Description

Genome. The total genetic material of an organism, that is, an organism's complete DNA sequence, is called a genome. The human genome is extremely complex, and the estimated number of genes has varied considerably during the past years. GENCODE 19 contained 20,719 protein-coding genes. A study has mapped peptides detected in 7 large-scale proteomics studies to approximately 60% of the protein-coding genes in the GENCODE annotation of the human genome (12). If one excludes nonprotein-coding genes from the human protein-coding gene catalog, the total number of genes in the human genome is reduced to approximately 19,000. Approximately 80% of the genes are expressed in the brain, and 5000 of these exclusively in the brain and not in other organs. The human genome contains 3 billion nucleotides, whereas the genome of unicellular organisms contains 1 million to 10 million nucleotides. DNA sequence information provides only a static snapshot of the various ways in which the cell might use its proteins, whereas the life of the cell is a dynamic process.

Genomic technologies. Technologies relevant to neurologic disorders are covered in detail in a book chapter on this topic (22). They are described here briefly.

Gel electrophoresis. This well-known method requires expensive hardware. It has many advantages over homogeneous systems, such as analysis of size, shape, and charge of molecules. Disadvantages are tedious gel preparation and longer run times. More accurate and easier-to-use systems are emerging. Various refinements of gel electrophoresis for application in genomics include the following:

Pulsed-field gel electrophoresis. This technique enables separation of large DNA molecules, which are difficult to separate with conventional gel electrophoresis. Two alternating fields are used, and separation of DNA fragments depends on their conformational properties.

2-D gel electrophoresis. This technique offers the highest resolution separations available for proteins when gels of sufficient size are used. This level of resolution, almost 2 orders of magnitude better than any competing technique, makes it uniquely suited to the study of protein components of cells. The major problem with this technique is that most of the spots cannot be sequenced, as they are beyond the capacity of current high-sensitivity sequencers.

Capillary electrophoresis. This technique uses fused silica capillaries and is employed for separation of DNA molecules based on the charge-to-mass ratio of the analytes. The advantages of capillary electrophoresis over gel electrophoresis are that it is faster, less labor-intensive, can be readily automated and requires picogram quantities of samples rather than microgram quantities.

DNA libraries. There are 2 main types of DNA libraries: genomic and cDNA.

Genomic libraries. These are constructed by partially digesting the human genomic DNA with restriction enzymes. Libraries that contain clones only from specific human chromosomes are now available. These are created cloning DNA from chromosomes separated on the basis of size.

cDNA libraries. A DNA library is the mixture of cDNAs from a given tissue. Techniques are available to insert cDNA into specialized DNA loops called vectors, which can reproduce inside bacterial cells. Such cDNA libraries are often preferable to genomic libraries as a source of cloned genes for the following reasons:

Screening cDNA libraries is a highly valuable approach for discovering important information about neurologic functions and diseases. This method has been used to identify genes that are involved in Huntington disease.

Detection of gene mutations. The final challenge in gene identification is to determine which one of the candidate genes is mutated in a patient with a certain disease. Several types of mutations responsible for inherited disorders have been identified by this approach. Biochip technology is now being applied for detection of genetic mutations.

Microarrays and DNA chips. Microarray and DNA chip technology (also known as gene chip or "biochip") are rapid methods of sequencing and analyzing genes. It is comprised of DNA probes formatted on a microscale plus the instruments needed to handle samples (automated robotics), read the reporter molecules (scanners), and analyze the data (bioinformatic tools). This technology is expected to revolutionize gene analysis. Advances in microarray technology enable massive parallel mining of biological data, with biochips providing hybridization-based expression monitoring, polymorphism detection, and phenotyping on a genomic scale. Applications of biochip technology relevant to neurologic disorders are:

An example of the application of biochip technology is the identification of whether a gene is turned on or off by measuring its ability to bind to the corresponding DNA sequences on the chips. This is important because the functions of most genes are still unknown even though the genome has been sequenced. This approach is particularly useful in disorders where multiple genes are involved. For example, gene expression has been used to monitor gene activity in the brains of various mouse strains following drug-induced seizures.

Although microarrays can be used to examine thousands of genes in 1 experiment and obtain gene profiles, the drawback of microarrays is based on hybridization. Gene expression levels are measured by fluorescence from hybridization, but quantification of the fluorescence of numerous spots on a chip is often unreliable and varies from 1 experiment to another. Furthermore, DNA samples can hybridize to more than 1 spot, thus generating misleading results. Next generation sequencing overcomes problems of microarrays by generating actual sequence reads and is ideal for detecting genetic mutations. Gene expression can be more accurately obtained by counting sequence reads.

