# Presentation: 394035

## Handling and analyzing data from a genome-wide association study

- Laura Scott
- Biostatistics Department
- Center for Statistical Genetics
- University of Michigan

## Outline

- Storing large amounts of genotype data
- Quality control
- Generating initial association analysis
- Viewing results
- Imputation of missing SNP genotypes
- Storing results and planning specialized analysis

## Genotype data is huge

-  500,000 SNPs * 2000 cases + controls = 1,000,000,000 genotypes!
- Need compact ways to store data
- If store each genotype as 00, 01, or 11 will have file that looks like
- Person   100011001010001001001100....
-          000000100001000010001000....

## Genotype data is huge

-  500,000 SNPs * 2000 cases + controls = 1,000,000,000 genotypes!
- Need compact ways to store data
- If store each genotype as 00, 01, or 11 will have file that looks like
- SNP       100011001010001001001100....
-          000000100001000010001000....

## Genotype data is huge

-  500,000 SNPs * 2000 cases + controls = 1,000,000,000 genotypes!
- Need compact ways to store data
- If store each genotype as 00, 01, or 11 will have file that looks like
- 		         100011001010001001001100....
-          000000100001000010001000....
- Total file space for 300K SNPs: 4 Gigabytes
- Largest chromosome file: .4 Gigabytes

## Need to do extensive planning for genotype data before it arrives

- Chromosome datasets are too large for SAS and other commonly used analytic packages
- Need programs to select and write out genotype data in multiple formats
- Tests of procedures with large-scale trial datasets

## Gather other data needed for analysis 

- SNP information
- Chromosome
- Position
- SNP annotation
- Gene
- Function
- Translation of called allele to a standard allele
- Example forward strand of given genome build

## How good is the data?

- Identify and remove bad samples and SNPs
- Compute summary statistics
- Percent successfully genotyped samples
- Average genotyping success rate
- Duplicate sample error rate
- Non-Mendelian inheritance error rates (errors not consistent with normal transmission of chromosomes in family members)

## Identify bad samples and remove

- Poor quality samples
- Sample genotype success rate < 95 to 97.5%
- Greater proportion of heterozygous genotypes than expected
- Related individuals (if independent samples)
- Based on pair-wise comparisons of similarity of genotypes
- Sample switches
- Wrong sex
- Regions of homozygosity in cell line

## Identify poor quality SNPs and remove

- Expected proportions of genotypes are not consistent with observed allele frequency (Hardy Weinberg Equilibrium (HWE))
- HWE p-value < 10-4 to 10-6
- Look for deviation from expected distribution of p-values under the null
- Genotyping success rate < 95%
- Duplicate sample or Non-Mendelian error rate  is elevated
- Differential missingness in cases and controls

## Programs are available for large scale quality control analysis

**Plink**
- Duplicate error rates, sample relatedness, HWE,..
- Develop by Shaun Purcell
- http://pngu.mgh.harvard.edu/~purcell/plink/
**GAINQC ** Software used for the quality control analysis of the GAIN project
- Duplicate error rates, sample relatedness, HWE,..
- Developed by Shyam Gopalakrishnan and Goncalo Abecasis
- Available from gopalakr@umich.edu

## Initial analysis is straightforward once have everything in place 

- Case/control association
- Use test that is not affected by deviations from HWE
- Cochran-Armitage test for trend
- Equivalent to score test in logistic regression
- TDT or other family-based test
- Quantitative trait association

## Programs are available for large scale case-control or family-based analysis

**Plink**
- Case/control, tdt, quantitative traits
- Develop by Shaun Purcell
- http://pngu.mgh.harvard.edu/~purcell/plink/
**Merlin**
- Quantitative traits in independent samples or families, ability to impute genotypes for untyped individuals based on genotyped family members
- Developed by Goncalo Abecasis
- http://www.sph.umich.edu/csg/abecasis/Merlin/

## Are the results believable?

- Are stronger associations correlated with poorer quality control measures?
- Is there a strong deviation from expected distribution of p-values?
- Is there confounding from differences in the genetic origins of case and control samples (population stratification)?
- Genomic control
- Eigenstrat analysis

## Seeing from many different angles is believing (sometimes)

- Plink graphical output
- User added custom tracks in the UCSC browser
- http://genome.ucsc.edu/
- http://genome.ucsc.edu/goldenPath/help/hgTracksHelp.html#CustomTracks
- Homemade graphes

