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There are two key signatures of pediatric cancers: (a) higher prevalence of germline alterations and (b) heterogeneity in alteration types. Recent population-based assessments have demonstrated that children with birth defects (BDs) are more likely to develop cancer even without chromosomal anomalies; therefore, explorations of genetic alterations in children with BDs and cancers could provide new insights into the underlying mechanisms for pediatric tumor development. We performed whole-genome sequencing (WGS) on blood-derived DNA for 1556 individuals without chromosomal anomalies, including 454 BD probands with at least one type of malignant tumor, 757 cancer-free children with BDs, and 345 healthy individuals, focusing on copy number variation (CNV) analysis. Roughly half of the children with BD-cancer have CNVs that are not identified in BD-only/healthy individuals, and CNVs are not evenly distributed among these patients. Strong heterogeneity was observed, with a limited number of cancer predisposition genes containing CNVs in more than three patients. Moreover, functional enrichments of genes with CNVs showed that dozens of patients have variations related to the same biological pathways, such as deletions of genes with neurological functions and duplications of immune response genes. Phenotype clustering uncovered recurrences of patients with sarcoma: A notable enrichment was observed involving non-coding RNA regulators, showing strong signals related to growth and cancer regulations in functional analysis. In conclusion, we conducted one of the first genomic studies exploring the impact of CNVs on cancer development in children with BDs, unveiling new insights into the underlying biological processes., (© 2024 The Author(s). Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Atopic dermatitis (AD) is a highly heritable and common inflammatory skin condition affecting children and adults worldwide. Multi-ancestry approaches to AD genetic association studies are poised to boost power to detect genetic signal and identify ancestry-specific loci contributing to AD risk. Here, we present a multi-ancestry GWAS meta-analysis of twelve AD cohorts from five ancestral populations totaling 56,146 cases and 602,280 controls. We report 101 genomic loci associated with AD, including 15 loci that have not been previously associated with AD or eczema. Fine-mapping, QTL colocalization, and cell-type enrichment analyses identified genes and cell types implicated in AD pathophysiology. Functional analyses in keratinocytes provide evidence for genes that could play a role in AD through epidermal barrier function. Our study provides new insights into the etiology of AD by harnessing multiple genetic and functional approaches to unveil the mechanisms by which AD-associated variants impact genes and cell types.

Background: Patients with birth defects (BD) exhibit an elevated risk of cancer. We aimed to investigate the potential link between pediatric cancers and BDs, exploring the hypothesis of shared genetic defects contributing to the coexistence of these conditions., Methods: This study included 1454 probands with BDs (704 females and 750 males), including 619 (42.3%) with and 845 (57.7%) without co-occurrence of pediatric onset cancers. Whole genome sequencing (WGS) was done at 30X coverage through the Kids First/Gabriella Miller X01 Program., Results: 8211 CNV loci were called from the 1454 unrelated individuals. 191 CNV loci classified as pathogenic/likely pathogenic (P/LP) were identified in 309 (21.3%) patients, with 124 (40.1%) of these patients having pediatric onset cancers. The most common group of CNVs are pathogenic deletions covering the region ChrX:52,863,011-55,652,521, seen in 162 patients including 17 males. Large recurrent P/LP duplications >5MB were detected in 33 patients., Conclusions: This study revealed that P/LP CNVs were common in a large cohort of BD patients with high rate of pediatric cancers. We present a comprehensive spectrum of P/LP CNVs in patients with BDs and various cancers. Notably, deletions involving E2F target genes and genes implicated in mitotic spindle assembly and G2/M checkpoint were identified, potentially disrupting cell-cycle progression and providing mechanistic insights into the concurrent occurrence of BDs and cancers., (Copyright © 2023. Published by Elsevier Inc.)

