Phenome-wide association study (PheWAS) in EMR-linked pediatric cohorts, genetically links PLCL1 to speech language development and IL5-IL13 to Eosinophilic Esophagitis

Eosinophilic Esophagitis and Inflammatory Bowel Disease: What Are the Differences?

Eosinophilic esophagitis (EoE) and inflammatory bowel disease (IBD) are chronic inflammatory disorders of the gastrointestinal tract, with EoE predominantly provoked by food and aeroallergens, whereas IBD is driven by a broader spectrum of immunopathological and environmental triggers. This review presents a comprehensive comparison of the pathophysiological and therapeutic strategies for EoE and IBD. We examine the current understanding of their underlying mechanisms, particularly the interplay between environmental factors and genetic susceptibility. A crucial element in both diseases is the integrity of the epithelial barrier, whose disruption plays a central role in their pathogenesis. The involvement of eosinophils, mast cells, B cells, T cells, dendritic cells, macrophages, and their associated cytokines is examined, highlighting the importance of targeting cytokine signaling pathways to modulate immune-epithelial interactions. We propose that advances in computation tools will uncover the significance of G-protein coupled receptors (GPCRs) in connecting immune and epithelial cells, leading to novel therapies for EoE and IBD.

Pathophysiology of Eosinophilic Esophagitis

Eosinophilic esophagitis (EoE) is a chronic, progressive immune-mediated disease associated with antigen-driven type 2 inflammation and symptoms of esophageal dysfunction. Research over the last 2 decades has dramatically furthered our understanding of the complex interplay between genetics, environmental exposures, and cellular and molecular interactions involved in EoE. This review provides an overview of our current understanding of EoE pathogenesis.

Advancements and progress in juvenile idiopathic arthritis: A Review of pathophysiology and treatment

Juvenile idiopathic arthritis (JIA) is a chronic clinical condition characterized by arthritic features in children under the age of 16, with at least 6 weeks of active symptoms. The etiology of JIA remains unknown, and it is associated with prolonged synovial inflammation and structural joint damage influenced by environmental and genetic factors. This review aims to enhance the understanding of JIA by comprehensively analyzing relevant literature. The focus lies on current diagnostic and therapeutic approaches and investigations into the pathoaetiologies using diverse research modalities, including in vivo animal models and large-scale genome-wide studies. We aim to elucidate the multifactorial nature of JIA with a strong focus towards genetic predilection, while proposing potential strategies to improve therapeutic outcomes and enhance diagnostic risk stratification in light of recent advancements. This review underscores the need for further research due to the idiopathic nature of JIA, its heterogeneous phenotype, and the challenges associated with biomarkers and diagnostic criteria. Ultimately, this contribution seeks to advance the knowledge and promote effective management strategies in JIA.

High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population

Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.

Breaking down the complex pathophysiology of eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is a chronic and progressive immune-mediated disease of the esophagus associated with antigen-driven type 2 inflammation and symptoms of esophageal dysfunction. Our understanding of EoE pathophysiology has evolved since its initial recognition more than 20 years ago and has translated into diagnostic and novel therapeutic approaches that are affecting patient care. The mechanisms underlying disease development and progression are influenced by diverse factors, such as genetics, age, allergic comorbidities, and allergen exposures. Central to EoE pathophysiology is a dysregulated feed-forward cycle that develops between the esophageal epithelium and the immune system. Allergen-induced, type 2-biased immune activation by the esophageal epithelium propagates a cycle of impaired mucosal barrier integrity and allergic inflammation, eventually leading to tissue remodeling and progressive organ dysfunction. Herein, we review the current understanding of fundamental pathophysiological mechanisms contributing to EoE pathogenesis.

Language abnormalities in schizophrenia: binding core symptoms through contemporary empirical evidence

Both the ability to speak and to infer complex linguistic messages from sounds have been claimed as uniquely human phenomena. In schizophrenia, formal thought disorder (FTD) and auditory verbal hallucinations (AVHs) are manifestations respectively relating to concrete disruptions of those abilities. From an evolutionary perspective, Crow (1997) proposed that "schizophrenia is the price that Homo sapiens pays for the faculty of language". Epidemiological and experimental evidence points to an overlap between FTD and AVHs, yet a thorough investigation examining their shared neural mechanism in schizophrenia is lacking. In this review, we synthesize observations from three key domains. First, neuroanatomical evidence indicates substantial shared abnormalities in language-processing regions between FTD and AVHs, even in the early phases of schizophrenia. Second, neurochemical studies point to a glutamate-related dysfunction in these language-processing brain regions, contributing to verbal production deficits. Third, genetic findings further show how genes that overlap between schizophrenia and language disorders influence neurodevelopment and neurotransmission. We argue that these observations converge into the possibility that a glutamatergic dysfunction in language-processing brain regions might be a shared neural basis of both FTD and AVHs. Investigations of language pathology in schizophrenia could facilitate the development of diagnostic tools and treatments, so we call for multilevel confirmatory analyses focused on modulations of the language network as a therapeutic goal in schizophrenia.

KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design

Family-based designs can eliminate confounding due to population substructure and can distinguish direct from indirect genetic effects, but these designs are underpowered due to limited sample sizes. Here, we propose KnockoffTrio, a statistical method to identify putative causal genetic variants for father-mother-child trio design built upon a recently developed knockoff framework in statistics. KnockoffTrio controls the false discovery rate (FDR) in the presence of arbitrary correlations among tests and is less conservative and thus more powerful than the conventional methods that control the family-wise error rate via Bonferroni correction. Furthermore, KnockoffTrio is not restricted to family-based association tests and can be used in conjunction with more powerful, potentially nonlinear models to improve the power of standard family-based tests. We show, using empirical simulations, that KnockoffTrio can prioritize causal variants over associations due to linkage disequilibrium and can provide protection against confounding due to population stratification. In applications to 14,200 trios from three study cohorts for autism spectrum disorders (ASDs), including AGP, SPARK, and SSC, we show that KnockoffTrio can identify multiple significant associations that are missed by conventional tests applied to the same data. In particular, we replicate known ASD association signals with variants in several genes such as MACROD2, NRXN1, PRKAR1B, CADM2, PCDH9, and DOCK4 and identify additional associations with variants in other genes including ARHGEF10, SLC28A1, ZNF589, and HINT1 at FDR 10%.

