A Precision Medicine Tool for Patients With Multiple Sclerosis (the Open MS BioScreen): Human-Centered Design and Development.

BACKGROUND: Patients with multiple sclerosis (MS) face several challenges in accessing clinical tools to help them monitor, understand, and make meaningful decisions about their disease course. The University of California San Francisco MS BioScreen is a web-based precision medicine tool initially designed to be clinician facing. We aimed to design a second, openly available tool, Open MS BioScreen, that would be accessible, understandable, and actionable by people with MS. OBJECTIVE: This study aimed to describe the human-centered design and development approach (inspiration, ideation, and implementation) for creating the Open MS BioScreen platform. METHODS: We planned an iterative and cyclical development process that included stakeholder engagement and iterative feedback from users. Stakeholders included patients with MS along with their caregivers and family members, MS experts, generalist clinicians, industry representatives, and advocacy experts. Users consisted of anyone who wants to track MS measurements over time and access openly available tools for people with MS. Phase I (inspiration) consisted of empathizing with users and defining the problem. We sought to understand the main challenges faced by patients and clinicians and what they would want to see in a web-based app. In phase II (ideation), our multidisciplinary team discussed approaches to capture, display, and make sense of user data. Then, we prototyped a series of mock-ups to solicit feedback from clinicians and people with MS. In phase III (implementation), we incorporated all concepts to test and iterate a minimally viable product. We then gathered feedback through an agile development process. The design and development were cyclical-many times throughout the process, we went back to the drawing board. RESULTS: This human-centered approach generated an openly available, web-based app through which patients with MS, their clinicians, and their caregivers can access the site and create an account. Users can enter information about their MS (basic level as well as more advanced concepts), visualize their data longitudinally, access a series of algorithms designed to empower them to make decisions about their treatments, and enter data from wearable devices to encourage realistic goal setting about their ambulatory activity. Agile development will allow us to continue to incorporate precision medicine tools, as these are validated in the clinical research arena. CONCLUSIONS: After engaging intended users into the iterative human-centered design of the Open MS BioScreen, we will now monitor the adaptation and dissemination of the tool as we expand its functionality and reach. The insights generated from this approach can be applied to the development of a number of self-tracking, self-management, and user engagement tools for patients with chronic conditions.

Author Correction: Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations.

Asthma is a complex disease with striking disparities across racial and ethnic groups. Despite its relatively high burden, representation of individuals of African ancestry in asthma genome-wide association studies (GWAS) has been inadequate, and true associations in these underrepresented minority groups have been inconclusive. We report the results of a genome-wide meta-analysis from the Consortium on Asthma among African Ancestry Populations (CAAPA; 7009 asthma cases, 7645 controls). We find strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations, including the chromosome 17q12-q21 locus and the chr12q13 region, a novel (and not previously replicated) asthma locus recently identified by the Trans-National Asthma Genetic Consortium (TAGC). An additional seven loci reported by TAGC show marginal evidence for association in CAAPA. We also identify two novel loci (8p23 and 8q24) that may be specific to asthma risk in African ancestry populations.

Harnessing electronic medical records to advance research on multiple sclerosis.

BACKGROUND: Electronic medical records (EMR) data are increasingly used in research, but no studies have yet evaluated similarity between EMR and research-quality data and between characteristics of an EMR multiple sclerosis (MS) population and known natural MS history. OBJECTIVES: To (1) identify MS patients in an EMR system and extract clinical data, (2) compare EMR-extracted data with gold-standard research data, and (3) compare EMR MS population characteristics to expected MS natural history. METHODS: Algorithms were implemented to identify MS patients from the University of California San Francisco EMR, de-identify the data and extract clinical variables. EMR-extracted data were compared to research cohort data in a subset of patients. RESULTS: We identified 4142 MS patients via search of the EMR and extracted their clinical data with good accuracy. EMR and research values showed good concordance for Expanded Disability Status Scale (EDSS), timed-25-foot walk, and subtype. We replicated several expected MS epidemiological features from MS natural history including higher EDSS for progressive versus relapsing-remitting patients and for male versus female patients and increased EDSS with age at examination and disease duration. CONCLUSION: Large real-world cohorts algorithmically extracted from the EMR can expand opportunities for MS clinical research.

Systematic integration of biomedical knowledge prioritizes drugs for repurposing.

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

Identifying tagging SNPs for African specific genetic variation from the African Diaspora Genome.

A primary goal of The Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to develop an 'African Diaspora Power Chip' (ADPC), a genotyping array consisting of tagging SNPs, useful in comprehensively identifying African specific genetic variation. This array is designed based on the novel variation identified in 642 CAAPA samples of African ancestry with high coverage whole genome sequence data (~30× depth). This novel variation extends the pattern of variation catalogued in the 1000 Genomes and Exome Sequencing Projects to a spectrum of populations representing the wide range of West African genomic diversity. These individuals from CAAPA also comprise a large swath of the African Diaspora population and incorporate historical genetic diversity covering nearly the entire Atlantic coast of the Americas. Here we show the results of designing and producing such a microchip array. This novel array covers African specific variation far better than other commercially available arrays, and will enable better GWAS analyses for researchers with individuals of African descent in their study populations. A recent study cataloging variation in continental African populations suggests this type of African-specific genotyping array is both necessary and valuable for facilitating large-scale GWAS in populations of African ancestry.

Long-term evolution of multiple sclerosis disability in the treatment era.

OBJECTIVE: To characterize the accrual of long-term disability in a cohort of actively treated multiple sclerosis (MS) patients and to assess whether clinical and magnetic resonance imaging (MRI) data used in clinical trials have long-term prognostic value. METHODS: This is a prospective study of 517 actively managed MS patients enrolled at a single center. RESULTS: More than 91% of patients were retained, with data ascertained up to 10 years after the baseline visit. At this last assessment, neurologic disability as measured by the Expanded Disability Status Scale (EDSS) was stable or improved compared to baseline in 41% of patients. Subjects with no evidence of disease activity (NEDA) by clinical and MRI criteria during the first 2 years had long-term outcomes that were no different from those of the cohort as a whole. 25-OH vitamin D serum levels were inversely associated with short-term MS disease activity; however, these levels had no association with long-term disability. At a median time of 16.8 years after disease onset, 10.7% (95% confidence interval [CI] = 7.2-14%) of patients reached an EDSS ≥ 6, and 18.1% (95% CI = 13.5-22.5%) evolved from relapsing MS to secondary progressive MS (SPMS). INTERPRETATION: Rates of worsening and evolution to SPMS were substantially lower when compared to earlier natural history studies. Notably, the NEDA 2-year endpoint was not a predictor of long-term stability. Finally, the data call into question the utility of annual MRI assessments as a treat-to-target approach for MS care. Ann Neurol 2016;80:499-510.

Multidimensional reduction of multicentric cohort heterogeneity: An alternative method to increase statistical power and robustness.

Modern clinical research takes advantage of multicentric cohorts to increase sample size and gain in statistical power. However, combining individuals from different recruitment centers provides heterogeneity in the dataset that needs to be accounted for to obtain robust results. Sophisticated statistical multivariate models adjusting for center effect can be implemented, but they can become unstable and can be complex to interpret with the increasing number of covariates to consider. Here, we present a multidimensional reduction technique to identify heterogeneity in a French multicentric cohort of hematopoietic stem cell transplantations and characterize a homogeneous subgroup prior to performing simple statistical univariate analyses. The exclusion of outliers allowed the identification of two genetic factors associated with post-transplantation overall survival. We therefore provide proof-of-concept that a sample size reduction method can efficiently account for heterogeneity and center effect in multicentric cohorts while increasing statistical power and robustness for discovery of new association signals.

Association of HLA Genetic Risk Burden With Disease Phenotypes in Multiple Sclerosis.

