Search

Autism spectrum disorder (ASD) represents a heterogeneous group of neurodevelopmental disorders and is largely attributable to genetic risk factors. Phenotypic and genetic heterogeneity of ASD have been well-recognized; however, genetic substrates for endophenotypes that constitute phenotypic heterogeneity are not yet known. In the present study, we compiled data from the Autism Genetic Resource Exchange, which contains the demographic and detailed phenotype information of 11,961 individuals. Notably, the whole-genome sequencing data available from MSSNG and iHART for 3833 individuals in this dataset was used to perform an endophenotype-wide association study. Using a linear mixed model, genome-wide association analyses were performed for 29 endophenotype scores and 0.58 million common variants with variant allele frequency ≥ 5%. We discovered significant associations between 9 genetic variants and 6 endophenotype scores comprising neurocognitive development and severity scores for core symptoms of ASD at a significance threshold of p –7. Of note, the Stereotyped Behaviors and Restricted Interests total score in Autism Diagnostic Observation Schedule Module 3 was significantly associated with multiple variants in the VPS13B gene, a causal gene for Cohen syndrome and a candidate gene for syndromic ASD. Our findings yielded loci with small effect sizes due to the moderate sample size and, thus, require validation in another cohort. Nonetheless, our endophenotype-wide association analysis extends previous candidate gene discovery in the context of genotype and endophenotype association. As a result, these candidate genes may be responsible for specific traits that constitute core symptoms and neurocognitive function of ASD rather than the disorder itself.

Background The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype–metabotype associations. However, these associations have not been characterized in children. Results We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h2 (> 0.8) for 15.9% of features and low h2 (h2 were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h2. For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p –12 (= 5 × 10–8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride (m/z 781.7483, retention time (RT) 89.3 s); CALN1 and Tridecanol (m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. Conclusion Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene–environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene–environment interaction toward healthy aging trajectories.

Coronavirus disease 2019 (COVID‐19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), is a pandemic as of early 2020. Upon infection, SARS‐CoV‐2 attaches to its receptor, that is, angiotensin‐converting enzyme 2 (ACE2), on the surface of host cells and is then internalized into host cells via enzymatic machineries. This subsequently stimulates immune response factors. Since the host immune response and severity of COVID‐19 vary among individuals, genetic risk factors for severe COVID‐19 cases have been investigated. Our research group recently conducted a survey of genetic variants among SARS‐CoV‐2‐interacting molecules across populations, noting near absence of difference in allele frequency spectrum between populations in these genes. Recent genome‐wide association studies have identified genetic risk factors for severe COVID‐19 cases in a segment of chromosome 3 that involves six genes encoding three immune‐regulatory chemokine receptors and another three molecules. The risk haplotype seemed to be inherited from Neanderthals, suggesting genetic adaptation against pathogens in modern human evolution. Therefore, SARS‐CoV‐2 uses highly conserved molecules as its virion interaction, whereas its immune response appears to be genetically biased in individuals to some extent. We herein review the molecular process of SARS‐CoV‐2 infection as well as our further survey of genetic variants of its related immune effectors. We also discuss aspects of modern human evolution., SARS‐CoV‐2 uses highly conserved molecules as its virion interaction, whereas its immune response appears to be genetically biased in individuals to some extent. The risk haplotype seemed to be inherited from Neanderthals, suggesting genetic adaptation against pathogens in modern human evolution.

Individuals are exposed to a wide variety of chemicals over their lifetime, yet current understanding of mixture toxicology is still limited. We present a two-step analytical method using a gas chromatograph-triple quadrupole mass spectrometer that requires less than 1 mL of sample. The method is applied to 183 plasma samples from a study population of children with autism spectrum disorder, their parents, and unrelated neurotypical children. We selected 156 environmental chemical compounds and ruled out chemicals with detection rates less than 20% of our study cohort (n = 61), as well as ones not amenable to the selected extraction and analytical methods (n = 34). The targeted method then focused on remaining chemicals (n = 61) plus 8 additional polychlorinated biphenyls (PCBs). Persistent pollutants, such as p,p’-dichlorodiphenyldichloroethylene (p,p’-DDE) and PCB congeners 118 and 180, were detected at high frequencies and several previously unreported chemicals, including 2,4,6-trichlorophenol, isosafrole, and hexachlorobutadiene, were frequently detected in our study cohort. This work highlights the benefits of employing a multi-step analytical method in exposure studies and demonstrates the efficacy of such methods for reporting novel information on previously unstudied pollutant exposures.

Precision medicine promises to revolutionize treatment, shifting therapeutic approaches from the classical one-size-fits-all to those more tailored to the patient’s individual genomic profile, lifestyle and environmental exposures. Yet, to advance precision medicine’s main objective—ensuring the optimum diagnosis, treatment and prognosis for each individual—investigators need access to large-scale clinical and genomic data repositories. Despite the vast proliferation of these datasets, locating and obtaining access to many remains a challenge. We sought to provide an overview of available patient-level datasets that contain both genotypic data, obtained by next-generation sequencing, and phenotypic data—and to create a dynamic, online catalog for consultation, contribution and revision by the research community. Datasets included in this review conform to six specific inclusion parameters that are: (i) contain data from more than 500 human subjects; (ii) contain both genotypic and phenotypic data from the same subjects; (iii) include whole genome sequencing or whole exome sequencing data; (iv) include at least 100 recorded phenotypic variables per subject; (v) accessible through a website or collaboration with investigators and (vi) make access information available in English. Using these criteria, we identified 30 datasets, reviewed them and provided results in the release version of a catalog, which is publicly available through a dynamic Web application and on GitHub. Users can review as well as contribute new datasets for inclusion (Web: https://avillachlab.shinyapps.io/genophenocatalog/; GitHub: https://github.com/hms-dbmi/GenoPheno-CatalogShiny).

Parkinson's disease (PD) is a neurodegenerative disorder associated with loss of striatal dopamine, secondary to degeneration of midbrain dopamine (mDA) neurons in the substantia nigra, rendering cell transplantation a promising therapeutic strategy. To establish human induced pluripotent stem cell-based (hiPSC-based) autologous cell therapy, we report a platform of core techniques for the production of mDA progenitors as a safe and effective therapeutic product. First, by combining metabolism-regulating microRNAs with reprogramming factors, we developed a method to more efficiently generate clinical-grade iPSCs, as evidenced by genomic integrity and unbiased pluripotent potential. Second, we established a "spotting"-based in vitro differentiation methodology to generate functional and healthy mDA cells in a scalable manner. Third, we developed a chemical method that safely eliminates undifferentiated cells from the final product. Dopaminergic cells thus express high levels of characteristic mDA markers, produce and secrete dopamine, and exhibit electrophysiological features typical of mDA cells. Transplantation of these cells into rodent models of PD robustly restores motor function and reinnervates host brain, while showing no evidence of tumor formation or redistribution of the implanted cells. We propose that this platform is suitable for the successful implementation of human personalized autologous cell therapy for PD.

