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Elisabeth B. Binder, Joseph F. Cubells: Chapter 3. Genetics and Genomics, in The American Psychiatric Publishing Textbook of Psychopharmacology, 4th Edition. Edited by Alan F. Schatzberg, Charles
- Nemeroff. Copyright ©2009 American Psychiatric Publishing, Inc. DOI: 10.1176/appi.books.9781585623860.407883. Printed 5/10/2009 from www.psychiatryonline.com
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Chapter 3. Genetics and Genomics
GENETICS AND GENOMICS: INTRODUCTION
Genetics and genomics have become among the most important tools in modern psychiatric research. Spurred by the completion of the human genome sequence in
February 2001, the number of psychiatric genetic studies has increased dramatically in the past two decades (Lander et al. 2001; Venter et al. 2001). The following
chapter will attempt to cover the basic methodologies and concepts, and define key terms, currently used in psychiatric genetics and genomics. Our goal is to
facilitate interpretation by working physicians and scientists in the field of psychopharmacology of the avalanche of genetic and genomic data that are
accumulating in the human neuroscience literature. Throughout this chapter, terms in common use in the genetics and genomics literature will be italicized upon
their first use and definition.
EPIDEMIOLOGICAL BASIS FOR GENETIC CONTRIBUTIONS TO NEUROBEHAVIORAL DISORDERS
Insights From Adoption and Twin Studies
Genetic epidemiological studies have established that most psychiatric disorders, as well as many nonpathological human behavioral traits, have a substantial
genetic component. Investigations in genetic epidemiology therefore provide the scientific foundation for molecular genetic and genomic studies of human
behavior and behavioral disorders (Kendler 1993, 2001; Plomin and Kosslyn 2001). Genetic epidemiology uses family, twin, and adoption studies to assess the
contribution of familial, environmental, and genetic factors to a trait of interest. Family studies can establish that a given disorder “runs in families” but cannot
easily distinguish whether such familiality is due to genetic or environmental factors. An everyday example of the distinction between genetic and familial (but
environmental) traits is the difference between the ability to acquire language (a genetic trait that distinguishes humans from other species) and the native
language spoken by a given person, which is familial, but entirely environmentally determined.
Adoption and twin studies distinguish between genetic and environmental influences on traits by accounting for each separately. Adoption studies investigate
whether an individual’s risk for a psychiatric disease depends on the mental health status of the biological or adoptive parents to disentangle genetic (i.e.,
similarity to biological parents, who have had little or no interaction with the adoptee) from environmental (i.e., similarity to adoptive parents, who have provided
the adoptee with his or her family/social environment) influence (Cadoret 1986; Tienari and Wynne 1994; Tienari et al. 2004). Practical, ethical, and legal
obstacles make large-scale adoption studies very difficult to conduct. Twin studies, while also quite challenging, are more tractable, and large twin registries are
now available across the world (Busjahn 2002). These studies have provided the bulk of strong evidence supporting genetic contributions to psychiatric disorders
and human behavioral traits.
In twin studies one determines what the probability of one twin being affected with a given trait or disorder, given the affectation status of the co-twin. This
degree of correlation between twins for the investigated trait is then compared between monozygotic (MZ) and dizygotic (DZ) twins to gain information on the
degree of genetic and environmental influence on a certain trait. MZ twins result from a separation of the zygote to yield two genetically identical embryos. DZ
twins result from the separate fertilization of two eggs in the same pregnancy. DZ twins thus only share on average 50% of their genes, similar to siblings born in
separate pregnancies. While neither the pre- or postnatal environments of twins are perfectly identical, to a first approximation MZ and DZ twins are equally
correlated for relevant environmental exposures (Kendler and Gardner 1998). The trait correlation between MZ and DZ twins therefore allows estimation of the
degree to which additive genetic, shared, or individual-specific environment contributes to the likelihood of a given trait. Figure 3–1 summarizes relative
contributions of each factor, estimated from patterns of correlation between MZ and DZ twins (for a methodological review of this topic, see Bulik et al. 2000).
FIGURE 3–1. Patterns of intrapair correlations and source of variance implied.
Intrapair correlations around zero imply effects of individual-specific environment. Equal intrapair correlations greater than zero that are equal for monozygotic (MZ) and dizygotic
(DZ) twins imply effects of shared environment. Correlations for MZ twins that are twice as great as those for DZ twins imply additive genetic effects. Correlations for MZ twins that
are greater than—but not twice as great as—those for DZ twins imply additive genetic effects and shared environment effects. The correlations of less than 1.0 in the last three
examples are likely mediated by individual-specific environment effects.
Twin studies have firmly established important genetic contributions for all psychiatric disorders, with heritability estimates (i.e., the proportion of risk for a
disorder attributable to the additive effects of genes) ranging from 30% from 80% for most common psychiatric disorders (see Table 3–1). Interestingly, the
importance of shared environment seems to be of significant relevance mostly in schizophrenia, whereas in most other disorders, including anxiety and mood
disorders, individual-specific but not shared environment is the major environmental contributor to susceptibility.
TABLE 3–1. Heritability scores for major psychiatric illnesses, with focus on results of recent meta-analyses
Disorder Heritability N in meta-analysis
References
Autism >0.8
Bailey et al. 1995; Rutter 2000
Schizophrenia 0.81 (0.73–0.9) 12 studies (Sweden, US, England, Norway, Denmark, Finland, Germany) Sullivan et al. 2003 (meta-analysis)
Bipolar disorder 0.79–0.85
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Disorder Heritability N in meta-analysis
References
Major depression 0.37 (0.31–0.42) 5 studies (UK, Sweden, US), N >21,000
Sullivan et al. 2000 (meta-analysis)
Panic disorder 0.43 (0.32–0.53) 3 studies, N >9,000
Hettema et al. 2001 (meta-analysis)
Generalized anxiety disorder 0.32 (0.24–0.39) 2 studies, N >12,000
Hettema et al. 2001 (meta-analysis)
Specific phobias 0.25–0.35
Kendler et al. 1992, 2001b
Social phobias 0.20–0.30
Kendler et al. 1992, 2001b
Agoraphobia 0.37–0.39
Kendler et al. 1992, 2001b
Obsessive-compulsive disorder 0.45–0.65 (children)
0.27–0.47 (adults)
van Grootheest et al. 2005 (review)
Anorexia nervosa 0.56 (0.00–0.86) Swedish twin registry, N >30,000
Bulik et al. 2006
Bulimia nervosa 0.28–0.83
Bulik et al. 2000 (review)
Alcohol dependence 0.48–0.73 (men)
0.51–0.65 (women)
Tyndale 2003 (review)
Nicotine addiction 0.4–0.7
Li 2003; Tyndale 2003 (reviews)
Antisocial personality disorder 0.32 51 studies
Rhee and Waldman 2002 (meta-analysis)
Genetic epidemiology can also contribute to the exploration of more complex questions, such as whether genetic risk factors are shared among different
psychiatric disorders and gender and whether they can moderate the effects of environmental risk factors (Kendler 2001) and can thus lead the design of follow-up
molecular genetic studies. For example, a twin study has suggested that genetic risk factors for major depression could in part act by increasing vulnerability to
stressful life events (Kendler 1995). This finding has been corroborated in recent years by the now well-replicated interaction of functional alleles of the locus
encoding the serotonin transporter protein with stressful life events to predict depression (Caspi et al. 2003; Kaufman et al. 2004; Kendler et al. 2005; Sjoberg et
- 2006; Surtees et al. 2006; Wilhelm et al. 2006). Genetic epidemiological and more specifically twin studies have therefore been an important foundation of
psychiatric genetics and are likely to continue to contribute more elaborate disease models for future molecular genetic analysis. The major limitation of these
studies, however, is that the estimated heritability using twin studies is only an estimate of the aggregate genetic effect. Heritability does not give information on
the contributions of specific genes to risk for a disorder. These questions, the answers to which ultimately will shed light on the underlying developmental
neurobiology underlying psychiatric illness, require molecular genetic methods.
Psychiatric Disorders Are Complex Genetic Disorders
Genetic epidemiological studies have also established that psychiatric disorders are likely not single-gene disorders inherited in a Mendelian fashion (i.e., in a clear
recessive, dominant, or X-linked fashion), although rare families in which psychiatric phenotypes are inherited this way have been reported (Brunner et al. 1993).
A genetic disorder can be complex for several reasons:
Incomplete penetrance. Not everybody carrying the disease allele(s) becomes ill.
Phenocopy. Individuals, even within the same families, can exhibit similar or identical traits because of environmental factors.
Locus heterogeneity. Variants in different genes can lead to similar or identical disease phenotypes.
Allelic heterogeneity. Different patterns of variation within the same gene or genes can lead to similar or identical disease phenotypes.
Polygenic inheritance. Additive or interactive effects of variation at multiple genes (i.e., epistatic effects) are necessary for an illness to manifest.
