Genome-wide association analysis of composite sleep health scores in 413,904 individuals.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, and together may provide a more complete picture of sleep health, while also illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p<8.3e-9), along with 341 previously reported loci (p<5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2=0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections to behavioral, psychological, and cardiometabolic traits.

Hepatic triglyceride content is intricately associated with numerous metabolites and biochemical pathways.

BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is characterized by the pathological accumulation of triglycerides in hepatocytes and is associated with insulin resistance, atherogenic dyslipidaemia and cardiometabolic diseases. Thus far, the extent of metabolic dysregulation associated with hepatic triglyceride accumulation has not been fully addressed. In this study, we aimed to identify metabolites associated with hepatic triglyceride content (HTGC) and map these associations using network analysis. METHODS: To gain insight in the spectrum of metabolites associated with hepatic triglyceride accumulation, we performed a comprehensive plasma metabolomics screening of 1363 metabolites in apparently healthy middle aged (age 45-65) individuals (N = 496) in whom HTGC was measured by proton magnetic resonance spectroscopy. An atlas of metabolite-HTGC associations, based on univariate results, was created using correlation-based Gaussian graphical model (GGM) and genome scale metabolic model network analyses. Pathways associated with the clinical prognosis marker fibrosis 4 (FIB-4) index were tested using a closed global test. RESULTS: Our analyses revealed that 118 metabolites were univariately associated with HTGC (p-value <6.59 × 10-5 ), including 106 endogenous, 1 xenobiotic and 11 partially characterized/uncharacterized metabolites. These associations were mapped to several biological pathways including branched amino acids (BCAA), diglycerols, sphingomyelin, glucosyl-ceramide and lactosyl-ceramide. We also identified a novel possible HTGC-related pathway connecting glutamate, metabolonic lactone sulphate and X-15245 using the GGM network. These pathways were confirmed to be associated with the FIB-4 index as well. The full interactive metabolite-HTGC atlas is provided online: https://tofaquih.github.io/AtlasLiver/. CONCLUSIONS: The combined network and pathway analyses indicated extensive associations between BCAA and the lipids pathways with HTGC and the FIB-4 index. Moreover, we report a novel pathway glutamate-metabolonic lactone sulphate-X-15245 with a potential strong association with HTGC. These findings can aid elucidating HTGC metabolomic profiles and provide insight into novel drug targets for fibrosis-related outcomes.

Normal range CAG repeat size variations in the HTT gene are associated with an adverse lipoprotein profile partially mediated by body mass index.

Tandem cytosine-adenine-guanine (CAG) repeat sizes of 36 or more in the huntingtin gene (HTT) cause Huntington's disease (HD). Apart from neuropsychiatric complications, the disease is also accompanied by metabolic dysregulation and weight loss, which contribute to a progressive functional decline. Recent studies also reported an association between repeats below the pathogenic threshold (<36) for HD and body mass index (BMI), suggesting that HTT repeat sizes in the non-pathogenic range are associated with metabolic dysregulation. In this study, we hypothesized that HTT repeat sizes < 36 are associated with metabolite levels, possibly mediated through reduced BMI. We pooled data from three European cohorts (n = 10 228) with genotyped HTT CAG repeat size and metabolomic measurements. All 145 metabolites were measured on the same targeted platform in all studies. Multilevel mixed-effects analysis using the CAG repeat size in HTT identified 67 repeat size metabolite associations. Overall, the metabolomic profile associated with larger CAG repeat sizes in HTT were unfavorable-similar to those of higher risk of coronary artery disease and type 2 diabetes-and included elevated levels of amino acids, fatty acids, low-density lipoprotein (LDL)-, very low-density lipoprotein- and intermediate density lipoprotein (IDL)-related metabolites while with decreased levels of very large high-density lipoprotein (HDL)-related metabolites. Furthermore, the associations of 50 metabolites, in particular, specific very large HDL-related metabolites, were mediated by lower BMI. However, no mediation effect was found for 17 metabolites related to LDL and IDL. In conclusion, our findings indicate that large non-pathogenic CAG repeat sizes in HTT are associated with an unfavorable metabolomic profile despite their association with a lower BMI.

Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts.

OBJECTIVE: Type 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed ß-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed. RESEARCH DESIGN AND METHODS: Here we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD). RESULTS: Despite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway. CONCLUSIONS: Our findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

Agreement of aptamer proteomics with standard methods for measuring venous thrombosis biomarkers.

BACKGROUND: Venous thromboembolism (VTE) is a complex disease with an incidence rate of about 1 in 1000 per year. Despite the availability of validated biomarkers for VTE, unprovoked events account for 50% of first events. Therefore, emerging high-throughput proteomics are promising methods for the expansion of VTE biomarkers. One such promising high-throughput platform is SomaScan, which uses a large library of synthetic oligonucleotide ligands known as aptamers to measure thousands of proteins. OBJECTIVE: The aim of this study was to evaluate the viability of the aptamer-based SomaScan platform for VTE studies by examining its agreement with standard laboratory methods. METHODS: We examined the agreement between eight established VTE biomarkers measured by SomaScan and standard laboratory immunoassay and viscosity-based instruments in 54 individuals (27 cases and 27 controls) from the Thrombophilia, Hypercoagulability and Environmental Risks in Venous Thromboembolism study. We performed the agreement analysis by using a regression model and predicting the estimates and the 95% prediction interval (PI) of the laboratory instrument values using SomaScan values. RESULTS: SomaScan measurements exhibited overall poor agreement, particularly for D-dimer (average fit, 492.7 ng/mL; 95% PI, 110.0-1998.2) and fibrinogen (average fit, 3.3 g/L; 95% PI, 2.0-4.7). CONCLUSION: Our results indicate that SomaScan measurement had poor agreement with the standard laboratory measurements. These results may explain why some genome-wide association studies with VTE proteins measured by SomaScan did not confirm previously identified loci. Therefore, SomaScan should be considered with caution in VTE studies.

The lipid profile for the prediction of prednisolone treatment response in patients with inflammatory hand osteoarthritis: The HOPE study.

Objective: To explore the use of lipidomics for prediction of prednisolone treatment response in patients with inflammatory hand osteoarthritis. Design: The Hand Osteoarthritis Prednisolone Efficacy (HOPE) study included patients (n â€‹= â€‹92) with symptomatic inflammatory hand osteoarthritis, fulfilling the ACR criteria. The present analyses comprised only patients randomized to prednisolone treatment (10 â€‹mg daily, n â€‹= â€‹40). Response to prednisolone treatment was defined according to the OARSI-OMERACT responder criteria at six weeks. Baseline blood samples were obtained non-fasted. Lipid species were quantified in erythrocytes with the Lipidyzer™ platform (Sciex). Oxylipins were analyzed in plasma using an in-house LC-MS/MS platform. Elastic net regularized regression was used to predict prednisolone treatment response based on common patient characteristics alone and including the patients' lipid profile. ROC analyses with 1000 bootstrapped area under the curve (AUC) was used to determine the discriminatory accuracy of the models. Results: Among included patients, 78% fulfilled the OARSI-OMERACT responder criteria. From the general patient characteristics, elastic net selected baseline hand function as only predictor of treatment response, with an AUC of 0.78 (0.56; 0.97). Addition of lipidomics resulted in an AUC of 0.92 (0.78; 0.99) and 0.85 (0.65; 0.98) for inclusion of the Lipidyzer™ platform and oxylipin platform, respectively. Conclusion: Our results suggest that the patients' lipid profile may improve the discriminative accuracy of the prediction of prednisolone treatment response in patients with inflammatory hand osteoarthritis compared to prediction by commonly measured patient characteristics alone. Hence, lipidomics may be a promising field for biomarker discovery for prediction of anti-inflammatory treatment response.

A Workflow for Missing Values Imputation of Untargeted Metabolomics Data.

Metabolomics studies have seen a steady growth due to the development and implementation of affordable and high-quality metabolomics platforms. In large metabolite panels, measurement values are frequently missing and, if neglected or sub-optimally imputed, can cause biased study results. We provided a publicly available, user-friendly R script to streamline the imputation of missing endogenous, unannotated, and xenobiotic metabolites. We evaluated the multivariate imputation by chained equations (MICE) and k-nearest neighbors (kNN) analyses implemented in our script by simulations using measured metabolites data from the Netherlands Epidemiology of Obesity (NEO) study (n = 599). We simulated missing values in four unique metabolites from different pathways with different correlation structures in three sample sizes (599, 150, 50) with three missing percentages (15%, 30%, 60%), and using two missing mechanisms (completely at random and not at random). Based on the simulations, we found that for MICE, larger sample size was the primary factor decreasing bias and error. For kNN, the primary factor reducing bias and error was the metabolite correlation with its predictor metabolites. MICE provided consistently higher performance measures particularly for larger datasets (n > 50). In conclusion, we presented an imputation workflow in a publicly available R script to impute untargeted metabolomics data. Our simulations provided insight into the effects of sample size, percentage missing, and correlation structure on the accuracy of the two imputation methods.

Causes and consequences of the opioid epidemic in the Netherlands: a population-based cohort study.

Over the past decade opioid use has risen globally. The causes and consequences of this increase, especially in Europe, are poorly understood. We conducted a population-based cohort study using national statistics on analgesics prescriptions, opioid poisoning hospital admissions and deaths in the Netherlands from 2013 to 2017. Pain prevalence and severity was determined by using results of 2014-2017 Health Interview Surveys. Between 2013 and 2017 the proportion of residents receiving opioid prescription rose from 4.9% to 6.0%, and the proportion of those receiving NSAIDs decreased from 15.5% to 13.7%. Self-reported pain prevalence and severity remained constant, as 44.7% of 5,119 respondents reported no pain-impeded activities-of-daily-living in 2014 (aRR, 1.00 [95% CI, 0.95-1.06] in 2017 vs 2014). Over the observation period, the incidence of opioid poisoning hospitalization and death increased from 8.6 to 12.9 per 100,000 inhabitants. The incidence of severe outcomes related to opioid use increased, as 3.9% of 1,343 hospitalized for opioid poisoning died in 2013 and 4.6% of 2,055 in 2017. We demonstrated that NSAIDs prescription decreased and opioid prescription increased in the Netherlands since 2013, without an increase in pain prevalence and severity. Consequently, the incidence of severe outcomes related to opioids increased.

Integrated Analysis of Whole Exome Sequencing and Copy Number Evaluation in Parkinson's Disease.

Genetic studies of the familial forms of Parkinson's disease (PD) have identified a number of causative genes with an established role in its pathogenesis. These genes only explain a fraction of the diagnosed cases. The emergence of Next Generation Sequencing (NGS) expanded the scope of rare variants identification in novel PD related genes. In this study we describe whole exome sequencing (WES) genetic findings of 60 PD patients with 125 variants validated in 51 of these cases. We used strict criteria for variant categorization that generated a list of variants in 20 genes. These variants included loss of function and missense changes in 18 genes that were never previously linked to PD (NOTCH4, BCOR, ITM2B, HRH4, CELSR1, SNAP91, FAM174A, BSN, SPG7, MAGI2, HEPHL1, EPRS, PUM1, CLSTN1, PLCB3, CLSTN3, DNAJB9 and NEFH) and 2 genes that were previously associated with PD (EIF4G1 and ATP13A2). These genes either play a critical role in neuronal function and/or have mouse models with disease related phenotypes. We highlight NOTCH4 as an interesting candidate in which we identified a deleterious truncating and a splice variant in 2 patients. Our combined molecular approach provides a comprehensive strategy applicable for complex genetic disorders.

Validation of Ion TorrentTM Inherited Disease Panel with the PGMTM Sequencing Platform for Rapid and Comprehensive Mutation Detection.

Quick and accurate molecular testing is necessary for the better management of many inherited diseases. Recent technological advances in various next generation sequencing (NGS) platforms, such as target panel-based sequencing, has enabled comprehensive, quick, and precise interrogation of many genetic variations. As a result, these technologies have become a valuable tool for gene discovery and for clinical diagnostics. The AmpliSeq Inherited Disease Panel (IDP) consists of 328 genes underlying more than 700 inherited diseases. Here, we aimed to assess the performance of the IDP as a sensitive and rapid comprehensive gene panel testing. A total of 88 patients with inherited diseases and causal mutations that were previously identified by Sanger sequencing were randomly selected for assessing the performance of the IDP. The IDP successfully detected 93.1% of the mutations in our validation cohort, achieving high overall gene coverage (98%). The sensitivity for detecting single nucleotide variants (SNVs) and short Indels was 97.3% and 69.2%, respectively. IDP, when coupled with Ion Torrent Personal Genome Machine (PGM), delivers comprehensive and rapid sequencing for genes that are responsible for various inherited diseases. Our validation results suggest the suitability of this panel for use as a first-line screening test after applying the necessary clinical validation.

Revisiting the morbid genome of Mendelian disorders.

