DATABASES AND GENOME PROJECTS

Am J Pharmacogenomics 2005; 5 (4): 233-246 1175-2203/05/0004-0233/$34.95/0 © 2005 Adis Data Information BV. All rights reserved.

The Autism Genome Project Goals and Strategies Diane Hu-Lince, David W. Craig, Matthew J. Huentelman and Dietrich A. Stephan Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA

Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 1. Clinical Description of Autism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 2. The Search for Candidate Genes in Autism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 2.1 Genetic Insight into Autism through Cytogenetically Visible Chromosomal Abnormalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 2.2 Hypothesis Driven Genetic Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 2.3 Whole Genome Linkage Scan Results in Autism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 3. The Autism Genome Project: Phase I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 3.1 Whole-Genome Linkage Scanning in Complex Traits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 3.2 Single Nucleotide Polymorphisms (SNP) Genotyping and Microsatellite Marker Genotyping . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 3.2.1 Accuracy and Reproducibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 3.2.2 Informativeness of SNPs versus Microsatellites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 3.2.3 Linkage Disequilibrium as a Confounding Factor in High Density SNP Linkage Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 3.2.4 Availability of Analysis Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 3.2.5 The GeneChip® DNA Analysis Software (GDAS) Port . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 3.2.6 Common Existing Analysis Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 3.2.7 New Analysis Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 3.2.8 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 4. The Autism Genome Project: Phase II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

Abstract

Autism is a complex neurodevelopmental disorder with a broad spectrum of symptoms and varying severity. Currently, no biological diagnosis exists. Although there has been a significant increase in autism genetics research recently, validated susceptibility genes for the most common, sporadic forms of autistic disorder, as well as familial autism, have yet to be identified. The identification of autism-susceptibility genes will not only assist in the identification and/or development of better medications that can help improve the health and neurodevelopment of children with autism, but will also allow for better perinatal diagnosis. The Autism Genome Project (AGP) is a large-scale, collaborative genetics research project initiated by the National Alliance for Autism Research and the National Institutes of Health, and is aimed at sifting through the human genome in search of autism-susceptibility genes. Phase I of the AGP will consist of genome-wide scans utilizing both SNP array and microsatellite technologies. Linkage analysis will subsequently be performed on approximately 1500 pedigrees as will downstream fine-mapping and sequencing of the critical linkage intervals. Ultimately, the vision will be to identify the exact nucleotide variants within genes which give rise to predisposition. The AGP intends to move the field of autism clinical management forward by answering questions about the causal mechanisms underlying the pathophysiology of autism. From this knowledge, therapeutic targets for drug treatments, and ultimately, a newborn screening diagnostic that would allow for early intervention, can begin to be developed.

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In recent years, interest in deciphering autism has increased markedly. This may be attributed to a sudden reported increase in the incidence of autism, and better diagnosis. Autism now ranks as the leading childhood developmental disorder in the US, with an incidence of 1 in 166 births and a rate four times higher in boys than in girls.[1-3] There are currently no specific diagnostic criteria to determine which children are at risk before the onset of the disorder, and no effective therapeutic once the disorder strikes. Progress toward finding susceptibility genes for autism has been impeded because of the multigenic nature of the disorder, a lack of access to tissue at the site of pathology, and a lack of robust rodent models of the trait. Recently, the Autism Genome Project (AGP) has been nucleated by both the National Alliance for Autism Research (NAAR) and National Institutes of Health (NIH) in an attempt to identify the community-specific nucleotide variants that are implicated as influencing the phenotype. The AGP is a large-scale, collaborative genetics research project drawing on very large patient cohorts from around the globe to garner sufficient statistical power to tease out the multiple underlying genetic roots of this disorder. Over 40 academic and research institutions across North America and Europe (table I) will band together to contribute approximately 7000 samples from approximately 1500 multiplex families (a family with at least two children affected with autistic spectrum disorder) to this effort. This will be the largest genome scan for autism ever performed. The first phase of the AGP will consist of genome-wide scans utilizing both single nucleotide polymorphisms (SNP) and microsatellite technologies. The Translational Genomics Research Institute (TGen), a nonprofit biomedical research institute, in conjunction with Affymetrix, Inc., will perform the genome scan utilizing high-density SNP genotyping arrays. A concurrent microsatellite marker-based linkage scan on the identical cohort will be conducted by the Center for Inherited Disease Research (CIDR) [figure 1]. Linkage analysis will subsequently be performed on the approximately 1500 pedigrees by a data coordinating site. This article highlights the current state of knowledge regarding the genetics of autism. Many other excellent reviews are available in the literature.[4-7] 1. Clinical Description of Autism Autism is a neurodevelopmental disorder characterized by varying degrees of impairment in three behavioral domains: (i) social interaction; (ii) language, communication, and imaginative play; and (iii) range of interests and activities.[8,9] For the past 50 years diagnosis of autism has relied on clinical presentation only. Currently there is no single biological or clinical marker for autism. © 2005 Adis Data Information BV. All rights reserved.

Hu-Lince et al.

