Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records

STUDY OBJECTIVES

The objectives of this study were to understand the relative comorbidity burden of obstructive sleep apnea (OSA), determine whether these relationships were modified by sex or age, and identify patient subtypes defined by common comorbidities.

METHODS

Cases with OSA and noncases (controls) were defined using a validated electronic health record (EHR)-based phenotype and matched for age, sex, and time period of follow-up in the EHR. We compared prevalence of the 20 most common comorbidities between matched cases and controls using conditional logistic regression with and without controlling for body mass index. Latent class analysis was used to identify subtypes of OSA cases defined by combinations of these comorbidities.

RESULTS

In total, 60,586 OSA cases were matched to 60,586 controls (from 1,226,755 total controls). Patients with OSA were more likely to have each of the 20 most common comorbidities compared with controls, with odds ratios ranging from 3.1 to 30.8 in the full matched set and 1.3 to 10.2 after body mass index adjustment. Associations between OSA and these comorbidities were generally stronger in females and patients with younger age at diagnosis. We identified 5 distinct subgroups based on EHR-defined comorbidities: High Comorbidity Burden, Low Comorbidity Burden, Cardiovascular Comorbidities, Inflammatory Conditions and Less Obesity, and Inflammatory Conditions and Obesity.

CONCLUSIONS

Our study demonstrates the power of leveraging the EHR to understand the relative health burden of OSA, as well as heterogeneity in these relationships based on age and sex. In addition to enrichment for comorbidities, we identified 5 novel OSA subtypes defined by combinations of comorbidities in the EHR, which may be informative for understanding disease outcomes and improving prevention and clinical care. Overall, this study adds more evidence that OSA is heterogeneous and requires personalized management.

CITATION

Te TT, Keenan BT, Veatch OJ, Boland MR, Hubbard RA, Pack AI. Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records. J Clin Sleep Med. 2024;20(4):521-533.

Screening effects of HCN channel blockers on sleep/wake behavior in zebrafish

Hyperpolarization-activated cyclic nucleotide-gated (HCN) ion channels generate electrical rhythmicity in various tissues although primarily heart, retina and brain. The HCN channel blocker compound, Ivabradine (Corlanor), is approved by the US Food and Drug Administration (FDA) as a medication to lower heart rate by blocking hyperpolarization activated inward current in the sinoatrial node. In addition, a growing body of evidence suggests a role for HCN channels in regulation of sleep/wake behavior. Zebrafish larvae are ideal model organisms for high throughput drug screening, drug repurposing and behavioral phenotyping studies. We leveraged this model system to investigate effects of three HCN channel blockers (Ivabradine, Zatebradine Hydrochloride and ZD7288) at multiple doses on sleep/wake behavior in wild type zebrafish. Results of interest included shorter latency to daytime sleep at 0.1 μM dose of Ivabradine (ANOVA, p: 0.02), moderate reduction in average activity at 30 μM dose of Zatebradine Hydrochloride (ANOVA, p: 0.024) in daytime, and increased nighttime sleep at 4.5 μM dose of ZD7288 (ANOVA, p: 0.036). Taken together, shorter latency to daytime sleep, decrease in daytime activity and increased nighttime sleep indicate that different HCN channel antagonists affected different parameters of sleep and activity.

Pleiotropic effects of a high confidence Autism Spectrum Disorder gene, arid1b, on zebrafish sleep

Sleep fulfills critical functions in neurodevelopment, such as promoting synaptic plasticity, neuronal wiring, and brain connectivity which are critical phenomena in Autism Spectrum Disorder (ASD) pathophysiology. Sleep disturbance, specifically insomnia, accompanies ASD and is associated with more severe core symptoms (e.g., social impairment). It is possible that focusing on identifying effective ways to treat sleep problems can help alleviate other ASD-related symptoms. A body of evidence indicates shared mechanisms and neurobiological substrates between sleep and ASD and investigation of these may inform therapeutic effects of improving sleep at both behavioral and molecular levels. In this study, we tested if sleep and social behavior were different in a zebrafish model with the arid1b gene mutated compared to controls. This gene was selected for study as expert curations conducted for the Simons Foundation for Autism Research Institute (SFARI) Gene database define it is as a 'high confidence' ASD gene (i.e., clearly implicated) encoding a chromatin remodeling protein. Homozygous arid1b mutants displayed increased arousability and light sleep compared to their heterozygous and wild type counterparts, based on testing a mechano-acoustic stimulus presenting different vibration frequencies of increasing intensity to detect sleep depth. In addition, decreased social preference was observed in arid1b heterozygous and homozygous mutant zebrafish. The behavioral phenotypes reported in our study are in line with findings from mouse models and human studies and demonstrate the utility of zebrafish as a vertebrate model system with high throughput phenotyping in the investigation of changes in sleep in models relevant to ASD. Furthermore, we demonstrate the importance of including assessments of arousal threshold when studying sleep using in vivo models.

