Human Genomic Information Research Articles

Predicting variant deleteriousness in non-human species: applying the CADD approach in mouse

BackgroundPredicting the deleteriousness of observed genomic variants has taken a step forward with the introduction of the Combined Annotation Dependent Depletion (CADD) approach, which trains a classifier on the wealth of available human genomic information. This raises the question whether it can be done with less data for non-human species. Here, we investigate the prerequisites to construct a CADD-based model for a non-human species.ResultsPerformance of the mouse model is competitive with that of the human CADD model and better than established methods like PhastCons conservation scores and SIFT. Like in the human case, performance varies for different genomic regions and is best for coding regions. We also show the benefits of generating a species-specific model over lifting variants to a different species or applying a generic model. With fewer genomic annotations, performance on the test set as well as on the three validation sets is still good.ConclusionsIt is feasible to construct species-specific CADD models even when annotations such as epigenetic markers are not available. The minimal requirement for these models is the availability of a set of genomes of closely related species that can be used to infer an ancestor genome and substitution rates for the data generation.

SAFETY: Secure gwAs in Federated Environment through a hYbrid Solution.

Recent studies demonstrate that effective healthcare can benefit from using the human genomic information. Consequently, many institutions are using statistical analysis of genomic data, which are mostly based on genome-wide association studies (GWAS). GWAS analyze genome sequence variations in order to identify genetic risk factors for diseases. These studies often require pooling data from different sources together in order to unravel statistical patterns, and relationships between genetic variants and diseases. Here, the primary challenge is to fulfill one major objective: accessing multiple genomic data repositories for collaborative research in a privacy-preserving manner. Due to the privacy concerns regarding the genomic data, multi-jurisdictional laws and policies of cross-border genomic data sharing are enforced among different countries. In this article, we present SAFETY, a hybrid framework, which can securely perform GWAS on federated genomic datasets using homomorphic encryption and recently introduced secure hardware component of Intel Software Guard Extensions to ensure high efficiency and privacy at the same time. Different experimental settings show the efficacy and applicability of such hybrid framework in secure conduction of GWAS. To the best of our knowledge, this hybrid use of homomorphic encryption along with Intel SGX is not proposed to this date. SAFETY is up to 4.82 times faster than the best existing secure computation technique.

Secure count query on encrypted genomic data

Human genomic information can yield more effective healthcare by guiding medical decisions. Therefore, genomics research is gaining popularity as it can identify potential correlations between a disease and a certain gene, which improves the safety and efficacy of drug treatment and can also develop more effective prevention strategies [1]. To reduce the sampling error and to increase the statistical accuracy of this type of research projects, data from different sources need to be brought together since a single organization does not necessarily possess required amount of data. In this case, data sharing among multiple organizations must satisfy strict policies (for instance, HIPAA and PIPEDA) that have been enforced to regulate privacy-sensitive data sharing. Storage and computation on the shared data can be outsourced to a third party cloud service provider, equipped with enormous storage and computation resources. However, outsourcing data to a third party is associated with a potential risk of privacy violation of the participants, whose genomic sequence or clinical profile is used in these studies. In this article, we propose a method for secure sharing and computation on genomic data in a semi-honest cloud server. In particular, there are two main contributions. Firstly, the proposed method can handle biomedical data containing both genotype and phenotype. Secondly, our proposed index tree scheme reduces the computational overhead significantly for executing secure count query operation. In our proposed method, the confidentiality of shared data is ensured through encryption, while making the entire computation process efficient and scalable for cutting-edge biomedical applications. We evaluated our proposed method in terms of efficiency on a database of Single-Nucleotide Polymorphism (SNP) sequences, and experimental results demonstrate that the execution time for a query of 50 SNPs in a database of 50,000 records is approximately 5 s, where each record contains 500 SNPs. And, it requires 69.7 s to execute the query on the same database that also includes phenotypes.

Human Molecular Genetics Has Not Yet Contributed to Measurable Public Health Advances.

For more than half a century, we have lived in a world dominated by the idea that the gene is the central and primary agent in biology, an era some have called the "Genetic Age." Each decade since the discovery of the double-helical structure of DNA in 1953 has seen scientific advances in genetics, discoveries that have led to at least 17 Nobel Prizes. Although the time span occupied by the Genetic Age has also been a time of great public health advances, no advance in human molecular genetics can be shown to have had any measurable effect on any public health parameter of importance. It is hard to think of another field of biomedical research in which such massive public fund investments have had less public health impact to date than human molecular genetics. The only arena in which precision medicine, with its reliance on human genomic information, is likely to be helpful, is selected inherited diseases. To measurably alter the health of the population, the focus of biomedical research should be on the molecular, cellular, clinical, and population effects of the external agents and exposures that drive the incidence of most health conditions.

Systematic Proteogenomic Approach To Exploring a Novel Function for NHERF1 in Human Reproductive Disorder: Lessons for Exploring Missing Proteins.

One of the major goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to fill the knowledge gaps between human genomic information and the corresponding proteomic information. These gaps are due to "missing" proteins (MPs)-predicted proteins with insufficient evidence from mass spectrometry (MS), biochemical, structural, or antibody analyses-that currently account for 2579 of the 19587 predicted human proteins (neXtProt, 2017-01). We address some of the lessons learned from the inconsistent annotations of missing proteins in databases (DB) and demonstrate a systematic proteogenomic approach designed to explore a potential new function of a known protein. To illustrate a cautious and strategic approach for characterization of novel function in vitro and in vivo, we present the case of Na(+)/H(+) exchange regulatory cofactor 1 (NHERF1/SLC9A3R1, located at chromosome 17q25.1; hereafter NHERF1), which was mistakenly labeled as an MP in one DB (Global Proteome Machine Database; GPMDB, 2011-09 release) but was well known in another public DB and in the literature. As a first step, NHERF1 was determined by MS and immunoblotting for its molecular identity. We next investigated the potential new function of NHERF1 by carrying out the quantitative MS profiling of placental trophoblasts (PXD004723) and functional study of cytotrophoblast JEG-3 cells. We found that NHERF1 was associated with trophoblast differentiation and motility. To validate this newly found cellular function of NHERF1, we used the Caenorhabditis elegans mutant of nrfl-1 (a nematode ortholog of NHERF1), which exhibits a protruding vulva (Pvl) and egg-laying-defective phenotype, and performed genetic complementation work. The nrfl-1 mutant was almost fully rescued by the transfection of the recombinant transgenic construct that contained human NHERF1. These results suggest that NHERF1 could have a previously unknown function in pregnancy and in the development of human embryos. Our study outlines a stepwise experimental platform to explore new functions of ambiguously denoted candidate proteins and scrutinizes the mandated DB search for the selection of MPs to study in the future.

The Evolution and Ecology of Resistance in Cancer Therapy.

Despite continuous deployment of new treatment strategies and agents over many decades, most disseminated cancers remain fatal. Cancer cells, through their access to the vast information of human genome, have a remarkable capacity to deploy adaptive strategies for even the most effective treatments. We note there are two critical steps in the clinical manifestation of treatment resistance. The first, which is widely investigated, requires deployment of a mechanism of resistance that usually involves increased expression of molecular machinery necessary to eliminate the cytotoxic effect of treatment. However, the emergence of a resistant phenotype is not in itself clinically significant. That is, resistant cells affect patient outcomes only when they form a sufficiently large population to allow tumor progression and treatment failure. Importantly, proliferation of the resistant phenotype is by no means certain and, in fact, depends on complex Darwinian dynamics governed by the costs and benefits of the resistance mechanisms in the context of the local environment and competing populations. Attempts to target molecular machinery of resistance have had little clinical success largely because of the diversity within the human genome-therapeutic interruption of one mechanism simply results in its replacement by an alternative. We explore an alternative strategy for overcoming treatment resistance that seeks to understand and exploit the critical evolutionary dynamics that govern proliferation of the resistant phenotypes. In general, this approach has shown that, although emergence of resistance mechanisms in cancer cells to every current therapy is inevitable, proliferation of the resistant phenotypes is not and can be delayed and even prevented with sufficient understanding of the underlying ecoevolutionary dynamics.