Pediatric acute respiratory distress syndrome (PARDS), though both common and deadly in critically ill children, lacks targeted therapies. The development of effective pharmacotherapies has been limited, in part, by lack of clarity about the pathobiology of pediatric ARDS. Epithelial lung injury, vascular endothelial activation, and systemic immune activation are putative drivers of this complex disease process. Prior studies have used either hypothesis-driven (e.g., candidate genes and proteins, in vitro investigations) or unbiased (e.g., genome-wide association, transcriptomic, metabolomic) approaches to predict clinical outcomes and to define subphenotypes. Advances in multiple omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have permitted more comprehensive investigation of PARDS pathobiology. However, omics studies have been limited in children compared to adults, and analyses across multiple tissue types are lacking. Here, we synthesized existing literature on the molecular mechanism of PARDS, summarized our interrogation of publicly available genomic databases to determine the association of candidate genes with PARDS phenotypes across multiple tissues and cell types, and integrated recent studies that used single-cell RNA sequencing (scRNA-seq). We conclude that novel profiling methods such as scRNA-seq, which permits more comprehensive, unbiased evaluation of pathophysiological mechanisms across tissue and cell types, should be employed to investigate the molecular mechanisms of PRDS toward the goal of identifying targeted therapies.

Background and ObjectivesOpsoclonus-myoclonus syndrome (OMS) is a rare autoimmune disorder associated with neuroblastoma in children, although idiopathic and postinfectious etiologies are present in children and adults. Small cohort studies in homogenous populations have revealed elevated rates of autoimmunity in family members of patients with OMS, although no differentiation between paraneoplastic and nonparaneoplastic forms has been performed. The objective of this study was to investigate the prevalence of autoimmune disease in first-degree relatives of pediatric patients with paraneoplastic and nonparaneoplastic OMS.MethodsA single-center cohort study of consecutively evaluated children with OMS was performed. Parents of patients were prospectively administered surveys on familial autoimmune disease. Rates of autoimmune disease in first-degree relatives of pediatric patients with OMS were compared using Fisher exact t test and χ2 analysis: (1) between those with and without a paraneoplastic cause and (2) between healthy and disease (pediatric multiple sclerosis [MS]) controls from the United States Pediatric MS Network.ResultsThirty-five patients (18 paraneoplastic, median age at onset 19.0 months; 17 idiopathic, median age at onset 25.0 months) and 68 first-degree relatives (median age 41.9 years) were enrolled. One patient developed systemic lupus erythematosus 7 years after OMS onset. Paraneoplastic OMS was associated with a 50% rate of autoimmune disease in a first-degree relative compared with 29% in idiopathic OMS (p = 0.31). The rate of first-degree relative autoimmune disease per OMS case (14/35, 40%) was higher than healthy controls (86/709, 12%; p < 0.001) and children with pediatric MS (101/495, 20%; p = 0.007).DiscussionIn a cohort of pediatric patients with OMS, there were elevated rates of first-degree relative autoimmune disease, with no difference in rates observed between paraneoplastic and idiopathic etiologies, suggesting an autoimmune genetic contribution to the development of OMS in children.

The developmental and epileptic encephalopathies (DEEs) are heterogeneous disorders with a strong genetic contribution, but the underlying genetic etiology remains unknown in a significant proportion of individuals. To explore whether statistical support for genetic etiologies can be generated on the basis of phenotypic features, we analyzed whole-exome sequencing data and phenotypic similarities by using Human Phenotype Ontology (HPO) in 314 individuals with DEEs. We identified a de novo c.508C>T (p.Arg170Trp) variant in AP2M1 in two individuals with a phenotypic similarity that was higher than expected by chance (p = 0.003) and a phenotype related to epilepsy with myoclonic-atonic seizures. We subsequently found the same de novo variant in two individuals with neurodevelopmental disorders and generalized epilepsy in a cohort of 2,310 individuals who underwent diagnostic whole-exome sequencing. AP2M1 encodes the mu-subunit of the adaptor protein complex 2 (AP-2), which is involved in clathrin-mediated endocytosis (CME) and synaptic vesicle recycling. Modeling of protein dynamics indicated that the p.Arg170Trp variant impairs the conformational activation and thermodynamic entropy of the AP-2 complex. Functional complementation of both the mu-subunit carrying the p.Arg170Trp variant in human cells and astrocytes derived from AP-2 mu conditional knockout mice revealed a significant impairment of CME of transferrin. In contrast, stability, expression levels, membrane recruitment, and localization were not impaired, suggesting a functional alteration of the AP-2 complex as the underlying disease mechanism. We establish a recurrent pathogenic variant in AP2M1 as a cause of DEEs with distinct phenotypic features, and we implicate dysfunction of the early steps of endocytosis as a disease mechanism in epilepsy.

Comprehensive and accurate detection of variants from whole-genome sequencing (WGS) is a strong prerequisite for translational genomic medicine; however, low concordance between analytic pipelines is an outstanding challenge. We processed a European and an African WGS samples with 70 analytic pipelines comprising the combination of 7 short-read aligners and 10 variant calling algorithms (VCAs), and observed remarkable differences in the number of variants called by different pipelines (max/min ratio: 1.3~3.4). The similarity between variant call sets was more closely determined by VCAs rather than by short-read aligners. Remarkably, reported minor allele frequency had a substantial effect on concordance between pipelines (concordance rate ratio: 0.11~0.92; Wald tests, P

Parkinson's disease (PD) is one of the most common neurodegenerative disorders, which affects about 0.3% of the general population. As the population in the developed world ages, this creates an escalating burden on society both in economic terms and in quality of life for these patients and for the families that support them. Although currently available pharmacological or surgical treatments may significantly improve the quality of life of many patients with PD, these are symptomatic treatments that do not slow or stop the progressive course of the disease. Because motor impairments in PD largely result from loss of midbrain dopamine neurons in the substantia nigra pars compacta, PD has long been considered to be one of the most promising target diseases for cell-based therapy. Indeed, numerous clinical and preclinical studies using fetal cell transplantation have provided proof of concept that cell replacement therapy may be a viable therapeutic approach for PD. However, the use of human fetal cells as a standardized therapeutic regimen has been fraught with fundamental ethical, practical, and clinical issues, prompting scientists to explore alternative cell sources. Based on groundbreaking establishments of human embryonic stem cells and induced pluripotent stem cells, these human pluripotent stem cells have been the subject of extensive research, leading to tremendous advancement in our understanding of these novel classes of stem cells and promising great potential for regenerative medicine. In this review, we discuss the prospects and challenges of human pluripotent stem cell-based cell therapy for PD.

Physiological status and pathological changes in an individual can be captured by metabolic state that reflects the influence of both genetic variants and environmental factors such as diet, lifestyle and gut microbiome. The totality of environmental exposure throughout lifetime – i.e., exposome – is difficult to measure with current technologies. However, targeted measurement of exogenous chemicals and untargeted profiling of endogenous metabolites have been widely used to discover biomarkers of pathophysiologic changes and to understand functional impacts of genetic variants. To investigate the coverage of chemical space and interindividual variation related to demographic and pathological conditions, we profiled 169 plasma samples using an untargeted metabolomics platform. On average, 1,009 metabolites were quantified in each individual (range 906 – 1,038) out of 1,244 total chemical compounds detected in our cohort. Of note, age was positively correlated with the total number of detected metabolites in both males and females. Using the robust Qn estimator, we found metabolite outliers in each sample (mean 22, range from 7 to 86). A total of 50 metabolites were outliers in a patient with phenylketonuria including the ones known for phenylalanine pathway suggesting multiple metabolic pathways perturbed in this patient. The largest number of outliers (N=86) was found in a 5-year-old boy with alpha-1-antitrypsin deficiency who were waiting for liver transplantation due to cirrhosis. Xenobiotics including drugs, diets and environmental chemicals were significantly correlated with diverse endogenous metabolites and the use of antibiotics significantly changed gut microbial products detected in host circulation. Several challenges such as annotation of features, reference range and variance for each feature per age group and gender, and population scale reference datasets need to be addressed; however, untargeted metabolomics could be immediately deployed as a biomarker discovery platform and to evaluate the impact of genomic variants and exposures on metabolic pathways for some diseases.

Background Within fossil- and solid-fuel dependent geographic locations, mechanisms of air pollution-induced asthma remains unknown. In particular, sources of greater genetic susceptibility to airborne carcinogen, namely, benzo[ a ]pyrene (B[ a ]P) has never been investigated beyond that of a few well known genes. Objectives To deepen our understanding on how the genotypic variations within the candidate genes contribute to the variability in the children's susceptibility to ambient B[ a ]P on doctor-diagnosed asthma. Methods Clinically confirmed asthmatic versus healthy control children (aged, 7–15) were enrolled from historically polluted and rural background regions in Czech Republic. Contemporaneous ambient B[ a ]P concentration was obtained from the routine monitoring network. The sputum DNA was genotyped for 95 genes. B[ a ]P interaction with SNPs was studied by two-stage, semi-agnostic screening of 621 SNPs. Results The median B[ a ]P within the highly polluted urban center was 8-times higher than that in the background region (7.8 vs. 1.1 ng/m 3 ) during the period of investigation. Within the baseline model, which considered B[ a ]P exposure-only, the second tertile range was associated with a significantly reduced odds (aOR = 0.28) of asthma (95% CI, 0.16 to 0.50) compared to those at the lowest range. However, the highest range of B[ a ]P was associated with 3.18-times greater odds of the outcome (95% CI, 1.77 to 5.71). Within the gene-environment interaction models, joint occurrence of a high B[ a ]P exposure range and having a high-risk genotype at CTLA4 gene (rs11571316) was associated with 9-times greater odds (95% CI, 4.56–18.36) of the asthma diagnosis. Similarly, rs11571319 at CTLA4 and a high B[ a ]P exposure range was associated with a 8-times greater odds (95% CI, 3.95–14.27) of asthma diagnosis. Furthermore, having TG + GG genotypes on rs1031509 near STAT4 was associated with 5-times (95% CI, 3.03–8.55) greater odds of asthma diagnosis at the highest B[ a ]P range, compared to the odds at the reference range. Also CYP2E1 AT + TT genotypes (rs2070673) was associated with 5-times (95% CI, 3.1–8.8) greater odds of asthma diagnosis at the highest B[ a ]P exposure. Conclusions The children, who jointly experience a high B[ a ]P exposure (6.3–8.5 ng/m3) as well as susceptible genotypes in CTLA4 (rs11571316 and rs11571319), STAT4 (rs1031509), and CYP2E1 (rs2070673), respectively, are associated with a significantly greater odds of having doctor-diagnosed asthma, compared to those with neither risk factors.

This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAP bead-based multiplexed immunoassay. To identify the performance of each biomarker in predicting cancer over benign tumors, we used a repeated leave-group-out cross-validation strategy. The prediction models using multimarkers were evaluated to develop a urinary ovarian cancer panel. After the exclusion of 12 borderline tumors, the urinary concentration of 17 biomarkers exhibited significant differences between 158 OCs and 125 benign tumors. Human epididymis protein 4 (HE4), vascular cell adhesion molecule (VCAM), and transthyretin (TTR) were the top three biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples with OC (mean area under the receiver operating characteristic curve (AUC) 0.822, 95% CI: 0.772&ndash, 0.869), whereas TTR showed the highest efficacy in early-stage OC (AUC 0.789, 95% CI: 0.714&ndash, 0.856). Overall, HE4 was the most informative biomarker, followed by creatinine, carcinoembryonic antigen (CEA), neural cell adhesion molecule (NCAM), and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multimarker panel consisting of HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity (SN) at 70.6% specificity (SP) to predict OC over the benign tumor. This panel performed well regardless of disease status and demonstrated an improved performance by including menopausal status. In conclusion, the urinary biomarker panel with HE4, creatinine, CEA, and TTR provided promising efficacy in predicting OC over benign tumors in women with pelvic masses. It was also a non-invasive and easily available diagnostic tool.

Genome sequencing is positioned as a routine clinical work-up for diverse clinical conditions. A commonly used approach to highlight candidate variants with potential clinical implication is to search over locus- and gene-centric knowledge databases. Most web-based applications allow a federated query across diverse databases for a single variant; however, sifting through a large number of genomic variants with combination of filtering criteria is a substantial challenge. Here we describe the Clinical Genome and Ancestry Report (CGAR), an interactive web application developed to follow clinical interpretation workflows by organizing variants into seven categories: (1) reported disease-associated variants, (2) rare- and high-impact variants in putative disease-associated genes, (3) secondary findings that the American College of Medical Genetics and Genomics recommends reporting back to patients, (4) actionable pharmacogenomic variants, (5) focused reports for candidate genes, (6) de novo variant candidates for trio analysis, and (7) germline and somatic variants implicated in cancer risk, diagnosis, treatment and prognosis. For each variant, a comprehensive list of external links to variant-centric and phenotype databases are provided. Furthermore, genotype-derived ancestral composition is used to highlight allele frequencies from a matched population since some disease-associated variants show a wide variation between populations. CGAR is an open-source software and is available at https://tom.tch.harvard.edu/apps/cgar/.

Inclusion body myositis is a late onset treatment-refractory autoimmune disease of skeletal muscle associated with a blood autoantibody (anti-cN1A), an HLA autoimmune haplotype, and muscle pathology characterized by cytotoxic CD8+ T cell destruction of myofibres. Here, we report on translational studies of inclusion body myositis patient muscle compared with a diverse set of other muscle disease samples. Using available microarray data on 411 muscle samples from patients with inclusion body myositis (n = 40), other muscle diseases (n = 265), and without neuromuscular disease (normal, n = 106), we identified a signature of T-cell cytotoxicity in inclusion body myositis muscle coupled with a signature of highly differentiated CD8 T-cell effector memory and terminally differentiated effector cells. Further, we examined killer cell lectin-like receptor G1 (KLRG1) as a marker of this population of cells, demonstrated the correlation of KLRG1 gene expression with lymphocyte cytotoxicity across 28 870 human tissue samples, and identified the presence of KLRG1 on pathogenic inclusion body myositis muscle invading T cells and an increase in KLRG1 expressing T cells in inclusion body myositis blood. We examined inclusion body myositis muscle T-cell proliferation by Ki67 immunohistochemistry demonstrating that diseased muscle-invading T cells are minimally or non-proliferative, in accordance with known properties of highly differentiated or terminally differentiated T cells. We found low expression of KLRG1 on infection-protective human lymphoid tissue central memory T cells and autoimmune-protective human blood regulatory T cells. Targeting highly differentiated cytotoxic T cells could be a favourable approach to treatment of inclusion body myositis.

Background Presenting features of inflammatory bowel disease (IBD) are non-specific. We hypothesized that mRNA profiles could (1) identify genes and pathways involved in disease pathogenesis; (2) identify a molecular signature that differentiates IBD from other conditions; (3) provide insight into systemic and colon-specific dysregulation through study of the concordance of the gene expression. Methods Children (8–18 years) were prospectively recruited at the time of diagnostic colonoscopy for possible IBD. We used transcriptome-wide mRNA profiling to study gene expression in colon biopsies and paired whole blood samples. Using blood mRNA measurements, we fit a regression model for disease state prediction that was validated in an independent test set of adult subjects (GSE3365). Results Ninety-eight children were recruited [39 Crohn’s disease, 18 ulcerative colitis, 2 IBDU, 39 non-IBD]. There were 1,118 significantly differentially (IBD vs non-IBD) expressed genes in colon tissue, and 880 in blood. The direction of relative change in expression was concordant for 106/112 genes differentially expressed in both tissue types. The regression model from the blood mRNA measurements distinguished IBD vs non-IBD disease status in the independent test set with 80% accuracy using only 6 genes. The overlap of 5 immune and metabolic pathways in the two tissue types was significant (p

Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. © 2016 Wiley Periodicals, Inc.

Display Omitted Method for predicting disease genes based on a processes affected in comorbidities.The approach uses a stochastic Petri Net model and is applied to ASD and IBD.Two genes are predicted for ASD and one gene is predicted for IBD. We propose a model-driven methodology aimed to shed light on complex disorders. Our approach enables exploring shared etiologies of comorbid diseases at the molecular pathway level. The method, Comparative Comorbidities Simulation (CCS), uses stochastic Petri net simulation for examining the phenotypic effects of perturbation of a network known to be involved in comorbidities to predict new roles for mutations in comorbid conditions. To demonstrate the utility of our novel methodology, we investigated the molecular convergence of autism spectrum disorder (ASD) and inflammatory bowel disease (IBD) on the autophagy pathway. In addition to validation by domain experts, we used formal analyses to demonstrate the models self-consistency. We then used CCS to compare the effects of loss of function (LoF) mutations previously implicated in either ASD or IBD on the autophagy pathway. CCS identified similar dynamic consequences of these mutations in the autophagy pathway. Our method suggests that two LoF mutations previously implicated in IBD may contribute to ASD, and one ASD-implicated LoF mutation may play a role in IBD. Future targeted genomic or functional studies could be designed to directly test these predictions.

The COVID-19 pandemic highlighted healthcare disparities in multiple countries. As such morbidity and mortality vary significantly around the globe between populations and ethnic groups. Underlying medical conditions and environmental factors contribute higher incidence in some populations and a genetic predisposition may play a role for severe cases with respiratory failure. Here we investigated whether genetic variation in the key genes for viral entry to host cells—ACE2 and TMPRSS2—and sensing of viral genomic RNAs (i.e., TLR3/7/8) could explain the variation in incidence across diverse ethnic groups. Overall, these genes are under strong selection pressure and have very few nonsynonymous variants in all populations. Genetic determinant for the binding affinity between SARS-CoV-2 and ACE2 does not show significant difference between populations. Non-genetic factors are likely to contribute differential population characteristics affected by COVID-19. Nonetheless, a systematic mutagenesis study on the receptor binding domain of ACE2 is required to understand the difference in host-viral interaction across populations., Highlights • Protein residues in active domains of the essential genes related viral susceptibility are highly conserved among population. • No evidence was found for ethnic group difference in ACE2, TMPRSS2, and genes associated with viral binding and endocytosis. • Disproportionately high incidence and mortality rates in African Americans in the US could be due to non-genetic factors.

Clinicians and researchers must contextualize a patient's genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care.Three of the nation's leading children's hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model.Harmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype-phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications.The GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.

Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers and characterise their function, we present an analysis of a single paired- end post-mortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both post- mortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.

Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers (and characterise their function), we present an analysis of a single paired-end post-mortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both post-mortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.

Background & Aims The I148M variant because of the substitution of C to G in PNPLA3 (rs738409) is associated with the increased risk of nonalcoholic fatty liver disease (NAFLD). In liver, I148M variant reduces hydrolytic function of PNPLA3, which results in hepatic steatosis; however, its association with the other clinical phenotype such as adiposity and metabolic diseases is not well established. Methods To identify the impact of I148M variant on clinical risk factors of NAFLD, we recruited 1363 generally healthy Korean males after excluding alcoholic and secondary causes of hepatic steatosis. Central adiposity was assessed by computed tomography, and hepatic steatosis was evaluated by abdominal ultrasonography. Results The participants were predominantly middle-aged (49.0 ± 7.1 years; range 30–60 years), and the frequency of NAFLD was 44.2%. The rs738409-G allele carriers had a 1.19-fold increased risk for NAFLD (minor allele frequency 0.43; allelic odds ratio 1.38; P = 4.3 × 10−5). Interestingly, the rs738409 GG carriers showed significantly lower levels of visceral and subcutaneous adiposity (P

To evaluate the coverage and accuracy of whole-exome sequencing (WES) across vendors. Blood samples from three trios underwent WES at three vendors. Relative performance of the three WES services was measured for breadth and depth of coverage. The false-negative rates (FNRs) were estimated using the segregation pattern within each trio. Mean depth of coverage for all genes was 189.0, 124.9, and 38.3 for the three vendor services. Fifty-five of the American College of Medical Genetics and Genomics 56 genes, but only 56 of 63 pharmacogenes, were 100% covered at 10 × in at least one of the nine individuals for all vendors; however, there was substantial interindividual variability. For the two vendors with mean depth of coverage >120 ×, analytic positive predictive values (aPPVs) exceeded 99.1% for single-nucleotide variants and homozygous indels, and sensitivities were 98.9–99.9%; however, heterozygous indels showed lower accuracy and sensitivity. Among the trios, FNRs in the offspring were 0.07–0.62% at well-covered variants concordantly called in both parents. The current standard of 120 × coverage for clinical WES may be insufficient for consistent breadth of coverage across the exome. Ordering clinicians and researchers would benefit from vendors’ reports that estimate sensitivity and aPPV, including depth of coverage across the exome.

Rationale: Pediatric pulmonary hypertension (PH) is a heterogeneous condition with varying natural history and therapeutic response. Precise classification of PH subtypes is, therefore, crucial for individualizing care. However, gaps remain in our understanding of the spectrum of PH in children. Objective: We seek to study the manifestations of PH in children and to assess the feasibility of applying a network-based approach to discern disease subtypes from comorbidity data recorded in longitudinal data sets. Methods and Results: A retrospective cohort study comprising 6 943 263 children (, National Institutes of Health (U.S.) (Award U01HL121518)

As whole genome sequencing (WGS) uncovers variants associated with rare and common diseases, an immediate challenge is to minimize false-positive findings due to sequencing and variant calling errors. False positives can be reduced by combining results from orthogonal sequencing methods, but costly. Here, we present variant filtering approaches using logistic regression (LR) and ensemble genotyping to minimize false positives without sacrificing sensitivity. We evaluated the methods using paired WGS datasets of an extended family prepared using two sequencing platforms and a validated set of variants in NA12878. Using LR or ensemble genotyping based filtering, false-negative rates were significantly reduced by 1.1- to 17.8-fold at the same levels of false discovery rates (5.4% for heterozygous and 4.5% for homozygous single nucleotide variants (SNVs); 30.0% for heterozygous and 18.7% for homozygous insertions; 25.2% for heterozygous and 16.6% for homozygous deletions) compared to the filtering based on genotype quality scores. Moreover, ensemble genotyping excluded > 98% (105,080 of 107,167) of false positives while retaining > 95% (897 of 937) of true positives in de novo mutation (DNM) discovery in NA12878, and performed better than a consensus method using two sequencing platforms. Our proposed methods were effective in prioritizing phenotype-associated variants, and an ensemble genotyping would be essential to minimize false-positive DNM candidates.

