Current perspectives on the genetic analysis of autism

American Journal of Medical Genetics Part C (Semin. Med. Genet.) 142C:24–32 (2006)

A R T I C L E

Current Perspectives on the GeneticAnalysis of AutismHILARY COON*

Although no definitive genetic mutations leading to autism susceptibility have been established, the field hasmany new resources to tackle this difficult problem. Numbers of families at many research sites are now large, andnew collaborations among these groups will allow for collections of subjects with enough statistical power todetect relatively small gene effects. New technological advances in genotyping will allow for more fine-grainedgenetic analysis, and more sophisticated techniques have been developed to address the vast amounts of dataacquired. Researchers have also begun to focus on intermediate phenotypes associated with autism, such aselevated serotonin, increased head circumference, social difficulties, or language impairment or delay. Thesephenotypes may help to stratify affected cases into more genetically homogeneous subgroups, and may alsooccur in their clinically unaffected relatives. The study of intermediate phenotypes may allow investigators to findcommon gene variants that lead to autism susceptibility. Here we discuss the major intermediate phenotypes, andgive an overview of current genetic analysis techniques. � 2006 Wiley-Liss, Inc.

KEY WORDS: autism; intermediate phenotype; genetic linkage; genetic association

INTRODUCTION

Autism researchers now have an

impressive array of methods available

for the analysis of genetic data. These

methods, together with dense marker

data, detailed diagnostic data, and many

intermediate phenotypes associated with

autism combine to provide the autism

genetics community with rich resources

to search for autism susceptibility genes.

Collaborative efforts have now resulted

in large, informative collections of

families. Results from multiple genome

scans in different samples and with

different subsets of the clinical pheno-

type are beginning to suggest some

convergence of regions, and to direct

new research. Within the next 5–

10 years, even larger consortium efforts

will begin to reveal results on family

collections larger than any yet studied.

An additional benefit of large colla-

borative studies may be the ability to

characterize genetic and/or phenotypic

subsets of subjects.

In addition to the availability of

large collaborative samples, the autism

genetics research community can now

take advantage of approximately 9 million

single nucleotide polymorphisms (SNPs;

e.g., http://www.ncbi.nlm.nih.gov/SNP)

and detailed data describing the non-

random associations of alleles at close

genetic markers among populations

(linkage disequilibrium) across the gen-

ome (HapMap [Daly et al., 2001;

Gabriel et al., 2002; Gibbs et al., 2003;

Hinds et al., 2005]). Analysis methods

have been hard pressed to keep up with

the massive influx of genetic data.

Linkage analysis has been extended to

handle larger datasets [Gudbjartsson

et al., 2000; Abecasis et al., 2002], and

many new methods for family-based

association analysis have been developed

[Boehnke and Langefeld, 1998; Horvath

and Laird, 1998; Spielman and Ewens,

1998; Clayton, 1999; Martin et al.,

2000; Seltman et al., 2003]. Effects of

different causal genes across different

families (heterogeneity] can be tested

Hilary Coon, Ph.D., is Associate Professorof Psychiatry and Principal Investigator forthe Genetics Component of the Utah AutismResearch Program at the University of Utah.She is a statistical geneticist with interest inthe analysis of complex traits.

Grant sponsor: NICHD; Grant number: 5U19 HD035476 and R01 MH069359; Grantsponsor: Utah Autism Foundation.

*Correspondence to: Hilary Coon, Ph.D.,Utah Neurodevelopmental Genetics Project,421 Wakara Way, Suite 143, Salt LakeCity, UT 84108. E-mail: [email protected]

DOI 10.1002/ajmg.c.30079

Collaborative efforts have now

resulted in large, informative

collections of families. Results

from multiple genome scans in

different samples and with

different subsets of the clinical

phenotype are beginning to

suggest some convergence of

regions, and to direct new

research. Within the next

5–10 years, even larger

consortium efforts will begin

to reveal results on family

collections larger than any

yet studied.

� 2006 Wiley-Liss, Inc.

[Devlin and Roeder, 1999; Pritchard

et al., 2000a,b], and techniques have

been developed to investigate gene-by-

gene interactions [Devlin et al., 2003;

Ritchie et al., 2003a,b].

The complexity of the phenotype

of autism poses challenges for genetic

analysis. Autism involves deficits in the

three behavioral domains of social inter-

action, communication, and repetitive

and stereotyped behaviors. Autism com-

prises a heterogeneous spectrum that

also includes Asperger syndrome and

PDD-Not Otherwise Specified (PDD-

NOS [American Psychiatric Associa-

tion, 1994]). Additional phenotypes

associated with autism will be described

below. Recent studies have suggested

that autism is more common than pre-

viously thought (from 0.0016 to 0.004)

[Bertrand et al., 2001; Chakrabarti and

Fombonne, 2001; Muhle et al., 2004].

There is strong evidence for the

genetic etiology of autism [Folstein and

Rutter, 1977; Steffenburg et al., 1989;

Bailey et al., 1996, 1998; Smalley and

Collins, 1996; Szatmari et al., 1998;

Fombonne, 1999; Chakrabarti and

Fombonne, 2001]. Heritability esti-

mates are high, and twin studies suggest

non-Mendelian inheritance, with the

involvement of at least two and perhaps

many interacting genes [Pickles et al.,

1995; Risch et al., 1999; Pritchard,

2001]. The recurrence risk, twin con-

cordance data, and extended familial

clinical phenotypes support the hypoth-

esis that autism results from a strong,

though not absolute, genetic influence.

It is likely that genetic and environ-

mental risk factors play complex, inter-

acting roles in causing autism [Jones and

Szatmari, 2002].

Genetic studies of autism are well

underway. There are now 12 published

genome scans of autism spectrum dis-

orders [IMGSAC, 1998; Barrett et al.,

1999; Philippe et al., 1999; Buxbaum

et al., 2001; IMGSAC, 2001; Liu et al.,

2001; Auranen et al., 2002; Shao et al.,

2002; Yonan et al., 2003; Buxbaum et al.,

2004; Ylisaukkooja et al., 2004; McCau-

ley et al., 2005]. Evidence of locations of

possible susceptibility genes can be

found in multiple locations across the

genome from these studies, with most

current efforts centering around candi-

date regions on chromosome 7q, chro-

mosome 2q, and chromosome 17q in

the region of the serotonin transporter

locus SLC6A4.

METHODS

Figure 1 gives an overview of the

processes involved in the genetic analysis

of autism. The sections that follow will

take each step in more detail.

New Phenotypes

Autism genetics research has begun to

take advantage of a wider array of

phenotypes than clinical diagnosis. Phe-

notypes associated with autism, called

intermediate phenotypes, may serve one

of two purposes. First, intermediate

phenotypes may serve to stratify affected

cases into more genetically homoge-

neous subgroups. Second, phenotypes

that occur in affected subjects may also

occur in their clinically unaffected

relatives. These phenotypes may be

indicators of susceptibility genes, present

singly in clinically unaffected family

Define and choose phenotypes for analysis Rates and distribution in autism subjects and relatives (or control sample), sufficient variation, segregation in pedigrees, heritability

Check data/ preliminary analysis Genotypes: misinheritance, map distance, adminxture, allele frequencies

Phenotypes: outliers, distributions, effects of covariates

Linkage analysis (pedigree study)

Affection status with or without intermediate phenotypes

Association analysis (case/control or family-based

association study) Simple chi-square tests, transmission

disequilibrium tests, family based association analysis

Choose marker set (pedigree linkage study) Traditional DNA markers, Single

Nucleotide Polymorphisms (SNPs)

Choose candidate gene(s) (association study)

Positional (under linkage peaks), functional variants in known genes

Verify findings Secondary analyses. For linkage, check if results are robust to: allele

frequencies, genetic models, maps, analysis methods. For candidate genes verify findings with, expression studies. For both, verify findings with

independent replication studies. OPTION: Refine phenotype or define phenotype subset, then return to the

top.

Figure 1. Schematic of steps in the genetic analysis of autism.

Evidence of locations of possible

susceptibility genes can be found

in multiple locations across the

genome from these studies, with

most current efforts centering

around candidate regions on

chromosome 7q, chromosome

2q, and chromosome 17q in

the region of the serotonin

transporter locus SLC6A4.

ARTICLE AMERICAN JOURNAL OF MEDICAL GENETICS PART C (SEMIN. MED. GENET.): DOI 10.1002/ajmg.c 25

members, but present together with

other susceptibility genes or environ-

mental factors in persons with autism.

The study of an intermediate phenotype

may therefore reveal one of many

common interacting genes. The follow-

ing brief review presents several promis-

ing intermediate phenotypes for autism.