The Autism Diagnostic Interview-Revised (ADI-R) has been used extensively as a comprehensive interview to aid in the diagnosis and treatment of autism.[10] Developmental abnormalities are evident by the age of 3 years and persist into adulthood.[2,6] Though it is difficult to accurately estimate the population prevalence of autism, it is probably within the range of 4–6 per 10 000 and some speculate even in the range of 40 per 10 000.[3,11] This significant increase in prevalence rates may be a reflection of changes in case definition and improved recognition. For reasons unknown, the prevalence in males is four times that of females.[3] The first clinical descriptions of autism were brought forth by psychiatrist Leo Kanner and pediatrician Hans Asperger in the early 1940s. Evidence to suggest that autism is genetic stems from family and twin studies. Family studies support the recurrence risk to siblings to be 2–6%, significantly higher than the risk to the general population (<0.1–0.2%).[12] Twin studies have also shown the importance of genetic factors in the etiology of autism. Depending on either narrow or broad phenotype, the concordance rate when one monozygotic (MZ) twin is diagnosed with autism is 70–90%. In contrast, a 0–25% concordance rate is observed among same-sex dizygotic (DZ) twins.[13-16] The overlap of autism with known genetic disorders such as fragile X,[17,18] tuberous sclerosis,[19] Prader-Willi/Angelman syndrome,[20] and neurofibromatosis[21] further supports a genetic predisposition to autism. Moreover, cytogenic abnormalities associated with autism phenotypes have been found on practically every chromosome, most notably chromosomes 7, 15, and X.[22] Despite evidence from family and twin studies, few significant genetic linkages to autism have been identified. The plethora of cytogenetic abnormalities and linkage regions found to be associated with autism phenotypes suggest that genetic heterogeneity is likely to be frequent across sporadic patients. The rate by which autism frequency drops among first, second, and third degree relatives is another indication that disease susceptibility arises from the combined effects of multiple genes.[23,24] Whole-genome screens performed on multiplex families with semblances of Mendelian inheritance patterns suggest that as many as ten or more genes may be interacting individually or in concert to cause the autism phenotype.[8,25] Additionally, allelic heterogeneity is also likely to be frequent. An example of allelic heterogeneity can be found in the monogenic disorder cystic fibrosis, whereby many different mutations in a single gene (CFTR) have been found to contribute to the disorder.[26] Similarly, distinct defects in the same gene may lead to the same or different patterns of autism. This, and the multigenic nature of autism, poses a considerable challenge for genetic analysis. Am J Pharmacogenomics 2005; 5 (4)

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Table I. Participants in the Autism Genome Project Autism Genetics Cooperative (AGC) The International Molecular Genetic The Collaborative Programs of Study of Autism Consortium (IMGSAC) Excellence (CPEA)

Autism Genetics Resource Exchange (AGRE)

Duke University Medical Center (Durham, NC, USA) University of North Carolina (Chapel Hill, NC, USA) University of South Carolina (Columbia, SC, USA) University of Iowa (Iowa City, IA, USA) Stanford University (Palo Alto, CA, USA) Mt Sinai Medical Center (New York, NY, USA) Vanderbilt University (Nashville, TN, USA) INSERM U513 (Creteil, France) Institut Pasteur (Paris, France) Goteborg University (Goteborg, Sweden) McMaster University (Hamilton, ON, Canada) University of Toronto (Toronto, ON, Canada) Trinity College of Dublin (Dublin, Ireland) University College of Dublin (Dublin, Ireland) Instituto Gulbenkian de Ciencia (Oeiras, Portugal)

University of California at Los Angeles (Los Angeles, CA, USA)

Institute of Psychiatry (London, England) Wellcome Trust Centre for Human Genetics University of Oxford (Oxford, England) University Section of Child and Adolescent Psychiatry University of Oxford (Oxford, England) Newcomen Centre at Guy’s Hospital (London, England) University of Newcastle (Newcastle Upon Tyne, England) University of Manchester (Manchester, England) Porton Down, ECACC (Wiltshire, England) Videnscenter og Centre for Autisme/ National Center for Autism (Virum, Denmark) Centre d’Etudes et de Recherches en Psychopathologie (CERPP) [Toulouse, France] Deutsches Krebsforschungszentrum (Heidelberg, Germany) J.W. Goethe Universitat (Frankfurt, Germany) “Agia Sophia” Children’s Hospital/ University Department of Child Psychiatry (Athens, Greece) University of Bologna (Bologna, Italy) AZU-Department of Child and Adolescent Psychiatry (Ultrecht, The Netherlands) McGill University/Montreal Children’s Hospital (Montreal, QC, Canada) University of Michigan (Ann Arbor, MI, USA) Yale University (New Haven, CT, USA) University of Chicago (Chicago, IL, USA)

2. The Search for Candidate Genes in Autism Diseases with complex inheritance are presumed to be caused by multiple genes interacting with one another and/or with environmental factors.[27] Examples of complex genetic diseases include asthma, diabetes mellitus, epilepsy, hypertension, bipolar disorder, schizophrenia, and autism. The genetic etiology of many complex diseases has been progressively studied in order to gain a better understanding of their pathogenesis with the eventual goal © 2005 Adis Data Information BV. All rights reserved.

Boston University (Boston, MA, USA) University of California at Los Angeles (Los Angeles, CA, USA) University of Rochester Medical Center (Rochester, NY, USA) University of Pittsburgh (Pittsburgh, PA, USA) University of California at Irvine (Irvine, CA, USA) Yale University (New Haven, CT, USA) University of Washington (Seattle, WA, USA) University of Utah (Salt Lake City, UT, USA)

of improving diagnostic tools, preventative strategies, and therapies.[28] Genetic mapping has been a very powerful tool for identifying human disease genes. In the last 20 years, family-based linkage analysis has led to the identification of many genes that control Mendelian traits but only a paucity of genes underlying complex diseases.[29] Unlike the predicted pattern of inheritance seen in autosomal or X-linked recessive and dominant disorders, the discovery of susceptibility genes in complex human diseases has Am J Pharmacogenomics 2005; 5 (4)

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43 institutions 7000 DNA samples 1500 multiplex families

Phase 1 Linkage analysis

Phase 2

Single nucleotide polymorphism array technology (TGen, Affymetrix, Inc.)