Whole-genome sequencing (WGS) studies are uncovering disease-associated variants in both rare and nonrare diseases. Utilizing the next-generation sequencing for WGS requires a series of computational methods for alignment, variant detection, and annotation, and the accuracy and reproducibility of annotation results are essential for clinical implementation. However, annotating WGS with up to date genomic information is still challenging for biomedical researchers. Here, we present one of the fastest and highly scalable annotation, filtering, and analysis pipeline-gNOME-to prioritize phenotype-associated variants while minimizing false-positive findings. Intuitive graphical user interface of gNOME facilitates the selection of phenotype-associated variants, and the result summaries are provided at variant, gene, and genome levels. Moreover, the enrichment results of specific variants, genes, and gene sets between two groups or compared with population scale WGS datasets that is already integrated in the pipeline can help the interpretation. We found a small number of discordant results between annotation software tools in part due to different reporting strategies for the variants with complex impacts. Using two published whole-exome datasets of uveal melanoma and bladder cancer, we demonstrated gNOME's accuracy of variant annotation and the enrichment of loss-of-function variants in known cancer pathways. gNOME Web server and source codes are freely available to the academic community (http://gnome.tchlab.org).

Autism spectrum disorder (ASD) is one of the most prevalent neurodevelopmental disorders with high heritability, yet a majority of genetic contribution to pathophysiology is not known. Siblings of individuals with ASD are at increased risk for ASD and autistic traits, but the genetic contribution for simplex families is estimated to be less when compared to multiplex families. To explore the genomic (dis-) similarity between proband and unaffected sibling in simplex families, we used genome-wide gene expression profiles of blood from 20 proband-unaffected sibling pairs and 18 unrelated control individuals. The global gene expression profiles of unaffected siblings were more similar to those from probands as they shared genetic and environmental background. One hundred eighty nine genes were significantly differentially expressed between proband-sib pairs (nominal p-value < 0.01) after controlling for age, sex, and family effects. Probands and siblings were distinguished into two groups by cluster analysis with these genes. Overall, unaffected siblings were equally distant from the centroid of probands and from that of unrelated controls with the differentially expressed genes. Interestingly, 5 of 20 siblings had gene expression profiles that were more similar to unrelated controls than to their matched probands. In summary, we found a set of genes that distinguished probands from the unaffected siblings, and a subgroup of unaffected siblings who were more similar to probands. The pathways that characterized probands compared to siblings using peripheral blood gene expression profiles were the up-regulation of ribosomal, spliceosomal, and mitochondrial pathways, and the down-regulation of neuroreceptor-ligand, immune response and calcium signaling pathways. Further integrative study with structural genetic variations such as de novo mutations, rare variants, and copy number variations would clarify whether these transcriptomic changes are structural or environmental in origin.

Summary Linking putatively pathogenic variants to the tissues they affect is necessary for determining the correct diagnostic workup and therapeutic regime in undiagnosed patients. Here, we explored how gene expression across healthy tissues can be used to infer this link. We integrated 6,665 tissue-wide transcriptomes with genetic disorder knowledge bases covering 3,397 diseases. Receiver-operating characteristics (ROC) analysis using expression levels in each tissue and across tissues indicated significant but modest associations between elevated expression and phenotype for most tissues (maximum area under ROC curve = 0.69). At extreme elevation, associations were marked. Upregulation of disease genes in affected tissues was pronounced for genes associated with autosomal dominant over recessive disorders. Pathways enriched for genes expressed and associated with phenotypes highlighted tissue functionality, including lipid metabolism in spleen and DNA repair in adipose tissue. These results suggest features useful for evaluating the likelihood of particular tissue manifestations in genetic disorders. The web address of an interactive platform integrating these data is provided.

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine.

With the advent of whole-genome sequencing made clinically available, the number of incidental findings is likely to rise. The false-positive incidental findings are of particular clinical concern. We provide estimates on the size of these false-positive findings and classify them into four broad categories. Whole-genome sequences (WGS) of nine individuals were scanned with several comprehensive public annotation databases and average estimates for the number of findings. These estimates were then evaluated in the perspective of various sources of false-positive annotation errors. At present there are four main sources of false-positive incidental findings: erroneous annotations, sequencing error, incorrect penetrance estimates, and multiple hypothesis testing. Of these, the first two are likely to be addressed in the near term. Conservatively, current methods deliver hundreds of false-positive incidental findings per individual. The burden of false-positives in whole-genome sequence interpretation threatens current capabilities to deliver clinical-grade whole-genome clinical interpretation. A new generation of population studies and retooling of the clinical decision-support approach will be required to overcome this threat. Genet Med 2012:14(4):399–404

Background— Alternative mRNA splicing is an important mechanism for regulation of gene expression. Altered mRNA splicing occurs in association with several types of cancer, and a small number of disease-associated changes in splicing have been reported in heart disease. However, genome-wide approaches have not been used to study splicing changes in heart disease. We hypothesized that mRNA splicing is different in diseased hearts compared with control hearts. Methods and Results— We used the Affymetrix Exon array to globally evaluate mRNA splicing in left ventricular myocardial RNA from controls (n=15) and patients with ischemic cardiomyopathy (n=15). We observed a broad and significant decrease in mRNA splicing efficiency in heart failure, which affected some introns to a greater extent than others. The profile of mRNA splicing separately clustered ischemic cardiomyopathy and control samples, suggesting distinct changes in mRNA splicing between groups. Reverse transcription–polymerase chain reaction validated 9 previously unreported alternative splicing events. Furthermore, we demonstrated that splicing of 4 key sarcomere genes, cardiac troponin T ( TNNT2 ), cardiac troponin I ( TNNI3 ), myosin heavy chain 7 ( MYH7 ), and filamin C, gamma ( FLNC ), was significantly altered in ischemic cardiomyopathy and in dilated cardiomyopathy and aortic stenosis. In aortic stenosis samples, these differences preceded the onset of heart failure. Remarkably, the ratio of minor to major splice variants of TNNT2 , MYH7 , and FLNC classified independent test samples as control or disease with >98% accuracy. Conclusions— Our data indicate that mRNA splicing is broadly altered in human heart disease and that patterns of aberrant RNA splicing accurately assign samples to control or disease classes.

