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The two best predictors of children's educational achievement available from birth are parents’ socioeconomic status (SES) and, recently, children's inherited DNA differences that can be aggregated in genome‐wide polygenic scores (GPS). Here, we chart for the first time the developmental interplay between these two predictors of educational achievement at ages 7, 11, 14 and 16 in a sample of almost 5,000 UK school children. We show that the prediction of educational achievement from both GPS and SES increases steadily throughout the school years. Using latent growth curve models, we find that GPS and SES not only predict educational achievement in the first grade but they also account for systematic changes in achievement across the school years. At the end of compulsory education at age 16, GPS and SES, respectively, predict 14% and 23% of the variance of educational achievement. Analyses of the extremes of GPS and SES highlight their influence and interplay: In children who have high GPS and come from high SES families, 77% go to university, whereas 21% of children with low GPS and from low SES backgrounds attend university. We find that the associations of GPS and SES with educational achievement are primarily additive, suggesting that their joint influence is particularly dramatic for children at the extreme ends of the distribution., Genome‐wide polygenic scores (GPS) and socioeconomic status (SES) accounted together for 27% of the variance in educational achievement from age 7 through 16 years. The prediction of GPS and SES on educational achievement was primarily additive, with their joint influence being particularly pronounced at the extreme ends of the distribution.77% of children with high GPS from high SES families went on to university, compared to 21% of children with low GPS from low SES homes.

In this paper we describe a combination of paper-based sensors and a novel smart-phone application for on-site quantification of colorimetric readouts as an ultra-low cost solution to monitoring water quality. The system utilizes a paper-based analytical device (μPAD) that produces a colorimetric signal that is dependent on the concentration of a specific target; a cell phone equipped with a camera for capturing images of two μPADs – one tested with a water sample and the other tested with clean water that is used as a control; and an on-site image processing app that uses a novel algorithm for quantifying color intensity and relating this to contaminant concentration. The cell phone app utilizes a pixel counting algorithm that performs with less bias and user subjectivity than the typically used lab-based software, ImageJ . The use of a test and control strip reduces bias from variations in ambient lighting, making it possible to acquire and process images on-site. The cell phone is also able to GPS tag the location of the test, and transmit results to a newly developed website, WaterMap.ca™, that displays the quantitative results from the water samples on a map. We demonstrate our approach using a previously developed μPAD that detects the presence of organophosphate pesticides based on the inhibition of immobilized acetylcholinesterase by these contaminants. The objective of this paper is to highlight the importance and potential of developing and integrated monitoring system consisting of μPADs, cell-phones and a centralized web portal for low-cost monitoring environmental contaminants at a large-scale.