Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth\u27s future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple “big-leaf” approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified the first- and second-order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks for a tropical site with an extent of 1×1∘. While the photosynthetic capacity parameter (Vc,max25) is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity

Epigenetic Contributions to Clinical Risk Prediction of Cardiovascular Disease

BACKGROUND: Cardiovascular disease (CVD) is among the leading causes of death worldwide. The discovery of new omics biomarkers could help to improve risk stratification algorithms and expand our understanding of molecular pathways contributing to the disease. Here, ASSIGN-a cardiovascular risk prediction tool recommended for use in Scotland-was examined in tandem with epigenetic and proteomic features in risk prediction models in ≥12 657 participants from the Generation Scotland cohort.METHODS: Previously generated DNA methylation-derived epigenetic scores (EpiScores) for 109 protein levels were considered, in addition to both measured levels and an EpiScore for cTnI (cardiac troponin I). The associations between individual protein EpiScores and the CVD risk were examined using Cox regression (n cases≥1274; n controls≥11 383) and visualized in a tailored R application. Splitting the cohort into independent training (n=6880) and test (n=3659) subsets, a composite CVD EpiScore was then developed. RESULTS: Sixty-five protein EpiScores were associated with incident CVD independently of ASSIGN and the measured concentration of cTnI ( P&lt;0.05), over a follow-up of up to 16 years of electronic health record linkage. The most significant EpiScores were for proteins involved in metabolic, immune response, and tissue development/regeneration pathways. A composite CVD EpiScore (based on 45 protein EpiScores) was a significant predictor of CVD risk independent of ASSIGN and the concentration of cTnI (hazard ratio, 1.32; P=3.7×10 - 3; 0.3% increase in C-statistic). CONCLUSIONS: EpiScores for circulating protein levels are associated with CVD risk independent of traditional risk factors and may increase our understanding of the etiology of the disease.</p

Local CpG density affects the trajectory and variance of age-associated DNA methylation changes

Acknowledgements We thank Riccardo Marioni, Chris Haley, Ailith Ewing, David Porteous, Chris Ponting, Rob Illingworth, Tamir Chandra, Sara Hagg, Yunzhang Wang, Chantriolnt-Andreas Kapourani, Nick Gilbert, Hannes Becher and members of the Sproul lab for helpful discussions about the study and the manuscript. This work has made use of the resources provided by the University of Edinburgh digital research services and the MRC IGC compute cluster. We are grateful to all the families who took part in the Generation Scotland study along with the general practitioners and the Scottish School of Primary Care for their help in recruiting them, and the entire Generation Scotland team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, healthcare assistants, and nurses. Peer review information Anahita Bishop and Kevin Pang were the primary editors of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Review history The review history is available as Additional file 3. Funding DS is a Cancer Research UK Career Development fellow (reference C47648/A20837), and work in his laboratory is also supported by an MRC university grant to the MRC Human Genetics Unit. LK is a cross-disciplinary postdoctoral fellow supported by funding from the University of Edinburgh and Medical Research Council (MC_UU_00009/2). S.R.C. and I.J.D. were supported by a National Institutes of Health (NIH) research grant R01AG054628, and S.R.C is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (221890/Z/20/Z). AMM is supported by the Wellcome Trust (104036/Z/14/Z, 216767/Z/19/Z, 220857/Z/20/Z) and UKRI MRC (MC_PC_17209, MR/S035818/1). PMV acknowledges support from the Australian National Health and Medical Research Council (1113400) and the Australian Research Council (FL180100072). DMH is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Reference 213674/Z/18/Z). We thank the LBC1936 participants and team members who contributed to the study. Further study information can be found at https://www.ed.ac.uk/lothian-birth-cohorts. The LBC1936 is supported by a jointly funded grant from the BBSRC and ESRC (BB/W008793/1), and also by Age UK (Disconnected Mind project), the Medical Research Council (G0701120, G1001245, MR/M013111/1, MR/R024065/1), and the University of Edinburgh. Genotyping of LBC1936 was funded by the BBSRC (BB/F019394/1), and methylation typing of LBC1936 was supported by Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, The Wellcome Trust Institutional Strategic Support Fund, The University of Edinburgh, and The University of Queensland. Work on Generation Scotland was supported by a Wellcome Strategic Award “STratifying Resilience and Depression Longitudinally” (STRADL; 104036/Z/14/Z) to AMM, KLE, and others, and an MRC Mental Health Data Pathfinder Grant (MC_PC_17209) to AMM. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). DNA methylation profiling and analysis of the GS:SFHS samples was supported by Wellcome Investigator Award 220857/Z/20/Z and Grant 104036/Z/14/Z (PI: AM McIntosh) and through funding from NARSAD (Ref: 27404; awardee: Dr DM Howard) and the Royal College of Physicians of Edinburgh (Sim Fellowship; Awardee: Dr HC Whalley).Peer reviewedPublisher PD

Methylome-wide association study of early life stressors and adult mental health

The environment and events that we are exposed to in utero, during birth and in early childhood influence our future physical and mental health. The underlying mechanisms that lead to these outcomes are unclear, but long-term changes in epigenetic marks, such as DNA methylation, could act as a mediating factor or biomarker. DNA methylation data were assayed at 713 522 CpG sites from 9537 participants of the Generation Scotland: Scottish Family Health Study, a family-based cohort with extensive genetic, medical, family history and lifestyle information. Methylome-wide association studies of eight early life environment phenotypes and two adult mental health phenotypes (major depressive disorder and brief resilience scale) were conducted using DNA methylation data collected from adult whole blood samples. Two genes involved with different developmental pathways (PRICKLE2, Prickle Planar Cell Polarity Protein 2 and ABI1, Abl-Interactor-1) were annotated to CpG sites associated with preterm birth (P < 1.27 × 10(−9)). A further two genes important to the development of sensory pathways (SOBP, Sine Oculis Binding Protein Homolog and RPGRIP1, Retinitis Pigmentosa GTPase Regulator Interacting Protein) were annotated to sites associated with low birth weight (P < 4.35 × 10(−8)). The examination of methylation profile scores and genes and gene-sets annotated from associated CpGs sites found no evidence of overlap between the early life environment and mental health conditions. Birth date was associated with a significant difference in estimated lymphocyte and neutrophil counts. Previous studies have shown that early life environments influence the risk of developing mental health disorders later in life; however, this study found no evidence that this is mediated by stable changes to the methylome detectable in peripheral blood