BACKGROUND: The pathogenicity of many Mendelian variants has been challenged by large-scale sequencing efforts. However, many rare and benign "disease mutations" are difficult to analyze due to their rarity. The Saudi Arabian variome is enriched for homozygosity due to inbreeding, a key advantage that can be exploited for the critical examination of previously published variants. RESULTS: We collated all "disease-related mutations" listed in the Human Gene Mutation Database (HGMD) and ClinVar, including "variants of uncertain significance" (VOUS). We find that the use of public databases including 1000 Genomes, ExAC, and Kaviar can reclassify many of these variants as likely benign. Our Saudi Human Genome Program (SHGP) can reclassify many variants that are rare in public databases. Furthermore, SGPD allows us to observe many previously reported variants in the homozygous state and our extensive phenotyping of participants makes it possible to demonstrate the lack of phenotype for these variants, thus challenging their pathogenicity despite their rarity. We also find that 18 VOUS BRCA1 and BRCA2 variants that are listed in BRCA Exchange are present at least once in the homozygous state in patients who lack features of Fanconi anemia. Reassuringly, we could reciprocally demonstrate that none of those labeled as "pathogenic" were observed in the homozygous statue in individuals who lack Fanconi phenotype in our database. CONCLUSION: Our study shows the importance of revisiting disease-related databases using public resources as well as of population-specific resources to improve the specificity of the morbid genome of Mendelian diseases in humans.

Characterizing the morbid genome of ciliopathies.

BACKGROUND: Ciliopathies are clinically diverse disorders of the primary cilium. Remarkable progress has been made in understanding the molecular basis of these genetically heterogeneous conditions; however, our knowledge of their morbid genome, pleiotropy, and variable expressivity remains incomplete. RESULTS: We applied genomic approaches on a large patient cohort of 371 affected individuals from 265 families, with phenotypes that span the entire ciliopathy spectrum. Likely causal mutations in previously described ciliopathy genes were identified in 85% (225/265) of the families, adding 32 novel alleles. Consistent with a fully penetrant model for these genes, we found no significant difference in their "mutation load" beyond the causal variants between our ciliopathy cohort and a control non-ciliopathy cohort. Genomic analysis of our cohort further identified mutations in a novel morbid gene TXNDC15, encoding a thiol isomerase, based on independent loss of function mutations in individuals with a consistent ciliopathy phenotype (Meckel-Gruber syndrome) and a functional effect of its deficiency on ciliary signaling. Our study also highlighted seven novel candidate genes (TRAPPC3, EXOC3L2, FAM98C, C17orf61, LRRCC1, NEK4, and CELSR2) some of which have established links to ciliogenesis. Finally, we show that the morbid genome of ciliopathies encompasses many founder mutations, the combined carrier frequency of which accounts for a high disease burden in the study population. CONCLUSIONS: Our study increases our understanding of the morbid genome of ciliopathies. We also provide the strongest evidence, to date, in support of the classical Mendelian inheritance of Bardet-Biedl syndrome and other ciliopathies.

A first-line diagnostic assay for limb-girdle muscular dystrophy and other myopathies.

BACKGROUND: Fifty random genetically unstudied families (limb-girdle muscular dystrophy (LGMD)/myopathy) were screened with a gene panel incorporating 759 OMIM genes associated with neurological disorders. Average coverage of the CDS and 10 bp flanking regions of genes was 99 %. All families were referred to the Neurosciences Clinic of King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Patients presented with muscle weakness affecting the pelvic and shoulder girdle. Muscle biopsy in all cases showed dystrophic or myopathic changes. Our main objective was to evaluate a neurological gene panel as a first-line diagnostic test for LGMD/myopathies. RESULTS: Our panel identified the mutation in 76 % of families (38/50; 11 novel). Thirty-four families had mutations in LGMD-related genes with four others having variants not typically associated with LGMD. The majority of cases had recessive inheritance with homoallelic pathogenic variants (97.4 %, 37/38), as expected considering the high rate of consanguinity in the study population. In one case, we detected a heterozygous mutation in DNAJB responsible for LGMD-1E. Our cohort included seven different subtypes of LGMD2. Mutations of DYSF were the most commonly identified cause of disease followed by that in CAPN3 and FKRP. Non-LGMD myopathies were due to mutations in genes associated with congenital disorder of glycosylation (ALG2), rigid spine muscular dystrophy 1 (SEPN1), inclusion body myopathy2/Nonaka myopathy (GNE), and neuropathy (WNK1). Whole exome sequencing (WES) of patients who remained undiagnosed with the neurological panel did not improve our diagnostic yield. CONCLUSIONS: Our neurological panel achieved a high clinical sensitivity (76 %) and is an effective first-line laboratory test in patients with LGMD and other myopathies. This sensitive, cost-effective, and rapid assay significantly assists clinical practice especially in these phenotypically and genetically heterogeneous disorders. Moreover, the application of the American College of Medical Genetics (ACMG) and Association for Molecular Pathology (AMP) guidelines applied in the classification of variant pathogenecity provides a clear interpretation for physicians on the relevance of such findings.

