Expect delivery in 20 days. Seller Inventory More information about this seller Contact this seller. Condition: New. New Book. Shipped from UK. Established seller since Seller Inventory FM Book Description Cambridge University Press , Language: English. Brand new Book. The first authoritative review on the parasomnias - disorders that cause abnormal behavior during sleep - this book contains many topics never before covered in detail. The behaviors associated with parasomnias may lead to injury of the patient or bed-partner, and may have forensic implications.
These phenomena are common but often unrecognized, misdiagnosed, or ignored in clinical practice. With increasing awareness of abnormal behaviors in sleep, the book fulfils the need for in-depth descriptions of clinical and research aspects of these disorders, including differential diagnosis, pathophysiology, morbidity, and functional consequences of each condition, where known. Appropriate behavioral and pharmacological treatments are addressed in detail.
There are authoritative sections on disorders of arousal, parasomnias usually associated with REM sleep, sleep-related movement disorders and other variants, and therapy of parasomnias. Sleep specialists, neurologists, psychiatrists, psychologists and other healthcare professionals with an interest in sleep disorders will find this book essential reading. Seller Inventory AAA Brand new book, sourced directly from publisher.
Dispatch time is working days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. Book Description Cambridge University Press. Giuseppe Plazzi Edited by Michael J. Publisher: Cambridge University Press , WL ] RC Every effort has been made in preparing this book to provide accurate and up-to-date information which is in accord with accepted standards and practice at the time of publication. Although case histories are drawn from actual cases, every effort has been made to disguise the identities of the individuals involved.
Nevertheless, the authors, editors and publishers can make no warranties that the information contained herein is totally free from error, not least because clinical standards are constantly changing through research and regulation. The authors, editors and publishers therefore disclaim all liability for direct or consequential damages resulting from the use of material contained in this book.
Readers are strongly advised to pay careful attention to information provided by the manufacturer of any drugs or equipment that they plan to use. Contents Foreword vii Preface ix List of contributors xi List of abbreviations xv. Methods in complex trait analysis: mapping the genetic basis of sleep using model organisms 13 Amelie Baud and Jonathan Flint. Genetic epidemiology of sleep and sleep disorders 33 Christer Hublin and Jaakko Kaprio. Drosophila model systems for genetic sleep research 43 Stephane Dissel and Paul J.
Caenorhabditis elegans and zebrafish in sleep research 54 David A. Prober and David M. Optogenetic control of arousal neurons 66 Antoine Adamantidis, Matthew E. Carter, and Luis de Lecea. The role of metabolic genes in sleep regulation 91 Matthew S. Thimgan and Karen D. Schilli A systems biology approach for uncovering the genetic landscape for multiple sleep wake traits Peng Jiang, Andrew Kasarskis, Christopher J.
Winrow, John J. Renger, and Fred W. Turek Genetic control of the circadian pacemaker Ethan Buhr and Joseph S. Takahashi Epigenetic basis of circadian rhythms and sleep disorders Irfan A. Qureshi and Mark F. Section 3: Sleep physiology and homeostasis Genetic interaction between circadian and homeostatic regulation of sleep Valrie Mongrain and Paul Franken Genetic approaches to understanding circadian entrainment Till Roenneberg and Karla V.
Allebrandt Animal models for cognitive deficits induced by sleep deprivation Laurent Seugnet and Paul Salin. Individual differences in sleep duration and responses to sleep loss Devon A. Grant and Hans P. Van Dongen Clock polymorphisms associated with human diurnal preference Simon N. Archer and Derk-Jan Dijk Sleep and long-term memory storage Jennifer H. Choi and Ted Abel Sleep and synaptic homeostasis Chiara Cirelli and Giulio Tononi.
Section 4: Insomnias Heritability and genetic factors in chronic insomnia Yves Dauvilliers and Charles M. Genome-wide association studies in narcolepsy Hyun Hor Genetic disorders producing symptomatic narcolepsy Seiji Nishino and Takashi Kanbayashi Section 6: Sleep-related breathing disorders Linkage and candidate gene studies of obstructive sleep apnea Annette C. Fedson, Thorarinn Gislason, and Allan I. Section 7: Circadian rhythm sleep disorders Genetics of familial advanced sleep phase S. Christin Chong, Louis J.
