Background:Breast cancer is multifactorial. Focusing on limited risk factors may miss high-risk individuals. Methods:We assessed the performance and overlap of various risk factors in identifying high-risk individuals for invasive breast cancer (BrCa) and ductal carcinoma in situ (DCIS) in 161,849 European-ancestry and 18,549 Asian-ancestry women. Discriminatory ability was evaluated using the area under the receiver operating characteristic curve (AUC). High-risk criteria included: 5-year absolute risk ≥1·66% by the Gail model [GAILbinary]; first-degree family history of breast cancer [FHbinary]; 5-year absolute risk ≥1·66% by a 313-variants polygenic risk score [PRSbinary]; and carriers of pathogenic variants in breast cancer predisposition genes [PTVbinary]. Findings:The 5-year absolute risk by PRS outperformed the Gail model in predicting BrCa (Europeansvs controls: AUCPRS=0·635 [0·632-0·638] vs AUCGail=0·492 [0·489-0·495]; Asiansvs controls: AUCPRS=0·564 [0·556-0·573] vs AUCGail=0·506 [0·497-0·514]). PRSbinary and GAILbinary identified more high-risk European than Asia individuals. High-risk proportions were higher among BrCa (16-26%) and DCIS (20-33%) compared to controls (9-15%) among young Europeans and all Asians. Fewer than 7% of BrCa, 10% of DCIS, and 3% of controls were classified as high-risk by multiple risk classifiers. Overlap between PRSbinary and PTVbinary was minimal (<0·65% Europeans, <0·15% Asians) compared to the proportion at high risk using PTVbinary alone (Europeans: 4·6%, Asians: 4·4%) and PRSbinary alone (Europeans: 13·9%, Asians: 8·5%). PRSbinary and FHbinary uniquely identified 5-6% and 9-11% of young BrCa, respectively. Interpretation:The incomplete overlap between high-risk individuals identified by PRSbinary, GAILbinary, FHbinary, and PTVbinary highlights the need for a comprehensive approach to breast cancer risk prediction.

Clinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyze germline sequencing data of BRCA1 and BRCA2 from 96,691 female breast cancer cases and 302,116 controls from three studies: the BRIDGES study of the Breast Cancer Association Consortium, the Cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observe 11,207 BRCA1 and BRCA2 variants, with 6909 being coding, covering 23.4% of BRCA1 and BRCA2 VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control likelihood ratio (ccLR) evidence is highly consistent with ClinVar assertions for (likely) benign or pathogenic variants; exhibiting 99.1% sensitivity and 95.3% specificity for BRCA1 and 93.3% sensitivity and 86.6% specificity for BRCA2. This approach provides case-control evidence for 787 unclassified variants; these include 579 with strong or moderate benign evidence and 10 with strong pathogenic evidence for which ccLR evidence is sufficient to alter clinical classification.

The role of germline genetics in adjuvant aromatase inhibitor (AI) treatment efficacy in ER-positive breast cancer is poorly understood. We employed a two-stage candidate gene approach to examine associations between survival endpoints and common germline variants in 753 endocrine resistance-related genes. For a discovery cohort, we screened the Breast Cancer Association Consortium database (n ≥ 90,000 cases) and retrieved 2789 AI-treated patients. Cox model-based analysis revealed 125 variants associated with overall, distant relapse-free, and relapse-free survival (p-value ≤ 1E-04). In validation analysis using five independent cohorts (n = 8857), none of the six selected candidates representing major linkage blocks at CELA2B/CASP9, NR1I2/GSK3B, LRP1B, and MIR143HG (CARMN) were validated. We discuss potential reasons for the failed validation and replication of published findings, including study/treatment heterogeneity and other limitations inherent to genomic treatment outcome studies. For the future, we envision prospective longitudinal studies with sufficiently long follow-up and endpoints that reflect the dynamic nature of endocrine resistance.