Fine mapping of linkage regions

Microsatellite technology (CIDR)

Nucleotide variant identification Mouse model generation

Fig. 1. The Autism Genome Project process (organized by the National Alliance for Autism Research [NAAR] and the National Institutes of Health [NIH]). CIDR = Center for Inherited Disease Research; TGen = Translational Genomics Research Institute.

been complicated by a host of factors including considerable etiological heterogeneity, the influence of any single gene-variant on diseases status having a small effect, and the associated requirement for large samples. In addition, mapping of human susceptibility loci can be hampered by any or combinations of the following: incomplete penetrance, high population frequency, phenocopies, genetic heterogeneity, possible epistasis, and pleiotropy.[27-30] Nonetheless, genetic linkage analysis has successfully identified a number of susceptibility genes for complex diseases such as type 1 diabetes mellitus[31-33] and Alzheimer disease.[34-36] Also, some rare Mendelian families can provide insight into the more common sporadic forms of the trait. In recent years several approaches have been employed to locate autism susceptibility genes. Among these are cytogenetic studies, hypothesis-driven studies of very specific biological processes, and genome-wide linkage scans. A brief overview of the results of each approach is given here. Table II gives a summary of the current positive associations found between candidate gene allelic variants and autism. 2.1 Genetic Insight into Autism through Cytogenetically Visible Chromosomal Abnormalities

Structural chromosomal abnormalities are usually of one of three types: (i) deletions, where material is lost from a single chromosome; (ii) inversions, where there are two breaks within a single chromosome and the broken segment inverts and reattaches to form a chromosome that is structurally out of sequence; and (iii) translocations, where there is an exchange of material between two or more chromosomes. It is thought that less than 10% of autism cases result from chromosomal abnormalities;[62,63] however, cytogenetic scans have provided some insight into locating specific genes or chromosomal loci potentially associated with autism. Cytogenetic abnormalities on chromosome 15 have been identified in several individuals with autism, indicating that this region may harbor potential susceptibility genes for autism.[22,49,63,64] Various studies have described duplications,[49,63,65-67] dele© 2005 Adis Data Information BV. All rights reserved.

tions,[53,63,68,69] and inversions[68,69] at locus 15q11-q13. Several chromosome 15 abnormalities resulting in features characteristic of autism are inherited duplications of maternal origin,[63,65] which suggests the possibility of an imprinting mechanism in the regulation of gene expression. This is characterized to varying degrees by language delay, ataxia, epilepsy, mental retardation, and facial dysmorphology. Hence, the phenotypic overlap with the autistic phenotype implicates this region as a potential autism hotspot. One of the potential candidate genes within this locus is the γaminobutyric acid (GABAA) receptor gene cluster, containing genes coding for the α5, β3 and γ3 receptor units (GABRA5, GABRB3, and GABRG3, respectively). GABA is expressed in early development and is the principal inhibitory neurotransmitter in the mammalian CNS, controlling excitability in the adult brain.[70] One initial study identified an association at 155CA-2, a microsatellite repeat polymorphism within the GABRB3 gene;[49] this finding was replicated in another study.[50] Several other studies, however, have not been able to replicate this finding.[71-73] In addition to the GABAA cluster, the ubiquitin protein ligase E3A gene (UBE3A) has also been implicated in the autistic phenotype.[53] This gene is expressed predominantly in the human brain and is regulated by mechanisms involved in imprinting and possibly silencing by antisense RNA transcribed from the paternal chromosome.[53,66,74] One sibling pair study revealed association at D15S122, a microsatellite marker located at the 5′ end of the UBE3A gene.[53] It is noteworthy to mention that individuals who possess an abnormality at chromosome 15q11-q13 do not necessarily develop autism. This finding further strengthens the multigenic nature of the disorder and suggests that multiple genes on different chromosomes may be acting individually or in concert. The chromosome 7q22-23 region has also been associated with chromosomal translocations.[22,75-77] Reelin, a protein that is encoded by the RELN gene at the site of the translocation at 7q22, is a large secreted glycoprotein potentially involved in neural migration during development. Specific mutations in RELN have been identified and cause autosomal recessive lissencephaly, a disorder of failed neuronal migration.[78] One study of autistic disorder reported transmission of longer triplet repeats (CGG) in the 5′UTR of RELN, conferring vulnerability to autistic disorder.[43] Another study concluded that the overall transmission disequilibrium was not significant but that ‘large’ alleles (≥11 repeats) were preferentially transmitted.[42] Further studies, however, have found no evidence for an association between the trait and the RELN trinucleotide repeat.[79-81] A plethora of other genes located at the 7q22-q33 locus have also shown an association with the autistic phenotype, though further validation is warranted.[40,77,82-84] Am J Pharmacogenomics 2005; 5 (4)

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Table II. Positive associations found between candidate gene allelic variants and autism Genea

Physical location

Phenotype

Study size and design

Reference

SLC25A12

2q24-q33

Autism

411 Autistic families; linkage and association

37

HLA-DRB1

6p21

Autism

50 Patients versus 79 controls

38

GRIK2

6q21

Autism

107 trios and 57 multiplex families; linkage and association

39

LAMB1

7q31

Autism

48 individuals

40

30 families

41

NRCAM

7q31

Autism

48 individuals

40

RELN

7q22

ASD-with delayed phrase speech

126 families

42

7q22

Autism

95 individuals; linkage and association 172 trios; TDT

43

GRM8

7q31

Autism

196 families; TDT

44

WNT2

7q31-q33

Autism with severe 50 families language abnormality

45

7q32

Autism

170 multiplex families; TDT with 76 markers

46

D721804 b D7S2533

b

7q33

Autism

170 multiplex families; TDT with 76 markers

46

EN2

7q36

Autism

138 trios; TDT

47

HRAS

11p15

Autism

Case versus control (n = 55; n = 55)

48

GABRB3

15q11.2-q13

Autism

140 multiplex families; LD

49

GABRB3

15q11-q13

Autism

80 families; TDT

50

GABRA5

15q11.2-q13

Autism

123 multiplex families; LD

51

GABRG3

15q11.2-q13

Autism

226 families; PDT

52

UBE3A

15q11-q13

Autism

94 multiplex families; LD

53

OMG

17q11

Autism (DQ >30)

Case versus control (n = 37; n = 101)

54

BLMH

17q11

Autism

81 trios; TDT

55

SLC6A4

17q11-q12

Autism

86 trios; TDT

56

17q11-q12

Autism

52 trios; TDT

57

17q11-q12

Autism

33 families

58

17q11-q12

Autism

71 patients; TDT

59

ADA

20q13

Autism

118 patients versus 126 controls

60

DXS287 b

Xq23

Infantile autism

Case control

61

a

All genes are reported using approved symbols according to the Human Genome Nomenclature Committee database (see URL: http:// www.gene.ucl.ac.uk/cgi-bin/nomenclature/searchgenes.pl), except where otherwise indicated.

b

Chromosomal marker.

