- Email: [email protected]
Bridging genetics and genomics in neurology
Neurol Clin N Am 20 (2002) 867–877
Bridging genetics and genomics in neurology Vivian G. Cheung, MDa,b,*, Richard S. Spielman, PhDb a
Department of Pediatrics, University of Pennsylvania, School of Medicine, 3516 Civic Center Blvd., RM 516, Abramson, Philadelphia, PA 19104, USA b Department of Genetics, University of Pennsylvania, School of Medicine, 415 Curie Blvd., 464 CRB, Philadelphia, PA 19104, USA
One of the fastest growing fields in biological sciences is genomics. Its impact on genetics, which stimulated its development, has been tremendous. Its impact on other fields such as neuroscience is equally substantial. In the past several years, the field of genomics has delivered detailed genetic maps of several organisms including mouse and human, and the complete genome sequences of many microorganisms and more recently the draft sequence of the 3 billion basepair human genome [1]. It has stimulated the development of technologies for high-throughput genome analysis such as fluorescent sequencer and DNA microarray technologies. These technologies were used to generate a detailed catalogue of genes that constitute our genome and to decipher the functions of those genes. This broad scope of genomics that covers genetic map, genome sequence and gene function has important influence on neurology and neuroscience. It has led to the identification of genes for various neurologic disorders and a better understanding of the genes that function in specific brain regions. In this article, the authors explore the impact of genomics on our understanding of the genetic basis of neurologic diseases.
Genes and diseases Mendelian disease One of the first successes of disease gene mapping was the identification of the gene for Huntington disease; however, the cloning of the Huntington disease gene was a long and tedious process. It took ten years from localizing the Huntington disease gene to chromosome 4 to identifying the gene * Corresponding author. E-mail address: [email protected] (V.G. Cheung). 0733-8619/02/$ - see front matter Ó 2002, Elsevier Science (USA). All rights reserved. PII: S 0 7 3 3 - 8 6 1 9 ( 0 2 ) 0 0 0 0 3 - 8
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itself [2,3]. Genomics has contributed to accelerating this process by providing a human genetic map that is densely populated with polymorphic markers. As of October 2001, there were over 8,000 microsatellite markers and 1.4 million single nucleotide polymorphism markers for the human genome [4,5] (http://www.ncbi.nlm.nih.gov/SNP/index.html). This simplifies the process of determining the candidate gene regions for the disease of interest. Once a candidate region is defined, then one can search for the disease-causing gene in the region. Before the Human Genome Project (HGP), this was a daunting task. Now one can perform an in silico gene search without the laborious work confronted by the Huntington Disease Research Collaborative Group. The DNA sequence of the human genome allows us to define genes and their protein products, which will then be available for design of more specific pharmacological targets. In the past, design of unique agents against human gene products was difficult. With the precise information of human genome sequence in hand, this process will become less tedious. The sequences of the regulatory regions of the genome will also serve as important therapeutic targets. Understanding the role of these regions in controlling gene functions will provide new understanding of the genetic and physiologic bases of human diseases. The availability of polymorphic markers and information about genes in our genome, along with the advances in genotyping technologies, have enhanced greatly the search for Mendelian disease genes. For example, among neurologic diseases, the genes for Peroxisome biogenesis disorder (PEX1), Limb-girdle muscular dystrophy (LGMD 2G), Generalized epilepsy with febrile seizures plus type 2 (SCN1A) and Spinocerebellar ataxia type 10 (SCA10) were identified using the resources provided by the HGP [1,6–10]. Undoubtedly, many other Mendelian disease genes will be discovered in similar manner. However, genes for complex traits remain a challenge. Complex genetic disease In contrast to the genetic disorders mentioned above, complex diseases do not exhibit Mendelian patterns in families, although there is a genetic contribution to the underlying susceptibility. The most characteristic aspect of this non-Mendelian behavior is the feature called ‘‘incomplete penetrance.’’ This term describes the fact that although some people who have the susceptibility gene(s) develop the disease, others (perhaps most) do not. The exact biological causes of incomplete penetrance are not clear, but certainly include both environmental and genetic components. Informational approaches such as that utilizing the HGP are ideal for understanding the genetic components that alter disease penetrance. In addition, it is often said that complex diseases are due to the simultaneous contributions of several or many genes. Although this is a likely explanation for some of the complexity of these diseases, it has not yet been shown to contribute substantially to any of the common complex diseases.