Background Breast cancer is multifactorial. Focusing on limited risk factors may miss high-risk individuals. Methods We assessed the performance and overlap of various risk factors in identifying high-risk individuals for invasive breast cancer (BrCa) and ductal carcinoma in situ (DCIS) in 161,849 European-ancestry and 18,549 Asian-ancestry women. Discriminatory ability was evaluated using the area under the receiver operating characteristic curve (AUC). High-risk criteria included: 5-year absolute risk greater or equal to 1.66% by the Gail model [GAILbinary]; first-degree family history of breast cancer [FHbinary]; 5-year absolute risk greater or equal to 1.66% by a 313-variants polygenic risk score [PRSbinary]; and carriers of pathogenic variants in breast cancer predisposition genes [PTVbinary]. Findings The 5-year absolute risk by PRS outperformed the Gail model in predicting BrCa (Europeansvs controls: AUCPRS=0.635 [0.632-0.638] vs AUCGail=0.492 [0.489-0.495]; Asiansvs controls: AUCPRS=0.564 [0.556-0.573] vs AUCGail=0.506 [0.497-0.514]). PRSbinary and GAILbinary identified more high-risk European than Asia individuals. High-risk proportions were higher among BrCa (16-26%) and DCIS (20-33%) compared to controls (9-15%) among young Europeans and all Asians. Fewer than 7% of BrCa, 10% of DCIS, and 3% of controls were classified as high-risk by multiple risk classifiers. Overlap between PRSbinary and PTVbinary was minimal (<0.65% Europeans, <0.15% Asians) compared to the proportion at high risk using PTVbinary alone (Europeans: 4.6%, Asians: 4.4%) and PRSbinary alone (Europeans: 13.9%, Asians: 8.5%). PRSbinary and FHbinary uniquely identified 5-6% and 9-11% of young BrCa, respectively. Interpretation The incomplete overlap between high-risk individuals identified by PRSbinary, GAILbinary, FHbinary, and PTVbinary highlights the need for a comprehensive approach to breast cancer risk prediction. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study is funded by the Agency for Science, Technology and Research (A*STAR) and PRECISION Health Research, Singapore (PRECISE). The breast cancer genome-wide association analyses in BCAC were supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministere de l'Economie, de la Science et de l'Innovation du Quebec through Genome Quebec and grant PSR-SIIRI-701, The National Institutes of Health (U19 CA148065, X01HG007492), Cancer Research UK (C1287/A10118, C1287/A16563, C1287/A10710), and The European Union (HEALTH-F2-2009-223175 and H2020 633784 and 634935). All studies and funders are listed in Additional Materials (BCAC Funding and Acknowledgments). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the A*STAR Institutional Review Board (reference number: 2022-041). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Availability of data and materials The data used in our analyses are available upon reasonable request through BCAC, subject to data access committee approval.

Abstract Background Pathological complete response (pCR) is an established surrogate marker for prognosis in patients with breast cancer (BC) after neoadjuvant chemotherapy. Individualized pCR prediction based on clinical information available at biopsy, particularly immunohistochemical (IHC) markers, may help identify patients who could benefit from preoperative chemotherapy. Methods Data from patients with HER2-negative BC who underwent neoadjuvant chemotherapy from 2002 to 2020 (n = 1166) were used to develop multivariable prediction models to estimate the probability of pCR (pCR-prob). The most precise model identified using cross-validation was implemented in an online calculator and a nomogram. Associations among pCR-prob, prognostic IHC3 distant recurrence and disease-free survival were studied using Cox regression and Kaplan–Meier analyses. The model’s utility was further evaluated in independent external validation cohorts. Results 273 patients (23.4%) achieved a pCR. The most precise model had across-validated area under the curve (AUC) of 0.84, sensitivity of 0.82, and specificity of 0.71. External validation yielded AUCs between 0.75 (95% CI, 0.70–0.81) and 0.83 (95% CI, 0.78–0.87). The higher the pCR-prob, the greater the prognostic impact of pCR status (presence/absence): hazard ratios decreased from 0.55 (95% central range, 0.07–1.77) at 0% to 0.20 (0.11–0.31) at 50% pCR-prob. Combining pCR-prob and IHC3 score further improved the precision of disease-free survival prognosis. Conclusions A pCR prediction model for neoadjuvant therapy decision-making was established. Combining pCR and recurrence prediction allows identification of not only patients who benefit most from neoadjuvant chemotherapy, but also patients with a very unfavorable prognosis for whom alternative treatment strategies should be considered.

The 313-variant polygenic risk score (PRS313) provides a promising tool for clinical breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed. We explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer diagnosis, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 223,316 females without breast cancer diagnosis from the UK Biobank. The mean PRS was calculated by country in the BCAC dataset and by country of birth in the UK Biobank. We explored different approaches to reduce the observed heterogeneity in the mean PRS across the countries, and investigated the implications of the distribution variability in risk prediction. The mean PRS313 differed markedly across European countries, being highest in individuals from Greece and Italy and lowest in individuals from Ireland. Using the overall European PRS313 distribution to define risk categories, leads to overestimation and underestimation of risk in some individuals from these countries. Adjustment for principal components explained most of the observed heterogeneity in the mean PRS. The mean estimates derived when using an empirical Bayes approach were similar to the predicted means after principal component adjustment. Our results demonstrate that PRS distribution differs even within European ancestry populations leading to underestimation or overestimation of risk in specific European countries, which could potentially influence clinical management of some individuals if is not appropriately accounted for. Population-specific PRS distributions may be used in breast cancer risk estimation to ensure predicted risks are correctly calibrated across risk categories.