ASD = autistic spectrum disorder; DQ = development quotient; LD = linkage disequilibrium; PDT = pedigree disequilibrium test; TDT = transmission disequilibrium test.

2.2 Hypothesis Driven Genetic Studies

A priori knowledge of biologically plausible gene candidates and pathways is the basis for hypothesis driven research. These studies predict the involvement of certain candidate genes based on clinical and empirical evidence. Studies have shown that serotonin reuptake inhibitors, dopamine antagonists as well as androgenic drugs, have a positive effect on autistic behavior.[85] Hence, genes that code for neurotransmitters or receptors of these substances are potential candidates for autism. © 2005 Adis Data Information BV. All rights reserved.

Serotonin is a neurotransmitter involved in many functions, including mood, appetite, and sensory perception. Altered serotonin levels may contribute to the core behavioral characteristics of autism.[85,86] The serotonin transporter gene (SLC6A4, also known as 5-HTT) has been studied extensively by many researchers. One study found preferential inheritance of a short promoter variant of SLC6A4 in affected individuals, while others have reported that affected family members inherited the long promoter variant more frequently.[57,58] However, other autistic populations have not Am J Pharmacogenomics 2005; 5 (4)

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shown an association between autism and serotonin transporter promoter variants. Dopamine is a neurotransmitter of the catecholamine family, which also includes norepinephrine and epinephrine and is involved in emotional behavior. One study has shown that children with autism have abnormal dopaminergic activity in the low medial prefrontal cortex[87] as well as elevated levels of catecholamines in the blood, urine, and cerebrospinal fluid.[88,89] Studies of the dopamine receptors D2, D3, and D5, the tyrosine hydroxylase gene (TH) and the dopamine β hydroxylase gene (DBH), however, have given few positive results.[89,90] Other avenues of interest include the homeo box (HOX) genes that regulate hindbrain development, differentiation of the urogenital system and appendicular skeletal growth,[91,92] the glutamatergic system,[93,94] and the arginine-vasopressin system.[95] 2.3 Whole Genome Linkage Scan Results in Autism

In linkage analysis, the goal is to identify a region of the genome that co-segregates with a trait through multiple meiotic events. The strategy of linkage analysis is to utilize polymorphic markers (microsatellite markers or SNPs) to discriminate which alleles are inherited through each meiotic event. Nonparametric, or model-free, linkage analysis approaches are typically favored for complex traits because they make no assumptions about disease transmission. This approach also relies on the use of sibling pairs to identify increased sharing of alleles among affected family members.[96] To date, at least 13 whole genome scans and several follow-up linkage studies targeting particular regions of the genome have been published in efforts to identify susceptibility genes in autism.[8,97-108] With the exception of regions on 2q where two studies found strong linkage signals[97,102] and chromosome 16p, where three studies showed suggestive signals,[97,100,101] most of these studies did not report any significant results and only give indications to either possible genetic loci or mild-to-moderate evidence for linkage. Whole-genome linkage scans conducted between 1998 and 2001 show the strongest evidence for linkage on chromosome 7q, whereas suggestive linkage was reported in three studies.[97,98,101] Many prior reviews have summarized these whole-genome linkage studies in detail.[4,62,109] Unfortunately, though some linkage peaks increased with the addition of new samples, suggestive linkage peaks from the first study decreased. Interestingly, the overlap of regions between studies or even in follow-up studies performed by the same group using the identical polymorphic marker panels in those independent cohorts is rare. Despite concerted efforts, no single gene has been identified that explains a significant proportion of cases of autism. It is becoming © 2005 Adis Data Information BV. All rights reserved.

increasingly evident that better and more powerful strategies aimed at identifying the genetic underpinnings of autism are warranted. 3. The Autism Genome Project: Phase I The completion of the Human Genome Project has paved the way for the study of complex human diseases. Recent progress in genomics, especially the development of high-throughput platforms for large-scale genotyping, has the potential to dramatically accelerate our ability to identify rare or, more likely, common polymorphisms that may be causal in both Mendelian and complex diseases. In phase I of the AGP, linkage analysis will be performed in a huge cohort of more than 1500 multiplex pedigrees collected from around the world using both the standard microsatellite linkage panels (at CIDR/NIH) and the Affymetrix >10 000 SNP array (GeneChip® Mapping 10K Array, Affymetrix, Inc., Santa Clara, CA, USA) and all data will be analyzed in an integrated fashion. By genotyping thousands of SNPs across the genome, it is possible to track regions of chromosomes that co-segregate through a pedigree with a trait. Historically, in a whole-genome scan, multiple affected members in a pedigree segregating a single-gene defect (Mendelian inheritance pattern) are genotyped using a set of 300–400 microsatellite markers spaced at 10 centiMorgan (cM) intervals across the genome. Microsatellite markers are polymorphic stretches of DNA that consist of tandem repeats of a simple sequence of nucleotides, which vary in length throughout the population.[110] If a gene that is responsible for the disease exists somewhere in the genome, affected family members are expected to have inherited the same disease-predisposing allele at the locus, and markers that lie physically near this disease gene will be transmitted along with the disease allele. With increasing density of the marker panel, the likelihood that at least one of these markers will be in sufficient proximity to the disease gene is significantly increased. As mentioned previously, the detection of genes for complex phenotypes is hampered by the relatively low genetic relative risk conferred by each locus. Bolstering information content by either genotyping more families or adding more markers could potentially decrease false-positive results and increase the detection of true susceptibility loci. While the use of microsatellite markers spaced at intervals of ~10cM across the genome has been the traditional method employed in genome-wide linkage scans, SNPs are beginning to emerge as an alternative to whole-genome genotyping because of their ease of use and ability to perform massively parallel genotyping. SNPs are variations in DNA sequence that occur when a single nucleotide, adenine (A), thymine (T), cytosine (C), or Am J Pharmacogenomics 2005; 5 (4)