\u201cLet\u2019s pull these technologies out of the ivory tower\u201d: The politics, ethos, and ironies of participant-driven genomic research

This paper investigates how groups of ‘citizen scientists’ in non-traditional settings and primarily online networks claim to be challenging conventional genomic research processes and norms. Although these groups are highly diverse, they all distinguish their efforts from traditional university- or industry-based genomic research as being ‘participant-driven’ in one way or another. Participant-driven genomic research (PDGR) groups often work from ‘labs’ that consist of servers and computing devices as much as wet lab apparatus, relying on information-processing software for data-driven, discovery-based analysis rather than hypothesis-driven experimentation. We interviewed individuals from a variety of efforts across the expanding ecosystem of PDGR, including academic groups, start-ups, activists, hobbyists, and hackers, in order to compare and contrast how they relate their stated objectives, practices, and political and moral stances to institutions of expert scientific knowledge production. Results reveal that these groups, despite their diversity, share commitments to promoting alternative modes of housing, conducting, and funding genomic research and, ultimately, sharing knowledge. In doing so, PDGR discourses challenge existing approaches to research governance as well, especially the regulation, ethics, and oversight of human genomic information management. Interestingly, the reaction of the traditional genomics research community to this revolutionary challenge has not been negative: in fact, the community seems to be embracing the ethos espoused by PDGR, at the highest levels of science policy. As conventional genomic research assimilates the ethos of PDGR, the movement’s ‘democratizing’ views on research governance are likely to become normalized as well, creating new tensions for science policy and research ethics.

A Computational Study of Three Frequent Mutations of EGFR and their Effects on Protein Dimer Formation and Non-Small Cell Lung Cancer Drug Resistance

Drug resistance is a major problem for non-small cell lung cancer (NSCLC) treatment due to mutations in patients’ DNA sequences. It is now possible to obtain the human genome information easily based on the high-throughput sequencing technology, so personalized medicine can become a reality. Based on mutation data of 168 patients with stage IIIB and IV NSCLC. We use computational method to predict the homo-dimers and hetero-dimers formation and compute the binding free energy of complexes (between drugs and proteins). For the gefitinib and erlotinib as two common drugs used in patient's therapy, we compute the possible 3D structure of epidermal growth factor receptor (EGFR) mutant- inhibitor complex. Rosetta and Amber are used for molecular dynamics analysis and simulation. The PRISM protocol is used to predict the binding energy based on similar protein-protein interaction surfaces. Multiple factors, including the mutant proteins surface geometry change, the number of hydrogen bonds change and the electronic change of the surface, are taken into account when in evaluating the binding free energy. Our results suggest that the mutation position is very important for dimer formation and it affects the drug’s binding strength with EGFR. Mutations such as L858R and T790M which do not happen on the protein interaction surface can hardly affect the formation of dimers. Patients with the delE746_A750 mutation can obtain a good therapy by using gefitinib instead of erlotinib. By comparing the binding free energy to form a homo- or heterodimers, we find that the L858R mutant will incline to form a hetero-dimer rather than a homo-dimer. Keywords: Dimer formation, EGFR, drug resistance, mutation, hetero-dimer, homo-dimer.

The Genetic Basis of Coronary Artery Disease and Atrial Fibrillation: A Search for Disease Mechanisms and Therapeutic Targets.

ORONARY ARTERY disease (CAD) and arrhythmias such as atrial fibrillation (AF) significantly contribute to perioperative mortality and morbidity. The 30-day postoperative mortality rate is increased significantly in patients with AF (6.4%) and (CAD) (2.9%). 1 The VISION trial found that 11.6% of patients undergoing noncardiac surgery had increased troponin levels postoperatively, and these patients had an increased 30-day mortality compared with the reference group. 2 Postoperative AF patients have longer hospital stays and increased mortality. 3–6 Postoperative AF is common not only after cardiac surgery, but is seen after major noncardiac surgery as well. As anesthesiologists continue to focus on perioperative care for surgical patients, they must examine all options for decreasing mortality and morbidity for patients with coronary artery disease and arrhythmias undergoing noncardiac surgery. The genetic predisposition for CAD and AF has garnered more attention in the past decade due to innovative technology and ready access to human genomic information that has improved the ability to identify single nucleotide polymorphisms in the genome. Although advances continue to be made in the management of CAD and AF, prevention likely will be the most effective way to decrease ischemic heart disease and atrial fibrillation in the patient population. Medical management has an important role for perioperative prevention of major adverse cardiac events; however, the 2014 ACC/AHA guidelines reflect important contributions from recent studies regarding the safety and efficacy of perioperative beta-blockade. The only class I recommendation for perioperative beta-blockade is the continuation of beta-blockers for patients who have been taking them chronically. 1 The initiation of beta-blockers for patients with high-risk myocardial ischemia or for patients with multiple risk factors is a class IIb recommendation. There is continued concern about the risk of perioperative stroke related to betablockers and the safest time to initiate beta-blockers before surgery. Perioperative myocardial revascularization has been examined as a method to decrease major perioperative adverse cardiac events. The CARP trial showed that at medial longterm follow-up, there was no difference in mortality between revascularized and nonrevascularized patients undergoing vascular surgery even in high-risk groups such as those with multivessel coronary artery disease 7 Therefore, medical and surgical interventions have limited ability to decrease perioperative risk when there is a clear unmet need for better risk assessment and management strategy. This review will focus on the recent progress made in establishing the genetic basis for coronary artery disease and atrial fibrillation. The clinical relevance of CAD and perioperative myocardial infarction are closely linked; however, they do not share many genetic variants. These genetic differences will be highlighted using specific genes as examples, including 9p21. A thorough understanding of the genes and their contribution to these disease processes may lead to improved risk stratification and prevention of these common perioperative complications.