Dermatomyositis (DM) is an autoimmune disease of unknown cause primarily affecting skeletal muscle and skin. The characteristic muscle pathology of DM is notable for capillary abnormalities and small, abnormal appearing muscle fibers around the periphery of some muscle fascicles bordering the perimysial connective tissue, a lesion called perifascicular atrophy (PFA). Recent microarray and pathological studies have pointed towards a mechanism of tissue injury in DM associated with the overexpression of type 1 interferon-inducible genes. Over 85% of the highest expressed transcripts in muscle from DM compared to normal and other inflammatory myopathies are from genes known to be induced by type 1 interferons.1 Plasmacytoid dendritic cells (pDCs), cells that produce high levels of type 1 interferons, have infiltrated DM muscle1 and skin.2 Expression of the type 1 interferon-inducible protein MxA occurs in the characteristic sites of DM pathology, the perifascicular myofibers and capillaries. These findings suggest that injury to capillaries and myofibers may result directly from the intracellular overproduction of one or more type 1 interferon-inducible transcripts or proteins. Interferon-stimulated gene 15 (ISG15), a type 1 interferon-inducible protein, is an ubiquitin-like modifier with poorly understood function. The enzymatic pathway for ISG15 conjugation to a target protein involves three conjugating enzymes, the E1 Ube1L,3 the E2 Ube2L6,4 and the E3 HERC5,5, 6 and a deconjugating enzyme USP18.7 Immunoprecipitates of ISG15 from interferon-β stimulated HeLa cells contain many putative ISG15-conjugated proteins.8 The conjugation of ISG15 to proteins in a human tissue sample has only been reported in 3 endometrial and 2 colon cancer biopsy specimens;9 the identities of any such proteins are unknown. We previously reported that ISG15 is the single most overexpressed gene in DM muscle compared to both normal muscle and muscle from patients with other inflammatory myopathies.1 It is also highly expressed in circulating mononuclear blood cells of patients with DM.10 In this study, we have examined the ISG15 pathway in DM and other inflammatory myopathy muscle biopsy specimens and in cultured human skeletal muscle exposed to type 1 interferons.

MicroRNAs are recently discovered regulators of gene expression and are becoming increasingly recognized as important regulators of heart function. Genome-wide profiling of microRNAs in human heart failure has not been reported previously. We measured expression of 428 microRNAs in 67 human left ventricular samples belonging to control ( n = 10), ischemic cardiomyopathy (ICM, n = 19), dilated cardiomyopathy (DCM, n = 25), or aortic stenosis (AS, n = 13) diagnostic groups. miRNA expression between disease and control groups was compared by ANOVA with Dunnett's post hoc test. We controlled for multiple testing by estimating the false discovery rate. Out of 428 microRNAs measured, 87 were confidently detected; 43 were differentially expressed in at least one disease group. In supervised clustering, microRNA expression profiles correctly grouped samples by their clinical diagnosis, indicating that microRNA expression profiles are distinct between diagnostic groups. This was further supported by class prediction approaches, in which the class (control, ICM, DCM, AS) predicted by a microRNA-based classifier matched the clinical diagnosis 69% of the time ( P < 0.001). These data show that expression of many microRNAs is altered in heart disease and that different types of heart disease are associated with distinct changes in microRNA expression. These data will guide further studies of the contribution of microRNAs to heart disease pathogenesis.

Objective To apply gene expression profiling to the study of peripheral blood mononuclear cells from patients with inflammatory myopathies, in order to provide insight into disease pathogenesis and identify potential biomarkers associated with disease activity. Methods We used Affymetrix whole-genome microarrays to measure the expression of ∼38,500 genes in 65 blood and 15 muscle samples from 44 patients with dermatomyositis (DM), polymyositis (PM), inclusion body myositis (IBM), myasthenia gravis, or genetically determined myopathies and from 12 healthy volunteers. In 9 patients, 2 samples were obtained at different time points, when disease was either active or improving, and these paired blood samples were also compared. Bioinformatics techniques were used to identify genes with significant differential expression among diagnostic categories and in relation to disease activity. We corroborated the microarray data with quantitative real-time reverse transcriptase–polymerase chain reaction. Results Most patients with active DM or PM, but not patients with IBM, had significant and high up-regulation of the type I interferon-α/β (IFNα/β)–inducible genes in blood. Furthermore, the up-regulation of these genes correlated with disease activity in DM and PM, with down-regulation occurring when disease was controlled with treatment. Conclusion DM and PM are diseases characterized by the systemic overexpression of IFNα/β-inducible genes. The magnitude of the overexpression of these genes is higher in DM and correlates with disease activity in both disorders. Although PM and IBM have been modeled as having similar immunologic processes occurring within muscle, there are substantial differences in the expression of IFNα/β-inducible genes in blood in these diseases.

BACKGROUND There are 346 serologically defined red blood cell (RBC) antigens and 33 serologically defined platelet (PLT) antigens, most of which have known genetic changes in 45 RBC or six PLT genes that correlate with antigen expression. Polymorphic sites associated with antigen expression in the primary literature and reference databases are annotated according to nucleotide positions in cDNA. This makes antigen prediction from next‐generation sequencing data challenging, since it uses genomic coordinates. STUDY DESIGN AND METHODS The conventional cDNA reference sequences for all known RBC and PLT genes that correlate with antigen expression were aligned to the human reference genome. The alignments allowed conversion of conventional cDNA nucleotide positions to the corresponding genomic coordinates. RBC and PLT antigen prediction was then performed using the human reference genome and whole genome sequencing (WGS) data with serologic confirmation. RESULTS Some major differences and alignment issues were found when attempting to convert the conventional cDNA to human reference genome sequences for the following genes: ABO, A4GALT, RHD, RHCE, FUT3, ACKR1 (previously DARC), ACHE, FUT2, CR1, GCNT2, and RHAG. However, it was possible to create usable alignments, which facilitated the prediction of all RBC and PLT antigens with a known molecular basis from WGS data. Traditional serologic typing for 18 RBC antigens were in agreement with the WGS‐based antigen predictions, providing proof of principle for this approach. CONCLUSION Detailed mapping of conventional cDNA annotated RBC and PLT alleles can enable accurate prediction of RBC and PLT antigens from whole genomic sequencing data.

Insulin-like growth factor 1 (IGF1) was considered a potential candidate for the treatment of heart failure. However, some animal studies and clinical trials have questioned whether elevating IGF1 chronically is beneficial. Secondary effects of increased serum IGF1 levels on other tissues may explain these unfavorable results. The aim of the current study was to examine the role of IGF1 in cardiac myocytes in the absence of secondary effects, and to elucidate downstream signaling pathways and transcriptional regulatory effects of the IGF1 receptor (IGF1R). Transgenic mice overexpressing IGF1R in the heart displayed cardiac hypertrophy, which was the result of an increase in myocyte size, and there was no evidence of histopathology. IGF1R transgenics also displayed enhanced systolic function at 3 months of age, and this was maintained at 12-16 months of age. The phosphoinositide 3-kinase (PI3K)-Akt-p70S6K1 pathway was significantly activated in hearts from IGF1R transgenics. Cardiac hypertrophy induced by overexpression of IGF1R was completely blocked by a dominant negative PI3K(p110alpha) mutant, suggesting IGF1R promotes compensated cardiac hypertrophy in a PI3K(p110alpha)-dependent manner. This study suggests that targeting the cardiac IGF1R-PI3K(p110alpha) pathway could be a potential therapeutic strategy for the treatment of heart failure.