The Autism Genome Project

guanine (G), in the genome sequence is altered. For example, a SNP might change the DNA sequence from GAGCCTA to GTGCCTA. While 99.9% of the human genome is identical between two individuals, it is estimated that a SNP can be found approximately every kilobase.[111-113] A map of closely spaced SNPs may offer equal or superior power to detect linkage, compared with low-density microsatellite maps even though they are individually less informative (only bi-allelic) than microsatellites.[114,115] Though there are several types of polymorphisms in the human genome (SNPs, repeat polymorphisms, and deletions or insertions, ranging in size from a single base pair to thousands of base pairs), DNA sequence variation in the human genome is predominantly in the form of SNPs.[116] SNPs can have very different consequences at the phenotypic level depending on the location. For example, SNPs residing in the coding regions of genes that alter the function or structure of the encoded proteins are the cause of classic single nucleotide mutations in most of the known recessively or dominantly inherited monogenic disorders. Other groups of SNPs alter the primary structure of a protein involved in drug metabolism and are, thus, obvious targets for pharmacogenetic intervention.[117] Missense SNPs that reside in the coding regions of genes also contribute to common diseases. Examples of missense SNPs include SNPs found in the apolipoprotein E (APOE) gene[118] and the factor V Leiden mutation.[119] An assessment of the risk of a particular disease can be accomplished by analyzing the SNP in question. Lastly, SNPs that reside in the regulatory regions of genes might influence the risk of common disease. Because most SNPs are found in noncoding regions of the human genome, they have no known direct impact on the phenotype of the individual. Thus, these types of SNPs are useful genetic markers to localize nearby disease-causing events through classic linkage analysis, association analysis, and linkage disequilibrium studies in both founder and outbred populations.[111-113,120] The use of SNPs in highdensity genotyping offers an alternative to microsatellites because of the sheer number of markers, which provide higher overall information that is more evenly distributed.[121] One of the first high-density whole-genome studies demonstrated that a carefully selected set of 2988 SNPs is more informative than the Marshfield Clinic screening microsatellite version.[122] 3.1 Whole-Genome Linkage Scanning in Complex Traits

The successful use of high-density, whole-genome SNP genotyping for linkage analysis has been demonstrated in several studies in the literature. For example, Puffenberger et al.[123] identified the causative mutation in the TSPYL gene (encoding © 2005 Adis Data Information BV. All rights reserved.

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testis-specific protein-like 1) from a 3.6 Mb autozygous region in a study of 21 individuals with sudden infant death with dysgenesis of the testes (SIDDT) syndrome using the GeneChip® Mapping 10K Array and Assay Kit containing >10 000 SNPs on a single silicon array. In 36 individuals, Shrimpton et al.[124] identified a single missense mutation in the HOXD10 gene from a 6 Mb region in an autosomal dominant form of Charcot-Marie-Tooth disease also using the Affymetrix GeneChip® Mapping 10K Array. In addition, Sellick et al.[125] were able to identify three loci for three separate diseases (two of which were novel), using the GeneChip® Mapping 10K Array. Moreover, there have been several examples of linkage studies in complex diseases where the informativeness of the 10K SNP panel was compared with the common microsatellite panels. For example, Schaid et al.[126] identified in 467 men with prostate cancer from 167 families several genetic loci including chromosomes 8, 2, 6, and 12 that were missed using 402 microsatellite markers with the ABI PRISM® Linkage Mapping Set (Applied Biosystems, Foster City, CA, USA). This study also provided a direct comparison between traditional microsatellite markers and SNP markers for genotyping, revealing that the information content increased from 41% for microsatellite markers to 61% for SNPs. The finding that the ~10 000 SNPs have a higher and more evenly distributed total information content than ~400 microsatellite markers has been corroborated by other groups using simulation approaches, as calculated by the program Merlin.[114,127] Other examples of linkage studies that compare SNPs to a microsatellite panel include studies on rheumatoid arthritis and bipolar disorder.[115,128] It was found that the SNP panel consistently gave better coverage and a higher information content (IC) compared with the microsatellite assays and, in addition, that linkage peaks identified would have gone undetected in previous whole-genome scans. Moreover, Murray et al.[129] were able to demonstrate that the information content for the Illumina® Linkage III SNP panel (Illumina, Inc., San Diego, CA, USA) was higher than both a 10cM and 5cM genome scan using conventional microsatellite markers. Additionally, there has been one study whereby 811 microsatellites have been compared with both the GeneChip® Mapping 10K Array platform, and the BeadArray™ platform (Illumina, Inc.). It was found that the GeneChip® system extracts marginally more information, that the BeadArray™ system exhibits slightly higher call rates, and that both high-density SNP platforms illustrate a higher information content than the 811 Applied Biosystems high-density linkage mapping microsatellite marker set (HDLMS) [GeneChip® 91.5%, BeadArray™ 90.7%, HDLMS 82.8%].[130] The sheer density of coverage in the genome that SNPs provide has been shown to allow genes to be honed in on at a much quicker Am J Pharmacogenomics 2005; 5 (4)