IMPORTANCE: Although multiple HLA alleles associated with multiple sclerosis (MS) risk have been identified, genotype-phenotype studies in the HLA region remain scarce and inconclusive. OBJECTIVES: To investigate whether MS risk-associated HLA alleles also affect disease phenotypes. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional, case-control study comprising 652 patients with MS who had comprehensive phenotypic information and 455 individuals of European origin serving as controls was conducted at a single academic research site. Patients evaluated at the Multiple Sclerosis Center at University of California, San Francisco between July 2004 and September 2005 were invited to participate. Spinal cord imaging in the data set was acquired between July 2013 and March 2014; analysis was performed between December 2014 and December 2015. MAIN OUTCOMES AND MEASURES: Cumulative HLA genetic burden (HLAGB) calculated using the most updated MS-associated HLA alleles vs clinical and magnetic resonance imaging outcomes, including age at onset, disease severity, conversion time from clinically isolated syndrome to clinically definite MS, fractions of cortical and subcortical gray matter and cerebral white matter, brain lesion volume, spinal cord gray and white matter areas, upper cervical cord area, and the ratio of gray matter to the upper cervical cord area. Multivariate modeling was applied separately for each sex data set. RESULTS: Of the 652 patients with MS, 586 had no missing genetic data and were included in the HLAGB analysis. In these 586 patients (404 women [68.9%]; mean [SD] age at disease onset, 33.6 [9.4] years), HLAGB was higher than in controls (median [IQR], 0.7 [0-1.4] and 0 [-0.3 to 0.5], respectively; P = 1.8 × 10-27). A total of 619 (95.8%) had relapsing-onset MS and 27 (4.2%) had progressive-onset MS. No significant difference was observed between relapsing-onset MS and primary progressive MS. A higher HLAGB was associated with younger age at onset and the atrophy of subcortical gray matter fraction in women with relapsing-onset MS (standard ß = -1.20 × 10-1; P = 1.7 × 10-2 and standard ß = -1.67 × 10-1; P = 2.3 × 10-4, respectively), which were driven mainly by the HLA-DRB1*15:01 haplotype. In addition, we observed the distinct role of the HLA-A*24:02-B*07:02-DRB1*15:01 haplotype among the other common DRB1*15:01 haplotypes and a nominally protective effect of HLA-B*44:02 to the subcortical gray atrophy (standard ß = -1.28 × 10-1; P = 5.1 × 10-3 and standard ß = 9.52 × 10-2; P = 3.6 × 10-2, respectively). CONCLUSIONS AND RELEVANCE: We confirm and extend previous observations linking HLA MS susceptibility alleles with disease progression and specific clinical and magnetic resonance imaging phenotypic traits.

Meta-analysis of genome-wide association studies reveals genetic overlap between Hodgkin lymphoma and multiple sclerosis.

BACKGROUND: Based on epidemiological commonalities, multiple sclerosis (MS) and Hodgkin lymphoma (HL), two clinically distinct conditions, have long been suspected to be aetiologically related. MS and HL occur in roughly the same age groups, both are associated with Epstein-Barr virus infection and ultraviolet (UV) light exposure, and they cluster mutually in families (though not in individuals). We speculated if in addition to sharing environmental risk factors, MS and HL were also genetically related. Using data from genome-wide association studies (GWAS) of 1816 HL patients, 9772 MS patients and 25 255 controls, we therefore investigated the genetic overlap between the two diseases. METHODS: From among a common denominator of 404 K single nucleotide polymorphisms (SNPs) studied, we identified SNPs and human leukocyte antigen (HLA) alleles independently associated with both diseases. Next, we assessed the cumulative genome-wide effect of MS-associated SNPs on HL and of HL-associated SNPs on MS. To provide an interpretational frame of reference, we used data from published GWAS to create a genetic network of diseases within which we analysed proximity of HL and MS to autoimmune diseases and haematological and non-haematological malignancies. RESULTS: SNP analyses revealed genome-wide overlap between HL and MS, most prominently in the HLA region. Polygenic HL risk scores explained 4.44% of HL risk (Nagelkerke R(2)), but also 2.36% of MS risk. Conversely, polygenic MS risk scores explained 8.08% of MS risk and 1.94% of HL risk. In the genetic disease network, HL was closer to autoimmune diseases than to solid cancers. CONCLUSIONS: HL displays considerable genetic overlap with MS and other autoimmune diseases.