Gene–environment interaction. A disorder manifests in response to environmental factors only in the context of predisposing genetic variants. An extreme example of such
interaction is phenylketonuria, where exposure to dietary phenylalanine causes severe neurobehavioral impairment in individuals carrying two mutant copies of the locus
encoding phenylalanine hydroxylase; limitation of dietary phenylalanine prevents the neurobehavioral disorder.
High frequency of the disorder and the predisposing alleles. It appears increasingly likely that common disorders such as schizophrenia, diabetes mellitus, stroke, or
hypertension represent final common outcomes to a variety of combinations of environmental and genetic predisposing factors. Thus, two individuals, even within the same
family, might manifest clinically indistinguishable disorders for different reasons.
Other genetic mechanisms of inheritance. Alternative genetic mechanisms—for example, mitochondrial inheritance or alteration of the genome across generations, such as
occurs in trinucleotide-repeat-expansion disorders (e.g., Huntington’s disease, fragile X syndrome) or in epigenetic disorders—may be operable in producing a disorder.
Epigenetic disorders result from alterations in the genetic material that do not involve changes in the base pair sequence of DNA. Examples of epigenetic disease include the
imprinted disorders Angelman syndrome and Prader-Willi syndrome, in which parent-of-origin–dependent chemical modification of DNA produces different phenotypic outcomes
from the same chromosomal deletion. Newton and Duman recently reviewed possible roles of epigenetic mechanisms in the action of psychotropic drugs (Newton and Duman
2006) and in neuronal plasticity (Duman and Newton 2007).
From the cumulative evidence of psychiatric genetic studies so far, one can conclude that psychiatric disorders best fit a polygenic mode of inheritance, with two or
more polymorphic loci contributing to these disorders, including unipolar depression (Johansson et al. 2001; Kendler et al. 2006), bipolar disorder (Blackwood and
Muir 2001), schizophrenia (Sobell et al. 2002), and autism (Folstein and Rosen-Sheidley 2001). However, it is still relatively unclear how many loci contribute to
each disorder. The inheritance of schizophrenia, for example, fits models including only a few loci as well as very large numbers of loci (Risch 1990a, 1990b;
Sullivan et al. 2003). Data from gene-mapping studies suggest that different loci are indeed likely to contribute to schizophrenia and bipolar disorder in different
individuals or families (meta-analyses [Levinson et al. 2003; Lewis et al. 2003; Segurado et al. 2003]), strongly supporting the hypothesis that locus heterogeneity
is an important factor in schizophrenia. Thus, Bleuler (1951) appears to have been correct when he referred to dementia praecox as “the group of schizophrenias.”
As already noted, susceptibility genes are likely to interact with environment, gender, and other genes, making the search for genes for psychiatric disorders even
more complex (Kendler and Greenspan 2006). Twin studies have produced evidence of genetic interactions with stressful life events predicting major depression
(Kendler et al. 1995a) and with early rearing environment to predict schizophrenia, conduct disorder, and drug abuse (Cadoret et al. 1995a, 1995b; Tienari et al.
2004). These gene–environment interactions have now been substantiated by several molecular genetic studies (e.g., Binder et al. 2008; Bradley et al. 2008; Caspi
et al. 2002, 2003, 2005), suggesting that future genetic and genomic studies will need to include analysis of both sets of factors. Furthermore, it is likely that there
are gender-specific predisposing genes for psychiatric disorders. Data from twin studies suggest that the combined genetic factors predisposing to major
depression, phobias, and alcoholism may differ in some respects for men and women (Kendler and Prescott 1999; Kendler and Walsh 1995; Kendler et al. 2001a,
2002, 2006; Prescott and Kendler 2000; Prescott et al. 2000), and this has been supported in molecular genetic studies by the identification of gender-specific loci
for major depression (e.g., Abkevich et al. 2003; Zubenko et al. 2002). Finally, gene–gene interactions may be relevant for these disorders (Risch 1990b).
Response to Drug Treatment
In contrast to disease susceptibility, genetic epidemiological studies on responses to psychotropic drugs are rare. There is some evidence from family studies that
suggests an important contribution of genetic factors in antidepressant response. Already in the early 1960s, studies on the effects of tricyclic antidepressants
(TCAs) in relatives suggested that response to these drugs was similar among family members (Angst 1961; Pare et al. 1962). O’Reilly et al. (1994) reported a
familial aggregation of response to tranylcypromine, a monoamine oxidase inhibitor, in a large family with major depression. These initial reports were followed by
only a few systematic studies. Franchini et al. (1998) indicated a possible genetic basis of response to the selective serotonin reuptake inhibitor (SSRI)
fluvoxamine in 45 pairs of relatives. In light of these data, some groups have used or proposed to use response to certain antidepressant drugs or mood stabilizers
as an additional phenotype in classical linkage analyses for mood disorders in the hope of identifying genetically more homogeneous families (Serretti et al. 1998;
Turecki et al. 2001).
Nonetheless, family studies supporting a genetic basis of response to psychotropic drugs are sparse, certainly reflecting the extreme difficulties inherent in
conducting well-controlled family studies of therapeutic responses to medications. It has been proposed that genetic modifiers for response to treatment to
psychotropic drugs may be easier to detect than associations with disease susceptibility, as the genetic contribution to these traits may be less complex
(Weinshilboum 2003). So far, the data are insufficient to support or refute that contention.
HUMAN GENETIC VARIATION
As mentioned above, genetic epidemiological studies can only indicate the presence of an aggregate genetic effect but not which type and how many variations
contribute to the effect. This next section will give an overview of the types of variation that occur in the human genome and will provide examples for implicationsPrint: Chapter 3. Genetics and Genomics http://www.psychiatryonline.com/popup.aspx?aID=407887&print=yes…
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of each of them for psychiatric disorders (see also Figure 3–2).
FIGURE 3–2. Chromosomes, genes, and genetic variation.
Panel A shows a representation of chromosome 6 (Chr6), which spans 170 megabases of DNA. Panel B shows a zoomed-in representation of the area highlighted in red in panel A.
This region contains 31 genes within 2 megabases. Genes can be transcribed from both strands of the DNA. The arrows indicate the direction of transcription and translation of the
respective genes. In the region shown, three copy number variants (CNVs) have been identified. All three span several genes. Panel C shows a zoomed-in representation of the gene
for FK506 binding protein 5 (FKBP5) highlighted with a red frame in panel B. The gene spans 115 kilobases and is composed of 11 exons (translated into protein). The intervening
introns are transcribed into RNA but are spliced out to form the mature mRNA that serves as the template for translation. The transcription start is at exon 1. In this gene, more than
60 SNPs have been genotyped within the HapMap Project. Their positions are indicated by the triangles. Panel D shows sequence examples for three common polymorphisms.
Source. Representations from www.hapmap.org.
Variation on a Chromosomal Scale
Variation in Chromosomal Number
The human genome has approximately 3 billion bases that are distributed over 23 chromosome pairs, with 22 pairs of autosomes and 1 pair of sex chromosomes, X
and Y. The most obvious genetic variations can be observed at the light microscope level in the karyotype. This approach visualizes metaphase chromosomes using
histological procedures, allowing identification of each specific pair of chromosomes and variations in the total number of chromosomes, such as unisomies and
trisomies. Several of the known variations of total chromosome number have an associated psychiatric phenotype. For example, Down syndrome is a complex
neurodevelopmental disorder that results in variable levels of mental retardation, and in old age, dementia strikingly similar to Alzheimer’s disease (Visootsak and
Sherman 2007). Down syndrome results from trisomy 21 (i.e., inheritance of three copies of chromosome 21, due to meiotic nondysjunction during oogenesis.
Turner syndrome, in which there is only a single X chromosome (i.e., an XO karyotype), is associated with nonverbal learning disabilities, particularly in arithmetic,
select visuospatial skills, and processing speed (Sybert and McCauley 2004).
Translocations
Karyotypic examination and other cytogenetic techniques such as fluorescent in situ hybridization (FISH) can reveal additional large-scale chromosomal
abnormalities, such as translocations, deletions, or duplications of large regions of chromosomes. In a large Scottish pedigree, a balanced translocation between
chromosomes 1 and 11 appears causally linked to a series of major psychiatric disorders, including schizophrenia, bipolar disorder, recurrent major depression,
and conduct disorder (St. Clair et al. 1990). This balanced translocation (which exchanged parts of chromosome 1 with parts of chromosome 11 to produce two
abnormal chromosomes, but no net loss of chromosomal material) disrupts two genes at the translocation breakpoint on chromosome 1, termed “disrupted in
schizophrenia” (DISC) 1 and 2 (Millar et al. 2000, 2001). Subsequent molecular analysis has provided strong evidence that variation in DISC1 can alter the risk for
schizophrenia; the locus is presently considered by most a “confirmed” schizophrenia locus (Porteous et al. 2006).