Identification of single nucleotide polymorphisms or SNPs. These are small stretches of DNA that differ in only 1 base and represent the most common form of genetic variation. Single nucleotide polymorphisms, commonly referred to as SNPs, serve to distinguish 1 individual's genetic material from that of another. There are no exact figures on the frequency of occurrence of single nucleotide polymorphisms in the human genome, but they occur about once every 1250 bases along the 3 billion base pairs, ie, the “letters” that make up the genetic code. Studies suggest approximately 5 single nucleotide polymorphisms per gene, but not every gene has a single nucleotide polymorphism. Single nucleotide polymorphisms comprise approximately 80% of all known polymorphisms. Approximately 85 million single nucleotide polymorphisms have been identified already in various databases but only a small fraction of these are well characterized and validated. Several technologies are used for their identification, of which the most important are based on DNA microarrays or biochip technology. Single nucleotide polymorphisms have the following relation to an individual's disease and drug response:

Copy number variations. Copy number variations refer to variation from one person to another in the number of copies of a gene or DNA sequence. Copy number variation is a source of genetic diversity in humans. Numerous copy number variations are being identified with various genome analysis technologies, including array comparative genomic hybridization, single nucleotide polymorphism genotyping, and DNA sequencing. Some diseases are associated with copy number variations rather than Single nucleotide polymorphisms. Although copy number variations confer a risk of disease, they may not be sufficient by themselves to lead to a specific disease outcome, and additional risk factors may account for the variation. Considerable variation has been observed in the phenotypes associated with several recurrent specific copy number variations that are relatively prevalent (13). This study, by showing that the phenotypic variation of some genomic disorders may be partially explained by the presence of additional large variants, may help in understanding the causes of some neurologic diseases.

Sequencing. Most genetic disorders are caused by point mutations. Deletions are less frequent and may be overlooked by DNA mapping. It is difficult to find the location of a gene buried in the tangle of chromosomal DNA in the nucleus; sequencing of individual nucleotide bases may be required. DNA sequence analysis is a multistep process comprising sample preparation, generation of labeled fragments by sequencing reactions, electrophoretic separation of fragments, data acquisition, assembly into a finished sequence, and most importantly, functional interpretation. Sequencing is also used to determine protein sequences, but it is difficult to determine protein function from sequence. Sequencing is now automated. Sequencing technologies are described in a special report on this topic (24). Apart from their impact on hereditary neurologic diseases, high-throughput genome sequencing technologies will improve our understanding of sporadic neurologic diseases as well, particularly those with low-penetrant mutations in the gene for hereditary diseases or de novo mutations (48).

RNA sequencing is a powerful tool for studying the effect of the transcriptome on phenotypes such as disease susceptibility and response to pharmaceuticals. Applications include the following:

The exome is the part of the genome formed by exons or the sequences which when transcribed remain within the mature RNA after introns are removed by RNA splicing. It differs from a transcriptome in that it consists of all DNA that is transcribed into mature RNA in cells of any type. Although it comprises a very small fraction of the genome, mutations in the exome harbor 85% of disease-causing mutations, and exome sequencing is an efficient strategy to determine the genetic basis of several Mendelian or single gene disorders. Exome sequencing could enable the discovery of much of the copy number variation that is responsible for many common and rare diseases. Exome sequencing was used to detect mutation of TRPV4 (transient receptor potential cation channel, subfamily V, member 4), a calcium-permeable nonselective cation channel that can cause Charcot-Marie-Tooth disease type 2C disease (30).

Study of gene expression in the CNS. The human brain has a more complex pattern of gene expression than any other region of the body. The molecular events in neurologic disorders are caused or paralleled by specific gene expression changes. Analysis of these changes provides an understanding of the disease at the molecular level. Gene expression profiling also provides some information about mitochondrial disorders because of a bidirectional information flow between the mitochondrion and the cell nucleus.

Techniques for analysis of gene expression can monitor 1 gene at a time, eg, RT-PCR methods, or can simultaneously analyze thousands of genes in a small brain sample. These techniques include expressed sequence tags, sequencing of cDNA libraries, differential display, subtractive hybridization, serial analysis of gene expression; and the high-density DNA microarrays. Gene expression measurements may be used to identify genes that are abnormally regulated as a secondary consequence of a disease state, or to identify the response of brain cells to pharmacological treatments.

