Figure 3. Genotype characteristics of the ﬁltered 5.3 K SNPs. We ﬁltered the 50 K SNPs from the Aﬀymetrix Xba chip ending with a set of about 5.3 K SNPs. Genotype characteristics were evaluated for this set of SNPs on a collection of 155 cell lines. Panels (A) and (B) show the heterozygosity frequency and the log of the p-value of the Hardy– Weinberg equilibrium test, respectively. In the context of identifying SNPs for the SPIA panel, we expect the best SNPs being the ones with minor allele frequency close to 0.5 and Heterozygosity frequency close to 0.5 (i.e. P-value Hardy–Weinberg = 1). would then best deﬁne the most accurate and parsimo- nious panel identifying both which SNPs to use and how many are needed. However, genotype information is not yet available for all cell lines. Instead, we used a computational approach for the selection of candidate SNPs using available empirical genotype data from a broad set of cancer cell lines (n = 155) derived from diﬀerent organs (e.g. breast, prostate, lung, etc.), and applied an empirical learning approach followed by validation on an independent cell line dataset. The cell lines used in this study are listed in Supplementary Material, Table 1 (‘List of cell lines used for SPIA study’). Applying general ﬁltering criteria to the 50 K genotype data, as detailed in Materials and Methods, resulted in a set of 5.3 K SNPs, which genotype characteristics graphi- cally represented in Figure 3. In brief, we divided the cell line set into training and testing sets and using the set of ﬁltered SNPs, we selected the SNPs that both satisﬁed Hardy–Weinberg equilibrium constraints and exhibited overall call rates of >80% across the dataset. After 1000 iterations of training and testing, the SNPs selected at each iteration were ranked based on the selection rate at the end of the iterative procedure. The iterative SNP selection procedure is detailed in Methods. Similar results in terms of SNP suitability were obtained when SNPs were selected based on concordance with a random allele distribution (e.g. 1/3 frequency for each 2-SNP allele). Set of best SNPs One hundred and thirty three cell lines were used for the selection of the best SNPs through the iterative process, as described in SNP panel selection. Supplementary Table 2 Nucleic Acids Research, 2008, Vol. 36, No. 7 2451 shows the ranked list of SNPs (top list) ordered by selection rate after the 1000 iterations. The table also reports the mean value of heterozygosity frequency for each SNP evaluated on the sampled test set of each iteration. Supplementary Figure 2 represents the location along the genome of all the selected SNPs and highlights the 100 top ranked. Comparison of pair-wise distances using multiple SNP panels and implementation of statistical test (SPIA) To quantitatively measure the relatedness of two samples, we determined a genotype distance function D, which is proportional to the number of genotype mismatches between the two samples (see Materials and Methods, ‘Genotype distance’ and Figure 4, Panel A). It also allows to easily compare multiple pairs of samples and to rank them based on relatedness. In our initial cell line data set, using 5.3K SNPs, the pair-wise mean distance D is 0.4735 and the standard deviation 0.0357 (min = 0 and max = 0.5765). Eleven cell line pairs exhibited unusually strong similarity (see Table 1). Some have previously been reported to be similar: M14 and MDA.MB435, NCI.ADR.RES and OVCAR.8, and SNB.19 and U251 (Cancer Genome Project, http://www.sanger.ac.uk/ genetics/CGP/Genotyping/synlinestable.shtml). Interestingly, the MCF7 breast cancer cell line showed a remarkably short distance from two other breast cancer lines (BT.20 and KPL.1), suggesting a high degree of genetic similarity. This diﬀerence is signiﬁcantly lower than the reproducibility error we evaluated for the same platform (0.02%, data not shown), suggesting that these cell lines may be derived from a single individual. MCF7 and BT.20 have been reported as being ER positive and ER negative, respectively, and as having diﬀerent pheno- types (24, 25). Similarities between MCF-7 and KPL-1 has been previously reported (http://www.sanger.ac.uk/ genetics/CGP/Genotyping/synlinestable.shtml). To our knowledge no report exists on the similarity between MCF7 and BT.20. This ﬁnding will require independent validation. After excluding the 9 cell lines involved in the detected similar pairs (N=11), we were left with 146 distinct cell lines. Of these, 133 were then used to identify and rank the best SNPs through a repeated hold-out training and testing approach, and 13 were used as an independent validation set. To measure the eﬀect of the SNP selection process on the ability to distinguish diﬀerent cell lines and to determine the minimum number of SNPs required to identify genetically similar cell lines, we evaluated the pair- wise distances using several sets of SNPs sampled from the 100 top ranked ones. We varied the number of single loci randomly selecting 80, 60, 40 and 20 SNPs. In Table 2 the mean distances as evaluated between all the possible pairs of two subsets of cell lines are reported; the ﬁrst subset contains the 133 cell lines used in the selection process, whereas the second set is the independent validation set. Table 2 also includes the mean distance values evaluated on the 50 K SNPs contained on the 50 K Xba chip and on the 5.3 K set of ﬁltered SNPs. We can appreciate how