Uncovering the genetic landscape for multiple sleep-wake traits

Despite decades of research in defining sleep-wake properties in mammals, little is known about the nature or identity of genes that regulate sleep, a fundamental behaviour that in humans occupies about one-third of the entire lifespan. While genome-wide association studies in humans and quantitativ...

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Veröffentlicht in:PloS one 2009-04, Vol.4 (4), p.e5161-e5161
Hauptverfasser: Winrow, Christopher J, Williams, Deanna L, Kasarskis, Andrew, Millstein, Joshua, Laposky, Aaron D, Yang, He S, Mrazek, Karrie, Zhou, Lili, Owens, Joseph R, Radzicki, Daniel, Preuss, Fabian, Schadt, Eric E, Shimomura, Kazuhiro, Vitaterna, Martha H, Zhang, Chunsheng, Koblan, Kenneth S, Renger, John J, Turek, Fred W
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container_issue 4
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container_title PloS one
container_volume 4
creator Winrow, Christopher J
Williams, Deanna L
Kasarskis, Andrew
Millstein, Joshua
Laposky, Aaron D
Yang, He S
Mrazek, Karrie
Zhou, Lili
Owens, Joseph R
Radzicki, Daniel
Preuss, Fabian
Schadt, Eric E
Shimomura, Kazuhiro
Vitaterna, Martha H
Zhang, Chunsheng
Koblan, Kenneth S
Renger, John J
Turek, Fred W
description Despite decades of research in defining sleep-wake properties in mammals, little is known about the nature or identity of genes that regulate sleep, a fundamental behaviour that in humans occupies about one-third of the entire lifespan. While genome-wide association studies in humans and quantitative trait loci (QTL) analyses in mice have identified candidate genes for an increasing number of complex traits and genetic diseases, the resources and time-consuming process necessary for obtaining detailed quantitative data have made sleep seemingly intractable to similar large-scale genomic approaches. Here we describe analysis of 20 sleep-wake traits from 269 mice from a genetically segregating population that reveals 52 significant QTL representing a minimum of 20 genomic loci. While many (28) QTL affected a particular sleep-wake trait (e.g., amount of wake) across the full 24-hr day, other loci only affected a trait in the light or dark period while some loci had opposite effects on the trait during the light vs. dark. Analysis of a dataset for multiple sleep-wake traits led to previously undetected interactions (including the differential genetic control of number and duration of REM bouts), as well as possible shared genetic regulatory mechanisms for seemingly different unrelated sleep-wake traits (e.g., number of arousals and REM latency). Construction of a Bayesian network for sleep-wake traits and loci led to the identification of sub-networks of linkage not detectable in smaller data sets or limited single-trait analyses. For example, the network analyses revealed a novel chain of causal relationships between the chromosome 17@29cM QTL, total amount of wake, and duration of wake bouts in both light and dark periods that implies a mechanism whereby overall sleep need, mediated by this locus, in turn determines the length of each wake bout. Taken together, the present results reveal a complex genetic landscape underlying multiple sleep-wake traits and emphasize the need for a systems biology approach for elucidating the full extent of the genetic regulatory mechanisms of this complex and universal behavior.
