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)

Interaction diversity within quantified insect food webs in restored and adjacent intensively managed meadows.

1. We studied the community and food-web structure of trap-nesting insects in restored meadows and at increasing distances within intensively managed grassland at 13 sites in Switzerland to test if declining species diversity correlates with declining interaction diversity and changes in food-web structure. 2. We analysed 49 quantitative food webs consisting of a total of 1382 trophic interactions involving 39 host/prey insect species and 14 parasitoid/predator insect species. Species richness and abundance of three functional groups, bees and wasps as the lower trophic level and natural enemies as the higher trophic level, were significantly higher in restored than in adjacent intensively managed meadows. Diversity and abundance of specific trophic interactions also declined from restored to intensively managed meadows. 3. The proportion of attacked brood cells and the mortality of bees and wasps due to natural enemies were significantly higher in restored than in intensively managed meadows. Bee abundance and the rate of attacked brood cells of bees declined with increasing distance from restored meadows. These findings indicate that interaction diversity declines more rapidly than species diversity in our study system. 4. Quantitative measures of food-web structure (linkage density, interaction diversity, interaction evenness and compartment diversity) were higher in restored than in intensively managed meadows. This was reflected in a higher mean number of host/prey species per consumer species (degree of generalism) in restored than in intensively managed meadows. 5. The higher insect species and interaction diversity was related to higher plant species richness in restored than in intensively managed meadows. In particular, bees and natural enemies reacted positively to increased plant diversity. 6. Our findings provide empirical evidence for the theoretical prediction that decreasing species richness at lower trophic levels should reduce species richness at higher trophic levels, and in addition lead to even stronger reductions in interaction diversity at these higher levels. Species at higher trophic levels may thus benefit relatively more than species at lower trophic levels from habitat restoration in the grassland ecosystems studied. We also demonstrate enhanced compartment diversity and lower interaction evenness in restored than in intensively managed meadows, both of which are theoretically positively associated with increased ecosystem stability in restored meadows