Disease-causing mutations and pharmacogenomic variants are of primary interest for clinical whole-genome sequencing. However, estimating genetic liability for common complex diseases using established risk alleles might one day prove clinically useful. We compared polygenic scoring methods using a case–control data set with independently discovered risk alleles in the MedSeq Project. For eight traits of clinical relevance in both the primary-care and cardiomyopathy study cohorts, we estimated multiplicative polygenic risk scores using 161 published risk alleles and then normalized them using the population median estimated from the 1000 Genomes Project. Our polygenic score approach identified the overrepresentation of independently discovered risk alleles in cases as compared with controls using a large-scale genome-wide association study data set. In addition to normalized multiplicative polygenic risk scores and rank in a population, the disease prevalence and proportion of heritability explained by known common risk variants provide important context in the interpretation of modern multilocus disease risk models. Our approach in the MedSeq Project demonstrates how complex trait risk variants from an individual genome can be summarized and reported for the general clinician and also highlights the need for definitive clinical studies to obtain reference data for such estimates and to establish clinical utility. Genet Med 17 7, 536–544.

As more research studies incorporate next-generation sequencing (including whole-genome or whole-exome sequencing), investigators and institutional review boards face difficult questions regarding which genomic results to return to research participants and how. An American College of Medical Genetics and Genomics 2013 policy paper suggesting that pathogenic mutations in 56 specified genes should be returned in the clinical setting has raised the question of whether comparable recommendations should be considered in research settings. The Clinical Sequencing Exploratory Research (CSER) Consortium and the Electronic Medical Records and Genomics (eMERGE) Network are multisite research programs that aim to develop practical strategies for addressing questions concerning the return of results in genomic research. CSER and eMERGE committees have identified areas of consensus regarding the return of genomic results to research participants. In most circumstances, if results meet an actionability threshold for return and the research participant has consented to return, genomic results, along with referral for appropriate clinical follow-up, should be offered to participants. However, participants have a right to decline the receipt of genomic results, even when doing so might be viewed as a threat to the participants’ health. Research investigators should be prepared to return research results and incidental findings discovered in the course of their research and meeting an actionability threshold, but they have no ethical obligation to actively search for such results. These positions are consistent with the recognition that clinical research is distinct from medical care in both its aims and its guiding moral principles.

Autism Spectrum Disorders (ASD) is a spectrum of highly heritable neurodevelopmental disorders in which known mutations contribute to disease risk in 20% of cases. Here, we report the results of the largest blood transcriptome study to date that aims to identify differences in 170 ASD cases and 115 age/sex-matched controls and to evaluate the utility of gene expression profiling as a tool to aid in the diagnosis of ASD. The differentially expressed genes were enriched for the neurotrophin signaling, long-term potentiation/depression, and notch signaling pathways. We developed a 55-gene prediction model, using a cross-validation strategy, on a sample cohort of 66 male ASD cases and 33 age-matched male controls (P1). Subsequently, 104 ASD cases and 82 controls were recruited and used as a validation set (P2). This 55-gene expression signature achieved 68% classification accuracy with the validation cohort (area under the receiver operating characteristic curve (AUC): 0.70 [95% confidence interval [CI]: 0.62–0.77]). Not surprisingly, our prediction model that was built and trained with male samples performed well for males (AUC 0.73, 95% CI 0.65–0.82), but not for female samples (AUC 0.51, 95% CI 0.36–0.67). The 55-gene signature also performed robustly when the prediction model was trained with P2 male samples to classify P1 samples (AUC 0.69, 95% CI 0.58–0.80). Our result suggests that the use of blood expression profiling for ASD detection may be feasible. Further study is required to determine the age at which such a test should be deployed, and what genetic characteristics of ASD can be identified.

Multiple studies have examined the risk of prenatal antidepressant exposure and risk for autism spectrum disorder (ASD) or attention-deficit hyperactivity disorder (ADHD), with inconsistent results. Precisely estimating such risk, if any, is of great importance in light of the need to balance such risk with the benefit of depression and anxiety treatment. We developed a method to integrate data from multiple New England health systems, matching offspring and maternal health data in electronic health records to characterize diagnoses and medication exposure. Children with ASD or ADHD were matched 1:3 with children without neurodevelopmental disorders. Association between maternal antidepressant exposure and ASD or ADHD liability was examined using logistic regression, adjusting for potential sociodemographic and psychiatric confounding variables. In new cohorts of 1245 ASD cases and 1701 ADHD cases, along with age-, sex- and socioeconomic status matched controls, neither disorder was significantly associated with prenatal antidepressant exposure in crude or adjusted models (adjusted odds ratio 0.90, 95% confidence interval 0.50−1.54 for ASD; 0.97, 95% confidence interval 0.53−1.69 for ADHD). Pre-pregnancy antidepressant exposure significantly increased risk for both disorders. These results suggest that prior reports of association between prenatal antidepressant exposure and neurodevelopmental disease are likely to represent a false-positive finding, which may arise in part through confounding by indication. They further demonstrate the potential to integrate data across electronic health records studies spanning multiple health systems to enable efficient pharmacovigilance investigation.

We previously identified a circulating autoantibody against a 43 kDa muscle autoantigen in sporadic inclusion body myositis (IBM) and demonstrated the feasibility of an IBM diagnostic blood test. Here, we sought to identify the molecular target of this IBM autoantibody, understand the relationship between IBM autoimmunity and muscle degeneration, and develop an IBM blood test with high diagnostic accuracy.IBM blood samples were screened using mass spectrometry and a synthetic human peptidome. Plasma and serum samples (N=200 patients) underwent immunoblotting assays, and results were correlated to clinical features. Muscle biopsy samples (n=30) were examined by immunohistochemistry and immunoblotting. Exome or whole genome sequencing was performed on DNA from 19 patients.Both mass spectrometry and screening of a 413,611 human peptide library spanning the entire human proteome identified cytosolic 5'-nucleotidase 1A (cN1A; NT5C1A) as the likely 43 kDa IBM autoantigen, which was then confirmed in dot blot and Western blot assays using recombinant cN1A protein. Moderate reactivity of anti-cN1A autoantibodies was 70% sensitive and 92% specific, and high reactivity was 34% sensitive and 98% specific for the diagnosis of IBM. One to 3 major cN1A immunodominant epitopes were identified. cN1A reactivity by immunohistochemistry accumulated in perinuclear regions and rimmed vacuoles in IBM muscle, localizing to areas of myonuclear degeneration.Autoantibodies against cN1A are common in and highly specific to IBM among muscle diseases, and may provide a link between IBM's dual processes of autoimmunity and myodegeneration. Blood diagnostic testing is feasible and should improve early and reliable diagnosis of IBM.

Member of the Autism Genome Project Consortium: Astrid M. Vicente Although autism is a highly heritable neurodevelopmental disorder, attempts to identify specific susceptibility genes have thus far met with limited success. Genome-wide association studies using half a million or more markers, particularly those with very large sample sizes achieved through meta-analysis, have shown great success in mapping genes for other complex genetic traits. Consequently, we initiated a linkage and association mapping study using half a million genome-wide single nucleotide polymorphisms (SNPs) in a common set of 1,031 multiplex autism families (1,553 affected offspring). We identified regions of suggestive and significant linkage on chromosomes 6q27 and 20p13, respectively. Initial analysis did not yield genome-wide significant associations; however, genotyping of top hits in additional families revealed an SNP on chromosome 5p15 (between SEMA5A and TAS2R1) that was significantly associated with autism (P = 2 x 10(-7)). We also demonstrated that expression of SEMA5A is reduced in brains from autistic patients, further implicating SEMA5A as an autism susceptibility gene. The linkage regions reported here provide targets for rare variation screening whereas the discovery of a single novel association demonstrates the action of common variants.