Biomarkers of vascular complications in type 2 diabetes

People with T2D are at substantially increased risk of developing severe complications, (coronary heart disease, stroke, retinopathy and nephropathy). Clinicians have an important responsibility to identify those patients who, because they are at high risk, will benefit more from a preventive program or intensified therapy. However, due to the limited accuracy of the predictive tests, and limited effectiveness of preventive measures, clinicians and their patients show a suboptimal compliance to such programs. An improved risk assessment will positively impact cost/benefit ratios, opening the door for new intervention. Research involving human genetic or genomic information becomes increasingly powerful and, in conjunction with other novel biomarkers, together with personal or health data, will offer new tools for harnessing risk factors underlying complex (multi-factorial) diseases such as T2D and its complications. Altogether, there is a rationale to develop early biomarkers with improved predictive value for vascular complications of T2D by integrating patients' genetic information with traditional and emerging biomarkers.

Patient privacy in the genomic era.

According to many scientists and clinicians, genomics is taking on a key role in the field of medicine. Impressive advances in genome sequencing have opened the way to a variety of revolutionary applications in modern healthcare. In particular, the increasing understanding of the human genome, and of its relation to diseases and response to treatments brings promise of improvements in better preventive and personalized medicine. However, this progress raises important privacy and ethical concerns that need to be addressed. Indeed, each genome is the ultimate identifier of its owner and, due to its nature, it contains highly personal and privacy-sensitive data. In this article, after summarizing recent advances in genomics, we discuss some important privacy issues associated with human genomic information and methods put in place to address them.

Causal gene identification using combinatorial V-structure search

With the advances of biomedical techniques in the last decade, the costs of human genomic sequencing and genomic activity monitoring are coming down rapidly. To support the huge genome-based business in the near future, researchers are eager to find killer applications based on human genome information. Causal gene identification is one of the most promising applications, which may help the potential patients to estimate the risk of certain genetic diseases and locate the target gene for further genetic therapy. Unfortunately, existing pattern recognition techniques, such as Bayesian networks, cannot be directly applied to find the accurate causal relationship between genes and diseases. This is mainly due to the insufficient number of samples and the extremely high dimensionality of the gene space. In this paper, we present the first practical solution to causal gene identification, utilizing a new combinatorial formulation over V-Structures commonly used in conventional Bayesian networks, by exploring the combinations of significant V-Structures. We prove the NP-hardness of the combinatorial search problem under a general settings on the significance measure on the V-Structures, and present a greedy algorithm to find sub-optimal results. Extensive experiments show that our proposal is both scalable and effective, particularly with interesting findings on the causal genes over real human genome data.

How bioinformatics influences health informatics: usage of biomolecular sequences, expression profiles and automated microscopic image analyses for clinical needs and public health.

