Genes, Brain and Behavior (2009) 8: 753–757 © 2009 The AuthorsJournal compilation © 2009 Blackwell Publishing Ltd/International Behavioural and Neural Genetics Society

Examination of tetrahydrobiopterin pathway genesin autism

N. C. Schnetz-Boutaud†, B. M. Anderson†, K. D.

Brown†, H. H. Wright‡, R. K. Abramson‡, M. L.

Cuccaro§, J. R. Gilbert§, M. A. Pericak-Vance§

and J. L. Haines∗,†

†Center for Human Genetics Research and Department ofMolecular Physiology and Biophysics, Vanderbilt UniversityMedical Center, Nashville, TN, ‡W.S. Hall Psychiatric Institute,University of South Carolina, Columbia, SC, and §Miami Institutefor Human Genomics, University of Miami, Miller School ofMedicine, Miami, FL, USA∗Corresponding author: J. L. Haines, PhD, Center for HumanGenetics Research, Vanderbilt University Medical Center, 519Light Hall, Nashville, TN 37232-0700, USA.E-mail: jonathan@chgr.mc.vanderbilt.edu

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 with 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 nine 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 pathway

alone, but not across the serotonin, dopamine and BH4

pathways.

Keywords: Association, autism, linkage, SNPs, tetrahydro-biopterin

Received 11 February 2009, revised 19 June 2009, acceptedfor publication 5 July 2009

Autism, a complex and heterogeneous neurodevelopmentaldisorder with a largely unknown etiology, is characterized byimpairments in social interaction and communication, alongwith the presence of repetitive and stereotypic behaviors orinterests. Autism is one of the several disorders that fallswithin the diagnostic spectrum of pervasive developmen-tal disorders (American Psychiatric Association 2000), whichvary according to severity and the presence of specific fea-tures (e.g. Aspergers disorder and pervasive developmental

disorder-not otherwise specified). Considered the proto-typic autism spectrum disorder, the prevalence of autismis estimated at 0.1–0.3%; prevalence estimates for autismspectrum disorders as a whole range from 0.3% to 0.6%(Fombonne 2003). In the last decade, the reported preva-lence of autism has increased by 3- to 14-fold, ostensiblybecause of changes in autism awareness, case definitionand broadening diagnostic criteria (Fombonne 2005).

The genetic component in autism is undeniable. Twinstudies 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 pairswhen the broader phenotype of related social and languageabnormalities are included (Folstein & Rutter 1977; Rutteret al. 1990a,b). While this evidence confirms heritability inautism, linkage genomic screens and genome-wide associ-ation studies have until now only found suggestive regionslinked to autism (Arking et al. 2008; Auranen et al. 2002;Barrett et al. 1999; Buxbaum et al. 2001; Cantor et al. 2005;International Molecular Genetic Study Autism Consortium(IMGSAC) 2001; Liu et al. 2001; Philippe et al. 1999; Rischet al. 1999; Shao et al. 2002; Szatmari et al. 2007) and onereplicated association with a common polymorphism of smalleffect (Ma et al. 2009; Wang et al. 2009). A specific, commonfunctional risk locus has yet to be identified.

As a complement to such genome-wide linkage studiesand genome-wide association studies, candidate gene stud-ies can be performed either to follow up on the gene(s) inthe linkage/association region or to examine genes with bio-logical relevance to autism. Although candidate gene studieshave found numerous positive associations, none have beenwidely replicated. Various possible causes underlie this fail-ure to replicate, such as the substantial heterogeneity in thedisease phenotype and the likelihood that several suscepti-bility loci may interact together leading to an increased riskof autism (Veenstra-Vanderweele & Cook 2004).

