Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Mendelian randomization supports bidirectional causality between telomere length and clonal hematopoiesis of indeterminate potential

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in the Trans-Omics for Precision Medicine (TOPMed) program (n = 63,302) and UK Biobank (n = 47,080). Bidirectional Mendelian randomization studies were consistent with longer genetically imputed LTL increasing propensity to develop CHIP, but CHIP then, in turn, hastens to shorten measured LTL (mLTL). We also demonstrated evidence of modest mediation between CHIP and CAD by mLTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD

Seville. Cigaret factory (Carmen) New University

Seville, Andalusia, Spai

Model free endurance markers based on the second derivative of blood lactate curves.

Blood lactate assessment is used regularly by exercise physiologists as a predictor of endurance performance. Typically fingertip blood samples are collected at selected running speeds on a treadmill and a plot of lactate concentration against workload (e.g. treadmill speed) is presented. Several features of the lactate curve have been suggested as markers for endurance. Typically these features, or endurance markers, are used to monitor changes in aerobic fitness, set training regimes and predict endurance performance. Determination of these markers however can be problematic (Weltman, 1995). In order to explore the structure of lactate curves, Functional Data Analysis (FDA) techniques are applied to provide graphical summaries. A new nonparametric endurance marker is then presented which corresponds to the point of maximum acceleration in the lactate curve. Using smoothing splines it is easily calculated by examining the second derivative of the smoothed lactate response. In addition we propose a simple discrete approximation to this marker

Model free endurance markers based on the second derivative of blood lactate curves

Blood lactate assessment is used regularly by exercise physiologists as a predictor of endurance performance. Typically fingertip blood samples are collected at selected running speeds on a treadmill and a plot of lactate concentration against workload (e.g. treadmill speed) is presented. Several features of the lactate curve have been suggested as markers for endurance. Typically these features, or endurance markers, are used to monitor changes in aerobic fitness, set training regimes and predict endurance performance. Determination of these markers however can be problematic (Weltman, 1995). In order to explore the structure of lactate curves, Functional Data Analysis (FDA) techniques are applied to provide graphical summaries. A new nonparametric endurance marker is then presented which corresponds to the point of maximum acceleration in the lactate curve. Using smoothing splines it is easily calculated by examining the second derivative of the smoothed lactate response. In addition we propose a simple discrete approximation to this marker

Strategic Decision Making: Influence Patterns in Public and Private Sector Organizations

This paper pursues the question of whether differences exist between influence patterns in decision making in public and private sector organizations. Results are reported from an analysis of the interest units appearing in 150 strategic decision-making processes studied in 30 British organizations. We conclude that, while there is an overall similarity in the involvement of types of interest units in the two sectors, there are notable differences in the influence exerted