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Regardless of the uncertainties about the underlying genetic causes, an important feature is that many of the well-known complex genetic diseases are common. In general, it is easier to analyze genetic patterns when the disease is rare. Thus for the geneticist, the high prevalence is another complicating feature that is not usually found in Mendelian diseases. Typical examples of the common complex diseases are diabetes (both type 1 and type 2), Alzheimer disease, asthma, and schizophrenia. The main goal of contemporary genetic analysis of complex diseases is to locate and characterize all the genes that contribute, and to explain how variation in these genes produces susceptibility to disease. For this task, the HGP has provided a wealth of materials and information, but a discipline that integrates the essential features of genetics and genomics has not yet emerged. In existing genetic analysis, the main difficulties arise from the incomplete penetrance. Classical linkage methods use genetic information from both affected and unaffected family members. In complex diseases, because of incomplete penetrance, the genetic (genotype) status of unaffected family members is usually very uncertain, so many of these cannot be used with confidence in the analysis. Difficulties like these in applying conventional linkage analysis have led to the development of new approaches, designed specifically for locating genes that contribute to complex diseases. A ‘‘genome scan’’ is a search for linkage throughout the genome, usually carried out with a set of genetic landmarks or ‘‘markers,’’ spaced approximately evenly over all the chromosomes. To avoid the problems of incomplete penetrance, in complex diseases this is often done using affected siblings only, or affected sibling and their parents, but ignoring other unaffected family members. This approach has become the method of choice in many studies of complex diseases [11,12]. The methods based on disease association use a fundamentally different approach; these focus on candidate genes or candidate regions already identified by linkage, or implicated by prior knowledge. In these methods, one first identifies variant allelic forms in the candidate gene (or candidate region); without this step it is not possible to test whether inherited variation in the gene plays a role. Instead of basing the assessment on a test for linkage, however a test for association evaluates evidence for correlation between disease and variant, in the population as a whole. Unlike a linkage study, this approach requires specifying in advance a gene or region suspected to play a role in disease. The two methods differ because they are applied to different samples of subjects with different expectations; genetic linkage results in cosegregation of disease and marker among related people, while association results in co-occurrence of disease and marker in a sample of unrelated people. Either of these phenomena can occur in the absence of the other; however, the principles of population genetics support the following expectation. A marker very closely linked to a disease susceptibility locus is more likely to be associated with the disease than a marker that is loosely linked or unlinked. This is the rationale for using the strength of association
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as a metric to localize genes that contribute to complex disease. It follows that once a site in the genome has been precisely identified as contributing to a disease, the evidence from each approach should support the other. Compared to monogenic Mendelian diseases, the complex diseases require much more elaborate study designs and analyses. Nevertheless, the approaches described here have led to much improved understanding in some complex diseases like late-onset Alzheimer disease (AD) [13] and MS [14]. The authors expect, however, that further advances in understanding will be greatly aided by the accomplishments of the HGP.
Gene expression studies in neurology About 50% of the genes in our genome are expressed in the nervous system. To understand the function of the highly complex human nervous system, we need to understand the function of these genes and their interactions. This knowledge will contribute to our understanding of the pathogenesis of diseases such as strokes and dementia as well as complex behaviors such as learning and sleep. Today, despite the availability of imaging studies such as MRI and PET scans, some aspects of the practice of neurology are still limited to descriptive analysis of patients’ symptoms and localization of those symptoms to gross anatomical areas of the brain. In time, genomics with its tools that study gene functions and genetic networks will integrate with anatomical studies. These tools are likely to have a major impact in elucidating the molecular basis of many neurologic diseases and eventually the development of therapies. The merging of traditional medical approaches and genomics will lead to an understanding of the normal function of many genes in the development and maintenance of our nervous system. Overview of gene expression technologies Several high-throughput technologies are now available for studying gene expression profiles. These include differential display, serial analysis of gene expression and cDNA microarrays [15–18]. The authors will not review these techniques in detail here. Depending on the goal of the experiment, one or several of these techniques can be used to assay large number of genes. Differential display and similar techniques such as suppressive subtractive hybridization and representational difference analysis are hybridizationbased subtractive methods that allow identification of genes that are expressed differently between two samples [15,19]. It is relatively easy to set up these techniques since no special equipment is needed and one does not need to know the list of genes to be interrogated in advance; however, one obtains mostly absent/present information but not quantitative information. In contrast, serial analysis of gene expression (SAGE) provides highly quantitative
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information. It provides an excellent estimate of the fold differences in expression levels of genes between two or more samples. But to perform SAGE, one must have the ability to perform and analyze a large number of sequencing reactions. The third type of technique is microarray. There are two types of microarrays, oligonucleotide array such as the GeneChip by Affymetrix and spotted cDNA microarrays. Both types of microarrays require specialized equipment and knowledge of the genes one wishes to interrogate. To synthesize oligonucleotide arrays, one needs to know the sequences of genes of interest and in spotted arrays, one needs the cDNA clones representing the genes of interest. In recent years, the HGP has provided the sequences and clones for many genes thus microarray technology has become increasing popular as a method for gene expression profiling. Sequences and structures of genes (such as intron/exon borders) are available freely at many sites (http://genome.ucsc.edu; http://www.ncbi.nlm.nih. gov/Entrez; http://www.ensembl.org). Full-length and sequence-verified cDNA clones are available commercially at reasonable cost (http://www. ncbi.nlm.nih.gov/ncicgap). High density synthetic oligonucleotide arrays There are several types of oligonucleotide arrays ranging from spotted oligonucleotide arrays to those arrays based on light-directed synthesis. The authors will limit this discussion to GeneChip probe arrays, which are constructed by combining photolithography and solid phase DNA synthesis [20,21]. A solid support is treated with photolabile protecting groups. Light is directed through a mask to deprotect and activate sites on the solid support. Protected nucleotides are added to those activated sites. This process is repeated, activating different sites and adding different nucleotides until the final synthesis of all DNA probes is achieved. This process is efficient — in each round, one of the 4 nucleotides is added to multiple sites simultaneously; thus it takes only 4N cycles to synthesize an array containing probes of length N regardless of the number of probes on the array. High-density arrays are available containing genes for a wide range of organisms including yeast, E. coli, mouse and human. The human arrays (U133 set with 2 arrays) contain about 33,000 known genes with 6,500 splice variants and 4,500 genes with alternative polyadenylation sites. To make the arrays, Affymetrix must have the sequences of the genes/ESTs of interest. As the human genome project is generating the sequences of increasing numbers of genes, one can expect to see a whole genome human array in the near future. Each gene on the array is represented by several 20-mer oligonucleotides. The oligonucleotides and probe sets are designed to maximize hybridization specificity. To perform expression profiling using the GeneChips, fluorescently labeled RNA samples are hybridized onto the arrays and the hybridization
