Outlining the mission profile of agricultural tractors through CAN-BUS data analytics

Tractor manufacturers need to know how farmers use their agricultural tractors for an optimal machine design. Tractor usage is not easy to assess due to the large variability of field operations. However, modern tractors embed sensors integrated into the CAN-BUS network and their data is accessible through the ISO 11,783 protocol. Even though this technology has been available for a long time, the use of CAN-BUS data for outlining the tractor usage is still limited, because a proper post-processing method is lacking. This study aimed to present a novel classification scheme of CAN-BUS data which permits to outline the tractor usage. On a tractor, a CAN-BUS data logger and a GNSS receiver were installed, and real-world data were recorded for 579 h. Thus, data was obtained in the most realistic condition. Tractor positions were classified using GIS layers while operating conditions were classified depending on the usage of the tractor's subsystems. The method highlights that showed to be able to detect the 97% of the logged data and that the tractor operated on the field in working, on idle, and moving duties for 65%, 18% and 16% of the time, respectively. The method allows a far more precise outline of tractor usage opening opportunities to obtain large benefits from massively collected CAN-BUS data

Statistical design of personalized medicine interventions: The Clarification of Optimal Anticoagulation through Genetics (COAG) trial

<p>Abstract</p> <p>Background</p> <p>There is currently much interest in pharmacogenetics: determining variation in genes that regulate drug effects, with a particular emphasis on improving drug safety and efficacy. The ability to determine such variation motivates the application of personalized drug therapies that utilize a patient's genetic makeup to determine a safe and effective drug at the correct dose. To ascertain whether a genotype-guided drug therapy improves patient care, a personalized medicine intervention may be evaluated within the framework of a randomized controlled trial. The statistical design of this type of personalized medicine intervention requires special considerations: the distribution of relevant allelic variants in the study population; and whether the pharmacogenetic intervention is equally effective across subpopulations defined by allelic variants.</p> <p>Methods</p> <p>The statistical design of the Clarification of Optimal Anticoagulation through Genetics (COAG) trial serves as an illustrative example of a personalized medicine intervention that uses each subject's genotype information. The COAG trial is a multicenter, double blind, randomized clinical trial that will compare two approaches to initiation of warfarin therapy: genotype-guided dosing, the initiation of warfarin therapy based on algorithms using clinical information and genotypes for polymorphisms in <it>CYP2C9 </it>and <it>VKORC1</it>; and clinical-guided dosing, the initiation of warfarin therapy based on algorithms using only clinical information.</p> <p>Results</p> <p>We determine an absolute minimum detectable difference of 5.49% based on an assumed 60% population prevalence of zero or multiple genetic variants in either <it>CYP2C9 </it>or <it>VKORC1 </it>and an assumed 15% relative effectiveness of genotype-guided warfarin initiation for those with zero or multiple genetic variants. Thus we calculate a sample size of 1238 to achieve a power level of 80% for the primary outcome. We show that reasonable departures from these assumptions may decrease statistical power to 65%.</p> <p>Conclusions</p> <p>In a personalized medicine intervention, the minimum detectable difference used in sample size calculations is not a known quantity, but rather an unknown quantity that depends on the genetic makeup of the subjects enrolled. Given the possible sensitivity of sample size and power calculations to these key assumptions, we recommend that they be monitored during the conduct of a personalized medicine intervention.</p> <p>Trial Registration</p> <p>clinicaltrials.gov: NCT00839657</p

Use of Pharmacogenetic and Clinical Factors to Predict the Therapeutic Dose of Warfarin