Because these studies have implicated the serotoninand dopamine pathways in autism, they have beeninvestigated extensively (Anderson et al. 2008, 2009). Suchstudies have shown that deficiencies in tetrahydrobiopterin(BH4), an essential cofactor for dopamine and serotoninbiosynthesis in the central nervous system (Fig. 1), canresult in severe neurological disorders. These disordersare characterized by monoamine-neurotransmitter deficiencycaused by mutations in the genes that encode theenzymes responsible for BH4 biosynthesis and regeneration(Bonafe et al. 2001). GTP cyclohydrolase I (GCH1), forexample, has been implicated in Segawa’s disease,also known as dihydroxyphenylalanine (DOPA)-responsive

doi: 10.1111/j.1601-183X.2009.00521.x 753

Schnetz-Boutaud et al.

Figure 1: Biosynthesis and metabolism of BH4. Adapted from Richardson et al. (2007). Dotted lines refer to regulatory influences.5-HIAA, 5-hydroxyindoleacetic acid; GCH1, GTP cyclohydrolase I; GCHFR, GTP cyclohydrolase I feedback regulatory protein; HVA,homovanillic acid; L-DOPA, L-3,4-hydroxyphenylalanine; NA, noradrenaline; NOS, nitric oxide synthase; PAH, phenylalanine hydroxylase;PCBD1, pterin-4a-carbinolamine dehydratase; PTS, 6-pyruvoyl-tetrahydropterin synthase; QDPR, dihydropteridine reductase; SPR,sepiapterin reductase; TH, tyrosine-3-hydroxylase; TPH, tryptophan-5-hydroxylase.

dystonia (Ichinose et al. 2008). The complexity of thispathway, as well as the number of enzymes involvedin the synthesis and recycling of BH4, has been studiedexhaustively (Thony et al. 2000).

The role of BH4 as the cofactor and a common factorin these pathways has prompted us to examine the BH4pathway’s role in autism.

Materials and methods

DatasetOur analysis was conducted on a dataset consisting of 403 non-Hispanic Caucasian American families collected in the southeastUnited States by the Center for Human Genetics Research atVanderbilt University and the Miami Institute for Human Genomics atthe University of Miami (Table 1). Probands for the study consistedof individuals between the ages of 3 and 21 years who were clinicallydiagnosed with autism using Diagnostic and Statistical Manual(DSM)-IV criteria. The clinical diagnosis of autism was confirmedon the basis of clinical evaluation using DSM-IV diagnostic criteriasupported by the Autism Diagnostic Interview-Revised (ADI-R) andmedical records. Exclusion criteria for participation in the largergenetics 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 orwalk by 24 months) or identified metabolic, genetic or progressiveneurological disorders. Parents/caregivers were informed of thepurposes, risks and benefits of participating in this project andprovided informed consent.

Molecular analysisGenomic DNA was extracted from blood using standard pro-tocols and a commercial system (Puregene; Gentra Systems,

Table 1: Distribution of study dataset

Total Multiplex Trios Unaffected sibs

Overall dataset 403 151 252 231Male-only subset 303 89 214 185

Minneapolis, MN, USA). All single nucleotide polymorphisms(SNPs) were identified using the Ensembl (www.ensembl.org),dbSNP (www.ncbi.nlm.nih.gov/projects/SNP) and Applied Biosys-tems (http://www.appliedbiosystems.com/) databases. SNPs weregenotyped using the ABI 7900 Taqman system (Oliveira et al. 2003).Genes were selected on the basis of their involvement in the tetrahy-drobiopterin pathway, including those that play a critical role in thesynthesis and metabolism of tetrahydrobiopterin (Thony et al. 2000).Multiple SNPs (when available) spanning each gene were chosenusing a hierarchy of non-synonymous coding change, minor allelefrequency > 0.10 and location within the gene. Laboratory person-nel were blinded to pedigree structure, affection status and locationof quality control samples. Duplicate quality control samples wereplaced both within and across 384-well plates, and equivalent geno-types were required for all quality control samples to ensure accurategenotyping. Hardy–Weinberg calculations were performed for eachmarker, and Mendelian inconsistencies were identified using PED-

CHECK (O’Connell & Weeks 1998). Suspect genotypes were re-read orretested. All SNPs were required to pass 95% genotyping efficiencyto be considered for analysis.