Ptek, and Ying-Hui Fu Delayed sleep phase disorder, circadian genes, sleep homeostasis and light sensitivity Simon N. Archer and Derk-Jan Dijk. Section 8: Parasomnias and sleep-related movement disorders Family and genome-wide association studies of restless legs syndrome Eva C. Schulte and Juliane Winkelmann. Section 5: Narcolepsy and hypersomnias Orexin hypocretin and narcolepsy Takeshi Sakurai and Seiji Nishino. Genomic variants and genotypephenotype interactions in pediatric sleep-related breathing disorders Leila Kheirandish-Gozal and David Gozal.
Section 9: Psychiatric and medical disorders Boivin Section Medication effects This is a beautiful and useful book for the numerous worlds sleep and dream clinicians and researchers hypnologists and oneirologists and sleep researchers and we should thank the editors, Paul Shaw, Mehdi Tafti, and Michael Thorpy. Although I am not a specialist in genetics, I have been introduced to this discipline by one of my best students and coworkers, Jean Louis Vataltx, who pioneered this field in , by reporting in Nature a study in inbred mice.
Certainly, genetic studies of the sleepwake cycle help the physiologist and I was interested to read the paper from the University of Missouri reporting the role of metabolic genes in sleep regulation. I was wondering which relationships between sleep deprivation and cognitive deficits paper No. In this field, may I suggest that results obtained in animal models should not be applied to humans. Some 30 years ago, I had the rare opportunity to study a man, continuously recorded by EEG, who. He was not sleepy and did not show any memory disturbances nor cognitive deficits and was able to complete several difficult cognitive tests.
Electroencephalogr Clin Neurophysiol. French , we had the following question: what was the function of sleep? However, it was only later that I recognized the very important role of genetics. In studying the patterns of rapid eye movements during dreaming in man, we found that these patterns were genetically controlled since they were identical in monozygotic twins, reared together or apart.
This result opened the hypothesis that dreaming REM sleep might be an iterative genetic programming of the psychological individuation in man. Thus, long life to the future of genetic studies of dreaming. The genetics of sleep and sleep disorders is still largely unknown and not well understood; however, new studies show the importance not only for understanding brain physiology but for sleep disorders and the circadian regulation that influences most body systems. In order to understand the physiology and pathophysiology of sleep, genetic studies are being developed that include new genetic techniques to tell us not only about brain regions that are activated or deactivated by sleep and alertness but also help us understand the pathophysiological mechanisms involved.
This book, Genetics of Sleep and Sleep Disorders, details the important advances in the genetics of sleep disorders that hold promise to help us understand the underlying physiology and pathophysiology of sleep that will also aid in the diagnosis of sleep disorders.
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There has been a major increase during the last decades in knowledge of the genetics of sleep and sleep disorders. Genetic epidemiologic studies have contributed considerably; however, there are marked differences in the level of knowledge between different aspects of sleep and individual disorders. Linkage, genome-wide association, and sequencing are yielding new insights into the basis of sleep traits. Mutations in the clock genes have been associated with Mendelian alterations of circadian rhythms and candidate gene association studies have been reported for a variety of sleep disorders.
Most sleep disorders are considered to be complex genetic disorders. Recent progress has been made in identifying the genetic basis of narcolepsy and RLS and genomewide association studies have demonstrated several genetic loci associated with their pathogenesis. The genetic basis remains to be determined for the more prevalent sleep disorders, insomnia and obstructive sleep apnea. Epigenetic mechanisms are being recognized as playing a major part in gene regulation of sleep.
In the future whole-genome sequencing may clarify the genetic basis of complex traits including. This book represents the first major overview of the accumulated scientific developments in genetics to the study of sleep and sleep disorders. No previous book has been published which comprehensively focuses on genetics of sleep and its disorders. This book accumulates the most recently available information on genetics and epigenetics and is written by top specialists in the field, geneticists, sleep disorders physicians and sleep researchers, from the Americas, Europe, and Asia.
The chapters are arranged in five major sections: an introductory section on principles of genetics and genomics, genetics of sleep and circadian rhythms, sleep physiology and homeostasis, genetics of the sleep disorders including, insomnia, sleep-related breathing disorders, circadian rhythm disorders, parasomnias and sleep-related movement disorders, psychiatric and medical disorders associated with sleep and finally therapeutics.
The introductory section comprises chapters on linkage and associations, complex trait analysis, and genome-wide association studies, including the fundamentals and methodology of genetic methods. The second section addresses genetics of normal sleep and circadian sleepwake rhythms and includes epidemiology, and presentations on Drosophila, C. Section three presents the genetics of the electrocephalographic basis of normal sleep, homeostasis and circadian entrainment, sleep deprivation and effects on memory and synaptic plasticity.
