for Thu., May 7:
Wang et al. (2008) Nature 456: 470-476.
Berninger et al. (2008) Methods 44: 13-21.

Analysis with sample data.
Bowtie: http://bowtie-bio.sourceforge.net/index.shtml
Cluster and TreeView: http://rana.lbl.gov/EisenSoftware.htm or http://bonsai.ims.u-tokyo.ac.jp/
Gene's files: http://www.snl.salk.edu/~lovci/BIOM262
7
 May 11 - May 15
T 1-4
LBR205		 Bruce Hamilton: Detecting selection with haplotype data
Class discussion: Signatures of positive selection. Consideratgions of population structure. Approaches and assumptions.
(BMS Retreat Thursday)
Papers for Tue., May 12:
Sabeti et al. (2002) Nature 419: 832-837.
Sabeti et al. (2007) Nature 449: 913-918.

Sweep software
8
 May 18 - May 22
TTh 1-4
LBR205		 X.-D. Fu and Gene Yeo: Analysis of high-throughput sequencing data, part B
Intorduction: genome organization, types of functional elements. Nucleosome mapping. ChIPseq mapping of histone modifications and DNA-protein interactions. CLIPseq mapping of RNA-protein interactions. Peak-finding statistics, background models (Poisson distribution), identification of enriched functional regions.
Papers for Tue., May 19:
Schones et al. (2008) Cell 132:887-898.
Heintzman et al. (2009) Nature advanced online.

Paper for Thu., May 21:
Yeo et al. (2009) Nature Struc. Mol. Biol, 16:130-137.

Hands-on analysis of high throughput sequencing data led by Gene and Fu
9
 May 25 - May 29
TTh 1-4
LBR205		 Bruce Hamilton: Gene expression, part A
Experimental methods for quantifying gene expression.  Sources of variance in gene expression measurements.  Replication experiments vs. repeated measurements.  Estimates of experimental variance.  Hypothesis tests with gene expression data: paired samples vs. population means, parametric vs. nonparametric tests.
Papers:
Toledo-Arana et al. (2009) Nature, in press
advanced online doi:10.1038/nature08080
Lee et al. (2009) Nature 459: 274-277
Concepcion et al. (2009) PLoS Genetics e1000484.

Data analysis with Excel
Web interfaces for SISA and VassarStats
10
 June 1 - June 5
TTh 1-4
LBR205		 Elizabeth Winzeler: Gene expression, part B
Introduction to genome-wide gene expression measurements collected using microarrays; the use clustering and gene set enrichment algorithms to evaluate gene expression results; and discussion of motif finding software for discovering nucleic acid sequences that control coordinated gene expression patterns.
Papers and software:
Eisen et al. (1998) Proc Natl Acad Sci, 95: 14863-14868.
Cluster Ananlysis with Cluster and TreeView

Subramanian et al. (2005) Proc Natl Acad Sci, 102: 15545-15550.
Analysis for Gene Set Enrichment with GSEA

Additional reading:
1. Hughes et al. (2000) Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J Mol Biol 296: 1205-1214.
2. Tompa et al. (2005) Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 23: 137-144.
3. Roth et al. (1998) Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation.  Nat Biotechnol 16: 939-945.
4. Cho et al. (1998) A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell 2: 65-73.
Some books suggested for additional background and reference:

The Lady Tasting Tea: How statisitics revolutionized science in the twentieth century. David Salsburg, 2001. Henry Holt & Co., New York, NY.

Principles of Biostatistics. Marcello Pagano and Kimberlee Gauvreau, 2000. Duxbury, Pacific Grove, CA.

The Cambridge Dictionary of Statistics in the Medical Sciences. B.S. Everitt, 1995. Cambridge University Press, New York, NY.

Statistics: An introduction using R. Michael J. Crawley, 2005. John Wiley & Sons, West Sussex, England.

Biostatistical Analysis. Jerrold H. Zar, 4th Edition 1998 (5th Edition 2009). Prentice Hall.