Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma.

BACKGROUND: Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma. METHODS: The subjects included asthma patients of European ancestry (n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest. RESULTS: The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67-0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70-0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (TPSAB1, FBXL16), asthma symptoms and severity (ABCA7, CNN2, PTRN3, and BSG/CD147), airway remodeling (ELANE, FSTL3), mucin production (GAL3ST), leukotriene synthesis (GPX4), allergic asthma (ZFPM1, SBNO2), and others. CONCLUSIONS: An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction.

Quantitative Foveal Structural Metrics as Predictors of Visual Acuity in Human Albinism.

Purpose: To assess the degree to which quantitative foveal structural measurements account for variation in best-corrected visual acuity (BCVA) in human albinism. Methods: BCVA was measured and spectral domain optical coherence tomography (SD-OCT) images were acquired for 74 individuals with albinism. Categorical foveal hypoplasia grades were assessed using the Leicester Grading System for Foveal Hypoplasia. Foveal anatomical specialization (foveal versus parafoveal value) was quantified for inner retinal layer (IRL) thickness, outer segment (OS) length, and outer nuclear layer (ONL) thickness. These metrics, participant sex, and age were used to build a multiple linear regression of BCVA. This combined linear model's predictive properties were compared to those of categorical foveal hypoplasia grading. Results: The cohort included three participants with type 1a foveal hypoplasia, 23 participants with type 1b, 33 with type 2, ten with type 3, and five with type 4. BCVA ranged from 0.08 to 1.00 logMAR (mean ± SD: 0.53 ± 0.21). IRL ratio, OS ratio, and ONL ratio were measured in all participants and decreased with increasing severity of foveal hypoplasia. The best-fit combined linear model included all three quantitative metrics and participant age expressed as a binary variable (divided into 0-18 years and 19 years or older; adjusted R2 = 0.500). This model predicted BCVA more accurately than a categorical foveal hypoplasia model (adjusted R2 = 0.352). Conclusions: A quantitative model of foveal specialization accounts for more variance in BCVA in albinism than categorical foveal hypoplasia grading. Other factors, such as optical aberrations and eye movements, may account for the remaining unexplained variance.

Estimating the efficacy of pharmacogenomics over a lifetime.

It is well known that common variants in specific genes influence drug metabolism and response, but it is currently unknown what fraction of patients are given prescriptions over a lifetime that could be contraindicated by their pharmacogenomic profiles. To determine the clinical utility of pharmacogenomics over a lifetime in a general patient population, we sequenced the genomes of 300 deceased Marshfield Clinic patients linked to lifelong medical records. Genetic variants in 33 pharmacogenes were evaluated for their lifetime impact on drug prescribing using extensive electronic health records. Results show that 93% of the 300 deceased patients carried clinically relevant variants. Nearly 80% were prescribed approximately three medications on average that may have been impacted by these variants. Longitudinal data suggested that the optimal age for pharmacogenomic testing was prior to age 50, but the optimal age is greatly influenced by the stability of the population in the healthcare system. This study emphasizes the broad clinical impact of pharmacogenomic testing over a lifetime and demonstrates the potential application of genomic medicine in a general patient population for the advancement of precision medicine.

Heritable Risk and Protective Genetic Components of Glaucoma Medication Non-Adherence.