Recent exome-wide association studies have explored the role of coding variants in breast cancer risk, highlighting the role of rare variants in multiple genes including BRCA1, BRCA2, CHEK2, ATM and PALB2, as well as new susceptibility genes e.g., MAP3K1. These genes, however, explain a small proportion of the missing heritability of the disease. Much of the missing heritability likely lies in the non-coding genome. We evaluated the role of rare variants in the 5' and 3' untranslated regions (UTRs) of 18,676 genes, and 35,201 putative promoter regions, using whole-genome sequencing data from UK Biobank on 8,001 women with breast cancer and 92,534 women without breast cancer. Burden tests and SKAT-O tests were performed in UTR and promoter regions. For UTR regions of 35 putative breast cancer susceptibility genes, we additionally performed a meta-analysis with a large breast cancer case-control dataset. Associations for 8 regions at P<0.0001 were identified, including several with known roles in tumorigenesis. The strongest evidence of association was for variants in the 5' UTR of CDK5R1 (P=8.5x10-7). These results highlight the potential role of non-coding regulatory regions in breast cancer susceptibility. ### Competing Interest Statement JRBP and EJG are employees of Insmed Innovation UK and holds stock/stock options in Insmed Inc. JRBP also receives research funding from GSK and engages in paid consultancy for WW International Inc. ### Funding Statement The research has been conducted using the UK Biobank Resource under Application Number 28126. N.W. was supported by the International Alliance for Cancer Early Detection, an alliance between Cancer Research UK (C14478/A29329), Canary Center at Stanford University, the University of Cambridge, OHSU Knight Cancer Institute, University College London, and the University of Manchester. Quality control of the UK Biobank sequencing data has been funded by the Medical Research Council (unit programs: MC\_UU\_12015/2, MC\_UU\_00006/2). The BRIDGES project was supported by the European Union Horizon 2020 research and innovation programs BRIDGES (grant number, 634935 to P.D A.G.-N., A,M.D. and D.F.E) and B-CAST (633784 to M.K.S. M.K.S., P.D.P.P. and D.F.E.), the Wellcome Trust (v203477/Z/16/Z to S.H.T ad D.F.E), and Cancer Research UK (C1287/A16563). Details regarding funding of specific BRIDGES studies are provided in the Supplementary Material. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The organisations providing ethical approval for the contributing studies are summarised by Dorling et al, NEJM, https://www.nejm.org/doi/full/10.1056/NEJMoa1913948 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data from UK Biobank are available through application to UK Biobank. Data from the Breast Cancer Association Consortium (BCAC) used in the present study are available upon reasonable request through the BCAC Data Access Coordinating Committee. All data produced in the present study are available upon reasonable request to the authors

Co-observation of a gene variant with a pathogenic variant in another gene that explains the disease presentation has been designated as evidence against pathogenicity for commonly used variant classification guidelines. Multiple variant curation expert panels have specified, from consensus opinion, that this evidence type is not applicable for the classification of breast cancer predisposition gene variants. Statistical analysis of sequence data for 55,815 individuals diagnosed with breast cancer from the BRIDGES sequencing project was undertaken to formally assess the utility of co-observation data for germline variant classification. Our analysis included expected loss-of-function variants in 11 breast cancer predisposition genes and pathogenic missense variants in BRCA1, BRCA2, and TP53. We assessed whether co-observation of pathogenic variants in two different genes occurred more or less often than expected under the assumption of independence. Co-observation of pathogenic variants in each of BRCA1, BRCA2, and PALB2 with the remaining genes was less frequent than expected. This evidence for depletion remained after adjustment for age at diagnosis, study design (familial versus population-based), and country. Co-observation of a variant of uncertain significance in BRCA1, BRCA2, or PALB2 with a pathogenic variant in another breast cancer gene equated to supporting evidence against pathogenicity following criterion strength assignment based on the likelihood ratio and showed utility in reclassification of missense BRCA1 and BRCA2 variants identified in BRIDGES. Our approach has applicability for assessing the value of co-observation as a predictor of variant pathogenicity in other clinical contexts, including for gene-specific guidelines developed by ClinGen Variant Curation Expert Panels.