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rate. This not only allows loci to be defined more precisely, but also provides the framework for conducting whole-genome casecontrol association studies. This type of study seeks to identify SNPs that statistically associate to complex, multi-genic diseases due to their close proximity to other, possibly unmapped, polymorphisms.[131,132] However, the number of SNPs required to realistically conduct a whole-genome association study is believed to be in the order of hundreds of thousands of SNPs spaced evenly across the genetic blueprint.[28] The AGP is a large-scale, collaborative genetics research project aimed at mapping the human genome in the search for autismsusceptibility genes. Together with over 40 academic and research institutions across North America and Europe, approximately 7000 samples from around 1500 multiplex families (a family with two or more children affected with autistic spectrum disorder according to the international classification of disease criteria[133]) will be combined to form the largest genome-wide linkage scan for autism to date. Phase I of the NAAR Genome Project will consist of genome-wide scans utilizing both SNP array and microsatellite technologies. Linkage analysis will subsequently be performed on the approximately 1500 pedigrees. Ultimately, the vision will be to identify the exact nucleotide variants within genes which give rise to predisposition. 3.2 Single Nucleotide Polymorphisms (SNP) Genotyping and Microsatellite Marker Genotyping

As stated previously, while linkage studies have historically relied on multi-allelic microsatellite markers, SNP markers are emerging as an alternative approach. High-density SNP genotyping offers an alternative to microsatellites in that while they are biallelic markers, the sheer number of markers typed provides higher overall information which is more evenly distributed.[134] The AGP will be comprehensive not only in that it is powered well for identification of predisposing alleles, but also because each DNA sample will be genotyped on both the Affymetrix GeneChip® Mapping 10K SNP array and the ~400 microsatellite marker panel. The utility of having every sample in the cohort genotyped on the identical set of markers will allow full integration of the assembled pedigrees – something that is never fully possible with meta-analyses post hoc. The Affymetrix GeneChip® DNA array technology is a direct extension of the more commonly used expression profiling GeneChip® assays.[135] In the 10K SNP genotyping assay, fluorescently labeled fragments of DNA containing specific SNPs are genotyped by whether or not they hybridize to a sequencespecific oligomer probe set tiled on a silicon wafer.[135] The GeneChip® Mapping Array assay uses whole-genome PCR ampli© 2005 Adis Data Information BV. All rights reserved.

fication of digested genomic DNA ligated to universal adaptors.[136-138] DNA is purified, biotin labeled, fragmented, and hybridized to a microarray. For each SNP, 40 tiled oligomer probes with 10 mismatched calls and 10 perfect matched calls interrogate each of 2 possible SNP alleles.[138] At the time of publication, three mapping panels were available using this platform: Affymetrix GeneChip® Mapping 10K Array, Affymetrix GeneChip® Mapping 100K Array, and an Affymetrix GeneChip® Mapping 500K Array in a two chip set. Array calls for each SNP are made by comparing the relative allele signal for each SNP to a trained database[139] (10K SNP panel) or by using a model-based algorithm[140] (100K SNP panel). The trained database model determines if the two relative allele signal (RAS) discriminators from both the sense and anti-sense direction fall into one of three silhouettes (homozygous AA, heterozygous AB, homozygous BB) derived from a training set. In comparison, the model-based algorithm calculates a log likelihood of all possible genotype models, homozygous, heterozygous, or no call, according to the hybridization signals for the 40 probes. A Wilcoxon Signed Rank test is used to compute confidence intervals. The model-based algorithm is necessary at higher densities since a training set is not always available for SNPs only found at low frequencies. Through these conservative calling algorithms, call accuracy is reported to be 99.9% and call rates (the number of SNPs successfully genotyped on each array) average 95%.[134] In our experience there are a set of approximately 400 SNPs out of the >10 000 which are consistently not called and account for the vast majority of the failed genotypes in a systematic fashion. 3.2.1 Accuracy and Reproducibility Accuracy

To assess the accuracy of the Affymetrix GeneChip® Mapping 10K SNP array platform, we have used three different methods. First, the genotypes generated by the Mapping 10K array were compared with genotypes generated by a single base extension (SBE or mini-sequencing) method across 40 Caucasian samples used by The SNP Consortium (TSC) allele frequency project. There was an average of 546 SNPs compared for each sample, for a total of 21 858 genotypes generated for this accuracy measure. 108 discordant genotypes were identified yielding a concordance rate of 99.5%. This represents a lower bound of accuracy, as we do not know if discordant genotypes were due to SBE errors or Mapping 10K errors. In the second method, sixty SNPs were genotyped in six individuals by di-deoxy sequencing analysis. There was only one discordant genotype among 341 compared, giving an overall concordance of 99.7%. The third method used PEDCHECK[141] to look at Mendel errors in five Centre d’Etude du Polymorphisme Humain (CEPH) trios.[134] Here, 104 errors Am J Pharmacogenomics 2005; 5 (4)

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were seen in 162 404 genotypes, giving an estimated accuracy of 99.9%. This number is likely to be an overestimate of accuracy, as some genotyping errors will go undetected due to uninformative meioses. Reproducibility

To assess reproducibility, 16 individuals were each genotyped 9 times for a total of 1 623 443 genotypes. 192 did not match the consensus genotypes, giving a reproducibility of 99.99%. In another set of experiments, 24 samples were tested in triplicate. Ten of 24 samples had 100% reproducibility across three replicates. Thus, the SNP arrays have an accuracy of between 99.5 and 99.9% by several independent estimates.[115,134] 3.2.2 Informativeness of SNPs versus Microsatellites

