2015
DOI: 10.1016/bs.mie.2014.10.046
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Genetics of Human Sleep Behavioral Phenotypes

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Cited by 25 publications
(27 citation statements)
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“…Moreover, the phase of entrainment of humans is not a stable trait; it rather depends on many internal and external factors. It has a genetic basis (11), is age-and sex-dependent (10), and depends on light exposure level (12,13) and on the season (12,14) as well as on the location within the time zone (10).…”
Section: Resultsmentioning
confidence: 99%
“…Moreover, the phase of entrainment of humans is not a stable trait; it rather depends on many internal and external factors. It has a genetic basis (11), is age-and sex-dependent (10), and depends on light exposure level (12,13) and on the season (12,14) as well as on the location within the time zone (10).…”
Section: Resultsmentioning
confidence: 99%
“…Inherent genetic and biological tendencies are subject to external factors, which can be confounding. Some of these confounds include considerable variability in assessed phenotypes, overlapping psychological pathology, environmental factors like medication or other drugs, social and cultural influences, and school/work pressures, all of which make objective assessments of sleep in large populations difficult to obtain (Chong et al, 2015;Hsu et al, 2015;Veatch, Keenan, Gehrman, Malow, & Pack, 2017).…”
Section: Methodsmentioning
confidence: 99%
“…The first step in studying the genetics of these traits is to identify affected and unaffected individuals in families through rigorous phenotyping. Careful attention must be paid to extreme, yet normal (nongenetic), variation in sleep onset and duration, adherence to established thresholds (defined to select for extreme phenotypes), and environmental or psychological influences that interfere with the phenotype of interest (Hsu et al, 2015). Subjects are recruited either by referral from clinicians or by self-referral.…”
Section: Methodsmentioning
confidence: 99%
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“…Understanding the cause for extreme points or statistical outliers in a given dataset may reveal useful information about normal and abnormal physiology. For example, genetic variants in sleep timing were identified in patients with Advanced Sleep Wake Phase Disorder (ASWPD) and Delayed Sleep Wake Phase Disorder (DSWPD), in which there can be large differences between social and circadian time 19,20 …”
Section: Moving To Applicationsmentioning
confidence: 99%