Clinical heterogeneity of PLA2G6-related Parkinsonism: analysis of two Saudi families.

BACKGROUND: Recessive mutations in PLA2G6 have been associated with different neurodegenerative disorders, including infantile neuroaxonal dystrophy, neurodegeneration with brain iron accumulation and more recently, early-onset dystonia parkinsonism. METHOD: Targeted-next generation sequencing using a custom Neurology panel, containing 758 OMIM-listed genes implicated in neurological disorders, was carried out in two index cases from two different Saudi families displaying early-onset levodopa-responsive Parkinsonism with pyramidal signs and additional clinical features. The detected mutations were verified in the index cases and available family members by direct sequencing. RESULTS AND CONCLUSION: We identified a previously described PLA2G6 homozygous p.R741Q mutation in three affected and two asymptomatic individuals from two Saudi families. Our finding reinforces the notion of the broadness of the clinical spectrum of PLA2G6-related neurodegeneration.

Genetic spectrum of Saudi Arabian patients with antenatal cystic kidney disease and ciliopathy phenotypes using a targeted renal gene panel.

BACKGROUND: Inherited cystic kidney disorders are a common cause of end-stage renal disease. Over 50 ciliopathy genes, which encode proteins that influence the structure and function of the primary cilia, are implicated in cystic kidney disease. METHODS: To define the phenotype and genotype of cystic kidney disease in fetuses and neonates, we correlated antenatal ultrasound examination and postnatal renal ultrasound examination with targeted exon sequencing, using a renal gene panel. A cohort of 44 families in whom antenatal renal ultrasound scanning findings in affected cases included bilateral cystic kidney disease, echogenic kidneys or enlarged kidneys was investigated. RESULTS: In this cohort, disease phenotypes were severe with 36 cases of stillbirth or perinatal death. Extra renal malformations, including encephalocele, polydactyly and heart malformations, consistent with ciliopathy phenotypes, were frequently detected. Renal gene panel testing identified causative mutations in 21 out of 34 families (62%), where patient and parental DNA was available. In the remaining 10 families, where only parental DNA was available, 7 inferred causative mutations were found. Together, mutations were found in 12 different genes with a total of 13 novel pathogenic variants, including an inferred novel variant in NEK8. Mutations in CC2D2A were the most common cause of an antenatal cystic kidney disease and a suspected ciliopathy in our cohort. CONCLUSIONS: In families with ciliopathy phenotypes, mutational analysis using a targeted renal gene panel allows a rapid molecular diagnosis and provides important information for patients, parents and their physicians.

Unbiased targeted next-generation sequencing molecular approach for primary immunodeficiency diseases.

BACKGROUND: Molecular genetics techniques are an essential diagnostic tool for primary immunodeficiency diseases (PIDs). The use of next-generation sequencing (NGS) provides a comprehensive way of concurrently screening a large number of PID genes. However, its validity and cost-effectiveness require verification. OBJECTIVES: We sought to identify and overcome complications associated with the use of NGS in a comprehensive gene panel incorporating 162 PID genes. We aimed to ascertain the specificity, sensitivity, and clinical sensitivity of the gene panel and its utility as a diagnostic tool for PIDs. METHODS: A total of 162 PID genes were screened in 261 patients by using the Ion Torrent Proton NGS sequencing platform. Of the 261 patients, 122 had at least 1 known causal mutation at the onset of the study and were used to assess the specificity and sensitivity of the assay. The remaining samples were from unsolved cases that were biased toward more phenotypically and genotypically complicated cases. RESULTS: The assay was able to detect the mutation in 117 (96%) of 122 positive control subjects with known causal mutations. For the unsolved cases, our assay resulted in a molecular genetic diagnosis for 35 of 139 patients. Interestingly, most of these cases represented atypical clinical presentations of known PIDs. CONCLUSIONS: The targeted NGS PID gene panel is a sensitive and cost-effective diagnostic tool that can be used as a first-line molecular assay in patients with PIDs. The assay is an alternative choice to the complex and costly candidate gene approach, particularly for patients with atypical presentation of known PID genes.