Initiation of warfarin therapy using trial-and-error dosing is problematic. our goal was to develop and validate a pharmacogenetic algorithm. in the derivation cohort of 1,015 participants, the independent predictors of therapeutic dose were: VKORC1 polymorphism −1639/3673 g>a (−28% per allele), body surface area (Bsa) (+11% per 0.25 m2), CYP2C9*3 (−33% per allele), CYP2C9*2 (−19% per allele), age (−7% per decade), target international normalized ratio (inr) (+11% per 0.5 unit increase), amiodarone use (−22%), smoker status (+10%), race (−9%), and current thrombosis (+7%). This pharmacogenetic equation explained 53−54% of the variability in the warfarin dose in the derivation and validation (N = 292) cohorts. For comparison, a clinical equation explained only 17−22% of the dose variability (P < 0.001). in the validation cohort, we prospectively used the pharmacogenetic-dosing algorithm in patients initiating warfarin therapy, two of whom had a major hemorrhage. To facilitate use of these pharmacogenetic and clinical algorithms, we developed a nonprofit website, http://www.WarfarinDosing.org

Relations between lipoprotein(a) concentrations, LPA genetic variants, and the risk of mortality in patients with established coronary heart disease: a molecular and genetic association study

BACKGROUND: Lipoprotein(a) concentrations in plasma are associated with cardiovascular risk in the general population. Whether lipoprotein(a) concentrations or LPA genetic variants predict long-term mortality in patients with established coronary heart disease remains less clear. METHODS: We obtained data from 3313 patients with established coronary heart disease in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. We tested associations of tertiles of lipoprotein(a) concentration in plasma and two LPA single-nucleotide polymorphisms ([SNPs] rs10455872 and rs3798220) with all-cause mortality and cardiovascular mortality by Cox regression analysis and with severity of disease by generalised linear modelling, with and without adjustment for age, sex, diabetes diagnosis, systolic blood pressure, BMI, smoking status, estimated glomerular filtration rate, LDL-cholesterol concentration, and use of lipid-lowering therapy. Results for plasma lipoprotein(a) concentrations were validated in five independent studies involving 10 195 patients with established coronary heart disease. Results for genetic associations were replicated through large-scale collaborative analysis in the GENIUS-CHD consortium, comprising 106 353 patients with established coronary heart disease and 19 332 deaths in 22 studies or cohorts. FINDINGS: The median follow-up was 9·9 years. Increased severity of coronary heart disease was associated with lipoprotein(a) concentrations in plasma in the highest tertile (adjusted hazard radio [HR] 1·44, 95% CI 1·14-1·83) and the presence of either LPA SNP (1·88, 1·40-2·53). No associations were found in LURIC with all-cause mortality (highest tertile of lipoprotein(a) concentration in plasma 0·95, 0·81-1·11 and either LPA SNP 1·10, 0·92-1·31) or cardiovascular mortality (0·99, 0·81-1·2 and 1·13, 0·90-1·40, respectively) or in the validation studies. INTERPRETATION: In patients with prevalent coronary heart disease, lipoprotein(a) concentrations and genetic variants showed no associations with mortality. We conclude that these variables are not useful risk factors to measure to predict progression to death after coronary heart disease is established. FUNDING: Seventh Framework Programme for Research and Technical Development (AtheroRemo and RiskyCAD), INTERREG IV Oberrhein Programme, Deutsche Nierenstiftung, Else-Kroener Fresenius Foundation, Deutsche Stiftung für Herzforschung, Deutsche Forschungsgemeinschaft, Saarland University, German Federal Ministry of Education and Research, Willy Robert Pitzer Foundation, and Waldburg-Zeil Clinics Isny

PPARA polymorphism influences the cardiovascular benefit of fenofibrate in type 2 diabetes: Findings from accord-lipid