Large-scale real-world data analysis identifies comorbidity patterns in schizophrenia

Schizophrenia affects >3.2 million people in the USA. However, its comorbidity patterns have not been systematically characterized in real-world populations. To address this gap, we conducted an observational study using a cohort of 86 million patients in a nationwide health insurance dataset. We identified participants with schizophrenia and those without schizophrenia matched by age, sex, and the first three digits of zip code. For each phenotype encoded in phecodes, we compared their prevalence in schizophrenia patients and the matched non-schizophrenic participants, and we performed subgroup analyses stratified by age and sex. Results show that anxiety, posttraumatic stress disorder, and substance abuse commonly occur in adolescents and young adults prior to schizophrenia diagnoses. Patients aged 60 and above are at higher risks of developing delirium, alcoholism, dementia, pelvic fracture, and osteomyelitis than their matched controls. Type 2 diabetes, sleep apnea, and eating disorders were more prevalent in women prior to schizophrenia diagnosis, whereas acute renal failure, rhabdomyolysis, and developmental delays were found at higher rates in men. Anxiety and obesity are more commonly seen in patients with schizoaffective disorders compared to patients with other types of schizophrenia. Leveraging a large-scale insurance claims dataset, this study identified less-known comorbidity patterns of schizophrenia and confirmed known ones. These comorbidity profiles can guide clinicians and researchers to take heed of early signs of co-occurring diseases.

Type 2 Inflammation in Eosinophilic Esophagitis: From Pathophysiology to Therapeutic Targets

Eosinophilic esophagitis (EoE) is a chronic immune-mediated disease of the esophagus characterized clinically by symptoms related to esophageal dysfunction and histologically by eosinophil-predominant inflammation, whose incidence is rising. It significantly affects patients’ quality of life and, if left untreated, results in fibrotic complications. Although broad consensus has been achieved on first-line therapy, a subset of patients remains non-responder to standard therapy. The pathogenesis of EoE is multifactorial and results from the complex, still mostly undefined, interaction between genetics and intrinsic factors, environment, and antigenic stimuli. A deep understanding of the pathophysiology of this disease is pivotal for the development of new therapies. This review provides a comprehensive description of the pathophysiology of EoE, starting from major pathogenic mechanisms (genetics, type 2 inflammation, epithelial barrier dysfunction, gastroesophageal reflux, allergens, infections and microbiota) and subsequently focusing on the single protagonists of type 2 inflammation (involved cells, cytokines, soluble effectors, surface proteins and transcription factors) that could represent present and future therapeutic targets, while summarizing previous therapeutic approaches in literature.

Bidirectional crosstalk between eosinophils and esophageal epithelial cells regulates inflammatory and remodeling processes

Eosinophils accumulate adjacent to epithelial cells in the mucosa of patients with eosinophilic esophagitis (EoE), yet the bidirectional communication between these cells is not well understood. Herein, we investigated the crosstalk between human eosinophils and esophageal epithelial cells. We report that blood-derived eosinophils have prolonged survival when cocultured with epithelial cells; 96 ± 1% and 30 ± 6% viability was observed after 7 and 14 days of coculture, respectively, compared with 1 ± 0% and 0 ± 0% of monoculture. In the presence of IL-13 and epithelial cells, eosinophils had greater survival (68 ± 1%) at 14 days compared with cocultures lacking IL-13. Prolonged eosinophil viability did not require cellular contact and was observed when eosinophils were cultured in conditioned media from esophageal epithelial cells; neutralizing GM-CSF attenuated eosinophil survival. The majority of eosinophil transcripts (58%) were dysregulated in cocultured eosinophils compared with freshly isolated cells. Analysis of epithelial cell transcripts indicated that exposure to eosinophils induced differential expression of a subset of genes that were part of the EoE esophageal transcriptome. Collectively, these results uncover a network of crosstalk between eosinophils and esophageal epithelial cells involving epithelial mediated eosinophil survival and reciprocal changes in cellular transcripts, events likely to occur in EoE.

Peripheral Expression of MACROD2 Gene Is Reduced Among a Sample of Turkish Children with Autism Spectrum Disorder

Background

Genomic variations in mono-ADP ribosylhydrolase 2 (MACROD2) have been associated with autism spectrum disorder (ASD) in recent genome-wide studies and case reports. In this study, we aimed to evaluate the MACROD2 expression profile in patients with ASD.

Methods

The study group included 100 children with a DSM-5 diagnosis of ASD, and the control group consisted of 105 healthy controls. Blood samples were obtained from all participants in this study, and the gene expression level was determined using quantitative reverse transcription PCR (RT-qPCR). Statistical analysis was performed with R 3.4.0 and Statistical Program for Social Sciences (SPSS for Windows, 21.0).

Results

The mean ages of the participants in the study and control groups were 9.22 ± 3.62 and 9.27 ± 3.86 years, respectively. There was no significant difference concerning gender (P = .944) and age (P = .914) between the 2 groups. MACROD2 gene expression was found to be decreased in the study group compared to the control group (study group = 5.73, control group = 89.56; fold change =-3.967; P < .001). While the level of MACROD2 expression was not correlated with the ASD severity, it was associated with the severity of the hyperactivity/impulsivity symptoms (P = .008).

Conclusions

This is the first study in the literature investigating the peripheral expression of the MACROD2 gene. We showed that the expression level of MACROD2 was decreased in patients with ASD when compared to the control group. As the relationship between the MACROD2 gene expression profile and ASD remains to be further investigated, this study may provide an insight for further studies.

Methodology in phenome-wide association studies: a systematic review

Phenome-wide association study (PheWAS) has been increasingly used to identify novel genetic associations across a wide spectrum of phenotypes. This systematic review aims to summarise the PheWAS methodology, discuss the advantages and challenges of PheWAS, and provide potential implications for future PheWAS studies. Medical Literature Analysis and Retrieval System Online (MEDLINE) and Excerpta Medica Database (EMBASE) databases were searched to identify all published PheWAS studies up until 24 April 2021. The PheWAS methodology incorporating how to perform PheWAS analysis and which software/tool could be used, were summarised based on the extracted information. A total of 1035 studies were identified and 195 eligible articles were finally included. Among them, 137 (77.0%) contained 10 000 or more study participants, 164 (92.1%) defined the phenome based on electronic medical records data, 140 (78.7%) used genetic variants as predictors, and 73 (41.0%) conducted replication analysis to validate PheWAS findings and almost all of them (94.5%) received consistent results. The methodology applied in these PheWAS studies was dissected into several critical steps, including quality control of the phenome, selecting predictors, phenotyping, statistical analysis, interpretation and visualisation of PheWAS results, and the workflow for performing a PheWAS was established with detailed instructions on each step. This study provides a comprehensive overview of PheWAS methodology to help practitioners achieve a better understanding of the PheWAS design, to detect understudied or overstudied outcomes, and to direct their research by applying the most appropriate software and online tools for their study data structure.