The usual method for the study of gene expression in the brain is by obtaining tissue sections and examining them for the expression of a certain gene using a fluorescent probe. When these genes are illuminated under a fluorescence microscope, the regions where the gene is most highly activated within the nervous system are clearly shown. The nervous system provides abundant opportunities to study gene expression because of the presence of numerous genes that carry out a wide range of functions. However, the development of a probe for each gene that could potentially be expressed in the brain, and then the utilization of these probes to test for the presence or absence of gene expression, is a challenging task.

Three-dimensional gene expression patterns in the brain can be mapped by analysis of spatially registered voxels (cubes) by a process analogous to the images reconstructed in functional brain imaging systems. Consistent gene expression differences between normal and Alzheimer disease brains can be demonstrated by this approach.

Proteomics. There is an increasing interest in proteomics because it represents the genome at work. The classical concept of “1 gene, 1 protein” is no longer valid because 1 gene codes for more than 1 protein and the human body may contain more than half a million different proteins (3-D structures), each having slightly or different functions. Neuroproteomics is described in a separate MedLink Neurology article.

Transcriptomics. The focus of decoding genomic information previously has been mostly on proteomics and mRNA (cDNA) analysis. A limitation of this approach is that the information contained within the genome is first expressed in the form of "primary transcripts" before it is processed into mRNA and proteins. The primary transcripts may not lead to the formation of mRNA and proteins but perform crucial cellular functions directly. Transcriptomics is the study of the entire set of RNA transcripts of an organism. For study of a transcriptome, samples for genotyping and whole exome/genome sequencing generally come from peripheral blood or saliva. Transcriptome and epigenome profiling can also be performed on peripheral blood and CSF in addition to other biomarker studies.

Epigenomics. The epigenome is a record of the chemical changes to the DNA and histone proteins of an organism, which can be inherited by an organism's offspring. The epigenome is involved in regulation of gene expression, development, and tissue differentiation. Unlike the underlying the genome, which is largely static within an individual, the epigenome can be altered by environmental conditions. Changes in the epigenome can result in changes in function of the genome. Neurologic disorders are not only associated with genomic mutations and transcriptomic dysregulations, but also with changes in the epigenome. Among the various types of epigenomic modifications, DNA methylation, histone modifications, and expression levels of microRNAs (miRNAs) have been the most widely studied. DNA methylation at CpG dinucleotides is an important epigenetic regulator in mammalian cells. It is implicated in the development of the human brain as well as plasticity underlying learning and memory. Widespread reconfiguration occurs in the methylome, and the conserved non-CG methylation accumulates in the neuronal genome during development (33). An example of DNA methylation in the brain is Rett syndrome, which is secondary to methyl-CpG-binding protein 2 (MECP2) mutations in most of the cases. MECP2 and its differential binding affinity for non-CpG and hydroxymethylation may affect the function of this protein in the nervous system (27).

Experimental studies have investigated the role of epigenomics in rapid behavioral adaptation. This is important, for example, for understanding the basis of exposure to the original fear-inducing thought in a safe environment as a strategy to cope with posttraumatic stress disorders. Fear extinction experiments in mice revealed a key role for ten-eleven translocation 3 (Tet3), which belongs to a family of enzymes that regulates gene activity and adaptive behavioral responses through DNA demethylation activity by creating 5-hydroxymethylcytosine (31).

Genetic biomarkers of neurologic disorders. A biomarker is defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, an alteration from normal at DNA, RNA, or protein level, or pharmacologic responses to a therapeutic intervention (23; 25). The expression of a distinct gene can enable its identification in a tissue with none of the surrounding cells expressing the specific biomarker and is also referred to as a genomic biomarker. Currently available molecular diagnostic technologies have been used to detect biomarkers of various diseases such as cancer, metabolic disorders, infections, and diseases of the central nervous system. Some of the newly discovered molecular biomarkers also form the basis of innovative molecular diagnostic tests. Biomarkers relevant to neurologic disorders can be detected in blood or cerebrospinal fluid. Some biomarkers are also detected by brain imaging. Due to multiple factors involved in the pathogenesis of several neurologic disorders, an ideal biomarker profile should integrate genomic and proteomic biomarkers, electrophysiological studies, and imaging such as MRI. Apart from their value for early diagnosis, these profiles can form the basis for development of therapeutics for a disease and help to link diagnostics and therapeutics, which is an important feature of personalized medicine.