Section four discusses the role of genetics in the understanding of the sleep disorders including, insomnia, narcolepsy and the hypersomnias, sleep-related breathing. This volume is intended primarily for sleep disorder specialists, sleep researchers, and geneticists; however, it is suitable for neurologists, psychiatrists, and any professional and researcher interested in the interdisciplinary field of sleep medicine. It will be of use for neurology, psychiatry and genetics residents and fellows, clinical psychologists, advanced graduate medical students, neuropsychologists, house officers, and other mental health and social workers who want to get an understanding the genetic basis of the.
We are greatly indebted to all the authors who have contributed to this book and are appreciative of the help of the staff of the Cambridge University Press in getting this book in print so quickly so that the contents are up-to-date and current. As findings in this area are rapidly advancing it is anticipated that future editions of this volume Genetics of Sleep and Sleep Disorders will take these developments into account. Matthew E. Karla V. Jennifer H. RodaRani Konadhode Ralph H. Irfan A.
David M. John J. Benjamin M. Elizabeth J. Allan I. Dheeraj Pelluru Ralph H. Priyattam J. Shiromani Ralph H. Fred W. Matthew S. Christopher J. Somatostatin serotonin-specific reuptake inhibitors slow-wave activity sharp-wave ripple slow-wave sleep TAL-effector nuclease T-cell receptor alpha transmission disequilibrium test TenEleven Translocation tet-operator tyrosine hydroxylase time in bed toll-like receptor 4 tuberomammilary nucleus transient receptor potential total sleep deprivation tetracycline transactivator Upstream Activation Sequence untranslated region ventrolateral preoptic area variable-number tandem repeats ventral tegmental area wild-type X chromosome inactivation zinc finger nuclease.
Introduction Human genetics is one of the most promising approaches to identifying the cellular underpinnings of human diseases and traits. For diseases whose etiology is largely unknown, identifying genes that contribute risk can lead to novel biological insights and potentially reveal proteins and pathways to target with therapeutics. Historically, the search for such genetic variation that influences phenotype has been particularly successful in rare genetic disorders, termed Mendelian disease, that are caused by severe mutations in DNA: classic examples of such diseases include hemochromatosis, cystic fibrosis and phenylketonuria .
For these diseases, DNA changes in particular genes lead to deficient or altered protein that in turn results in a cascade of physiological outcomes, ultimately culminating in the medical sequelae that define the disease. Not only have these findings helped elucidate the biological pathways important to these phenotypes, but also understanding the damaged cellular processes has been proven to be relevant to patients medical treatment.
A primary goal of human genetics is to understand disease biology and ultimately aid in the identification of novel therapeutic design. The application of genetics to severe rare diseases that follow clear inheritance patterns in families has led to the successful identification of the root cause in many instances. These Mendelian diseases are almost completely caused by genetic factors, which explains the success of genetics to unequivocally determine the cause. In contrast, complex traits are characterized by the combination of many genetic and environmental factors that together create the phenotype.
An additional consequence of this complex trait architecture is that the familial clustering of the trait does not follow a clear and predictable inheritance pattern. For most complex phenotypes, we do not understand the bulk of the underlying pathophysiology, in spite of the fact that many of these traits are clearly heritable. Since the nineteenth century, scientists and physicians have studied twins and families for complex phenotypes and identified clear evidence of heritability. The fact that traits tend to run in families and that more genetically similar family members tend to be more phenotypically similar provides empirical support of the genetic hypothesis.
Consequently, the identification of genetic variants is possible and provides the opportunity to gain insight into the biological processes relevant to human disease. Twin and family studies in sleep phenotypes have revealed significant heritability; the earliest observation of sleep phenotypes being heritable was made in when Geyer reported higher sleep profile concordance in monozygotic twins than dizogotic twins . As with many traits, the majority of sleep disorders and sleep-related traits are complex phenotypes. However, there are some examples of familial diseases that present with disordered sleep as either a primary or secondary finding.
Phenotypes in both these categories include diseases such as restless leg syndrome RLS and narcolepsycataplexy as well as quantitative traits in normal individuals including duration and quality of sleep. A number of instances of sleep disorders segregating in a Mendelian fashion within large families have been documented, but there are also well-established studies of heritability of sleep and sleep disorders as complex traits as discussed later in this chapter . Identifying genes for heritable Mendelian and complex traits alike requires genetic mapping, i. Genetic mapping is accomplished by correlating DNA variation with phenotype.
