Exome sequencing resolves apparent incidental findings and reveals further complexity of SH3TC2 variant alleles causing Charcot-Marie-Tooth neuropathy

Abstract

Background: The debate regarding the relative merits of whole genome sequencing (WGS) versus exome sequencing (ES) centers around comparative cost, average depth of coverage for each interrogated base, and their relative efficiency in the identification of medically actionable variants from the myriad of variants identified by each approach. Nevertheless, few genomes have been subjected to both WGS and ES, using multiple next generation sequencing platforms. In addition, no personal genome has been so extensively analyzed using DNA derived from peripheral blood as opposed to DNA from transformed cell lines that may either accumulate mutations during propagation or clonally expand mosaic variants during cell transformation and propagation.

Methods: We investigated a genome that was studied previously by SOLiD chemistry using both ES and WGS, and now perform six independent ES assays (Illumina GAII (x2), Illumina HiSeq (x2), Life Technologies' Personal Genome Machine (PGM) and Proton), and one additional WGS (Illumina HiSeq).

Results: We compared the variants identified by the different methods and provide insights into the differences among variants identified between ES runs in the same technology platform and among different sequencing technologies. We resolved the true genotypes of medically actionable variants identified in the proband through orthogonal experimental approaches. Furthermore, ES identified an additional SH3TC2 variant (p.M1?) that likely contributes to the phenotype in the proband.

Conclusions: ES identified additional medically actionable variant calls and helped resolve ambiguous single nucleotide variants (SNV) documenting the power of increased depth of coverage of the captured targeted regions. Comparative analyses of WGS and ES reveal that pseudogenes and segmental duplications may explain some instances of apparent disease mutations in unaffected individuals.

Keywords: Exome sequencing; Incidental findings; Personal genomes; Precision medicine; SH3TC2; Whole-genome sequencing.

Figure 1

Confirmation and segregation by endonuclease restriction digestion of mutations presumed to represent incidental findings. The upper gel shows restriction digestion using the HphI restriction enzyme for the p.G608D mutation in ABCD1, causative for adrenoleukodystrophy (ALD). All tested individuals, none of which presented with ALD, are shown to be heterozygous for the mutation, including all males whom are hemizygous for genes on the × chromosome. The lower gel shows restriction digestion using the BtgI restriction enzyme for the p.T879K mutation in IGHMBP2, putatively causative for spinal muscular atrophy with respiratory distress type 1 (SMARD1). Random segregation and zygosity for this variant can be observed in this family; none of the individual subjects present with SMARD.

Figure 2

Diagram depicting the differences between the number of variants identified by WGS and ES of the same individual genome. WGS identified 3,420,306 SNPs throughout the genome, including 18,829 coding SNPs (cSNPs). Targeted exome sequence (ES) focuses on capturing most of the coding variation contained in 197,583 exonic regions (VCRome 2.1 design). From this, ES identified 21,772 concordant cSNPs among four Illumina sequencing runs and 18,063 high-quality cSNP variants concordant among all six exome sequencing experiments. Of these, 3,709 cSNPs difered from the cSNPs identified by the original SOLiD WGS approach.

Figure 3

Overlap of SNVs identified in four targeted exome sequencing replicates of the same individual's DNA in the Illumina platform. (a) Comparison of identified coding SNPs (cSNPs) within and between sequencing technologies (GAII vs. HiSeq). There is a high percentage of shared identified SNPs both within and between the different technologies. (b) Comparison of identified InDels within and between sequencing technologies. We observe less overlap between the two technologies probably due to a higher rate of false positive InDels.

Figure 4

Distribution of variant fraction vs. coverage per base for SNPs called only in single exome sequencing runs, not replicated in any other exome run. For the six different exome sequencing runs, the majority of single-run called SNPs fall below the 0.20-0.25 variant fraction for reliable heterozygous calls; those single-run calls with higher variant fractions (>0.20) generally fall below the 10x coverage cutoff. The run that had the most 'private' calls was the PGM run; however the majority of these private calls fall within the low variant fraction portion of the distribution suggesting that most of them are false positives. Interestingly, the distribution of 'private' SNPs in the Proton exome sequencing run is more scattered and distributed in the coverage and variant fraction ranges usually observed for true positive variant calls.

Figure 5

Analysis of missed variants between Illumina ES runs. (a) Distribution of variant fraction and coverage per base for missed SNPs in Illumina exome sequencing experiments. In the four Illumina exome sequencing runs, the variant fraction vs. coverage distribution of SNPs called in one run but not in the other three runs is as expected. The majority of single-run calls cluster in the lower range of variant fraction, below the standard threshold of 0.20-0.25 for reliable heterozygous calls. (b) Classification of filtered-out variants that differed between Illumina exome sequencing runs. Comparison of SNP variants called between exome sequencing runs using the same Illumina sequencing platforms (GAII vs. GAII and HiSeq1 vs. HiSeq2) shows that the most common parameter for differences between sequencing runs is the filtering-out of variants due to strand bias in one run versus another; the second parameter is the low variant ratio and third low quality of the called variants which in many cases is influenced by mis-mapping of reads to different other locations in the genome besides the specific target.

Figure 6

(a) Genomic landscape of the region containing the ABCD1 gene and the repeated 9.7 kb segment reported as a partial pseudogene. This segment comprises exons 7 to 10 of the ABCD1 gene and is repeated in four other locations in the reference genome with approximately 95% identity. (b) Comparison of Sanger sequencing of the reported mutation (p.Gly608Asp) through direct PCR of the segment containing exon 8 and 9 of ABCD1 using genomic DNA as a template (upper) and nested PCR of the same segment after long-range PCR amplification of an approximately 10 kb segment using primers specific to the ABCD1 locus outside of the repeated segment (lower).

Figure 7

Flanking sequence of the genomic mutation T → C at position chr5:148,422,778 in SH3TC2. The coding SNV transition c.1A>G causes a substitution of valine for methionine at the initiation codon. An alternative in frame ATG codon is present 90 codons downstream with a less conserved Kozak sequence than the upstream initiation codon. The wild-type allele can be recognized by two different restriction enzymes, FatI and CviAII, but the mutation destroys the restriction site (data not shown). Conversely, transition T→C creates a restriction site that can be recognized by the endonuclease AflIII. The mutation segregates with the axonal neuropathy phenotype and co-segregates as a complex allele (p.M1?; Y169H) with the nonsense mutation p.R954X to cause the CMT phenotype in this family.

Figure 8

Segregation of nonsense and complex alleles of SH3TC2 in a family with Charcot-Marie-Tooth neuropathy. Previously reported nonsense variant (p.R954X) was inherited from the maternal line; reported missense p.Y169H and newly identified p.M1? variants are in cis inherited from the paternal line and segregating with the axonal neuropathy phenotype in the family. Individuals that inherited both the nonsense and complex allele present with CMT neuropathy.

Figure 9

Comparison of base pair coverage across the whole SH3TC2 gene and flanking regions of the three different mutations identified in the proband. Exome sequencing (ES) provides saturation of base calling reads at >100x. (a) Coverage across the whole SH3TC2 gene. (b) Coverage across the p.M1? mutation. (c) Coverage across the p.Y169H mutation. (d) Coverage across the p.R954X mutation.