Statistical analysisGenotype efficiency, Hardy–Weinberg equilibrium and linkagedisequilibrium (LD) were checked using HAPLOVIEW (Barrett et al.2005). If any SNP fell below 95% genotype efficiency, a SNPin high LD with the failed SNP was added to ensure continuedcoverage of the gene. Linkage analysis was conducted on thesubset of families with multiple affected children using two-point

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Tetrahydrobiopterin pathway genes in autism

heterogeneity LOD scores (HLOD) calculated using FASTLINK andHOMOG (Ott 1999). Both recessive and dominant models with diseaseallele frequencies of 0.01 and 0.001, respectively, were analyzed.This approach is robust for detecting linkage signals when theunderlying model is unknown or complex (Hodge 1994). Associationwas evaluated using the pedigree disequilibrium test (PDT) (Martinet al. 2003). This method provides valid and robust tests for allelicassociation in trios and extended families. The genotype-PDT (Geno-PDT) tested genotypic association with the risk of autism (Martinet al. 2003). Taking into account the 4:1 ratio of males to femalesaffected with autism, the HLOD, PDT and Geno-PDT were also runin a subset of families containing only affected males (male-only,N = 303).

Multifactor dimensionality reduction (MDR) analysis was used todetect multilocus interactions (Ritchie et al. 2001, 2007). BecauseMDR is designed for case-control data, we extracted the affectedchild from any family with a complete parent–child trio (one randomchild per family for multiplex families). We constructed ‘pseudo’controls using the non-transmitted alleles of the parents (Collinset al. 2006; Ma et al. 2005). We tested for all two-way and three-wayinteractions. All P-values are reported as nominal P-values unlessotherwise stated.

Results

When we genotyped 25 SNPs in nine genes of the BH4pathway (Table 2), we observed a LOD = 1.4 for rs2597773

in QDPR (dihydropteridine reductase) on chromosome 4(Table S1, Supporting Information). In the male-only subset, aLOD score of 1.5 was observed for this SNP, whereas a LODscore of 1.7 was seen in this gene for rs2252995 (Table S1,Supporting Information). Using the PDT, PTS (6-pyruvoyl-tetrahydropterin synthase, chromosome 11) showed signif-icant association with P = 0.009 (P = 0.20 when correctedfor multiple comparisons) for rs2518352 and P = 0.01 forrs3819331. In the male-only dataset, we observed similarassociations in that same gene.

After we performed a case/pseudocontrol association anal-ysis on the most significant gene, PTS, a marginal associationwas observed for rs2518352 and rs3819331 (Table S2,Supporting Information). The MDR analysis detected a sig-nificant two-way interaction between rs2518352 (PTS) andrs6730083 (SPR, sepiapterin reductase), for which the predic-tion accuracy was 60.2% and the empiric P = 0.03 (Table S3,Supporting Information). We also tested for potential interac-tions with nominally significant SNPs from our examinationof genes in the serotonin and dopamine pathways (Andersonet al. 2008, 2009), but no significant interactions were found(Table S3, Supporting Information).

