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Application of embryonic lethal or other obvious phenotypes to characterize the clinical significance of genetic variants found in trans with known deleterious mutations.

Judkins T, Hendrickson BC, Deffenbaugh AM, Eliason K, Leclair B, Norton MJ, Ward BE, Pruss D, Scholl T

Myriad Genetic Laboratories, Salt Lake City, Utah 84108, USA.

This work describes an approach to characterize the clinical significance of genetic variants detected during the genetic testing of BRCA1 in patients from hereditary breast/ovarian cancer families. Results from transgenic mice and extensive clinical testing support the hypothesis that biallelic BRCA1 mutations result in embryonic lethality. Therefore, it is reasonable to conclude that variants of uncertain clinical significance found to reside in trans with known deleterious mutations impart reduced risk for cancer. This approach was applied to a large data set of 55,630 patients who underwent clinical BRCA1 screening by whole gene direct DNA sequencing. Fourteen common single nucleotide polymorphisms (SNPs) were used to assign 10 previously defined common, recurrent, or canonical haplotypes in 99% of these cases. From a total of 1,477 genetic variants detected in these patients, excluding haplotype-tagging SNPs, 877 (59%) could be unambiguously assigned to one or more haplotypes. In 41 instances, variants previously classified as being of uncertain clinical significance, mostly missense variants, were excluded as fully penetrant mutations due to their coincidence in trans with known deleterious mutations. From a total of 1,150 patients that harbored these 41 variants, 956 carried one as the sole variant of uncertain clinical significance reported. This approach could have widespread application to other disease genes where compound heterozygous mutations are incompatible with life or result in obvious phenotypes. This largely computational technique is advantageous because it relies upon existing clinical data and is likely to prove informative for prevalent genetic variants in large data sets.

Published 3 November 2005 in Cancer Res, 65(21): 10096-103.
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