Study Objectives: To identify genetic susceptibility variants in pediatric obstructive sleep apnea in European American and African American children., Methods: A phenotyping algorithm using electronic medical records was developed to recruit cases with OSA and control subjects from the Center for Applied Genomics at Children's Hospital of Philadelphia (CHOP). Genome-wide association studies (GWAS) were performed in pediatric OSA cases and control subjects with European American (EA) and African American (AA) ancestry followed by meta-analysis and sex stratification., Results: The algorithm accrued 1486 subjects (46.3% European American, 53.7% African American). We identified genomic loci at 1p36.22 and 15q26.1 that associated with OSA risk in EA and AA, respectively. We also revealed a shared risk locus at 18p11.32 (rs114124196, p = 1.72 × 10-8) across EA and AA populations. Additionally, association at 1q43 (rs12754698) and 2p25.1 (rs72775219) was identified in the male-only analysis of EA children with OSA, while association at 8q21.11 (rs6472959), 11q24.3 (rs4370952) and 15q21.1 (rs149936782) was detected in the female-only analysis of EA children and association at 18p11.23 (rs9964029) was identified in the female-only analysis of African-American children. Moreover, the 18p11.32 locus was replicated in an EA cohort (rs114124196, p = 8.8 × 10-3)., Conclusions: We report the first GWAS for pediatric OSA in European Americans and African Americans. Our results provide novel insights to the genetic underpins of pediatric OSA., (© The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Pubertal growth patterns correlate with future health outcomes. However, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Translation And Rotation (SITAR) growth curve analysis on ~ 56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analyses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank., Results: Large-scale growth modeling enables an unprecedented view of adolescent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide significant loci and leverage trans-ancestry data to perform fine-mapping. Our data reveals genetic relationships between pediatric height growth and health across the life course, with different growth trajectories correlated with different outcomes. For instance, a faster tempo of pubertal growth correlates with higher bone mineral density, HOMA-IR, fasting insulin, type 2 diabetes, and lung cancer, whereas being taller at early puberty, taller across puberty, and having quicker pubertal growth were associated with higher risk for atrial fibrillation., Conclusion: We report novel genetic associations with the tempo of pubertal growth and find that genetic determinants of growth are correlated with reproductive, glycemic, respiratory, and cardiac traits in adulthood. These results aid in identifying specific growth trajectories impacting lifelong health and show that there may not be a single "optimal" pubertal growth pattern., (© 2023. The Author(s).)

LIGHT (homologous to lymphotoxins, exhibits inducible expression, and competes with HSV glycoprotein D for herpes virus entry mediator, a receptor expressed by T lymphocytes), encoded by the TNFSF14 gene, is a cytokine belonging to the TNF superfamily. On binding to its receptors, herpes virus entry mediator and lymphotoxin β receptor, it activates inflammatory responses. We conducted this study to determine whether plasma LIGHT levels are elevated in Crohn's disease (CD) in a pediatric population with the aim of nominating this cytokine as a therapeutic target. We used a single-molecule immunoassay to determine the circulating levels of free LIGHT in plasma from pediatric patients with CD in our biobank (n = 183), a panel of healthy pediatric (n = 9) or adult (n = 22) reference samples, and pediatric biobank controls (n = 19). We performed correlational analyses between LIGHT levels and the clinical characteristics of the CD cohort, including age, Montreal classification, family history, medical/surgical therapy, and routine blood test parameters. LIGHT levels were greatly elevated in CD, with an average of 305 versus 32.4 pg/ml for controls from the biobank (p < 0.0001). The outside reference samples showed levels of 57 pg/ml in pediatric controls and 55 pg/ml in adults (p < 0.0001). We found a statistically significant correlation between white blood cell count and free LIGHT (p < 0.046). We conclude that free, soluble LIGHT is increased 5- to 10-fold in pediatric CD across an array of disease subtypes and characteristics., (Copyright © 2023 by The American Association of Immunologists, Inc.)

Background: Children with birth defects (BD) are more likely to develop cancer and the increased risk of cancer persists into adulthood. Prior population-based assessments have demonstrated that even non-chromosomal BDs are associated with at least two-fold increase of cancer risk. Identification of variants that are associated with malignant tumor in BD patients without chromosomal anomalies may improve our understanding of the underlying molecular mechanisms and provide clues for early cancer detection in children with BD., Methods: In this study, whole genome sequencing (WGS) data of blood-derived DNA for 1653 individuals without chromosomal anomalies were acquired from the Kids First Data Resource Center (DRC), including 541 BD probands with at least one type of malignant tumors, 767 BD probands without malignant tumor, and 345 healthy family members who are the parents or siblings of the probands. Recurrent variants exclusively seen in cancer patients were selected and mapped to their corresponding genomic regions. The targeted genes/non-coding RNAs were further reduced using random forest and forward feature selection (ffs) models., Results: The filtered genes/non-coding RNAs, including variants in non-coding areas, showed enrichment in cancer-related pathways. To further support the validity of these variants, blood WGS data of additional 40 independent BD probands, including 25 patients with at least one type of cancers from unrelated projects, were acquired. The counts of variants of interest identified in the Kid First data showed clear deviation in the validation dataset between BD patients with cancer and without cancer. Furthermore, a deep learning model was built to assess the predictive abilities in the 40 patients using variants of interest identified in the Kids First cohort as feature vectors. The accuracies are ~ 75%, with the noteworthy observation that variants mapped to non-coding regions provided the highest accuracy (31 out of 40 patients were labeled correctly)., Conclusion: We present for the first time a panorama of genetic variants that are associated with cancers in non-chromosomal BD patients, implying that our approach may potentially serve for the early detection of malignant tumors in patients with BD., (© 2022. The Author(s).)

