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Current perspectives on the genetic analysis of autism.

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American Journal of Medical Genetics Part C (Semin. Med. Genet.) 142C:24 –32 (2006)
Current Perspectives on the Genetic
Analysis of Autism
Although no definitive genetic mutations leading to autism susceptibility have been established, the field has
many new resources to tackle this difficult problem. Numbers of families at many research sites are now large, and
new collaborations among these groups will allow for collections of subjects with enough statistical power to
detect relatively small gene effects. New technological advances in genotyping will allow for more fine-grained
genetic analysis, and more sophisticated techniques have been developed to address the vast amounts of data
acquired. Researchers have also begun to focus on intermediate phenotypes associated with autism, such as
elevated serotonin, increased head circumference, social difficulties, or language impairment or delay. These
phenotypes may help to stratify affected cases into more genetically homogeneous subgroups, and may also
occur in their clinically unaffected relatives. The study of intermediate phenotypes may allow investigators to find
common gene variants that lead to autism susceptibility. Here we discuss the major intermediate phenotypes, and
give an overview of current genetic analysis techniques. ß 2006 Wiley-Liss, Inc.
KEY WORDS: autism; intermediate phenotype; genetic linkage; genetic association
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 phenotype are beginning to suggest some
Hilary Coon, Ph.D., is Associate Professor
of Psychiatry and Principal Investigator for
the Genetics Component of the Utah Autism
Research Program at the University of Utah.
She is a statistical geneticist with interest in
the analysis of complex traits.
Grant sponsor: NICHD; Grant number: 5
U19 HD035476 and R01 MH069359; Grant
sponsor: Utah Autism Foundation.
*Correspondence to: Hilary Coon, Ph.D.,
Utah Neurodevelopmental Genetics Project,
421 Wakara Way, Suite 143, Salt Lake
City, UT 84108. E-mail:
DOI 10.1002/ajmg.c.30079
ß 2006 Wiley-Liss, Inc.
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.
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.
An additional benefit of large collaborative 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;
and detailed data describing the nonrandom associations of alleles at close
genetic markers among populations
(linkage disequilibrium) across the genome (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
[Devlin and Roeder, 1999; Pritchard
et al., 2000a,b], and techniques have
been developed to investigate gene-bygene 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 interaction, communication, and repetitive
and stereotyped behaviors. Autism comprises a heterogeneous spectrum that
also includes Asperger syndrome and
PDD-Not Otherwise Specified (PDDNOS [American Psychiatric Association, 1994]). Additional phenotypes
associated with autism will be described
below. Recent studies have suggested
that autism is more common than previously 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 estimates 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 concordance data, and extended familial
clinical phenotypes support the hypothesis that autism results from a strong,
though not absolute, genetic influence.
It is likely that genetic and environmental risk factors play complex, interacting 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 disorders [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; McCauley 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 candidate regions on chromosome 7q, chromosome 2q, and chromosome 17q in
the region of the serotonin transporter
locus SLC6A4.
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.
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. Phenotypes associated with autism, called
intermediate phenotypes, may serve one
of two purposes. First, intermediate
phenotypes may serve to stratify affected
cases into more genetically homogeneous 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
Choose candidate gene(s)
(association study)
Choose marker set
(pedigree linkage study)
Positional (under linkage peaks),
functional variants in known genes
Traditional DNA markers, Single
Nucleotide Polymorphisms (SNPs)
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
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
Figure 1.
Schematic of steps in the genetic analysis of autism.
members, but present together with
other susceptibility genes or environmental factors in persons with autism.
The study of an intermediate phenotype
may therefore reveal one of many
common interacting genes. The following brief review presents several promising intermediate phenotypes for autism.
