Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023:2023:9961341.
doi: 10.1155/2023/9961341. Epub 2023 Sep 14.

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

Maria Zanti  1 Denise G O'Mahony  1 Michael T Parsons  2 Hongyan Li  3 Joe Dennis  4 Kristiina Aittomäkkiki  5 Irene L Andrulis  6   7 Hoda Anton-Culver  8 Kristan J Aronson  9 Annelie Augustinsson  10 Heiko Becher  11 Stig E Bojesen  12   13   14 Manjeet K Bolla  4 Hermann Brenner  15   16   17 Melissa A Brown  18 Saundra S Buys  19 Federico Canzian  20 Sandrine M Caputo  21   22 Jose E Castelao  23 Jenny Chang-Claude  24   25 GC-HBOC study CollaboratorsKamila Czene  26 Mary B Daly  27 Arcangela De Nicolo  28 Peter Devilee  29   30 Thilo Dörk  31 Alison M Dunning  32 Miriam Dwek  33 Diana M Eccles  34 Christoph Engel  35   36 D Gareth Evans  37   38 Peter A Fasching  39 Manuela Gago-Dominguez  40 Montserrat García-Closas  41 José A García-Sáenz  42 Aleksandra Gentry-Maharaj  43 Willemina R R Geurts-Giele  44 Graham G Giles  45   46   47 Gord Glendon  6 Mark S Goldberg  48   49 Encarna B Gómez Garcia  50 Melanie Güendert  51   52   53 Pascal Guénel  54 Eric Hahnen  55   56 Christopher A Haiman  57 Per Hall  26   58 Ute Hamann  59 Elaine F Harkness  60   61   62 Frans B L Hogervorst  63 Antoinette Hollestelle  64 Reiner Hoppe  65   66 John L Hopper  46 Claude Houdayer  67 Richard S Houlston  68 Anthony Howell  69 ABCTB InvestigatorsMilena Jakimovska  70 Anna Jakubowska  71   72 Helena Jernström  10 Esther M John  73   74 Rudolf Kaaks  24 Cari M Kitahara  75 Stella Koutros  41 Peter Kraft  76   77 Vessela N Kristensen  78   79 James V Lacey  80   81 Diether Lambrechts  82   83 Melanie Léoné  84 Annika Lindblom  85   86 Jan Lubiński  71 Michael Lush  4 Arto Mannermaa  87   88   89 Mehdi Manoochehri  59 Siranoush Manoukian  90 Sara Margolin  58   91 Maria Elena Martinez  92   93 Usha Menon  43 Roger L Milne  45   46   47 Alvaro N Monteiro  94 Rachel A Murphy  95   96 Susan L Neuhausen  97 Heli Nevanlinna  98 William G Newman  37   38 Kenneth Offit  99   100 Sue K Park  101   102   103 Paul James  104   105 Paolo Peterlongo  106 Julian Peto  107 Dijana Plaseska-Karanfilska  70 Kevin Punie  108 Paolo Radice  109 Muhammad U Rashid  59   110 Gad Rennert  111 Atocha Romero  112 Efraim H Rosenberg  113 Emmanouil Saloustros  114 Dale P Sandler  115 Marjanka K Schmidt  116   117   118 Rita K Schmutzler  55   56   119 Xiao-Ou Shu  120 Jacques Simard  121 Melissa C Southey  45   47   122 Jennifer Stone  46   123 Dominique Stoppa-Lyonnet  21   124   125 Rulla M Tamimi  77   126 William J Tapper  34 Jack A Taylor  115   127 Soo Hwang Teo  128   129 Lauren R Teras  130 Mary Beth Terry  131 Mads Thomassen  132 Melissa A Troester  133 Celine M Vachon  134 Ana Vega  135   136   137 Maaike P G Vreeswijk  30 Qin Wang  4 Barbara Wappenschmidt  55   56 Clarice R Weinberg  138 Alicja Wolk  139   140 Wei Zheng  120 Bingjian Feng  141 Fergus J Couch  142 Amanda B Spurdle  2 Douglas F Easton  4   32 David E Goldgar  141 Kyriaki Michailidou  1   4
Affiliations

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

Maria Zanti et al. Hum Mutat. 2023.