Information content is a function of marker heterozygosity, distance between markers, and pedigree structure. While each individual SNP is less informative than a microsatellite marker in most cases (e.g. on the Mapping 10K array the average heterozygosity is 0.37, whereas on the CIDR panel the average heterozygosity is 0.72), the greater number of SNPs in aggregate leads to higher information content at any particular point in the genome.[134] The benefits of higher information content are higher logarithm of odds (LOD) scores, smaller linkage intervals, and the ability to find linkage intervals that were missed by lower information content approaches. Matsuzaki et al.[138] were able to replicate a linkage for chronic mucocutaneous candidiasis and thyroid disease with a higher LOD score, along with similar p-values and a decrease in the linkage interval from 35.28cM to 17.73cM. Sellick et al.[125] were able to identify novel regions linked to neonatal diabetes, and separately, craniosynostosis, that were missed by previous microsatellite scans on the same samples. Kennedy et al.[134] made several direct comparisons of information content on a group of 10 423 SNPs compared with 365 microsatellite markers in a set of 550 samples. The SNPs showed a much higher entropy score (a measurement of information content) compared with the microsatellite markers (mean IC = 0.54 for 365 microsatellites vs mean IC = 0.74 for 11 555 SNPs).[138] 3.2.3 Linkage Disequilibrium as a Confounding Factor in High Density SNP Linkage Analysis

There exists the potential for linkage disequilibrium between markers to skew multipoint LOD calculations because the intermarker density averages 210kb with the >10 000 SNPs on the Affymetrix GeneChip® 10K SNP array. This does not affect the accuracy of 2-point LOD score calculations, but will skew multipoint LOD calculations because alleles of adjacent markers do not segregate independently. The ability to define haplotypes as ‘virtual markers’ and use these in the multipoint LOD calculation will © 2005 Adis Data Information BV. All rights reserved.

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eventually provide a solution to this problem. Haplotyping will be performed by taking advantage of informative meioses within pedigrees as well as any information that can be gleaned from the Haplotype Mapping (HapMap) project.[142] The Varia software (Agilent Technologies, Palo Alto, CA, USA) [described below] does have this function built in, and we are currently using this software in conjunction with the standard packages discussed in the next section. 3.2.4 Availability of Analysis Tools

A number of freely available analysis tools have been utilized by the genetic analysis community for a number of years. All of these tools work with the Affymetrix GeneChip® 10K SNP genotype data. There are a number of options for 10K array analysis which include conversion tools to import Affymetrix files into common linkage packages (GeneChip® DNA Analysis Software [GDAS] Port), standard analysis tools which handle microsatellite data and also handle SNP array genotype data (Merlin,[127] GeneHunter,[143] LINKAGE suite,[144] SimWalk;[145] Mega2 converts between all file formats), new tools have been created specifically for GeneChip® 10K SNP data that have graphical user interfaces and seamless connection to genomic annotations (Varia and dChipSNP), and finally new tools, algorithms, and approaches are sure to be developed as a result of the Genetics Analysis Workshop 2004 which focused on comparing the Affymetrix GeneChip® Mapping 10K array to the standard ABI PRISM® microsatellite panel. 3.2.5 The GeneChip® DNA Analysis Software (GDAS) Port

This module formats the Affymetrix output file (‘GDAS analysis file’ containing SNP ID, TSC ID, dbSNP-assigned reference SNP [rs] ID, genetic map location, relative allele signals, SNP calls, and call zones [www.affymetrix.com]) for import into common analysis packages. This module is available from Affymetrix at no cost. The two input files are: • Pedigree file (.txt) family ID, individual ID, father, mother, sex, affection status (1 unaffected, 2 is affected). The standard pedfile is modified by making an additional column before the family ID column, which contains the individual ID from the GDAS output column header for the calls ending in the prefix “*_call”. • GDAS analysis file (.txt) should be updated with respect to genetic marker information from NetAffx at least every three months. The deCODE marker map (deCODE Genetics, Reykjavik, Iceland) is used because it has the highest resolution. The deCODE marker order map is really a linkage map derived from the latest decode microsatellite genetic map with the SNPs plotted relative to the microsatellite markers using genome sequence data. The NetAffx™ tool (Affymetrix) also enables Am J Pharmacogenomics 2005; 5 (4)

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downloading Caucasian, African American, and Asian allele frequencies for the analysis packages. Files in the correct format can be exported, and Mendelian inheritance of each marker is checked. If a marker does not pass this error check, the specific genotype call data is deleted and not used. 3.2.6 Common Existing Analysis Tools

The existing analysis tools include Merlin, GeneHunter, and Simwalk. Using these tools, we have obtained results using the 10K SNP array and compared those results to linkage data obtained using microsatellite marker panels. Multi-tool analysis of data generated from the GeneChip® 10K SNP array has been done on multiple pedigrees to illustrate that all common analysis tools work with the SNP data and produce LOD scores often in excess of those produced using microsatellite markers. Both parametric analysis (Varia and M-Link) and nonparametric analysis (Merlin and SimWalk2) can be done. A script for GeneHunter is being written to reduce the marker files to <400 markers each. 3.2.7 New Analysis Tools

There are currently two new analysis tools which are mature and which we have utilized. These tools not only perform standard linkage using standard analysis algorithms or modified algorithms, but also provide a host of visualization tools to query the genotypes, haplotypes, haplotype breakpoints, etc. These are: • Varia from Silicon Genetics (now part of Agilent Technologies). We have worked with Silicon Genetics to develop a new software package entitled Varia. This analysis solution can import Affymetrix GDAS files directly. • dChipSNP. This is an implementation of the Lander-Green algorithm for doing parametric linkage analysis. Data can be imported from GDAS analysis files directly into dChipSNP, and the software allows elegant visualization of linkage results, SNP genotypes for haplotype breakpoint mapping, and gene content in the interval. The basic dChipSNP was described in a recent publication by Leiberfarb et al.[146] 3.2.8 Workflow

Workflow is dramatically different between a microsatellite genome scan and a SNP array genome scan. Overall, with similar infrastructures available for each, SNP 10K arrays generate two orders of magnitude increase in throughput, with complete elimination of the need to check allele calling manually. • Microsatellites are generally genotyped using PCR amplification of a single marker in 96 or 384 well format. Individual marker amplicons are fluorescently tagged and size selected, so that up to 16 markers can be combined into a single well after amplification. Thus, 16 plates of PCR are robotically combined © 2005 Adis Data Information BV. All rights reserved.