This study identifies and characterizes a novel type of splicing mutation in RAD50 deficiency, a rare genetic disorder.

Introduction/Background Bevacizumab is a monoclonal antibody against soluble VEGF, active in the treatment of various cancer entities and one standard in the primary treatment of patients with advanced ovarian cancer. Toxicity profile of bevacizumab is favorable, however, hypertension and renal toxicites are specific side effects, which might lead to therapy discontinuation. No predictive biomarkers are available to identify patients with high risks of these toxicities. Methodology AGO-OVAR17 was a multicenter, open-label, randomized phase III trial including 927 ovarian cancer patients proofing that a treatment duration with bevacizumab of 15 months was equal effective to 30 months. German patients treated within the trial (N=764) could be included to our subproject, if they were still in the trial. After informed consent, genomic DNA was extracted from peripheral blood and subjected to SNPtype array genotyping of 24 polymorphisms known or suspected to regulate VEGF levels or VEGF activity. Genotypes were associated with clinical toxicity variables using linear or logistic regression analyses in STATA. Results During the recruitment period of 16 months, 131 patients were included. Genotyping was successful in all patients. 22 of the 24 variants passed QC and showed call rates > 99%. Association analyses with clinical variables revealed a potential association of rs58159269 and rs1885657, two linked variants in VEGFA, with residual disease. Delayed occurrence of toxicity events was observed for three carriers of the rare allele of rs114694170, a variant upstream of MEF2C (p=0.03). Conclusion We found borderline associations of VEGFA variants with residual disease and of a rare variant rs114694170 with toxicity after bevacizumab. Larger studies will be needed to replicate these initial results. Disclosures Nothing to disclose.

Cervical cancer is the fourth most common cancer in females. Genome‐wide association studies (GWASs) have proposed cervical cancer susceptibility variants at the HLA locus on chromosome 6p21. To corroborate these findings and investigate their functional impact in cervical tissues and cell lines, we genotyped nine variants from cervical cancer GWASs (rs17190106, rs535777, rs1056429, rs2763979, rs143954678, rs113937848, rs3117027, rs3130214, and rs9477610) in a German hospital‐based series of 1122 invasive cervical cancers, 1408 dysplasias, and 1196 healthy controls. rs17190106, rs1056429 and rs143954678/rs113937848 associated with cervical malignancies overall, while rs17190106 and rs535777 associated specifically with invasive cancer (OR = 0.69, 95% CI = 0.55–0.86, p = 0.001) or adenocarcinomas (OR = 1.63, 95%CI = 1.17–2.27, p = 0.004), respectively. We tested these and one previously genotyped GWAS variant, rs9272117, for potential eQTL effects on 36 gene transcripts at the HLA locus in 280 cervical epithelial tissues. The strongest eQTL pairs were rs9272117 and HLA‐DRB6 (p = 1.9x10E‐5), rs1056429 and HLA‐DRB5 (p = 2.5x10E‐4), and rs535777 and HLA‐DRB1 (p = 2.7x10E‐4). We also identified transcripts that were specifically upregulated (DDX39B, HCP5, HLA‐B, LTB, NFKBIL1) or downregulated (HLA‐C, HLA‐DPB2) in HPV+ or HPV16+ samples. In comparison, treating cervical epithelial cells with proinflammatory cytokine γ‐IFN led to a dose‐dependent induction of HCP5, HLA‐B, HLA‐C, HLA‐DQB1, HLA‐DRB1, HLA‐DRB6, and repression of HSPA1L. Taken together, these results identify relevant genes from both the MHC class I and II regions that are inflammation‐responsive in cervical epithelium and associate with HPV (HCP5, HLA‐B, HLA‐C) and/or with genomic cervical cancer risk variants (HLA‐DRB1, HLA‐DRB6). They may thus constitute important contributors to the immune escape of precancerous cells after HPV‐infection.

Known risk loci for endometrial cancer explain approximately one third of familial endometrial cancer. However, the association of germline copy number variants (CNVs) with endometrial cancer risk remains relatively unknown. We conducted a genome-wide analysis of rare CNVs overlapping gene regions in 4115 endometrial cancer cases and 17,818 controls to identify functionally relevant variants associated with disease. We identified a 1.22-fold greater number of CNVs in DNA samples from cases compared to DNA samples from controls (p = 4.4 × 10–63). Under three models of putative CNV impact (deletion, duplication, and loss of function), genome-wide association studies identified 141 candidate gene loci associated (p < 0.01) with endometrial cancer risk. Pathway analysis of the candidate loci revealed an enrichment of genes involved in the 16p11.2 proximal deletion syndrome, driven by a large recurrent deletion (chr16:29,595,483-30,159,693) identified in 0.15

