Abstract
Background: The prevalence of insomnia and hypersomnia in depressed individuals is substantially higher than that found in the general population. Unfortunately, these concurrent sleep problems can have profound effects on the disease course. Although the full biology of sleep remains to be elucidated, a recent genome-wide association (GWAS) of insomnia, and other sleep traits in over 1 million individuals was recently published and provides many promising hits for genetics of insomnia in a population-based sample. Methods: Using data from the largest available GWAS of insomnia and other sleep traits, we sought to test if sleep variable PRS scores derived from population-based studies predicted sleep variables in samples of depressed cases [Psychiatric Genomics Consortium - Major Depressive Disorder subjects (PGC MDD)]. A leave-one-out analysis was performed to determine the effects that each individual study had on our results. Results: The only significant finding was for insomnia, where p-value threshold, p = 0.05 was associated with insomnia in our PGC MDD sample (R2 = 1.75−3, p = 0.006). Conclusion: Our results reveal that <1% of variance is explained by the variants that cover the two significant p-value thresholds, which is in line with the fact that depression and insomnia are both polygenic disorders. To the best of our knowledge, this is the first study to investigate genetic overlap between the general population and a depression sample for insomnia, which has important treatment implications, such as leading to novel drug targets in future research efforts.
Original language | English |
---|---|
Article number | 734077 |
Pages (from-to) | 734077 |
Journal | Frontiers in psychiatry |
Volume | 12 |
DOIs | |
Publication status | Published - 3 Dec 2021 |
Keywords
- hypersomnia
- insomnia
- major depressive disorder
- polygenic risk
- sleep
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In: Frontiers in psychiatry, Vol. 12, 734077, 03.12.2021, p. 734077.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Potential Genetic Overlap Between Insomnia and Sleep Symptoms in Major Depressive Disorder
T2 - A Polygenic Risk Score Analysis
AU - Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
AU - Melhuish Beaupre, Lindsay M.
AU - Tiwari, Arun K.
AU - Gonçalves, Vanessa F.
AU - Zai, Clement C.
AU - Marshe, Victoria S.
AU - Lewis, Cathryn M.
AU - Martin, Nicholas G.
AU - McIntosh, Andrew M.
AU - Adams, Mark J.
AU - Baune, Bernhard T.
AU - Levinson, Doug F.
AU - Boomsma, Dorret I.
AU - Penninx, Brenda W.J.H.
AU - Breen, Gerome
AU - Hamilton, Steve
AU - Awasthi, Swapnil
AU - Ripke, Stephan
AU - Jones, Lisa
AU - Jones, Ian
AU - Byrne, Enda M.
AU - Hickie, Ian B.
AU - Potash, James P.
AU - Shi, Jianxin
AU - Weissman, Myrna M.
AU - Milaneschi, Yuri
AU - Shyn, Stanley I.
AU - Geus, Eco J.C.de
AU - Willemsen, Gonneke
AU - Brown, Gregory M.
AU - Kennedy, James L.
N1 - Funding Information: This project was supported by the Frederick Banting and Charles Best Canada Graduate Scholarship (LM), the Granville Nickerson Fellowship in Pharmacogenetics (AT), Brain and Behavior Research Foundation: NARSAD (AT), McLaughlin Centre Accelerator Grant (2019-2020) (AT), CAMH Foundation (VG), Brain and Behavior Research Foundation (NARSAD Young Investigator) (VG), McLaughlin Centre Accelerator Grant (VG), Larry and Judy Tanenbaum Foundation (JK). The NTR/NESDA dataset was funded by the: Netherlands Organization for Scientific Research (NWO) and MagW/ZonMW grants 904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004, 463-06-001, 451-04-034, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192, Center for Medical Systems Biology (CSMB, NWO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI–NL, 184.021.007); the European Science Foundation (ESF, EU/QLRT-2001-01254), the European Community’s Seventh Framework Program (FP7/2007-2013), ENGAGE (HEALTH-F4-2007-201413); the European Science Council (ERC Advanced, 230374), Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the Avera Institute, Sioux Falls, South Dakota (USA) and the National Institutes of Health (NIH, R01D0042157-01A, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995), the Netherlands Organization for Scientific Research (Geestkracht program grant 10-000-1002); the Center for Medical Systems Biology (CSMB, NWO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL), VU University’s Institutes for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam, University Medical Center Groningen, Leiden University Medical Center, National Institutes of Health (NIH, R01D0042157-01A, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995), Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. Computing was supported by BiG Grid, the Dutch e-Science Grid, which was financially supported by NWO. Funding Information: This project was supported by the Frederick Banting and Charles Best Canada Graduate Scholarship (LM), the Granville Nickerson Fellowship in Pharmacogenetics (AT), Brain and Behavior Research Foundation: NARSAD (AT), McLaughlin Centre Accelerator Grant (2019-2020) (AT), CAMH Foundation (VG), Brain and Behavior Research Foundation (NARSAD Young Investigator) (VG), McLaughlin Centre Accelerator Grant (VG), Larry and Judy Tanenbaum Foundation (JK). The NTR/NESDA dataset was funded by the: Netherlands