methods show low to moderate positive correlations, with the highest RCC between LRT and MutationTaster being 0.62 (Table 3). We also calculated the Pearson’s correlation coefficients (data not shown) for each pair of the methods. They are all smaller than their corresponding RCCs, which suggests stronger nonlinear correlation between those scores. Missing Data and Imputation The numbers of missing data of PhyloP, SIFT, Polyphen2, LRT, and MutationTaster per chromosome can be derived from Table 1. Among them, PhyloP has the lowest missing data rate (0.004%). As for the four prediction methods, MutationTaster has the lowest missing data rate (5.2%), followed by LRT (7.7%), SIFT (10.7%), and Polyphen2 (12.5%). This is partially because SIFT and Polyphen2 do not provide prediction scores for stop-gain (a mutation that changes an AA codon to a stop codon) or stop-loss (a mutation that changes a stop codon to an AA codon) and LRT does not provide prediction scores for stop-loss. An advantage of having multiple prediction scores for the same NS is that it facilitates better imputation for missing scores. Some statistical methods for detecting association between (groups) of rare variants and phenotypes (e.g., VT test) [Price et al., 2010] use prediction scores to weight NSs to increase their detecting power. If scores are missing, typically they are imputed using the average of the nonmissing scores (of the same algorithm) in the sample. As we observed in Figure 1, the prediction scores are bimodal distributed. Therefore, using the average score to impute may not be a good choice. Although there are only low to moderate correlations between different types of scores, it is still possible to make full use of those relationships to obtain better imputation scores. We chose the program BPCAfill [Oba et al., 2003] to impute the missing scores in dbNSFP, because of its higher accuracy and faster computation comparing to other methods [Aittokallio, 2010]. Although BPCAfill is originally designed for imputing missing expression data for microarray analyses, it can be applied to any data set with correlated columns or rows. To evaluate the performance, (1) we compiled a subset of dbNSFP, in which all five scores are NOT missing; (2) for each missing pattern observed in dbNSFP, we randomly picked the same number of NSs as those with the missing pattern in dbNSFP, and marked their correspond- ing scores as missing (e.g., dbNSFP has 334,421 NSs with LRT and MutationTaster scores missing while all other three scores are present; we randomly picked 334,421 NSs from the subset and marked their LRT and MutationTaster scores as missing); and (3) after BPCAfill imputed the missing scores, we compared the imputed scores with the corresponding original scores. Figure 2 shows the histograms of the error (imputed score minus original score) distribution from two different imputation strategies: imputing with BPCAfill or with the average scores of the same algorithm. Imputation errors for each algorithm with different missing patterns are summarized in Supp. Table S1. Comparing the imputation accuracies for different prediction scores over all missing patterns, LRT has the smallest mean squared error (MSE) of 0.035, followed by SIFT (0.054) and PhyloP (0.066), whereas MutationTaster and Polyphen2 have the largest MSEs of 0.133 and 0.142, respectively (Supp. Table S1). A t-test of the difference between mean error and 0 shows that imputations with both BPCAfill and the average scores were unbiased (data not shown), with average errors equal to 6.25 10 6 (BPCAfill) and 3.6 10 5 (average score), respectively. However, the distribu- tion of the errors using BPCAfill had a significantly smaller standard deviation than the other strategy (0.303 comparing to 0.341, P-valueo10 14, F-test, one tailed). Therefore, we concluded that BPCAfill can produce significantly better imputation scores than simply imputing with the average score. Companion Search Program A companion search program written in Java (search_dbNSFP) is freely available for download along with dbNSFP. It can run crossplatform on a wide range of computers, as long as a proper Java Runtime Environment is installed. It enables a simple search for a NS, a chromosome position or a gene. It runs on a command line with a basic format ‘‘java search_dbNSFP -i infile -o outfile,’’ where infile and outfile are the user designated input file name and output file name, respectively. The input file contains one or more lines, with each line representing a query. The query formats for NS, chromosome position and gene are presented in Table 4. The output file contains all NSs that match the query information. For example, the output of a gene query will contain all NSs in that gene, and that of a genome position query will contain all NSs on that position. If there is more than one result for a NS query due to different annotations (see Data Sources and Processing), all A B Figure 2. Distributions of imputation errors by using BPCAfill (A) or using the average score of the same algorithm (B). HUMAN MUTATION, Vol. 32, No. 8, 894–899, 2011 897