Limits...
Uncovering the genetic landscape for multiple sleep-wake traits.

Winrow CJ, Williams DL, Kasarskis A, Millstein J, Laposky AD, Yang HS, Mrazek K, Zhou L, Owens JR, Radzicki D, Preuss F, Schadt EE, Shimomura K, Vitaterna MH, Zhang C, Koblan KS, Renger JJ, Turek FW - PLoS ONE (2009)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Depression and Circadian Rhythms, Merck Research Laboratories, West Point, Pennsylvania, USA.

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.

Show MeSH

Related in: MedlinePlus

52 QTL for 20 sleep-wake traits.52 QTL shown by chromosome and cM positions that were identified for each of the sleep-wake traits listed in Table 1. The colored bands represent the position of the peak LOD score for each QTL and the fill of the bands denote the time period for the trait linkage as shown in the insert legend. Based on the factor analysis depicted in Table 1, the traits are grouped into 1 of 5 categories designated by the color of the bands as noted in the insert legend. The precise peak (in cM and Mb) and LOD score of the QTL, as well as the specific sleep-wake trait represented by each of the colored bands, are provided in Table 2. Further information on the size of the QTL are provided in Supporting Information Table S3.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2664962&req=5

pone-0005161-g001: 52 QTL for 20 sleep-wake traits.52 QTL shown by chromosome and cM positions that were identified for each of the sleep-wake traits listed in Table 1. The colored bands represent the position of the peak LOD score for each QTL and the fill of the bands denote the time period for the trait linkage as shown in the insert legend. Based on the factor analysis depicted in Table 1, the traits are grouped into 1 of 5 categories designated by the color of the bands as noted in the insert legend. The precise peak (in cM and Mb) and LOD score of the QTL, as well as the specific sleep-wake trait represented by each of the colored bands, are provided in Table 2. Further information on the size of the QTL are provided in Supporting Information Table S3.

Mentions: Linkage analysis was conducted with a set of 2,310 informative SNPs across the 19 autosomes from 269 N2 mice for which both complete and high quality genotype and sleep-wake phenotype data were obtained. The 48-hr sleep recording period was partitioned into two 24-hr periods and further into a light and dark phase yielding four recording time domains per animal during which each sleep trait was computed (see Supporting Information for further details on the statistical methods). Linkage analysis revealed a total of 52 significant QTL (comprising a minimum of 20 genomic loci) for the traits studied in this cross with LOD scores ranging from 2.5 to 7.6 (Fig. 1 and Table 2). Over half of these (28) reflected trait variation occurring across the full 24-hr day, indicating that much of the genetic control of sleep acts consistently across the light and dark periods. However, 12 additional QTL, termed “mixed-effect QTL”, reflected trait variation across the full 24-hr period where the direction and/or magnitude of the effect of the locus on the trait is statistically different between the light and dark periods. This indicates that the genotype at a locus can have the opposite or a quantifiably different effect on the same trait during the light versus the dark phase. In some cases, this effect was quite dramatic, as was observed for the wake min QTL at Q17@29 (LOD 7.6) where the estimated effect of the BALB genotype at this locus was 8.1 min in the light but −24.7 min in the dark (Supporting Information Table S3). Finally, some QTL were only detected in the dark (N = 9) or in the light (N = 3), indicating that the genotype at some loci only influenced sleep-wake traits during certain periods of the 24-hr day.


Uncovering the genetic landscape for multiple sleep-wake traits.

Winrow CJ, Williams DL, Kasarskis A, Millstein J, Laposky AD, Yang HS, Mrazek K, Zhou L, Owens JR, Radzicki D, Preuss F, Schadt EE, Shimomura K, Vitaterna MH, Zhang C, Koblan KS, Renger JJ, Turek FW - PLoS ONE (2009)

52 QTL for 20 sleep-wake traits.52 QTL shown by chromosome and cM positions that were identified for each of the sleep-wake traits listed in Table 1. The colored bands represent the position of the peak LOD score for each QTL and the fill of the bands denote the time period for the trait linkage as shown in the insert legend. Based on the factor analysis depicted in Table 1, the traits are grouped into 1 of 5 categories designated by the color of the bands as noted in the insert legend. The precise peak (in cM and Mb) and LOD score of the QTL, as well as the specific sleep-wake trait represented by each of the colored bands, are provided in Table 2. Further information on the size of the QTL are provided in Supporting Information Table S3.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2664962&req=5

pone-0005161-g001: 52 QTL for 20 sleep-wake traits.52 QTL shown by chromosome and cM positions that were identified for each of the sleep-wake traits listed in Table 1. The colored bands represent the position of the peak LOD score for each QTL and the fill of the bands denote the time period for the trait linkage as shown in the insert legend. Based on the factor analysis depicted in Table 1, the traits are grouped into 1 of 5 categories designated by the color of the bands as noted in the insert legend. The precise peak (in cM and Mb) and LOD score of the QTL, as well as the specific sleep-wake trait represented by each of the colored bands, are provided in Table 2. Further information on the size of the QTL are provided in Supporting Information Table S3.
Mentions: Linkage analysis was conducted with a set of 2,310 informative SNPs across the 19 autosomes from 269 N2 mice for which both complete and high quality genotype and sleep-wake phenotype data were obtained. The 48-hr sleep recording period was partitioned into two 24-hr periods and further into a light and dark phase yielding four recording time domains per animal during which each sleep trait was computed (see Supporting Information for further details on the statistical methods). Linkage analysis revealed a total of 52 significant QTL (comprising a minimum of 20 genomic loci) for the traits studied in this cross with LOD scores ranging from 2.5 to 7.6 (Fig. 1 and Table 2). Over half of these (28) reflected trait variation occurring across the full 24-hr day, indicating that much of the genetic control of sleep acts consistently across the light and dark periods. However, 12 additional QTL, termed “mixed-effect QTL”, reflected trait variation across the full 24-hr period where the direction and/or magnitude of the effect of the locus on the trait is statistically different between the light and dark periods. This indicates that the genotype at a locus can have the opposite or a quantifiably different effect on the same trait during the light versus the dark phase. In some cases, this effect was quite dramatic, as was observed for the wake min QTL at Q17@29 (LOD 7.6) where the estimated effect of the BALB genotype at this locus was 8.1 min in the light but −24.7 min in the dark (Supporting Information Table S3). Finally, some QTL were only detected in the dark (N = 9) or in the light (N = 3), indicating that the genotype at some loci only influenced sleep-wake traits during certain periods of the 24-hr day.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Depression and Circadian Rhythms, Merck Research Laboratories, West Point, Pennsylvania, USA.

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.

Show MeSH
Related in: MedlinePlus