Potential Pathophysiological Pathways in the Complex Relationships between OSA and Cancer

Several epidemiological and clinical studies have suggested a relationship between obstructive sleep apnea (OSA) and a higher incidence or severity of cancer. This relationship appears to be dependent on a myriad of factors. These include non-modifiable factors, such as age and gender; and modifiable or preventable factors, such as specific comorbidities (especially obesity), the use of particular treatments, and, above all, the histological type or location of the cancer. Heterogeneity in the relationship between OSA and cancer is also related to the influences of intermittent hypoxemia (a hallmark feature of OSA), among others, on metabolism and the microenvironment of different types of tumoral cells. The hypoxia inducible transcription factor (HIF-1α), a molecule activated and expressed in situations of hypoxemia, seems to be key to enabling a variety of pathophysiological mechanisms that are becoming increasingly better recognized. These mechanisms appear to be operationally involved via alterations in different cellular functions (mainly involving the immune system) and molecular functions, and by inducing modifications in the microbiome. This, in turn, may individually or collectively increase the risk of cancer, which is then, further modulated by the genetic susceptibility of the individual. Here, we provide an updated and brief review of the different pathophysiological pathways that have been identified and could explain the relationship between OSA and cancer. We also identify future challenges that need to be overcome in this intriguing field of research.

A practical approach to identifying autistic adults within the electronic health record

The electronic health record (EHR) provides valuable data for understanding physical and mental health conditions in autism. We developed an approach to identify charts of autistic young adults, retrieved from our institution's de-identified EHR database. Clinical notes within two cohorts were identified. Cohort 1 charts had at least one International Classification of Diseases (ICD-CM) autism code. Cohort 2 charts had only autism key terms without ICD-CM codes, and at least four notes per chart. A natural language processing tool parsed medical charts to identify key terms associated with autism diagnoses and mapped them to Unified Medical Language System Concept Unique Identifiers (CUIs). Average scores were calculated for each set of charts based on captured CUIs. Chart review determined whether patients met criteria for autism using a classification rubric. In Cohort 1, of 418 patients, 361 were confirmed to have autism by chart review. Sensitivity was 0.99 and specificity was 0.68 with positive predictive value (PPV) of 0.97. Specificity improved to 0.81 (sensitivity was 0.95; PPV was 0.98) when the number of notes was limited to four or more per chart. In Cohort 2, 48 of 136 patients were confirmed to have autism by chart review. Sensitivity was 0.95, specificity was 0.73, and PPV was 0.70. Our approach, which included using key terms, identified autism charts with high sensitivity, even in the absence of ICD-CM codes. Relying on ICD-CM codes alone may result in inclusion of false positive cases and exclusion of true cases with autism.

Next-generation sequencing and analysis of consecutive patients referred for connective tissue disorders

Heritable connective tissue disorders (HCTDs) consist of a wide array of genetic disorders such as Ehlers-Danlos syndrome, Marfan syndrome, and osteogenesis imperfecta. The diagnosis relies on clinical presentation and family history to guide genetic testing with next-generation sequencing (NGS) for identification of gene variants in HCTDs. NGS was performed on a cohort of 100 consecutive, unrelated patients referred for a connective tissue disorder at Fulgent Genetics, an accredited commercial laboratory. One hundred seventeen gene variants were found in 76 patients with 10 recognized pathogenic or likely pathogenic variants seen in nine patients. The remaining variants were grouped as unknown clinical significance with 36 meeting three out of four pathogenicity criteria, or potentially pathogenic, as defined in our study in 33 patients. They were judged as potentially pathogenic for clinical care and management with disease surveillance based on the specific gene and phenotypic presentation. Gene variants in collagen-related proteins were the most frequent with ZNF469 and ADAMTSL2 variants most often identified. Joint hypermobility was the most frequent clinical finding. Variants were found in 76% of patients who had distinct clinical features of a HCTD. The data were stratified to provide insight into frequency and types of variants, their classification, and clinical manifestations.

Connective Tissue Disorders and Fragile X Molecular Status in Females: A Case Series and Review

Fragile X syndrome (FXS) is the most common inherited cause of intellectual disabilities and the second most common cause after Down syndrome. FXS is an X-linked disorder due to a full mutation of the CGG triplet repeat of the FMR1 gene which codes for a protein that is crucial in synaptogenesis and maintaining functions of extracellular matrix-related proteins, key for the development of normal neuronal and connective tissue including collagen. In addition to neuropsychiatric and behavioral problems, individuals with FXS show physical features suggestive of a connective tissue disorder including loose skin and joint laxity, flat feet, hernias and mitral valve prolapse. Disturbed collagen leads to hypermobility, hyperextensible skin and tissue fragility with musculoskeletal, cardiovascular, immune and other organ involvement as seen in hereditary disorders of connective tissue including Ehlers−Danlos syndrome. Recently, FMR1 premutation repeat expansion or carrier status has been reported in individuals with connective tissue disorder-related symptoms. We examined a cohort of females with features of a connective tissue disorder presenting for genetic services using next-generation sequencing (NGS) of a connective tissue disorder gene panel consisting of approximately 75 genes. In those females with normal NGS testing for connective tissue disorders, the FMR1 gene was then analyzed using CGG repeat expansion studies. Three of thirty-nine females were found to have gray zone or intermediate alleles at a 1:13 ratio which was significantly higher (p < 0.05) when compared with newborn females representing the general population at a 1:66 ratio. This association of connective tissue involvement in females with intermediate or gray zone alleles reported for the first time will require more studies on how the size variation may impact FMR1 gene function and protein directly or in relationship with other susceptibility genes involved in connective tissue disorders.

Clinical genetics evaluation and testing of connective tissue disorders: a cross-sectional study

BACKGROUND

Heritable connective tissue disorders (HCTDs) consist of heterogeneous syndromes. The diagnosis of HCTDs is aided by genomic biotechnologies (e.g., next-generation sequencing panels) facilitating the discovery of novel variants causing disease.