Deletions
Microdeletions occurring on the long arm of chromosome 22 have received considerable attention as cytogenetic risk factors for the development of schizophrenia
(Karayiorgou and Gogos 2004). The 22q11 deletion syndrome (DS), in which 1.5–3 million base pairs of DNA are missing on one copy of 22q, includes a spectrum
of disorders affecting structures associated with development of the fourth branchial arch and migration of neural crest cells (e.g., the great vessels of the heart,
the oropharynx, facial midline, and thymus and parathyroid glands). Originally described as distinct disease syndromes prior to the elucidation of their common
molecular etiology, 22q11DS includes velocardiofacial syndrome (VCFS), DiGeorge syndrome, and conotruncal anomaly face syndrome. Following an initial report
of early-onset psychosis in patients with VCFS (Shprintzen et al. 1992), Pulver and colleagues examined psychiatric symptoms in adults with VCFS (Pulver et al.
1994) and in a cohort of patients ascertained for schizophrenia (Karayiorgou et al. 1995). The latter study identified two previously undiagnosed cases in 200
patients, verified by fluorescent in situ hybridization to carry 22q11 deletions. These findings, together with earlier reports of suggestive linkage of 22q11–22q12
(Gill et al. 1996; Pulver et al. 2000), strongly suggested that a gene or genes in the 22q11DS region could contribute to risk for schizophrenia.
Duplications
Duplications of the long arm of chromosome 15 (15q11–13) are the most frequent cytogenetic anomalies in autism spectrum disorders, occurring in approximately
1%–2% of cases (Cook 2001). This duplication syndrome cannot be clinically differentiated from idiopathic autism spectrum disorders (Veenstra-VanderWeele andPrint: Chapter 3. Genetics and Genomics http://www.psychiatryonline.com/popup.aspx?aID=407887&print=yes…
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Cook 2004), indicating that a complete workup of autism should include testing for this cytogenetic abnormality, as well as for several others (Martin and
Ledbetter 2007). Interestingly, deletion of this same region of 15q is associated with Angelman syndrome when the deletion occurs on the maternal copy of
chromosome 15, and with Prader-Willi syndrome when the deletion occurs on the paternal chromosome (or more rarely, when two maternal copies of chromosome
15 are present, and the paternal chromosome is missing entirely, a condition known as maternal disomy). Both syndromes manifest as quite distinct but dramatic
neurobehavioral disorders (Nicholls and Knepper 2001; Vogels and Fryns 2002).
Molecular Variation in the Genome
The majority of genetic and genomic studies in neuropsychiatry conducted to date have examined variation at the molecular level, which would be undetectable
with methods appropriate for the kinds of variation described above. To introduce this section, we provide basic definitions of terms.
Definition of Alleles, Genotypes, Haplotypes
The definition of alleles, genotypes, and haplotypes is common to all the types of polymorphisms discussed below. An allele is a variation in DNA sequence that
occurs at a particular polymorphic site on one chromosome. Every individual with a normal set of chromosomes has two alleles for each polymorphism on the
autosomes (nonsex chromosomes, numbers 1–22). On the sex chromosomes, men have only one allele each for all polymorphisms located on the X and Y
chromosomes, whereas women carry two copies of each X-linked allele. A genotype is the combined description for the variation at a particular corresponding
point on homologous chromosomes and is expressed as two alleles. When the alleles on both chromosomes are the same, it is a homozygous genotype. When the
alleles differ, it is a heterozygous genotype. A haplotype, a term derived from abbreviation of “haploid genotype,” is the sequence of alleles along an adjacent
series of polymorphic sites on a single chromosome. When genotypic data are available from three generations, haplotypes in the third generation can be
unambiguously deduced. In the absence of sufficient family-based data (e.g., in case–control studies of unrelated individuals), some haplotypes are ambiguous
because the combination of genotypes at the polymorphic sites under study can be explained by more than one set of possible chromosomal arrangements of the
component alleles. In such cases, methods such as estimation maximization (EM) can be used to infer the most likely haplotype (Hawley and Kidd 1995; Long et al.
1995).
Copy Number Variation
Genome-scale investigations enabled by the sequencing of the human genome and the advent of microarray-based comparative genomic hybridization have
recently revealed a previously unappreciated form of polymorphic variation in the human genome: chromosomal regions containing one or more genes can
sometimes be deleted or, alternatively, occur in multiple copies, with the number of copies differing among individuals (Nadeau and Lee 2006; Sebat et al. 2004).
Such copy number variants (CNVs) occur normally in human populations, and a preliminary map of such variants is now available (Redon et al. 2006). They have
recently been associated with marked differences in gene expression (Stranger et al. 2007). CNVs can also be associated with predisposition to disease, including
neurobehavioral disorders such as autism and schizophrenia (International Schizophrenia Consortium 2008; Sebat et al. 2007; Stefansson et al. 2008). Research in
this exciting new area is in its infancy but has already contributed importantly to the genetics of psychiatric disorders (Cook and Scherer 2008).
Copy number variation of the cytochrome P450 gene CYP2D6, which is important for the metabolism of many antidepressants, neuroleptics, and mood stabilizers
(Kirchheiner et al. 2004), provides a prominent example of the importance of CNVs to pharmacogenetics. The presence in the genome of copy-number variation at
this locus was inferred through biochemical–genetic studies predating the molecular era and was subsequently confirmed by molecular studies. The reported range
of copy numbers of CYP2D6 is from 0 to 13. The number of functional CYP2D6 gene copies directly correlates with plasma levels of metabolized drugs, such as the
TCA nortriptyline (Bertilsson et al. 2002). Patients with 0 or 1 functional copy of the gene attain therapeutic plasma levels of nortriptyline with very low doses and
can easily reach potentially toxic concentrations with typical or high doses. Patients with 2–4 copies, on the other hand, would require high-normal doses to even
reach therapeutic plasma levels (Kirchheiner et al. 2001). In the case of the one reported patient with 13 gene copies, even high-normal doses did not produce
therapeutic plasma concentrations (Dalen et al. 1998).
Insertion/Deletion Polymorphisms
Microscopic insertions and deletions (much smaller than CNVs—on the order of one to hundreds of base pairs [bp]) are another important type of genetic variation.
The most famous insertion/deletion polymorphism in psychiatric genetics is a common functional polymorphism in the promoter region of the serotonin
transporter gene SLC6A4, referred to as the 5-HT transporter gene–linked polymorphic region ( 5-HTTLPR). It consists of a repetitive region containing 16
imperfect repeat units of 22 bp, located approximately 1,000 bp upstream of the transcriptional start site (Heils et al. 1996; Lesch et al. 1996). The 5-HTTLPR is
polymorphic because of the insertion/deletion of the repeat units 6–8 (of the 16 repeats), which produces a short (S) allele that is 44 bp shorter than the long (L)
allele. Although the 5-HTTLPR was originally described as biallelic, rare (<<5%) very-long and extra-long alleles have been described in Japanese and African
Americans (Gelernter et al. 1999). Numerous additional variants within the repetitive region also occur (Nakamura et al. 2000). Thus, although most studies
continue to treat this complex region as biallelic, this is an oversimplification that may be hiding additional genetic information. The 5-HTTLPR has been associated
with different basal activity of the transporter, most likely related to differential transcriptional activity (Heils et al. 1996; Lesch et al. 1996). The long variant (L
allele) of this polymorphism has been shown to lead to a higher serotonin reuptake by the transporter in vitro. It is also noteworthy that the function of this
insertion/deletion polymorphism may be influenced by a single nucleotide polymorphism (SNP) that occurs with the L allele (Hu et al. 2006). However a positron
emission tomography study could not identify differences in serotonin transporter binding potential by 5-HTTLPR genotype, even when including the information of
the additional SNP, in healthy control subjects or patients with major depression (Parsey et al. 2006). This polymorphism has shown associations with a multitude
of psychiatric disorders and related phenotypes. The best established are an association with response to treatment with SSRI (Serretti et al. 2007) and the
moderation of the influence of life events on the development of depression (Caspi et al. 2003; Kaufman et al. 2004; Kendler et al. 2005; Surtees et al. 2006;
Wilhelm et al. 2006; Zalsman et al. 2006).