Table 2: Overall dataset and male only association analysis results

Overall MO

PDT PDT

Gene SNP Chr Sum Geno Sum Geno

SPR rs6730083 2 0.107 0.196 0.259 0.458QDPR rs2252995 4 0.676 0.916 0.882 0.989QDPR rs2597773 4 0.871 0.779 0.881 0.873QDPR rs2315248 4 0.432 0.676 0.792 0.965PCBD1 rs12218438 10 0.601 0.715 0.110 0.186PTS rs2518352 11 0.009 0.017 0.012 0.339PTS rs3819331 11 0.012 0.029 0.087 0.157TH rs2070762 11 0.297 0.590 0.478 0.768PAH rs1801153 12 0.906 0.927 0.621 0.795PAH rs1522307 12 0.412 0.143 0.131 0.024TPH2 rs4341582 12 0.205 0.185 0.829 0.714TPH2 rs10784941 12 0.239 0.549 0.776 0.963TPH2 rs1386494 12 0.882 0.944 0.646 0.581TPH2 rs2171363 12 0.610 0.511 0.345 0.241TPH2 rs1386492 12 0.820 0.799 0.563 0.793TPH2 rs4760816 12 1.000 0.815 0.565 0.532TPH2 rs10506645 12 0.594 0.721 0.395 0.537TPH2 rs1487278 12 1.000 0.364 0.833 0.632TPH2 rs1487280 12 0.938 0.913 0.375 0.254GCH1 rs10133662 14 0.796 0.890 0.897 0.862GCH1 rs9671371 14 0.555 0.822 0.866 0.967GCH1 rs3783638 14 0.314 0.549 0.290 0.526GCH1 rs3783641 14 0.702 0.824 0.409 0.641GCHFR rs2016546 15 0.398 0.398 1.000 1.000GCHFR rs2301176 15 0.625 0.848 0.553 0.777

The pedigree disequilibrium test (PDT) was performed on all SNPs for association analysis. Values in boldface indicate a P < 0.05. Chr,chromosome; Geno, Geno-PDT is a genotype test; MO, families where only males are affected; SNP, single nucleotide polymorphism; Sum,sum-PDT is an allelic test.

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We used the Collaborative Autism Project (CAP) genome-wide association study (GWAS) dataset as our replicationdataset (Ma et al. 2009), which consists of 487 parent–childtrios collected using the same criteria as those in thecurrent study. We selected the SNP genotyping data forour nine genes including 100 kb on each side of thegenes to include regulatory elements (Table S4, SupportingInformation). We observed a marginal association in QDPR,P = 0.05, GCH1, P = 0.01, and PAH, P = 0.01 (Table S5,Supporting Information). No association was seen for PTS.The MDR analysis on this dataset did not detect a gene–geneinteraction (Table S6, Supporting Information).

Discussion

Although the result of a double-blind placebo-controlledcrossover study indicated a possible effect of BH4 treatmentin children with autistic disorder (Danfors et al. 2005), ourprevious studies showed only modest association withautism for the genes in the dopamine and serotoninpathways (Anderson et al. 2008, 2009). In the dopaminepathway study, however, we found that YWHAB (tyrosine3-monooxygenase/tryptophan 5-monooxygenase activationprotein, beta polypeptide), the activation protein for tyrosine-3-hydroxylase (TH), has a nominal association with thedisorder. TH uses BH4 as a cofactor to metabolize tyrosineinto L-DOPA, the precursor of dopamine. The results fromthis present study were modest, showing only a marginalassociation with PTS, one of the genes responsible for BH4’sbiosynthesis. However, this association did not withstand thecorrection for multiple testing and was not replicated in theGWAS dataset.

When the hypothesis that gene–gene interaction mightplay an important role in the disease was tested using MDR,a modest association was detected between PTS and SPRin a two-way interaction model in the overall dataset. Giventhat PTS and SPR are two consecutive genes in the BH4synthesis (Fig. 1), the fact that they show epistasis mayrequire further investigation. Using the data from our previousstudies of the dopamine and serotonin pathways, we ranMDR on the three pathways combined, but no gene–geneinteraction was detected.

The underlying assumption that common variations inthese genes are responsible for modulating the risk ofautism also had an impact on our analysis. Although wecaptured a significant number of these variations, our studywas not fully comprehensive. Furthermore, Weiss et al.have hypothesized that rare variations (de novo mutation,deletions, duplications, point mutations) present in a largenumber of genes could account for as much as 90% ofidiopathic autism (Weiss et al. 2008). If true, this wouldindicate that our study might not have enough power toovercome the locus heterogeneity to detect rare variations.Given that such alternatives would significantly affect thepower of the PDT and MDR analysis, our data neverthelesssuggest at least a modest role for one BH4-related gene(PTS) in the risk of developing autism.

