Case-control meta-analysis of blood DNA methylation and autism spectrum disorder

Shan V. Andrews, Brooke Sheppard, Gayle C. Windham, Laura A. Schieve, Diana E. Schendel, Lisa A. Croen, Pankaj Chopra, Reid S. Alisch, Craig J. Newschaffer, Stephen T. Warren, Andrew P Feinberg, Daniele Daniele Fallin, Christine Marie Ladd-Acosta

Research output: Contribution to journalArticle

Abstract

Background: Several reports have suggested a role for epigenetic mechanisms in ASD etiology. Epigenome-wide association studies (EWAS) in autism spectrum disorder (ASD) may shed light on particular biological mechanisms. However, studies of ASD cases versus controls have been limited by post-mortem timing and severely small sample sizes. Reports from in-life sampling of blood or saliva have also been very limited in sample size and/or genomic coverage. We present the largest case-control EWAS for ASD to date, combining data from population-based case-control and case-sibling pair studies. Methods: DNA from 968 blood samples from children in the Study to Explore Early Development (SEED 1) was used to generate epigenome-wide array DNA methylation (DNAm) data at 485,512 CpG sites for 453 cases and 515 controls, using the Illumina 450K Beadchip. The Simons Simplex Collection (SSC) provided 450K array DNAm data on an additional 343 cases and their unaffected siblings. We performed EWAS meta-analysis across results from the two data sets, with adjustment for sex and surrogate variables that reflect major sources of biological variation and technical confounding such as cell type, batch, and ancestry. We compared top EWAS results to those from a previous brain-based analysis. We also tested for enrichment of ASD EWAS CpGs for being targets of meQTL associations using available SNP genotype data in the SEED sample. Findings: In this meta-analysis of blood-based DNA from 796 cases and 858 controls, no single CpG met a Bonferroni discovery threshold of p < 1.12 × 10- 7. Seven CpGs showed differences at p < 1 × 10- 5 and 48 at 1 × 10- 4. Of the top 7, 5 showed brain-based ASD associations as well, often with larger effect sizes, and the top 48 overall showed modest concordance (r = 0.31) in direction of effect with cerebellum samples. Finally, we observed suggestive evidence for enrichment of CpG sites controlled by SNPs (meQTL targets) among the EWAS CpG hits, which was consistent across EWAS and meQTL discovery p value thresholds. Conclusions: No single CpG site showed a large enough DNAm difference between cases and controls to achieve epigenome-wide significance in this sample size. However, our results suggest the potential to observe disease associations from blood-based samples. Among the seven sites achieving suggestive statistical significance, we observed consistent, and stronger, effects at the same sites among brain samples. Discovery-oriented EWAS for ASD using blood samples will likely need even larger samples and unified genetic data to further understand DNAm differences in ASD.

Original languageEnglish (US)
Article number40
JournalMolecular Autism
Volume9
Issue number1
DOIs
StatePublished - Jun 28 2018

Fingerprint

DNA Methylation
Meta-Analysis
Sample Size
Single Nucleotide Polymorphism
Siblings
Brain
Autism Spectrum Disorder
Hematologic Diseases
DNA
Saliva
Epigenomics
Cerebellum
Genotype
Population

Keywords

  • Autism spectrum disorders
  • DNA methylation
  • Epigenome
  • Peripheral blood
  • Simons Simplex Collection
  • Study to Explore Early Development

ASJC Scopus subject areas

  • Molecular Biology
  • Developmental Neuroscience
  • Developmental Biology
  • Psychiatry and Mental health

Cite this

Andrews, S. V., Sheppard, B., Windham, G. C., Schieve, L. A., Schendel, D. E., Croen, L. A., ... Ladd-Acosta, C. M. (2018). Case-control meta-analysis of blood DNA methylation and autism spectrum disorder. Molecular Autism, 9(1), [40]. https://doi.org/10.1186/s13229-018-0224-6

Case-control meta-analysis of blood DNA methylation and autism spectrum disorder. / Andrews, Shan V.; Sheppard, Brooke; Windham, Gayle C.; Schieve, Laura A.; Schendel, Diana E.; Croen, Lisa A.; Chopra, Pankaj; Alisch, Reid S.; Newschaffer, Craig J.; Warren, Stephen T.; Feinberg, Andrew P; Fallin, Daniele Daniele; Ladd-Acosta, Christine Marie.