Motivation: Type 2 diabetes is a chronic metabolic disease that involves both environmental and genetic factors. To understand the genetics of type 2 diabetes and insulin resistance, the DIabetes Genome Anatomy Project (DGAP) was launched to profile gene expression in a variety of related animal models and human subjects. We asked whether these heterogeneous models can be integrated to provide consistent and robust biological insights into the biology of insulin resistance. Results: We perform integrative analysis of the 16 DGAP data sets that span multiple tissues, conditions, array types, laboratories, species, genetic backgrounds and study designs. For each data set, we identify differentially expressed genes compared with control. Then, for the combined data, we rank genes according to the frequency with which they were found to be statistically significant across data sets. This analysis reveals RetSat as a widely shared component of mechanisms involved in insulin resistance and sensitivity and adds to the growing importance of the retinol pathway in diabetes, adipogenesis and insulin resistance. Top candidates obtained from our analysis have been confirmed in recent laboratory studies., National Institutes of Health (U.S.) (Roadmap for Medical Research, grant U54LM008748)

Aberrant transcriptional regulation contributes to the pathogenesis of both congenital and adult forms of heart disease. While the transcriptional regulator friend of Gata 2 (FOG2) is known to be essential for heart morphogenesis and coronary development, its tissue-specific function has not been previously investigated. Additionally, little is known about the role of FOG2 in the adult heart. Here we used spatiotemporally regulated inactivation of Fog2 to delineate its function in both the embryonic and adult mouse heart. Early cardiomyocyte- restricted loss of Fog2 recapitulated the cardiac and coronary defects of the Fog2 germline murine knockouts. Later cardiomyocyte-restricted loss of Fog2 (Fog2MC) did not result in defects in cardiac structure or coronary vessel formation. However, Fog2MC adult mice had severely depressed ventricular function and died at 8-14 weeks. Fog2MC adult hearts displayed a paucity of coronary vessels, associated with myocardial hypoxia, increased cardiomyocyte apoptosis, and cardiac fibrosis. Induced inactivation of Fog2 in the adult mouse heart resulted in similar phenotypes, as did ablation of the FOG2 interaction with the transcription factor GATA4. Loss of the FOG2 or FOG2-GATA4 interaction altered the expression of a panel of angiogenesis-related genes. Collectively, our data indicate that FOG2 regulates adult heart function and coronary angiogenesis.

Motivation: DNA copy number aberrations (CNAs) and gene expression (GE) changes provide valuable information for studying chromosomal instability and its consequences in cancer. While it is clear that the structural aberrations and the transcript levels are intertwined, their relationship is more complex and subtle than initially suspected. Most studies so far have focused on how a CNA affects the expression levels of those genes contained within that CNA. Results: To better understand the impact of CNAs on expression, we investigated the correlation of each CNA to all other genes in the genome. The correlations are computed over multiple patients that have both expression and copy number measurements in brain, bladder and breast cancer data sets. We find that a CNA has a direct impact on the gene amplified or deleted, but it also has a broad, indirect impact elsewhere. To identify a set of CNAs that is coordinately associated with the expression changes of a set of genes, we used a biclustering algorithm on the correlation matrix. For each of the three cancer types examined, the aberrations in several loci are associated with cancer-type specific biological pathways that have been described in the literature: CNAs of chromosome (chr) 7p13 were significantly correlated with epidermal growth factor receptor signaling pathway in glioblastoma multiforme, chr 13q with NF-kappaB cascades in bladder cancer, and chr 11p with Reck pathway in breast cancer. In all three data sets, gene sets related to cell cycle/division such as M phase, DNA replication and cell division were also associated with CNAs. Our results suggest that CNAs are both directly and indirectly correlated with changes in expression and that it is beneficial to examine the indirect effects of CNAs. Availability: The code is available upon request. Contact: peter_park@harvard.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

By providing genome-scale information on gene expression, microarray technology has gained popularity in diverse areas including clinical medicine. However, the analysis and interpretation of microarray data are often complicated. This chapter describes various strategies for microarray data analysis. The analysis starts with the scanned image of a microarray. The image information is processed and summarized to numerical values that represent the abundance of transcripts. Technical variability and systematic biases can be minimized with the proper procedures of background correction and normalization. Considerable numbers of genes are not expressed or not detected by microarray technology. Those genes can be filtered out before further statistical comparison to reduce the dimensionality of the problem. The next step in analysis involves statistical comparison, cluster analysis, and visualization. Genes from the same cluster are considered to be coexpressed and/or coregulated. Also, we can group coexpressed genes into categories by their biological function and cellular location. By combining prior knowledge and statistical results, we can make an inference based on the gene expression profiles.

Type 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein–protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein–protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder., Author Summary Type 2 diabetes mellitus currently affects millions of people. It is clinically characterized by insulin resistance in addition to an impaired glucose response and associated with numerous complications including heart disease, stroke, neuropathy, and kidney failure, among others. Accurate identification of the underlying molecular mechanisms of the disease or its complications is an important research problem that could lead to novel diagnostics and therapy. The main challenge stems from the fact that insulin resistance is a complex disorder and affects a multitude of biological processes, metabolic networks, and signaling pathways. In this report, the authors develop a network-based methodology that appears to be more sensitive than previous approaches in detecting deregulated molecular processes in a disease state. The methodology revealed that both insulin signaling and nuclear receptor networks are consistently and differentially expressed in many models of insulin resistance. The positive results suggest such network-based diagnostic technologies hold promise as potentially useful clinical and research tools in the future.

Mouse models mimicking human diseases are important tools in trying to understand the underlying mechanisms of many disease states. Several surgical models have been described that mimic human myocardial infarction (MI) and pressure-overload-induced cardiac hypertrophy. However, there are very few detailed descriptions for performing these surgical techniques in mice. Consequently, the number of laboratories that are proficient in performing cardiac surgical procedures in mice has been limited. Microarray technologies measure the expression of thousands of genes simultaneously, allowing for the identification of genes and pathways that may potentially be involved in the disease process. The statistical analysis of microarray experiments is highly influenced by the amount of variability in the experiment. To keep the number of required independent biological replicates and the associated costs of the study to a minimum, it is critical to minimize experimental variability by optimizing the surgical procedures. The aim of this publication was to provide a detailed description of techniques required to perform mouse cardiac surgery, such that these models can be utilized for genomic studies. A description of three major surgical procedures has been provided: 1) aortic constriction, 2) pulmonary artery banding, 3) MI (including ischemia-reperfusion). Emphasis has been placed on technical procedures with the inclusion of thorough descriptions of all equipment and devices employed in surgery, as well as the application of such techniques for expression profiling studies. The cardiac surgical techniques described have been, and will continue to be, important for elucidating the molecular mechanisms of cardiac hypertrophy and failure with high-throughput technology.