Published by Cambridge University Press. Cambridge University Press In some instances, the DNA variant being tested will in fact be the causal variant for the phenotype. In other instances, the DNA variant tested will simply be correlated with the truly causal variant. When two genetic variants are correlated, this correlation is referred to as linkage disequilibrium.
The two primary analytic techniques for genetic mapping are linkage and association. In linkage mapping, segments of the genome are tracked in families to determine whether exactly the same region of DNA is shared by members of the family that share phenotypic status. Historically, linkage has been extremely successful at the identification of Mendelian disease genes but has had limited success at the identification of risk loci for complex traits.
In contrast, association aims to correlate DNA variants with phenotype in the population, as variation that increases the chances of disease should be enriched in a case sample. In this chapter, we will discuss the methodological considerations surrounding linkage and association studies as well as results of both approaches as they relate to sleep and sleep disorders. The clear heritability of sleep-related phenotypes has spawned a number of efforts to identify regions in the genome that are suspect for contributing to disease or phenotype.
Consequently, a number of linkage and association studies have been carried out to determine the genetic factors that underlie these complex phenotypes. Here, we discuss these methods and the current state of results. Linkage The term linkage refers to the phenomenon whereby continuous stretches of DNA are inherited together during meiosis unless separated by recombination. Recombination refers to the process of chromosomal cross-over in which parts of chromosomes break and rejoin when homologous chromosomes align during meiosis Figure 1.
The further apart two loci, the more likely they will be separated by recombination during the lining up of homologous chromosomes and eventually end up in different daughter cells. Thus, in linkage the aim is to roughly decipher the location of a disease-causing gene relative to a nearby sequence by tracking the concordance between genetic markers, whose genomic positions are already known, and phenotype.
The earliest attempts at linkage mapping were carried out by Alfred Sturtevant in the laboratory of Thomas Morgan in the early s in Drosophila, when he realized that he could map the. Figure 1. A depiction of recombination during meiosis is shown. Linkage in families became feasible around when Botstein and colleagues proposed the idea of using restriction fragment length polymorphisms a type of variant that disrupts a restriction enzyme cut site and is therefore easy to assay throughout the genome to systematically map human genes associated with disease .
This breakthrough in methodology led to the mapping of the Huntingtons gene on chromosome 4 in  followed by the systemic documentation of dense genome-wide polymorphic sites and the subsequent mapping of now over 2, Mendelian diseases . The approach to linkage mapping involves assaying genetic markers throughout the genome within families where multiple members are affected by the disease of interest.
Earlier linkage studies were characterized by larger pedigrees with subsequent work extending to other study designs such as affected sibling pairs. The ideal genetic markers are ones that are easily assayed, ones that are sufficiently polymorphic across individuals to ensure a high frequency of heterozygosity and ones that are frequent throughout the genome so that a dense map can be achieved. Most early linkage studies used microsatellites, which are polymorphisms with variable-number tandem repeats VNTRs that are. More recently, linkage analysis has relied on single nucleotide polymorphisms SNPs , which are usually bi-allelic i.
For any category of variant, the first approach to linkage is to test each marker for linkage by comparing the odds of its being near in the genome to the disease-causing mutation versus the odds of its being independent of the disease-causing mutation that is, it is far enough away that assortment with the disease-causing mutation becomes independent. A variety of approaches to linkage analysis exist, the most classic of which is known as parametric or model-based linkage analysis.
Here, we assume that the trait in question is determined by a single locus and that familial resemblance is due only to this single locus according to the presumed inheritance pattern, whose parameters involve assumptions on the mode of inheritance as well as the penetrance of the diseasecausing allele. The statistic typically used for this test is known as a LOD logarithm of the odds score, which is calculated as: LOD log10 log The designation of the risk allele is typically achieved using the grand-parental generation of a pedigree, and counting subsequent meioses within a pedigree as R or NR requires that the meiosis be informative, a description that means we can determine the parental origin of an offsprings alleles.
A Under a dominant model with full penetrance, we use generation I to phase, which yields A as the risk allele. NR: non-recombinant. R: recombinant. If LOD never rises above 3, one assumes that there is not sufficient evidence to make a conclusion about linkage usually due to insufficient number of informative meioses in the pedigree , and if the LOD goes below 2, conventionally we presume that there is enough evidence to deem the two markers definitively unlinked at the corresponding theta.
Finally, LOD scores can be combined over multiple unrelated pedigrees to boost power to detect linkage, under the assumption that the same locus is causal in each family. The analysis can include variations on the chosen model. For example, penetrance may be age-dependent, such as in Huntingtons disease.
Alternatively, there may be sex-specific penetrances in the case of a disease that affects people differentially based on sex. Each factor of the proposed genetic model can therefore contribute to the final designation of recombination status; however, the investigator must specify parameters of the model and loss in power is correlated to the degree to which the chosen model is inappropriate.
Alternative methodologies for linkage analysis can be used, such as non-parametric linkage analysis and multipoint linkage analysis. Non-parametric linkage analysis also known as model-free does not assume a specific genetic model for disease. One such approach, known as the affected sib pair method, tests for excess sharing of marker alleles identical by.
Multipoint linkage analysis tests aim to determine the IBD states for all pairs of individuals across a pedigree by leveraging information from multiple markers. With the identification of these IBD states, a more formal test of excess IBD sharing based on sharing disease phenotype can be conducted.
These approaches are described in detail elsewhere . A number of disease and study characteristics that aid in successful linkage mapping included highly penetrant causal genetic variants, relatively little environmental influence on the phenotype, large families and minimal locus heterogeneity. Linkage has therefore been very successful in mapping Mendelian diseases although some loci, if of high enough effect, can be mapped via linkage in genetically complex diseases. The majority of sleep-related phenotypes that have been successfully mapped via linkage involve familial sleep disorders.
Two categories of disorders are described here: primary disorders of sleep, including narcolepsycataplexy as well as familial advanced sleep phase syndrome, and disorders with sleep disturbances, including RLS. Universally, success met by investigators using linkage usually involved large families with multiple affected members. However, further linkage attempts in narcolepsy have not been successful. Likely because of the heterogeneous nature of its genetic architecture, narcolepsy has seen more success with association testing which will be discussed in further detail later in this chapter.
Narcolepsy is a disorder characterized by excessive daytime sleepiness and abnormal rapid eye movement REM manifestations including sleep paralysis, hypnagogic hallucinations and sleep-onset REM periods . The strict definition of narcolepsy is narcolepsycataplexy, which refers to individuals whose narcoleptic symptoms include cataplexy, a sudden and transient loss of muscle tone. Familial forms of narcolepsy that follow a clear inheritance pattern are very rare. Nonetheless, in Dauvilliers et al. They successfully mapped a susceptibility locus to chromosome 21q LOD 4.
RLS is a disorder characterized by parasethesias described as an irresistible urge to move ones legs . These urges often occur at rest and cause sleep disturbance, leading to chronic sleep deprivation. RLS is fairly common, with the prevalence estimated to be between 1. The mode of inheritance is debated in the literature, with some families showing autosomal recessive and autosomal dominant inheritance patterns and other families exhibiting more complex inheritance pattern with environmental influence .
Nonetheless, linkage studies have been successful throughout the last decade. The first locus to be documented was on 12q in a French-Canadian sample under a recessive model maximum LOD score 3. This finding was then followed by the identification of four additional linkage peaks at 14q in an Italian family , 9p  and 2q  see Table 1. Although some. The initial study that showed it to be inherited in an autosomal dominant fashion was a linkage study on a large family with over 20 affected individuals . One linkage peak was identified on chromosome 2q LOD 5. Following this study, Xu et al.
Although the latter study did not use linkage but rather candidate gene sequencing, they provided strong evidence for the importance of this mutation by showing perfect segregation with disease and showing its absence in controls. Desautels et al. Association identified through candidate gene analysis. Association not yet replicated. Although loci are often named by the closest gene to the lead SNP, one cannot assume that the named gene is causal until definitive proof is provided. Here, we arbitrarily report the odds ratio from the study with the strongest association.
NR, not reported in the paper.
Later in the chapter, we discuss the use of association testing in RLS and success therein. Complex phenotypes Complex phenotypes are influenced by multiple genetic and non-genetic factors. As a result, these phenotypes cluster in families but do not follow any clear mode of inheritance.
Unlike the rare, highly penetrant mutations of Mendelian disease, the contributing genetic factors in complex traits are presumed to individually impart only a small risk for disease; the more risk factors an individual has, the more their risk for disease onset. Furthermore, environmental influence plays a large role in many complex phenotypes. These factors make linkage analysis ill-suited for discovering risk alleles, because any one allele will not segregate cleanly with disease.
