Estimating the proportion of pathogenic variants from breast cancer case-control data: Application to calibration of ACMG/AMP variant classification criteria.

Abstract

For genes with reliable estimates of disease risk associated with loss-of-function variants, case-control data can be used to estimate the proportion of variants of typical risk effect for defined groups of variants, of relevance for variant classification. A calculation was derived for a maximum likelihood estimate of the proportion of pathogenic variants of typical effect from case-control data and applied to rare variant counts for ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C, and RAD51D from published breast cancer studies: BEACCON (5770 familial cases and 5741 controls) and breast cancer risk after diagnostic sequencing (60,466 familial and population-based cases and 53,461 controls). There was significant evidence of pathogenic variants among rare noncoding variants, in particular deeper intronic variants, for BRCA1 (13%, p = 8.3 × 10-7 ), BRCA2 (6%, p = 0.016) and PALB2 (13%, p = 0.001). The estimated proportion of pathogenic missense variants varied markedly between genes, generally with enrichment in familial cases, for example, 9% for BRCA2 versus 60%-90% for CHEK2. Stratifying missense variants by position indicated that, for most genes, location within a functional domain significantly predicted pathogenicity, whereas location outside domains provided robust evidence against pathogenicity. Our approach provides novel insights into the spectrum of pathogenic variants of specific breast cancer genes and has wider application to inform gene-focused specifications of American College of Medical Genetics and Genomics (ACMG)/Association of Molecular Pathology (AMP) codes for variant curation.