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Validation and Replication
Overview
Definitions of validation and replication
Difficulties and limitations
Working examples from our group and others
Why?
False positive results still occur…. even after stringent QC, data pre-processing, complex analyses and alpha adjustments
The best ways of ensuring an observation is in fact real and meaningful is to:
• validate and replicate the findings
• perform longitudinal and functional studies to determine the true causal/biological effects
Validation vs. Replication
Validation
Verify that the methylation data generated are accurate and the results are reliable
Ideally, by repeating the experiment in the same samples but using different laboratory techniques
Several factors could result in erroneous data. For instance: • systematic errors associated with the laboratory methods
• experimental design issues (e.g. cases and controls on separate plates)
• handling errors (e.g. sample mix-ups)
Validation enables you to ensure the findings are due to true biological variation and not some unknown experimental artefact
Replication vs. Validation
Replication
Reproduce the findings in a independent dataset, i.e. different samples
Replication enables: • verification of the findings in a different dataset
• the findings to be generalised to the wider population
• a more precise estimate of the findings to be measured
• further exploration
The ideal scenario
Perform both
Validation proves the results are reliable but not necessarily generalisable to the wider population
Replication, if successful, proves the results are generalisable
But, if unsuccessful, you will not know why
• technical error in the first and/or second stage
• lack of power in the second stage
• subtle sample/phenotypic differences
• quite simply, a false positive finding due to chance in the first stage
In reality
Its not always possible to do both
• Epigenetic techniques are expensive
• Sites of interest may not be feasible on certain platforms
• Limited access to tissue samples
• Limited access to similar phenotypic cohorts
• Application of different study designs e.g. parent-offspring pairs, monozygotic twins, longitudinal studies may not be possible
Any attempt at validation and/or replication is better than nothing
Summary so far
Validation: Verify that the methylation data generated are accurate and the results are reliable
• same samples, different method
Replication: Reproduce the findings in an independent dataset
• different samples
Validation and replication are not the same thing, but both are valuable tools
Examples from our group
We have utilised a number of different processes:
Repeat the experiment in the same samples using a different methodology
Repeat the experiment in the same samples using a different source of tissue but the same technique
Include extra samples to increase robustness
Assess different measures
(e.g. expression, methylation, SNP genotypes)
Independent replication i.e. different samples but same experimental method and study design
LHON is a common mitochondrial disorder characterised by loss of central vision Hypothesis: Oxidative stress arising from mitochondrial dysfunction alters DNA methylation of the nuclear genome with consequences for the regulation of gene expression We measured DNA methylation of the nuclear genome using 27k array to identify differences between those with LHON phenotype and unaffected carriers
• Samples from four pedigrees from the North East of England.
Example 1. Leber’s Hereditary Optic Neuropathy (LHON)
Identify methylation differences associated with Leber’s hereditary optic neuropathy
Identify methylation differences associated with Leber’s hereditary optic neuropathy
UK family pedigrees with Leber’s hereditary optic neuropathy
Hannah Elliott, ongoing
Discovery
27k chip Identify differentially
methylated CpG sites (n=28)
Blood samples
2 CpG sites selected to take forward (p<0.05)
Bisulphite modification &
Pyrosequencing of 2 candidates (n=28)
Methylation levels strongly correlated (rho >0.6)
between techniques and trends in association for
both genes (p<0.1)
Validation
Blood samples
Bisulphite modification &
Pyrosequencing of 2 candidates (n=49)
With an additional 19 samples mainly from the
same families, one candidate remained
associated (p=0.006) the other did not (p>0.1)
Validation/Replication
Blood samples
Replication
Independent cohort
French family pedigrees
Bisulphite modification & Pyrosequencing
microarray expression analysis to identify genes with differential expression in preterm-born children defined as slow or rapid growers.
