Automated Feature Extraction from Population Wearable Device Data Identified Novel Loci Associated with Sleep and Circadian Rhythms

Mapping Intimacies ◽

10.1101/2020.03.31.017608 ◽

2020 ◽

Author(s):

Xinyue Li ◽

Hongyu Zhao

Keyword(s):

Feature Extraction ◽

Circadian Rhythms ◽

Large Scale ◽

Population Studies ◽

Wearable Devices ◽

Wearable Device ◽

Genome Wide Association Studies ◽

Physical Activity Monitoring ◽

Scale Population ◽

Shared Genetic

AbstractWearable devices have been increasingly used in research to provide continuous physical activity monitoring, but how to effectively extract features remains challenging for researchers. To analyze the generated actigraphy data in large-scale population studies, we developed computationally efficient methods to derive sleep and activity features through a Hidden Markov Model-based sleep/wake identification algorithm, and circadian rhythm features through a Penalized Multi-band Learning approach adapted from machine learning. Unsupervised feature extraction is useful when labeled data are unavailable, especially in large-scale population studies. We applied these two methods to the UK Biobank wearable device data and used the derived sleep and circadian features as phenotypes in genome-wide association studies. We identified 53 genetic loci with p<5×10-8 including genes known to be associated with sleep disorders and circadian rhythms as well as novel loci associated with Body Mass Index, mental diseases and neurological disorders, which suggest shared genetic factors of sleep and circadian rhythms with physical and mental health. Further cross-tissue enrichment analysis highlights the important role of the central nervous system and the shared genetic architecture with metabolism-related traits and the metabolic system. Our study demonstrates the effectiveness of our unsupervised methods for wearable device data when additional training data cannot be easily acquired, and our study further expands the application of wearable devices in population studies and genetic studies to provide novel biological insights.

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Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective

Handbook of Medical Image Computing and Computer Assisted Intervention ◽

10.1016/b978-0-12-816176-0.00021-1 ◽

2020 ◽

pp. 379-399 ◽

Cited By ~ 2

Author(s):

Guray Erus ◽

Mohamad Habes ◽

Christos Davatzikos

Keyword(s):

Machine Learning ◽

Large Scale ◽

Population Studies ◽

Imaging Biomarkers ◽

Scale Population

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Characterization of novel microsatellites and development of multiplex PCR for large-scale population studies in wild cherry, Prunus avium

Molecular Ecology Notes ◽

10.1111/j.1471-8286.2004.00673.x ◽

2004 ◽

Vol 4 (3) ◽

pp. 429-431 ◽

Cited By ~ 82

Author(s):

S. P. VAUGHAN ◽

K. RUSSELL

Keyword(s):

Multiplex Pcr ◽

Large Scale ◽

Prunus Avium ◽

Population Studies ◽

Wild Cherry ◽

Scale Population

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Apolipoprotein E genotyping by capillary electrophoretic analysis of restriction fragments

Clinical Chemistry ◽

10.1093/clinchem/43.8.1321 ◽

1997 ◽

Vol 43 (8) ◽

pp. 1321-1324 ◽

Cited By ~ 4

Author(s):

Gianluca De Bellis ◽

Giuliana Salani ◽

Silvia Panigone ◽

Ferruccio Betti ◽

Luigia Invernizzi ◽

...

Keyword(s):

Capillary Electrophoresis ◽

High Performance ◽

Large Scale ◽

Population Studies ◽

Electrophoretic Analysis ◽

Fragment Analysis ◽

Apo E ◽

Scale Population ◽

Capillary Electrophoretic ◽

High Performance Capillary Electrophoresis

Abstract We present the genotyping of apolipoprotein (apo) E by means of restriction fragment analysis of amplified genomic DNA by high-performance capillary electrophoresis and a replaceable non-gel-sieving matrix. This procedure streamlines the genotyping of apo E in large-scale population studies because of the automation and speed of capillary electrophoresis.

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Wearable-Based Mobile Health App in Gastric Cancer Patients for Postoperative Physical Activity Monitoring: Focus Group Study (Preprint)

10.2196/preprints.11989 ◽

2018 ◽

Author(s):

Jin-Ming Wu ◽

Te-Wei Ho ◽

Yao-Ting Chang ◽

ChungChieh Hsu ◽

Chia Jui Tsai ◽

...