Mental disorders present a global health concern and have limited treatment options. In today's medical practice, medications such as antidepressants are prescribed not only for depression but also for conditions such as anxiety and attention deficit hyperactivity disorder (ADHD). Therefore, identifying gene targets for specific disorders is important and offers improved precision. In this study, we performed a genetic analysis of six common mental disorders-ADHD, anxiety, depression, delays in mental development, intellectual disabilities (IDs) and speech/language disorder-in the ethnic minority of African Americans (AAs) using whole genome sequencing (WGS). WGS data were generated from blood-derived DNA from 4178 AA individuals, including 1384 patients with the diagnosis of at least one mental disorder. Mutation burden analysis was applied based on rare and deleterious mutations in the AA population between cases and controls, and further analyzed in the context of patients with single mental disorder diagnosis. Certain genes uncovered demonstrated significant P-values in mutation burden analysis. In addition, exclusive recurrences in specific type of disorder were scanned through gene-drug interaction databases to assess for availability of potential medications. We uncovered 15 genes harboring deleterious mutations, including 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR) and Uronyl 2-Sulfotransferase (UST) for ADHD; Farnesyltransferase, CAAX Box, Beta (FNTB) for anxiety; Xin Actin Binding Repeat Containing 2 (XIRP2), Natriuretic Peptide C (NPPC), Serine/Threonine Kinase 33 (STK33), Pannexin 1 (PANX1) and Neurotensin (NTS) for depression; RUNX Family Transcription Factor 3 (RUNX3), Tachykinin Receptor 1 (TACR1) and NADH:Ubiquinone Oxidoreductase Core Subunit S7 (NDUFS7) for delays in mental development; Hepsin (HPN) for ID and Collagen Type VI Alpha 3 Chain (COL6A3), Damage Specific DNA Binding Protein 1 (DDB1) and NADH:Ubiquinone Oxidoreductase Subunit A11 (NDUFA11) for speech/language disorder. Taken together, we have established critical insights into the development of new precision medicine approaches for mental disorders in AAs., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Asthma is the most common chronic condition in children and the third leading cause of hospitalization in pediatrics. The genome-wide association study catalog reports 140 studies with genome-wide significance. A polygenic risk score (PRS) with predictive value across ancestries has not been evaluated for this important trait., Objectives: This study aimed to train and validate a PRS relying on genetic determinants for asthma to provide predictions for disease occurrence in pediatric cohorts of diverse ancestries., Methods: This study applied a Bayesian regression framework method using the Trans-National Asthma Genetic Consortium genome-wide association study summary statistics to derive a multiancestral PRS score, used one Electronic Medical Records and Genomics (eMERGE) cohort as a training set, used a second independent eMERGE cohort to validate the score, and used the UK Biobank data to replicate the findings. A phenome-wide association study was performed using the PRS to identify shared genetic etiology with other phenotypes., Results: The multiancestral asthma PRS was associated with asthma in the 2 pediatric validation datasets. Overall, the multiancestral asthma PRS has an area under the curve (AUC) of 0.70 (95% CI, 0.69-0.72) in the pediatric validation 1 and AUC of 0.66 (0.65-0.66) in the pediatric validation 2 datasets. We found significant discrimination across pediatric subcohorts of European (AUC, 95% CI, 0.60 and 0.66), African (AUC, 95% CI, 0.61 and 0.66), admixed American (AUC, 0.64 and 0.70), Southeast Asian (AUC, 0.65), and East Asian (AUC, 0.73) ancestry. Pediatric participants with the top 5% PRS had 2.80 to 5.82 increased odds of asthma compared to the bottom 5% across the training, validation 1, and validation 2 cohorts when adjusted for ancestry. Phenome-wide association study analysis confirmed the strong association of the identified PRS with asthma (odds ratio, 2.71, P FDR  = 3.71 × 10 -65 ) and related phenotypes., Conclusions: A multiancestral PRS for asthma based on Bayesian posterior genomic effect sizes identifies increased odds of pediatric asthma., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To evaluate Mendelian causes of neurodegenerative disorders in a cohort of pediatric patients., Study Design: Patients enrolled in the Center for Applied Genomics Biobank at the Children's Hospital of Philadelphia with neurodegenerative symptoms were identified using an algorithm that consisted of including and excluding selected International Classification of Diseases, 9th and 10th edition codes. A manual chart review was then performed to abstract detailed clinical information., Results: Of approximately 100 000 patients enrolled in the Center for Applied Genomics Biobank, 76 had a neurodegenerative phenotype. After chart review, 7 patients were excluded. Of the remaining 69 patients, 42 had a genetic diagnosis (60.9%) and 27 were undiagnosed (39.1%). There were 32 unique disorders. Common diagnoses included Rett syndrome, mitochondrial disorders, and neuronal ceroid lipofuscinoses., Conclusions: The disorders encountered in our cohort demonstrate the diverse diseases and pathophysiology that contribute to pediatric neurodegeneration. Establishing a diagnosis often informed clinical management, although curative treatment options are lacking. Many patients who underwent genetic evaluation remained undiagnosed, highlighting the importance of continued research efforts in this field., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objective: Juvenile idiopathic arthritis (JIA) is the most common chronic immune-mediated joint disease among children and encompasses a heterogeneous group of immune-mediated joint disorders classified into 7 subtypes according to clinical presentation. However, phenotype overlap and biologic evidence suggest a shared mechanistic basis between subtypes. This study was undertaken to systematically investigate shared genetic underpinnings of JIA subtypes., Methods: We performed a heterogeneity-sensitive genome-wide association study encompassing a total of 1,245 JIA cases (classified into 7 subtypes) and 9,250 controls, followed by fine-mapping of candidate causal variants at each genome-wide significant locus, functional annotation, and pathway and network analysis. We further identified candidate drug targets and drug repurposing opportunities by in silico analyses., Results: In addition to the major histocompatibility complex locus, we identified 15 genome-wide significant loci shared between at least 2 JIA subtypes, including 10 novel loci. Functional annotation indicated that candidate genes at these loci were expressed in diverse immune cell types., Conclusion: This study identified novel genetic loci shared by JIA subtypes. Our findings identified candidate mechanisms underlying JIA subtypes and candidate targets with drug repurposing opportunities for JIA treatment., (© 2022 The Authors. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

