Optical proxies for terrestrial dissolved organic matter in estuaries and coastal waters

Dissolved organic matter (DOM) absorbance and fluorescence were used as optical proxies to track terrestrial DOM fluxes through estuaries and coastal waters by comparing models developed for several coastal ecosystems. Key to using these optical properties is validating and calibrating them with chemical measurements, such as lignin-derived phenols-a proxy to quantify terrestrial DOM. Utilizing parallel factor analysis (PARAFAC), and comparing models statistically using the OpenFluor database (http://www.openfluor.org) we have found common, ubiquitous fluorescing components which correlate most strongly with lignin phenol concentrations in several estuarine and coastal environments. Optical proxies for lignin were computed for the following regions: Mackenzie River Estuary, Atchafalaya River Estuary (ARE), Charleston Harbor, Chesapeake Bay, and Neuse River Estuary (NRE) (all in North America). The slope of linear regression models relating CDOM absorption at 350 nm (a350) to DOC and to lignin, varied 5-10-fold among systems. Where seasonal observations were available from a region, there were distinct seasonal differences in equation parameters for these optical proxies. The variability appeared to be due primarily to river flow into these estuaries and secondarily to biogeochemical cycling of DOM within them. Despite the variability, overall models using single linear regression were developed that related dissolved organic carbon (DOC) concentration to CDOM (DOC = 40 ± 2 × a350 + 138 ± 16; R2 = 0.77; N = 130) and lignin (Σ8) to CDOM (Σ8 = 2.03 ± 0.07 × a350 - 0.47 ± 0.59; R2 = 0.87; N = 130). This wide variability suggested that local or regional optical models should be developed for predicting terrestrial DOM flux into coastal oceans and taken into account when upscaling to remote sensing observations and calibrations

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)

The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape : A Large-Scale Genome-Wide Interaction Study

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age-and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to similar to 2.8M SNPs with BMI and WHRadjBMI in four strata (men 50y, women 50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR= 50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may providefurther insights into the biology that underlies weight change with age or the sexually dimorphism of body shape.Peer reviewe

Pattern formation via small RNA mobility

MicroRNAs and trans-acting siRNAs (ta-siRNAs) have important regulatory roles in development. Unlike other developmentally important regulatory molecules, small RNAs are not known to act as mobile signals during development. Here, we show that low-abundant, conserved ta-siRNAs, termed tasiR-ARFs, move intercellularly from their defined source of biogenesis on the upper (adaxial) side of leaves to the lower (abaxial) side to create a gradient of small RNAs that patterns the abaxial determinant AUXIN RESPONSE FACTOR3. Our observations have important ramifications for the function of small RNAs and suggest they can serve as mobile, instructive signals during development