Complex phenotypes are divided into two classes: continuous and categorical. A continuous trait is one that does not have a discrete scale i. Continuous traits are often studied in the general population to identify genes and pathways that play a role in dictating variation; however, selected samples such as extremes of the distribution are also used to boost power.
These groups may be ordered e. The most typical categorical traits studies are those of disease with affected and unaffected as categories. Although these diseases are usually studied using a case-control model, dichotomous traits are not unlike continuous traits in that they can be assumed to result from complex inheritance involving many genes, and the designation of one group over another is assumed to be based on an underlying liability distribution to which a threshold is applied see Figure 1.
Although not discussed in detail in this chapter, this idea is termed the Liability Threshold Model, with liability being ones. Heritability can thus be approximated by twice the difference between the phenotypic correlation of MZ twins and DZ twins. The other main approach historically taken is the comparison of parentoffspring pairs. In this case, heritability is the square of the correlation coefficient between mid-parent and offspring phenotypic scores. Understanding the heritability of a trait is a critical first step in setting expectations for results of genetic endeavors.
Methods for the estimation of heritability have been developed for multivariate traits as well as more complex family structures , but such methods are beyond the scope of this chapter. The liability threshold model suggests that for dichotomous traits influenced by many genetic factors each of small effect, an underlying distribution exists that depicts the distribution of liability toward a disease predisposition across a population. A threshold is applied to this distribution, above which individuals exhibit the trait cases and below which individuals do not controls.
The location of the threshold is determined by the disease prevalence that is, the area under the curve to the right of the threshold should be equal to the population prevalence of the disease. In practice, this distribution is not observed i. The study of complex traits involves analyzing variation of phenotype within a population of individuals, assumed to be a product of genetics and environment. While linkage analysis focused on specific crosses, complex trait analysis considers variation of a trait within a population and the degree to which genetic variation contributes to the phenotypic variation.
Inherently, therefore, the study of complex traits involves the study of populations, rather than families. For example, when studying duration of sleep, one would first observe the natural variation in sleep duration in a population and then try to estimate the degree to which that variation is due to genetics. The degree to which genetic factors contribute to phenotypic variance is termed heritability. Heritability is the proportion of phenotypic variance that is due to inherited factors influencing the trait. Typically these calculations are made by comparing close relatives.
The most frequent approach taken to estimate heritability is twin comparison: fraternal twins.
The Parasomnias and Other Sleep-Related Movement Disorders (Cambridge Medicine (Hardcover))
Methodology in studying complex phenotypes The large-scale success of linkage analysis for monogenic diseases naturally encouraged investigators to apply the same methodology to complex traits. However, it quickly became clear that this approach was underperforming in more common complex phenotypes despite strong heritability. As discussed, these traits are unlike Mendelian phenotypes in that they are highly polygenic, influenced by environment, and contributed to by genetic variants of individually very low effect.
These factors make linkage analysis much less powerful in identifying the molecular genetic basis of these traits. Association analysis, on the other hand, is an approach that tests for differences in allele frequencies that correlate with phenotype. The core test is to compare the allele frequencies between cases and controls or test for mean differences in a continuous trait conditional on genotype.
Compared to linkage analysis, this approach is better powered to detect such associations of weak effect as it is a test of means, rather than variances, and that large cohorts of unrelated individuals can be tested jointly, rather than focusing simply on large affected families. The first attempts at association testing were based on candidate gene studies where investigators compared variants between cases and controls within a single gene of interest, such as the mapping of the human leukocyte antigen locus to autoimmune disease  and the association between variants at APOE and Alzheimers disease .
In sleep, many candidate circadian genes thought to be involved in. Candidate gene association studies have been met with variable success. In the early s, investigators sought a more unbiased survey of the entire genome. This approach is known as a genome-wide association study GWAS , now the gold standard methodology for identifying genetic associations to complex traits. Much of the focus for GWAS in complex traits over the past decade has been on common variation.
In the European population, there are 10 million sites in the genome at which individuals genotypes vary . Although genetic variation across the allele frequency spectrum likely contributes to complex traits and disease, theoretical arguments grounded in population genetics predict the genetic architecture of common disease to be at least in part due to common variation hence the so-called common-variant common-disease hypothesis. This argument includes the typical late onset of many common diseases that precludes causal alleles from strong natural selection, causal alleles being neutral in the past and only now having an effect due to recently introduced changes in living situations, recent population expansion allowing detrimental alleles to rise in frequency and phenomena such as heterozygote-advantage .