Microsatellites: STRs and VNTRs
A very important class of polymorphisms, upon which molecular linkage studies and some association studies were based until very recently, is microsatellite
markers, also called short tandem repeats (STRs) or variable number of tandem repeats (VNTRs). The polymorphisms consist of simple sequences, such as GT or
GATA, that are repeated a variable number of times. An individual may thus have 5 GT repeats at a specific locus on one chromosome and 7 such repeats on the
other. When these regions are amplified by polymerase chain reaction (PCR), a technique for producing many copies of a specific small portion of the genome
based on DNA polymerase activity directed by specific DNA sequences, the difference in number of repeats results in differences in the length of the amplified
fragments (a difference of 4 bp in the current example), allowing efficient genotyping of these polymorphisms by gel electrophoresis, which separates DNA
fragments according to length. About 30,000 of these polymorphisms are now known in the human genome (Kawashima et al. 2006; Tamiya et al. 2005), and they
have served as markers for genomewide linkage analysis (see “Linkage Studies” subsection below for more detail). VNTRs, however, not only serve as genetic
markers for linkage analysis but also may produce functional variation within genes. An important example of such functional variation is a VNTR in the 3′
untranslated region (UTR) of the dopamine transporter gene (DAT). The repeat element consists of a 40-bp sequence that can occur with 3–11 repeats, with 9 and
10 repeats being the most common (Vandenbergh et al. 1992). Different effects of the 9 or 10 repeats on gene expression and DAT binding using single photon
emission computed tomography (SPECT) in humans have been reported, although the direction of these differences is controversial (Greenwood and Kelsoe 2003;
Inoue-Murayama et al. 2002; Martinez et al. 2001; Mill et al. 2002, 2005; van Dyck et al. 2005; VanNess et al. 2005), with the 9 or the 10 repeat alleles
respectively associated with higher expression of the DAT gene and higher DAT binding in different studies. Another example of a functional VNTR (48-bp repeat)
is a polymorphism in the third exon of the DRD4 locus (the locus encoding the D4 dopamine receptor), which results in a variable number of glutamine residues in
the third intracellular loop of the dopamine D 4 receptor protein (Van Tol et al. 1992). The allelomorphic proteins differ in their ligand-binding affinities, but all
couple to G proteins (Van Tol et al. 1992). Both polymorphisms have been associated with a multitude of psychiatric and behavioral phenotypes (many of which
require further investigation before their validity can be established with confidence).
Single Nucleotide Polymorphisms
The polymorphisms that have revolutionized (not only) psychiatric genetics are SNPs (Altshuler et al. 2000; Sachidanandam et al. 2001). SNPs consist of a
single-base difference at a particular site in the genome—in two-thirds of cases, a cytosine (C)-to-thymidine (T) exchange. Although theoretically the presence of
all four different bases at an SNP is possible, the vast majority of SNPs have only two alleles, although SNPs with three or four different alleles have occasionally
been reported. As of June 2008, close to 10 million SNPs have been catalogued in public databases (such as dbSNP [http://www.ncbi.nlm.nih.gov/projects/SNP]).
SNPs are so far the most common type of genetic variation and may represent up to 90% of all genetic variations (although this estimate may need revision as
knowledge about CNVs accumulates). Besides being very common (SNPs occur, on average, every 300 bases), SNPs are also amenable to high-throughput
genotyping methods (Kim and Misra 2007; Kwok 2000). Since SNPs are common (essentially every gene of interest has a number of known SNPs) and cheap to
genotype, they have become the markers of choice for psychiatric genetic studies. SNPs are now replacing STRs in genomewide linkage studies, as more genetic
information can be gleaned from 5,000–6,000 common SNPs across the genome than from the 300–400 markers typical of STR-based genome scans. SNPs are alsoPrint: Chapter 3. Genetics and Genomics http://www.psychiatryonline.com/popup.aspx?aID=407887&print=yes…
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the backbone of genetic association analysis.
Besides serving as genetic markers for chromosomal loci in association studies, SNPs can also have functional relevance themselves. SNPs in regulatory regions
can alter the transcriptional regulation of a gene, SNPs in regions relevant for mRNA splicing can alter splice sites, and SNPs in protein-coding exons can encode
differences in primary amino acid sequence. Interestingly, when one considers the occurrence of SNPs across the genome, one observes that SNPs are most dense
in intergenic and intronic regions, while they are scarcer in putative regulatory regions and exons, suggesting selection against putatively functional variants
(Altshuler et al. 2000; Sachidanandam et al. 2001). Within exons, SNPs that will not cause an amino acid exchange due to the degenerate code are called
synonymous SNPs as opposed to nonsynonymous SNPs, which lead to amino acid substitution. One nonsynonymous SNP that has repeatedly been associated with
psychiatric phenotypes is a valine-to-methionine exchange at amino acid position 108/158 in the soluble (S) or membrane-bound (MB) form of the
catechol-O-methyltransferase (COMT) peptide sequence due to a G-to-A exchange at position 472 of exon 4. This amino acid exchange dramatically affects the
temperature lability of this enzyme, with the methionine allelomorphic protein having only one-fourth of the enzyme activity at 37°C as the valine allelomorph
(Lachman et al. 1996; Lotta et al. 1995). It is presumed that individuals with the Val/Val genotype have a more rapid inactivation of centrally released dopamine
than individuals with the other genotypes, following an additive genetic model. This polymorphism seems to impact dopamine transmission, particularly in the
frontal cortex, where expression of the dopamine transporter protein is relatively low, and has been associated with differences in executive cognition and
functional activity of the prefrontal cortex during working memory tasks (Egan et al. 2001; Goldberg et al. 2003; Joober et al. 2002; Mattay et al. 2003). While it
was hypothesized that this polymorphism could also alter the risk for schizophrenia (particularly since COMT resides in the 22q11 region, the deletion of which has
been associated with schizophrenia), larger association studies and meta-analyses do not fully support that conclusion (Fan et al. 2005).
IDENTIFICATION OF DISEASE LOCI
These next paragraphs will describe specific examples of the different molecular genetic strategies to identify susceptibility genes for psychiatric disorders. We will
also discuss potential problems that hinder the identification of such genes.
Cytogenetic Studies
Disruption of chromosomal integrity by rare events such as balanced translocations has facilitated gene discovery in a variety of diseases and has significant
promise for similar applications in psychiatric illness (Pickard et al. 2005). Advances in techniques such as chromosome painting, FISH, and, most recently,
genomewide analysis of CNVs using microarrays have made it possible to identify disruptions in chromosomal architecture to single-base resolution, thus
facilitating identification of specific genes in families cosegregating mental illness and cytogenetic abnormalities. Such data may then be used to generate testable
hypotheses about the role of a particular gene in disease pathophysiology. The DISC1 and DISC2) genes provide clear examples of specific genes implicated in
mental illness following their discovery through careful molecular analysis of a cytogenetic anomaly. Since much more work has been published on DISC1, we
focus on that locus here.
As noted previously, DISC1 was initially identified by cytogenetic studies in which a large Scottish family with numerous relatives affected by major psychiatric
illness was shown to carry a balanced (1;11) (q42;q14.3) translocation that cosegregates with the presence of psychiatric illness. Linkage analysis using
psychiatric diagnosis as a phenotype and the translocation as a genetic marker yielded highly significant evidence supporting linkage of the translocation to
psychiatric illness, and particularly to schizophrenia (Blackwood et al. 2001; St. Clair et al. 1990). The translocation was shown to disrupt DISC1 and DISC2 on
chromosome 1 (Millar et al. 2000, 2001), whereas no known genes were disrupted on chromosome 11. DISC1 was shown to encode a novel protein, with no known
related proteins, that subsequently has been shown to play important roles in several key neuronal functions, including axonal transport and regulation of G
protein–mediated intracellular signaling (Porteous et al. 2006).
Linkage Studies
In the 1980s and 1990s, many genes for Mendelian disorders were successfully identified using linkage analysis in large families. Linkage relies on the principle
that on average, chromosomes from parents differ from those of offspring by only one meiotic crossover event. During meiosis, the cell divisions that produce
gametes by reducing the diploid genome (i.e., two chromosomes of each pair per cell) to a haploid genome (one copy of each chromosome per spermatocyte or
ooctye), homologous grandparental chromosomes (i.e., paternal and maternal to the parents of the offspring of interest) make physical contact and exchange
homologous regions (referred to as crossing over) to give rise to a new set of chromosomes in which each gametic chromosome is a mix of grandparental
sequences. This crossing-over process produces recombination that can then be tracked using molecular markers to delineate the ancestral origin of each
chromosomal region in the offspring. Classically, linkage studies use anywhere from several hundred up to a few thousand microsatellite markers, evenly spaced
across the genome. These markers are then genotyped in large families in which the disease of interest is common. To identify positions in the genome that may
be involved in the disease, one tracks whether marker alleles are inherited by affected relatives more often than expected by chance (i.e., linkage tests whether a
particular chromosomal region cosegregates with the disease). A quantitative measure of the likelihood of a particular pattern of marker–disease cosegregation is
the logarithm of odds (LOD) score. As the name implies, the LOD score is logarithmic, with LOD = 1 corresponding to 1:10 odds, LOD = 2 to 1:100 odds, and so
forth. Because of the high a priori probability that a given region is not linked to a given phenotype, a LOD score of 3.3 or greater is usually required before
significant linkage is accepted (Lander and Schork 1994). The results of linkage analyses are usually presented as plots of LOD scores at each marker, which are
arranged in their known order across individual chromosomes or the entire genome. These plots show a pattern of peaks and valleys. Linkage peaks identify
chromosomal regions (i.e., loci) where the LOD score is high, suggesting a high probability that a disease-linked variant resides nearby. Due to the spacing of the
markers and their multiallelic properties, the identified regions are usually large, comprising up to tens of mega bases and harboring dozens of genes (which are
then referred to as positional candidates because they reside under the linkage peak). Linkage peaks need to be followed up with additional fine mapping using
denser marker maps, including SNPs, to narrow in on the candidate gene(s) of interest.
