Drug Discovery for Schizophrenia / Edition 1 available in Hardcover
Drug Discovery for Schizophrenia / Edition 1
- ISBN-10:
- 1782620265
- ISBN-13:
- 9781782620266
- Pub. Date:
- 05/13/2015
- Publisher:
- RSC
Drug Discovery for Schizophrenia / Edition 1
Hardcover
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Drug Discovery for Schizophrenia
By Tatiana V Lipina, John C Roder
Royal Society of Chemistry
Copyright © 2015 The Royal Society of ChemistryAll rights reserved.
ISBN: 978-1-78262-249-9
CHAPTER 1
The Genetics of Schizophrenia
JAMES N. SAMSON AND ALBERT H. C. WONG
1.1 Introduction
If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.
Sun Tzu, The Art of War
The greatest difficulty in finding treatments for schizophrenia is that we do not know the enemy well enough. Revealing the complex etiology and pathophysiology of schizophrenia has posed a considerable challenge for researchers, but improving technology is now enhancing our ability to use the wellspring of information present in the genome to help find these answers. It is more than three decades since the first development of genome sequencing technology, and we have come to appreciate the intricate way in which variations in the genome can influence disease. Genetic research has provided insights into elucidating the pathophysiology of many diseases, and also promises to improve clinical outcomes through personalized treatments and targeted therapeutics. Studying the genetics of schizophrenia is important to discover genes and pathways that contribute to its development. The hope is that the symptoms of schizophrenia can be prevented or resolved by targeting therapeutics at these pathways. Still, treatment is most likely to be administered late in the development of the disorder, after diagnosable symptoms have already presented. By this time, the processes leading to the development of schizophrenia may have caused permanent changes; for example, alterations in brain morphology. Genetics can also help us to understand the underlying pathophysiology of the individual symptoms of schizophrenia, allowing for the development of targeted therapeutics to improve the lives of patients by treating symptoms after developmental pathways have become fixed. Whether to understand developmental processes or symptom pathophysiology, the study of the genetics of schizophrenia has great potential in helping us to understand the enemy, and hopefully, eventually, to conquer schizophrenia.
1.2 What Genetics Can Tell Us about Schizophrenia
It is now understood that genes and environment work together to influence the development of disease. The power of genetics to enable us to understand a disease is dependent upon how much of the variance in liability is contributed by genes compared to other factors. It is also important to consider the manner in which genes affect phenotype. The heritability and genetic architecture of schizophrenia tell us how genetic information can be used to understand the disorder.
1.2.1 The Heritability of Schizophrenia
The contribution of genes in determining a given phenotype can be quantified by estimating heritability. Heritability is a mathematical expression of the amount of variance in phenotype that is explained by genetic variation. This does not measure how much phenotypic variation is caused by genes; rather, it reflects the relative contribution of genetic vs. non-genetic factors in determining phenotype. Heritability is estimated by comparing the liability of developing a trait (schizophrenia, for example) between related and unrelated individuals. Twin studies have been invaluable for estimating heritability, as it is easier to differentiate between genetics and shared vs. differential environment in such studies. The concordance in phenotype between monozygotic (MZ) and dizygotic (DZ) twins gives a measure of the correlation between genotypic variation and presence of a trait. MZ concordance rates for schizophrenia have been reported between 41% and 65%, with DZ concordance ranging from 0% to 28%. Since DZ twins have approximately half the genetic variance of unrelated individuals, and MZ twins have identical genomes, heritability can be crudely calculated as twice the difference in concordance (r) between MZ and DZ twins (see eqn 1.1).
Heritability (h2) = 2(r(MZ) – r(DZ)) (1.1)
Hence, the heritability for schizophrenia estimated from twin studies is 81% (95% confidence interval 73–90%). A limitation of twin studies is that subjects are usually recruited from restricted environmental settings, typically from within the same hospital. Heritability estimates using family data from national records are lower, at 64–67%. This difference may be due to increased variance in environment and diagnostic interpretations when using subjects from national records. Either way, schizophrenia is clearly one of the most heritable neuropsychiatric disorders, demonstrating that genes have a large role to play.
The high estimated heritability for schizophrenia indicates that the genome contains information explaining much of the underlying patho-physiology of the disorder. So far, all the variants taken together from current genome-wide association study (GWAS) results have been calculated to account for 20–40% of the variation in liability for schizophrenia. Even using lower family-based estimates for comparison, current results do not account for all of the predicted heritability. The term "missing heritability" was coined to describe this discrepancy between the proportion of phenotypic variation explained by results from genetic studies and the total estimated heritability. Current evidence suggests that we may yet find a large proportion of this missing heritability within the genome. Part of the missing heritability may also be due to epigenetic DNA and chromatin modifications that alter gene expression without changing DNA sequence. Increased sample sizes and more complete coverage of variants with improved genotyping technologies have already uncovered many new significant schizophrenia-associated genetic loci. We can be optimistic that continuing efforts in interrogating the human genome will reveal ever increasing numbers of causal variants. This information promises to provide key insights into the mechanisms underlying the development of schizophrenia.
