Estimating heritability of drug-induced liver injury from common variants and implications for future study designs

Recent genome-wide association studies identified certain human leukocyote antigen (HLA) alleles as the major risk factors of drug-induced liver injuries (DILI). While these alleles often cause large relative risk, their predictive values are quite low due to low prevalence of idiosyncratic DILI. Finding additional risk factors is important for precision medicine. However, optimal design of further genetic studies is hindered by uncertain overall heritability of DILI. This is a common problem for low-prevalence pharmacological traits, since it is difficult to obtain clinical outcome data in families. Here we estimated the heritability (h2) of DILI from case-control genome-wide single nucleotide polymorphism data using a method based on random effect models. We estimated the proportion of h2 captured by common SNPs for DILI to be between 0.3 and 0.5. For co-amoxiclav induced DILI, chromosome 6 explained part of the heritability, indicating additional contributions from common variants yet to be found. We performed simulations to assess the robustness of the h2 estimate with limited sample size under low prevelance, a condition typical to studies on idiosyncratic pharmacological traits. Our findings suggest that common variants outside of HLA contribute to DILI susceptability; therefore, it is valuable to conduct further GWAS with expanded case collection

The potential for automated question answering in the context of genomic medicine: an assessment of existing resources and properties of answers

Knowledge gained in studies of genetic disorders is reported in a growing body of biomedical literature containing reports of genetic variation in individuals that map to medical conditions and/or response to therapy. These scientific discoveries need to be translated into practical applications to optimize patient care. Translating research into practice can be facilitated by supplying clinicians with research evidence. We assessed the role of existing tools in extracting answers to translational research questions in the area of genomic medicine. We: evaluate the coverage of translational research terms in the Unified Medical Language Systems (UMLS) Metathesaurus; determine where answers are most often found in full-text articles; and determine common answer patterns. Findings suggest that we will be able to leverage the UMLS in development of natural language processing algorithms for automated extraction of answers to translational research questions from biomedical text in the area of genomic medicine

Cancer Genetic Counselor Information Needs for Risk Communication: A Qualitative Evaluation of Interview Transcripts

Personalized medicine is a model of healthcare that is predictive, personalized, preventive and participatory (“P4 Medicine”). Genetic counselors are an ideal group to study when designing tools to support cancer P4 Medicine activities more broadly. The goal for this work was to gain a better understanding of the information cancer genetic counselors seek from their patients to facilitate effective information exchange for discussing risk. This was an analysis of a qualitative data set from interviews of eight cancer genetic counselors, recruited from three institutions. Genetic counselors at each site were interviewed using a semi-structured, open-ended questionnaire. A selective coding approach was used to determine major themes associated with genetic counseling information needs for communicating risk. We generated a model for understanding categories of genetic counseling information needs to support risk communication activities. Common activities for risk communication included risk assessment and tailoring communication. Categories of information needs included: (a) clinical patient characteristics, (b) social and cognitive patient characteristics and (c) patient motivation and goals for the genetic counseling session. A logical next step is for this model to inform the design of software systems for pre-visit patient planning and delivering just-in-time educational information to facilitate cancer risk communication activities

Developing a Prototype System for Integrating Pharmacogenomics Findings into Clinical Practice

Findings from pharmacogenomics (PGx) studies have the potential to be applied to individualize drug therapy to improve efficacy and reduce adverse drug events. Researchers have identified factors influencing uptake of genomics in medicine, but little is known about the specific technical barriers to incorporating PGx into existing clinical frameworks. We present the design and development of a prototype PGx clinical decision support (CDS) system that builds on existing clinical infrastructure and incorporates semi-active and active CDS. Informing this work, we updated previous evaluations of PGx knowledge characteristics, and of how the CDS capabilities of three local clinical systems align with data and functional requirements for PGx CDS. We summarize characteristics of PGx knowledge and technical needs for implementing PGx CDS within existing clinical frameworks. PGx decision support rules derived from FDA drug labels primarily involve drug metabolizing genes, vary in maturity, and the majority support the post-analytic phase of genetic testing. Computerized provider order entry capabilities are key functional requirements for PGx CDS and were best supported by one of the three systems we evaluated. We identified two technical needs when building on this system, the need for (1) new or existing standards for data exchange to connect clinical data to PGx knowledge, and (2) a method for implementing semi-active CDS. Our analyses enhance our understanding of principles for designing and implementing CDS for drug therapy individualization and our current understanding of PGx characteristics in a clinical context. Characteristics of PGx knowledge and capabilities of current clinical systems can help govern decisions about CDS implementation, and can help guide decisions made by groups that develop and maintain knowledge resources such that delivery of content for clinical care is supported