While classical linkage approaches have been very successful in identifying monogenic diseases that follow clear, simple patterns of inheritance, they have been
far less successful in complex psychiatric disease. Parametric linkage analysis requires the specification of an inheritance model (e.g., recessive or dominant),
information we do not have for psychiatric disorders as they clearly do not follow Mendelian inheritance. In addition, each family may have a different pattern of
inheritance, so that specifying one model for several pedigrees may decrease the power to detect a signal in some of the families. Nonparametric linkage analyses
that are mode of inheritance independent have been developed to address this problem. Also, linkage analysis requires that each person in a pedigree be
designated as either affected or unaffected, so one must decide, for example, in which category to place individuals with a single major depressive episode in a
bipolar pedigree. Linkage analysis studies therefore often run several different models and may also use different definitions of affected status and then report the
best LOD score, but here the threshold for significance also has to be adjusted for additional multiple testing, so that even higher LOD scores are required for
statistical significance. Linkage analyses in psychiatric disorders are further complicated by the fact that they explore complex psychiatric diseases, with likely
multiple (possibly additive or interacting) susceptibility genes and a strong environmental component, all of which cannot be modeled easily in these analyses. It
is therefore not surprising that linkage analyses have yielded very inconsistent data in the past. In an effort to overcome these problems by sheer sample size,
large meta-analyses have been conducted for linkage scans in schizophrenia and bipolar disorder (Badner and Gershon 2002; Levinson et al. 2003). In
schizophrenia, for example, 20 scans with 1,208 pedigrees and 2,945 affected individuals have been used for a combined analysis (Lewis et al. 2003). This study
yielded a locus on chromosome 2 segregating with schizophrenia with genomewide significance after correction for multiple testing, and 10 further regions have
been identified as strong schizophrenia candidates. A similar meta-analysis was also conducted for bipolar disorder (Segurado et al. 2003), with 948 affected when
bipolar I and schizoaffective disorder, bipolar type, were included as diagnoses and 1,733 when bipolar II disorder was also included. In this analysis, none of the
linkage peaks reached genomewide significance. These findings suggest that in bipolar disorder the effects of individual genes may be small, thus requiring even
greater sample sizes, or that heterogeneity across families is substantial.
Association Studies
Association studies are usually performed in case–control studies of unrelated individuals. In such studies, allele frequencies of markers are compared between a
case and a control population. Association has been shown to have more power than linkage studies to detect susceptibility genes that exert only a small effect on
disease risk (Risch and Merikangas 1996). Considering the strong evidence supporting complex inheritance for most psychiatric disorders, it appears that
association studies are the optimal study design to identify and/or test candidate genes for these disorders. Nonetheless, association studies have been fraught
with failures to replicate initially reported associations. For example, several meta-analyses of association studies of the DRD3 locus, encoding the dopamine D3
receptor, yielded conflicting results on whether a true association exists between an SNP in exon 2, which encodes either serine or glycine at amino acid position 9
(ser9gly), and risk for schizophrenia (Dubertret et al. 1998; Jonsson et al. 2003, 2004; Williams et al. 1998). Critical evaluation of such controversies requires an
understanding of several key concepts, which are important for the design and interpretation of association studies.
Linkage Disequilibrium
Association studies rely on the principle that even unrelated individuals share very small stretches of chromosomal DNA derived from a distant common ancestor.Print: Chapter 3. Genetics and Genomics http://www.psychiatryonline.com/popup.aspx?aID=407887&print=yes…
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In the case of a disease-predisposing mutation, some proportion of ill individuals will share that mutation arising from an original common ancestor. As the variant
is passed down from generation to generation, meiotic recombination events will shrink the size of the initial piece of ancestral chromosome that is inherited
together with the disease mutation. On these small ancestral stretches of chromosome (perhaps several thousand to several hundred thousand bp in length),
nonfunctional “marker” polymorphisms close to the disease mutation will “ride” through generations together with the disease variant. Such markers (SNPs, for
example) can thus serve as surrogate “tags” for the disease mutation. Note, however, that as generations pass and individuals become ever more distant relatives
(to a point where social/cultural “memory” of genetic relationship is usually lost), different families will produce recombination events at different points near the
disease mutation. Therefore, at a population level, unrelated individuals carrying the disease marker will carry variable lengths of DNA on which markers linked to
the disease variant ride. Stated another way, unrelated individuals carrying the disease variant will carry haplotypes that become more similar to each other as the
boundaries of the haplotypes are moved closer to the disease variant. Thus, the closer a marker is to the disease variant, the more likely it is to be originally linked
to the variant. When a marker is close enough to a disease marker that the number of recombination events in the population has not yet completely randomized,
and the chance is that it is on the same ancestral stretch of DNA (haplotype) as the disease variant, the marker and the disease variant are correlated and are said
to be in linkage disequilibrium (LD). Knowing the allele of a marker variant in LD with a disease variant can therefore predict the allele of the disease variant (i.e.,
the marker and the disease variant are not statistically independent).
Several factors influence the length of DNA over which LD occurs. The most important are the geographic origins of the population from which an individual is
drawn and the history of that population. For example, sub-Saharan African populations are ancient and reflect the greatest proportions of the total human pool of
variation, because most of the human species’ history predates migration of humans out of Africa. Therefore, on average, sub-Saharan African populations have
undergone the greatest number of recombination events since a given pair of unrelated individuals shared a common ancestor. Those individuals thus share only
short stretches of ancestral chromosomes (which are said to be identical by descent, or IBD). Non-African populations all derive from a relatively small number of
migrants who left Africa within approximately the last 100,000 years. Thus, Europeans, or Eastern Asians, or Native Americans share longer stretches of DNA IBD
within their respective continental groups than do sub-Saharan Africans (Daly et al. 2001; Gabriel et al. 2002; Patil et al. 2001; Reich et al. 2001). On the other
extreme, individuals from reproductively isolated populations, in which a small recent founder population has expanded with little admixture (i.e., introduction of
outside individuals into the mating pool), share longer blocks of ancestral chromosomes IBD (Shifman and Darvasi 2001). An example of such an isolate is the
Icelandic population, which derives almost exclusively from a small number of migrants who arrived on the island only several hundred years ago (Helgason et al.
2003).
Another factor contributing to the complexity of patterns of LD is the fact that recombination events do not occur with the same likelihood across all parts of the
genome (Phillips et al. 2003; Reich et al. 2002). In addition to a general pattern in which more telomeric regions of chromosomes are more likely to recombine
than centromeric regions, specific “hot spots” of recombination have been identified that are spaced unevenly along chromosomes. This variation across the
genome in the likelihood of recombination results in the observation that there are stretches in the genome (sometimes referred to as blocks) that have undergone
relatively little recombination over the generations and therefore exhibit strong LD among SNPs in the region. Such regions of high LD can span millions of bases,
whereas other regions in the same population have recombined more frequently, resulting in very short blocks of LD (e.g., <5,000 bp). It is important to note that
the observed haplotypes formed by the SNPs within a given stretch of chromosome usually represent only a small proportion of all possible haplotypes (for SNPs,
the number of possible haplotypes = 2n , where n = the number of SNPs defining the haplotype). For this reason, such regions of high LD are also called haplotype
blocks (Daly et al. 2001; Gabriel et al. 2002; Patil et al. 2001; Reich et al. 2001).
The HapMap Project
The HapMap Project is cataloguing LD structure (International HapMap Consortium 2003). This international consortium has set out to identify all ancestral or
haplotype blocks in four different populations: Caucasian Utah Mormons, Sub-Saharan African Yorubans, Han Chinese, and Japanese. Currently, phase 2 of the
project, with genotype data on SNPs with an average density of 1 SNP/ 1,000 bp, is available online (www.hapmap.org). These publicly available data allow
identification of the number of SNPs necessary to tag all ancestral blocks for a given gene, region of interest, or even the whole genome (Cardon and Abecasis
2003; Need and Goldstein 2006). While the data generated in this project have been invaluable for the field, one has to evaluate carefully whether the LD structure
of the populations characterized in HapMap is representative for the selected study population. HapMap tag SNPs seem to perform reasonably well in most
populations, the only caveat being that tag SNPs from Yorubans are not optimal for other African populations, especially African Americans, as they are an admixed
population in which the African ancestors of the population came from a larger geographical area (predominantly along the western coast of Sub-Saharan Africa)
than did the Yoruban population (Conrad et al. 2006; de Bakker et al. 2006).
Linkage disequilibrium and replication of association studies
As already noted, the literature contains myriad reported genetic associations to psychiatric disorders that have not replicated in subsequent studies. The most
important reason for such nonreplication is that until recently, the vast majority of association studies have been grossly underpowered and have examined only
one or a few SNPs. Given the huge a priori odds against a true genetic association at a given locus, even for candidate genes that “make biological sense,” many
reported associations are likely to be due to chance.