Broader Autism Phenotype (BAP)

The BAP is characterized by impair-

ments in the three core domains of autism

(social behaviors, communication, repe-

titive behaviors) present in a milder form

than required for diagnosis. Besides

The Broader Autism

Phenotype characterized by

impairments in the three core

domains of autism (social

behaviors, communication,

repetitive behaviors) present in

a milder form than required

for diagnosis.

traditional autism, families with an

autistic child show increased rates of

the BAP (12%–25%) [Folstein and

Piven, 1991; Bolton et al., 1994; Bailey

et al., 1996]. Piven and Folstein have

developed an algorithm that incorpo-

rates items measuring rigidity, friendship

patterns, pragmatic language and other

features to assign membership to the

BAP. Using this algorithm, 50% of

parents of autistic children manifest the

broader phenotype, compared to 2% of

parents with a Down syndrome child

[Piven et al., 1997a,b]. Szatmari et al.

[1995] have found similar results for

parents, but not in more extended family

members. New measures are being

developed specifically for genetic analy-

sis of the BAP. For example, the Social

Responsiveness Scale [Constantino

et al., 2000, 2003] is a new, quantitative

measure of social deficits characteristic

of autism spectrum disorders that shows

promise for future genetic studies of

autism.

Language function

Language function is a specific aspect of

the BAP that has been studied quite

intensively, and may define the most

heritable aspect of the BAP [Piven et al.,

1997b; Folstein et al., 1999; Lord et al.,

2001; Dawson et al., 2002]. Language

has already proven to be a useful

phenotype for refining genetic studies

of autism [Alarcon et al., 2002; Bradford

et al., 2001; Buxbaum et al., 2001].

Developmental language disorders, par-

ticularly specific language impairment,

have shown strong genetic etiology

[Rapin, 1996; Wassink and Piven,

2000; Bartlett et al., 2002; Newbury

and Monaco, 2002; Silverman et al.,

2002]. Language characteristics include

deficits in phonologic processing, langu-

age development, speech, and reading.

Familial deficits in these characteristics

have been identified in parents and

siblings of probands with autism [Plumet

et al., 1995; Piven et al., 1997b; Wolk and

Giesen, 2000; Silverman et al., 2002].

Language characteristics

include deficits in phonologic

processing, language

development, speech, and

reading. Familial deficits in

these characteristics have been

identified in parents and

siblings of probands

with autism.

Serotonin and autism

Elevated serotonin may identify families

at increased risk for autism, as suggested

by several lines of evidence. Studies of

blood levels in probands find an eleva-

tion in approximately 20%–30% of cases

[Anderson et al., 1987; Cook and

Leventhal, 1996; Anderson, 2002]. Ele-

vation of serotonin is present in family

members of autistic subjects with hyper-

serotonemia [Kuperman et al., 1985;

Abramson et al., 1989; Cooket al., 1990,

1994; Leventhal et al., 1990; Piven et al.,

1991]. Pharmacologic studies implicate

the serotonin system in autism [Cook

et al., 1994; McDougle et al., 1996;

Potenza and McDougle, 1997]. In addi-

tion to recent genome-wide linkage

findings on 17q, there is specific evi-

dence for the involvement of the

serotonin transporter protein gene on

17q (HTT) [Cook et al., 1997; Klauck

et al., 1997; Marazziti et al., 2000;

Yirmiya et al., 2001, Tordjman et al.,

2001; Kim et al., 2002].

Head circumference

Abnormalities in head circumference

in autism have been well documented.

In particular, macrocephaly (head

size over the 97th centile for age/sex)

occurs in about 20% of autistic indivi-

duals [Fombonne, 1999], and is also

common in autism spectrum disorders

[Woodhouse et al., 1996]. Investigations

Abnormalities in head

circumference in autism have

been well documented. In

particular, macrocephaly

(head size over the 97th centile

for age/sex) occurs in about

20% of autistic individuals and

is also common in autism

spectrum disorders.

of head circumference as a quantitative

phenotype show increases in quanti-

tative head circumference in autism

[Woodhouse et al., 1996; Lainhart et al.,

1997]. This increase in head size has

been shown through neuroimaging

studies to be due to abnormal enlarge-

ment of the brain. Head circumference

may prove to be an interesting and easily

measured stratification variable for asso-

ciation studies. Findings using this

simple measure on large data sets could

then be explored in interesting subsets

using more expensive MRI techniques.

Choosing intermediate phenotypes

To date, intermediate phenotypes stud-

ied have been primarily language

26 AMERICAN JOURNAL OF MEDICAL GENETICS PART C (SEMIN. MED. GENET.): DOI 10.1002/ajmg.c ARTICLE

delays, repetitive behaviors, and social

behaviors. Additional phenotypic traits

should be chosen with demonstrated

genetic etiology (significant heritabil-

ity). Heritability is the proportion of

variance in a phenotype attributable to

genes. Estimates of the heritability of

quantitative traits on large pedigrees can

be obtained using several analysis pro-

grams (e.g., SOLAR [Almasy and Blan-

gero, 1998] or PAP [Hasstedt and

Cartwright, 1979]).

Intermediate phenotypes may be

interdependent. The extent to which

phenotypes cosegregate in families can

be tested in the Pedigree Analysis

Package (PAP [Hasstedt and Cartwright,

1979]). The PAP model assumes that

variation in each phenotype is due to the

sum of independent effects of shared

genes and shared environment among

family members, as well as random

environmental effects specific to each

individual. The program estimates sepa-

rate genetic and environmental compo-

nents of the covariance between two

phenotypes. The genetic estimate indi-

cates the extent towhich the phenotypes

are influenced by genes common to

them both.

Data Checking and

Preliminary Analysis

Before embarking on an analysis of

genetic data, error checking should be

done, both for phenotypes and geno-

types. Such checking can potentially

avoid false negative findings. Labora-

tories use internal genotyping controls,

and have stringent criteria for accepting

or deleting uncertain genotypes using

lab checking protocols.

Genotype checking

Checking for remaining inconsistencies

can be done using various genotype

cleaning programs, such as PEDSYS

(http://www. sfbr.org/sfbr/public/soft-

ware/pedsys/pedsys.html).Software such

as CRIMAP [Lander and Green, 1987]

can be used to verify observed map

distances compared to fixed genetic map

distances. Data can sometimes indicate

inflated map distances, and tools such as

SimWalk2 can be used to identify geno-

types that generate the double recombi-

nants which inflate the map.

Ancestry and allele frequencies

Particularly with the creation of colla-

borative data sets, the issue of determin-

ing ancestry within samples is important.

Ancestry can be estimated from genetic

data using the STRUCTURE2 program

[Pritchard et al., 2000a], or a mixture

model based on principal component

analyses, as described by Zhu et al.

[2002]. The primary group within the

sample can first be analyzed, with

families of other ancestries subsequently

tested for homogeneity of linkage.

Heterogeneity can be evaluated specifi-

cally in linkage analysis by using one of

several available techniques [Vieland,

1998; Devlin et al., 2002; Logue and

Vieland, 2004; Wijsman and Yu, 2004].

Checking phenotypes: Distributions and

effects of covariates

Analysis methods are often sensitive to

outliers and deviations from the normal

distribution of quantitative traits being

analyzed. Each trait should be tested for

its distributional properties, and outliers

should be investigated. If the data are

non-normal, either a transformation

should be applied, or internal correc-

tions to the resulting scores should be

used, as implemented in many of the

current analysis programs. In addition,

several of the traits of interest for autism

may have strong effects of covariates that

should be taken into account before

genetic analysis. These effects may

include age, sex, and medications,

depending upon the trait in question.

While some genetic analysis software has

the capability of covariate adjustment,

careful scrutiny of these effects should

precede the final analysis.

Linkage Analysis: Basic Methods

and Refinements

We will briefly review the general

concepts of linkage. Linkage analysis is

based on the recombination of DNA

from parental gametes. When gametes

form during meiosis, physical crossing

over, breakage, and recombination of

chromosomes can occur, mixing up the

parental genetic material. The variations

in DNA sequence at a location (locus)

on a chromosome are called alleles. If

two loci are far enough apart, an odd or

even number of crossovers will happen

randomly between them and the recom-

binant and non-recombinant types of

chromosomes will occur in the gametes

with equal frequency. When this hap-

pens, the loci are unlinked, even though

they may be on the same chromosome.

In this case, the recombination fre-

quency (theta) is ½, because half of the

gametes have recombinant chromo-

somes and half have non-recombinant

chromosomes. Recombination frequ-

ency is proportional to physical distance

(map distance) on a chromosome, tho-

ugh correspondence between recombi-

nation and distance can be affected by

different male and female recombination

rates, recombination hot spots, and

higher recombination rates at telomeres.