The currently hyped expectation of personalized medicine is often associated with just achieving the information technology led integration of biomolecular sequencing, expression and histopathological bioimaging data with clinical records at the individual patients’ level as if the significant biomedical conclusions would be its more or less mandatory result. It remains a sad fact that many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known and, thus, most of the molecular and cellular data cannot be interpreted in terms of biomedically relevant conclusions. Whereas the historical trend will certainly be into the general direction of personalized diagnostics and cures, the temperate view suggests that biomedical applications that rely either on the comparison of biomolecular sequences and/or on the already known biomolecular mechanisms have much greater chances to enter clinical practice soon. In addition to considering the general trends, we exemplarily review advances in the area of cancer biomarker discovery, in the clinically relevant characterization of patient-specific viral and bacterial pathogens (with emphasis on drug selection for influenza and enterohemorrhagic E. coli) as well as progress in the automated assessment of histopathological images. As molecular and cellular data analysis will become instrumental for achieving desirable clinical outcomes, the role of bioinformatics and computational biology approaches will dramatically grow.Author summaryWith DNA sequencing and computers becoming increasingly cheap and accessible to the layman, the idea of integrating biomolecular and clinical patient data seems to become a realistic, short-term option that will lead to patient-specific diagnostics and treatment design for many diseases such as cancer, metabolic disorders, inherited conditions, etc. These hyped expectations will fail since many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known yet and, thus, most of the molecular and cellular data collected will not lead to biomedically relevant conclusions. At the same time, less spectacular biomedical applications based on biomolecular sequence comparison and/or known biomolecular mechanisms have the potential to unfold enormous potential for healthcare and public health. Since the analysis of heterogeneous biomolecular data in context with clinical data will be increasingly critical, the role of bioinformatics and computational biology will grow correspondingly in this process.

The Chills and Thrills of Whole Genome Sequencing

In recent years, whole genome sequencing (WGS) evolved from a futuristic-sounding research project to an increasingly affordable technology for determining complete genome sequences of complex organisms, including humans. This prompts a wide range of revolutionary applications, as WGS is a promising means for improving modern healthcare and providing a better understanding of the human genome, in particular its relation to diseases and response to treatments. However, this progress raises worrisome privacy and ethical issues, since, besides uniquely identifying its owner, the genome contains a treasure trove of highly personal and sensitive information. In this article, after summarizing recent advances in genomics, we discuss some important privacy issues associated with human genomic information and identify a number of particularly relevant research challenges.

HpvPDB: An Online Proteome Reserve for Human Papillomavirus

Human papillomavirus (HPV) infection is the leading cause of cancer mortality among women worldwide. The molecular understanding of HPV proteins has significant connotation for understanding their intrusion in the host and designing novel protein vaccines and anti-viral agents, etc. Genomic, proteomic, structural, and disease-related information on HPV is available on the web; yet, with trivial annotations and more so, it is not well customized for data analysis, host-pathogen interaction, strain-disease association, drug designing, and sequence analysis, etc. We attempted to design an online reserve with comprehensive information on HPV for the end users desiring the same. The Human Papillomavirus Proteome Database (hpvPDB) domiciles proteomic and genomic information on 150 HPV strains sequenced to date. Simultaneous easy expandability and retrieval of the strain-specific data, with a provision for sequence analysis and exploration potential of predicted structures, and easy access for curation and annotation through a range of search options at one platform are a few of its important features. Affluent information in this reserve could be of help for researchers involved in structural virology, cancer research, drug discovery, and vaccine design.

KAREBrowser: SNP database of Korea Association REsource Project

The International HapMap Project and the Human Genome Diversity Project (HGDP) provide plentiful resources on human genome information to the public. However, this kind of information is limited because of the small sample size in both databases. A Genome-Wide Association Study has been conducted with 8,842 Korean subjects as a part of the Korea Association Resource (KARE) project. In an effort to build a publicly available browsing system for genome data resulted from large scale KARE GWAS, we developed the KARE browser. This browser provides users with a large amount of single nucleotide polymorphisms (SNPs) information comprising 1.5 million SNPs from population-based cohorts of 8,842 samples. KAREBrowser was based on the generic genome browser (GBrowse), a webbased application tool developed for users to navigate and visualize the genomic features and annotations in an interactive manner. All SNP information and related functions are available at the web site http://ksnp.cdc. go.kr/karebrowser/.

Has the Genomic Revolution Failed?

Has the Genomic Revolution Failed?

