Untangling hydrological pathways and nitrate sources by chemical appraisal in a stream network of a reservoir catchment

The knowledge of water source contributions to streamflow is important for understanding chemical contamination origins and the status of biogeochemical cycling in stream networks of catchments. In this study, we evaluated whether a limited number of spatially distributed geochemical tracer data sampled during different hydrological seasons were sufficient to quantify water flow pathways and nitrate sources in a catchment. Six geochemical water constituents (δ<sup>2</sup>H, δ<sup>18</sup>O, Cl<sup>−</sup>, SO<sup>2−</sup><sub>4</sub>, Na<sup>+</sup>, NO<sup>−</sup><sub>3</sub> and K<sup>+</sup>) of precipitation, stream water, alluvial sediment pore water and shallow groundwater of a 352 km<sup>2</sup> agricultural catchment in the Alentejo region of Portugal were analysed. Exploratory data analysis and end-member mixing analysis (EMMA) were performed to estimate the water source mixing proportions. Residual analysis of principal components was used to identify the appropriate geochemical tracers and the number of end-members (water sources and flow paths), and their proportional contributions to streamflow were quantified. Spearman's rank correlation analysis was further used to identify nitrate origins in the streamflow. Results showed that, when using data from both wet and dry seasons, streamflow chemistry was strongly influenced by shallow groundwater. When only wet season data were modelled, streamflow chemistry was controlled and generated by three end-members: shallow groundwater, alluvial sediment pore water and precipitation. Isotope signatures of stream water were located mostly below the local meteoric water line (LMWL) and plotted along a local evaporation line (LEL), reflecting the permanence in the streamflow of shallow groundwater subjected to prior evaporation. Interpretation of isotope signatures during summer showed an isotopic enrichment in both streamflow and shallow groundwater. Measured and historical stream nitrate concentrations appeared to be strongly related to shallow groundwater. In addition, two hydrochemical data outliers for almost every solute from two sample points were identified by the analysis and could be related to local waste water outfalls. The results of this study have improved our understanding of water source contributions to streamflow in the catchment, and also yielded indications of nitrate consumption related to biogeochemical processes in the streamflow network. Moreover, we could conclude that the relatively limited geochemical spatial sample database used in this study was an adequate input for the end-member mixing analysis and diagnostic tools to quantify water sources and nitrate origins in the streamflow of the catchment

Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

© 2020, The Author(s). Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer’s disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (Poptimal) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD