dataesr/scientific-paragraphs-categorization huggingface dataset:
Genotypes were adjusted for 41 BRCA1 and 91 BRCA2 variants, while 3 BRCA1 and 2 BRCA2 variant genotypes were determined to have been called correctly by automated clustering. Genotype recalling was not performed for 12 BRCA1 and 34 BRCA2 variants due to the low quality of the genotype data; these variants were not considered further. After genotype cluster review and recalling, 16 BRCA1 and 19 BRCA2 variants were excluded from further analysis due to their high frequency (>0. 1%). Additionally, casecontrol LR calculations were not possible for four BRCA1 and six BRCA2 variants due to the absence of variant carriers in the postfiltering dataset. After these exclusions, casecontrol LR and logistic regression analyses were performed for 24 BRCA1 and 68 BRCA2 variants. It should be noted that some of the variants selected for the array have subsequently been classified or were those whose pathogenicity status were known and were included as positive or negative controls.
Statistical Analyses 2. 2. 1. Case-Control Likelihood Ratio Method. This method (detailed in Supplementary File 1) compares the likelihood of the distribution of the variant of interest among cases and controls under the hypothesis that the variant is associated with similar risks of the disease in question as the "average" pathogenic variant (H p ), compared to the likelihood under the hypothesis that it is a benign variant not associated with increased risk (H b ). These risks may be age-, sex-, and/or country-specific. Thus LR = Pr Data H p Pr Data H b , 2 where Data denotes observed data on carrier status of a variant of interest, case-control status, and age at diagnosis or interview, combined over all individuals in the dataset. In order to calculate the above LR, we follow a survival analysis framework.
The baseline incidence rates λ 0 t were taken from the age-specific background rates for England and Wales (1998-2002) (https://ci5. iarc. fr/CI5I-X/Default. aspx), and the age-specific breast cancer relative risks for pathogenic variant carriers β t were taken from the recent large-scale BRIDGES (Breast Cancer Risk after Diagnostic Gene Sequencing) project  [15]. To allow for possible carrier frequency differences by country, stratified LR calculations were performed within each country and then multiplied to provide a final LR. Likelihood ratios are further translated into ACMG/ AMP code strength categories according to published recommendations  [3]. Likelihood ratio estimates in favor of variant pathogenicity are scored as very strong, LR ≥ 350; strong, 350 > LR ≥ 18 7; moderate, 18 70 > LR ≥ 4 33; and supporting, 4 33 > LR ≥ 2 08.
aspx). Age-specific breast cancer incidences for Greece and North Macedonia were retrieved from the 2020 cancer registry (European Cancer Information System (ECIS), https://ecis. jrc. ec. europa. eu/) since cancer incidence data were not available for the years 1998-2019. Unstratified analyses were also performed for comparison. Detailed R scripts and preformatted Excel calculators (user can either input individual-level data or tabulated by age groups) for the calculation of case-control LRs can be found using the following GitHub link (https://github. com/ BiostatUnitCING/ccLR). The files provided can be used to derive estimates based on the RR from Dorling et al. [15] , Kuchenbaecker et al. [17] , or Antoniou et al. [16]. In addition, this method can also be used to compute case-control LRs for variants in other disease susceptibility genes by using age-specific penetrance estimates for the gene of interest (indicated by "custom" gene in the preformatted Excel calculators and R script).
Evaluation and Application of the Case-Control Analyses Methods. The simulated datasets were analyzed using the novel case-control LR method, logistic regression (adjusted by age), and Fisher's exact test. The case-control LR method was applied using age-specific breast cancer ORs for BRCA1 and BRCA2 PVs  [15]. For causal variants with a relative risk of 2 to 10, the power of the case-control LR method was estimated either as the probability of reaching at least supporting (LR ≥ 2 08) or at least strong pathogenic (LR ≥ 18 7) evidence. For benign variants with a relative risk of 1, the power of the case-control LR method was estimated either as the probability of reaching at least supporting (LR ≤ 0 48) or at least strong (LR ≤ 0 053) benign ACMG/AMP evidence. Correspondingly, type I error for pathogenicity was calculated as the probability of obtaining at least supporting or at least strong pathogenic ACMG/AMP evidence when the relative risk was set to 1.