Feasibility of incorporating genomic knowledge into electronic medical records for pharmacogenomic clinical decision support

In pursuing personalized medicine, pharmacogenomic (PGx) knowledge may help guide prescribing drugs based on a person’s genotype. Here we evaluate the feasibility of incorporating PGx knowledge, combined with clinical data, to support clinical decision-making by: 1) analyzing clinically relevant knowledge contained in PGx knowledge resources; 2) evaluating the feasibility of a rule-based framework to support formal representation of clinically relevant knowledge contained in PGx knowledge resources; and, 3) evaluating the ability of an electronic medical record/electronic health record (EMR/EHR) to provide computable forms of clinical data needed for PGx clinical decision support. Findings suggest that the PharmGKB is a good source for PGx knowledge to supplement information contained in FDA approved drug labels. Furthermore, we found that with supporting knowledge (e.g. IF age <18 THEN patient is a child), sufficient clinical data exists in University of Washington’s EMR systems to support 50% of PGx knowledge contained in drug labels that could be expressed as rules

Physician Attitudes toward Adopting Genome-Guided Prescribing through Clinical Decision Support

This study assessed physician attitudes toward adopting genome-guided prescribing through clinical decision support (CDS), prior to enlisting in the Clinical Implementation of Personalized Medicine through Electronic Health Records and Genomics pilot pharmacogenomics project (CLIPMERGE PGx). We developed a survey instrument that includes the Evidence Based Practice Attitude Scale, adapted to measure attitudes toward adopting genome-informed interventions (EBPAS-GII). The survey also includes items to measure physicians’ characteristics (awareness, experience, and perceived usefulness), attitudes about personal genome testing (PGT) services, and comfort using technology. We surveyed 101 General Internal Medicine physicians from the Icahn School of Medicine at Mount Sinai (ISMMS). The majority were residency program trainees (~88%). Prior to enlisting into CLIPMERGE PGx, most physicians were aware of and had used decision support aids. Few physicians, however, were aware of and had used genome-guided prescribing. The majority of physicians viewed decision support aids and genotype data as being useful for making prescribing decisions. Most physicians had not heard of, but were willing to use, PGT services and felt comfortable interpreting PGT results. Most physicians were comfortable with technology. Physicians who perceived genotype data to be useful in making prescribing decisions, had more positive attitudes toward adopting genome-guided prescribing through CDS. Our findings suggest that internal medicine physicians have a deficit in their familiarity and comfort interpreting and using genomic information. This has reinforced the importance of gathering feedback and guidance from our enrolled physicians when designing genome-guided CDS and the importance of prioritizing genomic medicine education at our institutions

Prioritizing Approaches to Engage Community Members and Build Trust in Biobanks: A Survey of Attitudes and Opinions of Adults within Outpatient Practices at the University of Maryland

Background: Achieving high participation of communities representative of all sub-populations is needed in order to ensure broad applicability of biobank study findings. This study aimed to understand potentially mutable attitudes and opinions commonly correlated with biobank participation in order to inform approaches to promote participation in biobanks. Methods: Adults from two University of Maryland (UMD) Faculty Physicians, Inc. outpatient practices were invited to watch a video and complete a survey about a new biobank initiative. We used: Chi-square to assess the relationship between willingness to join the biobank and participant characteristics, other potentially mutable attitudes and opinions, and trust in the UMD. We also used t-test to assess the relationship with trust in medical research. We also prioritize proposed actions to improve attitudes and opinions about joining biobanks according to perceived responsiveness. Results: 169 participants completed the study, 51% of whom indicated a willingness to join the biobank. Willingness to join the biobank was not associated with age, gender, race, or education but was associated with respondent comfort sharing samples and clinical information, concerns related to confidentiality, potential for misuse of information, trust in UMD, and perceived health benefit. In ranked order, potential actions we surveyed that might alleviate some of these concerns include: increase chances to learn more about the biobank, increase opportunities to be updated, striving to put community concerns first, including involving community members as leaders of biobank research, and involving community members in decision making. Conclusions: This study identified several attitudes and opinions that influence decisions to join a biobank, including many concerns that could potentially be addressed by engaging community members. We also demonstrate our method of prioritizing ways to improve attitudes and opinions about joining a biobank according to perceived responsiveness