Glaucoma is the leading cause of irreversible blindness, affecting 76 million globally. It is characterized by irreversible damage to the optic nerve. Pharmacotherapy manages intraocular pressure (IOP) and slows disease progression. However, non-adherence to glaucoma medications remains problematic, with 41-71% of patients being non-adherent to their prescribed medication. Despite substantial investment in research, clinical effort, and patient education protocols, non-adherence remains high. Therefore, we aimed to determine if there is a substantive genetic component behind patients' glaucoma medication non-adherence. We assessed glaucoma medication non-adherence with prescription refill data from the Marshfield Clinic Healthcare System's pharmacy dispensing database. Two standard measures were calculated: the medication possession ratio (MPR) and the proportion of days covered (PDC). Non-adherence on each metric was defined as less than 80% medication coverage over 12 months. Genotyping was done using the Illumina HumanCoreExome BeadChip in addition to exome sequencing on the 230 patients (1) to calculate the heritability of glaucoma medication non-adherence and (2) to identify SNPs and/or coding variants in genes associated with medication non-adherence. Ingenuity pathway analysis (IPA) was utilized to derive biological meaning from any significant genes in aggregate. Over 12 months, 59% of patients were found to be non-adherent as measured by the MPR80, and 67% were non-adherent as measured by the PDC80. Genome-wide complex trait analysis (GCTA) suggested that 57% (MPR80) and 48% (PDC80) of glaucoma medication non-adherence could be attributed to a genetic component. Missense mutations in TTC28, KIAA1731, ADAMTS5, OR2W3, OR10A6, SAXO2, KCTD18, CHCHD6, and UPK1A were all found to be significantly associated with glaucoma medication non-adherence by whole exome sequencing after Bonferroni correction (p < 10-3) (PDC80). While missense mutations in TINAG, CHCHD6, GSTZ1, and SEMA4G were found to be significantly associated with medication non-adherence by whole exome sequencing after Bonferroni correction (p < 10-3) (MPR80). The same coding SNP in CHCHD6 which functions in Alzheimer's disease pathophysiology was significant by both measures and increased risk for glaucoma medication non-adherence by three-fold (95% CI, 1.62-5.8). Although our study was underpowered for genome-wide significance, SNP rs6474264 within ZMAT4 (p = 5.54 × 10-6) was found to be nominally significant, with a decreased risk for glaucoma medication non-adherence (OR, 0.22; 95% CI, 0.11-0.42)). IPA demonstrated significant overlap, utilizing, both standard measures including opioid signaling, drug metabolism, and synaptogenesis signaling. CREB signaling in neurons (which is associated with enhancing the baseline firing rate for the formation of long-term potentiation in nerve fibers) was shown to have protective associations. Our results suggest a substantial heritable genetic component to glaucoma medication non-adherence (47-58%). This finding is in line with genetic studies of other conditions with a psychiatric component (e.g., post-traumatic stress disorder (PTSD) or alcohol dependence). Our findings suggest both risk and protective statistically significant genes/pathways underlying glaucoma medication non-adherence for the first time. Further studies investigating more diverse populations with larger sample sizes are needed to validate these findings.

FMR1 CGG Repeats and Stress Influence Self-Reported Cognitive Functioning in Mothers.

Variation in the FMR1 gene may affect aspects of cognition, such as executive function and memory. Environmental factors, such as stress, may also negatively impact cognitive functioning. Participants included 1,053 mothers of children with and without developmental disabilities. Participants completed self-report measures of executive function, memory, and stress (i.e., life events, parenting status), and provided DNA to determine CGG repeat length (ranging from 7 to 192 CGGs). Stress exposure significantly predicted greater self-reported difficulties in executive function and the likelihood of memory problems. Cubic CGG effects independently predicted executive function and memory difficulties, suggesting effects of both genetic variation and environmental stress exposure on cognitive functioning.

The effect of college degree attainment on neurodegenerative symptoms in genetically at-risk women.

Using longitudinal data, the present study examined the association between college degree attainment and the manifestation of neurodegenerative symptoms among women (n = 93) at elevated genetic risk. The neurodegenerative symptoms investigated in this study are due to FXTAS (Fragile X-associated Tremor/Ataxia Syndrome), a condition with onset after age 50. Those at risk for FXTAS have a mutation of a single gene found on the X chromosome. FXTAS is characterized by intention tremor, gait ataxia, executive function deficits, memory issues, and neuropathy. College degree attainment has been shown to provide neuroprotective effects in the general population, delaying the development of neurodegenerative conditions such as Alzheimer's disease. For this reason, college degree attainment is a potentially salient resource for those at risk of FXTAS. The results of the present research indicated significantly more severe FXTAS symptoms in women who did not attain a college degree as compared with those who were college graduates, although the two groups were similar in age, genetic risk, household income, health behaviors, and general health problems. Furthermore, symptoms in those who did not attain a college degree worsened over the 9-year study period at a significantly faster rate than the college graduates. The association between college degree attainment and FXTAS symptoms was significantly mediated by depression, which was lower among the graduates than those who did not attain a college degree. Thus, the present research is an example of how a sociodemographic factor can mitigate neurodegenerative conditions in genetically at-risk adults.

Advancing artificial intelligence-assisted pre-screening for fragile X syndrome.

BACKGROUND: Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism, is significantly underdiagnosed in the general population. Diagnosing FXS is challenging due to the heterogeneity of the condition, subtle physical characteristics at the time of birth and similarity of phenotypes to other conditions. The medical complexity of FXS underscores an urgent need to develop more efficient and effective screening methods to identify individuals with FXS. In this study, we evaluate the effectiveness of using artificial intelligence (AI) and electronic health records (EHRs) to accelerate FXS diagnosis. METHODS: The EHRs of 2.1 million patients served by the University of Wisconsin Health System (UW Health) were the main data source for this retrospective study. UW Health includes patients from south central Wisconsin, with approximately 33 years (1988-2021) of digitized health data. We identified all participants who received a code for FXS in the form of International Classification of Diseases (ICD), Ninth or Tenth Revision (ICD9 = 759.83, ICD10 = Q99.2). Only individuals who received the FXS code on at least two occasions ("Rule of 2") were classified as clinically diagnosed cases. To ensure the availability of sufficient data prior to clinical diagnosis to test the model, only individuals who were diagnosed after age 10 were included in the analysis. A supervised random forest classifier was used to create an AI-assisted pre-screening tool to identify cases with FXS, 5 years earlier than the time of clinical diagnosis based on their medical records. The area under receiver operating characteristic curve (AUROC) was reported. The AUROC shows the level of success in identification of cases and controls (AUROC = 1 represents perfect classification). RESULTS: 52 individuals were identified as target cases and matched with 5200 controls. AI-assisted pre-screening tool successfully identified cases with FXS, 5 years earlier than the time of clinical diagnosis with an AUROC of 0.717. A separate model trained and tested on UW Health cases achieved the AUROC of 0.798. CONCLUSIONS: This result shows the potential utility of our tool in accelerating FXS diagnosis in real clinical settings. Earlier diagnosis can lead to more timely intervention and access to services with the goal of improving patients' health outcomes.

Health profiles of adults with autism spectrum disorder: Differences between women and men.

The purpose of the present study was to investigate the hypothesis that women with autism have poorer health compared with men with autism, and compared with women without autism. Utilizing electronic health records drawn from a single health care system serving over 2 million individuals, 2119 adults with diagnosed autism spectrum disorders were compared with age- and sex-matched controls. When considering health care utilization, we found evidence of multiplicative risk for conditions within some domains (i.e., nutrition conditions, neurologic disease, psychiatric conditions, and sleep disorders) such that women with autism spectrum disorder (ASD) experienced double jeopardy-meaning they had greater rates of health care utilization within a domain than what would separately be expected by virtue of being a woman and having ASD. For other domains (i.e., endocrine disorders, gastrointestinal disorders), the risk was additive such that being a female and having ASD were both associated with higher health care utilization, but there were no significant interaction effects. It was only with respect to one domain (cardiovascular) that rates of health care utilization were reflective of neither ASD diagnosis nor sex. Overall, our findings suggest that women with ASD are a vulnerable subgroup with high levels of health care utilization. LAY SUMMARY: This study asked whether women with autism have poorer health compared with men with autism, and compared with women without autism. To answer this question, we used data from electronic health records. We found that women with autism spectrum disorder (ASD) were at the greatest risk for health problems such as nutrition conditions, neurologic disease, psychiatric conditions, and sleep disorders. More research on health of women with ASD is needed.

Mild Neurological Signs in FMR1 Premutation Women in an Unselected Community-Based Cohort.