However, even when a disorder-predisposing variant does occur within a candidate gene (i.e., when a true genetic association exists), several factors can lead to
nonreplication of association results. Differences in LD patterns between the population of the initial and the replication study can be one reason for such
nonreplication. If a marker polymorphism is in strong or complete LD with a true disease-predisposing variant at a given locus, then typing the marker in a
sufficiently large case–control study will detect an association to the disease, because the allelic variation at the marker accurately “tags” the true
disease-predisposing variant. If an association is detected, then replication is imperative. However, if an independent sample of cases and controls derives from a
population with a different ancestry, and hence a different pattern of LD between the marker and the true disease variant, the power of the originally “positive”
marker to detect the association can be greatly reduced if the marker does not tag the disease-predisposing variant as well (i.e., power is reduced to the degree
that LD between the marker and the disease-predisposing variant is lower). One implication of the foregoing scenario is that adequate evaluation of a given
candidate gene requires examination of a number of SNPs across that gene sufficient to represent most of the common sequence variation in the gene: from this
principle derives the concept of “tagging SNPs” (International HapMap Consortium 2003; Johnson et al. 2001). Useful software is now publicly available that
enables investigators to search the SNP databases and select SNPs that meet a desired criterion of correlation (LD) with each other. Selecting “tagging SNPs” in
this way accomplishes two important goals. First, as just discussed, such selection allows the researcher to capture most of the common variation at a candidate
gene. Second, selecting tagging SNPs allows the researcher to avoid the unnecessary cost and effort of genotyping redundant SNPs. Among the most useful and
widely used software for selecting tagging SNPs is Haploview, developed by Daly and colleagues (Barrett et al. 2005).
One attractive strategy for increasing the likelihood that a given candidate polymorphism is relevant to disease is to focus on variants that clearly alter the
structure or regulation of gene products. While it certainly makes sense that polymorphisms that definitely alter protein structure or gene expression (the most
obvious type being nonsynonymous SNPs) might be expected to alter more complex phenotypes, it is also quite possible that a presumed functional variant may in
fact only be in LD with the real causal mutation. For example, some evidence suggests that the COMT Val/Met polymorphism discussed above is merely a marker
for an upstream schizophrenia-associated variant (Bray et al. 2003; Shifman et al. 2002). As already discussed, variable LD of the Val/Met variant with some other
true schizophrenia-predisposing variant could explain the inconsistent results of the numerous association studies of this polymorphism in schizophrenia.
Stringent testing of the functionality of a specific human polymorphism is currently difficult. Almost invariably, tests of presumed functional variants also examine
other known or unknown polymorphisms due to LD with those other variants. Even if correlations with functional readouts are observed, such as changes in
expression or function in lymphoblastoid cell lines or differences in imaging parameters, one cannot definitely conclude that the specific polymorphism typed is
necessarily the functional one. To test this, one would need to hold all other genetic parameters in the experiment constant and just manipulate the variant of
interest on the same ancestral chromosome. So far these very stringent criteria for establishing functionality are rarely met, including for the most commonly
genotyped “functional” variants in psychiatric genetics.
One should also note that recent data from the Encyclopedia of DNA Elements (ENCODE) project suggest that the regulation of transcription is much more
complicated and widespread than previously assumed (ENCODE Project Consortium 2007). SNPs in genomic deserts (no genes close by) may be as important for
gene transcription regulation as SNPs located in classical promoter elements.
Population Stratification
Frequencies of specific alleles can vary to a great degree among different populations. The approximate frequency of the S allele of the 5-HTTLPR, for example, is
40%–50% in European, 70%–80% in East Asians, and 25% in African populations (Gelernter et al. 1999). Spurious associations can thus be found if subjects for a
genetic association study are sampled from genetically different subpopulations (with different marker allele frequencies) in different proportions in cases and
controls, for example. This could occur if an outcome is more prevalent in one subpopulation, perhaps because of different environmental exposure, so thatPrint: Chapter 3. Genetics and Genomics http://www.psychiatryonline.com/popup.aspx?aID=407887&print=yes…
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individuals from this subpopulation will be represented more frequently in the case as opposed to the control group. Differences in allele frequencies between
comparison groups can thus result from differences in the population structure of the sample alone, without any causal relationship to the outcome of interest.
This problem is called population stratification and is exemplified by the hypothetical example of the “chopsticks gene” (Hamer and Sirota 2000).
Several methods have been developed to address population stratification in genetic association studies. Family-based approaches to genetic association studies,
such as the transmission disequilibrium test (TDT; Spielman et al. 1993) and the haplotype relative risk (HRR) method (Knapp et al. 1993), are robust to
population stratification. In the TDT, rather than compare allele frequencies between cases and controls, genotypes are determined in probands and their parents.
To be informative, at least one parent must be heterozygous at the marker of interest (this is one of the disadvantages of the method in its original form: in some
cases, not all families can be used). The allele transmitted to the proband from each parent is recorded, as well as the nontransmitted allele. Under the null
hypothesis of no linkage or association, the expected chance of each allele’s transmission is 50%. Significant deviations from that chance provide evidence for
association (and linkage) between the trait and the marker. The HRR method is similar, except that the frequency of the nontransmitted alleles is compared to that
of the transmitted alleles over a large number of families. Each family thus provides its own control—since the proband is always matched for the population
background of the parents, population stratification cannot arise.
Recently, methods based on the TDT have been developed to allow transmission disequilibrium testing in larger family groups. Such family-based association tests
offer the advantage of using more of the available genetic information from each family (Horvath et al. 2001). The major disadvantage of the family-based
methods is that it is often impractical to gather families, especially in disorders such as schizophrenia or substance dependence, which are often associated with
familial estrangement, or age-related disorders such as Alzheimer’s disease, where surviving relatives (especially parents) may not be available. An additional
problem with family-based association studies is they generally have less statistical power than case–control studies (Risch and Merikangas 1996).
To control for population stratification in case–control studies, two sets of methods have been developed, both of which estimate, and then correct for, the degree
of stratification between comparison groups by typing a series of unlinked markers across the genome (Devlin and Roeder 1999; Pritchard et al. 2000). Using
these methods, called structured association (Rosenberg et al. 2002) and genomic control (Devlin and Roeder 1999), one can only determine stratification of the
level of the whole sample. A refinement of these methods employs ancestry informative markers (AIMs), which can estimate the proportional genetic heritage
from predefined ethnicities for each individual (Montana and Pritchard 2004; Parra et al. 2001). As opposed to the first approaches, AIMs are not randomly
selected unlinked markers, but markers are chosen to exhibit large differences in allele frequencies between specified populations (Northern Europeans vs.
sub-Saharan Africans, for example). Using such markers, one can demonstrate that some populations, such as self-identified African Americans and European
Americans, may show substantial admixture of European, African, and (to a lesser degree) Native American chromosomal ancestry (Parra et al. 1998, 2001). Each
individual’s proportion of predefined chromosomal ancestry can be estimated and then used as a covariate to correct for stratification in association analysis
(Frudakis et al. 2003).
Correction for Multiple Testing
The ease and cost-effectiveness with which SNPs or other markers can now be genotyped have led to the advent of whole-genome association studies, in which
100,000–1,000,000 SNPs are typed in large case–control comparisons. This approach, while creating the exciting opportunity to scan the genome in a
non-hypothesis-driven manner for genetic associations, suffers from the difficult problem that the number of statistical tests in each association study is
enormous, and thus the likelihood of false-positive associations is high. In a simple example, when one performs 100 statistical tests, one would expect that 5 of
them would show a P value of <0.05 just by chance. To decrease the likelihood of reporting and following false-positive associations, methods to correct for
multiple testing have been developed. The simplest and most common is the Bonferroni correction, in which the alpha level required for statistical significance
overall is divided by the number of statistical tests and thus, in this case, the number of tested SNPs. However, because not all SNPs are independent (see “Linkage
Disequilibrium” subsection above), Bonferroni correction is often overly conservative. Newer methods of correcting for multiple testing take into account the LD
among the tested SNPs (e.g., Nyholt 2004). An alternative is permutation-based methods, in which the outcome status (e.g., case or control status) is randomly
assigned and the genotypes remain fixed. The association statistic is recalculated for every permutation, and all P values are noted. This allows estimation of the
number of P values that are as small as or smaller than the P value based on the correct assignment of outcome status (Churchill and Doerge 1994). The corrected
empirical P value then is the number of P values smaller than the empirical one/number of total iterations. Because permutation-based correction can be
computationally intensive, newer, faster methods have been developed (Dudbridge and Koeleman 2004; Seaman and Muller-Myshok 2005). Another method to
control for multiple tests is the false discovery rate (Benjamini and Hochberg 1995). In this method one controls the expected proportion of false positives in a list
of potential positive associations. It is a less conservative comparison procedure with greater power than the methods mentioned above but at a cost of increasing
the likelihood of obtaining type I errors.