Connectomics and brain mapping. The term connectome was originally used to describe use of diffusion MRI to infer fiber tract architecture and brain anatomic connectivity for brain mapping. Tremendous amounts of data correlating anatomical, genomic, and functional data at the cell level are being analyzed using bioinformatic techniques to construct brain maps in health and disease, which will form an important basis for understanding and developing personalized approaches to neurologic disorders. The Human Connectome Project, sponsored by the National Institutes of Health, aims to build a “network map” that will facilitate research into brain disorders such as dyslexia, autism, Alzheimer disease, and schizophrenia. Large scale neuroimaging studies can be used to discover genetic variants that affect the brain. Screening of brain circuits for testing genetic associations in connectome-wide and genome-wide scans is feasible (34). Analysis of massive data, however, will be challenging.

Neuroimaging genomics. This term is applied to the integration of genomic and imaging data for investigating the mechanisms underlying neurologic disorders. Genome-wide association studies of brain imaging of thousands of persons have revealed promising findings. Extensions of this approach are now used to study the impact of epigenetics and gene-gene as well as gene-environment interactions, not only on brain structure, but also in brain function, to facilitate the translation of findings into clinical practice (35).

Systems biology and neurosciences. Systems biology is defined as the biology of dynamic interacting networks. It is also referred to as pathway, network, or integrative biology. An analysis of the structure and dynamics of a network of interacting elements provides insights that are not obvious from analysis of the isolated components of the system. A systems approach will facilitate the transfer of technologies relevant to genomic medicine from the preclinical to clinical phase. Methods for network analysis and systems biology can integrate multiple levels of data from research in neurosciences, correlate molecular pathways to nervous system function and form an important basis of genomic neurology.

Clinical applications

Applications of genomics in neurology. Completion of sequencing of the human genome will have its benefits for neurology, as it has been at the forefront of human genetics. Over 600 neurologic disorders have been mapped, of which causative mutations have been assigned to about 200 Mendelian disorders, each individually rare. Future gene hunting will include increasing emphasis on polygenic disorders. Identification of the genetic basis of disease predisposition will be more difficult than for diseases with Mendelian inheritance. Until now, the investigations of how a cell responds to a given stimulus have been conducted for 1 gene at a time. Technologies in development now will enable the investigator to prepare an array of clones representing thousands of genes and to find out which ones are active by the presence of their mRNA within the extracted material. Some of the goals relevant to neurology in the postgenomic era are:

Genomics is improving our understanding of neurologic diseases. The pathomechanism of many neurologic and psychiatric disorders is poorly understood, and genomic studies will not only contribute to better understanding but will also improve molecular diagnostics. The current diagnostic process is often long and complex, with most patients undergoing multiple invasive and costly investigations without ever reaching a conclusive diagnosis. The advent of massively parallel, next generation sequencing promises to revolutionize genetic testing and shorten the diagnostic process for many of these patients. This will be an important basis for the development of rational therapies in integrated healthcare of the future.

Genomics will have the following impact on healthcare:

Genomics-based therapy provides the opportunity to manage some of the neurologic diseases for which there are no satisfactory treatments at present. In the next decades, many of these diseases will succumb to therapies based on genomic technologies, perhaps eventually reducing the high cost of maintaining health. Important applications relevant to neurology that will result from the knowledge of the human genome are shown in Table 2.

Table 2. Applications of Genomics in Neurology

Study of neuropathology at the molecular level. Genomic technologies have enabled the study of neuropathology at the molecular level; this may be termed molecular neuropathology. In situ hybridization and polymerase chain reaction enable visualization of genes in single cells. The limitation of these techniques is the difficulty in analyzing the expression of multiple genes involved in the physiology of an individual cell. Expression profiles represent the molecular fingerprints of cells, and differences in the patterns between normal and diseased cells provide information about the molecular pathology at the single cell level. Analysis of messenger RNA levels for various genes in the central nervous system tissues may enhance understanding of the pathophysiology of neurologic disease. Comparison of expression levels of normal and diseased cells in neurologic disorders have both diagnostic and therapeutic implications. The eventual aim is detection of neurologic disease at the presymptomatic stage.

