Fast detection of de novo copy number variants from SNP arrays for case-parent trios

Abstract

BackgroundIn studies of case-parent trios, we define copy number variants (CNVs) in the offspring that differ from the parental copy numbers as de novo and of interest for their potential functional role in disease. Among the leading array-based methods for discovery of de novo CNVs in case-parent trios is the joint hidden Markov model (HMM) implemented in the PennCNV software. However, the computational demands of the joint HMM are substantial and the extent to which false positive identifications occur in case-parent trios has not been well described. We evaluate these issues in a study of oral cleft case-parent trios.ResultsOur analysis of the oral cleft trios reveals that genomic waves represent a substantial source of false positive identifications in the joint HMM, despite a wave-correction implementation in PennCNV. In addition, the noise of low-level summaries of relative copy number (log R ratios) is strongly associated with batch and correlated with the frequency of de novo CNV calls. Exploiting the trio design, we propose a univariate statistic for relative copy number referred to as the minimum distance that can reduce technical variation from probe effects and genomic waves. We use circular binary segmentation to segment the minimum distance and maximum a posteriori estimation to infer de novo CNVs from the segmented genome. Compared to PennCNV on simulated data, MinimumDistance identifies fewer false positives on average and is comparable to PennCNV with respect to false negatives. Genomic waves contribute to discordance of PennCNV and MinimumDistance for high coverage de novo calls, while highly concordant calls on chromosome 22 were validated by quantitative PCR. Computationally, MinimumDistance provides a nearly 8-fold increase in speed relative to the joint HMM in a study of oral cleft trios.ConclusionsOur results indicate that batch effects and genomic waves are important considerations for case-parent studies of de novo CNV, and that the minimum distance is an effective statistic for reducing technical variation contributing to false de novo discoveries. Coupled with segmentation and maximum a posteriori estimation, our algorithm compares favorably to the joint HMM with MinimumDistance being much faster.

A genome-wide study of de novo deletions identifies a candidate locus for non-syndromic isolated cleft lip/palate risk

“…This effect was much more prominent in the set of de novo deletions identified by PennCNV , consistent with a previous observation that MinimumDistance might be more robust to false positive identifications (see Figure 2 in [44]). When delineated via PennCNV , the control group had more than a three-fold de novo deletion rate, and less than a two-fold rate when de novo deletions were inferred with MinimumDistance (Table 2).…”

“…Each point represents a de novo deletion CNV segment, delineated through MinimumDistance [44] (lower half) or PennCNV [43] (upper half). The dashed lines represent the genome-wide significance levels for a family-wise error rate of 5%, derived via permutation tests.…”

Copy number polymorphisms near SLC2A9 are associated with serum uric acid concentrations

“…We further processed the log R ratios using the package ArrayTV [47] – an approach adapted from software for removing waves in high-throughput sequencing data [48]. A 6-state HMM comprising 5 distinct copy number states (0-4) implemented in the package VanillaICE (VI) and the stand-alone tool PennCNV were applied independently to each sample [13,14,49]. CNVs with fewer than 10 markers were excluded due to the level of noise of the log R ratios and the difficulty in assessing the validity of low-coverage CNVs without experimental validation.…”

Detecting Disease Variants in Case-Parent Trio Studies Using the Bioconductor Software Packagetrio

“…Additional functionality will for example include methods to test for epistatic interactions when markers are from the same haplotype block as proposed by Cordell and Clayton [], in addition to the ones currently available assuming unlinked markers [see Cordell, ]. We also plan a major extension of the available functionality in the future by incorporating methods for copy number variant analyses, such as the MinimumDistance approach to detect de novo DNA copy number deletions associated with disease [Scharpf et al., ; Younkin et al., ].…”