While stringent correction for multiple testing reduces the likelihood of reporting false-positive associations, true association may be dismissed as false positives
because, while nominally significant, the association does not reach the set threshold for correction for multiple testing. Some researchers thus advocate a
two-step design for association studies, in which a first sample is used as an exploratory sample, in which the alpha levels are more liberal, and then nominally
associated SNPs are tested in a second replication sample. Only associations that show significance in both samples are considered as potential true positives. This
approach has, for example, been used in the pharmacogenetic association study of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) sample
(McMahon et al. 2006).
Genomewide Association Studies
New Possibilities Due to the Human Genome Project
Despite 30–40 years of intense research on a series of promising markers, none has thus far been validated as diagnostic tools or predictors of treatment response.
In addition, most of the past approaches were hypothesis driven, relying on our limited knowledge of the pathophysiology of psychiatric disorders. With the
sequence of the human genome having being publicly available since February 2001 (Lander et al. 2001; Venter et al. 2001), an array of novel research tools has
become available that may yield unbiased, hypothesis-free insight into the pathophysiological underpinnings of certain psychiatric disorders. These novel tools
combine knowledge of the sequence of the human genome with miniaturized assays amenable to high-throughput processing for a parallel analysis of the whole
genome. Using these, one can investigate the whole genome at the level of the DNA (genomics), all expressed mRNA (expressomics—or, more commonly,
expression array or microarray analysis), and all proteins (proteomics) in a single experiment. These three approaches have to deal with increasing levels of
complexity, because the approximately 25,000 predicted human genes are expected to give rise to at least 10 times as many protein isoforms with a multitude of
posttranslational modifications, such as phosphorylation and glycosylation. Using these unbiased whole genome–based approaches, novel pathways and molecules
involved in the pathogenesis of psychiatric disorders may be identified. This chapter will mostly focus on genomewide SNP association studies and their impact on
psychiatric genetics; we recommend several reviews on the impact of microarray analysis and proteomics on pathophysiological concepts in psychiatry (Freeman
and Hemby 2004; Ginsberg et al. 2004; Mirnics et al. 2006; Tannu and Hemby 2006) for additional information in these areas.
Genomewide SNP Arrays
How many SNPs?
The presence of linkage disequilibrium in the human genome allows the investigator to evaluate a large extent of the common genetic variation with selected
markers. Estimates of the number of SNPs necessary to account for most of the common sequence variation across the genome (e.g., to account for SNPs with
minor allele frequencies of 1% or greater) have varied over time, with estimates ranging from as few as 10,000–100,000 to over a million SNPs. Thanks to the
HapMap and the ENCODE resequencing projects (International HapMap Consortium 2003 and www.hapmap.org), we are starting to have a better idea of how
efficiently we have and can cover the genetic information using SNP assays. These massive genotyping and resequencing projects have confirmed the segments of
long LD in the genome and thus the possibility of using tag SNPs (see discussion above) for each of these segments. Using the data from several hundred thousand
SNPs should be sufficient to cover most common variants in Caucasians; more SNPs will be necessary in African populations with shorter LD distances (for a
review, see Hirschhorn and Daly 2005). Based on the information in the HapMap and ENCODE databases, Barrett and Cardon (2006) estimated the coverage of
commercially available whole-genome SNP panels from Illumina (www.Illumina.com) and Affymetrix (www.affymetrix.com). The Illumina 300k chip offered
coverage of 75% in Caucasians and the Affymetrix 500K 65%. These values dropped dramatically for Yorubans, to 28% and 41%, respectively. While these
whole-genome arrays with 300,000 to 500,000 SNP assays offered relatively good coverage of more common variants, rare SNPs are not well covered. While these
earlier arrays still had relatively substantial holes in the genomewide coverage, especially in African populations, the development in this area is exponential. For
example, Affymetrix now offers a 1.8 million SNP array, and Illumina a 1 million SNP chip. Incomplete coverage should soon become a problem of the past
(Schuster 2008).
A major drawback of whole-genome LD-based approaches to association testing, such as those just outlined, is that the majority of sequence variation in thePrint: Chapter 3. Genetics and Genomics http://www.psychiatryonline.com/popup.aspx?aID=407887&print=yes…
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genome is rare, with minor allele frequencies too small to be detected with any power by even very dense maps of SNPs. Thus, to the degree that the genetic
underpinnings of complex diseases deviate from the “common disease, common variant” hypothesis, whole-genome SNP scans will be inadequate to detect
associated genes (Zwick et al. 2000). This concern is not merely theoretical. Thus, it is abundantly clear that in Mendelian diseases, such as cystic fibrosis, where
variation at only one locus accounts for the disease, a multitude of very rare sequence variants are causal in different families. Rare variation also clearly
contributes to complex diseases. For example, in familial autism, a subgroup of families who exhibit linkage to markers on chromosome 17q (Sutcliffe et al. 2005)
has demonstrated that a series of rare functional variants at SLC6A4, the serotonin transporter locus, accounts for much of the observed linkage in these families.
Common variation at the locus (e.g., the 5-HTTLPR discussed above) did not contribute to linkage to the disorder in these families. In all likelihood, complex
disorders will represent a mixture of contributions from rare and common variants, so that methods appropriate for both types of variation will need to be
developed and implemented. In regard to rare variation, the advent of whole-genome resequencing may contribute importantly to identifying such variants.
How many individuals?
Genomewide associations require massive correction for multiple testing so that P values below 10–7 or smaller have to be achieved for genomewide significance.
These necessary low alpha levels, together with the expected low odds ratios (ORs) associated with identified susceptibility variants, require large sample sizes for
adequate power. For example, to detect the effect of a susceptibility allele that has a frequency of 20% and an OR of 1.3 with a power of at least 80% and an even
more liberal alpha level of 10 –6, more than 2,500 cases are necessary. If the allele is rarer (e.g., 10% carriers), at least 6,000 cases would be required (W. Y.
Wang et al. 2005). Given that epidemiological studies suggest small effect sizes of multiple genes for psychiatric disorders, these calculations would apply to this
field as well.
How to reduce false-positive findings?
Because whole-genome association studies use a large number of tests, very strict levels of significance have been proposed (7.2 x 10–8; Dudbridge and Gusnanto
2008). While this strategy may avoid a large number of false positives (at least 25,000 SNPs are expected to show association with a P value of 0.05 or smaller in a
set of 500,000 SNPs), true positives may also be missed. This is especially relevant given that most studies in psychiatric genetics will likely be underpowered to
detect relevant effect sizes of 1.3 or smaller with rarer alleles. One possibility to reduce false negatives due to overly stringent correction for multiple testing
would be to genotype all SNPs in a smaller fraction of the sample (discovery sample) and use a liberal P value to select SNPs that are then genotyped in a second
larger sample (confirmation sample). Only SNPs passing this second stage would then be selected for replication in an independent sample (Hirschhorn and Daly
2005; H. Wang et al. 2006). Recently, guidelines for replicating genotype–phenotype associations, with focus on whole-genome associations, have been put
forward (Chanock et al. 2007).
Another strategy is to use convergent evidence from other genomewide approaches, such as linkage analyses, expression microarrays, and proteomics. While such
studies have not yet been published for whole-genome SNP association studies, this strategy has been successful with microarray and linkage data. John Kelsoe
and his colleagues used an approach that combined microarray analysis of animal models of mania and linkage analysis in families with bipolar disorder to identify
G protein–coupled receptor kinase 3 ( GRK3) as a promising candidate gene. This gene is involved in the homologous desensitization of G protein–coupled
receptors. The group had initially identified a linkage peak for bipolar disorder on chromosome 22q (Kelsoe et al. 2001; Lachman et al. 1997). That linked region,
however, spanned 32 cM, making it a challenging task to identify the causal gene by fine-mapping strategies. The group then used microarray analysis of different
brain regions in methamphetamine-treated rats and identified several genes that were regulated by this treatment that also mapped to previous linkage peaks
with bipolar disorder (Niculescu et al. 2000). One of these was GRK3, which maps to 22q. In addition to being regulated in the animal model, protein levels for this
gene were also found to be decreased in a subset of patient lymphoblastoid cell lines, and the magnitude of the decrease correlated with disease severity. By using
resequencing and SNP genotyping strategies, the group confirmed the association of 5’UTR and promoter variants of this gene with bipolar disorder (Barrett et al.
2003).
Current Results From Genomewide SNP Association Studies
In a landmark paper, Risch and Merikangas (1996) suggested that genomewide association studies will have more power to detect disease genes for complex
diseases than family-based studies. This bold suggestion was postulated 5 years before the sequence of the human genome was available and SNPs emerged as
potential tools for this type of study. Now, more than 10 years later, genomewide association studies with hundreds of thousands of SNPs in thousands of affected
individuals and controls are identifying novel hypothesis-free candidate genes for complex disorders. In the past few years, a number of large multicenter studies
have identified novel candidate genes for diabetes type 1 and type 2, metabolic diseases, several types of cancer, age-related macular degeneration, and a series
of autoimmune disorders using this approach (Altshuler et al. 2008). A series of genomewide association studies have now been published for addiction, bipolar
disorder, unipolar depression, schizophrenia, autism, and attention-deficit hyperactivity disorder (Psychiatric GWAS Consortium Steering Committee 2009). By
early 2009, genomewide association studies in 47 samples from these disorders will be completed, with well over 50,000 independent individuals in these studies.
