Fig. 6: Other factors affecting accuracy of prediction incorporating functional annotations. | Nature Genetics

Fig. 6: Other factors affecting accuracy of prediction incorporating functional annotations.

From: Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries

Fig. 6: Other factors affecting accuracy of prediction incorporating functional annotations.

a, Traits with low heritability tend to benefit more from using annotation data. The blue solid line indicates the linear regression of the data points and shading indicates the confident interval of the regression. b, GWASs with small sample sizes tend to benefit more from using annotation data. c, Improvement in prediction accuracy increases with the number of annotations upon the MAF and LD (+Baseline core/full = MAF + LD+Baseline core/full set of annotations). d, Full analysis of all SNPs and annotation data is superior to the stepwise analysis that prioritizes the top 1 M SNPs based on their annotations and fits them in the model. Each box plot in c and d shows the spread of data in ten cross-validations; the line is the middle (median), the box covers the middle half (IQR), the whiskers extend to 1.5 times the IQR and dots show outliers.

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