Identifying developmental stuttering and associated comorbidities in electronic health records and creating a phenome risk classifier

PURPOSE

This study aimed to identify cases of developmental stuttering and associated comorbidities in de-identified electronic health records (EHRs) at Vanderbilt University Medical Center, and, in turn, build and test a stuttering prediction model.

METHODS

A multi-step process including a keyword search of medical notes, a text-mining algorithm, and manual review was employed to identify stuttering cases in the EHR. Confirmed cases were compared to matched controls in a phenotype code (phecode) enrichment analysis to reveal conditions associated with stuttering (i.e., comorbidities). These associated phenotypes were used as proxy variables to phenotypically predict stuttering in subjects within the EHR that were not otherwise identifiable using the multi-step identification process described above.

RESULTS

The multi-step process resulted in the manually reviewed identification of 1,143 stuttering cases in the EHR. Highly enriched phecodes included codes related to childhood onset fluency disorder, adult-onset fluency disorder, hearing loss, sleep disorders, atopy, a multitude of codes for infections, neurological deficits, and body weight. These phecodes were used as variables to create a phenome risk classifier (PheRC) prediction model to identify additional high likelihood stuttering cases. The PheRC prediction model resulted in a positive predictive value of 83 %.

CONCLUSIONS

This study demonstrates the feasibility of using EHRs in the study of stuttering and found phenotypic associations. The creation of the PheRC has the potential to enable future studies of stuttering using existing EHR data, including investigations into the genetic etiology.

Building a Population Representative Pediatric Biobank: Lessons Learned From the Greater Cincinnati Childhood Cohort

Background: Biobanks can accelerate research by providing researchers with samples and data. However, hospital-based recruitment as a source for controls may create bias as who comes to the hospital may be different from the broader population. Methods: In an effort to broadly improve the quality of research studies and reduce costs and challenges associated with recruitment and sample collection, a group of diverse researchers at Cincinnati Children's Hospital Medical Center led an institution-supported initiative to create a population representative pediatric "Greater Cincinnati Childhood Cohort (GCC)." Participants completed a detailed survey, underwent a brief physician-led physical exam, and provided blood, urine, and hair samples. DNA underwent high-throughput genotyping. Results: In total, 1,020 children ages 3-18 years living in the 7 county Greater Cincinnati Metropolitan region were recruited. Racial composition of the cohort was 84% non-Hispanic white, 15% non-Hispanic black, and 2% other race or Hispanic. Participants exhibited marked demographic and disease burden differences by race. Overall, the cohort was broadly used resulting in publications, grants and patents; yet, it did not meet the needs of all potential researchers. Conclusions: Learning from both the strengths and weaknesses, we propose leveraging a community-based participatory research framework for future broad use biobanking efforts.

Eosinophilic esophagitis: novel concepts regarding pathogenesis and clinical manifestations

Eosinophilic esophagitis is a chronic disease whose incidence and prevalence are increasing, based on a genetic-driven interaction between environment and immune system. Several gene loci involved in the development of the disease have been identified. A two-step mechanism has been hypothesized: a thymic stromal lymphopoietin-induced allergic sensitization followed by upregulation of CAPN14-related esophageal-specific pathways. Environment seems to have a larger effect than genetic variants. Factors that could play a role are allergens, drugs, colonizing bacteria and possibly Helicobacter Pylori infection. Acting on these modifiable risk factors may be a tool to prevent the disease. EoE is characterized by a typical eosinophilic infiltrate limited to the esophageal epithelium, supported by a Th2-mediated immune response, found in other atopic conditions. The key of the pathogenesis is the disfunction of the epithelial barrier which allow the interaction between allergens and inflammatory cells. Eosinophilic-predominant inflammation leads to the typical wall remodeling, histologically characterized by epithelial and smooth muscle hyperplasia, lamina propria fibrosis and neo-angiogenesis. These alterations find their clinical expression in the pattern of symptoms: dysphagia, food impaction, chest pain, heartburn.

Replication and meta-analyses nominate numerous eosinophilic esophagitis risk genes

BACKGROUND

Eosinophilic esophagitis (EoE) is an emerging, chronic, rare allergic disease associated with marked eosinophil accumulation in the esophagus. Previous genome-wide association studies have provided strong evidence for 3 genome-wide susceptibility loci.

OBJECTIVE

We sought to replicate known and suggestive EoE genetic risk loci and conduct a meta-analysis of previously reported data sets.

METHODS

An EoE-Custom single-nucleotide polymophism (SNP) Chip containing 956 candidate EoE risk single-nucleotide polymorphisms was used to genotype 627 cases and 365 controls. Statistical power was enhanced by adding 1959 external controls and performing meta-analyses with 2 independent EoE genome-wide association studies.

RESULTS

Meta-analysis identified replicated association and genome-wide significance at 6 loci: 2p23 (2 independent genetic effects) and 5q22, 10p14, 11q13, and 16p13. Seven additional loci were identified at suggestive significance (P < 10-6): 1q31, 5q23, 6q15, 6q21, 8p21, 17q12, and 22q13. From these risk loci, 13 protein-coding EoE candidate risk genes were expressed in a genotype-dependent manner. EoE risk genes were expressed in disease-relevant cell types, including esophageal epithelia, fibroblasts, and immune cells, with some expressed as a function of disease activity. The genetic risk burden of EoE-associated genetic variants was markedly larger in cases relative to controls (P < 10-38); individuals with the highest decile of genetic burden had greater than 12-fold risk of EoE compared with those within the lowest decile.

CONCLUSIONS

This study extends the genetic underpinnings of EoE, highlighting 13 genes whose genotype-dependent expression expands our etiologic understanding of EoE and provides a framework for a polygenic risk score to be validated in future studies.