The cardiovascular benefits of fibrates have been shown to be heterogeneous and to depend on the presence of atherogenic dyslipidemia. We investigated whether genetic variability in the PPARA gene, coding for the pharmacological target of fibrates (PPAR-a), could be used to improve the selection of patients with type 2 diabetes who may derive cardiovascular benefit from addition of this treatment to statins. We identified a common variant at the PPARA locus (rs6008845, C/T) displaying a study-wide significant influence on the effect of fenofibrate on major cardiovascular events (MACE) among 3,065 self-reported white subjects treated with simvastatin and randomized to fenofibrate or placebo in the ACCORD-Lipid trial. T/T homozygotes (36% of participants) experienced a 51% MACE reduction in response to fenofibrate (hazard ratio 0.49; 95% CI 0.34–0.72), whereas no benefit was observed for other genotypes (Pinteraction 5 3.7 3 1024). The rs6008845-by-fenofibrate interaction on MACE was replicated in African Americans from ACCORD (N 5 585, P 5 0.02) and in external cohorts (ACCORD-BP, ORIGIN, and TRIUMPH, total N 5 3059, P 5 0.005). Remarkably, rs6008845 T/T homozygotes experienced a cardiovascular benefit from fibrate even in the absence of atherogenic dyslipidemia. Among these individuals, but not among carriers of other genotypes, fenofibrate treatment was associated with lower circulating levels of CCL11—a proinflammatory and atherogenic chemokine also known as eotaxin (P for rs6008845-by-fenofibrate interaction 5 0.003). The GTEx data set revealed regulatory functions of rs6008845 on PPARA expression in many tissues. In summary, we have found a common PPARA regulatory variant that influences the cardiovascular effects of fenofibrate and that could be used to identify patients with type 2 diabetes who would derive benefit from fenofibrate treatment, in addition to those with atherogenic dyslipidemia

CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits

Funding Information: This research has been conducted using the UK Biobank Resource. This research has been conducted using the Danish National Biobank resource. The authors are grateful to the Raine Study participants and their families, and to the Raine Study research staff for cohort co-ordination and data collection. QIMR is grateful to the twins and their families for their generous participation in these studies. We would like to thank staff at the Queensland Institute of Medical Research: Anjali Henders, Dixie Statham, Lisa Bowdler, Ann Eldridge, and Marlene Grace for sample collection, processing and genotyping, Scott Gordon, Brian McEvoy, Belinda Cornes and Beben Benyamin for data QC and preparation, and David Smyth and Harry Beeby for IT support. HBCS Acknowledgements: We thank all study participants as well as everybody involved in the Helsinki Birth Cohort Study. Helsinki Birth Cohort Study has been supported by grants from the Academy of Finland, the Finnish Diabetes Research Society, Folkhälsan Research Foundation, Novo Nordisk Foundation, Finska Läkaresällskapet, Juho Vainio Foundation, Signe and Ane Gyllenberg Foundation, University of Helsinki, Ministry of Education, Ahokas Foundation, Emil Aaltonen Foundation. Finrisk study is grateful for the THL DNA laboratory for its skillful work to produce the DNA samples used in this study and thanks the Sanger Institute and FIMM genotyping facilities for genotyping the samples. We thank the MOLGENIS team and Genomics Coordination Center of the University Medical Center Groningen for software development and data management, in particular Marieke Bijlsma and Edith Adriaanse. This work was supported by the Leenards Foundation (to Z.K.), the Swiss National Science Foundation (31003A_169929 to Z.K., Sinergia grant CRSII33-133044 to AR), Simons Foundation (SFARI274424 to AR) and SystemsX.ch (51RTP0_151019 to Z.K.). A.R.W., H.Y. and T.M.F. are supported by the European Research Council grant: 323195:SZ-245. M.A.T., M.N.W. and An.M. are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). For full funding information of all participating cohorts see Supplementary Note 2. Publisher Copyright: © 2017 The Author(s).There are few examples of robust associations between rare copy number variants (CNVs) and complex continuous human traits. Here we present a large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult samples from 26 cohorts. The study reveals five CNV associations at 1q21.1, 3q29, 7q11.23, 11p14.2, and 18q21.32 and confirms two known loci at 16p11.2 and 22q11.21, implicating at least one anthropometric trait. The discovered CNVs are recurrent and rare (0.01-0.2%), with large effects on height (> 2.4 cm), weight ( 5 kg), and body mass index (BMI) (> 3.5 kg/m(2)). Burden analysis shows a 0.41 cm decrease in height, a 0.003 increase in waist-to-hip ratio and increase in BMI by 0.14 kg/m2 for each Mb of total deletion burden (P = 2.5 x 10(-10), 6.0 x 10(-5), and 2.9 x 10(-3)). Our study provides evidence that the same genes (e.g., MC4R, FIBIN, and FMO5) harbor both common and rare variants affecting body size and that anthropometric traits share genetic loci with developmental and psychiatric disorders.Peer reviewe

