Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

@inproceedings{Vilhjlmsson2015ModelingLD,
  title={Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores},
  author={Bjarni J. Vilhj{\'a}lmsson and Jeongsam Yang and Hilary K. Finucane and Alexander Gusev and Sara Lindstr{\"o}m and Stephan Ripke and Giulio Genovese and Po-Ru Loh and Gaurav Bhatia and Ron Do and Tristian Hayeck and Hong-Hee Won and Sekar Kathiresan and Michele T. Pato and Carlos N. Pato and Rulla M. Tamimi and Eli Stahl and Noah Zaitlen and Bogdan Pasaniuc and Mikkel Heide Schierup and Phillip L. De Jager and Nikolaos A. Patsopoulos and Steven A. McCarroll and Mark J. Daly and Stephanie Purcell and Daniel I. Chasman and Benjamin M. Neale and M. E. Goddard and Peter M. Visscher and Peter Kraft and N. P. Patterson and Anthony George Price},
  year={2015}
}
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 LD-pruning markers and applying a P-value threshold to association statistics, but this discards information and may reduce predictive accuracy. We introduce a new method, LDpred, which infers the posterior mean causal effect size of each marker using a prior on effect sizes and LD… CONTINUE READING

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