METHODS

Detailed clinical exam data and CLIA-approved genetic testing results from next generation sequencing of 74 genes known to play a role in HCTDs were manually reviewed and analyzed in one hundred consecutive, unrelated patients with phenotypic features indicative of a HCTD referred over a 3.5-year period (2016-2020) to a specialized academic genetics clinic. The prevalence of symptoms was evaluated in the context of genetic variants. We also determined if symptoms among different organ systems were related and performed latent class analysis to identify distinct groups of patients based on symptomatology.

RESULTS

In the cohort of 100 consecutive, unrelated individuals there were four pathogenic, six likely pathogenic and 35 classified potentially pathogenic variants of unknown clinical significance. Patients with potentially pathogenic variants exhibited similar symptom profiles when compared to patients with pathogenic/likely pathogenic variants in the same genes. Although results did not meet a multiple testing corrected threshold, patients with connective tissue symptoms had suggestive evidence of increased odds of having skin (odds ratio 2.18, 95% confidence interval 1.12 to 4.24) and eye symptoms (odds ratio 1.89, 95% confidence interval 0.98 to 3.66) requiring further studies. The best performing latent class analysis results were identified when dividing the dataset into three distinct groups based on age, gender and presence or absence of symptoms in the skeletal, connective tissue, nervous, gastrointestinal and cardiovascular systems. These distinct classes of patients included individuals with: (1) minimal skeletal symptoms, (2) more skeletal but fewer connective tissue, nervous or gastrointestinal symptoms and (3) more nervous system symptoms.

CONCLUSIONS

We used novel approaches to characterize phenotype-genotype relationships, including pinpointing potentially pathogenic variants, and detecting unique symptom profiles in patients with features of HCTDs. This study may guide future diagnosis and disease/organ system monitoring with continued improvement and surveillance by clinicians for patients and their families.

Calculating genetic risk for dysfunction in pleiotropic biological processes using whole exome sequencing data

BACKGROUND

Numerous genes are implicated in autism spectrum disorder (ASD). ASD encompasses a wide-range and severity of symptoms and co-occurring conditions; however, the details of how genetic variation contributes to phenotypic differences are unclear. This creates a challenge for translating genetic evidence into clinically useful knowledge. Sleep disturbances are particularly prevalent co-occurring conditions in ASD, and genetics may inform treatment. Identifying convergent mechanisms with evidence for dysfunction that connect ASD and sleep biology could help identify better treatments for sleep disturbances in these individuals.

METHODS

To identify mechanisms that influence risk for ASD and co-occurring sleep disturbances, we analyzed whole exome sequence data from individuals in the Simons Simplex Collection (n = 2380). We predicted protein damaging variants (PDVs) in genes currently implicated in either ASD or sleep duration in typically developing children. We predicted a network of ASD-related proteins with direct evidence for interaction with sleep duration-related proteins encoded by genes with PDVs. Overrepresentation analyses of Gene Ontology-defined biological processes were conducted on the resulting gene set. We calculated the likelihood of dysfunction in the top overrepresented biological process. We then tested if scores reflecting genetic dysfunction in the process were associated with parent-reported sleep duration.

RESULTS

There were 29 genes with PDVs in the ASD dataset where variation was reported in the literature to be associated with both ASD and sleep duration. A network of 108 proteins encoded by ASD and sleep duration candidate genes with PDVs was identified. The mechanism overrepresented in PDV-containing genes that encode proteins in the interaction network with the most evidence for dysfunction was cerebral cortex development (GO:0,021,987). Scores reflecting dysfunction in this process were associated with sleep durations; the largest effects were observed in adolescents (p = 4.65 × 10^-3).

CONCLUSIONS

Our bioinformatic-driven approach detected a biological process enriched for genes encoding a protein-protein interaction network linking ASD gene products with sleep duration gene products where accumulation of potentially damaging variants in individuals with ASD was associated with sleep duration as reported by the parents. Specifically, genetic dysfunction impacting development of the cerebral cortex may affect sleep by disrupting sleep homeostasis which is evidenced to be regulated by this brain region. Future functional assessments and objective measurements of sleep in adolescents with ASD could provide the basis for more informed treatment of sleep problems in these individuals.

Synaptic dysfunction connects autism spectrum disorder and sleep disturbances: A perspective from studies in model organisms

Sleep disturbances (SD) accompany many neurodevelopmental disorders, suggesting SD is a transdiagnostic process that can account for behavioral deficits and influence underlying neuropathogenesis. Autism Spectrum Disorder (ASD) comprises a complex set of neurodevelopmental conditions characterized by challenges in social interaction, communication, and restricted, repetitive behaviors. Diagnosis of ASD is based primarily on behavioral criteria, and there are no drugs that target core symptoms. Among the co-occurring conditions associated with ASD, SD are one of the most prevalent. SD often arises before the onset of other ASD symptoms. Sleep interventions improve not only sleep but also daytime behaviors in children with ASD. Here, we examine sleep phenotypes in multiple model systems relevant to ASD, e.g., mice, zebrafish, fruit flies and worms. Given the functions of sleep in promoting brain connectivity, neural plasticity, emotional regulation and social behavior, all of which are of critical importance in ASD pathogenesis, we propose that synaptic dysfunction is a major mechanism that connects ASD and SD. Common molecular targets in this interplay that are involved in synaptic function might be a novel avenue for therapy of individuals with ASD experiencing SD. Such therapy would be expected to improve not only sleep but also other ASD symptoms.