Molecular neuropharmacology. The imprint of genomics is apparent in modern neuropharmacology, as it is studied at the molecular level. Discovery of genes for various neurotransmitter and ion channel receptors has enabled the elucidation of the action of drugs and facilitated the drug development process. Gene expression studies can help in identifying key molecules that participate in early pathogenesis, thus, pointing to novel drug targets and enabling the design of drugs to inhibit disease progression. Genomic-based technologies are now the most important part of the drug discovery process in the biopharmaceutical industry. Neurogenomics will revolutionize neurotherapeutics even though the process will be somewhat tedious. The eventual measure of success of advances in neurogenomics is translation into new approved drugs for the treatment of neurologic disorders, which is currently a challenge. However, generation of meaningful neurogenomics data by academic research will provide an opportunity for the biopharmaceutical industry to discover and develop new medicines that target more restricted patient populations, eg, fragile X syndrome and Duchenne muscular dystrophy (50). Gene-based medicines (antisense and gene therapies) will be an important part of neuropharmacology of the future.

In clinical neuropharmacology, pharmacogenomics and genotyping are more powerful than pharmacokinetics and therapeutic drug monitoring. Genotyping will be preferred to therapeutic drug monitoring because of the following advantages:

It is expected that several gene mutations will be identified in epilepsy using DNA biochips, eg, those in ion channel genes. Future drugs may be designed specifically according to the electrophysiological dysfunction as personalized medicines for epilepsy. Study of multidrug transporters is a fruitful area of epilepsy research. The knowledge that multidrug transporters are increased in epileptogenic areas opens potential new avenues for therapeutic intervention. Drugs can be developed to inhibit or bypass overexpressed transporters, or implantable devices can be used to deliver high concentrations of drugs directly into the epileptogenic brain parenchyma.

Study of genes for neurologic disorders. Most diseases have a genetic component. Until now, the investigations of how a cell responds to a given stimulus have been conducted 1 gene at a time. Technologies currently in development will enable investigators to prepare an array of clones representing thousands of genes and to find out which ones are active by the presence of their mRNA within the extracted material. In the past, most of the emphasis was on monogenic disorders such as Duchenne muscular dystrophy. Future gene hunting will include increasing emphasis on polygenic disorders such as Alzheimer disease (see the article titled Molecular diagnosis of neurogenetic disorders).

The list of genes for neurologic disorders that have been identified and those that have been sequenced continues to grow, and it is difficult to keep the lists updated. A list of some neurologic disorders with a genetic component and location of their genes on chromosomes, along with those involving other systems can be viewed on theNational Center for Biotechnology Information web site.

Cognitive genomics. A metaanalysis of genome-wide association studies by the Cognitive Genomics Consortium along with individual single nucleotide polymorphism and polygenic score analyses have linked 5 novel genomic loci to general cognitive function (47). Although the top 2 single nucleotide polymorphisms accounted for only approximately 0.1 percent of variance in cognitive performance, this finding provides an insight into the genetics of neurocognitive function and the pathophysiology of neuropsychiatric illness.

Genomic studies of nonhuman organisms relevant to neurology. Study of the genome of the yeast Saccharomyces cerevisiae facilitates an understanding of human gene function. Yeast does not have a nervous system, but expansions of trinucleotide repeats, prions, and other processes are analogous to those underlying many human neurologic diseases. Human genes can be linked to a phenotype in yeast to develop functional assays for mutation detection. Yeast as a model organism is already being used to identify new casease inhibitors that are under investigation as therapeutic agents for stroke and other neurologic disorders.

The Institute for Genomic Research has determined the complete genetic blueprint for Neisseria meningitidis, the primary causative agent of bacterial meningitis. A detailed description of the Neisseria meningitidis genome is available online at The Institute for Genomic Research. An understanding of the bacterium's pathogenicity determinants can be derived from the genome sequence. The detailed information on the microorganism's genetic structure has been used to identify novel vaccine candidates against meningococcal meningitis.

Creation of transgenic models of neurologic disorders. Animal models of human neurologic disease are assuming an important role in evaluation of various new treatments, including gene therapy. Spontaneously occurring genetic defects in animals are inadequate for serving as models of human disease, thus, the need for induced mutations. Mutations can be established in the animal genome by 1 of 2 approaches: nonhomologous recombinations (transgenic) or homologous recombinations (knock-out, null mutations). Transgenic animals are animals wherein foreign DNA has been integrated into the genome of all cells and can be transmitted to the offspring. Expression profiling of known genes in neurologic diseases can provide information to generate animal models by knockout or transgenic methods.

Redefinition and reclassification of diseases. Several diseases can now be described in molecular terms. Some defects can give rise to several disorders, and diseases will be reclassified on a molecular basis rather than according to symptoms and gross pathology. The implication of this is that the same drug can be used to treat several diseases with the same molecular basis. Another way of reclassification of human diseases will be subdivision of patient populations within the same disease group according to genetic markers and response to medications.

