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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Genes Brain Behav. Author manuscript; available in PMC 2010 November 1.
Published in final edited form as:
PMCID: PMC2784255
NIHMSID: NIHMS120933

Examination of Tetrahydrobiopterin Pathway Genes in Autism

Abstract

Autism is a complex disorder with a high degree of heritability and significant phenotypic and genotypic heterogeneity. Although candidate gene studies and genome-wide screens have failed to identify major causal loci associated with autism, numerous studies have proposed association to several variations in genes in the dopaminergic and serotonergic pathways. Because tetrahydrobiopterin (BH4) is the essential cofactor in the synthesis of these two neurotransmitters, we genotyped 25 SNPs in 9 genes of the BH4 pathway in a total of 403 families. Significant nominal association was detected in the gene for 6-pyruvoyl-tetrahydropterin synthase, PTS (chromosome 11), with p=0.009; this result was not restricted to an affected male only subset. Multilocus interaction was detected in the BH4 alone, but not across the serotonin, dopamine, and BH4 pathways.

Keywords: Autism, Tetrahydrobiopterin, SNPs, linkage, association

Introduction

Autism, a complex and heterogeneous neurodevelopmental disorder with a largely unknown etiology, is characterized by impairments in social interaction and communication, along with the presence of repetitive and stereotypic behaviors or interests. Autism is one of several disorders that falls within the diagnostic spectrum of Pervasive Developmental Disorders (American psychiatric Association 2000), which vary according to severity and the presence of specific features (e.g., Aspergers Disorder and Pervasive Developmental Disorder-Not Otherwise Specified). Considered the prototypic autism spectrum disorder, the prevalence of autism is estimated at 0.1–0.3%; prevalence estimates for autism spectrum disorders as a whole range from 0.3–0.6% (Fombonne, E. 2003). In the last decade, the reported prevalence of autism has increased by 3–14 fold, ostensibly because of changes in autism awareness, case definition, and broadening diagnostic criteria (Fombonne, E. 2005).

The genetic component in autism is undeniable. Twin studies show a concordance of 60% among monozygotic (MZ) twins and 0% among dizygotic (DZ) pairs for autism, which increases to 92% for MZ and 10% for DZ pairs when the broader phenotype of related social and language abnormalities are included (Folstein, S. et al, 1977; Rutter, M. et al, 1990b; Rutter, M. et al, 1990a). While this evidence confirms heritability in autism, linkage genomic screens and genome wide association studies have until now only found suggestive regions linked to autism (Arking, D.E. et al, 2008; Auranen, M. et al, 2002; Barrett, S. et al, 1999; Buxbaum, J.D. et al, 2001; Cantor, R.M. et al, 2005; International Molecular Genetic Study Autism Consortium (IMGSAC)2001; Liu, J. et al, 2001; Philippe, A. et al, 1999; Risch, N. et al, 1999; Shao, Y. et al, 2002; Szatmari, P. et al, 2007) and one replicated association to a common polymorphism of small effect (Ma, D. et al, 2009; Wang, K. et al, 2009). A specific, common functional risk locus has yet to be identified.

As a complement to such genome-wide linkage studies and genome wide association studies, candidate gene studies can be performed either to follow-up on the gene(s) in the linkage/association region or to examine genes with biological relevance to autism. While candidate gene studies have found numerous positive associations, none have been widely replicated. Various possible causes underlie this failure to replicate, such as the substantial heterogeneity in the disease phenotype and the likelihood that several susceptibility loci may interact together leading to an increased risk of autism (Veenstra-Vanderweele, J. et al, 2004).

Because these studies have implicated the serotonin and dopamine pathways in autism, they have been investigated extensively (Anderson, B.M. et al, 2008; Anderson, B.M. et al, 2009). Such studies have shown that deficiencies in tetrahydrobiopterin (BH4), an essential cofactor for dopamine and serotonin biosynthesis in the central nervous system (figure 1), can result in severe neurological disorders. These disorders are characterized by monoamine-neurotransmitter deficiency caused by mutations in the genes that encode the enzymes responsible for BH4 biosynthesis and regeneration (Bonafe, L. et al, 2001). GCH1 (GTP cyclohydrolase I), for example, has been implicated in Segawa’s disease, also known as DOPA-responsive dystonia (Ichinose, H. et al, 2008). The complexity of this pathway, as well as the number of enzymes involved in the synthesis and recycling of BH4, has been studied exhaustively (Thony, B. et al, 2000).

Figure 1
Biosynthesis and metabolism of BH 4

The role of BH4 as the cofactor and a common factor in these pathways has prompted us to examine the BH4 pathway’s role in autism.