On the Relationship between Diabetes and Obstructive Sleep Apnea: Evolution and Epigenetics

This review offers an overview of the relationship between diabetes, obstructive sleep apnea (OSA), obesity, and heart disease. It then addresses evidence that the traditional understanding of this relationship is incomplete or misleading. In the process, there is a brief discussion of the evolutionary rationale for the development and retention of OSA in light of blood sugar dysregulation, as an adaptive mechanism in response to environmental stressors, followed by a brief overview of the general concepts of epigenetics. Finally, this paper presents the results of a literature search on the epigenetic marks and changes in gene expression found in OSA and diabetes. (While some of these marks will also correlate with obesity and heart disease, that is beyond the scope of this project). We conclude with an exploration of alternative explanations for the etiology of these interlinking diseases.

PHIP gene variants with protein modeling, interactions, and clinical phenotypes

Variants in the pleckstrin homology domain-interacting protein (PHIP) gene are implicated in the clinical phenotype of Chung-Jansen syndrome, which includes dysmorphic features, cognitive dysfunction, aberrant behavior, and childhood onset obesity. Following a systematic literature review, 35 patients are reported to have unique PHIP variants impacting the encoded protein product. We summarize the status and frequency of these variants and relationship to clinical presentation. We also describe an additional patient with a rare, pathogenic variant due to a five base pair deletion leading to an altered codon at I307 but with a stop codon at 22 codons downstream; notably, a variant was identified at the same location as seen previously at protein position I307 in one other subject and a frameshift change at that protein position. We compare the clinical characteristics between the two patients and analyze whether certain types of gene defects impact clinical presentation in previously reported individuals. In addition, we predict structural protein models, which yielded unique differences between the wild-type and I307P-related mutant truncated proteins. Protein-protein interactions indicate involvement of POMC and related proteins with potential contribution to obesity, congenital, neuromuscular, and lipid disorders with heart, gastrointestinal, and rheumatoid diseases. With its surrounding proline-rich region, the I307P point mutation increases susceptibility to conformational rigidity and thermodynamic stability, ultimately impacting function as well as a stop codon downstream. Furthermore, the frameshift mutation seen in our patient may result in a truncated protein with a short abnormal region prior to the stop codon due to a five base pair deletion at I307 or target the protein for nonsense-mediated mRNA decay.

The dihydropyrimidine dehydrogenase gene contributes to heritable differences in sleep in mice

Many aspects of sleep are heritable, but only a few sleep-regulating genes have been reported. Here, we leverage mouse models to identify and confirm a previously unreported gene affecting sleep duration-dihydropyrimidine dehydrogenase (Dpyd). Using activity patterns to quantify sleep in 325 Diversity Outbred (DO) mice-a population with high genetic and phenotypic heterogeneity-a linkage peak for total sleep in the active lights off period was identified on chromosome 3 (LOD score = 7.14). Mice with the PWK/PhJ ancestral haplotype at this location demonstrated markedly reduced sleep. Among the genes within the linkage region, available RNA sequencing data in an independent sample of DO mice supported a highly significant expression quantitative trait locus for Dpyd, wherein reduced expression was associated with the PWK/PhJ allele. Validation studies were performed using activity monitoring and EEG/EMG recording in Collaborative Cross mouse strains with and without the PWK/PhJ haplotype at this location, as well as EEG and EMG recording of sleep and wake in Dpyd knockout mice and wild-type littermate controls. Mice lacking Dpyd had 78.4 min less sleep during the lights-off period than wild-type mice (p = 0.007; Cohen's d = -0.94). There was no difference in other measured behaviors in knockout mice, including assays evaluating cognitive-, social-, and affective-disorder-related behaviors. Dpyd encodes the rate-limiting enzyme in the metabolic pathway that catabolizes uracil and thymidine to β-alanine, an inhibitory neurotransmitter. Thus, data support β-alanine as a neurotransmitter that promotes sleep in mice.

Evaluating Sleep Disturbances in Children With Rare Genetic Neurodevelopmental Syndromes

BACKGROUND

Adequate sleep is important for proper neurodevelopment and positive health outcomes. Sleep disturbances are more prevalent in children with genetically determined neurodevelopmental syndromes compared with typically developing counterparts. We characterize sleep behavior in Rett (RTT), Angelman (AS), and Prader-Willi (PWS) syndromes to identify effective approaches for treating sleep problems in these populations. We compared sleep-related symptoms across individuals with these different syndromes with each other, and with typically developing controls.

METHODS

Children were recruited from the Rare Diseases Clinical Research Network consortium registries; unaffected siblings were enrolled as related controls. For each participant, a parent completed multiple sleep questionnaires including Pediatric Sleep Questionnaire (Sleep-Disordered Breathing), Children's Sleep Habits Questionnaire (CSHQ), and Pediatric Daytime Sleepiness Scale.

RESULTS

Sleep data were analyzed from 714 participants, aged two to 18 years. Young children with AS had more reported sleep problems than children with RTT or PWS. Older children with RTT had more reported daytime sleepiness than those with AS or PWS. Finally, all individuals with RTT had more evidence of sleep-disordered breathing when compared with individuals with PWS. Notably, typically developing siblings were also reported to have sleep problems, except for sleep-related breathing disturbances, which were associated with each of the genetic syndromes.

CONCLUSIONS

Individuals with RTT, AS, and PWS frequently experience sleep problems, including sleep-disordered breathing. Screening for sleep problems in individuals with these and other neurogenetic disorders should be included in clinical assessment and managements. These data may also be useful in developing treatment strategies and in clinical trials.