BACKGROUND: Premutation-sized (55-200) CGG repeat expansions in the FMR1 gene cause fragile X-associated tremor/ataxia syndrome (FXTAS). Most studies of premutation carriers utilized reverse ascertainment to identify patients, leading to a selection bias for larger repeats. As shorter CGG premutation repeats are common in the population, understanding their impact on health outcomes has a potentially large public health footprint. OBJECTIVE: The study's objective was to compare an unselected group of premutation carriers (n = 35, 55-101 CGG repeats) with matched controls (n = 61, 29-39 CGG repeats) with respect to FXTAS-type signs using structured neurological assessments. METHODS: Three neurologists independently rated signs, using an adapted version of the FXTAS Rating Scale (Leehey MA, Berry-Kravis E, Goetz CG, et al. FMR1 CGG repeat length predicts motor dysfunction in premutation carriers. Neurology. 2008). This was a double-blind study, as genetic status (premutation vs. control) was known neither by the participants nor by any of the neurologists. Analyses controlled potentially confounding comorbid conditions in the electronic health record (eg, osteoarthritis and stroke) and probed the association of age with signs. RESULTS: Although there was no overall difference between carriers and controls, among individuals without any potentially confounding comorbid diagnoses, there was a statistically significant age-associated elevation in FXTAS-type signs in premutation carriers compared to controls. CONCLUSIONS: Among those who do not have other comorbid diagnoses, women who have CGG repeats at the lower end of the premutation range may be at greater risk for ataxia and parkinsonism than their age peers, although their overall risk of developing such clinical features is low. This study should provide reassurance to those who share characteristics with the present cohort. © 2021 International Parkinson and Movement Disorder Society.

Novel EDGE encoding method enhances ability to identify genetic interactions.

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)-rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.

Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies.

Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate ("A-by-G" grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.

Artificial intelligence-assisted phenotype discovery of fragile X syndrome in a population-based sample.

PURPOSE: Fragile X syndrome (FXS), the most prevalent inherited cause of intellectual disability, remains underdiagnosed in the general population. Clinical studies have shown that individuals with FXS have a complex health profile leading to unique clinical needs. However, the full impact of this X-linked disorder on the health of affected individuals is unclear and the prevalence of co-occurring conditions is unknown. METHODS: We mined the longitudinal electronic health records from more than one million individuals to investigate the health characteristics of patients who have been clinically diagnosed with FXS. Additionally, using machine-learning approaches, we created predictive models to identify individuals with FXS in the general population. RESULTS: Our discovery-oriented approach identified the associations of FXS with a wide range of medical conditions including circulatory, endocrine, digestive, and genitourinary, in addition to mental and neurological disorders. We successfully created predictive models to identify cases five years prior to clinical diagnosis of FXS without relying on any genetic or familial data. CONCLUSION: Although FXS is often thought of primarily as a neurological disorder, it is in fact a multisystem syndrome involving many co-occurring conditions, some primary and some secondary, and they are associated with a considerable burden on patients and their families.

Assessing Foveal Structure in Individuals with TYR R402Q and S192Y Hypomorphic Alleles.

Purpose: To assess the impact of two TYR hypomorphic alleles (R402Q and S192Y) on foveal pit and foveal avascular zone (FAZ) morphology. Design: Prospective, cross-sectional study. Participants: A total of 164 participants with normal vision (67 male and 97 female; mean ± standard deviation [SD] age = 30.5 ± 12.8 years) were recruited. Methods: Sequencing of more than 100 pigmentation-related genes was performed, and results were reviewed for the presence or absence of the TYR polymorphisms R402Q (rs1126809) and S192Y (rs1042602). Volumetric scans of the macula were obtained for each participant using OCT, and retinal thickness maps were analyzed using custom software. OCT angiography was used to image the FAZ, which was manually segmented and measured. Linear mixed model analysis was used to assess associations between genotype and foveal pit morphology. Main Outcome Measures: Foveal pit depth, diameter, volume, and FAZ area in relation to the presence of hypomorphic alleles R402Q and S192Y on the TYR gene. Results: Heterozygosity for the TYR R402Q allele was associated with decreased pit diameter (P = 0.0094) and decreased FAZ area (P = 0.025). Homozygosity for the TYR R402Q allele was associated with reduced pit volume (P = 0.0005), decreased pit depth (P = 0.007), reduced pit diameter (P = 0.0052), and reduced FAZ area (P = 0.0012). Homozygosity for TYR S192Y was associated with reduced FAZ area (P = 0.016). Heterozygosity for the TYR S192Y allele was not associated with differences in foveal pit depth, diameter, volume, or FAZ area (P > 0.05). Conclusions: Although the role of the TYR R402Q and S192Y hypomorphic alleles in albinism remains controversial, our data suggest that these variants contribute to the extensive inter-individual variability in foveal morphology in the normal population. Our results contribute to the evolving picture of the relationship between ocular pigmentation and foveal morphology.

FMR1 Low Zone CGG Repeats: Phenotypic Associations in the Context of Parenting Stress.

The FMR1 gene on the X chromosome has varying numbers of CGG repeats. The modal number is 30, and expansion to >200 results in fragile X syndrome, but the copy number extends down to 6. Past research suggests that individuals whose CGGs are in the "low zone" (LZ; defined here as ≤ 25 CGGs) may be more environmentally-reactive than those with normal range repeats (26-40 CGGs)-a gene x environment interaction. Using a population-based DNA biobank, in our primary analysis we compared 96 mothers with LZ CGG repeats on both alleles to 280 mothers who had CGG repeats in the normal range. Secondarily, we conducted parallel analyses on fathers. We investigated how parents in these two CGG repeat categories differentially responded to stress, defined as parenting a child with disabilities. Significant gene x environment interactions indicated that LZ mothers who had children with disabilities had greater limitations (in executive functioning, depression, anxiety, daily health symptoms, and balance) than LZ mothers whose children did not have disabilities. In contrast, mothers with normal-range CGG repeats did not differ based on stress exposure. For fathers, a similar pattern was evident for one phenotype only (hand tremors). Although on average LZ CGGs are not associated with compromised functioning, the average masks differential response to the environment.

Development of an Integrated Platform Using Multidisciplinary Real-World Data to Facilitate Biomarker Discovery for Medical Products.

Translational multidisciplinary research is important for the Center for Devices and Radiological Health's efforts for utilizing real-world data (RWD) to enhance predictive evaluation of medical device performance in patient subpopulations. As part of our efforts for developing new RWD-based evidentiary approaches, including in silico discovery of device-related risk predictors and biomarkers, this study aims to characterize the sex/race-related trends in hip replacement outcomes and identify corresponding candidate single nucleotide polymorphisms (SNPs). Adverse outcomes were assessed by deriving RWD from a retrospective analysis of hip replacement hospital discharge data from the National Inpatient Sample (NIS). Candidate SNPs were explored using pre-existing data from the Personalized Medicine Research Project (PMRP). High-Performance Integrated Virtual Environment was used for analyzing and visualizing putative associations between SNPs and adverse outcomes. Ingenuity Pathway Analysis (IPA) was used for exploring plausibility of the sex-related candidate SNPs and characterizing gene networks associated with the variants of interest. The NIS-based epidemiologic evidence showed that periprosthetic osteolysis (PO) was most prevalent among white men. The PMRP-based genetic evidence associated the PO-related male predominance with rs7121 (odds ratio = 4.89; 95% confidence interval = 1.41-17.05) and other candidate SNPs. SNP-based IPA analysis of the expected gene expression alterations and corresponding signaling pathways suggested possible role of sex-related metabolic factors in development of PO, which was substantiated by ad hoc epidemiologic analysis identifying the sex-related differences in metabolic comorbidities in men vs. women with hip replacement-related PO. Thus, our in silico study illustrates RWD-based evidentiary approaches that may facilitate cost/time-efficient discovery of biomarkers for informing use of medical products.

Data-driven phenotype discovery of FMR1 premutation carriers in a population-based sample.

The impact of the FMR1 premutation on human health is the subject of considerable controversy. A fundamental unanswered question is whether carrying the premutation allele is directly correlated with clinical phenotypes. A challenging problem in past genotype-phenotype studies of the FMR1 premutation is ascertainment bias, which could lead to invalid research conclusions and negatively affect clinical practice. Here, we created the first population-based FMR1-informed biobank to find the pattern of health characteristics in premutation carriers. Our extensive phenotyping shows that premutation carriers experience a clinical profile that is significantly different from controls and is evident throughout adulthood. Comprehensive understanding of the clinical risk associated with this genetic variant is critical for premutation carriers, their families, and clinicians and has important implications for public health.

Preliminary outcomes of preemptive warfarin pharmacogenetic testing at a large rural healthcare center.