Genome-Phenome Wide Association in Maize and Arabidopsis Identifies a Common Molecular and Evolutionary Signature

Linking natural genetic variation to trait variation can help determine the functional roles ofdifferent genes. Variations of one or several traits are often assessed separately. High-throughput phenotyping and data mining can capture dozens or hundreds of traits from the same individuals. Here, we test the association between markers within a gene and many traits simultaneously. This genome-phenome wide association study (GPWAS) is both a multi-marker and multi-trait test. Genes identified using GPWAS with 260 phenotypic traits in maize were enriched for genes independently linked to phenotypic variation. Traits associated with classical mutants were consistent with reported phenotypes for mutant alleles. Genes linked to phenomic variation in maize using GPWAS shared molecular, population genetic, and evolutionary features with classical mutants in maize. Genes linked to phenomic variation in Arabidopsis using GPWAS are significantly enriched in genes with known loss-of-function phenotypes. GPWAS may be an effective strategy to identify genes in which loss-of-function alleles produce mutant phenotypes. The shared signatures present in classical mutants and genes identified using GPWAS may be markers for genes with a role in specifying plant phenotypes generally or pleiotropy specifically.

Phenome-Wide Scan Finds Potential Orofacial Risk Markers for Cancer

Cancer is a disease caused by a process that drives the transformation of normal cells into malignant cells. The late diagnosis of cancer has a negative impact on the health care system due to high treatment cost and decreased chances of favorable prognosis. Here, we aimed to identify orofacial conditions that can serve as potential risk markers for cancers by performing a phenome-wide scan (PheWAS). From a pool of 6,100 individuals, both genetic and epidemiological data of 1,671 individuals were selected: 350 because they were previously diagnosed with cancer and 1,321 to match to those individuals that had cancer, based on age, sex, and ethnicity serving as a comparison group. Results of this study showed that when analyzing the individuals affected by cancer separately, tooth loss/edentulism is associated with SNPs in AXIN2 (rs11867417 p = 0.02 and rs2240308 p = 0.02), and leukoplakia of oral mucosa is associated with both AXIN2 (rs2240308 p = 0.03) and RHEB (rs2374261 p = 0.03). These phenotypes did not show the same trends in patients that were not diagnosed with cancer, allowing for the conclusion that these phenotypes are unique to cases with higher cancer risk.

The genetic etiology of eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is a chronic allergic disease associated with marked mucosal eosinophil accumulation. Multiple studies have reported a strong familial component to EoE, with the presence of EoE increasing the risk for other family members with EoE. Epidemiologic studies support an important role for environmental risk factors as modulators of genetic risk. In a small percentage of cases, including patients who have Mendelian diseases with co-occurrent EoE, rare genetic variation with large effect sizes could mediate EoE and explain multigenerational incidence in families. Common genetic risk variants mediate genetic risk for the majority of patients with EoE. Across the 31 reported independent EoE risk loci (P < 10-5), most of the EoE risk variants are located in between genes (36.7%) or within the introns of genes (42.4%). Although some variants do change the amino acid sequence of genes (2.2%), only 3 of the 31 EoE risk loci harbor an amino acid-changing variant. Thus most EoE risk loci are outside of the coding regions of genes, suggesting a key role for gene regulation in patients with EoE, which is consistent with most other complex diseases.

High-fat diet-induced obesity worsens TH2 immune response and immunopathologic characteristics in murine model of eosinophilic oesophagitis

BACKGROUND

Eosinophilic oesophagitis (EoE) is an emergent chronic immune-mediated disease of the oesophagus, which affects both children and adults. It is clinically characterized by dysphagia, food impaction and oesophageal eosinophilia. Epidemiological studies indicate that obesity can worsen allergic symptoms; however, its effect on EoE immunopathological response has not been evaluated yet. This study aimed to assess the effect of obesity on allergic inflammation and T helper-2 profile in an EoE experimental model.

METHODS

Obesity was induced by high-fat feeding. After 7 weeks of diet, male BALB/c mice were subcutaneously sensitized and orally challenged with OVA.

RESULTS

Obesity itself induced a significant mast cell and eosinophil accumulation in the oesophagus, trachea, gut and lung. After allergy induction, this number was higher, when compared to lean-allergic mice. Moreover, obese-allergic mice showed higher remodelling area, in the oesophagus, associated with higher IL-5 and TSLP mRNA expression. In contrast, FoxP3 and IL-10 were less expressed in comparison with lean-allergic mice. In addition, the amount of CD11c⁺ MHCII⁺ PDL1⁺ dendritic cells was reduced, while the number of CD11c⁺ MHCII⁺ CD80⁺ DCs and CD3⁺ CD4⁺ GATA₃ ⁺ IL-4⁺ cells was increased in obese-allergic mice in the spleen and lymph nodes when compared to lean-allergic mice.

CONCLUSION

Obesity aggravated the immune histopathological characteristics in the EoE experimental model, which was associated with the reduction in the regulatory profile, and the increased inflammatory cells influx, related to the T_H 2 profile. Altogether, the data provide new knowledge about obesity as a risk factor, worsening EoE symptoms, and contribute for future treatment strategies for this specific profile.

PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies

Objective

Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as ≥2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for defining phenotypes would improve power for genetic association studies.

Methods

The PheProb approach employs unsupervised clustering to separate patients into 2 groups based on diagnosis codes. Subjects are assigned a probability of having the phenotype based on the number of diagnosis codes. This approach was developed using simulated EHR data and tested in a real world EHR cohort. In the latter, we tested the association between low density lipoprotein cholesterol (LDL-C) genetic risk alleles known for association with hyperlipidemia and hyperlipidemia codes (ICD-9 272.x). PheProb and thresholding approaches were compared.

Results

Among n = 1462 subjects in the real world EHR cohort, the threshold-based p-values for association between the genetic risk score (GRS) and hyperlipidemia were 0.126 (≥1 code), 0.123 (≥2 codes), and 0.142 (≥3 codes). The PheProb approach produced the expected significant association between the GRS and hyperlipidemia: p = .001.

Conclusions

PheProb improves statistical power for association studies relative to standard thresholding approaches by leveraging information about the phenotype in the billing code counts. The PheProb approach has direct applications where efficient approaches are required, such as in Phenome-Wide Association Studies.

The current state of omics technologies in the clinical management of asthma and allergic diseases

OBJECTIVE

To review the state of omics science specific to asthma and allergic diseases and discuss the current and potential applicability of omics in clinical disease prediction, treatment, and management.

DATA SOURCES

Studies and reviews focused on the use of omics technologies in asthma and allergic disease research and clinical management were identified using PubMed.

STUDY SELECTIONS

Publications were included based on relevance, with emphasis placed on the most recent findings.

