Opportunities and challenges for the computational interpretation of rare variation in clinically important genes

McInnes, Gregory; Sharo, Andrew G.; Koleske, Megan L.; Brown, Julia E.H.; Norstad, Matthew; Adhikari, Aashish N.; Wang, Sheng; Brenner, Steven E.; Halpern, Jodi; Koenig, Barbara A.; Magnus, David C.; Gallagher, Renata C.; Giacomini, Kathleen M.; Altman, Russ B.

doi:10.1016/j.ajhg.2021.03.003

Cited by 89Genomics and Rare DiseasesArtificial Intelligence in Healthcare and EducationMetabolism and Genetic Disorders

Opportunities and challenges for the computational interpretation of rare variation in clinically important genes

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McInnes, Gregory, Sharo, Andrew G., Koleske, Megan L., Brown, Julia E.H., Norstad, Matthew, Adhikari, Aashish N., Wang, Sheng, Brenner, Steven E. +6 more·2021·The American Journal of Human Genetics

DOI 10.1016/j.ajhg.2021.03.003PMC PMC8059338

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Abstract

Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

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Variants of uncertain significance are rapidly accumulating in variant databases and their classification represents a major challenge in clinical genetics.
“VUSs are rapidly accumulating in variant databases and their classification represents a major challenge in clinical genetics.1”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as [1]

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Clinical and genetical heterogeneity of late-onset multiple acyl-coenzyme A dehydrogenase deficiency
Grünert, S. C. (2014) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-014-0117-5 · Cited by 192 · cited 1× · see citing works
“For late-onset IEMs, genotype may predict age of onset, which can inform appropriate patient monitoring.(99) Similarly for PGx, if the potential consequences of prescribing a drug are life threatening, the clinician may select an alternate therapy.”
Background
Pharmacogenetics at Scale: An Analysis of the UK Biobank
McInnes, G., Lavertu, A., Sangkuhl, K., Klein, T. E., Whirl‐Carrillo, M., & Altman, R. B. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2122 · Cited by 184 · cited 1× · see citing works
“8 In the past, clinical practice overlooked the inﬂuence of genetics on drug response and—except for several extreme case 9 —used a standardized dose of any particular drug for most patients, with some trial-and-adjustment to determine the ideal drug and dosage.”
Background
A systematic approach to assessing the clinical significance of genetic variants
Duzkale, H., Shen, J., McLaughlin, H. M., Alfares, A. A., Kelly, M., Pugh, T. J., Funke, B., Rehm, H. L., & Lebo, M. S. (2013) · Clinical Genetics · doi:10.1111/cge.12257 · Cited by 183 · cited 1× · see citing works
“21 Variants in genes underlying IEMs frequently require functional characterization to be validated as causal.”
Background
Genetic Modifiers and Rare Mendelian Disease
Rahit, K. M. T. H., & Tarailo‐Graovac, M. (2020) · Genes · doi:10.3390/genes11030239 · Cited by 171 · cited 1× · see citing works
“As the ﬁeld matures to consider the role of genetic modiﬁers, 62 as well as social and environmental interactions, 63 genotypes of diverse individuals are needed to consider the effects of genetic modiﬁers and the environment on variants.”
Background
The evolving landscape of expanded carrier screening: challenges and opportunities
Kraft, S. A., Duenas, D. M., Wilfond, B. S., & Goddard, K. A. B. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0273-4 · Cited by 159 · cited 1× · see citing works
“However, interpretation of genetic screening results still faces signiﬁcant challenges, 19 especially in cases identifying variants of uncertain signiﬁcance (VUSs) where risk for inherited disease cannot be deﬁni-tively assessed and actionability is questionable.”
Background
Birth prevalence of disorders detectable through newborn screening by race/ethnicity
Feuchtbaum, L., Carter, J., Dowray, S., Currier, R. J., & Lorey, F. (2012) · Genetics in Medicine · doi:10.1038/gim.2012.76 · Cited by 143 · cited 2× · see citing works
“Newborn screening programs, with their mandatory collection and the near universal application of testing, provide a diverse and truly representative set of individuals.(16) That said, racial discrimination in healthcare and healthcare research is not simply resolvable through technical fixes.”
Background
Predicting functional effects of missense variants in voltage-gated sodium and calcium channels
Heyne, H. O., Baez-Nieto, D., Iqbal, S., Palmer, D. S., Brunklaus, A., May, P., Collaborative, E., Johannesen, K. M., Lauxmann, S., Lemke, J. R., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay6848 · Cited by 140 · see citing works
An optimized prediction framework to assess the functional impact of pharmacogenetic variants
Zhou, Y., Mkrtchian, S., Kumondai, M., Hiratsuka, M., & Lauschke, V. M. (2018) · The Pharmacogenomics Journal · doi:10.1038/s41397-018-0044-2 · Cited by 136 · cited 2× · see citing works
“Additionally, pharmacogenes are not under the same evolutionary constraint as genes involved in disease, limiting the effectiveness of most predictive algorithms.(47,53) To combine the best features of variant databases and computational predictors, automated systems that use both in tandem are already being tested to…”
BackgroundMethod
Molecular genetics and diagnosis of phenylketonuria: state of the art
Blau, N., Shen, N., & Carducci, C. (2014) · Expert Review of Molecular Diagnostics · doi:10.1586/14737159.2014.923760 · Cited by 124 · cited 1× · see citing works
“Some of these ultra-rare variants may be de novo mutations, and the individual may be the only person known to harbor that exact variant.(64) The vast majority of ultra-rare variants are absent from clinical databases, indicating that the current approach of cataloguing observed genetic variants fails when allele freq…”
Background
Pharmacogenomic Testing: Clinical Evidence and Implementation Challenges
Hippman, C., & Nislow, C. (2019) · Journal of Personalized Medicine · doi:10.3390/jpm9030040 · Cited by 123 · cited 1× · see citing works
“Genomic literacy across different healthcare professional roles is limited.(15,30) The disclosing of sequencing results should be contingent upon what has been previously explained to the patient/ parent about incidental findings and potential treatments.”
Background
The Evolution of PharmVar
Gaedigk, A., Sangkuhl, K., Whirl‐Carrillo, M., Twist, G. P., Klein, T. E., & Miller, N. (2018) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1275 · Cited by 122 · see citing works
Massively parallel variant characterization identifies <i>NUDT15</i> alleles associated with thiopurine toxicity
Suiter, C. C., Moriyama, T., Matreyek, K. A., Yang, W., Scaletti, E. R., Nishii, R., Yang, W., Hoshitsuki, K., Singh, M., Trehan, A., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1915680117 · Cited by 121 · cited 1× · see citing works
“The American Journal of Human Genetics 108 , 535–548, April 1, 2021 537 include enzyme activity assays 23 and transporter uptake assays.”
Method
MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease
Taroni, J., Grayson, P. C., Hu, Q., Eddy, S. F., Kretzler, M., Merkel, P. A., & Greene, C. S. (2019) · Cell Systems · doi:10.1016/j.cels.2019.04.003 · Cited by 120 · cited 1× · see citing works
“The resulting model may perform very well on Y because the ‘‘lessons’’ learned in modeling X transfer well to Y. 84–89 There are several ﬂavors of transfer learning that have been applied to applications in genetics and proteomics.”
Method
Mind the Gap: Putting Evidence into Practice in the Era of Learning Health Systems
Guise, J., Savitz, L. A., & Friedman, C. P. (2018) · Journal of General Internal Medicine · doi:10.1007/s11606-018-4633-1 · Cited by 102 · cited 1× · see citing works
“Learning health systems have long been proposed as models for improving health-care, 94–96 but integration of genetic data into such a system would allow for the accumulation of data to train more sophisticated predictive models as well as an opportunity to iteratively improve upon such algorithms.”
Background
Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions
Reisberg, S., Krebs, K., Lepamets, M., Kals, M., Mägi, R., Metsalu, K., Lauschke, V. M., Vilo, J., & Milani, L. A. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0337-5 · Cited by 101 · see citing works
If you build it, they will come: unintended future uses of organised health data collections
O’Doherty, K. C., Christofides, E., Yen, J., Bentzen, H. B., Burke, W., Hallowell, N., Koenig, B. A., & Willison, D. J. (2016) · BMC Medical Ethics · doi:10.1186/s12910-016-0137-x · Cited by 91 · cited 1× · see citing works
“government bodies, particularly in regard to informed consent and anonymization of data.(106) This requires careful consideration for how questions of beneficence regarding collections and distribution of quality care across populations can vary and ultimately widen health disparities.”
Background
Deep Learning for Genomics: A Concise OverviewPreprint
Yue, T., Wang, Y., Zhang, L., Gu, C., Xue, H., Wang, W., Lyu, Q., & Dun, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.00810 · Cited by 85 · see citing works
Sequencing Newborns:A Call for Nuanced Use of Genomic Technologies
Johnston, J., Lantos, J. D., Goldenberg, A. J., Chen, F., Parens, E., Koenig, B. A., & members of the NSIGHT Ethics and Policy Advisory Board (2018) · The Hastings Center Report · doi:10.1002/hast.874 · Cited by 85 · cited 4× · see citing works
“107 Taking the case of newborn DBSs, the current justiﬁcation for the mandatory nature of newborn screening rests on the potential harms to the child were they not screened for these treatable conditions (see Johnston et al., 2018 for a full historical justiﬁcation).”
BackgroundInfluential
Clinical Genetics Lacks Standard Definitions and Protocols for the Collection and Use of Diversity Measures
Popejoy, A. B., Crooks, K., Fullerton, S. M., Hindorff, L. A., Hooker, G. W., Koenig, B. A., Pino, N., Ramos, E. M., Ritter, D., Wand, H., et al. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.05.005 · Cited by 82 · cited 1× · see citing works
“15,30 The disclosing of sequencing results should be contingent upon what has been previously explained to the patient/ parent about incidental ﬁndings and potential treatments.”
Background
Toward predicting CYP2D6-mediated variable drug response from CYP2D6 gene sequencing data
van der Lee, M., Allard, W. G., Vossen, R. H. A. M., Baak-Pablo, R. F., Menafra, R., Deiman, B. A. L. M., Deenen, M. J., Neven, P., Johansson, I., Gastaldello, S., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abf3637 · Cited by 79 · see citing works
Privacy and ethical challenges in next-generation sequencing
Martinez‐Martin, N., & Magnus, D. C. (2019) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2019.1599685 · Cited by 76 · cited 1× · see citing works
“Uncertain and incidental (or secondary) results in clinical care should be considered in the context of existing slippages of fiduciary obligations—such as clinician biases and/or patient mistrust—that emerging tests may or may not be able to compensate for.(33) The NHGRI has called for greater diversity among the gen…”
Background
PharmGKB very important pharmacogene: SLCO1B1
Oshiro, C. M., Mangravite, L. M., Klein, T. E., & Altman, R. B. (2010) · Pharmacogenetics and Genomics · doi:10.1097/fpc.0b013e328333b99c · Cited by 72 · cited 1× · see citing works
“include enzyme activity assays(23) and transporter uptake assays.(24) Pharmacogenetic variation can further be validated as clinically important in pharmacokinetic/pharmacodynamic studies, whereby individuals with a particular genotype exhibit significantly different drug response compared with individuals with a diff…”
Background
Gene family information facilitates variant interpretation and identification of disease-associated genes in neurodevelopmental disorders
Lal, D., May, P., Samocha, K. E., Kosmicki, J. A., Robinson, E., Møller, R., Krause, R., Nüernberg, P., Weckhuysen, S., Jonghe, P. D., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00725-6 · Cited by 66 · see citing works
Carrier screening by next‐generation sequencing: health benefits and cost effectiveness
Azimi, M., Schmaus, K., Greger, V., Neitzel, D., Rochelle, R., & Dinh, T. (2016) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.204 · Cited by 64 · cited 1× · see citing works
“Carrier testing provides an opportunity to detect rare variants in IEMs and other disease-associated genes 18 before conception.”
Background
The functional genomics laboratory: functional validation of genetic variants
Rodenburg, R. (2018) · Journal of Inherited Metabolic Disease · doi:10.1007/s10545-018-0146-7 · Cited by 64 · cited 3× · see citing works
“, RNA sequencing [RNA-seq] and immunoblot) can reveal variant consequences on splicing and allele expression or differential protein expression, respectively.(22) The validation of clinically important variants relating to PGx is also complex.”
BackgroundInfluential
Leveraging Quality Improvement to Achieve Equity in Health Care
Green, A. R., Tan‐McGrory, A., Cervantes, M. C., & Betancourt, J. R. (2010) · The Joint Commission Journal on Quality and Patient Safety · doi:10.1016/s1553-7250(10)36065-x · Cited by 62 · cited 1× · see citing works
“This requires careful consideration for how questions of beneficence regarding collections and distribution of quality care across populations can vary and ultimately widen health disparities.(107) Taking the case of newborn DBSs, the current justification for the mandatory nature of newborn screening rests on the pot…”
Background
Transfer learning enables prediction of CYP2D6 haplotype function
McInnes, G., Dalton, R., Sangkuhl, K., Whirl‐Carrillo, M., Lee, S., Tsao, P. S., Gaedigk, A., Altman, R. B., & Woodahl, E. L. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008399 · Cited by 55 · cited 1× · see citing works
“Several methods have been developed specifically for the evaluation of alleles in pharmacogenes, namely CYP2D6 (MIM: 124030).(80,81) These purpose-built models outperform existing methods and are capable of assessing the impact of any combination of variants observed in a haplotype rather than single variants.”
Method
Comparative evolutionary pharmacogenetics of CYP2D6 in Ngawbe and Embera Amerindians of Panama and Colombia: role of selection versus drift in world populations.
Lf, J., Eichelbaum, M., Griese, E.-U., Inaba, T., & Td, A. (1999) · PubMed · Cited by 53 · see citing works
Warfarin Pharmacogenetics: An Illustration of the Importance of Studies in Minority Populations
Perera, M. A., Cavallari, L. H., & Johnson, J. A. (2014) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2013.209 · Cited by 49 · cited 1× · see citing works
“, African), which would only compound existing injustice in healthcare access for underrepresented populations.(59,60) Disparity in ancestry representation is especially stark in data sources for genome-wide association studies, where European ancestry disproportionately represents 81% of the dataset population.”
Background
Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance
Li, B., Mendenhall, J., Kroncke, B. M., Taylor, K. C., Huang, H., Smith, D. K., Vanoye, C. G., Blume, J. D., George, A. L., Sanders, C. R., et al. (2017) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.117.001754 · Cited by 45 · cited 1× · see citing works
“52 Despite their promise, such bespoke methods are constrained by the The American Journal of Human Genetics 108 , 535–548, April 1, 2021 539 limited data available for most genes, such as the number of known pathogenic variants and associated functional data.”
Method
Evaluation of CADD Scores in Curated Mismatch Repair Gene Variants Yields a Model for Clinical Validation and Prioritization
van der Velde, K. J., Kuiper, J., Thompson, B. A., Plazzer, J., van Valkenhoef, G., Haan, M., Jongbloed, J. D. H., Wijmenga, C., de Koning, T. J., Abbott, K. M., et al. (2015) · Human Mutation · doi:10.1002/humu.22798 · Cited by 43 · cited 1× · see citing works
“43 REVEL, a meta-predictor, uses the ensemble of scores from several prediction algorithms like CADD, each with different strengths and weaknesses, and is trained to differentiate rare unlabeled variants from HGMD pathogenic variants.”
Method
Professionally Responsible Disclosure of Genomic Sequencing Results in Pediatric Practice
McCullough, L. B., Brothers, K. B., Chung, W. K., Joffe, S., Koenig, B. A., Wilfond, B. S., & Yu, J. (2015) · PEDIATRICS · doi:10.1542/peds.2015-0624 · Cited by 37 · see citing works
A Gene-Specific Method for Predicting Hemophilia-Causing Point Mutations
Hamasaki‐Katagiri, N., Salari, R., Wu, A., Qi, Y., Schiller, T., Filiberto, A. C., Schisterman, E. F., Komar, A. A., Przytycka, T. M., & Kimchi‐Sarfaty, C. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.037 · Cited by 33 · see citing works
Pharmacogenomics and big genomic data: from lab to clinic and back again
Lavertu, A., McInnes, G., Daneshjou, R., Whirl‐Carrillo, M., Klein, T. E., & Altman, R. B. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy116 · Cited by 32 · cited 1× · see citing works
“PGx describes how an individual’s response to medication is influenced by genetic variation in pharmacogenes: genes encoding proteins involved in the pharmacokinetics and pharmacodynamics of a drug.(5) Many pharmacogenes have common genetic variants with known clinical significance.”
Background
Predicting changes to INa from missense mutations in human SCN5A
Clerx, M., Heijman, J., Collins, P. J., & Volders, P. G. A. (2018) · Scientific Reports · doi:10.1038/s41598-018-30577-5 · Cited by 31 · see citing works
Determinants of Riboflavin Responsiveness in Multiple Acyl-CoA Dehydrogenase Deficiency
Yıldız, Y., Talim, B., Haliloğlu, G., Topaloğlu, H., Akçören, Z., Dursun, A., Hs, S., Coşkun, T., & Tokatlı, A. (2019) · Pediatric Neurology · doi:10.1016/j.pediatrneurol.2019.06.015 · Cited by 30 · see citing works
A proposed approach to accelerate evidence generation for genomic-based technologies in the context of a learning health system
Lu, C. Y., Williams, M. S., Ginsburg, G. S., Toh, S., Brown, J. S., & Khoury, M. J. (2017) · Genetics in Medicine · doi:10.1038/gim.2017.122 · Cited by 27 · cited 1× · see citing works
“A genomic learning healthcare system (GLHS) would operate in much the same way, but with the addition that clinical decision support is provided on the basis of genetic data as well as clinical data.(34) In this proposed system, collection, sequencing, and analysis of patient data would be required as a first step and…”
Background
Gene-Specific Function Prediction for Non-Synonymous Mutations in Monogenic Diabetes Genes
Li, Q., Liu, X., Gibbs, R. A., Boerwinkle, E., Polychronakos, C., & Qu, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104452 · Cited by 25 · see citing works
Genome annotation across species using deep convolutional neural networks
Khodabandelou, G., Routhier, E., & Mozziconacci, J. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.278 · Cited by 23 · see citing works
Disparities in discovery of pathogenic variants for autosomal recessive non-syndromic hearing impairment by ancestry
Chakchouk, I., Zhang, D., Zhang, Z., Francioli, L. C., Santos‐Cortez, R. L. P., Schrauwen, I., & Leal, S. M. (2019) · European Journal of Human Genetics · doi:10.1038/s41431-019-0417-2 · Cited by 20 · cited 1× · see citing works
“For example, ClinVar was recently found to be missing a large number of hearing impairment variants that primarily affect individuals of African ancestry, 58 most likely indicative of a broader pattern.”
Background
Health-care providers’ perspectives on uncertainty generated by variant forms of newborn screening targets
Azzopardi, P. J., Upshur, R. E. G., Luca, S., Venkataramanan, V., Potter, B. K., Chakraborty, P. K., & Hayeems, R. Z. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0670-3 · Cited by 20 · see citing works
Ethical issues in genetic screening for cancer
Vineis, P. (1997) · Annals of Oncology · doi:10.1023/a:1008296719733 · Cited by 15 · cited 1× · see citing works
“28 Perhaps most thoroughly documented in cancer genetics, 29 the clinical return of ge-netic results is rarely straightforward.”
Background
Influence of PAH Genotype on Sapropterin Response in PKU: Results of a Single-Center Cohort Study
Leuders, S., Wolfgart, E., Ott, T., du Moulin, M., van Teeffelen-Heithoff, A., Vogelpohl, L., Och, U., Marquardt, T., Weglage, J., Feldmann, R., et al. (2013) · JIMD Reports · doi:10.1007/8904_2013_263 · Cited by 12 · cited 1× · see citing works
“Some IEMs respond to pharmaceutical interventions, and genotype may predict likelihood of response to a specific medication.(97,98) For late-onset IEMs, genotype may predict age of onset, which can inform appropriate patient monitoring.”
Background
Goal-directed therapy in patients with early acute kidney injury: a multicenter randomized controlled trial
Amêndola, C. P., Silva, J. M., Carvalho, T., Sanches, L. C., Silva, U. V. D. A. E., Almeida, R., Burdmann, E. A., Lima, E., Brandão, F. F. B., Ferreira, R. S., et al. (2018) · Clinics · doi:10.6061/clinics/2018/e327 · Cited by 11 · see citing works
Genome Functional Annotation across Species using Deep Convolutional Neural NetworksPreprint
Khodabandelou, G., Routhier, E., & Mozziconacci, J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/330308 · Cited by 7 · cited 1× · see citing works
“With the rapid growth in the availability of biological data, deep learning has also been extensively used in bioinformatics, 68–75 including transcription factor binding site prediction, 76 genome functional annotation, 77 and assessment of variant function.”
Method
Gene‐specific features enhance interpretation of mutational impact on acid α‐glucosidase enzyme activity
Adhikari, A. N. (2019) · Human Mutation · doi:10.1002/humu.23846 · Cited by 7 · see citing works
Unknown significance
Couzin-Frankel, J. (2014) · Science · doi:10.1126/science.346.6214.1167 · Cited by 6 · see citing works

Flagged first, then by citations

6 of the 115 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

[81]van der Lee M. Allard W.G. Rolf H.A. Baak-Pablo R.F. Menafra R. Birgit A.L. Deenen M.J. Neven P. Johansson I. Gastaldello S. A unifying model to predict variable drug response for personalised medicine bioRxiv 2020 10.1101/2020.03.02.967554 · DOI
[90]McInnes G. Dalton R. Sangkuhl K. Whirl-Carrillo M. Lee S.-B. Tsao P.S. Gaedigk A. Altman R.B. Woodahl E.L. Transfer learning enables prediction of CYP2D6 haplotype function bioRxiv 2020 10.1101/684357 PMC7660895 33137098 · DOI

Sorted by citations

Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria
Pejaver, V., Byrne, A. B., Feng, B.-J., Pagel, K. A., Mooney, S. D., Karchin, R., O’Donnell-Luria, A., Harrison, S. M., Tavtigian, S. V., Greenblatt, M. S., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.10.013 · Cited by 589 · see citing works
“VUSs are rapidly accumulating in variant databases and their classification represents a major challenge in clinical genetics.(1) To help standardize the approach of clinical genetic/ genomic testing laboratories, the American College of Medical Genetics and Genomics and the Association for”
Background
CADD v1.7: Using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions
Schubach, M., Maass, T., Nazaretyan, L., Röner, S., & Kircher, M. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad989 · Cited by 455 · see citing works
Targeting mutations in cancer
Waarts, M. R., Stonestrom, A. J., Park, Y. C., & Levine, R. L. (2022) · Journal of Clinical Investigation · doi:10.1172/jci154943 · Cited by 236 · see citing works
A deep catalogue of protein-coding variation in 983,578 individuals
Sun, K. Y., Bai, X., Chen, S., Bao, S., Zhang, C., Kapoor, M., Backman, J., Joseph, T., Maxwell, E., Mitra, G., et al. (2024) · Nature · doi:10.1038/s41586-024-07556-0 · Cited by 127 · see citing works
“Understanding VUSs is currently a bottleneck in the interpretation of variation in clinically relevant genes and a challenge in clinical management 19 .”
Background
The Role of Genome Sequencing in Neonatal Intensive Care Units
Kingsmore, S. F., & Cole, F. S. (2022) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120921-103442 · Cited by 95 · see citing works
Genomewide Association Studies in Pharmacogenomics
McInnes, G., Yee, S. W., Pershad, Y., & Altman, R. B. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2349 · Cited by 82 · see citing works
Challenge accepted: uncovering the role of rare genetic variants in Alzheimer’s disease
Khani, M., Gibbons, E., Bras, J., & Guerreiro, R. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00505-9 · Cited by 39 · see citing works
“And in cases with limited data, as often occurs for poorly studied genes and ultra-rare variants, alternative approaches such as transfer learning can be applied to study rare variants by using the information on genes similar to the gene of interest, for example [97].”
Background
The 3D mutational constraint on amino acid sites in the human proteome
Li, B., Roden, D. M., & Capra, J. A. (2022) · Nature Communications · doi:10.1038/s41467-022-30936-x · Cited by 39 · see citing works
Integration of artificial intelligence and multi-omics in kidney diseases
Zhou, S., Zhong, X., & Duan, L. (2022) · Fundamental Research · doi:10.1016/j.fmre.2022.01.037 · Cited by 35 · see citing works
“In genetic counseling, AI has also shown its superiority to improve the accuracy for variant calling, prediction, and classiﬁcation [164–166] .”
Ethical layering in AI-driven polygenic risk scores—New complexities, new challenges
Fritzsche, M.-C., Akyüz, K., Abadía, M. C., McLennan, S., Marttinen, P., Mayrhofer, M. T., & Buyx, A. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1098439 · Cited by 35 · see citing works
“Authors call for standards of fairness in order to diminish disparities caused by bias of ML in genetic risk prediction (Dias and Torkamani, 2019; McInnes et al., 2021).”
Background
Next-generation sequencing and bioinformatics in rare movement disorders
Zech, M., & Winkelmann, J. (2024) · Nature Reviews Neurology · doi:10.1038/s41582-023-00909-9 · Cited by 32 · see citing works
“Such approaches will be vital to optimizing the prioritization of different variant types, generating accurate pathogenicity predictions and enabling widespread applicability of multiomic analyses 88 .”
Influential
Functional genomics of OCTN2 variants informs protein-specific variant effect predictor for Carnitine Transporter Deficiency
Koleske, M. L., McInnes, G., Brown, J. E. H., Thomas, N., Hutchinson, K., Chin, M. Y., Koehl, A., Arkin, M. R., Schlessinger, A., Gallagher, R. C., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2210247119 · Cited by 31 · see citing works
“, OCTN2) could be refined with minimal data for related proteins implicated in IEM or PGx, producing better variant interpretation predictions than would be possible alone (17).”
Computational drug design targeting MYH7 for hypertrophic cardiomyopathy integrating molecular docking, Density Functional Theory, and Molecular Dynamics Simulations
Hanif, N., Arif, A. A. A., Ali, S. A. S., Anees, M. A. M., Anees, M. A. M., Arshad, A., & Asim, M. (2025) · Journal of Applied Biological Sciences · doi:10.71336/jabs.1477 · Cited by 23 · see citing works
“The introduction of genetic testing has been helpful in early detection and risk stratification but still present challenges in predicting outcome due to phenotypic variability [10].”
The Multi-Omic Approach to Newborn Screening: Opportunities and Challenges
Ashenden, A. J., Chowdhury, A., Anastasi, L. T., Lam, K., Rozek, T., Ranieri, E., Siu, C. W.-K., King, J. R., Mas, E., & Kassahn, K. S. (2024) · International Journal of Neonatal Screening · doi:10.3390/ijns10030042 · Cited by 20 · see citing works
“In a screening context, the follow-up or functional characterization of such variants is not feasible [47,48].”
Background
Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants
Capriotti, E., & Fariselli, P. (2022) · Human Genetics · doi:10.1007/s00439-021-02419-4 · Cited by 18 · see citing works
“These studies make variant interpretation a fundamental challenge in precision medicine (Fernald et al. 2011; Capriotti et al. 2012; McInnes et al. 2021).”
Background
Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations
Stenton, S. L., Pejaver, V. R., Bergquist, T. R., Biesecker, L. G., Byrne, A. B., Nadeau, E. A. W., Greenblatt, M. S., Harrison, S. M., Tavtigian, S. V., Predrag Radivojac, A., et al. (2024) · Genetics in Medicine · doi:10.1016/j.gim.2024.101213 · Cited by 18 · see citing works
From newborn screening to genomic medicine: challenges and suggestions on how to incorporate genomic newborn screening in public health programs
Dikow, N., Ditzen, B., Kölker, S., Hoffmann, G. F., & Schaaf, C. P. (2022) · Medizinische Genetik · doi:10.1515/medgen-2022-2113 · Cited by 15 · see citing works
“Computational approaches in variant interpretation are rapidly evolving and in the near future, a genomic learning healthcare system may help to guide clinical decisions in the context of VUS in genes associated with early-onset treatable conditions in newborns [25].”
Background
Genes to therapy: a comprehensive literature review of whole-exome sequencing in neurology and neurosurgery
Tan, J. K., Awuah, W. A., Ahluwalia, A., Sanker, V., Ben-Jaafar, A., Tenkorang, P. O., Aderinto, N., Mehta, A., Darko, K., Shah, M. H., et al. (2024) · European journal of medical research · doi:10.1186/s40001-024-02063-4 · Cited by 14 · see citing works
Variants of uncertain significance: At the crux of diagnostic odyssey
Zawar, A., Manoj, G., Nair, P. P., Deshpande, P., Suravajhala, R., & Suravajhala, P. (2025) · Gene · doi:10.1016/j.gene.2025.149587 · Cited by 14 · see citing works
A deep catalog of protein-coding variation in 985,830 individualsPreprint
Sun, K., Bai, X., Chen, S., Bao, S., Kapoor, M., Zhang, C., Backman, J., Joseph, T., Maxwell, E. K., Mitra, G., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.09.539329 · Cited by 14 · see citing works
“Understanding variants of unknown significance (VUS) is currently a bottleneck in interpretation of variation in clinically relevant genes and a challenge in clinical management 15 .”
Illustrious Implications of Nature-Inspired Computing Methods in Therapeutics and Computer-Aided Drug Design
Ahmad, S., Sheikh, K., Bano, N., Rafeeq, M. M., Mohammed, M. R. S., Yadav, M. K., & Raza, K. (2022) · Studies in computational intelligence · doi:10.1007/978-981-19-6379-7_15 · Cited by 13 · see citing works
Evolution, genetic diversity, and health
Palma-Martínez, M. J., Posadas-García, Y. S., Shaukat, A., López-Ángeles, B. E., & Sohail, M. (2025) · Nature Medicine · doi:10.1038/s41591-025-03558-1 · Cited by 9 · see citing works
“Machine-learning techniques, such as transfer learning, hold considerable potential for advancing the characterization of rare variant effects 113 .”
Pharmacogenetic profiling via genome sequencing in children with medical complexity
Pan, A. Y., Scodellaro, S., Khan, T. I., Ushcatz, I., Wu, W., Curtis, M., Cohen, E., Cohn, R. D., Hayeems, R. Z., Meyn, M. S., et al. (2022) · Pediatric Research · doi:10.1038/s41390-022-02313-3 · Cited by 12 · see citing works
“However, interpretation of ultra-rare and novel genetic variants in pharmacogenes, which can be detected by GS, remains challenging.(35) HLA genotyping remains beyond the analytical scope of GSPGx for now because of the complexity of that genomic region.”
Background
The recommendation of re-classification of variants of uncertain significance (VUS) in adult genetic disorders patients
Zhang, L., Shen, M., Shu, X., Zhou, J., Ding, J., Lin, H., Pan, B., Zhang, C., Wang, B., & Guo, W. (2024) · Journal of Human Genetics · doi:10.1038/s10038-024-01263-4 · Cited by 11 · see citing works
“With the widespread application of NGS especially whole exome sequencing (WES), VUS are rapidly accumulating in variant databases and their classi ﬁ cation represents a major challenge in clinical genetics [8].”
Molecular Docking: Metamorphosis in Drug Discovery
Danao, K. R., Nandurkar, D., Rokde, V. V., Shivhare, R. S., & Mahajan, U. N. (2022) · Biomedical engineering · doi:10.5772/intechopen.105972 · Cited by 9 · see citing works
Molecular Docking - Recent Advances
Istıflı, E. S. (2022) · Biomedical engineering · doi:10.5772/intechopen.100665 · Cited by 8 · see citing works
Effect of naturally-occurring mutations on the stability and function of cancer-associated NQO1: Comparison of experiments and computation
Pacheco‐García, J. L., Cagiada, M., Tienne-Matos, K., SALIDO, E. C., Lindorff‐Larsen, K., & Pey, A. L. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.1063620 · Cited by 8 · see citing works
“Advances in technologies for whole-genome or exome sequencing and high-throughput functional assays have increased our knowledge on the consequences of the genetic variability in humans and the relationship to disease (McInnes et al., 2021; Arnedo-Pac et al., 2022; Høie et al., 2022; Katsonis et al., 2022).”
Background
An Active Learning Framework Improves Tumor Variant Interpretation
Blee, A. M., Li, B., Pecen, T., Meiler, J., Nagel, Z. D., Capra, J. A., & Chazin, W. J. (2022) · Cancer Research · doi:10.1158/0008-5472.can-21-3798 · Cited by 7 · see citing works
In Silico Deciphering of the Potential Impact of Variants of Uncertain Significance in Hereditary Colorectal Cancer Syndromes
Fasano, C., Signorile, M. L., Marco, K. D., Forte, G., Disciglio, V., Sanese, P., Grossi, V., & Simone, C. (2024) · Cells · doi:10.3390/cells13161314 · Cited by 7 · see citing works
“VUS assessment is particularly important when the variant occurs in a clinically significant gene, as the interpretation of its structural and functional implications can be very useful in clinical practice and for the surveillance of hereditary disorders [11].”
Background
Counterintuitive structural and functional effects due to naturally occurring mutations targeting the active site of the disease‐associated NQO1 enzyme*
Pacheco‐García, J. L., Anoz‐Carbonell, E., Loginov, D. S., Kavan, D., SALIDO, E. C., Man, P., Medina, M., & Pey, A. L. (2022) · FEBS Journal · doi:10.1111/febs.16677 · Cited by 6 · see citing works
MLe-KCNQ2: An Artificial Intelligence Model for the Prognosis of Missense KCNQ2 Gene Variants
Sáez-Matía, A., Ibarluzea, M. G., M‐Alicante, S., Muguruza‐Montero, A., Núñez, E., Ramis, R., Ballesteros, Ó. R., Lasa-Goicuria, D., Fons, C., Gallego, M., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25052910 · Cited by 5 · see citing works
“Transfer learning offers an opportunity to leverage the power of deep learning in situations where data are limited [4].”
BackgroundInfluential
Refining the interpretation of variants of uncertain significance in hereditary cancer screening through integrated RNA sequencing
Sha, Y., Ortiz, J. B., Bristow, S. L., Loranger, K., Meng, L., Zhao, X., Xia, F., Parmar, S., ElNaggar, A. C., & Xu, W. (2024) · Genetics in Medicine Open · doi:10.1016/j.gimo.2024.101914 · Cited by 6 · see citing works
“34,35 As such, although RNA-seq shows promise in resolving VUS, its effectiveness is still hampered by the lack of comprehensive data on diverse populations.”
Artificial Intelligence and Omics in Health and Diseases
Sabha, S. U. (2025) · Springer eBooks · doi:10.1007/978-981-96-8176-1_5 · Cited by 5 · see citing works
Integrating the Genomic Revolution into Newborn Screening: Current Challenges and Future Perspectives
Tummolo, A., Ponzi, E., Simonetti, S., & Gentile, M. (2026) · Pediatric Reports · doi:10.3390/pediatric18010014 · Cited by 5 · see citing works
“Ultimately, this shift toward precision diagnostics enables personalized screening profiles that may facilitate earlier intervention and more tailored therapeutic strategies for rare genetic disorders [135,136].”
Rare diseases of ectoderm: Translating discovery to therapy
Wright, J. T., Abbott, B. M., Salois, M. N., Gugger, J. A., Parraga, S. P., Swanson, A. K., Fete, M. D., & Koster, M. I. (2022) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.63090 · Cited by 5 · see citing works
“Thus, targeted screening for gene mutations based on phenotype or family history is the current method of choice for identifying pathogenic mutations (McInnes et al., 2021).”
SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening
Zadorozhny, A. D., Rudik, A. V., Filimonov, D. A., & Lagunin, A. A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032463 · Cited by 5 · see citing works
“As the cost of sequencing decreased, the field of application increased, leading to the first cases of using NGS in NBS [3,4].”
BackgroundInfluential
Beyond the Exome: The Role of Noncoding and Regulatory Variants in Monogenic Diseases
Moustakli, E., Ζαγοριανάκου, Ν., Makrydimas, S., Μιλτιάδους, Α., Tzallas, A. T., & Makrydimas, G. (2025) · Current Issues in Molecular Biology · doi:10.3390/cimb47121038 · Cited by 4 · see citing works
“Accelerating translational research and fostering cooperation would be achieved by creating carefully vetted, publicly accessible libraries that connect verified noncoding variations to observed clinical outcomes [141].”
Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy
Zhou, Y., & Lauschke, V. M. (2022) · Handbook of experimental pharmacology · doi:10.1007/164_2022_596 · Cited by 4 · see citing works
Phenotypic Modulation of Cancer-Associated Antioxidant NQO1 Activity by Post-Translational Modifications and the Natural Diversity of the Human Genome
Pey, A. L. (2023) · Antioxidants · doi:10.3390/antiox12020379 · Cited by 4 · see citing works
Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2023) · Epigenetics & Chromatin · doi:10.1186/s13072-023-00497-4 · Cited by 4 · see citing works
Emerging Microbial Technologies
Prasad, M., Brar, B., Bala, K., & Singh, N. (2023) · Indian Journal of Microbiology · doi:10.1007/s12088-023-01103-7 · Cited by 5 · see citing works
“Second, microbial systems are well-characterized and amenable to genetic manipulation, allowing scientists to optimize production processes and tailor medicines to specific patient needs [14].”
Background
VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation
Heredia-Torrejón, M., Montañez, R., González‐Meneses, A., Carcavilla, A., Medina, M. Á., & Lechuga-Sancho, A. M. (2024) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-024-03307-6 · Cited by 4 · see citing works
Discovering predisposing genes for hereditary breast cancer using deep learning
Passi, G., Lieberman, S., Zahdeh, F., Murik, O., Renbaum, P., Beeri, R., Linial, M., May, D., Levy‐Lahad, E., & Schneidman‐Duhovny, D. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae346 · Cited by 3 · see citing works
“These new tools enable variant analysis at the protein level [21] opening the door for more robust studies of familial, rarely occurring variants [22], which would not be possible using classical population analysis.”
Method
Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities
Dukda, S., Kumar, M., Calcino, A., Schmitz, U., & Field, M. A. (2025) · Human Genomics · doi:10.1186/s40246-025-00811-z · Cited by 3 · see citing works
“Challenges include issues with the accuracy of existing variant classifications as well as the rising number of variants of uncertain significance (VUS) [150].”
The population frequency of human mitochondrial DNA variants is highly dependent upon mutational bias
Dunn, C. D. (2021) · Biology Open · doi:10.1242/bio.059072 · Cited by 3 · see citing works
Incorporating functional genomics into the pathology-supported genetic testing framework implemented in South Africa: A future view of precision medicine for breast carcinomas
Christowitz, C., Olivier, D. W., Schneider, J. W., Kotze, M. J., & Engelbrecht, A. (2024) · Mutation Research/Reviews in Mutation Research · doi:10.1016/j.mrrev.2024.108492 · Cited by 4 · see citing works
Precision Grounding: augmenting large language models with evidence-based databases for trustworthy genetic variant summarization
Du, X., Anna, N., Oates, M., Wang, Y., Wang, X., Plasek, J. M., Aronson, S., Lebo, M. S., & Zhou, L. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106424 · Cited by 4 · see citing works
Computational interpretation of human genetic variation
Bromberg, Y., & Predrag Radivojac, A. (2022) · Human Genetics · doi:10.1007/s00439-022-02483-4 · Cited by 2 · see citing works
“Even further contributing to the complexity of the field are the issues of safe deployment of the newly developed tools and mitigation of ethical challenges necessary for societal acceptance of both the research process and clinical application (Szabo 2019; McInnes et al. 2021).”
Background
Role of Genomics in Neonatal Care and Research—A Narrative Review
Kar, S. S., Dube, R., George, B. T., & Jhancy, M. (2024) · OBM Genetics · doi:10.21926/obm.genet.2404276 · Cited by 1 · see citing works
Functional Validation of a Novel Deep Intronic IMPG2 Variant Causing Pseudoexon Activation in Retinitis Pigmentosa with Macular Involvement
Zheng, G., Xu, C., Xie, F., Li, Q., Ou, Z., Wang, D., & Li, H. (2026) · The Application of Clinical Genetics · doi:10.2147/tacg.s573156 · Cited by 1 · see citing works
Health technology design considerations specific to Indigenous Data Sovereignty and implementation science
Calac, A. J., McMann, T., Yracheta, J., & Mackey, T. K. (2026) · Frontiers in Health Services · doi:10.3389/frhs.2026.1731352 · Cited by 1 · see citing works
Use of Next-Generation Sequencing and Whole-Exome Sequencing in the Diagnosis of Adult-Onset Familial Intrahepatic Cholestasis: Challenges in Interpreting Variants of Uncertain Significance
Conti, A., Gabrielli, F., Ferrari, S., Vaisfeld, A., Masi, C. D., Azzaroli, F., Piscaglia, F., & Vitale, G. (2026) · Diagnostics · doi:10.3390/diagnostics16152335 · Cited by 1 · see citing works
“These tools are becoming increasingly important for prioritizing variants that should have been further studied through experimental investigation [47].”
Impact of Genetic Variation on Drug Response
Huddart, R., & Altman, R. B. (2023) · Springer eBooks · doi:10.1007/978-981-99-1284-1_19 · Cited by 1 · see citing works
Healthcare applications of computational genomics
Rodrigues, J. J. P. C., Sikkander, A. R. M., Tripathi, S. L., Kumar, K., Mishra, S. R., & Theivanathan, G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30080-6.00012-2 · Cited by 1 · see citing works
Pathogenic variation types in human genes relate to diseases through Pfam and InterPro mapping
Babbi, G., Savojardo, C., Baldazzi, D., Martelli, P. L., & Casadio, R. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.966927 · Cited by 1 · see citing works
“In this scenario, harmonisation of disease definition is an issue for a better association of molecular events to phenotypes (McInnes et al., 2021).”
Background
Differences in Cardiac Mechanics among Genetically At-Risk First-Degree Relatives: The DCM Precision Medicine StudyPreprint
Wilcox, J. E., Beussink‐Nelson, L., Cao, J., Kumar, R. P., Jordan, E., Ni, H., Shah, S. J., Hershberger, R. E., & Kinnamon, D. D. (2023) · medRxiv · doi:10.1101/2023.05.30.23290123 · Cited by 1 · see citing works
Precision Grounding: Augmenting Large Language Models with Evidence-Based Databases for Trustworthy Genetic Variant SummarizationPreprint
Du, X., Nagy, A. A., Oates, M., Wang, Y., Wang, X., Plasek, J. M., Aronson, S., Lebo, M. S., & Zhou, L. N. (2025) · medRxiv · doi:10.1101/2025.06.09.25329279 · Cited by 1 · see citing works
Precision Grounding: Augmenting Large Language Models with Evidence-Based Databases for Trustworthy Genetic Variant SummarizationPreprint
Du, X., Nagy, A. A., Oates, M., Wang, Y., Wang, X., Plasek, J. M., Aronson, S., Lebo, M. S., & Zhou, L. N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5385545 · Cited by 2 · see citing works
Var|Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing dataPreprint
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.02.506346
Improved KCNQ2 gene missense variant interpretation with artificial intelligencePreprint
Sáez-Matía, A., Muguruza‐Montero, A., M‐Alicante, S., Núñez, E., Ramis, R., Ballesteros, Ó. R., Ibarluzea, M. G., Fons, C., Leonardo, A., Bergara, A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.20.513007
“The last limitation would be the variant reclassifications that regularly occur in clinical databases such as Clinvar [McInnes et al., 2021].”
BackgroundInfluential
Molecular Mechanisms, Genotype–Phenotype Correlations and Patient-Specific Treatments in Inherited Metabolic Diseases
Pey, A. L. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13010117
Data from An Active Learning Framework Improves Tumor Variant Interpretation
Blee, A. M., Li, B., Pecen, T., Meiler, J., Nagel, Z. D., Capra, J. A., & Chazin, W. J. (2023) · doi:10.1158/0008-5472.c.6513986.v1
Computational in silico genetic variant prediction tools in cardiovascular disease
Hellwig, L. D., Villar, J., & Turner, C. (2023) · Annals of Noninvasive Electrocardiology · doi:10.1111/anec.13079
Beispielhafte Implikationen von naturinspirierten Berechnungsmethoden auf Therapeutika und computergestützte Arzneimittelentwicklung
Ahmad, S., Sheikh, K., Bano, N., Rafeeq, M. M., Mohammed, M. R. S., Yadav, M. K., & Raza, K. (2024) · Springer eBooks · doi:10.1007/978-981-99-7808-3_15
Genome-Wise Analysis for Drug Targeting
Israr, J., Alam, S., Siddiqui, S., Misra, S., Gupta, D., & Kumar, A. (2024) · Springer eBooks · doi:10.1007/978-981-97-3169-5_6
Identification of novel variants in carbamoyl phosphate synthetase 1 gene and comparative pathogenicity assessments of CPS1 missense variants following ACMG/AMP-ClinGen recommendation for computational tools
Li, F., Cai, Q., Ji, W., Xu, M., Tian, G., & Zeng, F. (2025) · Molecular Genetics and Metabolism Reports · doi:10.1016/j.ymgmr.2025.101208
“However, VUS variants are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors [57].”
Innovations in Clinical Chemistry and Diabetes Management: Emerging Diagnostics and Therapeutic Approaches
Ismael, L. H. Y., Tarish, B. S. A., Nasir, W. S. K., Kadr, Z. M. S., & Kazim, E. W. J. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.305
Bio-Genetics and Disease: Diagnostic and Analytical Approaches
Juma, M., Jasab, S. J. H., Mohammed, S. H. H., & Alai, L. H. S. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.388
PPsFLP: End-to-End Privacy Preserved and Secure Federated Learning Paradigm of Medical Images
Kaur, G. (2025) · SN Computer Science · doi:10.1007/s42979-025-04107-w
Special Issue “Ion Conductance and Ion Regulation in Human Health and Disease”
Kaestner, L. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262110650
In utero precision medicine for severe, early-onset neurogenetic disease: scientific, clinical and ethical considerations
Borges, B., Brown, J. E. H., & Gano, D. L. (2025) · Pediatric Research · doi:10.1038/s41390-025-04487-y
Additional file 1 of Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2024) · Figshare · doi:10.6084/m9.figshare.26597048
Additional file 8 of Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2024) · Figshare · doi:10.6084/m9.figshare.26597069
Additional file 3 of Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2024) · Figshare · doi:10.6084/m9.figshare.26597054
Additional file 11 of Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2024) · Figshare · doi:10.6084/m9.figshare.26597078
Additional file 7 of Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2024) · Figshare · doi:10.6084/m9.figshare.26597066
Additional file 9 of Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2024) · Figshare · doi:10.6084/m9.figshare.26597072
Additional file 6 of Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2024) · Figshare · doi:10.6084/m9.figshare.26597063
Additional file 5 of Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2024) · Figshare · doi:10.6084/m9.figshare.26597060
Additional file 2 of Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2024) · Figshare · doi:10.6084/m9.figshare.26597051
Additional file 10 of Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2024) · Figshare · doi:10.6084/m9.figshare.26597075
Additional file 4 of Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data
Salma, M., Alaterre, E., Moreaux, J., & Soler, É. (2024) · Figshare · doi:10.6084/m9.figshare.26597057
Accelerated Identification and Preliminary Validation of a Pathogenic Missense Variant in the L1CAM Gene in a Pregnant Woman With Sonographic Anomalies Using AlphaMissense
Wang, Z., Shen, X., Xu, C., Wang, R., Teng, C., He, Y., Wu, W., & Hong, X. (2025) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.70169
“However, testing may also reveal variants of uncertain significance (VUSs), often amino acid substitutions, which pose challenges in clinical genetics (McInnes et al. 2021).”
Renal and hepatic disorders: Early indicators and diagnostic tools
Goel, F., Bhardwaj, V., Garg, V. K., Kumar, A., Singh, P., & Yadav, D. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33263-0.00010-3
Multi‐Level Genomic and Computational Analyses Identify a Novel IFT122 Variant Associated With Cranioectodermal Dysplasia 1 in a Consanguineous Saudi Family
Aljeaid, D., Almadiny, A., Nasser, K. K., Almutadares, M., & Issa, N. M. (2026) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.70230
“Although advanced genomic sequencing technologies have improved diagnostic yield, the interpretation of ultra-rare variants remains difficult and often requires the integration of phenotypic, genomic, and functional analyses (McInnes et al. 2021).”
Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS
Abbas, S. H., Wu, J., & Shi, T. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109580
Evaluating Genomic Language Models: Experimental Validation, Multi-Omics Integration, and Ethical-Legal Challenges
Refahi, M. S., Yoo, H., Hearne, G., Allegra, C., Brown, J. R., Sokhansanj, B. A., Woloszynek, S., Ramjattun, K., Allipilli, S. R., Robi Polikar, A., et al. (2026) · Springer eBooks · doi:10.1007/978-3-032-18509-9_3
Benchmarking CNN and LSTM Models for Genetic Mutation Classification across Diverse Sequence Encoding Techniques
Kurniawan, T. B., Dewi, D. A., & Ventayen, R. J. M. (2026) · Journal of Advances in Information Technology · doi:10.12720/jait.17.8.1498-1511
AI-Assisted WES Variant Interpretation in Neurodevelopmental Disorders: A Narrative Review of Existing Tools
Treccarichi, S., Vinci, M., Musumeci, A., Rando, R. G., Fallea, A., Brancato, D., Bruno, F., Federico, C., Saccone, S., Elia, M., et al. (2026) · Genes · doi:10.3390/genes17101276
“The interpretation of rare variants is particularly challenging in NDDs because of extensive genetic and phenotypic heterogeneity, variable expressivity, incomplete penetrance, and the large number of genes potentially contributing to overlapping clinical presentations [24,25].”

Opportunities and Challenges for Interpreting Rare Variation in Clinically Important GenesPreprint
McInnes, G., Sharo, A. G., Koleske, M. L., Brown, J. E. H., Norstad, M., Adhikari, A. N., Wang, S., Brenner, S. E., Halpern, J., Koenig, B. A., et al. (2020) · Preprints.org · doi:10.20944/preprints202011.0599.v1 · Cited by 1 · see citing works
P174: Understanding patterns in variants of uncertain significance to facilitate reclassification using machine-learning based variant effect predictors
Zhang, C., Zimmerman, D., & Roth, F. P. (2024) · Genetics in Medicine Open · doi:10.1016/j.gimo.2024.101071
Deep Learning for Rare Variant Interpretation
Petrov, J. (2025) · The Biosis Bulletin: Bioscience and Information science Journal · doi:10.5281/zenodo.19549575
Improved pathogenicity prediction for rare human missense variants
Wu, Y., Liu, H., Li, R., Sun, S., Weile, J., & Roth, F. P. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.08.012 · Cited by 166 · see citing works
Considerations for rare variants in drug metabolism genes and the clinical implications
Drögemöller, B. I., Wright, G. E. B., & Warnich, L. (2014) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1517/17425255.2014.903239 · Cited by 24 · see citing works

Authors: McInnes, G., Sharo, A. G., Koleske, M. L., Brown, J. E. H., Norstad, M., Adhikari, A. N., Wang, S., Brenner, S. E., Halpern, J., Koenig, B. A., Magnus, D. C., Gallagher, R. C., Giacomini, K. M., & Altman, R. B.
Affiliations: Stanford University (US)
University of California, Berkeley (US)
University of California, San Francisco (US)
Illumina (United States) (US)
University of Washington (US)
Stanford Medicine (US)
Corresponding author: Altman, Russ B.
ORCID: McInnes, Gregory
Sharo, Andrew G.
Koleske, Megan L.
Brown, Julia E.H.
Norstad, Matthew
Adhikari, Aashish N.
Wang, Sheng
Brenner, Steven E.
Halpern, Jodi
Koenig, Barbara A.
Magnus, David C.
Gallagher, Renata C.
Giacomini, Kathleen M.
Altman, Russ B.
Venue: The American Journal of Human Genetics
Publisher: Elsevier BV
Published: 2021-04
Volume: 108
Issue: 4
Pages: 535-548
ISSN: 0002-9297 (ISSN-L)
DOI: 10.1016/j.ajhg.2021.03.003
PMC: PMC8059338
OpenAlex: W3147241712
Cited by: 86
References: 115
Citations by year: 2026: 11 · 2025: 16 · 2024: 23 · 2023: 12 · 2022: 22 · 2021: 2
FWCI: 2.28
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: The American Journal of Human Genetics (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
eScholarship (California Digital Library) (submittedVersion)
Funders: National Science Foundation (DGE 1752814, U19HD077627)
National Center for Advancing Translational Sciences (UL1TR003142, HG010615, GM102365)
National Institutes of Health (U19HD077627, T32 LM012409)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
Keywords: rare variants, variants of uncertain significance, clinical variant interpretation, inborn errors of metabolism, newborn screening, pharmacogenomics, health disparities, precision medicine
MeSH terms: Machine Learning, Genetics, Medical, Humans, Infant, Newborn, Metabolism, Inborn Errors, Pharmacogenetics, Genetic Variation, Genome, Human, Genomics, Precision Medicine

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