Drug-resistant epilepsy classified by a phenotyping algorithm associates with NTRK2.

OBJECTIVE: Up to 40% of patients with epilepsy become drug resistant (DRE). Genetic factors are likely to play a role. While efforts have focused on the transporter and target hypotheses, neither of them fully explains the pan-pharmacoresistance seen in DRE. MATERIALS AND METHODS: In this study, we developed and used a phenotyping algorithm for the identification of DRE, responders, and epilepsy-free controls that were sequenced using a gene panel developed by the Pharmacogenomics Research Network (PGRN), which includes 82 genes involved in drug response. We tested the transporter hypothesis of DRE, the association between drug resistance and variants in the ATP-binding cassette family of genes previously associated with DRE, and also investigated potential new genetic factors. RESULTS: In the analysis of DRE vs controls, NTRK2 was significantly associated with DRE (rs76950094; P = 1.19 × 10-7 and gene-based P-value = 1.67 × 10-4 ). NTRK2 encodes TrkB, which is involved in the development and maturation of the central nervous system, and increased activation of TrkB signaling is suggested to promote epilepsy. CONCLUSION: Although the role of NTRK2 in DRE needs to be elucidated, these results support alternative mechanisms underlying DRE, complementary to the existing hypotheses, that should be evaluated.

Leveraging electronic health records to assess the role of ADRB2 single nucleotide polymorphisms in predicting exacerbation frequency in asthma patients.

Asthma is the leading chronic disease in children. Several studies have identified genetic biomarkers associated with susceptibility and severity in both adult and pediatric cases. In this study, we evaluated outcomes in 400 African American and European American pediatric cases all of whom were regular users of inhaled corticosteroids. Patients were stratified by genotype using two single nucleotide polymorphisms in the ß-2-adrenergic receptor (ADRB2) gene - rs1042713 and rs1042714, previously associated with asthma outcome. These correspond to nonsynonymous single nucleotide polymorphisms at positions 16 [arginine to glycine (Arg16Gly); rs1042713] and 27 [glutamic acid to glutamine (Glu27Gln); rs1042714], which are relatively common (minor allele frequencies ∼40-50%), and have been well characterized in asthma pharmacogenetics. We controlled for adherence to the National Heart, Lung and Blood Institute guidelines using deep mining of electronic health record data to determine treatment course. We found no significant effect for rs1042713 (Arg16Gly) but did identify an effect for rs1042714, where participants homozygous for Gln27 had increased exacerbations while taking inhaled corticosteroids in comparison with those who were either heterozygous or homozygous for Glu27. This is consistent with previous studies and demonstrates for the first time that the Glu27 variant in the ADRB2 gene is associated with increased frequencies of asthma exacerbations. Moreover, this study also lends an important proof-of-principle on how electronic health records linked to genotype can be efficiently and systematically mined to delineate health outcomes.

Identification of Four Novel Loci in Asthma in European American and African American Populations.

RATIONALE: Despite significant advances in knowledge of the genetic architecture of asthma, specific contributors to the variability in the burden between populations remain uncovered. OBJECTIVES: To identify additional genetic susceptibility factors of asthma in European American and African American populations. METHODS: A phenotyping algorithm mining electronic medical records was developed and validated to recruit cases with asthma and control subjects from the Electronic Medical Records and Genomics network. Genome-wide association analyses were performed in pediatric and adult asthma cases and control subjects with European American and African American ancestry followed by metaanalysis. Nominally significant results were reanalyzed conditioning on allergy status. MEASUREMENTS AND MAIN RESULTS: The validation of the algorithm yielded an average of 95.8% positive predictive values for both cases and control subjects. The algorithm accrued 21,644 subjects (65.83% European American and 34.17% African American). We identified four novel population-specific associations with asthma after metaanalyses: loci 6p21.31, 9p21.2, and 10q21.3 in the European American population, and the PTGES gene in African Americans. TEK at 9p21.2, which encodes TIE2, has been shown to be involved in remodeling the airway wall in asthma, and the association remained significant after conditioning by allergy. PTGES, which encodes the prostaglandin E synthase, has also been linked to asthma, where deficient prostaglandin E2 synthesis has been associated with airway remodeling. CONCLUSIONS: This study adds to understanding of the genetic architecture of asthma in European Americans and African Americans and reinforces the need to study populations of diverse ethnic backgrounds to identify shared and unique genetic predictors of asthma.

Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies.

BACKGROUND: Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is one of the most common causes of skin and soft tissue infections in the United States, and a variety of genetic host factors are suspected to be risk factors for recurrent infection. Based on the CDC definition, we have developed and validated an electronic health record (EHR) based CA-MRSA phenotype algorithm utilizing both structured and unstructured data. METHODS: The algorithm was validated at three eMERGE consortium sites, and positive predictive value, negative predictive value and sensitivity, were calculated. The algorithm was then run and data collected across seven total sites. The resulting data was used in GWAS analysis. RESULTS: Across seven sites, the CA-MRSA phenotype algorithm identified a total of 349 cases and 7761 controls among the genotyped European and African American biobank populations. PPV ranged from 68 to 100% for cases and 96 to 100% for controls; sensitivity ranged from 94 to 100% for cases and 75 to 100% for controls. Frequency of cases in the populations varied widely by site. There were no plausible GWAS-significant (p < 5 E -8) findings. CONCLUSIONS: Differences in EHR data representation and screening patterns across sites may have affected identification of cases and controls and accounted for varying frequencies across sites. Future work identifying these patterns is necessary.

Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder.

OBJECTIVE: Cohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD. METHODS: We extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children's Hospital (BCH) (N = 150) and Cincinnati Children's Hospital and Medical Center (CCHMC) (N = 152). Two algorithms were created: (1) rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2) predictive classifier. The positive predictive values (PPV) achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups. RESULTS: The rule-based algorithm produced the best PPV: (a) BCH: 0.885 vs. 0.273 (baseline); (b) CCHMC: 0.840 vs. 0.645 (baseline); (c) combined: 0.864 vs. 0.460 (baseline). A validation at Children's Hospital of Philadelphia yielded 0.848 (PPV). Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients) identified psychiatric, developmental, and seizure disorder clusters. CONCLUSIONS: In a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD.

Advantage of Whole Exome Sequencing over Allele-Specific and Targeted Segment Sequencing in Detection of Novel TULP1 Mutation in Leber Congenital Amaurosis.

BACKGROUND: Leber congenital amaurosis (LCA) is a severe form of retinal dystrophy with marked underlying genetic heterogeneity. Until recently, allele-specific assays and Sanger sequencing of targeted segments were the only available approaches for attempted genetic diagnosis in this condition. A broader next-generation sequencing (NGS) strategy, such as whole exome sequencing, provides an improved molecular genetic diagnostic capacity for patients with these conditions. MATERIALS AND METHODS: In a child with LCA, an allele-specific assay analyzing 135 known LCA-causing variations, followed by targeted segment sequencing of 61 regions in 14 causative genes was performed. Subsequently, exome sequencing was undertaken in the proband, unaffected consanguineous parents and two unaffected siblings. Bioinformatic analysis used two independent pipelines, BWA-GATK and SOAP, followed by Annovar and SnpEff to annotate the variants. RESULTS: No disease-causing variants were found using the allele-specific or targeted segment Sanger sequencing assays. Analysis of variants in the exome sequence data revealed a novel homozygous nonsense mutation (c.1081C > T, p.Arg361*) in TULP1, a gene with roles in photoreceptor function where mutations were previously shown to cause LCA and retinitis pigmentosa. The identified homozygous variant was the top candidate using both bioinformatic pipelines. CONCLUSIONS: This study highlights the value of the broad sequencing strategy of exome sequencing for disease gene identification in LCA, over other existing methods. NGS is particularly beneficial in LCA where there are a large number of causative disease genes, few distinguishing clinical features for precise candidate disease gene selection, and few mutation hotspots in any of the known disease genes.

The impact of the metabotropic glutamate receptor and other gene family interaction networks on autism.

Although multiple reports show that defective genetic networks underlie the aetiology of autism, few have translated into pharmacotherapeutic opportunities. Since drugs compete with endogenous small molecules for protein binding, many successful drugs target large gene families with multiple drug binding sites. Here we search for defective gene family interaction networks (GFINs) in 6,742 patients with the ASDs relative to 12,544 neurologically normal controls, to find potentially druggable genetic targets. We find significant enrichment of structural defects (P ≤ 2.40E-09, 1.8-fold enrichment) in the metabotropic glutamate receptor (GRM) GFIN, previously observed to impact attention deficit hyperactivity disorder (ADHD) and schizophrenia. Also, the MXD-MYC-MAX network of genes, previously implicated in cancer, is significantly enriched (P ≤ 3.83E-23, 2.5-fold enrichment), as is the calmodulin 1 (CALM1) gene interaction network (P ≤ 4.16E-04, 14.4-fold enrichment), which regulates voltage-independent calcium-activated action potentials at the neuronal synapse. We find that multiple defective gene family interactions underlie autism, presenting new translational opportunities to explore for therapeutic interventions.