Genes will be inherited together

(linked) if they are close on the same

chromosome because recombination

within a smaller distance is less likely.

In the case of linkage, recombinant

chromosomes occur in the gametes less

frequently (theta<½ of the time) than

non-recombinant chromosomes. The

most basic method of genetic linkage

involves assuming a trait is caused by one

single gene in a family, then counting

recombinations between each of many

genetic markers and the trait. Fewer

recombinations indicate the trait gene is

more likely to be close to that marker.

To quantify the degree of linkage in

a family, one assumes various values of

theta, then writes the probability of

observing the marker and the trait in

each family member given each degree

of recombination. The joint probability

of the whole family together given a

particular assumption about recombina-

tion is called the likelihood. The like-

lihood is also computed assuming no

linkage (theta¼½). When each like-

lihood, assuming linkage (a value of

theta<½), is compared with the like-

lihood of the family assuming no

linkage, the result is a likelihood ratio,

sometimes called an odds ratio. The log

of this odds ratio is the lod score, a


measure of the likelihood of linkage

versus no linkage. For any given family,

one value of theta will give the highest

odds ratio. If there is no linkage in the

family, the highest odds ratio will be 1.0

(the log of this odds ratio¼ lod

score¼0), and will occur when theta¼1/2. Lod scores can be summed across

multiple families to give a sample lod

score. If some families within the sample

are linked to a particular locus, while

others are not, genetic heterogeneity has

occurred. For a complex phenotype

such as autism, genetic heterogeneity is

likely. The use of intermediate pheno-

types may help to classify subsets of

families. In addition, lod scores can be

computed taking into account estimates

of heterogeneity. For a more complete

discussion of genetic linkage, see Ott

[1999].

Variance components linkage

Quantitative phenotypes complicate

linkage analysis. One approach to the

analysis of these phenotypes is variance

components linkage analysis, which

assumes that a quantitative trait is in-

fluenced by genetic and environmental

factors. The genetic factors may include

specific loci in addition to polygenic

influences. Tests for evidence for the

specific loci compare the observed and

expected covariance among the different

classes of relatives in the family. The a

priori correlation among two indivi-

duals in a family is the probability that

they share a particular allele identical

through a common ancestor (identical

by descent, or IBD). This probability is

called the coefficient of relationship, and

is defined as twice their kinship coeffi-

cient. For example, the expected prob-

ability that full sibs share an allele IBD is

½. For first cousins, the probability is 1/

8. The coefficient of relationship could

be thought of as the average IBD

probability. Given a particular map of

genotyped markers, one can also com-

pute a location-specific IBD probability

at each location. Observed covariances

among individuals are compared to

expected covariances computed first

assuming only the average IBD prob-

ability, then additionally with the pre-

sence of a locus-specific component.

The likelihood is computed when the

additive genetic variance at a specific

locus is fixed at zero, then compared to

the likelihood at that location when the

additive genetic variance is estimated.

The log10 of the difference between

these two likelihoods provides the lod

score at that location.

Methods are being developed to

check for gene–gene interaction effects

[e.g., Devlin et al., 2003]. Gene–gene

interaction effects could be substantially

larger than the main effects. Due to

issues of multiple testing, tests should

be confined to gene interactions that are

either compelling biologically, have

substantial main effects, or occur in

excellent candidate genes.

Methods are being developed to

check for gene–gene interaction

effects. Gene–gene interaction

effects could be substantially

larger than the main effects.

Due to issues of multiple

testing, tests should be confined

to gene interactions that are

either compelling biologically,

have substantial main effects,

or occur in excellent

candidate genes.

Linkage analysis of

intermediate phenotypes

Multivariate analysis may be helpful to

investigate the possibility of common

susceptibility genes underlying multiple

intermediate phenotypes for autism.

Preliminary cosegregation analysis

should inform decisions about clustering

phenotype data. This clustering could be

done prior to genetic analysis using a

number of standard clustering techni-

ques. It may also be possible to specify

an analysis of multiple quantitative

phenotypes, such as BAP, repetitive

behavior and language ability [Iturria

and Blangero, 2000].

Linkage analysis of SNPs in pedigrees

The explosion of SNP data in recent

years presents a feast for the genetics

researcher that is as yet difficult to digest.