References

American Psychiatric Association (2000) Diagnostic and StatisticalManual of Mental Disorders, 4th edn, Text Revision (DSM-IV-TR®).American Psychiatric Press Inc., Washington, D.C. 995.

Anderson, B.M., Schnetz-Boutaud, N., Bartlett, J., Wright, H.H.,Abramson, R.K., Cuccaro, M.L., Gilbert, J.R., Pericak-Vance, M.A.& Haines, J.L. (2008) Examination of association to autism of com-mon genetic variation in genes related to dopamine. Autism Res1, 364–369.

Anderson, B.M., Schnetz-Boutaud, N.C., Bartlett, J., Wotawa, A.M.,Wright, H.H., Abramson, R.K., Cuccaro, M.L., Gilbert, J.R.,Pericak-Vance, M.A. & Haines, J.L. (2009) Examination of asso-ciation of genes in the serotonin system to autism. Neurogenetics10, 209–216.

Arking, D.E., Cutler, D.J., Brune, C.W., Teslovich, T.M., West, K.,Ikeda, M., Rea, A., Guy, M., Lin, S., Cook, E.H. & Chakravarti, A.(2008) A common genetic variant in the neurexin superfamilymember CNTNAP2 increases familial risk of autism. Am J HumGenet 82, 160–164.

Auranen, M., Vanhala, R., Varilo, T., Ayers, K., Kempas, E., Ylisaukko-Oja, T., Sinsheimer, J.S., Peltonen, L. & Jarvela, I. (2002) Agenomewide screen for autism-spectrum disorders: evidence fora major susceptibility locus on chromosome 3q25-27. Am J HumGenet 71, 777–790.

Barrett, S., Beck, J.C., Bernier, R. et al. (1999) An autosomal genomicscreen for autism. Collaborative linkage study of autism. Am J MedGenet 88, 609–615.

Barrett, J.C., Fry, B., Maller, J. & Daly, M.J. (2005) Haploview:analysis and visualization of LD and haplotype maps. Bioinformatics21, 263–265.

Bonafe, L., Thony, B., Penzien, J.M., Czarnecki, B. & Blau, N. (2001)Mutations in the sepiapterin reductase gene cause a noveltetrahydrobiopterin-dependent monoamine-neurotransmitter defi-ciency without hyperphenylalaninemia. Am J Hum Genet 69,269–277.

Buxbaum, J.D., Silverman, J.M., Smith, C.J., Kilifarski, M.,Reichert, J., Hollander, E., Lawlor, B.A., Fitzgerald, M., Green-berg, D.A. & Davis, K.L. (2001) Evidence for a susceptibility genefor autism on chromosome 2 and for genetic heterogeneity. Am JHum Genet 68, 1514–1520.

Cantor, R.M., Kono, N., Duvall, J.A., Varez-Retuerto, A., Stone, J.L.,Alarcon, M., Nelson, S.F. & Geschwind, D.H. (2005) Replication ofautism linkage: fine-mapping peak at 17q21. Am J Hum Genet 76,1050–1056.

Collins, A.L., Ma, D., Whitehead, P.L., Martin, E.R., Wright, H.H.,Abramson, R.K., Hussman, J.P., Haines, J.L., Cuccaro, M.L.,Gilbert, J.R. & Pericak-Vance, M.A. (2006) Investigation of autismand GABA receptor subunit genes in multiple ethnic groups.Neurogenetics 7, 167–174.

Danfors, T., von Knorring, A.L., Hartvig, P., Langstrom, B., Moul-der, R., Stromberg, B., Torstenson, R., Wester, U., Watanabe, Y.& Eeg-Olofsson, O. (2005) Tetrahydrobiopterin in the treatment ofchildren with autistic disorder: a double-blind placebo-controlledcrossover study. J Clin Psychopharmacol 25, 485–489.

