a likelihood ratio approach for utilizing case-control data in the clinical classification of rare sequence variants: application to BRCA1 and BRCA2

Zanti, M.; O'Mahony, D.G.; Parsons, M.T.; Li, H.Y.; Dennis, J.; Aittomäkkiki, K.; ... ; GC-HBOC Study Collaborators (2023)

Article / Letter to editor

A large number of variants identified through clinical genetic testing in disease susceptibility genes are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion) can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analysis of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC) and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity-findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared with classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and preformatted Excel calculators for implementation of the method for rare variants in BRCA1, BRCA2, and other high-risk genes with known penetrance.

• All authors: Zanti, M.; O'Mahony, D.G.; Parsons, M.T.; Li, H.Y.; Dennis, J.; Aittomäkkiki, K.; Andrulis, I.L.; Anton-Culver, H.; Aronson, K.J.; Augustinsson, A.; Becher, H.; Bojesen, S.E.; Bolla, M.K.; Brenner, H.; Brown, M.A.; Buys, S.S.; Canzian, F.; Caputo, S.M.; Castelao, J.E.; Chang-Claude, J.; Czene, K.; Daly, M.B.; Nicolo, A. de; Devilee, P.; Dörk, T.; Dunning, A.M.; Dwek, M.; Eccles, D.M.; Engel, C.; Evans, D.G.; Fasching, P.A.; Gago-Dominguez, M.; García-Closas, M.; García-Sáenz, J.A.; Gentry-Maharaj, A.; Geurts-Giele, W.R.R.; Giles, G.G.; Glendon, G.; Goldberg, M.S.; Garcia, E.B.G.; Güendert, M.; Guénel, P.; Hahnen, E.; Haiman, C.A.; Hall, P.; Hamann, U.; Harkness, E.F.; Hogervorst, F.B.L.; Hollestelle, A.; Hoppe, R.; Hopper, J.L.; Houdayer, C.; Houlston, R.S.; Howell, A.; Investigators, A.; Jakimovska, M.; Jakubowska, A.; Jernström, H.; John, E.M.; Kaaks, R.; Kitahara, C.M.; Koutros, S.; Kraft, P.; Kristensen, V.N.; Lacey, J.; Lambrechts, D.; Léoné, M.; Lindblom, A.; Lush, M.; Mannermaa, A.; Manoochehri, M.; Manoukian, S.; Margolin, S.; Martinez, M.E.; Menon, U.; Milne, R.L.; Monteiro, A.N.; Murphy, R.A.; Neuhausen, S.L.; Nevanlinna, H.; Newman, W.G.; Offit, K.; Park, S.K.; James, P.; Peterlongo, P.; Peto, J.; Plaseska-Karanfilska, D.; Punie, K.; Radice, P.; Rashid, M.U.; Rennert, G.; Romero, A.; Rosenberg, E.H.; Saloustros, E.; Sandler, D.P.; Schmidt, M.K.; Schmutzler, R.K.; Shu, X.O.; Simard, J.; Southey, M.C.; Stone, J.; Stoppa-Lyonnet, D.; Tamimi, R.M.; Tapper, W.J.; Taylor, J.A.; Teo, S.H.; Teras, L.R.; Terry, M.B.; Thomassen, M.; Troester, M.A.; Vachon, C.M.; Vega, A.; Vreeswijk, M.P.G.; Wang, Q.; Wappenschmidt, B.; Weinberg, C.R.; Wolk, A.; Zheng, W.; Feng, B.J.; Couch, F.J.; Spurdle, A.B.; Easton, D.F.; Goldgar, D.E.; Michailidou, K.; GC-HBOC Study Collaborators
• Date: 2023-09-14
• Journal: Human Mutation: Variation, Informatics and Disease
• Volume: 2023
• DOI: doi:10.1155/2023/9961341