An analysis of genome-wide single nucleotide polymorphism data shows that individual and aggregate molecular genetic risk factors are shared between 5 neuropsychiatric disorders that are treated as distinct categories in clinical practice: autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, major depressive disorder, and schizophrenia (09). Pathway analysis supports a role for calcium channel signaling genes for all 5 disorders.

An example of the changing attitude toward the molecular basis of disease is the genetic basis of a link among migraine, anxiety, and depression. This information offers potential new indications for the numerous compounds that modulate the dopaminergic system. Clinical trials for the potentially new indications can be optimized by genotype analysis of patients with migraine, depression, and anxiety disorders.

Molecular diagnostics. This is the clinical application of molecular technologies related to DNA and RNA used to elucidate, diagnose, and monitor human disease. Knowledge of DNA sequences or their encoded proteins that have been gained from the study of genomics have been used to develop probes for molecular diagnostics. DNA arrays that measure levels of gene expression will become more useful in clinical diagnosis.

Gene therapy. Gene therapy involves the transfer of defined genetic material to specific target cells of a patient for the ultimate purpose of preventing or altering a certain disease state. DNA sequences, identified through study of genomics, can be transferred into cells to correct genetic defects, destroy abnormal cells, or regulate cellular function.

Personalized neurology. Personalized medicine, also referred to as individualized therapy, simply means the prescription of specific treatments and therapeutics best suited for an individual, taking into consideration genetic, epigenetic, and environmental factors that influence response to therapy (26). Application of this approach to neurologic disorders is termed “personalized neurology.” Neurogenomics has contributed to our understanding of disease risk as well as the underlying neurobiology of complex brain disorders. Integrated and interdisciplinary research should continue to uncover the mechanisms by which genetic and genomic variations influence disease initiation and progression under highly heterogeneous conditions to improve our ability to stratify patients for developing informed diagnostic and therapeutic approaches (02). These approaches will facilitate the development of personalized neurology.

Neurogenomics of disorders of the nervous system. Many neurologic conditions are caused by immensely heterogeneous gene mutations. The role of genetic factors in the etiology of complex diseases remains largely unresolved. Using genome-wide associations in millions of patient medical records, a study demonstrated that common variants associated with complex diseases are enriched in the genes indicated by the “Mendelian code” – a phenotypic code that links each complex disorder to a unique collection of Mendelian loci (04). The study identified widespread comorbidity between Mendelian-Mendelian and Mendelian-complex disease pairs. Neurogenomics of a few neurologic disorders are given as examples here.

Alzheimer disease. Identification of rare, disease-causing mutations in amyloid precursor protein (APP), PSEN1, and PSEN2 causing early-onset familial Alzheimer disease was followed by the discovery of APOE as the single most important risk factor for late-onset Alzheimer disease (LOAD). Apart from APOE, more than 20 genes are associated with Alzheimer disease.

Genome-wide scans were used to screen the brain’s connectivity pattern and the SPON1 variant at rs2618516 on chromosome 11 (21). Older persons who carried the connectivity variant rs2618516 had significantly milder clinical dementia scores.

Later genome-wide association studies delivered several additional Alzheimer disease susceptibility loci that are common in the general population, but exert only very small risk effects. As a result, a large proportion of the heritability of Alzheimer disease continues to remain unexplained by the currently known disease genes, and much of this may be accounted for by rare sequence variants that are being discovered by advances in high-throughput sequencing technologies.

Deficits in cerebral metabolic rates of glucose in the posterior cingulate region in subjects with Alzheimer disease and in APOEε4 carriers appear decades before the onset of cognitive deficits. RNA sequencing has identified differentially expressed mitochondria-related genes, including TRMT61B, FASTKD2, and NDUFA4L2, as well as immune response genes, including CLU, C3, and CD74 in astrocytes from the posterior cingulate region (43). These genes are implicated in amyloid beta generation or clearance and are potential targets for development of therapeutics to slow or arrest disease progression at earlier stages. Metaanalysis of data from several studies involving thousands of persons has shown the significant relationship of single nucleotide polymorphisms of sortilin-related receptor on susceptibility to Alzheimer disease: (1) rs641120 and rs1010159 increase risk in Asian populations; (2) rs689021 decreases risk in Caucasians; and (3) rs641120 decreases risk in both populations (08). A large genome-wide association metaanalysis of clinically diagnosed late-onset Alzheimer disease has confirmed 20 previous risk loci and identified 5 new genome-wide loci -- IQCK, ACE, ADAM10, ADAMTS1, and WWOX -- implicating immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein metabolism in pathogenesis (28). Results show that genetic variants affecting amyloid beta processing are associated not only with early-onset autosomal dominant Alzheimer disease, but also with late-onset Alzheimer disease.