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signals are analyzed using a fluorescent scanner. The intensity of the hybridization signals for each probe on the array is proportional to the abundance of that transcript in the sample. Affymetrix has designed software for analyzing the hybridization data. Other software packages are available to manage and to analyze the data generated using GeneChips [22]. Spotted cDNA microarrays Spotted cDNA microarrays are microscope slides containing immobilized DNA samples [17]. For expression analysis, the DNA samples are cDNA clones representing genes or ESTs. These microarrays can be made using robotic ‘‘microarrayers’’ that place nanoliter amount of DNA samples onto microscope slides (http://www.microarrays.org/; http://www.gene-chips.com/). To gain information about the DNA on the microarrays, one hybridizes fluorescently or radioactively-labeled samples onto the microarrays in manners similar to the northern and Southern blots. In a cDNA microarray experiment, one compares two or more samples that are each labeled with one fluorescent dye (for example, Cy3 or Cy5) that are co-hybridized onto a single microarray. This allows comparison of the relative abundance of transcripts corresponding to each gene/EST on the microarray between the samples. After the hybridization, the slides are scanned using laser scanners. The fluorescent intensity of each spot is proportional to the number of RNA molecules hybridized to each spotted DNA sample. The scanned images are analyzed using software that quantifies the spot intensities. As in many hybridization-based assays, detection of hybridization signals and subtraction of background noise are important aspects of array image analysis. Similar to the analysis tools for the GeneChips system, software packages that analyze spotted array images are beginning to address many of these issues. A more difficult aspect of microarray technology is the analysis of the data. A single experiment using either spotted microarrays and GeneChips allows researchers to interrogate tens of thousands of genes. Thus a simple experiment designed to compare two tissue types can generate thousands of data points. Determining the significance of the results is more challenging than traditional experiments that examine one or several genes. When testing many genes, the chance of finding one or more genes that are different between the two test samples by chance is increased. Genomic technologies have made it simple to test many genes and to explore many interactions not suspected a priori; however, a major drawback of performing multiple tests is the increased probability of declaring false positives. An additional problem of testing multiple genes is the unknown correlations between the genes. Many traditional parametric statistics rely on the assumption of independence between variables. This assumption is violated in microarray data since some of the genes are correlated and the correlation is usually unknown a priori. Several groups have proposed methods for analyzing the data using nonparametric methods that address the problems of
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multiple testing and the unknown correlations between genes. It is beyond the scope of this study to review these methods – many of these studies are available at the following website, http://linkage.rockefeller.edu/wli/ microarray/. Profiling gene expression in regions of the brain The human brain is a highly complex system composed of many gross anatomical parts that are further divided into microscopic divisions. Much information is available on the function of various parts of the brain from the frontal lobe to the hippocampus and the cerebellum. Most functional studies of the brain are performed by subdividing the brain in anatomical and physiologically distinct regions. With the availability of genomic technologies such as microarrays, one can now begin to make the link between genes and anatomy. By studying the genes that are expressed at different regions of the brain, one can identify the genes that participate in different functions specific for each region. This will help to better understand regionspecific neurological functions and facilitate the mapping of susceptibility genes for neurological disorders. For example, if a gene mapping study led to the discovery of a seizure susceptibility locus on a particular chromosomal region; much work still remains since likely many genes reside in that genomic region. But if one knows that within the region are one or more genes that are specific to the hippocampus, those genes would be excellent candidates for mutation analyses. Projects such as the BMAP (http://trans.nih.gov/resources/resources.htm) initiative and several recent publications demonstrate successes in defining genes specific for different brain regions [23–25]. First, these papers confirmed the speculation that a large number of genes are expressed in the brain. Two groups used the Affymetrix GeneChips to study over 13,000 genes and found that slightly over 50% of the genes are detected in one or more regions of the mouse brain [24,25]. It appears that the cerebellum has more uniquely expressed genes when compared to the cortex, amygdala, entorhinal cortex and midbrain [24]. Sandberg and colleagues found at least 23 genes were unique to the cerebellum and another 28 genes were not expressed in cerebellum but were present in other brain regions studied. These cerebellum-specific genes include mouse D-type cyclin, p21 activated kinase-3, protein tyrosine phosphatase STEP61, NMDA receptor subunit NR2C and glutamate receptor channel delta 2 subunits. Perhaps it is not surprising that the cerebellum, whose developmental origin is unique, has a specific set of genes that participate in growth and receptor signaling. The cells in cerebellum may respond to different growth stimuli and have developed signaling receptors unique for its function and survival. This regional specific gene expression pattern is also observed in microscopic substructures such as the amygdaloid subnuclei. Zirlinger and colleagues showed that different subnuclei of the amygdala have genes that are unique to each