Abstract

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 analyses 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 to classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and pre-formatted excel calculators for implementation of the method for rare variants in BRCA1, BRCA2 and other high-risk genes with known penetrance.

Keywords: ACMG/AMP; BRCA; PS4; VUS; case-control; likelihood ratio; variant classification.

PubMed Disclaimer

Conflict of interest statement

The following authors declare conflicts not directly relevant to this work as stated below. Usha Menon has a patent (no: EP10178345.4) for Breast Cancer Diagnostics and held personal shares in Abcodia between 1st April 2011 and 30 October 2021. She is a member of the Research Advisory Panel, Yorkshire Cancer Research, Trial Steering Committee, NOVEL, and Scientific Advisory Board of Tina's Wish. She has received grants from the Medical Research Council (MRC), Cancer Research UK, the National Institute for Health Research (NIHR), and The Eve Appeal. She is part of research collaborations with iLOF, RNG Guardian and Micronoma. All other authors declare that they have no conflict of interests.

Figures

Figure 1
Figure 1
Performance of the case-control likelihood ratio method and odds ratio analysis in providing at least strong ACMG/AMP evidence in favor of pathogenicity (LR ≥ 18.7) using simulated datasets. Power equals the probability of reaching at least strong pathogenic ACMG/AMP evidence. Genotype data simulations were carried out for causal variants conferring disease relative risk between 2 and 10. We performed 10,000 simulations for each case scenario. Results represent simulated case-control data for 20,000 (a–c), 30,000 (d–f), or 50,000 (g–i) breast cancer cases and controls and minor allele frequency of 0.00003 (a, d, g), 0.00005 (b, e, h), or 0.0001 (c, f, i). ccLR: case-control likelihood ratio; MAF: minor allele frequency; N: sample size.
Figure 2
Figure 2
Performance of the case-control likelihood ratio method in providing ACMG/AMP evidence against pathogenicity, using simulated datasets. Power equals to the probability of reaching at least supporting benign ACMG/AMP evidence (LR ≤0.48) when the relative risk was set to 1. We performed 10,000 simulations for each case scenario. Results represent simulated case-control data for 20,000, 30,000, or 50,000 breast cancer cases and controls and minor allele frequency of 0.00003, 0.00005, or 0.0001. ccLR: case-control likelihood ratio; MAF: minor allele frequency; N: sample size.

References

    1. Eccles D. M., Mitchell G., Monteiro A. N. A., et al. BRCA1 and BRCA2 genetic testing–pitfalls and recommendations for managing variants of uncertain clinical significance. Annals of Oncology . 2015;26(10):2057–2065. doi: 10.1093/annonc/mdv278. - DOI - PMC - PubMed
    1. Richards S., Aziz N., Bale S., et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine . 2015;17(5):405–424. doi: 10.1038/gim.2015.30. - DOI - PMC - PubMed
    1. Tavtigian S. V., Greenblatt M. S., Harrison S. M., et al. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genetics in Medicine . 2018;20(9):1054–1060. doi: 10.1038/gim.2017.210. - DOI - PMC - PubMed
    1. Goldgar D. E., Easton D. F., Deffenbaugh A. M., Monteiro A. N., Tavtigian S. V., Couch F. J. Integrated evaluation of DNA sequence variants of unknown clinical significance: application to BRCA1 and BRCA2. The American Journal of Human Genetics . 2004;75(4):535–544. doi: 10.1086/424388. - DOI - PMC - PubMed
    1. Goldgar D. E., Easton D. F., Byrnes G. B., et al. Genetic evidence and integration of various data sources for classifying uncertain variants into a single model. Human Mutation . 2008;29(11):1265–1272. doi: 10.1002/humu.20897. - DOI - PMC - PubMed

Publication types

Grants and funding