Background: In over half of pediatric cases, ADHD presents with comorbidities, and often, it is unclear whether the symptoms causing impairment are due to the comorbidity or the underlying ADHD. Comorbid conditions increase the likelihood for a more severe and persistent course and complicate treatment decisions. Therefore, it is highly important to establish an algorithm that identifies ADHD and comorbidities in order to improve research on ADHD using biorepository and other electronic record data., Methods: It is feasible to accurately distinguish between ADHD in isolation from ADHD with comorbidities using an electronic algorithm designed to include other psychiatric disorders. We sought to develop an EHR phenotype algorithm to discriminate cases with ADHD in isolation from cases with ADHD with comorbidities more effectively for efficient future searches in large biorepositories. We developed a multi-source algorithm allowing for a more complete view of the patient's EHR, leveraging the biobank of the Center for Applied Genomics (CAG) at Children's Hospital of Philadelphia (CHOP). We mined EHRs from 2009 to 2016 using International Statistical Classification of Diseases and Related Health Problems (ICD) codes, medication history and keywords specific to ADHD, and comorbid psychiatric disorders to facilitate genotype-phenotype correlation efforts. Chart abstractions and behavioral surveys added evidence in support of the psychiatric diagnoses. Most notably, the algorithm did not exclude other psychiatric disorders, as is the case in many previous algorithms. Controls lacked psychiatric and other neurological disorders. Participants enrolled in various CAG studies at CHOP and completed a broad informed consent, including consent for prospective analyses of EHRs. We created and validated an EHR-based algorithm to classify ADHD and comorbid psychiatric status in a pediatric healthcare network to be used in future genetic analyses and discovery-based studies., Results: In this retrospective case-control study that included data from 51,293 subjects, 5840 ADHD cases were discovered of which 46.1% had ADHD alone and 53.9% had ADHD with psychiatric comorbidities. Our primary study outcome was to examine whether the algorithm could identify and distinguish ADHD exclusive cases from ADHD comorbid cases. The results indicate ICD codes coupled with medication searches revealed the most cases. We discovered ADHD-related keywords did not increase yield. However, we found including ADHD-specific medications increased our number of cases by 21%. Positive predictive values (PPVs) were 95% for ADHD cases and 93% for controls., Conclusion: We established a new algorithm and demonstrated the feasibility of the electronic algorithm approach to accurately diagnose ADHD and comorbid conditions, verifying the efficiency of our large biorepository for further genetic discovery-based analyses., Trial Registration: ClinicalTrials.gov, NCT02286817 . First posted on 10 November 2014., Clinicaltrials: gov, NCT02777931 . First posted on 19 May 2016., Clinicaltrials: gov, NCT03006367 . First posted on 30 December 2016., Clinicaltrials: gov, NCT02895906 . First posted on 12 September 2016., (© 2022. The Author(s).)

Background: Asthma is a complex condition largely attributed to the interactions among genes and environments as a heterogeneous phenotype. Obesity is significantly associated with asthma development, and genetic studies on obese vs. non-obese asthma are warranted., Methods: To investigate asthma in the minority African American (AA) population with or without obesity, we performed a whole genome sequencing (WGS) study on blood-derived DNA of 4289 AA individuals, included 2226 asthma patients (1364 with obesity and 862 without obesity) and 2006 controls without asthma. The burden analysis of functional rare coding variants was performed by comparing asthma vs. controls and by stratified analysis of obese vs. non-obese asthma, respectively., Results: Among the top 66 genes with P < 0.01 in the asthma vs. control analysis, stratified analysis by obesity showed inverse correlation of natural logarithm (LN) of P value between obese and non-obese asthma (r = - 0.757, P = 1.90E-13). Five genes previously reported in a genome-wide association study (GWAS) on asthma, including TSLP, SLC9A4, PSMB8, IGSF5, and IKZF4 were demonstrated association in the asthma vs. control analysis. The associations of IKZF4 and IGSF5 are only associated with obese asthma; and the association of SLC9A4 is only observed in non-obese asthma. In addition, the association of RSPH3 (the gene is related to primary ciliary dyskinesia) is observed in non-obese asthma., Conclusions: These findings highlight genetic heterogeneity between obese and non-obese asthma in patients of AA ancestry., (© 2022. The Author(s).)

Mental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patients who have more than one type of mental disorder, especially for young toddlers who are not able to complete questionnaires or standardized rating scales for diagnosis. In the past decade, multiple genomic association signals have been reported for mental disorders, some of which present attractive drug targets. Concurrently, machine learning algorithms, especially deep learning algorithms, have been successful in the diagnosis and/or labeling of complex diseases, such as attention deficit hyperactivity disorder (ADHD) or cancer. In this study, we focused on eight common mental disorders, including ADHD, depression, anxiety, autism, intellectual disabilities, speech/language disorder, delays in developments, and oppositional defiant disorder in the ethnic minority of African Americans. Blood-derived whole genome sequencing data from 4179 individuals were generated, including 1384 patients with the diagnosis of at least one mental disorder. The burden of genomic variants in coding/non-coding regions was applied as feature vectors in the deep learning algorithm. Our model showed ~65% accuracy in differentiating patients from controls. Ability to label patients with multiple disorders was similarly successful, with a hamming loss score less than 0.3, while exact diagnostic matches are around 10%. Genes in genomic regions with the highest weights showed enrichment of biological pathways involved in immune responses, antigen/nucleic acid binding, chemokine signaling pathway, and G-protein receptor activities. A noticeable fact is that variants in non-coding regions (e.g., ncRNA, intronic, and intergenic) performed equally well as variants in coding regions; however, unlike coding region variants, variants in non-coding regions do not express genomic hotspots whereas they carry much more narrow standard deviations, indicating they probably serve as alternative markers., (© 2021. The Author(s).)

Wiedemann-Steiner syndrome (WSS) is an autosomal dominant disorder caused by monoallelic variants in KMT2A and characterized by intellectual disability and hypertrichosis. We performed a retrospective, multicenter, observational study of 104 individuals with WSS from five continents to characterize the clinical and molecular spectrum of WSS in diverse populations, to identify physical features that may be more prevalent in White versus Black Indigenous People of Color individuals, to delineate genotype-phenotype correlations, to define developmental milestones, to describe the syndrome through adulthood, and to examine clinicians' differential diagnoses. Sixty-nine of the 82 variants (84%) observed in the study were not previously reported in the literature. Common clinical features identified in the cohort included: developmental delay or intellectual disability (97%), constipation (63.8%), failure to thrive (67.7%), feeding difficulties (66.3%), hypertrichosis cubiti (57%), short stature (57.8%), and vertebral anomalies (46.9%). The median ages at walking and first words were 20 months and 18 months, respectively. Hypotonia was associated with loss of function (LoF) variants, and seizures were associated with non-LoF variants. This study identifies genotype-phenotype correlations as well as race-facial feature associations in an ethnically diverse cohort, and accurately defines developmental trajectories, medical comorbidities, and long-term outcomes in individuals with WSS., (© 2021 Wiley Periodicals LLC.)

