The Experts below are selected from a list of 258972 Experts worldwide ranked by ideXlab platform
Elisa Alducci - One of the best experts on this subject based on the ideXlab platform.
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Large scale multifactorial Likelihood quantitative Analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification
Human Mutation, 2019Co-Authors: Françoise Revillion, Michael T. Parsons, Emma Tudini, Eric Hahnen, Barbara Wappenschmidt, Lídia Feliubadaló, Cora M. Aalfs, Simona Agata, Kristiina Aittomäki, Elisa AlducciAbstract:The multifactorial Likelihood Analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co‐segregation, family cancer history profile, co‐occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case‐control information. Research and clinical data for multifactorial Likelihood Analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the Likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non‐pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene‐specific calibration of evidence types used for variant classification.
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large scale multifactorial Likelihood quantitative Analysis of brca1 and brca2 variants an enigma resource to support clinical variant classification
Human Mutation, 2019Co-Authors: Michael T. Parsons, Emma Tudini, Eric Hahnen, Barbara Wappenschmidt, Lídia Feliubadaló, Cora M. Aalfs, Simona Agata, Kristiina Aittomäki, Hongyan Li, Elisa AlducciAbstract:The multifactorial Likelihood Analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial Likelihood Analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the Likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.
Michele Tizzoni - One of the best experts on this subject based on the ideXlab platform.
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seasonal transmission potential and activity peaks of the new influenza a h1n1 a monte carlo Likelihood Analysis based on human mobility
arXiv: Populations and Evolution, 2009Co-Authors: Duygu Balcan, Chiara Poletto, Bruno Goncalves, Paolo Bajardi, Jose J Ramasco, Daniela Paolotti, Nicola Perra, Michele Tizzoni, Wouter Van Den BroeckAbstract:On 11 June the World Health Organization officially raised the phase of pandemic alert (with regard to the new H1N1 influenza strain) to level 6. We use a global structured metapopulation model integrating mobility and transportation data worldwide in order to estimate the transmission potential and the relevant model parameters we used the data on the chronology of the 2009 novel influenza A(H1N1). The method is based on the maximum Likelihood Analysis of the arrival time distribution generated by the model in 12 countries seeded by Mexico by using 1M computationally simulated epidemics. An extended chronology including 93 countries worldwide seeded before 18 June was used to ascertain the seasonality effects. We found the best estimate R0 = 1.75 (95% CI 1.64 to 1.88) for the basic reproductive number. Correlation Analysis allows the selection of the most probable seasonal behavior based on the observed pattern, leading to the identification of plausible scenarios for the future unfolding of the pandemic and the estimate of pandemic activity peaks in the different hemispheres. We provide estimates for the number of hospitalizations and the attack rate for the next wave as well as an extensive sensitivity Analysis on the disease parameter values. We also studied the effect of systematic therapeutic use of antiviral drugs on the epidemic timeline. The Analysis shows the potential for an early epidemic peak occurring in October/November in the Northern hemisphere, likely before large-scale vaccination campaigns could be carried out. We suggest that the planning of additional mitigation policies such as systematic antiviral treatments might be the key to delay the activity peak inorder to restore the effectiveness of the vaccination programs.
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seasonal transmission potential and activity peaks of the new influenza a h1n1 a monte carlo Likelihood Analysis based on human mobility
BMC Medicine, 2009Co-Authors: Duygu Balcan, Chiara Poletto, Bruno Goncalves, Paolo Bajardi, Jose J Ramasco, Daniela Paolotti, Nicola Perra, Michele TizzoniAbstract:On 11 June the World Health Organization officially raised the phase of pandemic alert (with regard to the new H1N1 influenza strain) to level 6. As of 19 July, 137,232 cases of the H1N1 influenza strain have been officially confirmed in 142 different countries, and the pandemic unfolding in the Southern hemisphere is now under scrutiny to gain insights about the next winter wave in the Northern hemisphere. A major challenge is pre-empted by the need to estimate the transmission potential of the virus and to assess its dependence on seasonality aspects in order to be able to use numerical models capable of projecting the spatiotemporal pattern of the pandemic. In the present work, we use a global structured metapopulation model integrating mobility and transportation data worldwide. The model considers data on 3,362 subpopulations in 220 different countries and individual mobility across them. The model generates stochastic realizations of the epidemic evolution worldwide considering 6 billion individuals, from which we can gather information such as prevalence, morbidity, number of secondary cases and number and date of imported cases for each subpopulation, all with a time resolution of 1 day. In order to estimate the transmission potential and the relevant model parameters we used the data on the chronology of the 2009 novel influenza A(H1N1). The method is based on the maximum Likelihood Analysis of the arrival time distribution generated by the model in 12 countries seeded by Mexico by using 1 million computationally simulated epidemics. An extended chronology including 93 countries worldwide seeded before 18 June was used to ascertain the seasonality effects. We found the best estimate R 0 = 1.75 (95% confidence interval (CI) 1.64 to 1.88) for the basic reproductive number. Correlation Analysis allows the selection of the most probable seasonal behavior based on the observed pattern, leading to the identification of plausible scenarios for the future unfolding of the pandemic and the estimate of pandemic activity peaks in the different hemispheres. We provide estimates for the number of hospitalizations and the attack rate for the next wave as well as an extensive sensitivity Analysis on the disease parameter values. We also studied the effect of systematic therapeutic use of antiviral drugs on the epidemic timeline. The Analysis shows the potential for an early epidemic peak occurring in October/November in the Northern hemisphere, likely before large-scale vaccination campaigns could be carried out. The baseline results refer to a worst-case scenario in which additional mitigation policies are not considered. We suggest that the planning of additional mitigation policies such as systematic antiviral treatments might be the key to delay the activity peak in order to restore the effectiveness of the vaccination programs.
John Ellis - One of the best experts on this subject based on the ideXlab platform.
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Likelihood Analysis of the pmssm11 in light of lhc 13 tev data
European Physical Journal C, 2018Co-Authors: Emanuele Bagnaschi, J C Costa, Kazuki Sakurai, M Borsato, O Buchmueller, M Citron, A De Roeck, Matthew J Dolan, John EllisAbstract:We use MasterCode to perform a frequentist Analysis of the constraints on a phenomenological MSSM model with 11 parameters, the pMSSM11, including constraints from $$\sim 36$$ /fb of LHC data at 13 TeV and PICO, XENON1T and PandaX-II searches for dark matter scattering, as well as previous accelerator and astrophysical measurements, presenting fits both with and without the $$(g-2)_\mu $$ constraint. The pMSSM11 is specified by the following parameters: 3 gaugino masses $$M_{1,2,3}$$ , a common mass for the first-and second-generation squarks $$m_{\tilde{q}}$$ and a distinct third-generation squark mass $$m_{\tilde{q}_3}$$ , a common mass for the first-and second-generation sleptons $$m_{\tilde{\ell }}$$ and a distinct third-generation slepton mass $$m_{\tilde{\tau }}$$ , a common trilinear mixing parameter A, the Higgs mixing parameter $$\mu $$ , the pseudoscalar Higgs mass $$M_A$$ and $$\tan \beta $$ . In the fit including $$(g-2)_\mu $$ , a Bino-like $$\tilde{\chi }^0_{1}$$ is preferred, whereas a Higgsino-like $$\tilde{\chi }^0_{1}$$ is mildly favoured when the $$(g-2)_\mu $$ constraint is dropped. We identify the mechanisms that operate in different regions of the pMSSM11 parameter space to bring the relic density of the lightest neutralino, $$\tilde{\chi }^0_{1}$$ , into the range indicated by cosmological data. In the fit including $$(g-2)_\mu $$ , coannihilations with $$\tilde{\chi }^0_{2}$$ and the Wino-like $$\tilde{\chi }^\pm _{1}$$ or with nearly-degenerate first- and second-generation sleptons are active, whereas coannihilations with the $$\tilde{\chi }^0_{2}$$ and the Higgsino-like $$\tilde{\chi }^\pm _{1}$$ or with first- and second-generation squarks may be important when the $$(g-2)_\mu $$ constraint is dropped. In the two cases, we present $$\chi ^2$$ functions in two-dimensional mass planes as well as their one-dimensional profile projections and best-fit spectra. Prospects remain for discovering strongly-interacting sparticles at the LHC, in both the scenarios with and without the $$(g-2)_\mu $$ constraint, as well as for discovering electroweakly-interacting sparticles at a future linear $$e^+ e^-$$ collider such as the ILC or CLIC.
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Likelihood Analysis of the sub gut mssm in light of lhc 13 tev data
European Physical Journal C, 2018Co-Authors: J C Costa, Emanuele Bagnaschi, Kazuki Sakurai, M Borsato, O Buchmueller, M Citron, A De Roeck, Matthew J Dolan, John EllisAbstract:We describe a Likelihood Analysis using MasterCode of variants of the MSSM in which the soft supersymmetry-breaking parameters are assumed to have universal values at some scale $$M_\mathrm{in}$$ below the supersymmetric grand unification scale $$M_\mathrm{GUT}$$ , as can occur in mirage mediation and other models. In addition to $$M_\mathrm{in}$$ , such ‘sub-GUT’ models have the 4 parameters of the CMSSM, namely a common gaugino mass $$m_{1/2}$$ , a common soft supersymmetry-breaking scalar mass $$m_0$$ , a common trilinear mixing parameter A and the ratio of MSSM Higgs vevs $$\tan \beta $$ , assuming that the Higgs mixing parameter $$\mu > 0$$ . We take into account constraints on strongly- and electroweakly-interacting sparticles from $$\sim 36$$ /fb of LHC data at 13 TeV and the LUX and 2017 PICO, XENON1T and PandaX-II searches for dark matter scattering, in addition to the previous LHC and dark matter constraints as well as full sets of flavour and electroweak constraints. We find a preference for $$M_\mathrm{in}\sim 10^5$$ to $$10^9 \,\, \mathrm {GeV}$$ , with $$M_\mathrm{in}\sim M_\mathrm{GUT}$$ disfavoured by $$\Delta \chi ^2 \sim 3$$ due to the $$\mathrm{BR}(B_{s, d} \rightarrow \mu ^+\mu ^-)$$ constraint. The lower limits on strongly-interacting sparticles are largely determined by LHC searches, and similar to those in the CMSSM. We find a preference for the LSP to be a Bino or Higgsino with $$m_{\tilde{\chi }^0_{1}} \sim 1 \,\, \mathrm {TeV}$$ , with annihilation via heavy Higgs bosons H / A and stop coannihilation, or chargino coannihilation, bringing the cold dark matter density into the cosmological range. We find that spin-independent dark matter scattering is likely to be within reach of the planned LUX-Zeplin and XENONnT experiments. We probe the impact of the $$(g-2)_\mu $$ constraint, finding similar results whether or not it is included.
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Likelihood Analysis of the pmssm11 in light of lhc 13 tev data
arXiv: High Energy Physics - Phenomenology, 2017Co-Authors: Emanuele Bagnaschi, J C Costa, Kazuki Sakurai, M Borsato, O Buchmueller, M Citron, A De Roeck, Matthew J Dolan, John EllisAbstract:We use MasterCode to perform a frequentist Analysis of the constraints on a phenomenological MSSM model with 11 parameters, the pMSSM11, including constraints from ~ 36/fb of LHC data at 13 TeV and PICO, XENON1T and PandaX-II searches for dark matter scattering, as well as previous accelerator and astrophysical measurements, presenting fits both with and without the $(g-2)_{\mu}$ constraint. The pMSSM11 is specified by the following parameters: 3 gaugino masses $M_{1,2,3}$, a common mass for the first-and second-generation squarks $m_{\tilde{q}}$ and a distinct third-generation squark mass $m_{\tilde{q}_3}$, a common mass for the first-and second-generation sleptons $m_{\tilde l}$ and a distinct third-generation slepton mass $m_{\tilde \tau}$, a common trilinear mixing parameter $A$, the Higgs mixing parameter $\mu$, the pseudoscalar Higgs mass $M_A$ and $\tan\beta$. In the fit including $(g-2)_{\mu}$, a Bino-like $\tilde\chi^0_1$ is preferred, whereas a Higgsino-like $\tilde \chi^0_1$ is favoured when the $(g-2)_{\mu}$ constraint is dropped. We identify the mechanisms that operate in different regions of the pMSSM11 parameter space to bring the relic density of the lightest neutralino, $\tilde\chi^0_1$, into the range indicated by cosmological data. In the fit including $(g-2)_{\mu}$, coannihilations with $\tilde \chi^0_2$ and the Wino-like $\tilde\chi^{\pm}_1$ or with nearly-degenerate first- and second-generation sleptons are favoured, whereas coannihilations with the $\tilde \chi^0_2$ and the Higgsino-like $\tilde\chi^{\pm}_1$ or with first- and second-generation squarks may be important when the $(g-2)_{\mu}$ constraint is dropped. Prospects remain for discovering strongly-interacting sparticles at the LHC as well as for discovering electroweakly-interacting sparticles at a future linear $e^+ e^-$ collider such as the ILC or CLIC.
Chiara Poletto - One of the best experts on this subject based on the ideXlab platform.
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Assessment of the Middle East respiratory syndrome coronavirus (MERS-CoV) epidemic in the Middle East and risk of international spread using a novel maximum Likelihood Analysis approach
Eurosurveillance, 2014Co-Authors: Chiara Poletto, C Pelat, Daniel Levy-bruhl, Yazdan Yazdanpanah, Pierre-yves Boëlle, Vittoria ColizzaAbstract:The emergence of the novel Middle East (ME) respiratory syndrome coronavirus (MERS-CoV) has raised global public health concerns regarding the current situation and its future evolution. Here we propose an integrative maximum Likelihood Analysis of both cluster data in the ME and importations in a set of European countries to assess the transmission scenario and incidence of sporadic infections. Our approach is based on a spatial-transmission model integrating mobility data worldwide and allows for variations in the zoonotic/environmental transmission and under-ascertainment. Maximum Likelihood estimates for the ME, considering outbreak data up to 31 August 2013, indicate the occurrence of a subcritical epidemic with a reproductive number R of 0.50 (95% confidence interval (CI): 0.30–0.77) associated with a daily rate of sporadic introductions p sp of 0.28 (95% CI: 0.12–0.85). Infections in the ME appear to be mainly dominated by zoonotic/environmental transmissions, with possible under-ascertainment (ratio of estimated to observed (0.116) sporadic cases equal to 2.41, 95% CI: 1.03–7.32). No time evolution of the situation emerges. Analyses of flight passenger data from ME countries indicate areas at high risk of importation. While dismissing an immediate threat for global health security, this Analysis provides a baseline scenario for future reference and updates, suggests reinforced surveillance to limit under-ascertainment, and calls for alertness in high importation risk areas worldwide.
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seasonal transmission potential and activity peaks of the new influenza a h1n1 a monte carlo Likelihood Analysis based on human mobility
arXiv: Populations and Evolution, 2009Co-Authors: Duygu Balcan, Chiara Poletto, Bruno Goncalves, Paolo Bajardi, Jose J Ramasco, Daniela Paolotti, Nicola Perra, Michele Tizzoni, Wouter Van Den BroeckAbstract:On 11 June the World Health Organization officially raised the phase of pandemic alert (with regard to the new H1N1 influenza strain) to level 6. We use a global structured metapopulation model integrating mobility and transportation data worldwide in order to estimate the transmission potential and the relevant model parameters we used the data on the chronology of the 2009 novel influenza A(H1N1). The method is based on the maximum Likelihood Analysis of the arrival time distribution generated by the model in 12 countries seeded by Mexico by using 1M computationally simulated epidemics. An extended chronology including 93 countries worldwide seeded before 18 June was used to ascertain the seasonality effects. We found the best estimate R0 = 1.75 (95% CI 1.64 to 1.88) for the basic reproductive number. Correlation Analysis allows the selection of the most probable seasonal behavior based on the observed pattern, leading to the identification of plausible scenarios for the future unfolding of the pandemic and the estimate of pandemic activity peaks in the different hemispheres. We provide estimates for the number of hospitalizations and the attack rate for the next wave as well as an extensive sensitivity Analysis on the disease parameter values. We also studied the effect of systematic therapeutic use of antiviral drugs on the epidemic timeline. The Analysis shows the potential for an early epidemic peak occurring in October/November in the Northern hemisphere, likely before large-scale vaccination campaigns could be carried out. We suggest that the planning of additional mitigation policies such as systematic antiviral treatments might be the key to delay the activity peak inorder to restore the effectiveness of the vaccination programs.
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seasonal transmission potential and activity peaks of the new influenza a h1n1 a monte carlo Likelihood Analysis based on human mobility
BMC Medicine, 2009Co-Authors: Duygu Balcan, Chiara Poletto, Bruno Goncalves, Paolo Bajardi, Jose J Ramasco, Daniela Paolotti, Nicola Perra, Michele TizzoniAbstract:On 11 June the World Health Organization officially raised the phase of pandemic alert (with regard to the new H1N1 influenza strain) to level 6. As of 19 July, 137,232 cases of the H1N1 influenza strain have been officially confirmed in 142 different countries, and the pandemic unfolding in the Southern hemisphere is now under scrutiny to gain insights about the next winter wave in the Northern hemisphere. A major challenge is pre-empted by the need to estimate the transmission potential of the virus and to assess its dependence on seasonality aspects in order to be able to use numerical models capable of projecting the spatiotemporal pattern of the pandemic. In the present work, we use a global structured metapopulation model integrating mobility and transportation data worldwide. The model considers data on 3,362 subpopulations in 220 different countries and individual mobility across them. The model generates stochastic realizations of the epidemic evolution worldwide considering 6 billion individuals, from which we can gather information such as prevalence, morbidity, number of secondary cases and number and date of imported cases for each subpopulation, all with a time resolution of 1 day. In order to estimate the transmission potential and the relevant model parameters we used the data on the chronology of the 2009 novel influenza A(H1N1). The method is based on the maximum Likelihood Analysis of the arrival time distribution generated by the model in 12 countries seeded by Mexico by using 1 million computationally simulated epidemics. An extended chronology including 93 countries worldwide seeded before 18 June was used to ascertain the seasonality effects. We found the best estimate R 0 = 1.75 (95% confidence interval (CI) 1.64 to 1.88) for the basic reproductive number. Correlation Analysis allows the selection of the most probable seasonal behavior based on the observed pattern, leading to the identification of plausible scenarios for the future unfolding of the pandemic and the estimate of pandemic activity peaks in the different hemispheres. We provide estimates for the number of hospitalizations and the attack rate for the next wave as well as an extensive sensitivity Analysis on the disease parameter values. We also studied the effect of systematic therapeutic use of antiviral drugs on the epidemic timeline. The Analysis shows the potential for an early epidemic peak occurring in October/November in the Northern hemisphere, likely before large-scale vaccination campaigns could be carried out. The baseline results refer to a worst-case scenario in which additional mitigation policies are not considered. We suggest that the planning of additional mitigation policies such as systematic antiviral treatments might be the key to delay the activity peak in order to restore the effectiveness of the vaccination programs.
Michael T. Parsons - One of the best experts on this subject based on the ideXlab platform.
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Large scale multifactorial Likelihood quantitative Analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification
Human Mutation, 2019Co-Authors: Françoise Revillion, Michael T. Parsons, Emma Tudini, Eric Hahnen, Barbara Wappenschmidt, Lídia Feliubadaló, Cora M. Aalfs, Simona Agata, Kristiina Aittomäki, Elisa AlducciAbstract:The multifactorial Likelihood Analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co‐segregation, family cancer history profile, co‐occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case‐control information. Research and clinical data for multifactorial Likelihood Analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the Likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non‐pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene‐specific calibration of evidence types used for variant classification.
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large scale multifactorial Likelihood quantitative Analysis of brca1 and brca2 variants an enigma resource to support clinical variant classification
Human Mutation, 2019Co-Authors: Michael T. Parsons, Emma Tudini, Eric Hahnen, Barbara Wappenschmidt, Lídia Feliubadaló, Cora M. Aalfs, Simona Agata, Kristiina Aittomäki, Hongyan Li, Elisa AlducciAbstract:The multifactorial Likelihood Analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial Likelihood Analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the Likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.