A fully Bayesian approach for the analysis of Whole-Genome Bisulfite Sequencing Data

Transcription

1 A fully Bayesian approach for the analysis of Whole-Genome Bisulfite Sequencing Data Leonardo Bottolo 1,2,3 1 Department of Medical Genetics, University of Cambridge, UK 2 The Alan Turing Institute, London, UK, 3 MRC Biostatistics Unit, University of Cambridge, UK Big Data and Medicine: Tools, Transformation and Translation - Tuesday 4th July 2017

2 A fully Bayesian approach for the analysis of Whole-Genome Bisulfite Sequencing Data Leonardo Bottolo 1,2,3 1 Department of Medical Genetics, University of Cambridge, UK 2 The Alan Turing Institute, London, UK, 3 MRC Biostatistics Unit, University of Cambridge, UK Big Data and Medicine: Tools, Transformation and Translation - Tuesday 4th July 2017

3 Collaborators Petros Dellaportas Enrico Petretto Owen Rackham

4 Epigenomics Genome-wide investigation of heritable phenotypes resulting from changes in a chromosome without alterations in the. DNA methylation is the most well-studied epigenetic mark.

5 Technologies overview Class Technique Assays CpG coverage Resolution Measurement HM450 2% single-base BV >90 % of Array BS HM450 EPIC CpGs + single-base BV 413,743 CpGs BS WGBS Oxidative >90% single-base PM OXBS-Seq BS-Seq BS Enzyme digestion + BS RRBS 10 20% single-base PM Enrichment MeDIP- Affinity 60 90% bp #reads per bin Seq enrichment MRE-Seq 1 6% single-base #reads per site

6 Technologies overview Class Technique Assays CpG coverage Resolution Measurement HM450 2% single-base BV >90 % of Array BS HM450 EPIC CpGs + single-base BV 413,743 CpGs BS WGBS Oxidative >90% single-base PM OXBS-Seq BS-Seq BS Enzyme digestion + BS RRBS 10 20% single-base PM Enrichment MeDIP- Affinity 60 90% bp #reads per bin Seq enrichment MRE-Seq 1 6% single-base #reads per site

7 Goal Detecting Differentially Methylated CpG sites (DMCs) or Regions (DMRs) is essential in comparative study (case/control) of diverse DNA methylation profiles.

8 Approximate Bayesian Bisulfite Analysis overview

9 ABBA overview

10 Replicates ABBA overview Cases Controls d

11 Replicates ABBA overview Cases Controls Average methylation level d

12 Replicates ABBA overview Cases Controls Average methylation level d Unobserved methylation profile

13 Replicates ABBA overview Cases Controls Average methylation level d Unobserved methylation profile

14 Replicates ABBA overview Cases Controls Average methylation level d Unobserved methylation profile

15 Replicates ABBA overview Cases Controls Average methylation level d Unobserved methylation profile Latent Gaussian Process

16 Replicates Methylation probability difference ABBA overview Cases Controls Average methylation level d Unobserved methylation profile

17 Replicates Methylation probability difference ABBA overview Cases Controls Differentially Methylated Region Average methylation level d Unobserved methylation profile

18 ABBA overview Cases Controls Differentially Methylated Region

19 Poster Approximate Bayesian Bisulfite Analysis Technical details about granularity of smoothing. FDR control for DMR calling. Extensions to include genetic/non-genetic confounding effects. Extensions to perform methylation Quantitative Trait Loci.

20 References Rackham, O.J.L., Langley, Oates, T., Vradi, E., S.R., Harmston, N., Srivastava, P.K., Behmoaras, J., Dellaportas, P., Bottolo, L. and Petretto, E.G. (2017). A Bayesian approach for analysis of wholegenome bisulfite sequencing data identifies disease-associated changes in DNA methylation. Genetics, 205, Mingas, G., Bottolo, L. and Bouganis, C.-S. (2017). Algorithms and architectures for large scale inference in state-space models. Int. J. Approx. Reason., 83, Owen, R., Dellaportas, P., Petretto, E., Bottolo, L. (2015). WGBS- Suite: a simulator for DNA methylation data and benchmarking of differential-methylation tools. Bioinformatics, 31,

21 Thank you!