Keyword(s):

Physical Activity ◽

Gastric Cancer ◽

Cancer Patients ◽

Mobile Health ◽

Early Discharge ◽

Wearable Devices ◽

Wearable Device ◽

Clinical Value ◽

Focus Group Study ◽

Physical Activity Monitoring

BACKGROUND Surgical cancer patients often have deteriorated physical activity (PA), which in turn, contributes to poor outcomes and early recurrence of cancer. Mobile health (mHealth) platforms are progressively used for monitoring clinical conditions in medical subjects. Despite prevalent enthusiasm for the use of mHealth, limited studies have applied these platforms to surgical patients who are in much need of care because of acutely significant loss of physical function during the postoperative period. OBJECTIVE The aim of our study was to determine the feasibility and clinical value of using 1 wearable device connected with the mHealth platform to record PA among patients with gastric cancer (GC) who had undergone gastrectomy. METHODS We enrolled surgical GC patients during their inpatient stay and trained them to use the app and wearable device, enabling them to automatically monitor their walking steps. The patients continued to transmit data until postoperative day 28. The primary aim of this study was to validate the feasibility of this system, which was defined as the proportion of participants using each element of the system (wearing the device and uploading step counts) for at least 70% of the 28-day study. “Definitely feasible,” “possibly feasible,” and “not feasible” were defined as ≥70%, 50%-69%, and <50% of participants meeting the criteria, respectively. Moreover, the secondary aim was to evaluate the clinical value of measuring walking steps by examining whether they were associated with early discharge (length of hospital stay <9 days). RESULTS We enrolled 43 GC inpatients for the analysis. The weekly submission rate at the first, second, third, and fourth week was 100%, 93%, 91%, and 86%, respectively. The overall daily submission rate was 95.5% (1150 days, with 43 subjects submitting data for 28 days). These data showed that this system met the definition of “definitely feasible.” Of the 54 missed transmission days, 6 occurred in week 2, 12 occurred in week 3, and 36 occurred in week 4. The primary reason for not sending data was that patients or caregivers forgot to charge the wearable devices (>90%). Furthermore, we used a multivariable-adjusted model to predict early discharge, which demonstrated that every 1000-step increment of walking on postoperative day 5 was associated with early discharge (odds ratio 2.72, 95% CI 1.17-6.32; P=.02). CONCLUSIONS Incorporating the use of mobile phone apps with wearable devices to record PA in patients of postoperative GC was feasible in patients undergoing gastrectomy in this study. With the support of the mHealth platform, this app offers seamless tracing of patients’ recovery with a little extra burden and turns subjective PA into an objective, measurable parameter.

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A large-scale genome-wide association analysis of lung function in the Chinese population identifies novel loci and highlights shared genetic etiology with obesity

European Respiratory Journal ◽

10.1183/13993003.00199-2021 ◽

2021 ◽

pp. 2100199

Author(s):

Zhaozhong Zhu ◽

Jiachen Li ◽

Jiahui Si ◽

Baoshan Ma ◽

Huwenbo Shi ◽

...

Keyword(s):

Lung Function ◽

Large Scale ◽

Causal Effect ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Etiology ◽

Trait Analysis ◽

Genome Wide ◽

Shared Genetic

Lung function is a heritable complex phenotype with obesity being one of its important risk factors. However, the knowledge of their shared genetic basis is limited. Most genome-wide association studies (GWASs) for lung function have been based on European populations, limiting the generalisability across populations. Large-scale lung function GWAS in other populations are lacking.We included 100 285 subjects from China Kadoorie Biobank (CKB). To identify novel loci for lung function, single-trait GWAS were performed on FEV1, FVC, FEV1/FVC in CKB. We then performed genome-wide cross-trait analysis between the lung function and obesity traits (body mass index [BMI], BMI-adjusted waist-to-hip ratio, and BMI-adjusted waist circumference) to investigate the shared genetic effects in CKB. Finally, polygenic risk scores (PRSs) of lung function were developed in CKB and its interaction with BMI's association on lung function were examined. We also conducted cross-trait analysis in parallel with CKB using 457 756 subjects from UK Biobank (UKB) for replication and investigation of ancestry specific effect.We identified 9 genome-wide significant novel loci for FEV1, 6 for FVC and 3 for FEV1/FVC in CKB. FEV1 and FVC showed significant negative genetic correlation with obesity traits in both CKB and UKB. Genetic loci shared between lung function and obesity traits highlighted important pathways, including cell proliferation, embryo and tissue development. Mendelian randomisation analysis suggested significant negative causal effect of BMI on FEV1 and on FVC in both CKB and UKB. Lung function PRSs significantly modified the effect of change-in-BMI on change-in-lung function during an average follow-up of 8 years.This large-scale GWAS of lung function identified novel loci and shared genetic etiology between lung function and obesity. Change-in-BMI might affect change-in-lung function differently according to a subject's polygenic background. These findings may open new avenue for the development of molecular-targeted therapies for obesity and lung function improvement.