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of them, therefore, confirming the classical definition of boundaries between the subnuclei [25]. Many of the genes that were identified to be specific to certain brain regions have unknown or poorly defined functions. The next steps will be in defining their functions and relating the gene expression studies to other functional analyses such as functional MRI. In addition, it will be important to integrate these findings with data collected in genetic mapping studies of neurobehavioral traits. As mentioned above, mapping genes for complex traits remains a major challenge; however, data from functional studies should enhance the identification of genes involved in complex neuropsychiatric disorders. Opportunities are now available to build the bridge between neuronal and genetic networks. This integrated knowledge from genes to neurons will allow us to begin to understand the complex networks that are responsible for complex behavior such as learning and emotions. High fidelity RNA profiling also has direct clinical applications within the field of neuro-oncology and pathology. Histological characteristics are typically used for clinical determination of tumor grade and prognosis. However, use of molecular markers (such as estrogen receptor status in breast cancer) has been used for determining prognosis and defining therapy. High throughput RNA analysis has the ability to look at profiles of markers quantitatively leading to superior predictions of tumor grade, and differentiation from benign lesions. It appears likely that this approach will be successful in the near future. Pharmacogenomics One direct application of genetics and genomics to clinical medicine is the improved understanding of why some people are more and others are less sensitive to certain medications. Pharmacogenetics is the study of the inherited differences between individuals in response to drugs. The differences in drug response are often due to variation in genes that affect the absorption, metabolism, clearance and excretion of drugs. The genetic constitutions of some people may allow them to absorb and to metabolize certain chemical compounds more or less effectively than others. This process will affect how individual patients respond to medications. In other cases, genetic locus heterogeneity may explain why some patients fail to respond to a treatment while others respond very well. Different genetic mechanisms can produce similar phenotypes. But the treatment of the diseases caused by different genetic mechanisms may be very different even though phenotypically the patients may be very similar. Genomics has enhanced pharmacogenetics by providing the tools and human genome sequences to facilitate the identification of genetic variations that affect individuals’ differences in response to drugs. New genotyping and sequencing technologies are allowing cheap and robust methods for screening patients for these variations. Soon, this technology will allow physicians
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to develop individualized treatment plans specific for each patient’s genetic profile. One of the great promises of pharmacogenomics is the ability to identify the most appropriate drug and dosage for a patient based on his/her genetic profile thus reducing adverse drug effects. The authors discuss two examples of how genetics plays a significant role in the practice of neurology. First is on the treatment for long QT syndrome and the other is for dementia. Genotype-specific therapy for long QT syndrome Long QT syndrome is characterized by prolongation of QT interval and abnormalities of T wave due to abnormal ventricular depolarization. Patients are susceptible to syncope, malignant arrhythmias and sudden death. At least five genes have been associated with this syndrome. They code for different ion channels that play a role in activation of cardiac action potential. One of the autosomal dominant forms of Long QT Syndrome is caused by mutations in the potassium channel gene, KCNQ1, on chromosome 11p15.5. Another form of autosomal dominant Long QT Syndrome is caused by mutations in the sodium channel gene, SCN5A, on chromosome 3p21-24. Patients with untreated Long QT syndrome are at very high risk for sudden cardiac death. There are several treatment options including betablocker therapy, pacemaker implantation and cardioverter-defribrillator implantation. The main medical therapy option is beta-blocker. However, responses to beta-blocker are not optimal in many patients. Increasingly, there is interest in implementing genotype-specific therapy to prevent life-threatening arrhythmias [26,27]. Sodium channel blockers such as Mexiletine(Danbury Pharmacal, Danbury, CT) should be effective for patients with LQT3 mutations (sodium channel-SCN5A) but will not be effective on patients with LQT1 mutations (potassium channel -KCNQ1). Until the genetics of this syndrome is determined, physicians cannot classify patients into responder and non-responder before therapy. This is one reason why ion channel blockers are not used as first choice in medical treatment of this syndrome because they are not effective on some patients. However, now that the genes for Long QT syndrome have been characterized, mutation analysis should be considered as part of the diagnostic procedure. This will allow prescription of medications specific to the molecular defects causing each patient’s symptoms. Genotype-specific therapy for Alzheimer disease Dementia is the 4th leading cause of death in the US. The direct and indirect cost for treatment and care for patients with dementia exceed $100 billion per year. Some forms of cognitive impairment can be successfully treated with acetylcholine esterase inhibitors (AChE-I) such as Tacrine (Warner-Lambert Co, Morris Plains, NJ). An original trial of Tacrine as a
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treatment for AD patients failed to reveal any effects; however, when the trial was conducted by stratifying AD patients by their ApoE genotypes, it was shown that patients with E4 allele failed to respond to Tacrine but patients with non-ApoE4 genotypes responded well [28]. As tools for determining individuals’ genetic profiles become cheaper and more reliable, we will be able to practice individualized medicine. Since our susceptibility to diseases differs and our risks of developing adverse reactions to drugs also differ, it is only practical that the next step in the advancement of clinical medicine is to develop preventive measures and treatment plans specific to one’s genetic constitution. To achieve this, as clinicians and scientists, we need to bridge the fields of genetics, genomics and medicine.
References [1] International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 2001;409:860–921. [2] Gusella JF, Wexler NS, Conneally PM, et al. A polymorphic DNA marker genetically linked to Huntington’s Disease. Nature 1983;306:234–8. [3] Huntington’s Disease Collaborative Research Group. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s Disease chromosome. Cell 1993; 72:971–83. [4] International SNP Map Working Group. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 2001;409:928–33. [5] Yu A, Zhao C, Fan Y, et al. Comparison of human genetic and sequence-based physical maps. Nature 2001;409:951–3. [6] Escayg A, et al. Mutations of SCN1A, encoding a neuronal sodium channel, in two families with GEFS+2. Nat Genet 2000;24:343–5. [7] Matsuura T, et al. Large expansion of the ATTCT pentanucleotide repeat in spinocerebellar ataxia type 10. Nat Genet 2000;26:191–4. [8] Moreira ES, et al. Limb-girdle muscular dystrophy type 2G is caused by mutations in the gene encoding the sarcomeric protein telethonin. Nat Genet 2000;24:163–6. [9] Portsteffen H, et al. Human PEX1 is mutated in complementation group 1 of the peroxisome biogenesis disorders. Nat Genet 1997;17:449–52. [10] Reuber BE, et al. Mutations in PEX1 are the most common cause of peroxisome biogenesis disorders. Nat Genet 1997;17:445–448. [11] International Molecular Genetic Study of Autism Consortium. A genome-wide screen for autism: strong evidence for linkage to chromosomes 2q, 7q and 16p. Am J Hum Genet 2001; 69:570–81. [12] Kuokkanen S, et al. Genomewide scan of multiple sclerosis in Finnish multiplex families. Am J Hum Genet 1997;61:1379–87. [13] Roses AD. A model for susceptibility polymorphisms for complex diseases: apolipoprotein E and Alzheimer disease. Neurogenetics 1997;1:3–11. [14] Ebers GC, Sadovnick AD. Association studies in multiple sclerosis. J Neuroimmunol 1994;53:117–22. [15] Liang P, Pardee AB. Differential display of eukaryotic messenger RNA by means of polymerase chain reaction. Science 1992;257:967–71. [16] Lockhart DJ, Dong H, Bryne MC, et al. Expression monitoring by hybridization to high density oligonucleotide arrays. Nat Biotechnol 1996;14:1675–80. [17] Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270:467–70.
