Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
PLoS Genet. 2013 Mar;9(3):e1003348. doi: 10.1371/journal.pgen.1003348. Epub 2013 Mar 21.

Power and predictive accuracy of polygenic risk scores.

Author information

  • Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom. frank.dudbridge@lshtm.ac.uk

Erratum in

  • PLoS Genet. 2013 Apr;9(4). doi: 10.1371/annotation/b91ba224-10be-409d-93f4-7423d502cba0.

Abstract

Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow.

PMID:
23555274
[PubMed - indexed for MEDLINE]
PMCID:
PMC3605113
Free PMC Article

Images from this publication.See all images (4)Free text

Figure 1
Figure 2
Figure 3
Figure 4
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Icon for Public Library of Science Icon for PubMed Central
    Loading ...
    Write to the Help Desk