Neuropsychiatric disorders, such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) share common clinical presentations, suggesting etiologic overlap. A substantial proportion of SNP-based heritability for neuropsychiatric disorders is attributable to genetic components, and genome-wide association studies (GWASs) focusing on individual diseases have identified multiple genetic loci shared between these diseases. Here, we aimed at identifying novel genetic loci associated with individual neuropsychiatric diseases and genetic loci shared by neuropsychiatric diseases. We performed multi-trait joint analyses and meta-analysis across five neuropsychiatric disorders based on their summary statistics from the Psychiatric Genomics Consortium (PGC), and further carried out a replication study of ADHD among 2726 cases and 16299 controls in an independent pediatric cohort. In the multi-trait joint analyses, we found five novel genome-wide significant loci for ADHD, one novel locus for BIP, and ten novel loci for MDD. We further achieved modest replication in our independent pediatric dataset. We conducted fine-mapping and functional annotation through an integrative multi-omics approach and identified causal variants and potential target genes at each novel locus. Gene expression profile and gene-set enrichment analysis further suggested early developmental stage expression pattern and postsynaptic membrane compartment enrichment of candidate genes at the genome-wide significant loci of these neuropsychiatric disorders. Therefore, through a multi-omics approach, we identified novel genetic loci associated with the five neuropsychiatric disorders which may help to better understand the underlying molecular mechanism of neuropsychiatric diseases.

Background/objectives: Melanocortin-4 receptor (MC4R) plays an essential role in food intake and energy homeostasis. More than 170 MC4R variants have been described over the past two decades, with conflicting reports regarding the prevalence and phenotypic effects of these variants in diverse cohorts. To determine the frequency of MC4R variants in large cohort of different ancestries, we evaluated the MC4R coding region for 20,537 eMERGE participants with sequencing data plus additional 77,454 independent individuals with genome-wide genotyping data at this locus., Subjects/methods: The sequencing data were obtained from the eMERGE phase III study, in which multisample variant call format calls have been generated, curated, and annotated. In addition to penetrance estimation using body mass index (BMI) as a binary outcome, GWAS and PheWAS were performed using median BMI in linear regression analyses. All results were adjusted for principal components, age, sex, and sites of genotyping., Results: Targeted sequencing data of MC4R revealed 125 coding variants in 1839 eMERGE participants including 30 unreported coding variants that were predicted to be functionally damaging. Highly penetrant unreported variants included (L325I, E308K, D298N, S270F, F261L, T248A, D111V, and Y80F) in which seven participants had obesity class III defined as BMI ≥ 40 kg/m 2 . In GWAS analysis, in addition to known risk haplotype upstream of MC4R (best variant rs6567160 (P = 5.36 × 10 -25 , Beta = 0.37), a novel rare haplotype was detected which was protective against obesity and encompassed the V103I variant with known gain-of-function properties (P = 6.23 × 10 -08 , Beta = -0.62). PheWAS analyses extended this protective effect of V103I to type 2 diabetes, diabetic nephropathy, and chronic renal failure independent of BMI., Conclusions: MC4R screening in a large eMERGE cohort confirmed many previous findings, extend the MC4R pleotropic effects, and discovered additional MC4R rare alleles that probably contribute to obesity.

Context: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice., Objective: Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment., Design, Patients, and Methods: Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS., Results: The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity", "type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension", and "sleep apnea" reaching phenome-wide significance., Conclusions: Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis., (© Endocrine Society 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Lipid levels are important markers for the development of cardio-metabolic diseases. Although hundreds of associated loci have been identified through genetic association studies, the contribution of genetic factors to variation in lipids is not fully understood, particularly in U.S. minority groups. We performed genome-wide association analyses for four lipid traits in over 45,000 ancestrally diverse participants from the Population Architecture using Genomics and Epidemiology (PAGE) Study, followed by a meta-analysis with several European ancestry studies. We identified nine novel lipid loci, five of which showed evidence of replication in independent studies. Furthermore, we discovered one novel gene in a PrediXcan analysis, minority-specific independent signals at eight previously reported loci, and potential functional variants at two known loci through fine-mapping. Systematic examination of known lipid loci revealed smaller effect estimates in African American and Hispanic ancestry populations than those in Europeans, and better performance of polygenic risk scores based on minority-specific effect estimates. Our findings provide new insight into the genetic architecture of lipid traits and highlight the importance of conducting genetic studies in diverse populations in the era of precision medicine., Competing Interests: The authors have declared that no competing interests exist.