Furthermore, from a practical standpoint, this type of variation is extremely convenient because there is widespread correlation among common variants due to their being relatively old in evolutionary history and recombination happening mostly at hotspots. This means that only a subset of variants needs to be genotyped in a given study to serve as a proxy for nearby DNA, and microarray technology can easily allow for cheap, direct genotyping of these hundreds of thousands and now a million SNPs . This chapter will therefore focus on common variation. The goal of GWAS is to test variants throughout the entire genome for a difference in the number of people carrying the minor or major allele between cases and controls or as a function of the trait.
A simplified approach to GWAS is described here and involves five steps: sample collection, genotyping, association testing, population stratification, and replication. Sample collection. The first step in a GWAS is collecting samples, with emphasis on power and appropriate matching of cases and controls. As power to detect association is in part a function of the number of samples, one can roughly predict the approximate number of samples needed to detect associations at different effect sizes i.
Typically, the associations we are well powered to catch first are those at relatively common SNPs and of high effect size. Power goes down as effect size and minor allele frequency go down, necessitating larger and larger sample sizes; for this reason, a number of researchers have forged international collaborations to carry out metaanalyses, where cohorts are combined to yield large sample sizes on the order of 10s to s of thousands of individuals. In addition to power considerations, cases and controls need to be well matched on any variable that could confound the analysis.
First and foremost, cases and controls should be of the same ethnic background so as to minimize the effects of population stratification see discussion below. Beyond this, investigators can try and match on any other variable that may confound the analysis for example, limiting enrollment to individuals of a certain age range. Finally, one should take great care to randomize samples with respect to the timing of their being assayed; separating cases and controls on the time frame in which they were genotyped as well as any platform differences can lead to very large batch effects.
With such large sample sizes, genotyping needs to be technologically easy and. There are many companies that offer cheap, high-throughput genotyping arrays . These technologies have grown from earliest implementations of , markers to assays with 2. Technologically, these arrays typically require DNA amplification followed by hybridization to the array with a set of probes that correspond to loci throughout the genome. Allelic discrimination is usually accomplished either through allele-specific primers or through allele-specific probes.
Measuring the strength of a platform includes the accuracy how well it agrees with the known genotype , call-rate how often it can confidently call a genotype , reproducibility how concordant the results are across replicates , how well it covers the genome as well as how easily it is multiplexed i. Although this chapter mainly focuses on SNP assays, for the past 5 years investigators have been looking beyond SNPs and toward submicroscopic structural variation in the genome known as copy number variation CNV. These types of variants are typically assayed via array comparative genomic hybridization aCGH as well as creative uses of the standard SNP chips to estimate CNV status.
For simplicity we will focus mainly on SNP analysis in this chapter, but similar principles of association testing apply when looking at CNVs. Quality control. Individuals are filtered on gender checks i. Ultimately, QC reduces the chance that an association is discovered due to an exogenous effect unrelated to the phenotype being studied, and it cleans up the data to maximize power to discover true associations .
Association testing. Once genotypes are collected across samples and QC is completed, each SNP is tested for association to disease. This can be accomplished using a simple chi-squared test or logistic regression if handling a case-control sample. Care should be taken to control for any confounding variables in the analysis by adding them as covariates. For example, if studying sleepiness as a quantitative score, age, sex and BMI are typically used as covariates in the analysis as differences in the trait attributable to the covariates can lead to association entirely explained by a the covariate .
When looking genome-wide, around 1 million tests are performed in any analysis, and therefore correction for multiple testing is critical. Using the accepted association threshold of 0. Population stratification. Population stratification refers to the presence of any systemic differences in allele frequencies between cases and controls or across individuals according to quantitative trait value that are related to ancestry and not to the phenotype being studied.
Two approaches to control for this are genomic control and principal components analysis. To correct using genomic control, one can divide all association 2 values by. Genomic control has also proven to be a useful metric for the identification of potential bias in the distribution of test statistics. Here, PCA is applied to genotype data to infer continuous axes of genetic variation and the first axis typically describes population substructure.
The principal components attributable to population stratification can then be used as covariates in the association test to remove association due to ancestry. More recently, methods to handle other sources of structure in the data beyond population substructure i. These methods involve the use of mixed models, and software tools are available for fast implementation .