Whole-genome SNP association studies have been successful in bipolar disorder, and polymorphisms in the genes encoding ankyrin G ( ANK3) and the alpha 1C
subunit of the L-type voltage-gated calcium channel ( CACNA1C) have emerged as new candidates for this disorder from data combining several large samples
(Ferreira et al. 2008; Schulze et al. 2008). The latter gene family may also be of relevance in schizophrenia (Moskvina et al. 2008). Interestingly, the independent
samples (Baum et al. 2008a, 2008b; Craddock et al. 2008; Sklar et al. 2008; Wellcome Trust Case Control Consortium 2007) mostly yielded no genomewide
significant associations or could not be replicated in single association studies, showing the importance of large sample sizes for these studies. The single
genomewide association study on unipolar depression published by the end of 2008, starting with data from the Genetic Association Information Network (GAIN)
initiative of the National Institutes of Health, did not show replication of the strongest, but not genomewide significant hit in five other samples with a total of
more than 7,000 cases and controls (Sullivan et al. 2008a). Whole-genome SNP association studies for schizophrenia have also been less convincing than for
bipolar disorder (Kirov et al. 2008; Lencz et al. 2007; Shifman et al. 2008; Sullivan et al. 2008b). The largest replication attempt of genomewide association studies
in schizophrenia, with more than 16,000 individuals, points to a locus around zinc finger protein 804A ( ZNF804A) that reaches genomewide significance when
patients with bipolar disorder are included in the meta-analysis (O’Donovan et al. 2008). Genomewide genetics studies in schizophrenia seem to point to the
importance of a combination of rare and common variants in this disorder, possibly explaining the relative lack of genomewide significant hits in genomewide
association studies. A whole-genome association study in schizophrenic patients revealed a colony-stimulating factor receptor ( CSF2RA) and an interleukin
receptor (IL3RA) as potential novel candidate genes for this disorder (Lencz et al. 2007). From this study in two independent samples, the authors could identify
an association with disease of common haplotypes in these two genes and also discovered an excess of rare nonsynonymous mutations in these genes, thus
supporting a model in which both common and rare mutations can contribute to disease susceptibility (Lewis et al. 2003). The importance of rare variants and
specifically structural variants in schizophrenia has been underlined by two large international genomewide studies identifying a series of CNVs associated with the
disorder, confirming previous hits in neurodevelopmental genes from earlier linkage and candidate gene association studies (International Schizophrenia
Consortium 2008; Stefansson et al. 2008).
The first sets of genomewide association data in psychiatric genetics provide new insights into these disorders. Although promising, these new data raise several
important issues:
Multiple genes of small effects contribute to psychiatric disease. To identify these effects with sufficient power, large sample sizes of several thousand cases will be required, and
international collaborations are essential.
Comparison with other complex diseases. The Wellcome Trust Case Control Consortium (2007) has published whole-genome association data for 14,000 cases of seven disorders
and 3,000 common controls. In this study, 2,000 cases of bipolar disorder were examined next to cases of coronary artery disease, Crohn’s disease, hypertension, rheumatoid
arthritis, and diabetes type 1 and type 2. The direct comparison of this association study for bipolar disorder with association in the other tested disorders is very interesting. In
bipolar disorder, no really strong clusters of association could be determined. For diabetes type 1, with a similar heritability (around 80%), on the other hand, at least five very
strong clusters of association could be identified, and even coronary artery disease and diabetes type 2, disorders with a prominent environmental component and lower
heritabilities, showed several clusters of very strong association. Psychiatric disorders may thus be even more complex than other complex disorders. The fact that bipolar
disorder, the most heritable affective disorder, did not perform well in the Wellcome Trust Case Control Consortium analysis in comparison with diabetes and coronary artery
disease, for example, shows that psychiatric genetics needs to invest in the identification of reliable biological phenotypes that better cluster genetically homogeneous patient
groups.
Importance of a mix of common and rare variants, with rare variants likely predominating in autism and schizophrenia. Linkage and association studies will need to be combined
with resequencing studies to identify these rare variants.
Importance of structural variations in psychiatric genetics. In schizophrenia and autism, investigation of structural variation has proven to be a very powerful tool to dissect the
genetics of these disorders (Cook and Scherer 2008; Marshall et al. 2008; Sebat et al. 2007; St. Clair 2009; Szatmari et al. 2007; Weiss et al. 2008). We are awaiting similar
studies in other psychiatric disorders.
Gene–Environment InteractionsPrint: Chapter 3. Genetics and Genomics http://www.psychiatryonline.com/popup.aspx?aID=407887&print=yes…
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To date, most genetic association studies have searched for simple associations between sequence variants and psychiatric disorders. This approach ignores the
clear reality that environmental factors contribute importantly to psychiatric illness. While many such factors remain unknown, several clearly are known. For
example, it is becoming increasingly clear that early childhood traumatic experiences substantially increase the risk of major depression and other mood disorders.
In their landmark paper, now replicated by several groups, Caspi et al. (2003) showed that genotype at the 5-HTTLPR interacts with exposure to early trauma to
increase the risk of depression differentially in carriers of the S allele. From a theoretical perspective, searching for such gene–environment interactions makes
sense. Thus, any statistical interaction is bound at the upper end of its effect size by the magnitude of the main effects being considered. When environmental
factors such as early trauma, which have large effect sizes, are examined together with genetic factors, which are expected generally to have small effect sizes,
the result can be greater power to detect gene–environment interactions than main effects of the gene. Thus, an argument can be made (which by no means is
accepted by all) that future genetic studies of psychiatric disorders and other complex disorders should be based on models that incorporate both genetic and
environmental factors.
CONCLUSION
Taken together, epidemiological, cytogenetic, linkage, and association studies in psychiatric genetics to date paint a picture of highly complex genetic influences on
psychiatric disorders. As Kendler (2005) pointed out, the phrase “a gene for. . . ” will very likely not apply to psychiatric genetics. And, as Kendler went on to note,
“The impact of individual genes on risk for psychiatric illness is small, often nonspecific, and embedded in a complex causal pathway” (Kendler 2005, p. 1243). The
field may need to adopt strategies that are better adapted to the most likely disease models. In the following we will mention some of the proposed strategies to
address this issue.
First, we may need to reconsider the way we define cases or the phenotype of interest. Our current classification schemes are not likely directly reflective of the
underlying biology—and thus the genetic determinants—of psychiatric disease. The currently used diagnostic algorithms (DSM-IV-TR [American Psychiatric
Association 2000] and ICD-10 [World Health Organization 1992]) group diagnoses by symptoms and clinical course, characteristics that may reflect not a common
biology but rather a final common pathway of several different pathophysiological disturbances. That recognition has led some to propose the use of intermediate
phenotypes—including neurophysiological, biochemical, cognitive, and endocrine measures (Gottesman and Gould 2003; Hasler et al. 2004)—in psychiatric genetic
studies in order to create biologically more homogeneous subgroups of patients and thus to increase the power to detect case–control associations. Another
important consideration is that a number of symptoms are common to several different DSM-IV-TR diagnoses, and the genetic susceptibility to develop these
symptoms may be common across disorders. In fact, there is evidence that the major psychiatric disorders may share susceptibility genes. A series of linkage
peaks and candidate gene associations overlaps between bipolar disorder and schizophrenia, for example (Craddock et al. 2006), and the cytogenetic disruption of
DISC1 leads to a variety of severe psychiatric disorders, ranging from recurrent unipolar disorder to schizophrenia (St. Clair et al. 1990).
Second, environmental measures should be included more consistently in genetic studies, including whole-genome association studies. Epidemiological (Kendler
1995) as well as molecular genetic studies have now repeatedly demonstrated the importance of gene–environment interactions in psychiatric disease (Caspi and
Moffitt 2006). Genetic effects may be obscured by unmeasured environmental effects, so that different environmental exposures in replication samples may be one
source of nonreplication of genetic association.
Finally, one should not forget that SNPs are just the most common and convenient type of genetic variant. Other types of variation, such as CNVs, may be equally
important (Redon et al. 2006; Sebat et al. 2004). Newer versions of whole-genome arrays now try to cover most copy number variations known to date, and
association with these may lead to surprising findings.
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Course Content
Introduction to Genetics & Genomics: Understanding the Basics
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The Building Blocks of Life: DNA and RNA
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The Central Dogma of Molecular Biology
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Basic Concepts in Genetics & Genomics Quiz
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Introduction to Genomics: Beyond the Genome
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Mendelian Inheritance: Understanding Genetic Traits
DNA Structure and Function: The Blueprint of Life
Genomic Technologies and Techniques: Tools of Discovery
Applications in Medicine and Biotechnology: Harnessing Genomic Insights
Ethical Considerations and Future Directions in Genomics
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