•

into a single plate which can be run on an automatic sequencer. Current sequencers can handle 96 wells at a time, so 4 runs of an ABI 3730xl are needed to genotype 16 markers in 384 individuals. The robotics, PCR machine infrastructure, and DNA sequencer capacity allow us to genotype 48 markers per day on 384 individuals. To do all ~400 microsatellites on 384 individuals would take about 2 weeks. Thus, the yearly maximum (working 45 weeks a year) that can be achieved using microsatellite markers at a mid-sized genome institute would be 8640 individuals genotyped with the microsatellite marker panel, with almost 3 500 000 genotypes generated. There would also have to be a significant team of scientists checking alleles and Mendelian inheritance of alleles in order to achieve this throughput. SNP arrays take 3 days each to run. One DNA sample is labeled with one primer and hybridized to one array which allows (on the 10K SNP array) >10 000 SNP genotypes to be called in that time-period. Because they can be run in parallel, data can be generated at a rate of 1000 arrays per week with 6 technicians and 3 scanners and 12 daisy-chained fluidics stations. The major bottleneck of verifying the integrity of each call is not necessary with the Affymetrix genotyping software. Calls are made automatically and have an accuracy of >99.9%. Additionally, a >95% call rate is the average seen. The genotype calling algorithm is extremely conservative so that if no unambiguous call can be made, the software assigns an ‘absent’ call for that genotype. There is a user interface available within Affymetrix GDAS program that allows any genotype to be queried with respect to signal strength and call confidence. 4. The Autism Genome Project: Phase II

The second phase of the AGP is focused on identifying the exact nucleotide changes that predispose to autism and reside within the linkage intervals derived from phase I. The general strategy for phase II will be to sequence each linkage interval to completion in probands from families which contribute the majority of the LOD score to each genetic interval. These will be different families for each positive linkage interval. Sequencing technologies have recently become available and costs have plummeted, enabling large-scale sequencing of intervals. If a functional nucleotide variant is found in a gene, and other significantly functional variants (i.e. ‘mutations’) are found in the same gene in different pedigrees from that linkage interval, it may become obvious as to what is the disease-causing gene. If no functional nucleotide variants are identified, variants will be tested through informatics to assess whether there is a probability of that variant being functionally relevant (nonsynonymous, evolutionariAm J Pharmacogenomics 2005; 5 (4)

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ly conserved nucleotide, etc.). The subset of variants that survive must reside on the at-risk allele, which will be tested by genotyping in the original pedigree structure. Those 50% of variants which reside on the correct allele will be prioritized with respect to functional significance again, and then gene-specific clustering will be performed across the pedigrees. If the genetic risk is imparted through a common nucleotide variant which is present in all probands on the correct allele, a larger case-control association study will be needed to solidify the association of that SNP with the phenotype. Phase II of the AGP is also ultimately interested in developing murine models for autism based on the genetic findings. Murine models will be essential for testing therapeutic interventions. Ultimately, we hope that the work performed in both phase I and II will move the entire field of autism clinical management forward in the next decade to the point where early and accurate newborn screening can be performed, and effective interventions can be initiated to ensure that the disorder does not strike. 5. Summary Autism is a major public health concern, conferring a deep burden on patients and their families. While some with autism are only mildly affected, most people with the condition will require lifelong supervision and care and have significant impairments. This poses a tremendous challenge to the health professionals who treat, and the families who live with and care for, these individuals. Early diagnosis and intervention will significantly improve a child’s long-term outcome and can reduce the challenges associated with the disorder, lessen disruptive behavior, provide for some degree of independence and increase a child’s ability to grow and develop new skills. Because autism requires lifelong treatment, any insight provided by the AGP study in identifying susceptibility genes responsible for the autistic phenotype could significantly alter a person’s lifestyle. The AGP will move the entire field of autism clinical management forward in that we hope the findings from the study will begin to answer questions about the causal mechanisms underlying the pathophysiology of autism. As well, more robust knockout mouse models using the candidate gene findings will aid in targeting new pathways and mechanisms. From here we can develop therapeutic targets for drug treatments; the ultimate goal is to develop a newborn screening diagnostic that would allow for early intervention. Interestingly, no drugs are currently available for use directly in autism. Defining key regulatory points in the disease pathogenesis that would potentially help in the development of new therapies targeting these regulatory events would provide families with much needed hope. © 2005 Adis Data Information BV. All rights reserved.

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Moreover, autism is a disorder with symptoms that are also associated with other brain-based conditions. Identifying the genetic causes of autism has the potential to yield significant advances for other disorders such as attention deficit-hyperactivity disorder, fragile X, and some forms of mental retardation. Over the past two decades 1200 human disease genes have been identified,[147] together with what we learn from the outcome of the AGP, a paradigm for other complex genetic traits may result. Of course, even after susceptibility genes are identified, many questions will still need to be answered. What is the relative importance of each gene on the disease process? How will we understand and treat the disease based on the genetic underpinnings that are identified? Key regulatory points in the disease pathogenesis will need to be defined and new therapies targeting these regulatory events will certainly need to be developed. Acknowledgments Dr Hu-Lince received a National Research Service Award Fellowship (P32 NS43932).

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Correspondence and offprints: Dr Dietrich A. Stephan, Neurogenomics Division, The Translational Genomics Research Institute, 445 North Fifth Street, 5th Floor, Phoenix, AZ 85004, USA.

Am J Pharmacogenomics 2005; 5 (4)