Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Reliability and factorial validity of Adolescent Time Inventory-Time Attitude (ATI-TA) Scores in Scottish and Northern Irish adolescents

The contemporary literature investigating the construct broadly known as time perspective is replete with methodological and conceptual concerns. These concerns focus on the reliability and factorial validity of measurement tools, and the sample-specific modification of scales. These issues continue to hamper the development of this potentially useful psychological construct. An emerging body of evidence has supported the six-factor structure of scores on the Adolescent Time Inventory-Time Attitudes Scale, as well as their reliability. The present study utilized data from the first wave of a longitudinal study in the United Kingdom to examine the reliability, validity, and cross-cultural invariance of the scale. Results showed that the hypothesized six-factor model provided the best fit for the data; all alpha and omega estimates were >. .70; scores on ATI-TA factors related meaningfully to self-efficacy scores; and the factor structure was invariant across both research sites. Results are discussed in the context of the extant temporal literature.