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A predictive model to diagnose pregnancy in guanacos (Lama guanicoe) using non-invasive methods

Canadian Journal of Zoology ◽

10.1139/cjz-2019-0070 ◽

2020 ◽

Vol 98 (1) ◽

pp. 13-20

Author(s):

A. Marozzi ◽

V.I. Cantarelli ◽

F.M. Gomez ◽

A. Panebianco ◽

L.R. Leggieri ◽

...

Keyword(s):

Predictive Model ◽

Large Scale ◽

Population Studies ◽

Lama Guanicoe ◽

Non Invasive ◽

Pregnant Females ◽

Metabolite Concentrations ◽

Scale Population ◽

Pregnancy Status ◽

Free Ranging

Pregnancy status is usually not included in ecological studies because it is difficult to evaluate. The use of non-invasive methods to determine pregnancy, without physically restraining individuals, would enable pregnancy to be included in population studies. In this study, we evaluated sex steroid hormones in plasma and fecal samples from pregnant and non-pregnant females to develop a pregnancy predictive model for guanacos (Lama guanicoe (Müller, 1776)). Samples were obtained during live-shearing management (i.e., capture, shear, and release) of guanacos. Enzyme immunoassays were used to evaluate progesterone (P4) and estradiol (E2) concentrations in plasma and pregnanediol glucuronides (PdG) and conjugated estrogens (EC) in feces. Mean hormonal and fecal metabolite concentrations were significantly higher in pregnant females than in non-pregnant females. A linear relationship was found between each hormone and its fecal metabolite. Finally, hormonal data were combined with an independent source of pregnancy diagnosis such as abdominal ballottement to develop a logistic regression model to diagnose pregnancy in non-handled individuals. The use of predictive models and non-invasive methods might be suitable to incorporate pregnancy information in large-scale population studies on guanaco and other free-ranging ungulates.

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Shared genetic architecture and casual relationship between leptin levels and type 2 diabetes: large-scale cross-trait meta-analysis and Mendelian randomization analysis

BMJ Open Diabetes Research & Care ◽

10.1136/bmjdrc-2019-001140 ◽

2020 ◽

Vol 8 (1) ◽

pp. e001140

Author(s):

Xinpei Wang ◽

Jinzhu Jia ◽

Tao Huang

Keyword(s):

Type 2 Diabetes ◽

Genetic Correlation ◽

Genetic Architecture ◽

Large Scale ◽

Meta Analysis ◽

Genetic Correlations ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Shared Genetic

ObjectiveWe aimed to estimate genetic correlation, identify shared loci and test causality between leptin levels and type 2 diabetes (T2D).Research design and methodsOur study consists of three parts. First, we calculated the genetic correlation of leptin levels and T2D or glycemic traits by using linkage disequilibrium score regression analysis. Second, we conducted a large-scale genome-wide cross-trait meta-analysis using cross-phenotype association to identify shared loci between trait pairs that showed significant genetic correlations in the first part. In the end, we carried out a bidirectional MR analysis to find out whether there is a causal relationship between leptin levels and T2D or glycemic traits.ResultsWe found positive genetic correlations between leptin levels and T2D (Rg=0.3165, p=0.0227), fasting insulin (FI) (Rg=0.517, p=0.0076), homeostasis model assessment-insulin resistance (HOMA-IR) (Rg=0.4785, p=0.0196), as well as surrogate estimates of β-cell function (HOMA-β) (Rg=0.4456, p=0.0214). We identified 12 shared loci between leptin levels and T2D, 1 locus between leptin levels and FI, 1 locus between leptin levels and HOMA-IR, and 1 locus between leptin levels and HOMA-β. We newly identified eight loci that did not achieve genome-wide significance in trait-specific genome-wide association studies. These shared genes were enriched in pancreas, thyroid gland, skeletal muscle, placenta, liver and cerebral cortex. In addition, we found that 1-SD increase in HOMA-IR was causally associated with a 0.329 ng/mL increase in leptin levels (β=0.329, p=0.001).ConclusionsOur results have shown the shared genetic architecture between leptin levels and T2D and found causality of HOMA-IR on leptin levels, shedding light on the molecular mechanisms underlying the association between leptin levels and T2D.

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