Broader Autism Phenotype (BAP)
The BAP is characterized by impairments in the three core domains of autism
(social behaviors, communication, repetitive 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 incorporates 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 analysis 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
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, particularly 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, language 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 elevation in approximately 20%–30% of cases
[Anderson et al., 1987; Cook and
Leventhal, 1996; Anderson, 2002]. Elevation of serotonin is present in family
members of autistic subjects with hyperserotonemia [Kuperman et al., 1985;
Abramson et al., 1989; Cook et 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 addition to recent genome-wide linkage
findings on 17q, there is specific evidence 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 individuals [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 quantitative 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 enlargement of the brain. Head circumference
may prove to be an interesting and easily
measured stratification variable for association 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 studied have been primarily language
delays, repetitive behaviors, and social
behaviors. Additional phenotypic traits
should be chosen with demonstrated
genetic etiology (significant heritability). 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 programs (e.g., SOLAR [Almasy and Blangero, 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 separate genetic and environmental components of the covariance between two
phenotypes. The genetic estimate indicates the extent to which 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 genotypes. Such checking can potentially
avoid false negative findings. Laboratories 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. 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 genotypes that generate the double recombinants which inflate the map.
Ancestry and allele frequencies
Particularly with the creation of collaborative data sets, the issue of determining 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 specifically 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 corrections 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 recombinant and non-recombinant types of
chromosomes will occur in the gametes
with equal frequency. When this happens, the loci are unlinked, even though
they may be on the same chromosome.
In this case, the recombination frequency (theta) is ½, because half of the
gametes have recombinant chromosomes and half have non-recombinant
chromosomes. Recombination frequency is proportional to physical distance
(map distance) on a chromosome, though correspondence between recombination 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 recombination is called the likelihood. The likelihood is also computed assuming no
linkage (theta ¼ ½). When each likelihood, assuming linkage (a value of
theta < ½), is compared with the likelihood 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 ratio ¼ lod
score ¼ 0), and will occur when theta ¼
1/2. 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 phenotypes 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
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 influenced 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 individuals 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 coefficient. For example, the expected probability 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 compute a location-specific IBD probability
at each location. Observed covariances
among individuals are compared to
expected covariances computed first
assuming only the average IBD probability, 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 techniques. 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 pedigrees using markers in linkage disequilibrium will require some methodological
development. While many current linkage analysis programs can handle arbitrary 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 problem is further complicated by the need
to estimate the interlocus associations
from the observed genotypes. One promising approach is the graphical modeling methods of Thomas and Camp
[2004]. A graphical model [Lauritzen,
1996] describes the pattern of associations 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 function. This work has recently been extended to handle diploid data [Thomas,
2005] using a two stage Markov chain
Monte Carlo process that iterates between 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 reconstruction 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.
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 technique or analysis program. Finally, independent 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 nonrandom 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 generations within a population has not yet
occurred. If there is LD between a traitcausing 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 compared 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 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.
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 estimated. 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 haplotype 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 traitcausing 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-transmitted 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 components/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 chromosome analysis [Ho and Bailey-Wilson,
Family-based association tests
Tests of association using information
from more extended relatives offer an
interesting alternative to the classic casecontrol 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 employed 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.
functional change in linkage disequilibrium with the associated variant, further 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 beyond the scope of this paper.
The genetic analysis of autism stands
poised at the threshold of discovery.
Clinicians have turned their considerable talents toward the development of
specific phenotype assessments that will
perhaps identify genetically homogeneous subsets of subjects with autism. In
addition, intermediate phenotypes associated with autism may allow the characterization of gene carriers within
families, multiplying the information
available to genetic research.
Molecular information has exploded in recent years, offering an unprecedented level of detail for genetic
analysis. Tools to dissect this information, while still in development, will
facilitate the characterization of susceptibility genes for autism in the near future.
FBAT is based on the original transmission disequilibrium test [Spielman et al.,
1993], which quantifies the transmission
of alleles of heterozygous parent to
affected offspring. When true association 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-offspring 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
This work was supported by 5 U19
HD035476, one of the NICHD Collaborative 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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