In: Molecular Autism, Vol. 9, No. 1, 40, 28.06.2018.

Research output: Contribution to journalArticle

Andrews, SV, Sheppard, B, Windham, GC, Schieve, LA, Schendel, DE, Croen, LA, Chopra, P, Alisch, RS, Newschaffer, CJ, Warren, ST, Feinberg, AP, Fallin, DD & Ladd-Acosta, CM 2018, 'Case-control meta-analysis of blood DNA methylation and autism spectrum disorder', Molecular Autism, vol. 9, no. 1, 40. https://doi.org/10.1186/s13229-018-0224-6
Andrews SV, Sheppard B, Windham GC, Schieve LA, Schendel DE, Croen LA et al. Case-control meta-analysis of blood DNA methylation and autism spectrum disorder. Molecular Autism. 2018 Jun 28;9(1). 40. https://doi.org/10.1186/s13229-018-0224-6
Andrews, Shan V. ; Sheppard, Brooke ; Windham, Gayle C. ; Schieve, Laura A. ; Schendel, Diana E. ; Croen, Lisa A. ; Chopra, Pankaj ; Alisch, Reid S. ; Newschaffer, Craig J. ; Warren, Stephen T. ; Feinberg, Andrew P ; Fallin, Daniele Daniele ; Ladd-Acosta, Christine Marie. / Case-control meta-analysis of blood DNA methylation and autism spectrum disorder. In: Molecular Autism. 2018 ; Vol. 9, No. 1.
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AU - Windham, Gayle C.

AU - Schieve, Laura A.

AU - Schendel, Diana E.

AU - Croen, Lisa A.

AU - Chopra, Pankaj

AU - Alisch, Reid S.

AU - Newschaffer, Craig J.

AU - Warren, Stephen T.

AU - Feinberg, Andrew P

AU - Fallin, Daniele Daniele

AU - Ladd-Acosta, Christine Marie

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N2 - Background: Several reports have suggested a role for epigenetic mechanisms in ASD etiology. Epigenome-wide association studies (EWAS) in autism spectrum disorder (ASD) may shed light on particular biological mechanisms. However, studies of ASD cases versus controls have been limited by post-mortem timing and severely small sample sizes. Reports from in-life sampling of blood or saliva have also been very limited in sample size and/or genomic coverage. We present the largest case-control EWAS for ASD to date, combining data from population-based case-control and case-sibling pair studies. Methods: DNA from 968 blood samples from children in the Study to Explore Early Development (SEED 1) was used to generate epigenome-wide array DNA methylation (DNAm) data at 485,512 CpG sites for 453 cases and 515 controls, using the Illumina 450K Beadchip. The Simons Simplex Collection (SSC) provided 450K array DNAm data on an additional 343 cases and their unaffected siblings. We performed EWAS meta-analysis across results from the two data sets, with adjustment for sex and surrogate variables that reflect major sources of biological variation and technical confounding such as cell type, batch, and ancestry. We compared top EWAS results to those from a previous brain-based analysis. We also tested for enrichment of ASD EWAS CpGs for being targets of meQTL associations using available SNP genotype data in the SEED sample. Findings: In this meta-analysis of blood-based DNA from 796 cases and 858 controls, no single CpG met a Bonferroni discovery threshold of p < 1.12 × 10- 7. Seven CpGs showed differences at p < 1 × 10- 5 and 48 at 1 × 10- 4. Of the top 7, 5 showed brain-based ASD associations as well, often with larger effect sizes, and the top 48 overall showed modest concordance (r = 0.31) in direction of effect with cerebellum samples. Finally, we observed suggestive evidence for enrichment of CpG sites controlled by SNPs (meQTL targets) among the EWAS CpG hits, which was consistent across EWAS and meQTL discovery p value thresholds. Conclusions: No single CpG site showed a large enough DNAm difference between cases and controls to achieve epigenome-wide significance in this sample size. However, our results suggest the potential to observe disease associations from blood-based samples. Among the seven sites achieving suggestive statistical significance, we observed consistent, and stronger, effects at the same sites among brain samples. Discovery-oriented EWAS for ASD using blood samples will likely need even larger samples and unified genetic data to further understand DNAm differences in ASD.

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