Background: Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms., Methods: We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category., Results: A total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 ± 1.38. Specifically, the average knowledge (K) score is 0.64 ± 0.66, interpretation (I) score is 0.33 ± 0.55, and programming (P) score is 0.40 ± 0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks., Conclusion: This study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Resting-state white blood cell (WBC) count is a marker of inflammation and immune system health. There is evidence that WBC count is not fixed over time and there is heterogeneity in WBC trajectory that is associated with morbidity and mortality. Latent class mixed modeling (LCMM) is a method that can identify unobserved heterogeneity in longitudinal data and attempts to classify individuals into groups based on a linear model of repeated measurements. We applied LCMM to repeated WBC count measures derived from electronic medical records of participants of the National Human Genetics Research Institute (NHRGI) electronic MEdical Record and GEnomics (eMERGE) network study, revealing two WBC count trajectory phenotypes. Advancing these phenotypes to GWAS, we found genetic associations between trajectory class membership and regions on chromosome 1p34.3 and chromosome 11q13.4. The chromosome 1 region contains CSF3R, which encodes the granulocyte colony-stimulating factor receptor. This protein is a major factor in neutrophil stimulation and proliferation. The association on chromosome 11 contain genes RNF169 and XRRA1; both involved in the regulation of double-strand break DNA repair.

Background: Implementing clinical phenotypes across a network is labor intensive and potentially error prone. Use of a common data model may facilitate the process., Methods: Electronic Medical Records and Genomics (eMERGE) sites implemented the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model across their electronic health record (EHR)-linked DNA biobanks. Two previously implemented eMERGE phenotypes were converted to OMOP and implemented across the network., Results: It was feasible to implement the common data model across sites, with laboratory data producing the greatest challenge due to local encoding. Sites were then able to execute the OMOP phenotype in less than one day, as opposed to weeks of effort to manually implement an eMERGE phenotype in their bespoke research EHR databases. Of the sites that could compare the current OMOP phenotype implementation with the original eMERGE phenotype implementation, specific agreement ranged from 100% to 43%, with disagreements due to the original phenotype, the OMOP phenotype, changes in data, and issues in the databases. Using the OMOP query as a standard comparison revealed differences in the original implementations despite starting from the same definitions, code lists, flowcharts, and pseudocode., Conclusion: Using a common data model can dramatically speed phenotype implementation at the cost of having to populate that data model, though this will produce a net benefit as the number of phenotype implementations increases. Inconsistencies among the implementations of the original queries point to a potential benefit of using a common data model so that actual phenotype code and logic can be shared, mitigating human error in reinterpretation of a narrative phenotype definition., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver illness with a genetically heterogeneous background that can be accompanied by considerable morbidity and attendant health care costs. The pathogenesis and progression of NAFLD is complex with many unanswered questions. We conducted genome-wide association studies (GWASs) using both adult and pediatric participants from the Electronic Medical Records and Genomics (eMERGE) Network to identify novel genetic contributors to this condition., Methods: First, a natural language processing (NLP) algorithm was developed, tested, and deployed at each site to identify 1106 NAFLD cases and 8571 controls and histological data from liver tissue in 235 available participants. These include 1242 pediatric participants (396 cases, 846 controls). The algorithm included billing codes, text queries, laboratory values, and medication records. Next, GWASs were performed on NAFLD cases and controls and case-only analyses using histologic scores and liver function tests adjusting for age, sex, site, ancestry, PC, and body mass index (BMI)., Results: Consistent with previous results, a robust association was detected for the PNPLA3 gene cluster in participants with European ancestry. At the PNPLA3-SAMM50 region, three SNPs, rs738409, rs738408, and rs3747207, showed strongest association (best SNP rs738409 p = 1.70 × 10 - 20 ). This effect was consistent in both pediatric (p = 9.92 × 10 - 6 ) and adult (p = 9.73 × 10 - 15 ) cohorts. Additionally, this variant was also associated with disease severity and NAFLD Activity Score (NAS) (p = 3.94 × 10 - 8 , beta = 0.85). PheWAS analysis link this locus to a spectrum of liver diseases beyond NAFLD with a novel negative correlation with gout (p = 1.09 × 10 - 4 ). We also identified novel loci for NAFLD disease severity, including one novel locus for NAS score near IL17RA (rs5748926, p = 3.80 × 10 - 8 ), and another near ZFP90-CDH1 for fibrosis (rs698718, p = 2.74 × 10 - 11 ). Post-GWAS and gene-based analyses identified more than 300 genes that were used for functional and pathway enrichment analyses., Conclusions: In summary, this study demonstrates clear confirmation of a previously described NAFLD risk locus and several novel associations. Further collaborative studies including an ethnically diverse population with well-characterized liver histologic features of NAFLD are needed to further validate the novel findings.