Folstein, S. & Rutter, M. (1977) Genetic influences and infantileautism. Nature 265, 726–728.

Fombonne, E. (2003) Epidemiological surveys of autism and otherpervasive developmental disorders: an update. J Autism DevDisord 33, 365–382.

Fombonne, E. (2005) Epidemiology of autistic disorder and other per-vasive developmental disorders. J Clin Psychiatry 66(Suppl. 10),3–8.

Hodge, S.E. (1994) What association analysis can and cannot tellus about the genetics of complex disease. Am J Med Genet 54,318–323.

Ichinose, H., Nomura, T. & Sumi-Ichinose, C. (2008) Metabolism oftetrahydrobiopterin: its relevance in monoaminergic neurons andneurological disorders. Chem Rec 8, 378–385.

756 Genes, Brain and Behavior (2009) 8: 753–757

Tetrahydrobiopterin pathway genes in autism

International Molecular Genetic Study Autism Consortium (IMGSAC)(2001) A genomewide screen for autism: strong evidence forlinkage to chromosomes 2q, 7q, and 16p. Am J Hum Genet 69,570–581.

Liu, J., Nyholt, D.R., Magnussen, P., Parano, E., Pavone, P.,Geschwind, D., Lord, C., Iversen, P., Hoh, J., Ott, J. & Gilliam, T.C.(2001) A genomewide screen for autism susceptibility loci. Am JHum Genet 69, 327–340.

Ma, D.Q., Whitehead, P.L., Menold, M.M., Martin, E.R., Shley-Koch, A.E., Mei, H., Ritchie, M.D., DeLong, G.R., Abramson, R.K.,Wright, H.H., Cuccaro, M.L., Hussman, J.P., Gilbert, J.R. &Pericak-Vance, M.A. (2005) Identification of significant associa-tion and gene-gene interaction of GABA receptor subunit genes inautism. Am J Hum Genet 77, 377–388.

Ma, D., Salyakina, D., Jaworski, J.M. et al. (2009) A genome-wideassociation study of autism reveals a common novel risk locus at5p14.1. Ann Hum Genet 73, 263–273.

Martin, E.R., Bass, M.P., Gilbert, J.R., Pericak-Vance, M.A. &Hauser, E.R. (2003) Genotype-based association test for generalpedigrees: the genotype-PDT. Genet Epidemiol 25, 203–213.

O’Connell, J.R. & Weeks, D.E. (1998) PedCheck: a program foridentification of genotype incompatibilities in linkage analysis. AmJ Hum Genet 63, 259–266.

Oliveira, S.A., Scott, W.K., Nance, M.A. et al. (2003) Associationstudy of Parkin gene polymorphisms with idiopathic Parkinsondisease. Arch Neurol 60, 975–980.

Ott, J. (1999) Analysis of Human Genetic Linkage, 3rd edn. JohnsHopkins University Press, Baltimore, MD. 424.

Philippe, A., Martinez, M., Guilloud-Bataille, M., Gillberg, C., Ras-tam, M., Sponheim, E., Coleman, M., Zappella, M., Aschauer, H.,Van, M.L., Penet, C., Feingold, J., Brice, A. & Leboyer, M. (1999)Genome-wide scan for autism susceptibility genes. Paris AutismResearch International Sibpair Study. Hum Mol Genet 8, 805–812.

Richardson, M.A., Read, L.L., Reilly, M.A., Clelland, J.D. & Clel-land, C.L. (2007) Analysis of plasma biopterin levels in psychiatricdisorders suggests a common BH4 deficit in schizophrenia andschizoaffective disorder. Neurochem Res 32, 107–113.

Risch, N., Spiker, D., Lotspeich, L. et al. (1999) A genomic screen ofautism: evidence for a multilocus etiology. Am J Hum Genet 65,493–507.

Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D.,Parl, F.F. & Moore, J.H. (2001) Multifactor-dimensionality reduc-tion reveals high-order interactions among estrogen-metabolismgenes in sporadic breast cancer. Am J Hum Genet 69,138–147.

Ritchie, M.D., Edwards, T.L., Fanelli, T.J. & Motsinger, A.A. (2007)Genetic heterogeneity is not as threatening as you might think.Genet Epidemiol 31, 797–800.

Rutter, M., Bolton, P., Harrington, R., Le, C.A., Macdonald, H. &Simonoff, E. (1990a) Genetic factors in child psychiatric disor-ders–I. A review of research strategies. J Child Psychol Psychiatry31, 3–37.

Rutter, M., Macdonald, H., Le, C.A., Harrington, R., Bolton, P. &Bailey, A. (1990b) Genetic factors in child psychiatric disorders–II.Empirical findings. J Child Psychol Psychiatry 31, 39–83.

Shao, Y., Wolpert, C.M., Raiford, K.L., Menold, M.M., Donnelly, S.L.,Ravan, S.A., Bass, M.P., McClain, C., Von, W.L., Vance, J.M.,Abramson, R.H., Wright, H.H., Shley-Koch, A., Gilbert, J.R.,DeLong, R.G., Cuccaro, M.L. & Pericak-Vance, M.A. (2002)Genomic screen and follow-up analysis for autistic disorder. Am JMed Genet 114, 99–105.

Szatmari, P., Paterson, A.D., Zwaigenbaum, L. et al. (2007) Mappingautism risk loci using genetic linkage and chromosomal rearrange-ments. Nat Genet 39, 319–328.

Thony, B., Auerbach, G. & Blau, N. (2000) Tetrahydrobiopterinbiosynthesis, regeneration and functions. Biochem J 347(Pt 1),1–16.

Veenstra-Vanderweele, J. & Cook, E.H. Jr. (2004) Molecular geneticsof autism spectrum disorder. Mol Psychiatry 9, 819–832.

Wang, K., Zhang, H., Ma, D. et al. (2009) Common genetic variantson 5p14.1 associate with autism spectrum disorders. Nature 459,528–533.

Weiss, L.A., Shen, Y., Korn, J.M. et al. (2008) Association betweenmicrodeletion and microduplication at 16p11.2 and autism. N EnglJ Med 358, 667–675.

Acknowledgments

We wish to thank both the patients with autism and their familymembers who agreed to participate in this study, as well asthe personnel of the Center for Human Genetics Researchat Vanderbilt University and the Miami Institute for HumanGenomics at the University of Miami. We would like to thankM. J. Allen for her excellent technical support. This researchwas supported in part by National Institutes of Health (NIH)program project grant NS026630 (MPV, JLH) and NIH R01 grantMH080647.

Supporting Information

Additional Supporting Information may be found in the onlineversion of this article:

Table S1: Two-point dominant and two-point recessivelinkage (LOD score) analysis results for the overall datasetand the male-only dataset

Table S2: Cases/pseudocontrols allelic and genotypicanalysis results

Table S3: Multifactor dimensionality reduction (MDR)analysis for the BH4 pathway only and for the combinedserotonin dopamine and BH4 pathways

Table S4: Base pair location for the nine genes of thetetrahydrobiopterin pathway adding 100 kb on each side(Build 36)

Table S5: Linkage and association analysis for the genesin the tetrahydrobiopterin pathway using the CollaborativeAutism Project (CAP) GWAS families

Table S6: Multifactor Dimensionality Reduction (MDR)analysis for the BH4 pathway only on the AGP GWAS dataset

As a service to our authors and readers, this journalprovides supporting information supplied by the authors.Such materials are peer-reviewed and may be re-organizedfor online delivery, but are not copy-edited or typeset.Technical support issues arising from supporting information(other than missing files) should be addressed to the authors.

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