Survival from ovarian cancer depends on the resection status after primary surgery. We performed genome-wide association analyses for resection status of 7705 ovarian cancer patients, including 4954 with high-grade serous carcinoma (HGSOC), to identify variants associated with residual disease. The most significant association with resection status was observed for rs72845444, upstream of MGMT , in HGSOC ( p = 3.9 × 10 −8 ). In gene-based analyses, PPP2R5C was the most strongly associated gene in HGSOC after stage adjustment. In an independent set of 378 ovarian tumours from the AGO-OVAR 11 study, variants near MGMT and PPP2R5C correlated with methylation and transcript levels, and PPP2R5C mRNA levels predicted progression-free survival in patients with residual disease. MGMT encodes a DNA repair enzyme, and PPP2R5C encodes the B56γ subunit of the PP2A tumour suppressor. Our results link heritable variation at these two loci with resection status in HGSOC.

Background:Nineteen genomic regions have been associated with high-grade serous ovarian cancer (HGSOC). We used data from the Ovarian Cancer Association Consortium (OCAC), Consortium of Investigators of Modifiers of BRCA1/BRCA2 (CIMBA), UK Biobank (UKBB), and FinnGen to identify novel HGSOC susceptibility loci and develop polygenic scores (PGS). Methods:We analyzed >22 million variants for 398,238 women. Associations were assessed separately by consortium and meta-analysed. OCAC and CIMBA data were used to develop PGS which were trained on FinnGen data and validated in UKBB and BioBank Japan. Results:Eight novel variants were associated with HGSOC risk. An interesting discovery biologically was finding that TP53 3'-UTR SNP rs78378222 was associated with HGSOC (per T allele relative risk (RR)=1.44, 95%CI:1.28-1.62, P=1.76×10-9). The optimal PGS included 64,518 variants and was associated with an odds ratio of 1.46 (95%CI:1.37-1.54) per standard deviation in the UKBB validation (AUROC curve=0.61, 95%CI:0.59-0.62). Conclusions:This study represents the largest GWAS for HGSOC to date. The results highlight that improvements in imputation reference panels and increased sample sizes can identify HGSOC associated variants that previously went undetected, resulting in improved PGS. The use of updated PGS in cancer risk prediction algorithms will then improve personalized risk prediction for HGSOC.

Objectives Disturbances of the central nervous system and immune system are thought to play a role in sudden infant death syndrome (SIDS). Dysregulated expression of sodium (Na + )/hydrogen (H + ) exchanger 3 (NHE3) in the brainstem and of interleukin 13 (IL13) in the lungs has been observed in SIDS. An association of single-nucleotide polymorphisms (SNPs) in NHE3 and IL13 with SIDS has been proposed, but controversial results were reported. Therefore, there is a need to revisit the association of SNPs in NHE3 and IL13 with SIDS. Methods Genotyping of rs71597645 (G1131A) and rs2247114 (C2405T) in NHE3 and rs20541 (+ 4464A/G) in IL13 was performed in 201 SIDS cases and 338 controls. A meta-analysis was performed after merging our data with previously published data (all from European populations). Results Polymorphisms rs2247114 ( NHE3 ) and rs20541 ( IL13 ) were significantly associated with SIDS overall and in multiple subgroups, but no association was found for rs71597645 ( NHE3 ). After combining our data with previously published data, a fixed-effect meta-analysis showed that rs2247114 in NHE3 retained a significant association with SIDS under a recessive model (OR 2.78, 95%CI 1.53 to 5.06; p = 0.0008). Conclusion Our findings suggest an association of NHE3 variant rs2247114 (C2405T), though not rs71597645 ( NHE3 ), with SIDS. A potential role of rs20541 ( IL13 ) still has to be elucidated. Especially NHE3 seems to be an interesting topic for future SIDS research.

Association results between minor alleles of 467 variants incorportated in cross tissue gene expression prediction model for the gene of CRHR1.

PDF file, 107K, Summary characteristics of the participating BCAC case-control studies.

Table S1: Description of the BCAC studies contributing to COGS. Table S2: Predicted effects of BRCA1 and BRCA2 variants included in the iCOGS array on protein function. Table S3: Frequency of BRCA1 and BRCA2 variants from iCOGS in breast cancer cases and controls. Table S4: Family studies of Y3035S showing scores for each family by constant relative risk and 75% penetrance. Supplementary References.

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.