Equivalently, type I error for evidence against pathogenicity was calculated as the probability of obtaining at least supporting or at least strong benign ACMG/AMP evidence when the relative risk was greater than one. The power of the OR methods was estimated as the probability of reaching the ACMG/AMP PS4 criterion (OR > 5 0, CI not including 1. 0, p value <0. 05). Following the analyses results of the simulated datasets, optimal LR cut-offs (to maximize power and minimize type I error) are used to define ACMG/AMP evidence strengths for the 92 variants included in the BCAC OncoArray dataset.
Simulated Datasets. Based on the simulation results for high-risk BRCA1 (RR > 9) and BRCA2 (RR > 5) variants, LR of strong and very strong evidence in favor of pathogenicity (LR ≥ 18 7) and of at least supporting evidence against 7 Human Mutation pathogenicity (LR ≤ 0 48) should be used in order to maintain a high power (>80%) and low type I error (<0. 05) (Supplementary Table  S2 ). Results for all measures in all simulated datasets show that the power to achieve strong evidence in favour of pathogenicity is consistently greater for the case-control LR method using age-specific breast cancer risks compared to standard OR analysis methods (Figure  1 , Supplementary Table  S2 ). The power to correctly categorize variants with a RR comparable to a typical BRCA1 PV was >80% in all scenarios except for small datasets (N ≤ 30,000) with causal variants present at a lower frequency (MAF = 0 00003) (Figure  1(a) ).
In addition, the case-control LR method can also be used to obtain evidence against pathogenicity, something that cannot be achieved using standard OR analysis methods. Results from simulated case-control datasets of benign variants (RR of 1, Figure  2 ) show that the casecontrol LR method using the age-specific RRs of the "average" BRCA1 PV exhibits adequate power (>80%) to identify variants with evidence against pathogenicity (LR ≤ 0 48) for larger datasets (N ≥ 30,000) and a MAF of 0. 0001. The implementation of the method to account for datasets with missing information, assuming the same age for all individuals, demonstrated reduced power and increased type I error in all simulations. However, the type I error was still less than 0. 05 in all cases (Supplementary Figures  S1  and S2 , Supplementary Table  S3 ).
BCAC OncoArray (i) 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. 8 Human Mutation 3. 2.
2. Case-Control LRs and ACMG/AMP Code Strengths. In the country-stratified baseline analysis (using the breast cancer ORs estimated from BRIDGES  [15] ), evidence in favor of pathogenicity (defined as LR ≥ 18 70 following the simulation cut-offs) was achieved for 6 variants (6. 5%) (Table  2 ), of which 3 variants were assigned very strong and another 3 strong strengths. Evidence against pathogenicity (defined as LR ≤ 0 48) was observed for 59 variants (64. 1%), of which 26 were assigned very strong, 14 strong, 7 moderate, and 12 supporting strengths. The results for the remaining 27 variants (29. 3%) were uninformative. Case-control LRs and corresponding ACMG/AMP code strengths for all 92 BRCA1 and BRCA2 variants are shown in Supplementary Table  S4. The different sensitivity analyses did not show any major discrepancies in the estimated LRs (Supplementary Table  S5 ).
Discussion This study provides a detailed description of the methodology to calculate case-control LRs for rare variants using case-control data based on age-and gene-specific relative risks and age information for noncarriers. The LRs are calculated by comparing the likelihood of the distribution of the variant of interest in cases and controls under the hypothesis that the variant has similar age-specific relative risks as the "average" pathogenic variant, compared to the hypothesis that it is not associated with increased (or decreased) disease risk. We evaluated the method using simulated datasets and further applied it to derive LRs for pathogenicity for individual variants from the analysis of genotype data from a large case-control study. These can now be used in combination with other evidence to inform variant classification-either according to ACMG/AMP classification standards and guidelines  [2, 3]  or using multifactorial likelihood modelling approaches  [4, 11].