RESULTS

Omics-based research is increasingly being used to differentiate asthma and allergic disease subtypes, identify biomarkers and pathological mediators, predict patient responsiveness to specific therapies, and monitor disease control. Although most studies have focused on genomics and transcriptomics approaches, increasing attention is being placed on omics technologies that assess the effect of environmental exposures on disease initiation and progression. Studies using omics data to identify biological targets and pathways involved in asthma and allergic disease pathogenesis have primarily focused on a specific omics subtype, providing only a partial view of the disease process.

CONCLUSION

Although omics technologies have advanced our understanding of the molecular mechanisms underlying asthma and allergic disease pathology, omics testing for these diseases are not standard of care at this point. Several important factors need to be addressed before these technologies can be used effectively in clinical practice. Use of clinical decision support systems and integration of these systems within electronic medical records will become increasingly important as omics technologies become more widely used in the clinical setting.

Poly(ADP-Ribosylation) in Age-Related Neurological Disease

A central and causative feature of age-related neurodegenerative disease is the deposition of misfolded proteins in the brain. To devise novel approaches to treatment, regulatory pathways that modulate these aggregation-prone proteins must be defined. One such pathway is post-translational modification by the addition of poly(ADP-ribose) (PAR), which promotes protein recruitment and localization in several cellular contexts. Mounting evidence implicates PAR in seeding the abnormal localization and accumulation of proteins that are causative of neurodegenerative disease. Inhibitors of PAR polymerase (PARP) activity have been developed as cancer therapeutics, raising the possibility that they could be used to treat neurodegenerative disease. We focus on pathways regulated by PAR in neurodegenerative disease, with emphasis on amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD).

An exploratory phenome wide association study linking asthma and liver disease genetic variants to electronic health records from the Estonian Biobank

The Estonian Biobank, governed by the Institute of Genomics at the University of Tartu (Biobank), has stored genetic material/DNA and continuously collected data since 2002 on a total of 52,274 individuals representing ~5% of the Estonian adult population and is increasing. To explore the utility of data available in the Biobank, we conducted a phenome-wide association study (PheWAS) in two areas of interest to healthcare researchers; asthma and liver disease. We used 11 asthma and 13 liver disease-associated single nucleotide polymorphisms (SNPs), identified from published genome-wide association studies, to test our ability to detect established associations. We confirmed 2 asthma and 5 liver disease associated variants at nominal significance and directionally consistent with published results. We found 2 associations that were opposite to what was published before (rs4374383:AA increases risk of NASH/NAFLD, rs11597086 increases ALT level). Three SNP-diagnosis pairs passed the phenome-wide significance threshold: rs9273349 and E06 (thyroiditis, p = 5.50x10-8); rs9273349 and E10 (type-1 diabetes, p = 2.60x10-7); and rs2281135 and K76 (non-alcoholic liver diseases, including NAFLD, p = 4.10x10-7). We have validated our approach and confirmed the quality of the data for these conditions. Importantly, we demonstrate that the extensive amount of genetic and medical information from the Estonian Biobank can be successfully utilized for scientific research.

Mechanisms of gastrointestinal allergic disorders

Gastrointestinal (GI) allergic disease is an umbrella term used to describe a variety of adverse, food antigen-driven, immune-mediated diseases. Although these diseases vary mechanistically, common elements include a breakdown of immunologic tolerance, a biased type 2 immune response, and an impaired mucosal barrier. These pathways are influenced by diverse factors such as diet, infections, exposure to antibiotics and chemicals, GI microbiome composition, and genetic and epigenetic elements. Early childhood has emerged as a critical period when these factors have a dramatic impact on shaping the immune system and therefore triggering or protecting against the onset of GI allergic diseases. In this Review, we will discuss the latest findings on the molecular and cellular mechanisms that govern GI allergic diseases and how these findings have set the stage for emerging preventative and treatment strategies.

Genetic, Inflammatory, and Epithelial Cell Differentiation Factors Control Expression of Human Calpain-14

Eosinophilic esophagitis (EoE) is a chronic, food-driven allergic disease resulting in eosinophilic esophageal inflammation. We recently found that EoE susceptibility is associated with genetic variants in the promoter of CAPN14, a gene with reported esophagus-specific expression. CAPN14 is dynamically up-regulated as a function of EoE disease activity and after exposure of epithelial cells to interleukin-13 (IL-13). Herein, we aimed to explore molecular modulation of CAPN14 expression. We identified three putative binding sites for the IL-13-activated transcription factor STAT6 in the promoter and first intron of CAPN14. Luciferase reporter assays revealed that the two most distal STAT6 elements were required for the ∼10-fold increase in promoter activity subsequent to stimulation with IL-13 or IL-4, and also for the genotype-dependent reduction in IL-13-induced promoter activity. One of the STAT6 elements in the promoter was necessary for IL-13-mediated induction of CAPN14 promoter activity while the other STAT6 promoter element was necessary for full induction. Chromatin immunoprecipitation in IL-13 stimulated esophageal epithelial cells was used to further support STAT6 binding to the promoter of CAPN14 at these STAT6 binding sites. The highest CAPN14 and calpain-14 expression occurred with IL-13 or IL-4 stimulation of esophageal epithelial cells under culture conditions that allow the cells to differentiate into a stratified epithelium. This work corroborates a candidate molecular mechanism for EoE disease etiology in which the risk variant at 2p23 dampens CAPN14 expression in differentiated esophageal epithelial cells following IL-13/STAT6 induction of CAPN14 promoter activity.

A PheWAS study of a large observational epidemiological cohort of African Americans from the REGARDS study

BACKGROUND

Cardiovascular disease, diabetes, and kidney disease are among the leading causes of death and disability worldwide. However, knowledge of genetic determinants of those diseases in African Americans remains limited.

RESULTS

In our study, associations between 4956 GWAS catalog reported SNPs and 67 traits were examined among 7726 African Americans from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study, which is focused on identifying factors that increase stroke risk. The prevalent and incident phenotypes studied included inflammation, kidney traits, cardiovascular traits and cognition. Our results validated 29 known associations, of which eight associations were reported for the first time in African Americans.

CONCLUSION

Our cross-racial validation of GWAS findings provide additional evidence for the important roles of these loci in the disease process and may help identify genes especially important for future functional validation.

Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals

Phenome-wide association studies (PheWASs) have been a useful tool for testing associations between genetic variations and multiple complex traits or diagnoses. Linking PheWAS-based associations between phenotypes and a variant or a genomic region into a network provides a new way to investigate cross-phenotype associations, and it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. We created a network of associations from one of the largest PheWASs on electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger's biobank; the samples were genotyped through the DiscovEHR project. We computed associations between 632,574 common variants and 541 diagnosis codes. Using these associations, we constructed a "disease-disease" network (DDN) wherein pairs of diseases were connected on the basis of shared associations with a given genetic variant. The DDN provides a landscape of intra-connections within the same disease classes, as well as inter-connections across disease classes. We identified clusters of diseases with known biological connections, such as autoimmune disorders (type 1 diabetes, rheumatoid arthritis, and multiple sclerosis) and cardiovascular disorders. Previously unreported relationships between multiple diseases were identified on the basis of genetic associations as well. The network approach applied in this study can be used to uncover interactions between diseases as a result of their shared, potentially pleiotropic SNPs. Additionally, this approach might advance clinical research and even clinical practice by accelerating our understanding of disease mechanisms on the basis of similar underlying genetic associations.

Pathophysiology of Eosinophilic Esophagitis

Eosinophilic esophagitis (EoE) is a chronic inflammatory disease of the esophagus associated with an atopic predisposition which appears to be increasing in prevalence over the last few decades. Symptoms stem from fibrosis, swelling, and smooth muscle dysfunction. In the past two decades, the etiology of EoE has been and is continuing to be revealed. This review provides an overview of the effects of genetics, environment, and immune function including discussions that touch on microbiome, the role of diet, food allergy, and aeroallergy. The review further concentrates on the pathophysiology of the disease with particular focus on the important concepts of the molecular etiology of EoE including barrier dysfunction and allergic hypersensitivity.

The use of electronic health records for psychiatric phenotyping and genomics

The widespread adoption of electronic health record (EHRs) in healthcare systems has created a vast and continuously growing resource of clinical data and provides new opportunities for population-based research. In particular, the linking of EHRs to biospecimens and genomic data in biobanks may help address what has become a rate-limiting study for genetic research: the need for large sample sizes. The principal roadblock to capitalizing on these resources is the need to establish the validity of phenotypes extracted from the EHR. For psychiatric genetic research, this represents a particular challenge given that diagnosis is based on patient reports and clinician observations that may not be well-captured in billing codes or narrative records. This review addresses the opportunities and pitfalls in EHR-based phenotyping with a focus on their application to psychiatric genetic research. A growing number of studies have demonstrated that diagnostic algorithms with high positive predictive value can be derived from EHRs, especially when structured data are supplemented by text mining approaches. Such algorithms enable semi-automated phenotyping for large-scale case-control studies. In addition, the scale and scope of EHR databases have been used successfully to identify phenotypic subgroups and derive algorithms for longitudinal risk prediction. EHR-based genomics are particularly well-suited to rapid look-up replication of putative risk genes, studies of pleiotropy (phenomewide association studies or PheWAS), investigations of genetic networks and overlap across the phenome, and pharmacogenomic research. EHR phenotyping has been relatively under-utilized in psychiatric genomic research but may become a key component of efforts to advance precision psychiatry.

Phenotypic Characterization of Eosinophilic Esophagitis in a Large Multicenter Patient Population from the Consortium for Food Allergy Research

BACKGROUND

Eosinophilic esophagitis (EoE) is increasingly common, but data on phenotypic aspects are still incomplete.

OBJECTIVES

To describe the clinical, endoscopic, and histopathologic features of a large number of children and adults with EoE across the United States.

METHODS

This was a multisite single visit registry enrolling subjects aged 6 months to 65 years with EoE. Participants provided responses regarding their medical history, with verification of the diagnosis and history by the study teams.

RESULTS

A total of 705 subjects were analyzed (median [interquartile range] age at enrollment 11.2 [6.7-17.7] years, 68.2% male, 87.9% whites). Of these, 67 subjects had concurrent gastrointestinal eosinophilia, with gastric mucosa most common. An age- and race-dependent time gap was present between symptom onset and time of diagnosis (adults and whites with longer gap). Food allergy and atopic dermatitis were associated with a decrease in this gap. Symptoms varied with age (more dysphagia and food impaction in adults) and with race (more vomiting in non-whites). Esophageal rings and strictures at diagnosis were more common in adults, although esophageal eosinophilia was comparable among age groups. Concomitant allergic disease (91%), infectious/immunologic disorders (44%), neurodevelopmental disorders (30%), and failure to thrive (21%) were common. Depression/anxiety increased with age. EoE was reported in 3% of parents and 4.5% of siblings.

CONCLUSIONS

Gastrointestinal eosinophilia is present in approximately 10% of patients with EoE; the symptom-diagnosis time gap is influenced by age, race, food allergy, and atopic dermatitis; symptoms vary with race; concurrent infectious/immunologic disorders and mental health disorders are common; and the level of esophageal eosinophils is comparable in patients with and without fibrostenotic features.

Genetic variants at the 16p13 locus confer risk for eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is a chronic inflammatory disease of the esophagus triggered by immune hypersensitivity to food. Herein, we tested whether genetic risk factors for known, non-allergic, immune-mediated diseases, particularly those involving autoimmunity, were associated with EoE risk. We used the high-density Immunochip platform, encoding 200,000 genetic variants for major auto-immune disease. Accordingly, 1214 subjects with EoE of European ancestry and 3734 population controls were genotyped and assessed using data directly generated or imputed from the previously published GWAS. We found lack of association of EoE with the genetic variants in the major histocompatibility complex (MHC) class I, II, and III genes and nearly all other loci using a highly powered study design with dense genotyping throughout the locus. Importantly, we identified an EoE risk locus at 16p13 with genome-wide significance (P_combined=2.05 × 10⁻⁹, odds ratio = 0.76−0.81). This region is known to encode for the genes CLEC16A, DEXI, and CIITI, which are expressed in immune cells and esophageal epithelial cells. Suggestive EoE risk were also seen 5q23 (intergenic) and 7p15 (JAZF1). Overall, we have identified an additional EoE risk locus at 16p13 and highlight a shared and unique genetic etiology of EoE with a spectrum of immune-associated diseases.

Recent Genetics and Epigenetics Approaches to PTSD

PURPOSE OF REVIEW

Following a life-threatening traumatic exposure, about 10% of those exposed are at considerable risk for developing posttraumatic stress disorder (PTSD), a severe and disabling syndrome characterized by uncontrollable intrusive memories, nightmares, avoidance behaviors, and hyperarousal in addition to impaired cognition and negative emotion symptoms. This review will explore recent genetic and epigenetic approaches to PTSD that explain some of the differential risk following trauma exposure.

