validation and replication - · validation and replication are not the same thing, but both are...

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


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




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



Cord blood samples


DNA


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

validation and replication - · validation and replication are not the same thing, but both are...

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