Integrating Expression Quantitative Brain Loci in ASD GWAS Analyses

Friday, May 13, 2016: 11:30 AM-1:30 PM
Hall A (Baltimore Convention Center)
C. Shu1, C. Ladd-Acosta2, A. Jaffe3, J. L. Daniels4, C. J. Newschaffer5, A. M. Reynolds6, D. E. Schendel7, L. A. Schieve8 and M. D. Fallin9, (1)Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, (2)Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, (3)Lieber Institute for Brain Development, Baltimore, MD, (4)University of North Carolina, Chapel Hill, NC, (5)A.J. Drexel Autism Institute, Philadelphia, PA, (6)University of Colorado - Denver, Aurora, CO, (7)Aarhus University, Aarhus, Denmark, (8)National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, (9)Wendy Klag Center for Autism and Developmental Disabilities, JHBSPH, Baltimore, MD
Background: Autism Spectrum Disorder (ASD) is highly heritable and there is evidence that common genetic variation plays a major role in this variability. However, genome-wide association studies (GWAS) thus far have had limited success. Genetics studies of other psychiatric disorders have shown enrichment for genetic variants that control brain expression, i.e. brain expression quantitative trait loci (eQTLs).  Limiting genome-wide single nucleotide polymorphism (SNP) analyses to subsets known to be brain eQTLs (denoted “eSNPs”), and/or located in genes known to show developmental brain expression patterns, can reduce the search space allowing important association signals to separate from signals simply due to millions of tests performed.

Objectives: To perform genome-scale SNP association analyses for ASD, limited to SNPs known to be brain eSNPs or known to be located in genes expressed in early neural development.  Further, to compare patterns of genome-wide association among SNP subsets defined by expression in specific brain regions.

Methods: Brain eSNPs and their proxy SNPs, based on linkage disequilibrium (LD) in 1000 genomes, were obtained from 6 published brain eQTLs studies, with annotation for 11 different brain tissue types. GWAS was performed on all SNPs, subsets of brain eSNPs, and brain tissue specific eSNPs after LD-based SNP pruning, using SNPs data from the Study to Explore Early Development (SEED) from 584 ASD cases and 725 non-ASD controls drawn from the general population. Similar annotation-based subsetting of Psychiatric Genomics Consortium (PGC) ASD SNP results are planned.  Comparisons were made by examining the patterns of QQ plots.

Results: A total of 288,675 brain eSNPs were obtained after LD pruning, along with brain tissue specific eSNPs in cerebellum(3,027), frontal cortex(1,450), hippocampus(764), inferior olivary nucleus(659), occipital cortex(695) , pons(440), putamen(425), substantia nigra(359), temporal cortex(1,420), thalamus(636), and intralobular white matter(1,034). GWAS based on brain eQTLs revealed SNPs (rs7625872, rs73861956) that separated from expectation in QQ plots, while no separation was observed in the overall GWAS analysis of SEED data. The QQ patterns were also differential by subset in analyses based on eSNPs for specific brain tissues, where only QQ plots of cerebellum, frontal cortex and temporal cortex eSNP subsets showed positive separation from expectation.

Conclusions: The findings reported here are consistent with literature on the key brain regions involved in ASD etiology, namely cerebellum, frontal cortex and temporal cortex. Subsetting GWAS analysis to brain eSNPs can provide further insight on the ASD common variant signals.

See more of: Genetics
See more of: Genetics