RECENT FINDINGS

A substantial portion of the variance explaining differential risk responses to trauma exposure may be explained by differential inherited and acquired genetic and epigenetic risk. This biological risk is complemented by alterations in the functional regulation of genes via environmentally induced epigenetic changes, including prior childhood and adult trauma exposure. This review will cover recent findings from large-scale genome-wide association studies as well as newer epigenome-wide studies. We will also discuss future "phenome-wide" studies utilizing electronic medical records as well as targeted genetic studies focusing on mechanistic ways in which specific genetic or epigenetic alterations regulate the biological risk for PTSD.

PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger

Most phenome-wide association studies (PheWASs) to date have used a small to moderate number of SNPs for association with phenotypic data. We performed a large-scale single-cohort PheWAS, using electronic health record (EHR)-derived case-control status for 541 diagnoses using International Classification of Disease version 9 (ICD-9) codes and 25 median clinical laboratory measures. We calculated associations between these diagnoses and traits with ∼630,000 common frequency SNPs with minor allele frequency > 0.01 for 38,662 individuals. In this landscape PheWAS, we explored results within diseases and traits, comparing results to those previously reported in genome-wide association studies (GWASs), as well as previously published PheWASs. We further leveraged the context of functional impact from protein-coding to regulatory regions, providing a deeper interpretation of these associations. The comprehensive nature of this PheWAS allows for novel hypothesis generation, the identification of phenotypes for further study for future phenotypic algorithm development, and identification of cross-phenotype associations.

A simulation study investigating power estimates in phenome-wide association studies

Background

Phenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance.

Results

We performed a PheWAS simulation study, where we investigated variations in statistical power based on different parameters, such as overall sample size, number of cases, case-control ratio, minor allele frequency, and disease penetrance. The simulation was performed on both binary and quantitative phenotypic measures. Our simulation on binary traits suggests that the number of cases has more impact on statistical power than the case to control ratio; also, we found that a sample size of 200 cases or more maintains the statistical power to identify associations for common variants. For quantitative traits, a sample size of 1000 or more individuals performed best in the power calculations. We focused on common genetic variants (MAF > 0.01) in this study; however, in future studies, we will be extending this effort to perform similar simulations on rare variants.

Conclusions

This study provides a series of PheWAS simulation analyses that can be used to estimate statistical power for some potential scenarios. These results can be used to provide guidelines for appropriate study design for future PheWAS analyses.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2135-0) contains supplementary material, which is available to authorized users.

Rare variants in drug target genes contributing to complex diseases, phenome-wide

The DrugBank database consists of ~800 genes that are well characterized drug targets. This list of genes is a useful resource for association testing. For example, loss of function (LOF) genetic variation has the potential to mimic the effect of drugs, and high impact variation in these genes can impact downstream traits. Identifying novel associations between genetic variation in these genes and a range of diseases can also uncover new uses for the drugs that target these genes. Phenome Wide Association Studies (PheWAS) have been successful in identifying genetic associations across hundreds of thousands of diseases. We have conducted a novel gene based PheWAS to test the effect of rare variants in DrugBank genes, evaluating associations between these genes and more than 500 quantitative and dichotomous phenotypes. We used whole exome sequencing data from 38,568 samples in Geisinger MyCode Community Health Initiative. We evaluated the results of this study when binning rare variants using various filters based on potential functional impact. We identified multiple novel associations, and the majority of the significant associations were driven by functionally annotated variation. Overall, this study provides a sweeping exploration of rare variant associations within functionally relevant genes across a wide range of diagnoses.

Eosinophilic Esophagitis: Pathophysiology and Definition

Eosinophilic esophagitis is an adaptive immune response to patient-specific antigens, mostly foods. Eosinophilic esophagitis is not solely IgE-mediated and is likely characterized by Th2 lymphocytes with an impaired esophageal barrier function. The key cytokines and chemokines are thymic stromal lymphopoeitin, interleukin-13, CCL26/eotaxin-3, and transforming growth factor-β, all involved in eosinophil recruitment and remodeling. Chronic food dysphagia and food impactions, the feared late complications, are related in part to dense subepithelial fibrosis, likely induced by interleukin-13 and transforming growth factor-β.

Current Scope and Challenges in Phenome-Wide Association Studies

PURPOSE OF REVIEW

Over many decades, researchers have been designing studies to investigate the relationship between genotypes and phenotypes to gain an understanding about the effect of genetics on disease. Recently, a high-throughput approach called phenome-wide associations studies (PheWAS) have been extensively used to identify associations between genetic variants and many diseases and traits simultaneously. In this review, we describe the value of PheWAS along with methodological issues and challenges in interpretation for current applications of PheWAS.

RECENT FINDINGS

PheWAS have uncovered a paradigm to identify new associations for genetic loci across many diseases. The application of PheWAS have been effective with phenotype data from electronic health records, epidemiological studies, and clinical trials data.

SUMMARY

The key strength of a PheWAS is to identify the association of one or more genetic variants with multiple phenotypes, which can showcase interconnections among the phenotypes due to shared genetic associations. While the PheWAS approach appears promising, there are a number of challenges that need to be addressed to provide additional robustness to PheWAS findings.

Eosinophilic esophagitis (EoE) genetic susceptibility is mediated by synergistic interactions between EoE-specific and general atopic disease loci

BACKGROUND

Eosinophilic esophagitis (EoE) is an esophageal inflammatory disease associated with atopic diseases. Thymic stromal lymphopoietin (TSLP) and calpain 14 (CAPN14) genetic variations contribute to EoE, but how this relates to atopy is unclear.

OBJECTIVE

The purpose of this study was to explore the relationship between EoE, atopy, and genetic risk.

METHODS

EoE-atopy enrichment was tested by using 700 patients with EoE and 801 community control subjects. Probing 372 single nucleotide polymorphisms (SNPs) in 63 atopy genes, we evaluated EoE associations using 412 nonatopic and 868 atopic disease control subjects. Interaction and stratified analyses of EoE-specific and atopy-related SNPs were performed.