Parkinson disease. Results of the largest case-control genome-wide association study so far indicate a substantial contribution of genetics to susceptibility for both early-onset and late-onset Parkinson disease, although most of the genetic components of Parkinson disease remain to be discovered (10). Five genes are now known to cause monogenic forms of Parkinson disease; these were identified using genetic linkage approaches, which require large pedigrees with affected and unaffected individuals. Two of these genes, SNCA and LRRK2, cause dominant forms of Parkinson disease, whereas mutations in PARK2, PINK1, and DJ-1 were shown to underlie recessive forms of the disease. Eleven loci were identified as risk factors for the development of common forms of Parkinson disease (International Parkinson's Disease Genomics Consortium (IPDGC) and Wellcome Trust Case Control Consortium 2 (WTCCC2) 2011). However, a significant proportion of inherited cases of Parkinson disease is unexplained genetically, and the cause of the disease remains somewhat elusive.

Exome sequencing has now been applied to Parkinson disease research and has the potential for use as a screening method to identify pathogenic mutations in some Parkinson disease patients (05). A major challenge of exome sequencing is the amount of data generated and the rapid evolution of methods to evaluate these data.

Multiple sclerosis. Genetic factors are responsible for the increased frequency of the disease seen in the relatives of individuals affected with multiple sclerosis. Genome-wide association studies have identified several risk loci, and variation within the major histocompatibility complex exerts the greatest individual effect on risk. Immunologically relevant genes are overrepresented among those mapping close to the identified loci and implicate T helper cell differentiation in the pathogenesis of multiple sclerosis (19).

Major histocompatibility complex (MHC) in chromosome 6p21.3 has been identified as multiple sclerosis susceptibility locus genome-wide in all studied populations, and there is evidence for the association of over 100 non-MHC loci with disease susceptibility (39). Research is in progress to fully characterize the genes that predispose to multiple sclerosis and modulate its presentation as well as clinical course, which will pose a major challenge in multiple sclerosis genetics research in the coming years. An important advance is functional characterization of the multiple sclerosis risk variant on chromosome 12p13.31 containing the gene TNFRSF1A, which encodes the tumor necrosis factor (TNF) receptor superfamily member 1A with apoptotic activity. TNFRSF1A shows association with multiple sclerosis risk and provides an insight into the pathophysiology that can lead to novel therapeutic strategies (32).

Epilepsy. Epilepsy is mostly a multifactorial disorder, but there are familial forms, and some epilepsy genes have been identified. Currently there are no genetic tests for epilepsy. Single-nucleotide polymorphism association analysis shows that malic enzyme 2 (ME2) gene predisposes to idiopathic generalized epilepsy (15). ME2 is a genome-coded mitochondrial enzyme that converts malate to pyruvate and is involved in neuronal synthesis of the neurotransmitter GABA. Disruption of synthesis of GABA predisposes to seizures, which are triggered when mutations at other genes are present.

Results of several genome-wide association studies suggest that, like other common diseases, associations with single nucleotide polymorphisms appear likely to account for a small fraction of the heritability of epilepsy, thus, fueling the effort to also search for alternative genetic contributors with an increased emphasis on rare variants with larger effects. It is possible that both common and rare variants contribute to an increased susceptibility to common epilepsy syndromes. Approaches that have been taken to identify genetic risk biomarkers of the common epilepsy syndromes as well as technologies that might expedite the discovery of these variants have been reviewed elsewhere (42). These include microarray-based, high-throughput, genotyping technology, and complementary interdisciplinary expertise of study teams including the need for meta-analyses under global collaborative frameworks.

Epileptic encephalopathies differ in genetic causes, and the genotype-phenotype correlations by next-generation sequencing can provide insights into the underlying pathogenic mechanisms (53). Integration of exome sequencing and gene panel testing into the diagnostic protocol for epileptic encephalopathy is clinically useful as well as cost-effective (40). Initial rapid screening for treatable causes should be followed by comprehensive genomic screening.

