PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor

Most human activity recognition datasets that are publicly available have data captured by using either smartphones or smartwatches, which are usually placed on the waist or the wrist, respectively. These devices obtain one set of acceleration and angular velocity in the x-, y-, and z-axis from the accelerometer and the gyroscope planted in these devices. The PLHI-MC10 dataset contains data obtained by using 3 BioStamp nPoint® sensors from 7 physically healthy adult test subjects performing different exercise activities. These sensors are the state-of-the-art biomedical sensors manufactured by MC10. Each of the three sensors was attached to the subject externally on three muscles-Extensor Digitorum (Posterior Forearm), Gastrocnemius (Calf), and Pectoralis (Chest)-giving us three sets of 3 axial acceleration, two sets of 3 axial angular velocities, and 1 set of voltage values from the heart. Using three different sensors instead of a single sensor improves precision. It helps distinguish between human activities as it simultaneously captures the movement and contractions of various muscles from separate parts of the human body. Each test subject performed five activities (stairs, jogging, skipping, lifting kettlebell, basketball throws) in a supervised environment. The data is cleaned, filtered, and synced

Meta-analysis of exome array data identifies six novel genetic loci for lung function [version 1; peer review:1 approved, 1 approved with reservations]

Background: Over 90 regions of the genome have been associated with lung function to date, many of which have also been implicated in chronic obstructive pulmonary disease. Methods: We carried out meta-analyses of exome array data and three lung function measures: forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and the ratio of FEV1 to FVC (FEV1/FVC). These analyses by the SpiroMeta and CHARGE consortia included 60,749 individuals of European ancestry from 23 studies, and 7,721 individuals of African Ancestry from 5 studies in the discovery stage, with follow-up in up to 111,556 independent individuals. Results: We identified significant (P&lt;2•8x10 -7 ) associations with six SNPs: a nonsynonymous variant in RPAP1, which is predicted to be damaging, three intronic SNPs (SEC24C, CASC17 and UQCC1) and two intergenic SNPs near to LY86 and FGF10. Expression quantitative trait loci analyses found evidence for regulation of gene expression at three signals and implicated several genes, including TYRO3 and PLAU. Conclusions: Further interrogation of these loci could provide greater understanding of the determinants of lung function and pulmonary disease.</p

Sixteen new lung function signals identified through 1000 Genomes Project reference panel imputation

Lung function measures are used in the diagnosis of chronic obstructive pulmonary disease. In 38,199 European ancestry individuals, we studied genome-wide association of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC with 1000 Genomes Project (phase 1)-imputed genotypes and followed up top associations in 54,550 Europeans. We identify 14 novel loci (P <5 x 10(-8)) in or near ENSA, RNU5F-1, KCNS3, AK097794, ASTN2, LHX3, CCDC91, TBX3, TRIP11, RIN3, TEKT5, LTBP4, MN1 and AP1S2, and two novel signals at known loci NPNT and GPR126, providing a basis for new understanding of the genetic determinants of these traits and pulmonary diseases in which they are altered.Peer reviewe

Author Correction:New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries

Correction to: Nature Genetics https://doi.org/10.1038/s41588-018-0321-7, published online 25 February 2019

Author Correction:New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries (Nature Genetics, (2019), 51, 3, (481-493), 10.1038/s41588-018-0321-7)

Correction to: Nature Geneticshttps://doi.org/10.1038/s41588-018-0321-7, published online 25 February 2019. In the version of the article initially published, unconsented individuals were erroneously included in SPIROMICS consortium results. The analysis has now been repeated with the unconsented individuals removed. The change in the results does not affect the conclusions in the paper. The corrections required to the paper are as follows: In the third paragraph of the “Association with FEV1/FVC and COPD in multiple ancestries” section: “(n = 6,979 cases and 3,915 controls)”, should be “(n = 6,964 cases and 3,904 controls)” and “P = 2.87 × 10–75” should be “P = 2.21 × 10–75”. In the fourth paragraph of the “Association with FEV1/FVC and COPD in multiple ancestries” section: “4.73 (95% CI: [3.79, 5.90]), P = 3.00 × 10−43”, should be “4.71 (95% CI: [3.77, 5.87]), P = 7.24 × 10−43”. In the Fig. 3b table, the SPIROMICS row: “1.54, 1.38, 1.72, 4.47 × 10–14, 988, 537”, should be “1.55, 1.39, 1.74, 6.80 × 10–14, 973, 526”; and the Meta-analysis row: “1.55, 1.48, 1.62, 1.48 × 10–75, 6,979, 3,915”, should be “1.55, 1.48, 1.62, 2.21 × 10–75, 6,964, 3,904”. In the final paragraph of the Discussion: “The 279-variant GRS we constructed was associated with a 4.73-fold increased relative risk…”, should be “The 279-variant GRS we constructed was associated with a 4.71-fold increased relative risk…” In the fifth paragraph of the “Effect of genetic risk score on COPD susceptibility in multiple ancestries” section in the Methods: “SPIROMICS (988 cases, 537 controls)”, should be “SPIROMICS (973 cases, 526 controls)”. In the third paragraph of the “Association with FEV1/FVC and COPD in multiple ancestries” section: “(n = 6,979 cases and 3,915 controls)”, should be “(n = 6,964 cases and 3,904 controls)” and “P = 2.87 × 10–75” should be “P = 2.21 × 10–75”. In the fourth paragraph of the “Association with FEV1/FVC and COPD in multiple ancestries” section: “4.73 (95% CI: [3.79, 5.90]), P = 3.00 × 10−43”, should be “4.71 (95% CI: [3.77, 5.87]), P = 7.24 × 10−43”. In the Fig. 3b table, the SPIROMICS row: “1.54, 1.38, 1.72, 4.47 × 10–14, 988, 537”, should be “1.55, 1.39, 1.74, 6.80 × 10–14, 973, 526”; and the Meta-analysis row: “1.55, 1.48, 1.62, 1.48 × 10–75, 6,979, 3,915”, should be “1.55, 1.48, 1.62, 2.21 × 10–75, 6,964, 3,904”. In the final paragraph of the Discussion: “The 279-variant GRS we constructed was associated with a 4.73-fold increased relative risk…”, should be “The 279-variant GRS we constructed was associated with a 4.71-fold increased relative risk…” In the fifth paragraph of the “Effect of genetic risk score on COPD susceptibility in multiple ancestries” section in the Methods: “SPIROMICS (988 cases, 537 controls)”, should be “SPIROMICS (973 cases, 526 controls)”. The correction is due to 26 unconsented SPIROMICS samples being originally included in the analysis. The analyses that previously included these samples have been rerun with data from these 26 samples removed. Supplementary Information accompanies the online version of this amendment and includes: Updated Supplementary Text and Figures wherein we have changed: On page 23 (description of SPIROMICS cohort) the number of COPD cases has been changed from 988 to 973 and controls from 537 to 526. Supplementary Figure 9 – the forest plots have been updated for the new results for association with 279 variants after reanalysis of SPIROMICS. Supplementary Table 20 – the demographics for SPIROMICS have been updated. Supplementary Table 21 – the results rows for the SPIROMICS and “Meta-analysis of 5 European-ancestry study groups” have been updated. Supplementary Table 22 – The “Meta-analysis of 5 European cohorts” columns have been updated after SPIROMICS reanalysis. Updated Supplementary Tables wherein we have changed: Supplementary Table 29 – columns X–Z (“Meta-analysis of 5 external European-ancestry COPD cohorts (Cases = 6,964; Controls = 3,904)”) after reanalysis of SPIROMICS data. Updated Supplementary Text and Figures wherein we have changed: On page 23 (description of SPIROMICS cohort) the number of COPD cases has been changed from 988 to 973 and controls from 537 to 526. Supplementary Figure 9 – the forest plots have been updated for the new results for association with 279 variants after reanalysis of SPIROMICS. Supplementary Table 20 – the demographics for SPIROMICS have been updated. Supplementary Table 21 – the results rows for the SPIROMICS and “Meta-analysis of 5 European-ancestry study groups” have been updated. Supplementary Table 22 – The “Meta-analysis of 5 European cohorts” columns have been updated after SPIROMICS reanalysis. Updated Supplementary Tables wherein we have changed: Supplementary Table 29 – columns X–Z (“Meta-analysis of 5 external European-ancestry COPD cohorts (Cases = 6,964; Controls = 3,904)”) after reanalysis of SPIROMICS data

Sixteen new lung function signals identified through 1000 Genomes Project reference panel imputation

Lung function measures are used in the diagnosis of chronic obstructive pulmonary disease. In 38,199 European ancestry individuals, we studied genome-wide association of forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and FEV1/FVC with 1000 Genomes Project (Phase 1) imputed genotypes and followed up top associations in 54,550 Europeans. We identify 14 novel loci (P<5x10-8) in or near ENSA, RNU5F-1, KCNS3, AK097794, ASTN2, LHX3, CCDC91, TBX3, TRIP11, RIN3, TEKT5, LTBP4, MN1, AP1S2, and two novel signals at known loci NPNT and GPR126, providing a basis for new understanding of the genetic determinants of these traits and pulmonary diseases in which they are altered

New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries

Abstract Reduced lung function predicts mortality and is key to the diagnosis of chronic obstructive pulmonary disease (COPD). In a genome-wide association study in 400,102 individuals of European ancestry, we define 279 lung function signals, 139 of which are new. In combination, these variants strongly predict COPD in independent populations. Furthermore, the combined effect of these variants showed generalizability across smokers and never smokers, and across ancestral groups. We highlight biological pathways, known and potential drug targets for COPD and, in phenome-wide association studies, autoimmune-related and other pleiotropic effects of lung function–associated variants. This new genetic evidence has potential to improve future preventive and therapeutic strategies for COPD