ABSTRACT
Background: Current clinical variant analysis pipelines focus on coding variants and intronic variants within 10-20 bases of an exon-intron boundary that may affect splicing. The impact of newer splicing prediction algorithms combined with in vitro splicing assays on rare variants currently considered Benign/Likely Benign (B/LB) is unknown. Methods: Exome sequencing data from 576 pediatric cancer patients enrolled in the Texas KidsCanSeq study were filtered for intronic or synonymous variants absent from population databases, predicted to alter splicing via SpliceAI (>0.20), and scored as potentially deleterious by CADD (>10.0). Total cellular RNA was extracted from monocytes and RT-PCR products analyzed. Subsequently, rare synonymous or intronic B/LB variants in a subset of genes submitted to ClinVar were similarly evaluated. Variants predicted to lead to a frameshifted splicing product were functionally assessed using an in vitro splicing reporter assay in HEK-293T cells. Results: KidsCanSeq exome data analysis revealed a rare, heterozygous, intronic variant (NM_177438.3(DICER1):c.574-26A>G) predicted by SpliceAI to result in gain of a secondary splice acceptor site. The proband had a personal and family history of pleuropulmonary blastoma consistent with DICER1 syndrome but negative clinical sequencing reports. Proband RNA analysis revealed alternative DICER1 transcripts including the SpliceAI-predicted transcript.Similar bioinformatic analysis of synonymous or intronic B/LB variants (n=31,715) in ClinVar from 61 Mendelian disease genes yielded 18 variants, none of which could be scored by MaxEntScan. Eight of these variants were assessed (DICER1 n=4, CDH1 n=2, PALB2 n=2) using in vitro splice reporter assay and demonstrated abnormal splice products (mean 66%; range 6% to 100%). Available phenotypic information from submitting laboratories demonstrated DICER1 phenotypes in 2 families (1 variant) and breast cancer phenotypes for PALB2 in 3 families (2 variants). Conclusions: Our results demonstrate the power of newer predictive splicing algorithms to highlight rare variants previously considered B/LB in patients with features of hereditary conditions. Incorporation of SpliceAI annotation of existing variant data combined with either direct RNA analysis or in vitro assays has the potential to identify disease-associated variants in patients without a molecular diagnosis.
ABSTRACT
Genome-wide DNA methylation studies have typically focused on quantitative assessments of CpG methylation at individual loci. Although methylation states at nearby CpG sites are known to be highly correlated, suggestive of an underlying coordinated regulatory network, the extent and consistency of inter-CpG methylation correlation across the genome, including variation between individuals, disease states, and tissues, remains unknown. Here, we leverage image conversion of correlation matrices to identify correlated methylation units (CMUs) across the genome, describe their variation across tissues, and annotate their regulatory potential using 35 public Illumina BeadChip datasets spanning more than 12,000 individuals and 26 different tissues. We identified a median of 18,125 CMUs genome-wide, occurring on all chromosomes and spanning a median of ~1 kb. Notably, 50% of CMUs had evidence of long-range correlation with other proximal CMUs. Although the size and number of CMUs varied across datasets, we observed strong intra-tissue consistency among CMUs, with those in testis encompassing those seen in most other tissues. Approximately 20% of CMUs were highly conserved across normal tissues (i.e. tissue independent), with 73 loci demonstrating strong correlation with non-adjacent CMUs on the same chromosome. These loci were enriched for CTCF and transcription factor binding sites, always found within putative TADs, and associated with the B compartment of chromosome folding. Finally, we observed significantly different, but highly consistent, patterns of CMU correlation between diseased and non-diseased states. Our first-generation, genome-wide, DNA methylation map suggests a highly coordinated CMU regulatory network that is sensitive to disruptions in its architecture.
