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.

Residential segregation, dividing walls and mental health: A population-based record linkage study

Background
Neighbourhood segregation has been described as a fundamental determinant of physical health, but literature on its effect on mental health is less clear. Whilst most previous research has relied on conceptualized measures of segregation, Northern Ireland is unique as it contains physical manifestations of segregation in the form of segregation barriers (or “peacelines”) which can be used to accurately identify residential segregation.
Methods
We used population-wide health record data on over 1.3 million individuals, to analyse the effect of residential segregation, measured by both the formal Dissimilarity Index and by proximity to a segregation barrier, on the likelihood of poor mental health.
Results
Using multi-level logistic regression models we found residential segregation measured by the Dissimilarity Index poses no additional risk to the likelihood of poor mental health after adjustment for area-level deprivation. However, residence in an area segregated by a “peaceline” increases the likelihood of antidepressant medication by 19% (OR=1.19, 95% CI: 1.14, 1.23) and anxiolytic medication by 39% (OR=1.39, 95% CI: 1.32, 1.48), even after adjustment for gender, age, conurbation, deprivation and crime.
Conclusions
Living in an area segregated by a ‘peaceline’ is detrimental to mental health suggesting segregated areas characterised by a heightened sense of ‘other’ pose a greater risk to mental health. The difference in results based on segregation measure highlights the importance of choice of measure when studying segregation.