Association of Chromosome 9p21 with Subsequent Coronary Heart Disease events:A GENIUS-CHD study of individual participant data

BACKGROUND:Genetic variation at chromosome 9p21 is a recognized risk factor for coronary heart disease (CHD). However, its effect on disease progression and subsequent events is unclear, raising questions about its value for stratification of residual risk. METHODS:A variant at chromosome 9p21 (rs1333049) was tested for association with subsequent events during follow-up in 103,357 Europeans with established CHD at baseline from the GENIUS-CHD Consortium (73.1% male, mean age 62.9 years). The primary outcome, subsequent CHD death or myocardial infarction (CHD death/MI), occurred in 13,040 of the 93,115 participants with available outcome data. Effect estimates were compared to case/control risk obtained from CARDIoGRAMPlusC4D including 47,222 CHD cases and 122,264 controls free of CHD. RESULTS:Meta-analyses revealed no significant association between chromosome 9p21 and the primary outcome of CHD death/MI among those with established CHD at baseline (GENIUS-CHD OR 1.02; 95% CI 0.99-1.05). This contrasted with a strong association in CARDIoGRAMPlusC4D OR 1.20; 95% CI 1.18-1.22; p for interaction Conclusions: In contrast to studies comparing individuals with CHD to disease free controls, we found no clear association between genetic variation at chromosome 9p21 and risk of subsequent acute CHD events when all individuals had CHD at baseline. However, the association with subsequent revascularization may support the postulated mechanism of chromosome 9p21 for promoting atheroma development

Subsequent Event Risk in Individuals with Established Coronary Heart Disease:Design and Rationale of the GENIUS-CHD Consortium

BACKGROUND: The "GENetIcs of sUbSequent Coronary Heart Disease" (GENIUS-CHD) consortium was established to facilitate discovery and validation of genetic variants and biomarkers for risk of subsequent CHD events, in individuals with established CHD. METHODS: The consortium currently includes 57 studies from 18 countries, recruiting 185,614 participants with either acute coronary syndrome, stable CHD or a mixture of both at baseline. All studies collected biological samples and followed-up study participants prospectively for subsequent events. RESULTS: Enrollment into the individual studies took place between 1985 to present day with duration of follow up ranging from 9 months to 15 years. Within each study, participants with CHD are predominantly of self-reported European descent (38%-100%), mostly male (44%-91%) with mean ages at recruitment ranging from 40 to 75 years. Initial feasibility analyses, using a federated analysis approach, yielded expected associations between age (HR 1.15 95% CI 1.14-1.16) per 5-year increase, male sex (HR 1.17, 95% CI 1.13-1.21) and smoking (HR 1.43, 95% CI 1.35-1.51) with risk of subsequent CHD death or myocardial infarction, and differing associations with other individual and composite cardiovascular endpoints. CONCLUSIONS: GENIUS-CHD is a global collaboration seeking to elucidate genetic and non-genetic determinants of subsequent event risk in individuals with established CHD, in order to improve residual risk prediction and identify novel drug targets for secondary prevention. Initial analyses demonstrate the feasibility and reliability of a federated analysis approach. The consortium now plans to initiate and test novel hypotheses as well as supporting replication and validation analyses for other investigators

Machine learning for classification of hypertension subtypes using multi-omics: a multi-centre, retrospective, data-driven study

Background: Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. Methods: This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. Findings: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers. Interpretation: We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment. Funding: European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1)

Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants