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Genetics play a key role in venous thromboembolism (VTE) risk, however established risk factors in European populations do not translate to individuals of African descent because of the differences in allele frequencies between populations. As part of the fifth iteration of the Critical Assessment of Genome Interpretation, participants were asked to predict VTE status in exome data from African American subjects. Participants were provided with 103 unlabeled exomes from patients treated with warfarin for non-VTE causes or VTE and asked to predict which disease each subject had been treated for. Given the lack of training data, many participants opted to use unsupervised machine learning methods, clustering the exomes by variation in genes known to be associated with VTE. The best performing method using only VTE related genes achieved an area under the ROC curve of 0.65. Here, we discuss the range of methods used in the prediction of VTE from sequence data and explore some of the difficulties of conducting a challenge with known confounders. In addition, we show that an existing genetic risk score for VTE that was developed in European subjects works well in African Americans.

Publisher's version (útgefin grein), Cutaneous squamous cell carcinoma (SCC) is one of the most common cancers in the United States. Previous genome-wide association studies (GWAS) have identified 14 single nucleotide polymorphisms (SNPs) associated with cutaneous SCC. Here, we report the largest cutaneous SCC meta-analysis to date, representing six international cohorts and totaling 19,149 SCC cases and 680,049 controls. We discover eight novel loci associated with SCC, confirm all previously associated loci, and perform fine mapping of causal variants. The novel SNPs occur within skin-specific regulatory elements and implicate loci involved in cancer development, immune regulation, and keratinocyte differentiation in SCC susceptibility. © 2020, The Author(s)., We thank the research participants and employees of 23andMe for contributing to this work. In addition, we thank the participants and staff of the Nurses’ Health Study and the Health Professionals Follow-up Study, for their valuable contributions, as well as the following state cancer registries for their help: A.L., A.Z., A.R., C.A., C.O., C.T., D.E., F.L., G.A., I.D., I.L., I.N., I.A., K.Y., L.A., M.E., M.D., M.A., M.I., N.E., N.H., N.J., N.Y., N.C., N.D., O.H., O.K., O.R., P.A., R.I., S.C., T.N., T.X., V.A., W.A. and W.Y. We assume full responsibility for analyses and interpretation of these data. EQTL data described in this paper were obtained from the GTEx Portal on 06/01/2018. We thank L. Tryggvadottir and G.H. Olafsdottir of the Icelandic Cancer Registry for assistance in the ascertainment of affected individuals. We thank D. Allain, V. Klee, and M. Bernhardt for ascertainment of cases and associated clinical data. The OSU Human Genetics Sample bank provided control samples. This work was supported in part by the National Human Genome Research Institute of the National Institutes of Health (grant number R44HG006981) and in part by NIH R01 CA49449, P01 CA87969, UM1 CA186107, UM1 CA167552, R03 CA219779, K23 CA211793 (K.Y.S.), and in part by Walther Cancer Foundation (J.H.). K.Y.S. is a Damon Runyon Clinical Investigator supported (in part) by the Damon Runyon Cancer Research Foundation. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University Rotterdam; Netherlands Organization for the Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam.

The large volume of data used in cancer diagnosis presents a unique opportunity for deep learning algorithms, which improve in predictive performance with increasing data. When applying deep learning to cancer diagnosis, the goal is often to learn how to classify an input sample (such as images or biomarkers) into predefined categories (such as benign or cancerous). In this article, we examine examples of how deep learning algorithms have been implemented to make predictions related to cancer diagnosis using clinical, radiological, and pathological image data. We present a systematic approach for evaluating the development and application of clinical deep learning algorithms. Based on these examples and the current state of deep learning in medicine, we discuss the future possibilities in this space and outline a roadmap for implementations of deep learning in cancer diagnosis.

Introduction African Americans have a higher incidence of venous thromboembolism (VTE) than European descent individuals. However, the typical genetic risk factors in populations of European descent are nearly absent in African Americans, and population-specific genetic factors influencing the higher VTE rate are not well characterized. Methods We performed a candidate gene analysis on an exome-sequenced African American family with recurrent VTE and identified a variant in Protein S (PROS1) V510M (rs138925964). We assessed the population impact of PROS1 V510M using a multicenter African American cohort of 306 cases with VTE compared to 370 controls. Additionally, we compared our case cohort to a background population cohort of 2203 African Americans in the NHLBI GO Exome Sequencing Project (ESP). Results In the African American family with recurrent VTE, we found prior laboratories for our cases indicating low free Protein S levels, providing functional support for PROS1 V510M as the causative mutation. Additionally, this variant was significantly enriched in the VTE cases of our multicenter case–control study (Fisher's Exact Test, P = 0.0041, OR = 4.62, 95% CI: 1.51–15.20; allele frequencies – cases: 2.45%, controls: 0.54%). Similarly, PROS1 V510M was also enriched in our VTE case cohort compared to African Americans in the ESP cohort (Fisher's Exact Test, P = 0.010, OR = 2.28, 95% CI: 1.26–4.10). Conclusions We found a variant, PROS1 V510M, in an African American family with VTE and clinical laboratory abnormalities in Protein S. Additionally, we found that this variant conferred increased risk of VTE in a case–control study of African Americans. In the ESP cohort, the variant is nearly absent in ESP European descent subjects (n = 3, allele frequency: 0.03%). Additionally, in 1000 Genomes Phase 3 data, the variant only appears in African descent populations. Thus, PROS1 V510M is a population-specific genetic risk factor for VTE in African Americans.

The field of pharmacogenomics is an area of great potential for near-term human health impacts from the big genomic data revolution. Pharmacogenomics research momentum is building with numerous hypotheses currently being investigated through the integration of molecular profiles of different cell lines and large genomic data sets containing information on cellular and human responses to therapies. Additionally, the results of previous pharmacogenetic research efforts have been formulated into clinical guidelines that are beginning to impact how healthcare is conducted on the level of the individual patient. This trend will only continue with the recent release of new datasets containing linked genotype and electronic medical record data. This review discusses key resources available for pharmacogenomics and pharmacogenetics research and highlights recent work within the field.

Cytochrome p450, family 2, subfamily F, polypeptide 2 (CYP4F2) catalyzes the NADPH-dependent oxidation of the terminal carbon of long and very long-chain fatty acids, the side chains of vitamin K (K1, K2) and vitamin E (tocopherols and tocotrienols), arachidonic acid (AA), and leukotriene B4 (LTB4). CYP4F2 localizes to the endoplasmic reticulum of cells. Although it is predominantly expressed in the liver and kidneys, there is some evidence that CYP4F2 is expressed in human enteric microsomes [1, 2]. In the liver and kidneys, CYP4F2 catalyzes the synthesis of 20-hydroxyeicosatetraeonic acid (20-HETE) from AA. Hepatic CYP4F2 also regulates the bioavailability of vitamin K and vitamin E, catalyzes the inactivation of LTB4, and the bioactivation of the anti-malarial drug pafuramidine. Although initial studies of CYP4F2 were focused on its role as a regulator of LTB4 and 20-HETE, current investigations focus on how variants of CYP4F2 affect warfarin drug dosing and safety. A fully interactive version of this brief review can accessed at: http://www.pharmgkb.org/gene/PA27121.

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype–phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype–phenotype relationships.

Genome-wide association studies (GWAS) are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power, and allows the effects of both protein coding variation as well as variation in expression to be combined into “gene level” effects.Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein coding regions. Here, we introduce a method that improves the predicted dose by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic (PK) and pharmacodynamic (PD) pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose, and identify a signature of eleven gene-tissue pairs that significantly augment the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction, in a population-specific manner.

Background Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects. Methods Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. Results We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Conclusions Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0495-0) contains supplementary material, which is available to authorized users.

The American College of Medical Genetics and Genomics (ACMG) recently released guidelines regarding the reporting of incidental findings in sequencing data. Given the availability of Direct to Consumer (DTC) genetic testing and the falling cost of whole exome and genome sequencing, individuals will increasingly have the opportunity to analyze their own genomic data. We have developed a web-based tool, PATH-SCAN, which annotates individual genomes and exomes for ClinVar designated pathogenic variants found within the genes from the ACMG guidelines. Because mutations in these genes predispose individuals to conditions with actionable outcomes, our tool will allow individuals or researchers to identify potential risk variants in order to consult physicians or genetic counselors for further evaluation. Moreover, our tool allows individuals to anonymously submit their pathogenic burden, so that we can crowd source the collection of quantitative information regarding the frequency of these variants. We tested our tool on 1092 publicly available genomes from the 1000 Genomes project, 163 genomes from the Personal Genome Project, and 15 genomes from a clinical genome sequencing research project. Excluding the most commonly seen variant in 1000 Genomes, about 20% of all genomes analyzed had a ClinVar designated pathogenic variant that required further evaluation.

Background Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations. Results Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association. Conclusions Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.

Summary BackgroundVKORC1 and CYP2C9 are important contributors to warfarin dose variability, but explain less variability for individuals of African descent than for those of European or Asian descent. We aimed to identify additional variants contributing to warfarin dose requirements in African Americans. MethodsWe did a genome-wide association study of discovery and replication cohorts. Samples from African-American adults (aged ≥18 years) who were taking a stable maintenance dose of warfarin were obtained at International Warfarin Pharmacogenetics Consortium (IWPC) sites and the University of Alabama at Birmingham (Birmingham, AL, USA). Patients enrolled at IWPC sites but who were not used for discovery made up the independent replication cohort. All participants were genotyped. We did a stepwise conditional analysis, conditioning first for VKORC1 −1639G→A, followed by the composite genotype of CYP2C9*2 and CYP2C9*3. We prespecified a genome-wide significance threshold of p

There is great variation in drug-response phenotypes, and a “one size fits all” paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.