Organization for Scientific Research (NWO) and MagW/ZonMW grants 904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004, 463-06-001, 451-04-034, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192, Center for Medical Systems Biology (CSMB, NWO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI?NL, 184.021.007); the European Science Foundation (ESF, EU/QLRT-2001-01254), the European Community's Seventh Framework Program (FP7/2007-2013), ENGAGE (HEALTH-F4-2007-201413); the European Science Council (ERC Advanced, 230374), Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the Avera Institute, Sioux Falls, South Dakota (USA) and the National Institutes of Health (NIH, R01D0042157-01A, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995), the Netherlands Organization for Scientific Research (Geestkracht program grant 10-000-1002); the Center for Medical Systems Biology (CSMB, NWO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL), VU University's Institutes for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam, University Medical Center Groningen, Leiden University Medical Center, National Institutes of Health (NIH, R01D0042157-01A, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995), Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. Computing was supported by BiG Grid, the Dutch e-Science Grid, which was financially supported by NWO. Publisher Copyright: Copyright © 2021 Melhuish Beaupre, Tiwari, Gonçalves, Zai, Marshe, Lewis, Martin, McIntosh, Adams, Baune, Levinson, Boomsma, Penninx, Breen, Hamilton, Awasthi, Ripke, Jones, Jones, Byrne, Hickie, Potash, Shi, Weissman, Milaneschi, Shyn, Geus, Willemsen, Brown, Kennedy and Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium.
PY - 2021/12/3
Y1 - 2021/12/3
N2 - Background: The prevalence of insomnia and hypersomnia in depressed individuals is substantially higher than that found in the general population. Unfortunately, these concurrent sleep problems can have profound effects on the disease course. Although the full biology of sleep remains to be elucidated, a recent genome-wide association (GWAS) of insomnia, and other sleep traits in over 1 million individuals was recently published and provides many promising hits for genetics of insomnia in a population-based sample. Methods: Using data from the largest available GWAS of insomnia and other sleep traits, we sought to test if sleep variable PRS scores derived from population-based studies predicted sleep variables in samples of depressed cases [Psychiatric Genomics Consortium - Major Depressive Disorder subjects (PGC MDD)]. A leave-one-out analysis was performed to determine the effects that each individual study had on our results. Results: The only significant finding was for insomnia, where p-value threshold, p = 0.05 was associated with insomnia in our PGC MDD sample (R2 = 1.75−3, p = 0.006). Conclusion: Our results reveal that <1% of variance is explained by the variants that cover the two significant p-value thresholds, which is in line with the fact that depression and insomnia are both polygenic disorders. To the best of our knowledge, this is the first study to investigate genetic overlap between the general population and a depression sample for insomnia, which has important treatment implications, such as leading to novel drug targets in future research efforts.
AB - Background: The prevalence of insomnia and hypersomnia in depressed individuals is substantially higher than that found in the general population. Unfortunately, these concurrent sleep problems can have profound effects on the disease course. Although the full biology of sleep remains to be elucidated, a recent genome-wide association (GWAS) of insomnia, and other sleep traits in over 1 million individuals was recently published and provides many promising hits for genetics of insomnia in a population-based sample. Methods: Using data from the largest available GWAS of insomnia and other sleep traits, we sought to test if sleep variable PRS scores derived from population-based studies predicted sleep variables in samples of depressed cases [Psychiatric Genomics Consortium - Major Depressive Disorder subjects (PGC MDD)]. A leave-one-out analysis was performed to determine the effects that each individual study had on our results. Results: The only significant finding was for insomnia, where p-value threshold, p = 0.05 was associated with insomnia in our PGC MDD sample (R2 = 1.75−3, p = 0.006). Conclusion: Our results reveal that <1% of variance is explained by the variants that cover the two significant p-value thresholds, which is in line with the fact that depression and insomnia are both polygenic disorders. To the best of our knowledge, this is the first study to investigate genetic overlap between the general population and a depression sample for insomnia, which has important treatment implications, such as leading to novel drug targets in future research efforts.
KW - hypersomnia
KW - insomnia
KW - major depressive disorder
KW - polygenic risk
KW - sleep
UR - http://www.scopus.com/inward/record.url?scp=85121440593&partnerID=8YFLogxK
U2 - https://doi.org/10.3389/fpsyt.2021.734077
DO - https://doi.org/10.3389/fpsyt.2021.734077
M3 - Article
C2 - 34925085
SN - 1664-0640
VL - 12
SP - 734077
JO - Frontiers in psychiatry
JF - Frontiers in psychiatry
M1 - 734077
ER -