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A recent analysis using family history weighting and co-observation classification modeling indicated that BRCA1 c.594-2A &gt; C (IVS9-2A &gt; C), previously described to cause exon 10 skipping (a truncating alteration), displays characteristics inconsistent with those of a high risk pathogenic BRCA1 variant. We used large-scale genetic and clinical resources from the ENIGMA, CIMBA and BCAC consortia to assess pathogenicity of c.594-2A &gt; C. The combined odds for causality considering case-control, segregation and breast tumor pathology information was 3.23 × 10 -8 Our data indicate that c.594-2A &gt; C is always in cis with c.641A &gt; G. The spliceogenic effect of c.[594-2A &gt; C;641A &gt; G] was characterized using RNA analysis of human samples and splicing minigenes. As expected, c.[594-2A &gt; C; 641A &gt; G] caused exon 10 skipping, albeit not due to c.594-2A &gt; C impairing the acceptor site but rather by c.641A &gt; G modifying exon 10 splicing regulatory element(s). Multiple blood-based RNA assays indicated that the variant allele did not produce detectable levels of full-length transcripts, with a per allele BRCA1 expression profile composed of ≈70-80% truncating transcripts, and ≈20-30% of in-frame Δ9,10 transcripts predicted to encode a BRCA1 protein with tumor suppression function.We confirm that BRCA1c.[594-2A &gt; C;641A &gt; G] should not be considered a high-risk pathogenic variant. Importantly, results from our detailed mRNA analysis suggest that BRCA-associated cancer risk is likely not markedly increased for individuals who carry a truncating variant in BRCA1 exons 9 or 10, or any other BRCA1 allele that permits 20-30% of tumor suppressor function. More generally, our findings highlight the importance of assessing naturally occurring alternative splicing for clinical evaluation of variants in disease-causing genes.

human molecular genetics online/human molecular genetics

Combined genetic and splicing analysis of BRCA1 c.[594-2A&gt;C; 641A&gt;G] highlights the relevance of naturally occurring in-frame transcripts for developing disease gene variant classification algorithms