Imputation of TPMT defective alleles for the identification of patients with high-risk phenotypes.

BACKGROUND: The activity of thiopurine methyltransferase (TPMT) is subject to genetic variation. Loss-of-function alleles are associated with various degrees of myelosuppression after treatment with thiopurine drugs, thus genotype-based dosing recommendations currently exist. The aim of this study was to evaluate the potential utility of leveraging genomic data from large biorepositories in the identification of individuals with TPMT defective alleles. MATERIAL AND METHODS: TPMT variants were imputed using the 1000 Genomes Project reference panel in 87,979 samples from the biobank at The Children's Hospital of Philadelphia. Population ancestry was determined by principal component analysis using HapMap3 samples as reference. Frequencies of the TPMT imputed alleles, genotypes and the associated phenotype were determined across the different populations. A sample of 630 subjects with genotype data from Sanger sequencing (N = 59) and direct genotyping (N = 583) (12 samples overlapping in the two groups) was used to check the concordance between the imputed and observed genotypes, as well as the sensitivity, specificity and positive and negative predictive values of the imputation. RESULTS: Two SNPs (rs1800460 and rs1142345) that represent three TPMT alleles ((*)3A, (*)3B, and (*)3C) were imputed with adequate quality. Frequency for the associated enzyme activity varied across populations and 89.36-94.58% were predicted to have normal TPMT activity, 5.3-10.31% intermediate and 0.12-0.34% poor activities. Overall, 98.88% of individuals (623/630) were correctly imputed into carrying no risk alleles (553/553), heterozygous (45/46) and homozygous (25/31). Sensitivity, specificity and predictive values of imputation were over 90% in all cases except for the sensitivity of imputing homozygous subjects that was 80.64%. CONCLUSION: Imputation of TPMT alleles from existing genomic data can be used as a first step in the screening of individuals at risk of developing serious adverse events secondary to thiopurine drugs.

AGC1 Deficiency Causes Infantile Epilepsy, Abnormal Myelination, and Reduced N-Acetylaspartate.

BACKGROUND: Whole exome sequencing (WES) offers a powerful diagnostic tool to rapidly and efficiently sequence all coding genes in individuals presenting for consideration of phenotypically and genetically heterogeneous disorders such as suspected mitochondrial disease. Here, we report results of WES and functional validation in a consanguineous Indian kindred where two siblings presented with profound developmental delay, congenital hypotonia, refractory epilepsy, abnormal myelination, fluctuating basal ganglia changes, cerebral atrophy, and reduced N-acetylaspartate (NAA). METHODS: Whole blood DNA from one affected and one unaffected sibling was captured by Agilent SureSelect Human All Exon kit and sequenced on the Illumina HiSeq2000. Mutations were validated by Sanger sequencing in all family members. Protein from wild-type and mutant fibroblasts was isolated to assess mutation effects on protein expression and enzyme activity. RESULTS: A novel SLC25A12 homozygous missense mutation, c.1058G>A; p.Arg353Gln, segregated with disease in this kindred. SLC25A12 encodes the neuronal aspartate-glutamate carrier 1 (AGC1) protein, an essential component of the neuronal malate/aspartate shuttle that transfers NADH and H(+) reducing equivalents from the cytosol to mitochondria. AGC1 activity enables neuronal export of aspartate, the glial substrate necessary for proper neuronal myelination. Recombinant mutant p.Arg353Gln AGC1 activity was reduced to 15% of wild type. One prior reported SLC25A12 mutation caused complete loss of AGC1 activity in a child with epilepsy, hypotonia, hypomyelination, and reduced brain NAA. CONCLUSIONS: These data strongly suggest that SLC25A12 disease impairs neuronal AGC1 activity. SLC25A12 sequencing should be considered in children with infantile epilepsy, congenital hypotonia, global delay, abnormal myelination, and reduced brain NAA.