Materials and Methods

Dataset

Our analysis was conducted on a dataset consisting of 403 non-Hispanic Caucasian American families collected in the Southeast United States by the Center for Human Genetics Research at Vanderbilt University and the Miami Institute for Human Genomics at the University of Miami (Table 1). Probands for the study consisted of individuals between the ages of 3 and 21 years who were clinically diagnosed with autism using Diagnostic and Statistical Manual (DSM)-IV criteria. The clinical diagnosis of autism was confirmed based on clinical evaluation using DSM-IV diagnostic criteria supported by the Autism Diagnostic Interview-Revised (ADI-R) and medical records. Exclusion criteria for participation in the larger genetics study included developmental level below 18 months, severe sensory problems (e.g., visual impairment or hearing loss), significant motor impairments (e.g., failure to sit by 12 months or walk by 24 months), or identified metabolic, genetic, or progressive neurological disorders. Parents/caregivers were informed of the purposes, risks, and benefits of participating in this project and provided informed consent.

Table 1
Distribution of Study Dataset

Molecular Analysis

Genomic DNA was extracted from blood using standard protocols and a commercial system (Puregene; Gentra Systems, Minneapolis, MN). All single nucleotide polymorphisms (SNPs) were identified using the Ensembl (www.ensembl.org), dbSNP (www.ncbi.nlm.nih.gov/projects/SNP), and AppliedBiosystems (http://www.appliedbiosystems.com/) databases. SNPs were genotyped using the ABI 7900 Taqman system (Oliveira, S.A. et al, 2003). Genes were selected based on their involvement in the tetrahydrobiopterin pathway, including those that play a critical role in the synthesis and metabolism of tetrahydrbiopterin (Thony, B. et al, 2000). Multiple SNPs (when available) spanning each gene were chosen using a hierarchy of nonsynonymous coding change, minor allele frequency > 0.10, and location within the gene. Laboratory personnel were blinded to pedigree structure, affection status, and location of quality control samples. Duplicate quality control samples were placed both within and across 384-well plates, and equivalent genotypes were required for all quality control samples to ensure accurate genotyping. Hardy-Weinberg calculations were performed for each marker, and Mendelian inconsistencies were identified using PedCheck (O’Connell, J.R. et al, 1998). Suspect genotypes were re-read or retested. All SNPs were required to pass 95% genotyping efficiency to be considered for analysis.

Statistical Analysis

Genotype efficiency, Hardy-Weinberg Equilibrium and linkage disequilibrium were checked using Haploview (Barrett, J.C. et al, 2005). If any SNP fell below 95% genotype efficiency, a SNP in high LD with the failed SNP was added to ensure continued coverage of the gene. Linkage analysis was conducted on the subset of families with multiple affected children using two-point heterogeneity LOD scores (HLOD) calculated using FASTLINK and HOMOG (Ott, J. 1999). Both recessive and dominant models with disease allele frequencies of 0.01 and 0.001, respectively, were analyzed. This approach is robust for detecting linkage signals when the underlying model is unknown or complex (Hodge, S.E. 1994). Association was evaluated using the Pedigree Disequilibrium Test (PDT) (Martin, E.R. et al, 2003). This method provides valid and robust tests for allelic association in trios and extended families. The Genotype-PDT (GenoPDT) tested genotypic association to the risk of autism (Martin, E.R. et al, 2003). Taking into account the 4:1 ratio of males to females affected with autism, the HLOD, PDT, and Geno-PDT were also run in a subset of families containing only affected males (male-only, N=303).

Multifactor Dimensionality Reduction (MDR) analysis was used to detect multilocus interactions (Ritchie, M.D. et al, 2001; Ritchie, M.D. et al, 2007). Since MDR is designed for case-control data, we extracted the affected child from any family with a complete parent-child trio (one random child per family for multiplex families). We constructed “pseudo” controls using the non-transmitted alleles of the parents (Collins, A.L. et al, 2006; Ma, D.Q. et al, 2005). We tested for all two way and three way interactions. All p-values are reported as nominal P values unless otherwise stated.

Results

When we genotyped 25 SNPs in 9 genes of the BH4 pathway (Table 2), we observed a LOD=1.4 for rs2597773 in QDPR (dihydropteridine reductase) on chromosome 4 (Table 1, supplementary material). In the male only subset, a LOD score of 1.5 was observed for this SNP, while a LOD score of 1.7 was seen in this gene for rs2252995 (Table 1, supplementary material). Using the PDT, PTS (6-pyruvoyl-tetrahydropterin synthase, chromosome 11) showed significant association with p=0.009 (p=0.20 when corrected for multiple comparisons) for rs2518352 and p=0.01 for rs3819331. In the male only dataset, we observed similar associations in that same gene.