Further, we provide user-friendly scripts and preformatted Excel cal-culators to facilitate the future implementation of this method for the calculation of case-control LRs. These resources may be readily applied for the calculation of LRs to be used in the classification of VUS in the BRCA1 and BRCA2 and other disease susceptibility genes with known penetrance values. Notably, our results demonstrate the improved performance of our LR-based method for assessing variant pathogenicity as it considers gene-and age-specific penetrance for carriers and age information for noncarriers. Using simulated case-control datasets, we show that the case-control LR method using age-specific breast cancer ORs from high-penetrance genes (e. g. , BRCA1 and BRCA2) outperforms other OR analysis methods. These observations reflect the fact that the method presented here is more suitable for the analysis of rare variants in a case-control setting.
We further provide cut-offs of LRs in favor or against pathogenicity to be used in a real setting. Analysis of the BCAC OncoArray data using our proposed method provided informative pathogenic ACMG/ AMP classification evidence for six out of the 92 variants analyzed. Furthermore, 59 variants reached evidence against pathogenicity, something that is not directly measured as a code strength through classical calculations of ORs. Given that, a priori, the vast majority of rare sequence variants (e. g. , BRCA1 and BRCA2) will be neutral with respect to risk, this is a key advantage of our approach. In contrast, using logistic regression analysis, the informative ACMG/AMP classification criterion PS4 (OR > 5 0, p value <0. 05, and CI not including 1. 0) was reached only for two variants. There are possible caveats that should be recognized.
The selection of cases or controls for a family history of cancer would affect the carrier probabilities. The likelihood ratios would then be inaccurate, but in principle, this could be considered by incorporating family history into the likelihoods, if known. Depletion of cases with known pathogenic variants by prior clinical sequencing could also bias the likelihood ratios; therefore, the method is best applied to 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.
It may also increase type I error because the likelihood ratio may be calculated for an age that is not appropriate for the dataset (for example, if the dataset consists predominantly of older individuals), although the type I error was still low in the simulations we considered. In the tabulated, preformatted calculator, we allow the user to incorporate individuals of unknown age at diagnosis or interview into any of the age groups specified. A conservative approach would be to include individuals of unknown age in the oldest age group. In this way, case-control genotypes from both existing data and new series, with and without age data, can be incorporated. However, we would like to emphasize that pooling series, particularly from different populations with different age/ethnicity structures or with different genotyping technologies, can lead to biased results.
Conclusions This manuscript describes in detail a novel method used for the calculation of the case-control LR to provide evidence of variant pathogenicity. This LR method is more informative compared to logistic regression analysis (or an OR calculation based on contingency tables and Fisher's exact test). It improves power as it considers age-and genespecific penetrance values and age information for noncarriers and can provide both evidence in favor of and against pathogenicity. In addition, this method can also be imple-mented towards the classification of VUS in any disease susceptibility gene for which disease penetrance has been reliably estimated. Open-access scripts and preformatted Excel calculators with code and instructions on how to use the method are available at the following address: https://github.
Acknowledgments This work was cofunded by the  Republic of Cyprus  through the  Research and Innovation Foundation  (Project:  CUL-TURE/AWARD-YR/0418/0017 ). ABS and MTP are supported by the  Australian National Health and Medical Research Funding  ( APP177524 ). DGO is funded by the  Telethon Cyprus (Telethon Cyprus :  33173233 ) through the  Cyprus Institute of Neurology and Genetics. BCAC  and individual BCAC and ENIGMA studies, funders, and grant numbers are detailed in the main text. BCAC is funded by the  European Union 's  Horizon 2020 Research and Innovation Programme  (grant numbers  634935  and  633784  for  BRIDGES  and B-CAST, respectively), and the  PERSPEC-TIVE I&I  project is funded by the  Government of Canada  through the  Genome Canada  and the  Canadian Institutes of Health Research , the  Ministère de l'Économie et de l'Innovation du Québec through Genome Québec , and the  Quebec Breast Cancer Foundation.
Additional funding for BCAC is provided via the Confluence project which is funded with intramural funds from the  National Cancer Institute Intramural Research Program  and  National Institutes of Health  and via the  CanRisk  project which is funded from the  Cancer Research UK  (grant  PPRPGM-Nov20/ 100002 ). Genotyping of the OncoArray was funded by the  NIH  Grant  U19 CA148065  and  Cancer Research UK  Grant  C1287/A16563 , and the  PERSPECTIVE  project was supported by the  Government of Canada  through  Genome Canada  and the  Canadian Institutes of Health Research  (grant  GPH-129344 ), and the  Ministère de l'Économie, Science et Innovation du Québec  through the  Genome Québec  and the  PSRSIIRI-701  grant and the  Quebec Breast Cancer Foundation. Funding for iCOGS came from the  European Community 's  Seventh Framework Programme  under grant agreement no.
The ABCS study was supported by the  Dutch Cancer Society  (grants  NKI 2007-3839  and  2009 4363 ). The  Australian Breast Cancer Tissue Bank  (ABCTB) was supported by the  National Health and Medical Research Council of Australia ,  The Cancer Institute NSW , and the  National Breast Cancer Foundation. The AHS study is supported by the intramural research program of the  National Institutes of Health , the  National Cancer Institute  (grant number  Z01-CP010119 ), and the  National Institute of Environmental Health Sciences  (grant number  Z01-ES049030 ). The work of the BBCC was partly funded by the  ELAN-Fond of the University Hospital of Erlangen. The BBCS is funded by the  Cancer Research UK and Breast Cancer 12 Human Mutation Now  and acknowledges NHS funding to the  NIHR Biomedical Research Centre  and the  National Cancer Research Network  (NCRN).
The BCEES was funded by the  National Health and Medical Research Council, Australia , and the  Cancer Council Western Australia  and acknowledges funding from the  National Breast Cancer Foundation  (JS). For the BCFR-NY, BCFR-PA, and  BCFR-UT , this work was supported by grant  UM1 CA164920  from the  National Cancer Institute. The BCINIS study is supported in part by the  Breast Cancer Research Foundation (BCRF). The  BREast Oncology GAlician Network  ( BREOGAN ) is funded by the  Acción Estratégica de Salud del Instituto de Salud Carlos III   FIS PI12/02125/ Cofinanciado and  FEDER PI17/00918/   Cofinanciado FEDER ;  Acción Estratégica  de Salud del Instituto de Salud Carlos III FIS Intrasalud (PI13/01136); Programa Grupos Emergentes, Cancer Genetics Unit, Instituto de Investigacion Biomedica Galicia Sur; Xerencia de Xestion Integrada de Vigo-SERGAS, Instituto de Salud Carlos III, Spain, Grant 10CSA012E; Consellería de Industria Programa Sectorial de Investigación Aplicada, PEME I + D e I + D Suma del Plan Gallego de Investigación, Desarrollo e Innovación Tecnológica de la Consellería de Industria de la Xunta de Galicia, Spain, Grant EC11-192; Fomento de la Investigación Clínica Independiente, Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain, Grant FEDER-Innterconecta; and Ministerio de Economia y Competitividad, Xunta de Galicia, Spain.