##  FUSION T2D association

## Many different ways to display similar data

- Zeggini et al. (2007) Science 316: 13361341;  Diabetes Genetic Initiative (2007)
-  Science  316:1331-1336; Scott et al., (2007) Science 316:1341-1345

## Getting more for your genotyping dollars: Imputation of SNP genotypes

- Impute/predict genotypes for:
- Missing data within genotyped markers
- Untyped markers
- Uses haplotype structure of existing sample such as HapMap samples to infer data for samples with sparser marker set

## Observed genotypes

**Study **
**Sample**

**HapMap**

**Gonçalo Abecasis**

**Notes:**
This imputation method uses the dense genotype data available from the HapMap CEU samples  and the linkage disequilibrium (LD) relationships of the SNPs to impute (predict) genotypes for a large number of SNPs that were not measured experimentally in our Finnish cases and controls. 

## Identify match among reference

**Gonçalo Abecasis**

**Notes:**
We probabilistically searched for mosaics of HapMap CEU chromosomes that matched each FUSION individual. 
That is, searched for similar stretches of flanking haplotype in the HapMap CEU reference sample.

## Phase chromosomes, impute missing genotypes

**Gonçalo Abecasis**

**Notes:**
Used these mosaics to impute the genotypes for autosomal SNPs genotyped in the HapMap CEU data but not genotyped in the Illumina HumanHap300 BeadChip

****
Markov model algorithm is used to model each haplotype, conditional on all others
Gibbs sampler is used to estimate parameters and update haplotypes
Each individual is updated conditional on all others
In parallel to updating haplotypes, estimate “error rates” and “crossover” probabilities

## Imputing genotype data allows much more thorough analysis

- Allows testing of untyped variation
- Allows easy combination of data across genotyping platforms
- Provides complete data for analysis with multiple SNPs

## Imputed data takes care to generate, analyze and understand    

- Requires large scale computing resources
- Need to assess quality of imputation
- Compare imputed gentoypes to actual genotypes
- Error rates are higher than for genotyped SNPs
- Works less well for rarer alleles
- Best to take account of uncertainty imputed SNPs in analysis
- Need ways to take into account fractional genotype counts

## Imputation programs are available

- IMPUTE
- Developed by Jonathan Marchini
- Nature Genetics, Advance online publication
- http://www.stats.ox.ac.uk/~marchini/#software
- Mach 1.0,  Markov Chain Haplotyping
- Developed by Goncalo Abecasis
- http://www.sph.umich.edu/csg/abecasis/MACH/

## Need to store results and prepare for large scale specialized analysis

- System to store, view, merge results
- SQL database
- Plink
- Testing speed of specialized analyses in different statistical packages
- Potential development of software to run large scale specialized analysis

## Summary:  Ideally what needs to happen before getting the data

- Ability to store, select and write out genotype data in multiple formats for quality control and association analysis
- Identification of primary quality control and analysis programs
- Systems to store, view, merge results
- Adequate computing resources to do intensive computing
- Testing of standard and specialized processes with large-scale trial datasets

## FUSION study 

***U Michigan***

**Michael Boehnke**
**Karen Conneely**
**Charles Ding **
**William Duren**
**Terry Gliedt**
**Larry Hu**
**Anne Jackson**
**Xiao-Yi Li**
**Andrew Skol**
**Heather Stringham**
**Peggy White**
**Cristen Willer**
**Fang Xiang**
**Rui Xiao**

***National Public ******Health Institute ******Helsinki***

**Jaakko Tuomilehto**
**Timo Valle**

***USC***

**Richard Bergman**
**Thomas Buchanan**
**Richard Watanabe**

**Randall Pruim**

***Calvin College***

**Kimberly Doheny**
**Elizabeth Pugh**

***CIDR***

**Francis Collins**
**Lori Bonnycastle**
**Peter Chines**
**Michael Erdos**
**Narisu Narisu****L. Prokunina-Olsson**
**Nancy Riebow**
**Andrew Sprau**
**Amy Swift**
**Maurine Tong**

***NHGRI / NIH***

**Karen Mohlke**
**Kyle Gaulton**
**Jason Luo **
**Li Qin**

***UNC-Chapel Hill***

***U Michigan***

**Gon****ç****alo Abecasis**
**Yun Li**
**Jun Ding**
**Paul Scheet**