RESULTS

Atopic disease was enriched in patients with EoE (P < .0001). Comparing patients with EoE and nonatopic control subjects, EoE associated strongly with IL-4/kinesin family member 3A (IL4/KIF3A) (P = 2.8 × 10^-6; odds ratio [OR], 1.87), moderately with TSLP (P = 1.5 × 10^-4; OR, 1.43), and nominally with CAPN14 (P = .029; OR, 1.35). Comparing patients with EoE with atopic disease control subjects, EoE associated strongly with ST2 (P = 3.5 × 10^-6; OR, 1.77) and nominally with IL4/KIF3A (P = .019; OR, 1.25); TSLP's association persisted (P = 4.7 × 10^-5; OR, 1.37), and CAPN14's association strengthened (P = .0001; OR, 1.71). Notably, there was gene-gene interaction between TSLP and IL4 SNPs (P = .0074). Children with risk alleles for both genes were at higher risk for EoE (P = 2.0 × 10^-10; OR, 3.67).

CONCLUSIONS

EoE genetic susceptibility is mediated by EoE-specific and general atopic disease loci, which can have synergistic effects. These results might aid in identifying potential therapeutics and predicting EoE susceptibility.

Embracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine

Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships between genetic loci and across traits. As we move toward making precision medicine a reality, whereby we make predictions about disease risk based on genomic profiles, we need to identify improved predictive models of the relationship between genome and phenome. Methods that embrace pleiotropy (the effect of one locus on more than one trait), and gene-environment (G×E) and gene-gene (G×G) interactions, will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on the genotype and environment of an individual, the whole premise of precision medicine.

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks

Motivation: Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL).

Results: We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51 key ‘hub’ diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes.

Contacts: rong.chen@mssm.edu or joel.dudley@mssm.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record

OBJECTIVE

To compare three groupings of Electronic Health Record (EHR) billing codes for their ability to represent clinically meaningful phenotypes and to replicate known genetic associations. The three tested coding systems were the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, the Agency for Healthcare Research and Quality Clinical Classification Software for ICD-9-CM (CCS), and manually curated "phecodes" designed to facilitate phenome-wide association studies (PheWAS) in EHRs.

METHODS AND MATERIALS

We selected 100 disease phenotypes and compared the ability of each coding system to accurately represent them without performing additional groupings. The 100 phenotypes included 25 randomly-chosen clinical phenotypes pursued in prior genome-wide association studies (GWAS) and another 75 common disease phenotypes mentioned across free-text problem lists from 189,289 individuals. We then evaluated the performance of each coding system to replicate known associations for 440 SNP-phenotype pairs.

RESULTS

Out of the 100 tested clinical phenotypes, phecodes exactly matched 83, compared to 53 for ICD-9-CM and 32 for CCS. ICD-9-CM codes were typically too detailed (requiring custom groupings) while CCS codes were often not granular enough. Among 440 tested known SNP-phenotype associations, use of phecodes replicated 153 SNP-phenotype pairs compared to 143 for ICD-9-CM and 139 for CCS. Phecodes also generally produced stronger odds ratios and lower p-values for known associations than ICD-9-CM and CCS. Finally, evaluation of several SNPs via PheWAS identified novel potential signals, some seen in only using the phecode approach. Among them, rs7318369 in PEPD was associated with gastrointestinal hemorrhage.

CONCLUSION

Our results suggest that the phecode groupings better align with clinical diseases mentioned in clinical practice or for genomic studies. ICD-9-CM, CCS, and phecode groupings all worked for PheWAS-type studies, though the phecode groupings produced superior results.

Genetics of eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is a chronic, allergic disease associated with marked mucosal eosinophil accumulation. EoE disease risk is multifactorial and includes environmental and genetic factors. This review will focus on the contribution of genetic variation to EoE risk, as well as the experimental tools and statistical methodology used to identify EoE risk loci. Specific disease-risk loci that are shared between EoE and other allergic diseases (TSLP, LRRC32) or unique to EoE (CAPN14), as well as Mendellian Disorders associated with EoE, will be reviewed in the context of the insight that they provide into the molecular pathoetiology of EoE. We will also discuss the clinical opportunities that genetic analyses provide in the form of decision support tools, molecular diagnostics, and novel therapeutic approaches.

A Subset of Autism-Associated Genes Regulate the Structural Stability of Neurons

Autism spectrum disorder (ASD) comprises a range of neurological conditions that affect individuals’ ability to communicate and interact with others. People with ASD often exhibit marked qualitative difficulties in social interaction, communication, and behavior. Alterations in neurite arborization and dendritic spine morphology, including size, shape, and number, are hallmarks of almost all neurological conditions, including ASD. As experimental evidence emerges in recent years, it becomes clear that although there is broad heterogeneity of identified autism risk genes, many of them converge into similar cellular pathways, including those regulating neurite outgrowth, synapse formation and spine stability, and synaptic plasticity. These mechanisms together regulate the structural stability of neurons and are vulnerable targets in ASD. In this review, we discuss the current understanding of those autism risk genes that affect the structural connectivity of neurons. We sub-categorize them into (1) cytoskeletal regulators, e.g., motors and small RhoGTPase regulators; (2) adhesion molecules, e.g., cadherins, NCAM, and neurexin superfamily; (3) cell surface receptors, e.g., glutamatergic receptors and receptor tyrosine kinases; (4) signaling molecules, e.g., protein kinases and phosphatases; and (5) synaptic proteins, e.g., vesicle and scaffolding proteins. Although the roles of some of these genes in maintaining neuronal structural stability are well studied, how mutations contribute to the autism phenotype is still largely unknown. Investigating whether and how the neuronal structure and function are affected when these genes are mutated will provide insights toward developing effective interventions aimed at improving the lives of people with autism and their families.

Phenome-Wide Association Studies as a Tool to Advance Precision Medicine

Beginning in the early 2000s, the accumulation of biospecimens linked to electronic health records (EHRs) made possible genome-phenome studies (i.e., comparative analyses of genetic variants and phenotypes) using only data collected as a by-product of typical health care. In addition to disease and trait genetics, EHRs proved a valuable resource for analyzing pharmacogenetic traits and developing reverse genetics approaches such as phenome-wide association studies (PheWASs). PheWASs are designed to survey which of many phenotypes may be associated with a given genetic variant. PheWAS methods have been validated through replication of hundreds of known genotype-phenotype associations, and their use has differentiated between true pleiotropy and clinical comorbidity, added context to genetic discoveries, and helped define disease subtypes, and may also help repurpose medications. PheWAS methods have also proven to be useful with research-collected data. Future efforts that integrate broad, robust collection of phenotype data (e.g., EHR data) with purpose-collected research data in combination with a greater understanding of EHR data will create a rich resource for increasingly more efficient and detailed genome-phenome analysis to usher in new discoveries in precision medicine.