Genomic, Clinical, and Behavioral Characterization of 15q11.2 BP1-BP2 Deletion (Burnside-Butler) Syndrome in Five Families

The 15q11.2 BP1-BP2 deletion (Burnside-Butler) syndrome is emerging as the most common cytogenetic finding in patients with neurodevelopmental or autism spectrum disorders (ASD) presenting for microarray genetic testing. Clinical findings in Burnside-Butler syndrome include developmental and motor delays, congenital abnormalities, learning and behavioral problems, and abnormal brain findings. To better define symptom presentation, we performed comprehensive cognitive and behavioral testing, collected medical and family histories, and conducted clinical genetic evaluations. The 15q11.2 BP1-BP2 region includes the TUBGCP5, CYFIP1, NIPA1, and NIPA2 genes. To determine if additional genomic variation outside of the 15q11.2 region influences expression of symptoms in Burnside-Butler syndrome, whole-exome sequencing was performed on the parents and affected children for the first time in five families with at least one parent and child with the 15q1l.2 BP1-BP2 deletion. In total, there were 453 genes with possibly damaging variants identified across all of the affected children. Of these, 99 genes had exclusively de novo variants and 107 had variants inherited exclusively from the parent without the deletion. There were three genes (APBB1, GOLGA2, and MEOX1) with de novo variants that encode proteins evidenced to interact with CYFIP1. In addition, one other gene of interest (FAT3) had variants inherited from the parent without the deletion and encoded a protein interacting with CYFIP1. The affected individuals commonly displayed a neurodevelopmental phenotype including ASD, speech delay, abnormal reflexes, and coordination issues along with craniofacial findings and orthopedic-related connective tissue problems. Of the 453 genes with variants, 35 were associated with ASD. On average, each affected child had variants in 6 distinct ASD-associated genes (x¯ = 6.33, sd = 3.01). In addition, 32 genes with variants were included on clinical testing panels from Clinical Laboratory Improvement Amendments (CLIA) approved and accredited commercial laboratories reflecting other observed phenotypes. Notably, the dataset analyzed in this study was small and reported results will require validation in larger samples as well as functional follow-up. Regardless, we anticipate that results from our study will inform future research into the genetic factors influencing diverse symptoms in patients with Burnside-Butler syndrome, an emerging disorder with a neurodevelopmental behavioral phenotype.

ADAMTSL2 gene variant in patients with features of autosomal dominant connective tissue disorders

Ehlers-Danlos syndrome (EDS) consists of a heterogeneous group of genetically inherited connective tissue disorders. A family with three affected members over two generations with features of Dermatosparaxic EDS (dEDS) autosomal dominant transmission was reported by Desai et al. and having a heterozygous nonsynonymous missense variant of ADAMTSL2 (c.1261G > A; p. Gly421Ser). Variation in this gene is also reported to cause autosomal recessive geleophysic dysplasia. We report five unrelated patients with the Gly421Ser variant identified from a large series of patients presenting with features of connective tissue disorders, each with a positive family history consistent with autosomal dominant transmission. Clinical features of a connective tissue disorder included generalized joint hypermobility and pain with fragility of internal and external tissues including of skin, dura, and arteries. Overall, our analyses including bioinformatics, protein modeling, and gene-protein interactions with the cases described would add evidence for the Gly421Ser variant in ADAMTSL2 as causative for variable expressivity of autosomal dominant connective tissue disorders.

An Automated Functional Annotation Pipeline That Rapidly Prioritizes Clinically Relevant Genes for Autism Spectrum Disorder

Human genetic studies have implicated more than a hundred genes in Autism Spectrum Disorder (ASD). Understanding how variation in implicated genes influence expression of co-occurring conditions and drug response can inform more effective, personalized approaches for treatment of individuals with ASD. Rapidly translating this information into the clinic requires efficient algorithms to sort through the myriad of genes implicated by rare gene-damaging single nucleotide and copy number variants, and common variation detected in genome-wide association studies (GWAS). To pinpoint genes that are more likely to have clinically relevant variants, we developed a functional annotation pipeline. We defined clinical relevance in this project as any ASD associated gene with evidence indicating a patient may have a complex, co-occurring condition that requires direct intervention (e.g., sleep and gastrointestinal disturbances, attention deficit hyperactivity, anxiety, seizures, depression), or is relevant to drug development and/or approaches to maximizing efficacy and minimizing adverse events (i.e., pharmacogenomics). Starting with a list of all candidate genes implicated in all manifestations of ASD (i.e., idiopathic and syndromic), this pipeline uses databases that represent multiple lines of evidence to identify genes: (1) expressed in the human brain, (2) involved in ASD-relevant biological processes and resulting in analogous phenotypes in mice, (3) whose products are targeted by approved pharmaceutical compounds or possessing pharmacogenetic variation and (4) whose products directly interact with those of genes with variants recommended to be tested for by the American College of Medical Genetics (ACMG). Compared with 1000 gene sets, each with a random selection of human protein coding genes, more genes in the ASD set were annotated for each category evaluated (p ≤ 1.99 × 10⁻²). Of the 956 ASD-implicated genes in the full set, 18 were flagged based on evidence in all categories. Fewer genes from randomly drawn sets were annotated in all categories (x = 8.02, sd = 2.56, p = 7.75 × 10⁻⁴). Notably, none of the prioritized genes are represented among the 59 genes compiled by the ACMG, and 78% had a pathogenic or likely pathogenic variant in ClinVar. Results from this work should rapidly prioritize potentially actionable results from genetic studies and, in turn, inform future work toward clinical decision support for personalized care based on genetic testing.

