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Successful sequencing experiments require judicious sample selection.However, this selection must often be performed on the basis of limitedpreliminary data. Predicting the statistical properties of the final samplebased on preliminary data can be challenging, because numerous uncertain modelassumptions may be involved. Here, we ask whether we can predict ``omics"variation across many samples by sequencing only a fraction of them. In theinfinite-genome limit, we find that a pilot study sequencing $5\%$ of apopulation is sufficient to predict the number of genetic variants in theentire population within $6\%$ of the correct value, using an estimatoragnostic to demography, selection, or population structure. To reach similaraccuracy in a finite genome with millions of polymorphisms, the pilot studywould require about $15\%$ of the population. We present computationallyefficient jackknife and linear programming methods that exhibit substantiallyless bias than the state of the art when applied to simulated data andsub-sampled 1000 Genomes Project data. Extrapolating based on the NHLBI ExomeSequencing Project data, we predict that $7.2\%$ of sites in the capture regionwould be variable in a sample of $50,000$ African-Americans, and $8.8\%$ in aEuropean sample of equal size. Finally, we show how the linear programmingmethod can also predict discovery rates of various genomic features, such asthe number of transcription factor binding sites across different cell types.

Predicting discovery rates of genomic features