1.2.2. The Genetic Architecture of Schizophrenia
Genetic studies have now discovered enough associated risk variants to give an empirical view of the genetic architecture of schizophrenia. Schizophrenia is a complex, highly polygenic disorder with multiple variants conferring risk. Numerous variants with population frequencies >1% have been associated with schizophrenia. Alongside these common variants, rare variants with frequencies <0.1%, resulting from de novo mutations and large-effect structural variations, are implicated in the disorder. The effect size of schizophrenia associated variants is inversely proportional to their population frequency. Effect size encompasses the idea of penetrance. Penetrance reflects the amount an individual variant contributes to a phenotype, with completely penetrant variants guaranteeing the presentation of a trait, and incomplete and low penetrant variants only incrementally increasing the probability of possessing that trait.
The allelic spectrum of schizophrenia risk is depicted in Figure 1.1. Multiple low effect common variants with odds ratios (ORs) typically <1.3 and moderate to high effect, but still incompletely penetrant rare variants contribute to risk. Risk variants can combine additively where each locus adds/subtracts a certain amount of risk, or multiplicatively where a certain number or arrangement of loci must be present to reach a threshold to increase risk. A purely additive model of gene interaction does not explain the genetics of schizophrenia. Therefore, the genetic architecture of schizophrenia probably involves thousands of risk variants which interact multiplicatively and are highly susceptible to genetic background, pleiotropy, and, of course, environmental effects. Unaffected individuals probably carry manageable numbers of risk variants, while the probability of developing schizophrenia rises sharply for individuals with a high burden of risk alleles. While this picture suggests that common variants are unlikely to be essential alone, the multiplicative nature of their effects means that single risk variants can still exert biologically meaningful effects, depending on the genetic background. Hence, associated common variants still warrant functional investigation alongside higher risk rare variants. The picture so far suggests that understanding the genetic causes of schizophrenia will involve understanding not only the functional relevance of individual genes, but also how multiple genes interact and converge on molecular pathways to affect behaviour.
1.3 The Tools of Genomics
New technologies are constantly improving our ability to read the human genome and detect genetic variation between individuals. Genomes vary in many ways, from single nucleotide polymorphisms (SNPs) to more severe structural variations such as copy number variations (CNVs). SNPs are the most common type of variation. Structural variants have a greater potential to cause disruptions in genes simply due to their size, and hence large-effect rare variants tend to be of the structural type; however, other variants can still have large effects. A simple schematic of the types of variation is given in Figure 1.2. Different techniques and technologies are better for detecting specific types of variation, so study design and genotyping methods must be tailored to the type of variation relevant to the specific research question.
Sequencing remains the gold standard to capture all of the variation in the genome. Next-generation sequencing (NGS) technology has greatly reduced the cost of sequencing and is now standard practice. NGS sequences millions of DNA fragments in parallel, with bases being identified optically in real time using "sequencing by synthesis" chemistry, greatly reducing time and costs. Third-generation sequencing and nanopore based technologies are on the horizon; these can sequence single DNA molecules without the need for amplification or cyclic-sequencing steps. Sequencing is still too expensive for large samples, so microarray technology remains the most used genotyping method in GWASs. Modern microarrays can now simultaneously interrogate up to 1 million SNPs, and are capable of detecting CNVs and microsatellites; however, inversions and translocations can only be detected by comparison of fully sequenced genomes. Only a fraction of SNPs can be interrogated on a microarray; however, un-interrogated SNPs can be mathematically predicted from reference genome data by imputation, greatly increasing coverage. High coverage is crucial for detecting disease variants as it is unlikely that the variants on an array are causal; rather, they are in linkage disequilibrium with true causal variants. Cheaper sequencing technologies, improved arrays, and better reference information for imputation will continue to increase our ability to find disease-associated variants.
Study design can greatly affect the ability to find genotype–phenotype associations. There are two major study designs used in human genetics: case–control and pedigree-based. Simpler case–control studies are better for finding associations with low effect size, but cannot discriminate between inherited and de novo variations. More complicated family-based designs can be used to evaluate linkage (co-segregation of genotypes and phenotypes from parents to offspring), test for associations, and identify de novo variants. Subject choice is important as families with a history of disease (multiplex pedigrees) may be enriched for rare causal variants, whereas affected subjects whose families have no history of disease (simplex pedigrees) may be enriched for de novo variants. It is also important to consider how data are analyzed. False discovery rate procedures to correct for multiple testing, test-replication designs, and pathway analysis for enrichment of functionally-related genes are all clever ways to increase statistical power without relying on massive sample sizes. Carefully considered study designs combined with constantly improving technologies are already generating results in the search for causal variants for schizophrenia, and we can expect continued progress in schizophrenia genetics.