Characterization of genetic and phenotypic heterogeneity of obstructive sleep apnea using electronic health records

Background

Obstructive sleep apnea (OSA) is defined by frequent episodes of reduced or complete cessation of airflow during sleep and is linked to negative health outcomes. Understanding the genetic factors influencing expression of OSA may lead to new treatment strategies. Electronic health records (EHRs) can be leveraged to both validate previously reported OSA-associated genomic variation and detect novel relationships between these variants and comorbidities.

Methods

We identified candidate single nucleotide polymorphisms (SNPs) via systematic literature review of existing research. Using datasets available at Geisinger (n = 39,407) and Vanderbilt University Medical Center (n = 24,084), we evaluated associations between 40 previously implicated SNPs and OSA diagnosis, defined using clinical codes. We also evaluated associations between these SNPs and OSA severity measures obtained from sleep reports at Geisinger (n = 6571). Finally, we used a phenome-wide association study approach to help reveal pleiotropic genetic effects between OSA candidate SNPs and other clinical codes and laboratory values available in the EHR.

Results

Most previously reported OSA candidate SNPs showed minimal to no evidence for associations with OSA diagnosis or severity in the EHR-derived datasets. Three SNPs in LEPR, MMP-9, and GABBR1 validated for an association with OSA diagnosis in European Americans; the SNP in GABBR1 was associated following meta-analysis of results from both clinical populations. The GABBR1 and LEPR SNPs, and one additional SNP, were associated with OSA severity measures in European Americans from Geisinger. Three additional candidate OSA SNPs were not associated with OSA-related traits but instead with hyperlipidemia and autoimmune diseases of the thyroid.

Conclusions

To our knowledge, this is one of the largest candidate gene studies and one of the first phenome-wide association studies of OSA genomic variation. Results validate genetic associates with OSA in the LEPR, MMP-9 and GABBR1 genes, but suggest that the majority of previously identified genetic associations with OSA may be false positives. Phenome-wide analyses provide evidence of mediated pleiotropy. Future well-powered genome-wide association analyses of OSA risk and severity across populations with diverse ancestral backgrounds are needed. The comprehensive nature of the analyses represents a platform for informing future work focused on understanding how genetic data can be useful to informing treatment of OSA and related comorbidities.

0386 Identification of Sleep Complaints Using Social Media: Effect of the Daylight Savings Time to Standard Time Transition

Introduction

Transitions to and from daylight savings time (DST) are natural experiments of circadian disruption and are associated with negative health consequences. Yet, the majority of the United States and several other countries still adopt these changes. Large observational studies focused on understanding the impact of DST transitions on sleep are difficult to conduct. Social media platforms, like Twitter, are powerful sources of human behavior data. We used machine learning to identify tweets reporting sleep complaints (TRSC) during the week of the standard time (ST)-DST transition. Next, we evaluated the circadian patterns of TRSC and compared their prevalence before and after the transition.

Methods

Using data publicly available via the Twitter API, we collected 500 tweets with evidence of sleep complaints, and manually annotated each tweet to validate true sleep complaints. Next, we calculated term frequency-inverse document frequency of each word in each tweet and trained a random forest to classify TRSC using a 3-fold cross-validation design. The trained model was then used to annotate a collection of tweets captured between Oct. 30, 2019-Nov. 6, 2019, overlapping with the DST-ST transition, which occurred on Nov. 3, 2019.

Results

Random forest demonstrated good performance in classifying TRSC (AUC[95%CI]=0.85[0.82-0.89]). This model was applied to 3,738,383 tweets collected around the DST-ST transition, and identified 11,044 TRSC. Posting of these tweets had a circadian pattern, with peak during nighttime. We found a higher frequency of TRSC after the DST-ST transition (0.33% vs. 0.27%, p&lt;0.00001), corresponding to a ~20% increase in the odds of reporting sleep complaints (OR[95%CI]=1.21[1.16-1.25]).

Conclusion

Using machine learning and Twitter data, we identified tweets reporting sleep complaints, described their circadian patterns and demonstrated that the prevalence of these types of tweets is significantly increased after the transition from DST to ST. These results demonstrate the applicability of social media data mining for public health in sleep medicine.

Support

NIH (K01LM012870); AASM Foundation (194-SR-18)

Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea

STUDY OBJECTIVES

We examined the performance of a simple algorithm to accurately distinguish cases of diagnosed obstructive sleep apnea (OSA) and noncases using the electronic health record (EHR) across six health systems in the United States.

METHODS

Retrospective analysis of EHR data was performed. The algorithm defined cases as individuals with ≥ 2 instances of specific International Classification of Diseases (ICD)-9 and/or ICD-10 diagnostic codes (327.20, 327.23, 327.29, 780.51, 780.53, 780.57, G4730, G4733 and G4739) related to sleep apnea on separate dates in their EHR. Noncases were defined by the absence of these codes. Using chart reviews on 120 cases and 100 noncases at each site (n = 1,320 total), positive predictive value (PPV) and negative predictive value (NPV) were calculated.