Ataxias. A pilot study has used heterogeneous ataxias as a model neurogenetic disorder to assess the introduction of next generation sequencing into clinical practice (37). The authors captured several known human ataxia genes by use of next generation sequencing in patients with ataxia who had been extensively investigated and were refractory to diagnosis. Numerous mutations were predicted in 8 different genes: PRKCG, TTBK2, SETX, SPTBN2, SACS, MRE11, KCNC3, and DARS2, of which 9 were novel including 1 causing a newly described recessive ataxia syndrome. Next generation sequencing was efficient, cost-effective, and enabled a molecular diagnosis in many cases. Functional analysis confirmed the pathogenicity of novel gene variants. The results have broad implications for neurology practice and the approach to diagnostics.

Congenital myopathies. Although several causative genes have been identified for each of the congenital myopathy groups, a specific genetic diagnosis is often not possible. Whole exome sequencing enables analysis of almost the entire coding region of an individual's DNA to investigate all known and novel disease genes for disorders such as these. Use of next generation sequencing, either whole genome or whole exome, enabled a genetic diagnosis for 47% of families in a cohort of 38 unrelated families with congenital myopathies (46).

Migraine. The pathophysiology of migraine is not well understood, and although some gene mutations have been associated with special forms of migraine, genetic influences on common migraine at the population level were previously unknown. Genome-wide analysis of a large population in Europe, including migraineurs and non-migraineurs, revealed that 2 single nucleotide polymorphisms, rs2651899 and rs10166942, were associated with migraine, but the association was not preferential for migraine with aura or without aura, or with any for specific features of migraine (06).

Syncope. There is a heritable component in syncope. A review of genome-wide association data has revealed a significant locus (rs12465214) associated with syncope and collapse, which was replicated in an independent cohort (17).

Myalgic encephalomyelitis/chronic fatigue syndrome. This is a complex illness that includes alterations in multiple body systems and results from the combined action of many genes and environmental factors. Genomic studies including single nucleotide polymorphisms have linked myalgic encephalomyelitis/chronic fatigue syndrome to genes coding for the glucocorticoid receptor, for serotonin, and for tryptophan hydroxylase, which are related to the body's ability to handle stress. Functional analysis of DNA testing results in a study on persons with myalgic encephalomyelitis/chronic fatigue syndrome found that most of the identified single nucleotide polymorphisms were related to immune system, hormone, metabolic, and extracellular matrix organization (41). The findings provide evidence of the biological basis of chronic fatigue syndrome and could lead to improved diagnostic tools and new therapies.

Psychiatric disorders. Most psychiatric disorders, including schizophrenia, major depression, and bipolar disorder, are considered polygenic. Psychiatric genetics has developed considerably as genome-wide studies have revealed interesting gene variants (14). Studying genes for single nucleotide polymorphisms is an excellent tool to discover genes for psychiatric disorders and potentially an excellent tool for psychopharmacogenetics as well. One study suggests that schizophrenia is associated with substantial brain structural heterogeneity beyond the mean differences, which may reflect higher sensitivity to environmental and genetic perturbations in patients that are not captured by the polygenic risk score (01). Genome-wide analysis has revealed an extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence (44). The sharing of genetic influences with intelligence provides new insights into an understanding of these disorders and their management.

Major depressive disorder. A study using haplotype-block-based regional heritability mapping has shown that the expression of TOX2 gene and a brain-specific long noncoding RNA RP1-269M15.3 in frontal cortex and nucleus accumbens basal ganglia, respectively, are significantly regulated by major depressive disorder–associated single nucleotide polymorphisms within this region (52). This study provides an important target within the TOX2 gene for further functional studies.

Genetic loci of brain disorders in brainstem. A study has used imaging-genetics data from a discovery sample of thousands of individuals to identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and mapped them to 305 genes of common brain disorders (11). Results showed differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.

Integration of diagnostics and therapeutics. The trend in healthcare in the next decade will be integration of diagnostics and therapeutics in personalized medicine. If patients can be diagnosed in terms of DNA mutations, alleles, or polymorphisms pertaining to a specific disease, their response to treatment can be vastly improved. This will be facilitated by gene-based disease management incorporating genetic tests that predict the safety and efficacy of therapeutic products. The identification of genes that influence the penetrance and expressivity of risk would be important in determining these risk profiles. As treatment becomes more specific to the genetic cause of disease, diagnostic tools that measure the activity of targeted genes will become crucial for disease management. Eventually, prior to the prescription of therapy, a diagnostic test must confirm the existence of diseased genes and their activity levels to improve treatment efficiency and cut patient care cost.