The critical analysis issue raised by the

addition of the SNP data concerns the

problem of linkage in the presence

of possible linkage disequilibrium (LD)

among markers within large pedigrees.

Many consortium projects will have

some combination of SNP data and

microsatellite data, and will need to

address this issue.

Linkage analysis in extended pedi-

grees using markers in linkage disequili-

brium will require some methodological

development. While many current link-

age analysis programs can handle arbi-

trary haplotype frequencies for a small

number of loci, they cannot produce

multipoint lod scores for a large number

of markers. Current approaches usually

involve suboptimal approximations,

such as selecting subsets of loci that

appear not to be associated. The pro-

blem is further complicated by the need

to estimate the interlocus associations

from the observed genotypes. One pro-

mising approach is the graphical model-

ing methods of Thomas and Camp

[2004]. A graphical model [Lauritzen,

1996] describes the pattern of associa-

tions between loci using a Markov graph

in which loci are represented by vertices

with the property that given the states of

its neighbors in the graph, a locus is

conditionally independent of all other

loci. Thomas and Camp [2004] showed

that such a graph can be estimated from a

sample of reconstructed haplotypes by

maximizing a penalized likelihood func-

tion. This work has recently been exten-

ded to handle diploid data [Thomas,

2005] using a two stage Markov chain

Monte Carlo process that iterates be-

tween reconstructing haplotypes given

the graphical model, and estimating

the model given the reconstructed

haplotypes. Thus, this process combines

estimating a graphical model for linkage

disequilibrium with haplotype recon-

struction and haplotype frequency

estimation. The method is currently

being extended to handle family data

by combining it with the Markov

chain Monte Carlo linkage analysis


method developed by Thomas et al.

[2000].

Verifying positive linkage findings

The clusters of markers responsible for

the linkage signal should be identified

and checked for errors and mapping

accuracy to determine the robustness of

the linkage signal to markers. Allele

frequencies should also be varied to

assess sensitivity. If an analysis was done

with a genetic model, these parameters

should also be varied to see how sensitive

the result is to the model assumptions.

Different analysis methods and software

should be used to determine if the result

is dependent upon a quirk in the tech-

nique or analysis program. Finally, inde-

pendent replication in a different sample,

though difficult for complex traits,

should be considered the gold standard

of genetic linkage replication.

Genetic Association

The fundamental principle underlying

genetic association is that of linkage

disequilibrium (LD). LD is the non-

random sharing alleles at close DNA

markers across unrelated subjects within

a population, as compared to allele

sharing only within a family (linkage).

The fundamental principle

underlying genetic association

is that of linkage disequilibrium

(LD). LD is the non-random

sharing alleles at close DNA

markers across unrelated

subjects within a population, as

compared to allele sharing only

within a family (linkage).

LD can happen if two loci are very close,

so that recombination across generat-

ions within a population has not yet

occurred. If there is LD between a trait-

causing gene and a marker, subjects with

the trait in the population will share

the same allele at the marker. Tests for

genetic association look for significant

differences in the frequency of an allele

among unrelated subjects with a trait

versus unrelated control subjects. A

somewhat different type of molecular

information can be obtained by using

comparative genome hybridization

(CGH), a microarray technique that

will reveal indication of genome-wide

chromosomal instability [see Oostlander

et al., 2004 for review]. By assessing

control populations, the frequency of

specific deletions in cases can be com-

pared to that in controls. In addition, the

number of deletion/insertions found in

autism subjects can be compared to the

number found in the controls using a

simple binomial test.

While previous association studies

of autism have been confined to cate-

gories of the clinical phenotype, future

studies will have a number of other

variables (e.g., language function, re-

gressive onset, head circumference,

blood assays) to explore.

While previous association

studies of autism have been

confined to categories of the

clinical phenotype, future

studies will have a number of

other variables (e.g., language

function, regressive onset, head

circumference, blood assays)

to explore.

For a candidate gene association

study, the design is usually to screen the

gene for variants using a subset of

subjects, rather than to screen the entire

sample. The extent of LD among the

informative SNPs must then be esti-

mated. Haplotype blocks (series of

markers in LD) can be defined using

software such as the HapBlockFinder

program [Zhang et al., 2002]. The

average haplotype block size has been

estimated at about 35 kb [Daly et al.,

2001; Gabriel et al., 2002]. Assuming

an approximate gene size of 54 kb

[Deloukas et al., 2001], an average gene

will be covered by about two haplo-

type blocks [Gabriel et al., 2002]. Once

blocks are defined, one can then select

two to three haplotype-tagging SNPs

(htSNPs) per block. In the SNP typing

phase, these htSNPs would then be

typed in the entire sample.