RESULTS

The algorithm showed excellent performance across sites, with a PPV (95% confidence interval) of 97.1 (95.6, 98.2) and NPV of 95.5 (93.5, 97.0). Similar performance was seen at each site, with all NPV and PPV estimates ≥ 90% apart from a somewhat lower PPV of 87.5 (80.2, 92.8) at one site. A modified algorithm of ≥ 3 instances improved PPV to 94.9 (88.5, 98.3) at this site, but excluded an additional 18.3% of cases. Thus, performance may be further improved by requiring additional codes, but this reduces the number of determinate cases.

CONCLUSIONS

A simple EHR-based case-identification algorithm for diagnosed OSA showed excellent predictive characteristics in a multisite sample from the United States. Future analyses should be performed to understand the effect of undiagnosed disease in EHR-defined noncases. This algorithm has wide-ranging applications for EHR-based OSA research.

Strategies for Pathway Analysis Using GWAS and WGS Data

Single-allele study designs, commonly used in genome-wide association studies (GWAS) as well as the more recently developed whole genome sequencing (WGS) studies, are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p-values are declared statistically significant due to issues arising from multiple testing and type I errors. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, making it difficult to separate true associations from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at the overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios. © 2018 by John Wiley & Sons, Inc.

Hi-MC: a novel method for high-throughput mitochondrial haplogroup classification

Effective approaches for assessing mitochondrial DNA (mtDNA) variation are important to multiple scientific disciplines. Mitochondrial haplogroups characterize branch points in the phylogeny of mtDNA. Several tools exist for mitochondrial haplogroup classification. However, most require full or partial mtDNA sequence which is often cost prohibitive for studies with large sample sizes. The purpose of this study was to develop Hi-MC, a high-throughput method for mitochondrial haplogroup classification that is cost effective and applicable to large sample sizes making mitochondrial analysis more accessible in genetic studies. Using rigorous selection criteria, we defined and validated a custom panel of mtDNA single nucleotide polymorphisms that allows for accurate classification of European, African, and Native American mitochondrial haplogroups at broad resolution with minimal genotyping and cost. We demonstrate that Hi-MC performs well in samples of European, African, and Native American ancestries, and that Hi-MC performs comparably to a commonly used classifier. Implementation as a software package in R enables users to download and run the program locally, grants greater flexibility in the number of samples that can be run, and allows for easy expansion in future revisions. Hi-MC is available in the CRAN repository and the source code is freely available at https://github.com/vserch/himc.

Pleiotropic genetic effects influencing sleep and neurological disorders

Research evidence increasingly points to the large impact of sleep disturbances on public health. Many aspects of sleep are heritable and genes influencing traits such as timing, EEG characteristics, sleep duration, and response to sleep loss have been identified. Notably, large-scale genome-wide analyses have implicated numerous genes with small effects on sleep timing. Additionally, there has been considerable progress in the identification of genes influencing risk for some neurological sleep disorders. For restless legs syndrome, implicated variants are typically in genes associated with neuronal development. By contrast, genes conferring risk for narcolepsy function in the immune system. Many genetic variants associated with sleep disorders are also implicated in neurological disorders in which sleep abnormalities are common; for example, variation in genes involved in synaptic homoeostasis are implicated in autism spectrum disorder and sleep-wake control. Further investigation into pleiotropic roles of genes influencing both sleep and neurological disorders could lead to new treatment strategies for a variety of sleep disturbances.

Shorter sleep duration is associated with social impairment and comorbidities in ASD

Sleep disturbance, particularly insomnia, is common in children with autism spectrum disorders (ASD). Furthermore, disturbed sleep affects core symptoms and other related comorbidities. Understanding the causes and consequences of sleep disturbances in children with ASD is an important step toward mitigating these symptoms. To better understand the connection between sleep duration and ASD severity, we analyzed ASD-related symptoms using the Autism Diagnostic Interview-Revised (ADI-R), Autism Diagnostic Observation Schedule (ADOS), IQ scores, and parent reports of the average amount of time slept per night that were available in the medical histories of 2,714 children with ASD in the Simons Simplex Collection (SSC). The mean (SD) sleep duration was 555 minutes. Sleep duration and severity of core ASD symptoms were negatively correlated, and sleep duration and IQ scores were positively correlated. Regression results indicated that more severe social impairment, primarily a failure to develop peer relationships, is the core symptom most strongly associated with short sleep duration. Furthermore, increased severity for numerous maladaptive behaviors assessed on the Child Behavior Checklist, as well as reports of attention deficit disorder, depressive disorder, and obsessive compulsive disorder were associated with short sleep duration. Severity scores for social/communication impairment and restricted and repetitive behaviors (RRB) were increased, and IQ scores were decreased, for children reported to sleep ≤420 minutes per night (lower 5th percentile) compared to children sleeping ≥660 minutes (upper 95th percentile). Our results indicate that reduced amounts of sleep are related to more severe symptoms in children with ASD. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1221-1238. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Sleep in Children With Autism Spectrum Disorders: How Are Measures of Parent Report and Actigraphy Related and Affected by Sleep Education?

Sleep disturbance is common in children with autism, resulting in a great need for effective treatments. To evaluate treatments for sleep disturbance in this population, it is critical to understand the relationship between measures of sleep captured by parent report and objective measures. The Children's Sleep Habits Questionnaire (CSHQ) and actigraphy-measured data from 80 children with autism and sleep-onset delay were evaluated. Reported problems with sleep-onset delay were concurrent with sleep duration problems in 66% of children, night wakings in 72% of children, and bedtime resistance in 66% of children; 38% of children were reported to have problems with all CSHQ insomnia domains. Actigraphy-measured sleep duration was correlated with estimates using CSHQ-reported bed and wake times.

