Targeted enrichment beyond the consensus coding DNA sequence exome reveals exons with higher variant densities

Abstract

Background: Enrichment of loci by DNA hybridization-capture, followed by high-throughput sequencing, is an important tool in modern genetics. Currently, the most common targets for enrichment are the protein coding exons represented by the consensus coding DNA sequence (CCDS). The CCDS, however, excludes many actual or computationally predicted coding exons present in other databases, such as RefSeq and Vega, and non-coding functional elements such as untranslated and regulatory regions. The number of variants per base pair (variant density) and our ability to interrogate regions outside of the CCDS regions is consequently less well understood.

Results: We examine capture sequence data from outside of the CCDS regions and find that extremes of GC content that are present in different subregions of the genome can reduce the local capture sequence coverage to less than 50% relative to the CCDS. This effect is due to biases inherent in both the Illumina and SOLiD sequencing platforms that are exacerbated by the capture process. Interestingly, for two subregion types, microRNA and predicted exons, the capture process yields higher than expected coverage when compared to whole genome sequencing. Lastly, we examine the variation present in non-CCDS regions and find that predicted exons, as well as exonic regions specific to RefSeq and Vega, show much higher variant densities than the CCDS.

Conclusions: We show that regions outside of the CCDS perform less efficiently in capture sequence experiments. Further, we show that the variant density in computationally predicted exons is more than 2.5-times higher than that observed in the CCDS.

Figure 1

Content and overlap of the VCR-set and REC-set designs. A hypothetical gene shows regions that would be targeted by the VCR-set (orange), REC-set (green), both designs (orange/green) and CCDS (blue). TFBS, transcription factor binding site.

Figure 2

GC content distributions for capture subregions. (a) REC-set capture and (b) VCR-set capture.

Figure 3

Coverage distributions for capture subregions. (a) Average coverage for subregions of REC-set design are shown as a proportion of the average coverage of the CCDS subregion. (b) Average coverage for subregions of VCR-set design are shown as a proportion of the average coverage of the CCDS subregion. 'R/V specific' refers to RefSeq/Vega exons not contained in the CCDS.

Figure 4

Normalized coverage distributions. (a) Coverage of genomic subregions, relative to the CCDS, after whole genome SOLiD sequencing. Green, regions specific to REC-set; orange, regions specific to VCR-set; blue, shared regions. 'R/V specific' refers to RefSeq/Vega exons not contained in the CCDS. (b) Proportional difference in relative coverage between capture-sequencing and WGS shows both enrichment (values > 1) and depletion (values < 1) of certain genomic subregions after capture. Green, regions specific to REC-set; orange, regions specific to VCR-set; blue shared regions.

Figure 5

Single nucleotide variant densities. (a) Number of SNV substitutions per base pair, of REC-set subregions, as a proportion of the SNV rate of the CCDS subregion. The absolute average value from SOLiD and Illumina sequencing is given above the data point. (b) Number of SNV substitutions per base pair, of VCR-set subregions, as a proportion of the SNV rate of the CCDS subregion. The absolute average value from SOLiD and Illumina sequencing is given above the data point. 'R/V specific' refers to RefSeq/Vega exons not contained in the CCDS.

Figure 6

Distribution of phyloP scores across the CCDS (blue), intronic (red) and predicted exons (green).

Figure 7

Minor allele frequency distributions for variants in HuRef subregions: predicted exons (green), CCDS exons (blue) and introns (red). 'Private' indicates the variant was not found in the Thousand Genomes Project.