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[18] Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene expression. Science 1995;270:484–7. [19] Lisitsyn N, Lisitsyn N, Wigler M. Cloning the differences between two complex genomes. Science 1993;259:946–51. [20] Fodor SP, et al. Light-directed, spatially addressable parallel chemical synthesis. Science 1991;251:767–73. [21] Lipshutz RJ, Fodor SP, Gingeras TR, Lockhart DJ. High density synthetic oligonucleotide arrays. Nat Genet 1999;21:20–4. [22] Li C, Wong WH. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA 2001;98:31–6. [23] Pavlidis P, Noble WS. Analysis of strain and regional variation in gene expression in mouse brain. GenomeBiology 2001;2:1–15. [24] Sandberg R, Yasuda R, Pankratz DG, et al. Regional and strain-specific gene expression mapping in the adult mouse brain. Proc Natl Acad Sci 2000;97:11038–43. [25] Zirlinger M, Kreiman G, Anderson DJ. Amygdala-enriched genes identified by microarray technology are restricted to specific amygdaloid subnuclei. Proc Natl Acad Sci 2001; 98:5270–5. [26] Phillips JR, Case CL. Evaluation and treatment of pediatric patients with congenital or acquired long QT interval syndromes. Prog Pediatr Cardiol 2001;13:101–10. [27] Schwartz PJ, et al. Genotype-Phenotype correlation in the long-QT syndrome: gene-specific triggers for life-threatening arrhythmias. Circulation 2001;103:89–95. [28] Poirier J, et al. Apolipoprotein E4 allele as a predictor of cholinergic deficitis and treatment outcome in Alzheimer disease. Proc Natl Acad Sci USA 1995;92:12260–4.
Bridging genetics and genomics in neurology Vivian G. Cheung, MDa,b,*, Richard S. Spielman, PhDb a
Department of Pediatrics, University of Pennsylvania, School of Medicine, 3516 Civic Center Blvd., RM 516, Abramson, Philadelphia, PA 19104, USA b Department of Genetics, University of Pennsylvania, School of Medicine, 415 Curie Blvd., 464 CRB, Philadelphia, PA 19104, USA
One of the fastest growing fields in biological sciences is genomics. Its impact on genetics, which stimulated its development, has been tremendous. Its impact on other fields such as neuroscience is equally substantial. In the past several years, the field of genomics has delivered detailed genetic maps of several organisms including mouse and human, and the complete genome sequences of many microorganisms and more recently the draft sequence of the 3 billion basepair human genome [1]. It has stimulated the development of technologies for high-throughput genome analysis such as fluorescent sequencer and DNA microarray technologies. These technologies were used to generate a detailed catalogue of genes that constitute our genome and to decipher the functions of those genes. This broad scope of genomics that covers genetic map, genome sequence and gene function has important influence on neurology and neuroscience. It has led to the identification of genes for various neurologic disorders and a better understanding of the genes that function in specific brain regions. In this article, the authors explore the impact of genomics on our understanding of the genetic basis of neurologic diseases.
Genes and diseases Mendelian disease One of the first successes of disease gene mapping was the identification of the gene for Huntington disease; however, the cloning of the Huntington disease gene was a long and tedious process. It took ten years from localizing the Huntington disease gene to chromosome 4 to identifying the gene * Corresponding author. E-mail address: [email protected] (V.G. Cheung). 0733-8619/02/$ - see front matter Ó 2002, Elsevier Science (USA). All rights reserved. PII: S 0 7 3 3 - 8 6 1 9 ( 0 2 ) 0 0 0 0 3 - 8
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itself [2,3]. Genomics has contributed to accelerating this process by providing a human genetic map that is densely populated with polymorphic markers. As of October 2001, there were over 8,000 microsatellite markers and 1.4 million single nucleotide polymorphism markers for the human genome [4,5] (http://www.ncbi.nlm.nih.gov/SNP/index.html). This simplifies the process of determining the candidate gene regions for the disease of interest. Once a candidate region is defined, then one can search for the disease-causing gene in the region. Before the Human Genome Project (HGP), this was a daunting task. Now one can perform an in silico gene search without the laborious work confronted by the Huntington Disease Research Collaborative Group. The DNA sequence of the human genome allows us to define genes and their protein products, which will then be available for design of more specific pharmacological targets. In the past, design of unique agents against human gene products was difficult. With the precise information of human genome sequence in hand, this process will become less tedious. The sequences of the regulatory regions of the genome will also serve as important therapeutic targets. Understanding the role of these regions in controlling gene functions will provide new understanding of the genetic and physiologic bases of human diseases. The availability of polymorphic markers and information about genes in our genome, along with the advances in genotyping technologies, have enhanced greatly the search for Mendelian disease genes. For example, among neurologic diseases, the genes for Peroxisome biogenesis disorder (PEX1), Limb-girdle muscular dystrophy (LGMD 2G), Generalized epilepsy with febrile seizures plus type 2 (SCN1A) and Spinocerebellar ataxia type 10 (SCA10) were identified using the resources provided by the HGP [1,6–10]. Undoubtedly, many other Mendelian disease genes will be discovered in similar manner. However, genes for complex traits remain a challenge. Complex genetic disease In contrast to the genetic disorders mentioned above, complex diseases do not exhibit Mendelian patterns in families, although there is a genetic contribution to the underlying susceptibility. The most characteristic aspect of this non-Mendelian behavior is the feature called ‘‘incomplete penetrance.’’ This term describes the fact that although some people who have the susceptibility gene(s) develop the disease, others (perhaps most) do not. The exact biological causes of incomplete penetrance are not clear, but certainly include both environmental and genetic components. Informational approaches such as that utilizing the HGP are ideal for understanding the genetic components that alter disease penetrance. In addition, it is often said that complex diseases are due to the simultaneous contributions of several or many genes. Although this is a likely explanation for some of the complexity of these diseases, it has not yet been shown to contribute substantially to any of the common complex diseases.