The BSUCH study was supported by the Dietmar-Hopp Foundation, the Helmholtz Society, and the German Cancer Research Center (DKFZ). CBCS is funded by the Canadian Cancer Society (grant # 313404) and the Canadian Institutes of Health Research. CCGP is supported by funding from the University of Crete. The CECILE study was supported by the Fondation de France, Institut National du Cancer (INCa), Ligue Nationale contre le Cancer, Agence Nationale de Sécurité Sanitaire, de l'Alimentation, de l'Environnement et du Travail (ANSES), and Agence Nationale de la Recherche (ANR). The CGPS was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council, and Herlev and Gentofte Hospital. The American Cancer Society funds the creation, maintenance, and updating of the CPS-II cohort.
The University of Westminster curates the DietCompLyf database was funded by Against Breast Cancer Registered Charity No. 1121258 and the NCRN. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by the Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, and Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), and Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation and the Stavros Niarchos Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro(AIRC), Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia, and Navarra, ISCIII RETIC (RD06/ 0020) (Spain); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk and MR/M012190/1 to EPIC-Oxford) (United Kingdom).
The ESTHER study was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. FHRISK and PROCAS are funded from NIHR grant PGfAR 0707-10031. DGE, AH, and WGN are supported by the NIHR Manchester Biomedical Research Centre (IS-BRC-1215-20007). The GC-HBOC (German Consortium of Hereditary Breast and Ovarian Cancer) is supported by the German Cancer Aid (grant nos. 110837 and 70114178, coordinator: Rita K. Schmutzler, Cologne) and the Federal Ministry of Education and Research, Germany (grant no. 01GY1901). This work was also funded by the European Regional Development Fund and Free State of Saxony, Germany (LIFE-Leipzig Research Centre for Civilization Diseases, project numbers 713-241202, 713-241202, 14505/2470, and 14575/2470). The GENICA was funded by the Federal Ministry of Education and Research (BMBF), Germany, grants 01KW9975/5, 01KW9976/8, 01KW9977/ 0, and 01KW0114; the Robert Bosch Foundation, Stuttgart; Deutsches Krebsforschungszentrum (DKFZ), Heidelberg; the Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, and the Department of Internal Medicine, Johanniter GmbH Bonn, Johanniter Krankenhaus, Bonn, Germany.
D. P. was supported by a grant 18-29-09129 of the Russian Foundation for Basic Research. E. K was supported by the mega grant from the Government of Russian Federation (2020-220-08-2197). Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, the Swedish Cancer Society, The Gustav V Jubilee Foundation, and Bert von Kantzows Foundation. The KARMA study was supported by Märit and Hans Rausing's Initiative Against Breast Cancer. The KBCP was financially supported by the Special Government Funding (VTR) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, and by the strategic funding of the University of Eastern Finland. LMBC is supported by the "Stichting tegen Kanker.
" DL is supported by the FWO. The MABCS study is funded by the Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov," MASA. The MARIE study was supported by the Deutsche Krebshilfe e. V. (70-2892-BR I, 106332, 108253, 108419, 110826, and 110828), the Hamburg Cancer Society, the German Cancer Research Center (DKFZ), and the Federal Ministry of Education and Research (BMBF) Germany (01KH0402). MBCSG is supported by grants from the Italian Association for Cancer Research (AIRC). The MCBCS was supported by the NIH grants R35CA253187, R01CA192393, R01CA116167, and R01CA176785, a NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), and the Breast Cancer Research Foundation. The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by the VicHealth and Cancer Council Victoria.
The Northern California Breast Cancer Family Registry (NC-BCFR) and Ontario Familial Breast Cancer Registry (OFBCR) were supported by a grant U01CA164920 from the USA National Cancer Institute of the National Institutes of Health. The Carolina Breast Cancer Study (NCBCS) was funded by the Komen Foundation, the National Cancer Institute (P50 CA058223, U54 CA156733, and U01 CA179715), and the North Carolina University Cancer Research Fund. The NHS was supported by the NIH grants P01 CA87969, UM1 CA186107, and U19 CA148065. The NHS2 was supported by NIH grants UM1 CA176726 and U19 CA148065. The ORIGO study was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). The PBCS was funded by the Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA.