# Presentation: 242689

## Factors to Consider in Selecting a Genotyping Platform

**Elizabeth Pugh**
**June 22, 2007**

## GWA Studies

**Genotype 300,000 to 1,000,000 SNPs**
**3 platforms, multiple products**
**Affymetrix **
**Illumina **
**Perlegen**
**How to choose?**

## What I can cover

**Basics of calling genotypes**
**Examples of good and bad data**
**Some things to consider**

## Basics of how it works

**Skipping chemistry...**
**Generate intensity data for 2 alleles**
**Assign genotypes based on clustering**
**These are ‘phenotypes’ – there is measurement error**
**No manual review of data – too many SNPs **

## A good SNP

## Same SNP different view

## Same SNP different view

## Another Good SNP

## And Another Good SNP

## Data Quality

**Most of the data is good for all platforms**
**Some samples, SNPs and genotypes fail**
**Have to find them without manual review**

## Ways to find bad data

**Use summary statistics across SNPs, samples**
**Include investigator and control replicates**
**Include control and where possible investigator trios**
**If use Hapmap controls can compare with caution to Hapmap genotypes – there are some errors in Hapmap data **

## Finding Bad SNPs

**Use qc checks**
**Call rate**
**Mendelian Inheritance**
**Replicates**
**HWE **
**Quality score, clustering**
**Note some bad SNPs will pass any qc filter**
**Some good SNPs may fail qc**

## Bad SNP caught by qc filter

## Bad SNP caught by qc filter

## Bad SNP caught by qc filter

## Bad SNP caught by qc filter

## Bad SNP caught by qc filter

## Bad SNP caught by qc filter

## Bad SNP caught by qc filter

## Bad SNP caught by qc filter

## Bad SNP caught by qc filter

## Yikes! Some of those are awful!

**Yes**
**We can find many, hopefully most of them but...**
**Use the intensity data to plot your most significant SNPs**
**Look at them before you publish**

## Use a lab that will give you intensity data

**If you have intensity data you can**
**Plot the intensities to check clustering**
**Cluster with a different algorithm**
**Recluster as algorithms get better **
**Recluster subsets or supersets of the data**
**Create your own metrics (e.g. number of samples with no or very low intensity)**

## Finding Bad Samples

**Look at sample level metrics starting with call rate**
**Bad samples - even water will have some genotypes**
**May want to remove possibly bad sample before clustering the data then make final sample decisions**

## Sample plotall SNPs for one sample sample call rate 99.8%

## Sample plot – Failed samplelow intensity Call freq 41%

## Failed samples tend to fall outside of clusters for many SNPs

## Failed samples tend to fall outside of clusters for many SNPs

## Can I use WGA samples?

**Whole Genome Amplified DNA performance ranges from awful to very good**
**Even WGA samples that work very well may perform poorly for some SNPs**
**Extra attention needed for clustering decisions and for analysis**
**Make sure lab knows sample type for each sample**

## WGA clustering with other samples

## WGA lower intensityCall freq 98%

## WGA failurecall rate 93%

## Multiple sample types in study

**Look at data by sample type (metrics and plots)**
**If they are not performing equivalently do lots of extra qc by sample type**
**If have to cluster separately even more qc and checks are needed**
**If sample type is not random may cause more headaches (e.g. different types for cases and controls)**

## Preventing Bad Data

**Discuss sample types with lab  what is their experiece? May want to test some before start project**
**Discuss plating with lab may wish to place controls uniquely or arrange males and females uniquely by plate**

## Preventing Bad Data

**Differences in intensity (batch effects) are not common but possible **
**May only be present for subset of SNPs**
**May want to mix cases and controls across plates to minimize effect of plate effect if it happens**

## Genotypes

**For good SNPs and samples some genotypes will fail**
**May not be called**
**May be called with low confidence or quality score**
**May be called wrong**

## 1 genotype not called

## 1 wrong genotype 

## Copy number

**With Affymetrix and Illumina intensity information can be used to infer copy number**
**Works very well with small numbers of samples and manual review**
**Not really a high throughput system – software not sensitive or specific enough ... Yet**

## Genome viewer

## Female Chr X

## Male chrX

## Known Frequent CNV chr 10

## Known Frequent CNV chr 10

## Choosing a Platform and ProductFactors to Consider

**Your study**
**Population**
**Study design**
**Sample types**
**Combining data with other studies**
**Interest in CNV’s**

**Product**
**Coverage of the genome **
**How many SNPs**
**Which SNPs (tagging, in or near genes)**
**Quality of data**
**Performance on your sample types**
**Information on CNV’s**

## Comparing PlatformsMake sure the numbers are comparable!

**QC rates reported – denominators can differ**
**Mendel errors per trio or per sample**
**Replicate errors per pair or per sample**

## Comparing PlatformsMake sure the numbers are comparable!

**SNPs on the chip are correlated with many others – often very strong correlation**
**There are multiple **
**measures of the strength of the correlation**
**Lists of SNPs to use as proxy for ‘Genome’**

## Cost?

**Hard to say**
**Changing rapidly **
**Generally increase with the numbers of SNPs on a chip**
**May decrease with number of samples in a study**
**Reagents (the chips) are only part of the cost**

## New Stuff!

## New GWA ArraysAffymetrix and Illumina

**~ 1 million SNPs**
**Enhanced copy number content**
**Different strategies**
**Improved coverage in YRI population**
**Illumina 1M – still pre-release**
**Same chemistry, same software, same probe designs, same lab workflow as other Infinium products**
**Affymetrix 6.0 – just released**
**Same chemistry & lab workflow as 5.0**
**Changes in probe design & software**

## More SNPs are better, right?

**Maybe not always**
**Methods that use the genotypes on samples plus Hapmap data to infer ungenotyped SNPs**
**Can use infered genotypes in analysis**
**Can combine data from studies that used different SNPs**
**more samples on fewer genotypes may give more power**
**Need enough genotypes for your population to infer SNPs**

## One or Two Stage Designs

**A year ago everyone was thinking about 2 stage designs**
**GWA scan on part of sample**
**Follow up a subset of significant results in rest of sample**
**Now may cost less to do GWA scan on all samples**

## Effect Size of 1.2 !!!!

**Recent GWA studies have found small effect sizes**
**May need many, many samples to have reasonable power**

## Choosing a platform

**Must balance coverage, QC and cost per sample to design the most powerful study you can**
**Costs, products, clustering, qc and analysis methods are changing rapidly**
**What is best will change**

## www.cidr.jhmi.edu

**www.cidr.jhmi.edu**

## The end