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Regardless of the uncertainties about the underlying genetic causes, an important feature is that many of the well-known complex genetic diseases are common. In general, it is easier to analyze genetic patterns when the disease is rare. Thus for the geneticist, the high prevalence is another complicating feature that is not usually found in Mendelian diseases. Typical examples of the common complex diseases are diabetes (both type 1 and type 2), Alzheimer disease, asthma, and schizophrenia. The main goal of contemporary genetic analysis of complex diseases is to locate and characterize all the genes that contribute, and to explain how variation in these genes produces susceptibility to disease. For this task, the HGP has provided a wealth of materials and information, but a discipline that integrates the essential features of genetics and genomics has not yet emerged. In existing genetic analysis, the main difficulties arise from the incomplete penetrance. Classical linkage methods use genetic information from both affected and unaffected family members. In complex diseases, because of incomplete penetrance, the genetic (genotype) status of unaffected family members is usually very uncertain, so many of these cannot be used with confidence in the analysis. Difficulties like these in applying conventional linkage analysis have led to the development of new approaches, designed specifically for locating genes that contribute to complex diseases. A ‘‘genome scan’’ is a search for linkage throughout the genome, usually carried out with a set of genetic landmarks or ‘‘markers,’’ spaced approximately evenly over all the chromosomes. To avoid the problems of incomplete penetrance, in complex diseases this is often done using affected siblings only, or affected sibling and their parents, but ignoring other unaffected family members. This approach has become the method of choice in many studies of complex diseases [11,12]. The methods based on disease association use a fundamentally different approach; these focus on candidate genes or candidate regions already identified by linkage, or implicated by prior knowledge. In these methods, one first identifies variant allelic forms in the candidate gene (or candidate region); without this step it is not possible to test whether inherited variation in the gene plays a role. Instead of basing the assessment on a test for linkage, however a test for association evaluates evidence for correlation between disease and variant, in the population as a whole. Unlike a linkage study, this approach requires specifying in advance a gene or region suspected to play a role in disease. The two methods differ because they are applied to different samples of subjects with different expectations; genetic linkage results in cosegregation of disease and marker among related people, while association results in co-occurrence of disease and marker in a sample of unrelated people. Either of these phenomena can occur in the absence of the other; however, the principles of population genetics support the following expectation. A marker very closely linked to a disease susceptibility locus is more likely to be associated with the disease than a marker that is loosely linked or unlinked. This is the rationale for using the strength of association
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as a metric to localize genes that contribute to complex disease. It follows that once a site in the genome has been precisely identified as contributing to a disease, the evidence from each approach should support the other. Compared to monogenic Mendelian diseases, the complex diseases require much more elaborate study designs and analyses. Nevertheless, the approaches described here have led to much improved understanding in some complex diseases like late-onset Alzheimer disease (AD) [13] and MS [14]. The authors expect, however, that further advances in understanding will be greatly aided by the accomplishments of the HGP.
Gene expression studies in neurology About 50% of the genes in our genome are expressed in the nervous system. To understand the function of the highly complex human nervous system, we need to understand the function of these genes and their interactions. This knowledge will contribute to our understanding of the pathogenesis of diseases such as strokes and dementia as well as complex behaviors such as learning and sleep. Today, despite the availability of imaging studies such as MRI and PET scans, some aspects of the practice of neurology are still limited to descriptive analysis of patients’ symptoms and localization of those symptoms to gross anatomical areas of the brain. In time, genomics with its tools that study gene functions and genetic networks will integrate with anatomical studies. These tools are likely to have a major impact in elucidating the molecular basis of many neurologic diseases and eventually the development of therapies. The merging of traditional medical approaches and genomics will lead to an understanding of the normal function of many genes in the development and maintenance of our nervous system. Overview of gene expression technologies Several high-throughput technologies are now available for studying gene expression profiles. These include differential display, serial analysis of gene expression and cDNA microarrays [15–18]. The authors will not review these techniques in detail here. Depending on the goal of the experiment, one or several of these techniques can be used to assay large number of genes. Differential display and similar techniques such as suppressive subtractive hybridization and representational difference analysis are hybridizationbased subtractive methods that allow identification of genes that are expressed differently between two samples [15,19]. It is relatively easy to set up these techniques since no special equipment is needed and one does not need to know the list of genes to be interrogated in advance; however, one obtains mostly absent/present information but not quantitative information. In contrast, serial analysis of gene expression (SAGE) provides highly quantitative
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information. It provides an excellent estimate of the fold differences in expression levels of genes between two or more samples. But to perform SAGE, one must have the ability to perform and analyze a large number of sequencing reactions. The third type of technique is microarray. There are two types of microarrays, oligonucleotide array such as the GeneChip by Affymetrix and spotted cDNA microarrays. Both types of microarrays require specialized equipment and knowledge of the genes one wishes to interrogate. To synthesize oligonucleotide arrays, one needs to know the sequences of genes of interest and in spotted arrays, one needs the cDNA clones representing the genes of interest. In recent years, the HGP has provided the sequences and clones for many genes thus microarray technology has become increasing popular as a method for gene expression profiling. Sequences and structures of genes (such as intron/exon borders) are available freely at many sites (http://genome.ucsc.edu; http://www.ncbi.nlm.nih. gov/Entrez; http://www.ensembl.org). Full-length and sequence-verified cDNA clones are available commercially at reasonable cost (http://www. ncbi.nlm.nih.gov/ncicgap). High density synthetic oligonucleotide arrays There are several types of oligonucleotide arrays ranging from spotted oligonucleotide arrays to those arrays based on light-directed synthesis. The authors will limit this discussion to GeneChip probe arrays, which are constructed by combining photolithography and solid phase DNA synthesis [20,21]. A solid support is treated with photolabile protecting groups. Light is directed through a mask to deprotect and activate sites on the solid support. Protected nucleotides are added to those activated sites. This process is repeated, activating different sites and adding different nucleotides until the final synthesis of all DNA probes is achieved. This process is efficient — in each round, one of the 4 nucleotides is added to multiple sites simultaneously; thus it takes only 4N cycles to synthesize an array containing probes of length N regardless of the number of probes on the array. High-density arrays are available containing genes for a wide range of organisms including yeast, E. coli, mouse and human. The human arrays (U133 set with 2 arrays) contain about 33,000 known genes with 6,500 splice variants and 4,500 genes with alternative polyadenylation sites. To make the arrays, Affymetrix must have the sequences of the genes/ESTs of interest. As the human genome project is generating the sequences of increasing numbers of genes, one can expect to see a whole genome human array in the near future. Each gene on the array is represented by several 20-mer oligonucleotides. The oligonucleotides and probe sets are designed to maximize hybridization specificity. To perform expression profiling using the GeneChips, fluorescently labeled RNA samples are hybridized onto the arrays and the hybridization
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signals are analyzed using a fluorescent scanner. The intensity of the hybridization signals for each probe on the array is proportional to the abundance of that transcript in the sample. Affymetrix has designed software for analyzing the hybridization data. Other software packages are available to manage and to analyze the data generated using GeneChips [22]. Spotted cDNA microarrays Spotted cDNA microarrays are microscope slides containing immobilized DNA samples [17]. For expression analysis, the DNA samples are cDNA clones representing genes or ESTs. These microarrays can be made using robotic ‘‘microarrayers’’ that place nanoliter amount of DNA samples onto microscope slides (http://www.microarrays.org/; http://www.gene-chips.com/). To gain information about the DNA on the microarrays, one hybridizes fluorescently or radioactively-labeled samples onto the microarrays in manners similar to the northern and Southern blots. In a cDNA microarray experiment, one compares two or more samples that are each labeled with one fluorescent dye (for example, Cy3 or Cy5) that are co-hybridized onto a single microarray. This allows comparison of the relative abundance of transcripts corresponding to each gene/EST on the microarray between the samples. After the hybridization, the slides are scanned using laser scanners. The fluorescent intensity of each spot is proportional to the number of RNA molecules hybridized to each spotted DNA sample. The scanned images are analyzed using software that quantifies the spot intensities. As in many hybridization-based assays, detection of hybridization signals and subtraction of background noise are important aspects of array image analysis. Similar to the analysis tools for the GeneChips system, software packages that analyze spotted array images are beginning to address many of these issues. A more difficult aspect of microarray technology is the analysis of the data. A single experiment using either spotted microarrays and GeneChips allows researchers to interrogate tens of thousands of genes. Thus a simple experiment designed to compare two tissue types can generate thousands of data points. Determining the significance of the results is more challenging than traditional experiments that examine one or several genes. When testing many genes, the chance of finding one or more genes that are different between the two test samples by chance is increased. Genomic technologies have made it simple to test many genes and to explore many interactions not suspected a priori; however, a major drawback of performing multiple tests is the increased probability of declaring false positives. An additional problem of testing multiple genes is the unknown correlations between the genes. Many traditional parametric statistics rely on the assumption of independence between variables. This assumption is violated in microarray data since some of the genes are correlated and the correlation is usually unknown a priori. Several groups have proposed methods for analyzing the data using nonparametric methods that address the problems of
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multiple testing and the unknown correlations between genes. It is beyond the scope of this study to review these methods – many of these studies are available at the following website, http://linkage.rockefeller.edu/wli/ microarray/. Profiling gene expression in regions of the brain The human brain is a highly complex system composed of many gross anatomical parts that are further divided into microscopic divisions. Much information is available on the function of various parts of the brain from the frontal lobe to the hippocampus and the cerebellum. Most functional studies of the brain are performed by subdividing the brain in anatomical and physiologically distinct regions. With the availability of genomic technologies such as microarrays, one can now begin to make the link between genes and anatomy. By studying the genes that are expressed at different regions of the brain, one can identify the genes that participate in different functions specific for each region. This will help to better understand regionspecific neurological functions and facilitate the mapping of susceptibility genes for neurological disorders. For example, if a gene mapping study led to the discovery of a seizure susceptibility locus on a particular chromosomal region; much work still remains since likely many genes reside in that genomic region. But if one knows that within the region are one or more genes that are specific to the hippocampus, those genes would be excellent candidates for mutation analyses. Projects such as the BMAP (http://trans.nih.gov/resources/resources.htm) initiative and several recent publications demonstrate successes in defining genes specific for different brain regions [23–25]. First, these papers confirmed the speculation that a large number of genes are expressed in the brain. Two groups used the Affymetrix GeneChips to study over 13,000 genes and found that slightly over 50% of the genes are detected in one or more regions of the mouse brain [24,25]. It appears that the cerebellum has more uniquely expressed genes when compared to the cortex, amygdala, entorhinal cortex and midbrain [24]. Sandberg and colleagues found at least 23 genes were unique to the cerebellum and another 28 genes were not expressed in cerebellum but were present in other brain regions studied. These cerebellum-specific genes include mouse D-type cyclin, p21 activated kinase-3, protein tyrosine phosphatase STEP61, NMDA receptor subunit NR2C and glutamate receptor channel delta 2 subunits. Perhaps it is not surprising that the cerebellum, whose developmental origin is unique, has a specific set of genes that participate in growth and receptor signaling. The cells in cerebellum may respond to different growth stimuli and have developed signaling receptors unique for its function and survival. This regional specific gene expression pattern is also observed in microscopic substructures such as the amygdaloid subnuclei. Zirlinger and colleagues showed that different subnuclei of the amygdala have genes that are unique to each