Table 2
Overall dataset and male only association analysis results

After we performed a case/pseudocontrol association analysis on the most significant gene, PTS, a marginal association was observed for rs2518352 and rs3819331 (Table 2, supplementary material). The MDR analysis detected a significant two-way interaction between rs2518352 (PTS) and rs6730083 (SPR, sepiapterin reductase), for which the predicted accuracy was 60.2% and the empiric p=0.03 (Table 3, supplementary material). We also tested for potential interactions with nominally significant SNPs from our examination of genes in the serotonin and dopamine pathways (Anderson, B.M. et al, 2008; Anderson, B.M. et al, 2009)but no significant interactions were found (Table 3, supplementary material).

We used the Collaborative Autism Project (CAP) Genome Wide Association Study (GWAS) dataset as our replication dataset (Ma, D. et al, 2009), which consists of 487 parent-child trios collected using the same criteria as those in the current study. We selected the SNP genotyping data for our 9 genes including 100 kb on each side of the genes to include regulatory elements (Table 4, supplementary material). We observed a marginal association in QDPR, p=0.05, GCH1 p=0.01 and PAH p=0.01 (Table 5, supplementary material). No association was seen for PTS. The MDR analysis on this dataset did not detect a gene-gene interaction (Table 6, supplementary material).

Discussion

Although the result of a double-blind placebo-controlled crossover study indicated a possible effect of BH4 treatment in children with autistic disorder (Danfors, T. et al, 2005), our previous studies showed only modest association to autism for the genes in the dopamine and serotonin pathways (Anderson, B.M. et al, 2008; Anderson, B.M. et al, 2009). In the dopamine pathway study, however, we found that YWHAB (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, beta polypeptide), the activation protein for tyrosine-3-hydroxylase (TH), has a nominal association to the disorder. TH uses BH4 as a cofactor to metabolize tyrosine into L-DOPA, the precursor of dopamine. The results from this present study were modest, revealing only a marginal association with PTS, one of the genes responsible for BH4’s biosynthesis. However, this association did not withstand the correction for multiple testing and was not replicated in the GWAS dataset.

When the hypothesis that gene-gene interaction might play an important role in the disease was tested using MDR, a modest association was detected between PTS and SPR in a 2-way interaction model in the overall dataset. Given that PTS and SPR are two consecutive genes in the BH4 synthesis (figure 1), the fact that they show epistasis may require further investigation. Using the data from our previous studies of the dopamine and serotonin pathways, we ran MDR on the 3 pathways combined, but no gene-gene interaction was detected.

The underlying assumption that common variations in these genes are responsible for modulating the risk of autism also had an impact on our analysis. Even though we captured a significant number of these variations, our study was not fully comprehensive. Furthermore, Weiss et al. have hypothesized that rare variations (de novo mutation, deletions, duplications, point mutations) present in a large number of genes could account for as much as 90% of idiopathic autism (Weiss, L.A. et al, 2008). If true, this would indicate that our study might not have enough power to overcome the locus heterogeneity to detect rare variations. Given that such alternatives would significantly impact the power of the PDT and MDR analysis, our data nevertheless suggest at least a modest role for one BH4-related gene (PTS) in the risk of developing autism.

Supplementary Material

Table 1

Table 1. Two point dominant and two point recessive linkage (LOD score) analysis results for the overall dataset and the male only dataset:

Table 2

Table 2. Cases/pseudocontrols Allelic and Genotypic analysis results:

Table 3

Table 3. Multifactor Dimensionality Reduction (MDR) analysis for the BH4 pathway only and for the combined serotonin dopamine and BH4 pathways:

Table 4

Table 4. Base pair location for the 9 genes of the tetrahydrobiopterin pathway adding 100kb on each side (Build 36)

Table 5

Table 5. Linkage and Association analysis for the genes in the tetrahydrobiopterin pathway using the Collaborative Autism Project (CAP) GWAS families:

Table 6

Table 6. Multifactor Dimensionality Reduction (MDR) analysis for the BH4 pathway only on the AGP GWAS dataset:

Acknowledgments

We wish to thank both the patients with autism and their family members who agreed to participate in this study, as well as the personnel of the Center for Human Genetics Research at Vanderbilt University and the Miami Institute for Human Genomics at the University of Miami. We would like to thank M.J. Allen for her excellent technical support. This research was supported in part by National Institutes of Health (NIH) program project grant NS026630 (MPV, JLH) and NIH R01 grant MH080647.

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