PURPOSE: As a preliminary evaluation of the outcomes of implementing pharmacogenetic testing within a large rural healthcare system, patients who received pre-emptive pharmacogenetic testing and warfarin dosing were monitored until June 2017. SUMMARY: Over a 20-month period, 749 patients were genotyped for VKORC1 and CYP2C9 as part of the electronic Medical Records and Genomics Pharmacogenetics (eMERGE PGx) study. Of these, 27 were prescribed warfarin and received an alert for pharmacogenetic testing pertinent to warfarin; 20 patients achieved their target international normalized ratio (INR) of 2.0-3.0, and 65% of these patients achieved target dosing within the recommended pharmacogenetic alert dose (± 0.5 mg/day). Of these, 10 patients had never been on warfarin prior to the alert and were further evaluated with regard to time to first stable target INR, bleeds and thromboembolic events, hospitalizations, and mortality. There was a general trend of faster time to first stable target INR when the patient was initiated at a warfarin dose within the alert recommendation versus a dose outside of the alert recommendation with a mean (± SD) of 34 (± 28) days versus 129 (± 117) days, respectively. No trends regarding bleeds, thromboembolic events, hospitalization, or mortality were identified with respect to the pharmacogenetic alert. The pharmacogenetic alert provided pharmacogenetic dosing information to prescribing clinicians and appeared to deploy appropriately with the correct recommendation based upon patient genotype. CONCLUSION: Implementing pharmacogenetic testing as a standard of care service in anticoagulation monitoring programs may improve dosage regimens for patients on anticoagulation therapy.

Association of a Primary Open-Angle Glaucoma Genetic Risk Score With Earlier Age at Diagnosis.

IMPORTANCE: Genetic variants associated with primary open-angle glaucoma (POAG) are known to influence disease risk. However, the clinical effect of associated variants individually or in aggregate is not known. Genetic risk scores (GRS) examine the cumulative genetic load by combining individual genetic variants into a single measure, which is assumed to have a larger effect and increased power to detect relevant disease-related associations. OBJECTIVE: To investigate if a GRS that comprised 12 POAG genetic risk variants is associated with age at disease diagnosis. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional study included individuals with POAG and controls from the Glaucoma Genes and Environment (GLAUGEN) study and the National Eye Institute Glaucoma Human Genetics Collaboration (NEIGHBOR) study. A GRS was formulated using 12 variants known to be associated with POAG, and the alleles associated with increasing risk of POAG were aligned in the case-control sets. In case-only analyses, the association of the GRS with age at diagnosis was analyzed as an estimate of disease onset. Results from cohort-specific analyses were combined with meta-analysis. Data collection started in August 2012 for the NEIGHBOR cohort and in July 2008 for the GLAUGEN cohort and were analyzed starting in March 2018. MAIN OUTCOMES AND MEASURES: Association of a 12 single-nucleotide polymorphism POAG GRS with age at diagnosis in individuals with POAG using linear regression. RESULTS: The GLAUGEN study included 976 individuals with POAG and 1140 controls. The NEIGHBOR study included 2132 individuals with POAG and 2290 controls. For individuals with POAG, the mean (SD) age at diagnosis was 63.6 (9.8) years in the GLAUGEN cohort and 66.0 (13.7) years in the NEIGHBOR cohort. For controls, the mean (SD) age at enrollment was 65.5 (9.2) years in the GLAUGEN cohort and 68.9 (11.4) years in the NEIGHBOR cohort. All study participants were European white. The GRS was strongly associated with POAG risk in case-control analysis (odds ratio per 1-point increase in score = 1.24; 95% CI, 1.21-1.27; P = 3.4 × 10-66). In case-only analyses, each higher GRS unit was associated with a 0.36-year earlier age at diagnosis (ß = -0.36; 95% CI, -0.56 to -0.16; P = 4.0 × 10-4). Individuals in the top 5% of the GRS had a mean (SD) age at diagnosis of 5.2 (12.8) years earlier than those in the bottom 5% GRS (61.4 [12.7] vs 66.6 [12.9] years; P = 5.0 × 10-4). CONCLUSIONS AND RELEVANCE: A higher dose of POAG risk alleles was associated with an earlier age at glaucoma diagnosis. On average, individuals with POAG with the highest GRS had 5.2-year earlier age at diagnosis of disease. These results suggest that a GRS that comprised genetic variants associated with POAG could help identify patients with risk of earlier disease onset impacting screening and therapeutic strategies.