Invited talk at the ICAMPAM 2019 conference, Maastricht

1. An Open Heuristic
method that helped
to gain New Insights
in human Sleep
Dr. Vincent van Hees, ICAMPAM, 28th of June 2019

2. Sleep patterns are associated with
metabolic disorders

3. Do these associations reflect causal
relationships?
 Key components to study causal relationships:
 Data on genetic variants
 Data on health
 Data on sleep behavior => Traditionally based on error prone self-reported sleep
 UK Biobank (N=103,000) raw data accelerometer offers an unparalleled opportunity to
investigate sleep
Wrist worn Axivity AX3 device
z
x
y
Tri-axial device

4. Objectives
1. Develop a method to detect sleep patterns
2. Find genetic variants associated with detected sleep patterns
3. Perform causality analyses

5. Our heuristic ‘sleep’ detection
 Better name: Sustained inactivity bout
 Interpretable as lack of posture change and lack
of movement, regardless of agreement with
neurological sleep
 Angle is a more visual concept than magnitude
of acceleration
 Embedded in Open Source R package GGIR
 https://github.com/wadpac/GGIR
 https://CRAN.R-project.org/package=GGIR
[van Hees et al. PLoSONE 2015, doi: 10.1371/journal.pone.0142533]
< 5º
> 5 minutes

6. Sleep Period Time-window
= sustained inactivity bouts
𝑥1 𝑥2 𝑥3 𝑥4-6 𝑥7 𝑥8
van Hees et al. PLoS One 2015 & van Hees et al. Scientific Reports 2018
Separating daytime inactivity/sleep

7. • No convincing gold standard exists for free-living conditions
• Heuristic method, ‘trained’ with unlabeled data from 20 random individuals.
Change in wrist angle over time invariant to sensor orientation
Threshold per
individual
My assumptions about what a sleep period is
[van Hees et al. Scientific Reports 2018, doi:10.1038/s41598-018-31266-z]
Sleep Period Time (SPT) Window

8. Method Evaluation
Sleep diary Polysomnography, one night
Older adults
(N=3750)
Sleep clinic
patients (N=28)
Healthy good
sleepers (N=22)
Mean Absolute Error
in sleep onset and waking time
40 minutes 71 minutes 38 minutes
Bias in estimated
Sleep Duration
- +30 minutes
(P=0.04, DF=27)
-6 minutes
(P=0.56, DF=21)
[van Hees et al. Scientific Reports 2018, doi:10.1038/s41598-018-31266-z]

9. SPT-window
8 measures of sleep / circadian rhythm
= sustained inactivity bout
𝑥1 𝑥2 𝑥3 𝑥4-6 𝑥7 𝑥8
Sleep duration = σ𝒊=𝟏
𝒏
𝒙𝒊 Sleep efficiency =
Sleep duration
SPT-window size
Number of sustained inactivity bouts
Sleep
midpoint
L5 Timing
(Duration) (Quality) (Timing)
Jones, van Hees, et al., Nature Communications 2019

10. L5 timeSleep midpoint
Sleep duration
variability
Sleep efficiency
Diurnal
inactivity
M10 time
No. of sleep
episodes
HTR1F
APOE
GPR139
KCNQ5
KCNH5
MEIS1
PAX8
ANK1
ALG10B
BTBD9
RGS16
MEIS1
MEIS1
PAX8
KCNH5
BTBD9
ALG10B
HTR1A
RELN
Up to 85,670 participants
~12 million HRC-imputed
genetic variants
hG
2: heritability estimate
hG
2=0.10 hG
2=0.09 hG
2=0.12
hG
2=0.19 hG
2=0.15
hG
2=0.03
hG
2=0.13 hG
2=0.22
Sleep duration
Jones, van Hees, et al., Nature Communications 2019
Replication in
6000 individuals
from CoLaus,
Whitehall and
Rotterdam
studies
Through genome-wide association analyses (GWAS)
we identify 47 distinct loci associated with our sleep
measures (P<5x10-8)

11. Insomnia causes an increased risk of
coronary artery disease
2 Sample MR IVW_P = 2x10-4
1 Sample MR IVW_P = 1x10-12
Lane et al. Nature Genetics 2019,
doi: 10.1038/s41588-019-0361-7

12. Being an early bird reduces risk of
schizophrenia and depression
Jones et al. Nature
Communications 2019, doi:
10.1038/s41467-018-08259-7 SNP effect on morningness
SNPeffectonschizophrenia(PGC)
2 Sample MR IVW_P = 1x10-4

13. Discussion points
 Meaning daytime sustained inactivity bouts => naps?
 If feasible, use longer measurements in future studies: 7 days is just a snapshot in time.
[*Wilkinson et al. Scientific Data 2016

14. Key underlying developments
1. Since 2007 wrist-worn Raw Data Accelerometry was advocated and explored by various
people in the field.s
2. Heuristic / interpretable sapproach to data analysis
3. Open Source Software, e.g. R package GGIR, 2012- present

15. Open Source Software (OSS) – Why?
 Reproducibility
 Software quality increases with more users and contributors
 Effective use of (public) research funding
 Usage not tight to developer: GGIR used in 100+ publications, I am co-author on ≈10
Making software Open Source can be embarrassing,
but not doing it is even more embarrassing!
Scientific publications become anecdotes if data and software are not available

16. Open Source Software – How & Who?
How?
 Put your source code online as early as possible in the development, e.g. via GitHub
 Use version control, e.g. git, to enable going back to specific time points
 Include an Open Source License file => Otherwise it is still copyright protected
 Document your code
Who should feel responsible for sustainability?
 Community: Developer + End-users

17. Also very important …
 Make research data more Findable, Accessible, Interoperable, and
Reproducible (FAIR)*
 More recognition for Research Software Engineers:
 We are an integral part of scientific advancement
 Initiatives world-wide: https://rse.ac.uk/, http://nl-rse.org/, https://us-rse.org/,
https://www.de-rse.org, http://nordic-rse.org/, https://www.software.ac.uk
[*Wilkinson et al. Scientific Data 2016

18. Take home messages
 Sleep measures:
 can be extracted from wrist-worn raw data accelerometry
 can be interpretable
 have provided new insights in the relationship between sleep and health
 Open source software and the people who develop it are critical for the
advancement of the physical behavior research field

19.  University of Exeter Medical School,
UK
 Andrew Wood
 Robin Beaumont
 Jessica Tyrrell
 Michael Weedon
 Timothy Frayling
 Melvyn Hillsdon
 Samuel Jones
 Genetics of Complex Traits team
 UCL, UK
 Jorgen Engmann
 Séverine Sabia
 University of Pennsylvania, USA
 Philip Gehrman
 Diego Mazzotti
 Broad Institute, USA
 Jacqueline Lane
 Hassan Dashti
 Richa Saxena
 Harvard TH Chan School of Medicine,
USA
 Henning Tiemeier
 Erasmus MC, Netherlands
 Ashley van der Spek
 Desana Kocevska
 Annemarie Luik
 Najaf Amin
 Lausanne University Hospital,
Switzerland
 Zoltán Kutalik
 Pedro Marques-Vidal
 University of Essex, UK
 Meena Kumari
 Chronogen Consortium:
 Martin Rutter
 Richa Saxena
 Debbie Lawlor
 Netherlands eScience Center
 Newcastle University
 Kirstie Anderson
 Sarah Charman
 UK Biobank staff and participants
Acknowledgements
Research funded by the Medical Research Council
David Hinds
Thanks to the 23andMe
research participants and
employees
© 23andMe, 2017