Download Background Autism spectrum disorder (ASD) is a

Calcium signaling and molecular adhesion processes may hold the key to genetic risk for Autism. A molecular pathway analysis on two independent samples
Antonio Drago(1), Marco Calabro'(2), Concetta Crisafulli(2), Uffe Birk Jensen(3)
P.1.a.017
1. Department of Clinical Medicine, Aarhus University ­ Psykiatrisk Forskningsenhed Vest, Herning, Denmark.
2. Department of Biomedical Science and Morphological and Functional Images, University of Messina, Via Consolare Valeria, 98125 Messina, Italy.
3. Department for Clinical Genetics, Aarhus University Hospital, Aarhus, Denmark
Background
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that
appears early in life and is characterized by limited interest and lacking
ability in social interactions, repetitive behavior and dysfunction in social
communication. Motor and intellectual deficits, together with mood and
sleep disorder and sensory and gastrointestinal abnormalities are also
common in ASD [1]. ASD affects up to 1% of the general population [2]
and has a genetic counterpart, resulting in an estimated concordance rate as
high as 60-70% in identical twins and 5 – 30% in siblings [3,4]. The
definition of the genetic basis of ASD is still to be achieved, de novo
mutations may explain a part of the missing heritability, as it was
consistently shown that they may play a role in ASD [15–18].
Nevertheless, the impact of de novo mutations is not as relevant as the one
brought by inheritance: it was estimated that 49% of the
genetic architecture of ASD is related to common inherited
variants, 3% by de novo mutations and 3% by rare inherited
variants [19]. Combined international GWAS databases
provide an unprecedented opportunity to describe the common
inherited variants that are associated with ASD.
Hypothesis under analysis
Enrichment in common genetic variations clusters in
specific molecular pathways and such clustering
can be replicated in independent ASD samples.
Methods
Genetic data were available from the NIMH. The Autism Dataset 4 sample
was chosen for the investigation sample. The Autism Dataset 3 served as a
replication sample. Plink [28] served for the TDI GWAS analysis and
genetic annotations. R [29] and dedicated packages
served for the permutations analysis and QQ-plot
creation. Haploview [30] served for the
identification of the known available SNPs
for each gene. The Gene Ontology Consortium
was interrogated to detect enriched GO annotations.
Investigation sample. TDI associations were
run and results annotated. Quality of results
was checked. Variations associated with
Autism at P<10E-4 were selected and
genes identified (genelist1). Enriched GO terms
were searched in genelist1 and genes within the enriched
GO terms were selected (genelist2). SNPs
belonging to genetlis2 were annotated. A simulation
test was run in R to avoid false positive finding.
Replication sample. SNPs were derived from genetlist2 and
the incidence of observed vs expected significant
associations was checked.
Authors declare no conflict of interest
References
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Results
The central nervous system development pathway (GO:0007417) was
enriched in both samples (table 1).The SNPs belonging to the index
pathway in the investigation sample (n=1124) were identified in the
replication sample. 519 SNPs were detected, that were belonging to the
index pathway from the investigation sample, were represented in the
replication sample and had the same direction of association with
Autism as in the investigation sample. The prevalence of SNPs
associated with Autism in the replication sample was 5.4% at a p level
of 0.05 and 1.9% at a p level of 0.01 (Figure 1). After the permutation
analysis, the prevalence of SNPs associated with Autism was confirmed
at a p level of <0.01, and not confirmed at a p level of <0.05.
Conclusion
Genes belonging to the GO:0007417 may hold the genetic variations
that drive the risk to ASD. In particular, genes that code for proteins
related to the adhesion processes may be associated with ASD. Further
analyses are necessary to confirm those results.
Geschwind, D. H. Advances in autism. Annu. Rev. Med. 60, 367–380 (2009).
Lai, M.-C., Lombardo, M. V. & Baron-Cohen, S. Autism. Lancet Lond. Engl. 383, 896–910 (2014).
Ozonoff, S. et al. Recurrence risk for autism spectrum disorders: a Baby Siblings Research Consortium study. Pediatrics 128, e488-495 (2011).
Bailey, A. et al. Autism as a strongly genetic disorder: evidence from a British twin study. Psychol. Med. 25, 63–77 (1995).
Levy, D. et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70, 886–897 (2011).
Marshall, C. R. et al. Structural variation of chromosomes in autism spectrum disorder. Am. J. Hum. Genet. 82, 477–488 (2008).
Sebat, J. et al. Strong association of de novo copy number mutations with autism. Science 316, 445–449 (2007).
Utami, K. H. The implications of de novo coding mutations in simplex autism families. Clin. Genet. 87, 428–429 (2015).
Gaugler, T. et al. Most genetic risk for autism resides with common variation. Nat. Genet. 46, 881–885 (2014).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
R Foundation for Statistical Computing. R: A language and environment for statistical computing. (2013).
Barrett, J. C., Fry, B., Maller, J. & Daly, M. J. Haploview: analysis and visualization of LD and haplotype maps. Bioinforma. Oxf. Engl. 21, 263–265 (2005).