1.4 What Genetics Has Told Us about Schizophrenia
Even in the late 2000s there was a worry that genome-wide screens were finding no true causal variants for schizophrenia. While early linkage studies were beginning to find significant loci, many of the most interesting findings were not replicated, and candidate genes tested from significant loci yielded no associations. Past GWASs yielded few strong results due to lack of power; however, newer GWASs and mega-analyses with combined sample sizes in the tens of thousands are now finding numerous significant associations. Furthermore, improving genotyping technology and analysis techniques are making it possible to determine the role of rare structural variation and de novo mutations in schizophrenia, and facilitate the identification of rare associated CNVs. Many schizophrenia risk variants are beginning to show biological relevance and potential as drug targets. There are now too many associated loci to mention in any adequate detail in this section; however, some of the more interesting results are highlighted.
1.4.1 Common Variation
Common SNPs account for a large amount of the variance in liability for schizophrenia. Using statistical models, it was estimated in 2012 that 23% of the variation in liability for schizophrenia is accounted for by common SNPs. Just 1 year later, that estimate had increased to at least 32%, and this number should continue to climb as better-powered studies discover more significant associations. A few of the more robust and interesting GWAS findings are summarized in Table 1.1. Some of the more interesting themes arising from genetic studies in terms of drug discovery are detailed below.
1.4.1.1 Receptors
Receptors represent obvious potential therapeutic targets. The DRD1 gene encoding the D1 dopamine receptor gained strong epidemiologic credibility from early genetic studies. While implicating the dopamine system was not a new finding, this is an example showing that genetic studies were corroborating established hypotheses. Common SNPs in the receptor genes CHRNA7 and GRM3 were also associated with schizophrenia, but, as was typical with early candidate gene studies, many studies also reported no associations. Nevertheless, concordant evidence supported a role for these receptors. CHRNA7 encodes a subunit of the ionotropic α-7 nicotinic acetylcholine receptor (nAChR). This receptor seems to function mainly to modulate neurotransmitter release in the striatum. There is evidence that α-7 nAChR agonists have efficacy in improving cognitive deficits in schizophrenia, and may be useful in combination with antipsychotics. Additionally, rare variants in CHRNA7 show strong associations with schizophrenia. GRM3 encodes the mGluR3 subunit of the metabotropic glutamate receptor (mGluR). Allosteric and orthosteric modulators of mGluR2/3 are available, and evidence from animals and early clinical trials is beginning to show that some of these agonists may have efficacy in treating the positive and negative symptoms of schizophrenia. These examples show how genetic research with concordant biological evidence allows us to tap new sources with therapeutic potential.
1.4.1.2 The Major Histocompatibility Complex
Associations within an exceptionally complex genomic region on chromosome 6 known as the major histocompatibility complex (MHC) are some of the most robust and consistent findings for schizophrenia. This region contains hundreds of genes in high linkage disequilibrium, thus making it difficult to identify specific genes underlying associated loci. Nevertheless, examining the general role of the MHC in immune function, autoimmunity, inflammation, and infection in relation to schizophrenia suggests intriguing new directions in schizophrenia research. Animal and in vitro studies are revealing a role for MHC molecules in neurodevelopment, neuronal and synaptic plasticity, learning, memory, and behavior. Furthermore, MHC molecule expression is altered in schizophrenia, and risk loci within the MHC have been associated with functional effects on brain morphology and cognition in humans. MHC associated autoimmune disorders, infections with certain pathogenic microbes, and prenatal maternal immune activation have been associated with increased schizophrenia risk; in addition, neuroinflammation and increased inflammatory markers are seen in patients with the disorder. Systematic reviews and meta-analyses show that anti-inflammatory drugs given in combination with antipsychotics decrease the severity of schizophrenia symptoms. A complete review of the intricate relationship between the immune system and the central nervous system as it relates to the MHC and schizophrenia is beyond the scope of this chapter; however, it is clear that associations in the MHC can inform our understanding. Additionally, it is likely that therapeutic strategies involving the immune system will improve the treatment of schizophrenia.
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Excerpted from Drug Discovery for Schizophrenia by Tatiana V Lipina, John C Roder. Copyright © 2015 The Royal Society of Chemistry. Excerpted by permission of Royal Society of Chemistry.
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