doi_str_mv 10.1371/journal.pone.0005161
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Analysis of a dataset for multiple sleep-wake traits led to previously undetected interactions (including the differential genetic control of number and duration of REM bouts), as well as possible shared genetic regulatory mechanisms for seemingly different unrelated sleep-wake traits (e.g., number of arousals and REM latency). Construction of a Bayesian network for sleep-wake traits and loci led to the identification of sub-networks of linkage not detectable in smaller data sets or limited single-trait analyses. For example, the network analyses revealed a novel chain of causal relationships between the chromosome 17@29cM QTL, total amount of wake, and duration of wake bouts in both light and dark periods that implies a mechanism whereby overall sleep need, mediated by this locus, in turn determines the length of each wake bout. 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While genome-wide association studies in humans and quantitative trait loci (QTL) analyses in mice have identified candidate genes for an increasing number of complex traits and genetic diseases, the resources and time-consuming process necessary for obtaining detailed quantitative data have made sleep seemingly intractable to similar large-scale genomic approaches. Here we describe analysis of 20 sleep-wake traits from 269 mice from a genetically segregating population that reveals 52 significant QTL representing a minimum of 20 genomic loci. While many (28) QTL affected a particular sleep-wake trait (e.g., amount of wake) across the full 24-hr day, other loci only affected a trait in the light or dark period while some loci had opposite effects on the trait during the light vs. dark. 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(biology)</subject><subject>Rodents</subject><subject>Sleep</subject><subject>Sleep - genetics</subject><subject>Sleep and wakefulness</subject><subject>Sleep, REM - genetics</subject><subject>Time 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Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Winrow, Christopher J</au><au>Williams, Deanna L</au><au>Kasarskis, Andrew</au><au>Millstein, Joshua</au><au>Laposky, Aaron D</au><au>Yang, He S</au><au>Mrazek, Karrie</au><au>Zhou, Lili</au><au>Owens, Joseph R</au><au>Radzicki, Daniel</au><au>Preuss, Fabian</au><au>Schadt, Eric E</au><au>Shimomura, Kazuhiro</au><au>Vitaterna, Martha H</au><au>Zhang, Chunsheng</au><au>Koblan, Kenneth S</au><au>Renger, John J</au><au>Turek, Fred W</au><au>Zaas, Aimee K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Uncovering the genetic landscape for multiple sleep-wake traits</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2009-04-10</date><risdate>2009</risdate><volume>4</volume><issue>4</issue><spage>e5161</spage><epage>e5161</epage><pages>e5161-e5161</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Despite decades of research in defining sleep-wake properties in mammals, little is known about the nature or identity of genes that regulate sleep, a fundamental behaviour that in humans occupies about one-third of the entire lifespan. While genome-wide association studies in humans and quantitative trait loci (QTL) analyses in mice have identified candidate genes for an increasing number of complex traits and genetic diseases, the resources and time-consuming process necessary for obtaining detailed quantitative data have made sleep seemingly intractable to similar large-scale genomic approaches. Here we describe analysis of 20 sleep-wake traits from 269 mice from a genetically segregating population that reveals 52 significant QTL representing a minimum of 20 genomic loci. While many (28) QTL affected a particular sleep-wake trait (e.g., amount of wake) across the full 24-hr day, other loci only affected a trait in the light or dark period while some loci had opposite effects on the trait during the light vs. dark. Analysis of a dataset for multiple sleep-wake traits led to previously undetected interactions (including the differential genetic control of number and duration of REM bouts), as well as possible shared genetic regulatory mechanisms for seemingly different unrelated sleep-wake traits (e.g., number of arousals and REM latency). Construction of a Bayesian network for sleep-wake traits and loci led to the identification of sub-networks of linkage not detectable in smaller data sets or limited single-trait analyses. For example, the network analyses revealed a novel chain of causal relationships between the chromosome 17@29cM QTL, total amount of wake, and duration of wake bouts in both light and dark periods that implies a mechanism whereby overall sleep need, mediated by this locus, in turn determines the length of each wake bout. Taken together, the present results reveal a complex genetic landscape underlying multiple sleep-wake traits and emphasize the need for a systems biology approach for elucidating the full extent of the genetic regulatory mechanisms of this complex and universal behavior.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>19360106</pmid><doi>10.1371/journal.pone.0005161</doi><tpages>e5161</tpages><oa>free_for_read</oa></addata></record>
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identifier ISSN: 1932-6203
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1932-6203
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source MEDLINE; Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; PubMed Central; Free Full-Text Journals in Chemistry; EZB Electronic Journals Library
subjects Algorithms
Analysis
Animals
Bayes Theorem
Bayesian analysis
Biology
Chromosome 17
Chromosome Mapping
Chromosomes, Mammalian
Crosses, Genetic
Electroencephalography
Electromyography
Emotional disorders
Eye movements
Factor Analysis, Statistical
Gene mapping
Genes
Genetic control
Genetic Linkage
Genetic research
Genetics
Genetics and Genomics/Animal Genetics
Genetics and Genomics/Complex Traits
Genomes
Genomics
Laboratories
Latency
Life span
Light emitting diodes
Lod Score
Male
Mammals
Mice
Mice, Inbred BALB C
Mice, Inbred Strains
Models, Genetic
Mutation
Neuroscience
Neuroscience/Behavioral Neuroscience
Polymorphism, Single Nucleotide
Population
Population genetics
Quantitative genetics
Quantitative trait loci
Quantitative Trait Loci - genetics
Reaction Time
Regulatory mechanisms (biology)
Rodents
Sleep
Sleep - genetics
Sleep and wakefulness
Sleep, REM - genetics
Time Factors
Trends
title Uncovering the genetic landscape for multiple sleep-wake traits
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