• Identify potential candidates for methylation analysis
Example 2. Postnatal growth and DNA methylation are associated with differential gene expression of TACSTD2 and childhood fat mass
Postnatal growth and DNA methylation are associated with differential gene expression of TACSTD2 and childhood fat mass
Postnatal growth and DNA methylation are associated with differential gene expression of TACSTD2 and childhood fat mass
CHILDREN BORN PRETERM: Newcastle Preterm birth cohort
Alix Groom et al, Diabetes 2012
Blood samples 11yrs
expression microarray slow vs rapid postnatal
growth (n=20)
RNA
Validation of top hit using Real time PCR
Analysis of relationship between methylation, expression and phenotype at age 11y
Bisulphite modification
DNA
Pyrosequencing analysis of candidate gene (n=94)
Saliva samples 11yrs
DNA
Bisulphite modification
Pyrosequencing analysis of candidate gene (n=68)
Postnatal growth and DNA methylation are associated with differential gene expression of TACSTD2 and childhood fat mass
CHILDREN BORN TERM: ALSPAC
Alix Groom et al, Diabetes 2012
Analysis of relationship between methylation and phenotype at age 9 and 15 years
Blood samples 7yrs
DNA
Bisulphite modification
Pyrosequencing analysis of candidate gene (n=178)
Cord blood samples
Bisulphite modification
DNA
Pyrosequencing analysis of candidate gene (n=173)
177 individuals from the population-based epidemiological ESTHER study: current smokers, former smokers, and those who had never smoked Illumina HumanMethylation 27K BeadChip
Smoking and methylation
Example 3 (not from our group)
Smoking and Methylation 177 individuals from ESTHER
study
Discovery
27k Chip Identify differentially
methylated CpG sites
Blood samples
1 CpG site selected to take
forward
Bisulphite modification & Sequenom EpiTYPER
analysis of discovery samples
Spearman correlation between methods: (rho =0.82) Smokers still hypomethylated at CpG site (Psmoking = 1.07x10-28)
Validation
Blood samples
Bisulphite modification & Sequenom EpiTYPER
analysis of 328 non-
overlapping subjects
Pronounced association with smoking remained
Replication
Blood samples
Looked at methylation in
surrounding regions using
Sequenom EpiTYPER
79 samples from the discovery study
Further discovery
Only CpG sites immediately next to
the main hit were associated with
smoking (41bp away)
…They then went on to test the same methylation site in a different cohort (Better replication?)
• Sequenom EpiTYPER analysis • This time looking at whether F2RL3 methylation was related to a clinical outcome
1206 individuals from the KAROLA prospective cohort study
• Experienced acute coronary syndrome, myocardial infarction or coronary intervention • Active follow up over 8 years
Smoking and Methylation
Methylation at F2RL3 associated with mortality in patients in this cohort ! The methylation data (CpG_4) reported in the main body of the paper IS NOT the same CpG site described in the original paper. This CpG is “CpG_2” – see supplementary data for results The strongest signal from the first round wasn’t the strongest association when linked to clinical outcome in a second cohort
Smoking and Methylation
Conclusions
Validation and replication are different
Ideally, attempt to do both
Plan for further functional work or analysis to identify true causal/biological effects
If you can….
Do it!
References
Breitling LP et al., Eur Heart J. 2012 Apr 17: Smoking, F2RL3 methylation, and prognosis in stable coronary heart disease Breitling LP et al., Am J Hum Genet. 2011 Apr 8;88(4):450-7. Epub 2011 Mar 31: Tobacco-smoking-related differential DNA methylation: 27K discovery and replication Groom A et al., Diabetes 2012 Feb;61(2):391-400. Epub 2011 Dec 21: Postnatal growth and DNA methylation are associated with differential gene expression of the TACSTD2 gene and childhood fat mass Hirschhorn JN and Daly MJ. Nat Rev Genet. 2005 Feb;6(2):95-108: Genome-wide association studies for common diseases and complex traits Rakyan VK et al., Nat Rev Genet. 2011 Jul 12;12(8):529-41. doi: 10.1038/nrg3000: Epigenome-wide association studies for common human diseases
Validation and Replication