Sleep in Autism Spectrum Disorders

Autism spectrum disorders (ASD) are common neurodevelopmental conditions, affecting 1 in 68 children. Sleep disturbance, particularly insomnia, is very common in children diagnosed with ASD, with evidence supporting overlapping neurobiological and genetic underpinnings. Disturbed sleep exacerbates core and related ASD symptoms and has a substantial negative impact on the entire family. Treatment of sleep disturbance holds promise for ameliorating many of the challenging behavioral symptoms that children with ASD and their families face. Behavioral and pharmacological studies indicate promising approaches to treating sleep disturbances in this population. Awareness of treatment options is particularly important as parents and clinicians may believe that sleep disturbance is part of autism and refractory to therapy. In addition, autism symptoms refractory to treatment with conventional psychiatric medications may improve when sleep is addressed. Additional evidence-based studies are needed, including those that address the underlying biology of this condition.

Melatonin in Children with Autism Spectrum Disorders: How Does the Evidence Fit Together?

Autism spectrum disorders (ASD) are prevalent neurodevelopmental conditions, affecting 1 in 68 children in the United States alone. Sleep disturbance, particularly insomnia, is very common in children diagnosed with ASD, with evidence supporting overlapping neurobiological and genetic underpinnings. One of the most well studied mechanisms related to ASD and insomnia is dysregulation of the melatonin pathway, which has been observed in many individuals with ASD compared to typically developing controls. Furthermore, variation in genes whose products regulate endogenous melatonin modify sleep patterns in humans and have also been implicated in some cases of ASD. However, the relationship between comorbid insomnia, melatonin processing, and genes that regulate endogenous melatonin levels in ASD is complex and requires further study to fully elucidate. The aim of this review is to provide an overview of the current findings related to the effects of genetic variation in the melatonergic pathway on risk for expression of sleep disorders in children with ASD. In addition, functional findings related to endogenous levels of melatonin and pharmacokinetic profiles in this patient population are evaluated.

Genetically meaningful phenotypic subgroups in autism spectrum disorders

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with strong evidence for genetic susceptibility. However, the effect sizes for implicated chromosomal loci are small, hard to replicate and current evidence does not explain the majority of the estimated heritability. Phenotypic heterogeneity could be one phenomenon complicating identification of genetic factors. We used data from the Autism Diagnostic Interview-Revised, Autism Diagnostic Observation Schedule, Vineland Adaptive Behavior Scales, head circumferences, and ages at exams as classifying variables to identify more clinically similar subgroups of individuals with ASD. We identified two distinct subgroups of cases within the Autism Genetic Resource Exchange dataset, primarily defined by the overall severity of evaluated traits. In addition, there was significant familial clustering within subgroups (odds ratio, OR ≈ 1.38-1.42, P < 0.00001), and genotypes were more similar within subgroups compared to the unsubgrouped dataset (Fst = 0.17 ± 0.0.0009). These results suggest that the subgroups recapitulate genetic etiology. Using the same approach in an independent dataset from the Autism Genome Project, we similarly identified two distinct subgroups of cases and confirmed this severity-based dichotomy. We also observed evidence for genetic contributions to subgroups identified in the replication dataset. Our results provide more effective methods of phenotype definition that should increase power to detect genetic factors influencing risk for ASD.

Strategies for pathway analysis from GWAS data

Genome-wide association studies (GWAS) are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p values are declared statistically significant due to issues arising from multiple testing and type I error concerns. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, as they are difficult to separate from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios.

Cloning of the papaya chromoplast-specific lycopene beta-cyclase, CpCYC-b, controlling fruit flesh color reveals conserved microsynteny and a recombination hot spot

Carotenoid pigments in fruits are indicative of the ripening process and potential nutritional value. Papaya (Carica papaya) fruit flesh color is caused by the accumulation of lycopene or beta-carotenoids in chromoplasts. It is a distinct feature affecting nutritional composition, fruit quality, shelf life, and consumer preference. To uncover the molecular basis of papaya flesh color, we took map-based cloning and candidate gene approaches using integrated genetic and physical maps. A DNA marker tightly linked to flesh color colocalized on a contig of the physical map with a cDNA probe of the tomato (Solanum lycopersicum) chromoplast-specific lycopene beta-cyclase, CYC-b. Candidate gene sequences were obtained from amplified fragments and verified by sequencing two bacterial artificial chromosomes containing the two alleles. Sequence comparison revealed a 2-bp insertion in the coding region of the recessive red flesh allele resulting in a frame-shift mutation and a premature stop codon. A color complementation test in bacteria confirmed that the papaya CpCYC-b is the gene controlling fruit flesh color. Sequence analysis of wild and cultivated papaya accessions showed the presence of this frame-shift mutation in all red flesh accessions examined. Evaluation of DNA markers near CpCYC-b revealed a recombination hot spot, showing that CpCYC-b is located in a gene-rich region with a recombination rate at 3.7 kb per centimorgan, more than 100-fold higher than the genome average at 400 kb per centimorgan. Conserved microsynteny of the CpCYC-b region is indicated by colinearity of two to four genes between papaya, Arabidopsis (Arabidopsis thaliana), grape (Vitis vinifera), and tomato. Our results enhanced our understanding of papaya flesh color inheritance and generated new tools for papaya improvement.