A method for rapid, targeted CNV genotyping identifies rare variants associated with neurocognitive disease

Abstract

Copy-number variants (CNVs) are substantial contributors to human disease. A central challenge in CNV-disease association studies is to characterize the pathogenicity of rare and possibly incompletely penetrant events, which requires the accurate detection of rare CNVs in large numbers of individuals. Cost and throughput issues limit our ability to perform these studies. We have adapted the Illumina BeadXpress SNP genotyping assay and developed an algorithm, SNP-Conditional OUTlier detection (SCOUT), to rapidly and accurately detect both rare and common CNVs in large cohorts. This approach is customizable, cost effective, highly parallelized, and largely automated. We applied this method to screen 69 loci in 1105 children with unexplained intellectual disability, identifying pathogenic variants in 3.1% of these individuals and potentially pathogenic variants in an additional 2.3%. We identified seven individuals (0.7%) with a deletion of 16p11.2, which has been previously associated with autism. Our results widen the phenotypic spectrum of these deletions to include intellectual disability without autism. We also detected 1.65-3.4 Mbp duplications at 16p13.11 in 1.1% of affected individuals and 350 kbp deletions at 15q11.2, near the Prader-Willi/Angelman syndrome critical region, in 0.8% of affected individuals. Compared to published CNVs in controls they are significantly (P = 4.7 x 10(-5) and 0.003, respectively) enriched in these children, supporting previously published hypotheses that they are neurocognitive disease risk factors. More generally, this approach offers a previously unavailable balance between customization, cost, and throughput for analysis of CNVs and should prove valuable for targeted CNV detection in both research and diagnostic settings.

Figure 1.

CNVs at 16p13.11 in individuals with ID. (A) SCOUT uses fluorescence intensity data generated for SNP genotypes, in this case at rs35597, to identify samples that harbor deletions or duplications. Each sample is assigned a score based on its intensity relative to the center of the cluster of all samples with the same genotype. Samples that harbor duplications exhibit greater total intensity and potentially aberrant allelic ratio (inferred genotypes marked in green), while deletions exhibit lesser intensity and lack SNP heterozygosity (inferred genotypes labeled in red). Samples analyzed as positive controls are labeled PC, while validated predictions are labeled as L1–L2 (“loss”) or G1–G9 (“gain”). Dashed circles are drawn manually for visual aid. Unlabeled samples that appear to be outliers at this probe are either outliers only at this probe or samples with insufficient DNA for validation. (B) SCOUT predictions were validated with targeted array-CGH experiments. Shown are the resulting data for the labeled samples in panel A, with normalized log-transformed test/reference ratios at each tested probe presented as vertical bars. Those probes that are more than 1.5 standard deviations above (below) the average for the entire array are colored green (red). The samples shown include eight validated duplications, two validated deletions, and one positive control deletion. Note that G9 was analyzed with a different array-CGH platform and is not shown here. Genome coordinates and segmental duplications are annotated at the top. The bottom shows RefSeq genes/isoforms annotated in this region.

Figure 2.

SCOUT assigns highly negative scores to deletions (red), near-zero scores to diploid intervals (black), and highly positive scores to duplications (green). Normalized histograms (Y-axis indicates percent of each category) for known deletions (red, n = 35), predicted and validated deletions (dark red, n = 35), known duplications (green, n = 29), and predicted and validated duplications (dark green, n = 23) are shown. SCOUT scores are binned by 1-unit increments with the center of each bin labeled on the X-axis. The vast majority of scores correspond to diploid sites (n = 10,566 or 98.9%).

Figure 3.

Reliability of SCOUT predictions as a function of threshold. Sensitivity (dashed lines/open squares) and false discovery rates (FDR, solid lines/solid squares) to detect deletions (red) and duplications (green) are plotted as a function of the absolute value of the SCOUT threshold, incremented by units of 0.25. These estimates exclude regions covered by only two probes and also exclude CNVs in positive control samples.