Case-control analysis methods

To test for association between a trait and

a single genetic variant, for example, a

functional variant in a gene, chi-square

methods can be used. Programs are

available [e.g., Estimating Haplotypes;

Xie and Ott, 1993; Zhao et al., 2000] to

allow the test of multiple variants and

haplotype blocks, and to specify the

allele frequency of the putative trait-

causing gene. These programs allow for

simulation of the data given the analysis

assumptions so that an empirical P-value

can be reported. If a case-control design

is to be used, controls should be carefully

chosen. The finding of differences in

allele frequencies between cases and

controls can be due simply to population

differences if the control sample is not

well matched to the population of the

case sample (admixture).

Transmission disequilibrium tests

Although case-control designs present

advantages in ease of ascertainment, trio

designs (affected subject and their two

parents) avoid admixture, and may

therefore be preferred. In the trio

design, transmission disequilibrium is

used to test for association [Terwilliger

and Ott, 1992; Spielman et al., 1993].

This method counts the non-transmit-

ted alleles from heterozygous parents as

the control comparison. The classical

TDT test [Spielman et al., 1993] has

been extended to include covariates in a

variety of frameworks: variance compo-

nents/quantitative traits [Allison, 1997;

Rabinowitz, 1997; Xiong et al., 1998;

Abecasis et al., 2000]; extended families

[Spielman and Ewens, 1998]; imprinting

[Weinberg et al., 1998]; and X chromo-

some analysis [Ho and Bailey-Wilson,

2000].

Family-based association tests

Tests of association using information

from more extended relatives offer an


interesting alternative to the classic case-

control or trio designs. The Family

Based Association Test (FBAT) and

associated programs [Laird et al., 2000;

Rabinowitz and Laird, 2000] can test for

association between a qualitative or

quantitative trait and genetic variants

in a pedigree sample. The method em-

ployed by these programs tests for linkage

as well as association, and again avoids

false positive results due to admixture.

The Family Based Association

Test (FBAT) and associated

programs can test for

association between a

qualitative or quantitative

trait and genetic variants in a

pedigree sample. The method

employed by these programs

tests for linkage as well as

association, and again avoids

false positive results due

to admixture.

FBAT is based on the original transmis-

sion disequilibrium test [Spielman et al.,

1993], which quantifies the transmission

of alleles of heterozygous parent to

affected offspring. When true associa-

tion exists, the transmission will deviate

from the expected 50%. The FBAT test

is an extension of this method that

performs an adaptation of this test in

pedigrees, rather than just parent-off-

spring pairs. FBAT also allows tests of

different genetic models (additive,

dominant, or recessive), and includes

an option to test association with

quantitative traits. The hbat command

in FBATallows one to test for association

with the haplotypes.

Verifying association findings

Positive association results should be

verified in an independent sample. In

addition, if the associated variant

changes gene function, or if there is a

functional change in linkage disequili-

brium with the associated variant, fur-

ther molecular studies can be designed to

verify the finding. Such experiments

may include knock out animal models or

gene expression studies, but the methods

involved in these experiments are be-

yond the scope of this paper.

CONCLUSION

The genetic analysis of autism stands

poised at the threshold of discovery.

Clinicians have turned their consider-

able talents toward the development of

specific phenotype assessments that will

perhaps identify genetically homoge-

neous subsets of subjects with autism. In

addition, intermediate phenotypes asso-

ciated with autism may allow the char-

acterization of gene carriers within

families, multiplying the information

available to genetic research.

Molecular information has explo-

ded in recent years, offering an unpre-

cedented level of detail for genetic

analysis. Tools to dissect this informa-

tion, while still in development, will

facilitate the characterization of suscept-

ibility genes for autism in the near future.

ACKNOWLEDGMENTS

This work was supported by 5 U19

HD035476, one of the NICHD Colla-

borative Programs of Excellence in

Autism, by R01 MH069359, and by

the Utah Autism Foundation. We thank

those on our staff whose work have made

this manuscript possible.

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Current perspectives on the genetic analysis of autism