Haplotype-based approach to known MS-associated regions increases the amount of explained risk.

Genome-wide association studies (GWAS), using single nucleotide polymorphisms (SNPs), have yielded 110 non-human leucocyte antigen genomic regions that are associated with multiple sclerosis (MS). Despite this large number of associations, however, only 28% of MS-heritability can currently be explained. Here we compare the use of multi-SNP-haplotypes to the use of single-SNPs as alternative methods to describe MS genetic risk. SNP-haplotypes (of various lengths from 1 up to 15 contiguous SNPs) were constructed at each of the 110 previously identified, MS-associated, genomic regions. Even after correcting for the larger number of statistical comparisons made when using the haplotype-method, in 32 of the regions, the SNP-haplotype based model was markedly more significant than the single-SNP based model. By contrast, in no region was the single-SNP based model similarly more significant than the SNP-haplotype based model. Moreover, when we included the 932 MS-associated SNP-haplotypes (that we identified from 102 regions) as independent variables into a logistic linear model, the amount of MS-heritability, as assessed by Nagelkerke's R-squared, was 38%, which was considerably better than 29%, which was obtained by using only single-SNPs. This study demonstrates that SNP-haplotypes can be used to fine-map the genetic associations within regions of interest previously identified by single-SNP GWAS. Moreover, the amount of the MS genetic risk explained by the SNP-haplotype associations in the 110 MS-associated genomic regions was considerably greater when using SNP-haplotypes than when using single-SNPs. Also, the use of SNP-haplotypes can lead to the discovery of new regions of interest, which have not been identified by a single-SNP GWAS.

Genetic contribution to multiple sclerosis risk among Ashkenazi Jews.

BACKGROUND: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system, with a strong genetic component. Over 100 genetic loci have been implicated in susceptibility to MS in European populations, the most prominent being the 15:01 allele of the HLA-DRB1 gene. The prevalence of MS is high in European populations including those of Ashkenazi origin, and low in African and Asian populations including those of Jewish origin. METHODS: Here we identified and extracted a total of 213 Ashkenazi MS cases and 546 ethnically matched healthy control individuals from two previous genome-wide case-control association analyses, and 72 trios (affected proband and two unaffected parents) from a previous genome-wide transmission disequilibrium association study, using genetic data to define Ashkenazi. We compared the pattern of genetic risk between Ashkenazi and non-Ashkenazi Europeans. We also sought to identify novel Ashkenazi-specific risk loci by performing association tests on the subset of Ashkenazi cases, controls, probands, and parents from each study. RESULTS: The HLA-DRB1*15:01 allele and the non-HLA risk alleles were present at relatively low frequencies among Ashkenazi and explained a smaller fraction of the population-level risk when compared to non-Ashkenazi Europeans. Alternative HLA susceptibility alleles were identified in an Ashkenazi-only association study, including HLA-A*68:02 and one or both genes in the HLA-B*38:01-HLA-C*12:03 haplotype. The genome-wide screen in Ashkenazi did not reveal any loci associated with MS risk. CONCLUSION: These results suggest that genetic susceptibility to MS in Ashkenazi Jews has not been as well established as that of non-Ashkenazi Europeans. This implies value in studying large well-characterized Ashkenazi populations to accelerate gene discovery in complex genetic diseases.

Precision medicine in chronic disease management: The multiple sclerosis BioScreen.

We present a precision medicine application developed for multiple sclerosis (MS): the MS BioScreen. This new tool addresses the challenges of dynamic management of a complex chronic disease; the interaction of clinicians and patients with such a tool illustrates the extent to which translational digital medicine-that is, the application of information technology to medicine-has the potential to radically transform medical practice. We introduce 3 key evolutionary phases in displaying data to health care providers, patients, and researchers: visualization (accessing data), contextualization (understanding the data), and actionable interpretation (real-time use of the data to assist decision making). Together, these form the stepping stones that are expected to accelerate standardization of data across platforms, promote evidence-based medicine, support shared decision making, and ultimately lead to improved outcomes.