Factors to Consider in Selecting a Genotyping Platform

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Factors to Consider in Selecting a Genotyping
Platform

• Elizabeth Pugh
• June 22, 2007

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GWA Studies

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

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What I can cover

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

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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

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A good SNP
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Same SNP different view
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Same SNP different view
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Another Good SNP
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And Another Good SNP
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Data Quality

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

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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

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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

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Bad SNP caught by qc filter
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Bad SNP caught by qc filter
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Bad SNP caught by qc filter
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Bad SNP caught by qc filter
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Bad SNP caught by qc filter
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Bad SNP caught by qc filter
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Bad SNP caught by qc filter
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Bad SNP caught by qc filter
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Bad SNP caught by qc filter
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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

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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)

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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

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Sample plotall SNPs for one sample sample call
rate 99.8
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Sample plot Failed samplelow intensity Call
freq 41
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Failed samples tend to fall outside of clusters
for many SNPs
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Failed samples tend to fall outside of clusters
for many SNPs
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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

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WGA clustering with other samples
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WGA lower intensityCall freq 98
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WGA failurecall rate 93
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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)

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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

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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

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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

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1 genotype not called
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1 wrong genotype
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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

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Genome viewer
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Female Chr X
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Male chrX
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Known Frequent CNV chr 10
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Known Frequent CNV chr 10
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Choosing a Platform and ProductFactors to
Consider

• Your study
• Population
• Study design
• Sample types
• Combining data with other studies
• Interest in CNVs

• 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 CNVs

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Comparing PlatformsMake 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

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Comparing PlatformsMake 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

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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

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New Stuff!
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New GWA ArraysAffymetrix 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

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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

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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

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Effect Size of 1.2 !!!!

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

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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

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www.cidr.jhmi.edu
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The end
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