Personalized Medicine: Progress and Promise

Personalized medicine is a broad and rapidly advancing field of health care that is informed by each person's unique clinical, genetic, genomic, and environmental information. Personalized medicine depends on multidisciplinary health care teams and integrated technologies (e.g., clinical decision support) to utilize our molecular understanding of disease in order to optimize preventive health care strategies. Human genome information now allows providers to create optimized care plans at every stage of a disease, shifting the focus from reactive to preventive health care. The further integration of personalized medicine into the clinical workflow requires overcoming several barriers in education, accessibility, regulation, and reimbursement. This review focuses on providing a comprehensive understanding of personalized medicine, from scientific discovery at the laboratory bench to integration of these novel ways of understanding human biology at the bedside.

Trade-Secret Model: Privacy Rights

![Figure][1] CREDIT: ISTOCKPHOTO.COM In their Policy Forum “Genomics, Biobanks, and the Trade-secret model” (15 April, p. [309][2]), R. Mitchell and his colleagues suggest that trade-secret law could be applied effectively to manage the use of human genomic information. It would be productive to assess the potential application of two other legal models as well: the individual's right to control his or her name and likeness, and the right to control public disclosure of private facts. Jurisdictions that recognize a right of personal privacy commonly include within that right the ability of an individual to control the use of his/her name and likeness for commercial advantage ([ 1 ][3]). An individual's name and visual image are deemed to be unique and highly personal qualities that each person should have the right to control. This right is viewed by the law as part of the individual's ability to protect the key aspects of his/her personality. Name and likeness have been interpreted to include other characteristics of an individual's personality, including the sound of his or her voice ([ 2 ][4]). It seems reasonable that the legal framework designed to help the individual to protect the integrity of his/her personality should also include the most intimate aspect of an individual's personality—personal genomic information. Personal privacy rights also frequently include the ability to control public disclosure of private facts about an individual ([ 3 ][5]). Arguably, genomic information includes the most private and personal facts associated with any individual. The right to control public disclosure of private facts appears to provide another legal vehicle for management of use of personal genomic information. Application of these traditional privacy rights can supplement legal approaches such as the trade-secret model proposed by Mitchell et al. There may also be circumstances in which the trade-secret model would not be appropriate but the traditional privacy rights could be applied. For example, it is unclear whether an individual can effectively assert a trade-secret claim when the secret he or she possesses is not actually understood by the individual asserting the protection. No such complications arise when a traditional personal privacy right is applied. 1. [↵][6] California Civil Code, Section 3344. 2. [↵][7] Massachusetts General Laws, Chapter 214, Section 1B. 3. [↵][8] California State Constitution, Article I, Section 1B and Florida State Constitution, Article I, Section 23. [1]: pending:yes [2]: http://www.sciencemag.org/content/332/6027/309.full [3]: #ref-1 [4]: #ref-2 [5]: #ref-3 [6]: #xref-ref-1-1 View reference 1 in text [7]: #xref-ref-2-1 View reference 2 in text [8]: #xref-ref-3-1 View reference 3 in text

A rigorous approach to facilitate and guarantee the correctness of the genetic testing management in human genome information systems

BackgroundRecent medical and biological technology advances have stimulated the development of new testing systems that have been providing huge, varied amounts of molecular and clinical data. Growing data volumes pose significant challenges for information processing systems in research centers. Additionally, the routines of genomics laboratory are typically characterized by high parallelism in testing and constant procedure changes.ResultsThis paper describes a formal approach to address this challenge through the implementation of a genetic testing management system applied to human genome laboratory. We introduced the Human Genome Research Center Information System (CEGH) in Brazil, a system that is able to support constant changes in human genome testing and can provide patients updated results based on the most recent and validated genetic knowledge. Our approach uses a common repository for process planning to ensure reusability, specification, instantiation, monitoring, and execution of processes, which are defined using a relational database and rigorous control flow specifications based on process algebra (ACP). The main difference between our approach and related works is that we were able to join two important aspects: 1) process scalability achieved through relational database implementation, and 2) correctness of processes using process algebra. Furthermore, the software allows end users to define genetic testing without requiring any knowledge about business process notation or process algebra.ConclusionsThis paper presents the CEGH information system that is a Laboratory Information Management System (LIMS) based on a formal framework to support genetic testing management for Mendelian disorder studies. We have proved the feasibility and showed usability benefits of a rigorous approach that is able to specify, validate, and perform genetic testing using easy end user interfaces.