samples assayed using multiple Illumina genome-wide single-nucleotide polymorphism (SNP) microarrays. These samples were studied as part of genome-wide association studies (dbGaP) for phenotypes unrelated to neurological disease (e.g. lipid concentration levels, blood pressure, asthma, etc.) (Supplementary Table 4). CNVs were called using a Hidden Markov Model (HMM)–based discovery method 14 with an overall precision of 0.892 in identifying large CNVs (>100 kbp) (validation rates of 6/615 and 19/2216). From this dataset, we identified 446,736 CNVs with an average of 53.6 events (rare and common) per individual (median size 1.9 kbp). Due to the increased probe density (most >550,000 probes), our control dataset provides increased CNV detection power and resolution when compared to the disease dataset, reducing the potential for spurious CNV enrichments within cases (see Methods).