Although much attention is given to controlling for technical artifacts, covariates and ancestry, unforeseen forces can lead to Type I errors. Therefore, an association that replicates in a set of independent individuals and ideally on a different genotyping platform is considered bona fide. Often, investigators will look to replicate in individuals of different ethnic backgrounds. When replicating, one need only take the top results and genotype them, Bonferonni correcting for the number of variants tested.
The final p-values reported are typically the combined exploration and replication statistics. Genome-wide association studies in sleep and sleep-related disorders GWAS has recently been employed in studying sleep phenotypes. Two general categories have been studied: diseases that manifest with sleep disturbance discrete traits and quantitative characteristics of sleep continuous traits. These are studied using a case-control setup. Quantitative characteristics of sleep include sleep quality, sleep pattern, sleep timing and EEG profiles, which are studied using linear regression.
Sleep diseases as dichotomous traits Early association results in narcolepsycataplexy were discovered through candidate gene association tests. In Honda et al. The large percentage of unaffected individuals carrying the variant suggests that other genetic contributing factors are likely to be involved in narcolepsycataplexy. In , Hallmayer et al. In , Miyagawa et al. These genetic findings implicate an autoimmune process that is responsible for the destruction of neurons.
A fourth locus was discovered in on chromosome 9p containing the gene PTPRD that was then replicated in an independent cohort . These genes demonstrate a potential role of developmental regulatory factors in RLS that affect spinal cord regulation of sensory perception and locomotor pattern generation, because many of these genes are known to play a role in the developing spinal cord . In general, however, for narcolepsy and RLS these findings only explain a small percentage of the genetic variation.
Other heritable disorders that manifest with sleep disturbances include bipolar disorder and ADHD. To date, ADHD meta-analysis has yet to identify any significant loci . Diseases such as insomnia and hypersomnia, dissociated REM sleep such as sleep paralysis and hypnagogic hallucinations as well as obstructive sleep apnea have been found to cluster in families but have yet to yield any genetic associations [57,60,61]. Phenotypic variability is one possible explanation. For example, many genetically driven risk factors can lead to separate forms of sleep apnea that break the disorder into different categories, such as upperairway anatomic features, variable lung capacity, and obesity .
These factors are likely phenotypes. Sleep as a quantitative trait A large part of the field has focused on sleep as a quantitative trait and found many components to be heritable. Consequently, a handful of cohorts have been gathered for GWAS to look for alleles throughout the genome that correlate with one of these traits. To date, only one such study has been successful. Gottlieb et al. This association is on chromosome 5 near the gene PDE4D, although it has yet to be replicated.
Other results include a tentative association between EEG profiles and PER3 via candidate gene association testing , but this was not verified by other groups . The current lack of results to quantitative sleep traits likely reflects the difficulty of articulating these phenotypes.follow url
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology
They are difficult to define and to measure even EEG, which is perhaps the most objective, yields a very noisy trace that requires significant transformation to be a stable trait to study. With larger sample sizes and better articulated measurements, the striking heritability of these traits may one day yield exciting results as to the genes that may help define their variance in humans.
Botstein D, Risch N. Discovering genotypes underlying human phenotypes: past successes for Mendelian disease, future approaches for complex disease. Nat Genet. Geyer H. Uber den Schlaf von Zwillingen. Conclusion So far, the genetics of sleep phenotypes remain largely undiscovered. The first promising steps have been taken to understand the biological basis of these traits. Linkage has been forged in many large families to yield a handful of loci for a subset of diseases in sleep. More recently, investigators have begun to organize large cohorts of individuals with which to conduct genomewide association studies.
Although still in the early stages, these studies have shed light on surprising pathways that may be relevant in the control of sleep, including immune-mediated processes and developmental regulatory pathways. In general, however, much more remains to be discovered and most of the observed heritability of sleep disorders and traits is yet unexplained. Based on early success, future efforts in GWAS with larger sample sizes are likely to be fruitful.
Owing to recent technological advances, genome sequencing in medical genetics to discover diseaserelevant variants is now a reality . Despite significant analytic challenges to overcome in studying such variation, fully sequencing exomes and genomes is gradually becoming technologically and economically feasible. The future of genetics of sleep and sleep-related disorders will likely include more rare variation discovered through sequencing.
The tools of linkage, genome-wide association, and eventually sequencing promise to yield new insights into the basis of sleep traits. These are early days for the field of sleep genetics, but leveraging international collaboration to expand sample sizes and rapidly advancing technology to explore genome sequencing will shed light on yet undiscovered genetic factors underlying the heritability of sleep.
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