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of them, therefore, confirming the classical definition of boundaries between the subnuclei [25]. Many of the genes that were identified to be specific to certain brain regions have unknown or poorly defined functions. The next steps will be in defining their functions and relating the gene expression studies to other functional analyses such as functional MRI. In addition, it will be important to integrate these findings with data collected in genetic mapping studies of neurobehavioral traits. As mentioned above, mapping genes for complex traits remains a major challenge; however, data from functional studies should enhance the identification of genes involved in complex neuropsychiatric disorders. Opportunities are now available to build the bridge between neuronal and genetic networks. This integrated knowledge from genes to neurons will allow us to begin to understand the complex networks that are responsible for complex behavior such as learning and emotions. High fidelity RNA profiling also has direct clinical applications within the field of neuro-oncology and pathology. Histological characteristics are typically used for clinical determination of tumor grade and prognosis. However, use of molecular markers (such as estrogen receptor status in breast cancer) has been used for determining prognosis and defining therapy. High throughput RNA analysis has the ability to look at profiles of markers quantitatively leading to superior predictions of tumor grade, and differentiation from benign lesions. It appears likely that this approach will be successful in the near future. Pharmacogenomics One direct application of genetics and genomics to clinical medicine is the improved understanding of why some people are more and others are less sensitive to certain medications. Pharmacogenetics is the study of the inherited differences between individuals in response to drugs. The differences in drug response are often due to variation in genes that affect the absorption, metabolism, clearance and excretion of drugs. The genetic constitutions of some people may allow them to absorb and to metabolize certain chemical compounds more or less effectively than others. This process will affect how individual patients respond to medications. In other cases, genetic locus heterogeneity may explain why some patients fail to respond to a treatment while others respond very well. Different genetic mechanisms can produce similar phenotypes. But the treatment of the diseases caused by different genetic mechanisms may be very different even though phenotypically the patients may be very similar. Genomics has enhanced pharmacogenetics by providing the tools and human genome sequences to facilitate the identification of genetic variations that affect individuals’ differences in response to drugs. New genotyping and sequencing technologies are allowing cheap and robust methods for screening patients for these variations. Soon, this technology will allow physicians
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to develop individualized treatment plans specific for each patient’s genetic profile. One of the great promises of pharmacogenomics is the ability to identify the most appropriate drug and dosage for a patient based on his/her genetic profile thus reducing adverse drug effects. The authors discuss two examples of how genetics plays a significant role in the practice of neurology. First is on the treatment for long QT syndrome and the other is for dementia. Genotype-specific therapy for long QT syndrome Long QT syndrome is characterized by prolongation of QT interval and abnormalities of T wave due to abnormal ventricular depolarization. Patients are susceptible to syncope, malignant arrhythmias and sudden death. At least five genes have been associated with this syndrome. They code for different ion channels that play a role in activation of cardiac action potential. One of the autosomal dominant forms of Long QT Syndrome is caused by mutations in the potassium channel gene, KCNQ1, on chromosome 11p15.5. Another form of autosomal dominant Long QT Syndrome is caused by mutations in the sodium channel gene, SCN5A, on chromosome 3p21-24. Patients with untreated Long QT syndrome are at very high risk for sudden cardiac death. There are several treatment options including betablocker therapy, pacemaker implantation and cardioverter-defribrillator implantation. The main medical therapy option is beta-blocker. However, responses to beta-blocker are not optimal in many patients. Increasingly, there is interest in implementing genotype-specific therapy to prevent life-threatening arrhythmias [26,27]. Sodium channel blockers such as Mexiletine(Danbury Pharmacal, Danbury, CT) should be effective for patients with LQT3 mutations (sodium channel-SCN5A) but will not be effective on patients with LQT1 mutations (potassium channel -KCNQ1). Until the genetics of this syndrome is determined, physicians cannot classify patients into responder and non-responder before therapy. This is one reason why ion channel blockers are not used as first choice in medical treatment of this syndrome because they are not effective on some patients. However, now that the genes for Long QT syndrome have been characterized, mutation analysis should be considered as part of the diagnostic procedure. This will allow prescription of medications specific to the molecular defects causing each patient’s symptoms. Genotype-specific therapy for Alzheimer disease Dementia is the 4th leading cause of death in the US. The direct and indirect cost for treatment and care for patients with dementia exceed $100 billion per year. Some forms of cognitive impairment can be successfully treated with acetylcholine esterase inhibitors (AChE-I) such as Tacrine (Warner-Lambert Co, Morris Plains, NJ). An original trial of Tacrine as a
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treatment for AD patients failed to reveal any effects; however, when the trial was conducted by stratifying AD patients by their ApoE genotypes, it was shown that patients with E4 allele failed to respond to Tacrine but patients with non-ApoE4 genotypes responded well [28]. As tools for determining individuals’ genetic profiles become cheaper and more reliable, we will be able to practice individualized medicine. Since our susceptibility to diseases differs and our risks of developing adverse reactions to drugs also differ, it is only practical that the next step in the advancement of clinical medicine is to develop preventive measures and treatment plans specific to one’s genetic constitution. To achieve this, as clinicians and scientists, we need to bridge the fields of genetics, genomics and medicine.
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