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.

Background: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals., Methods: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651)., Results: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β., Conclusions: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

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

MYCN amplification and 11q deletion are two inversely correlated prognostic factors of poor outcome in neuroblastoma. Here we identify common variants at 11q22.2 within MMP20 that associate with neuroblastoma cases harboring 11q deletion (rs10895322), using GWAS in 113 European-American cases and 5109 ancestry-matched controls. The association is replicated in 44 independent cases and 1902 controls. Our study yields novel insights into the genetic underpinnings of neuroblastoma, demonstrating that the inherited common variants reported contribute to the origin of intra-tumor genetic heterogeneity in neuroblastoma.Chromosomal abnormalities such as 11q deletion are associated with poor prognosis in neuroblastoma. Here, the authors perform a genome-wide association study and identify an association between a variant within a Matrix metalloproteinase (MMP) gene member, MMP20, and 11q-deletion subtype neuroblastoma.

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We conducted a genome-wide association study (GWAS) of anorexia nervosa (AN) using a stringently defined phenotype. Analysis of phenotypic variability led to the identification of a specific genetic risk factor that approached genome-wide significance (rs929626 in EBF1 (Early B-Cell Factor 1); P = 2.04 × 10 -7 ; OR = 0.7; 95% confidence interval (CI) = 0.61-0.8) with independent replication (P = 0.04), suggesting a variant-mediated dysregulation of leptin signaling may play a role in AN. Multiple SNPs in LD with the variant support the nominal association. This demonstrates that although the clinical and etiologic heterogeneity of AN is universally recognized, further careful sub-typing of cases may provide more precise genomic signals. In this study, through a refinement of the phenotype spectrum of AN, we present a replicable GWAS signal that is nominally associated with AN, highlighting a potentially important candidate locus for further investigation.

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

Birth weight (BW) has been shown to be influenced by both fetal and maternal factors and in observational studies is reproducibly associated with future risk of adult metabolic diseases including type 2 diabetes (T2D) and cardiovascular disease. These life-course associations have often been attributed to the impact of an adverse early life environment. Here, we performed a multi-ancestry genome-wide association study (GWAS) meta-analysis of BW in 153,781 individuals, identifying 60 loci where fetal genotype was associated with BW (P < 5 × 10 -8 ). Overall, approximately 15% of variance in BW was captured by assays of fetal genetic variation. Using genetic association alone, we found strong inverse genetic correlations between BW and systolic blood pressure (R g  = -0.22, P = 5.5 × 10 -13 ), T2D (R g  = -0.27, P = 1.1 × 10 -6 ) and coronary artery disease (R g  = -0.30, P = 6.5 × 10 -9 ). In addition, using large -cohort datasets, we demonstrated that genetic factors were the major contributor to the negative covariance between BW and future cardiometabolic risk. Pathway analyses indicated that the protein products of genes within BW-associated regions were enriched for diverse processes including insulin signalling, glucose homeostasis, glycogen biosynthesis and chromatin remodelling. There was also enrichment of associations with BW in known imprinted regions (P = 1.9 × 10 -4 ). We demonstrate that life-course associations between early growth phenotypes and adult cardiometabolic disease are in part the result of shared genetic effects and identify some of the pathways through which these causal genetic effects are mediated., Competing Interests: One of the authors discloses competing financial interests: Krina Zondervan has a scientific collaboration with Bayer HealthCare Ltd. and Population Diagnostics Inc.

Acute otitis media (AOM) is among the most common pediatric diseases, and the most frequent reason for antibiotic treatment in children. Risk of AOM is dependent on environmental and host factors, as well as a significant genetic component. We identify genome-wide significance at a locus on 6q25.3 (rs2932989, P meta =2.15 × 10 -09 ), and show that the associated variants are correlated with the methylation status of the FNDC1 gene (cg05678571, P=1.43 × 10 -06 ), and further show it is an eQTL for FNDC1 (P=9.3 × 10 -05 ). The mouse homologue, Fndc1, is expressed in middle ear tissue and its expression is upregulated upon lipopolysaccharide treatment. In this first GWAS of AOM and the largest OM genetic study to date, we identify the first genome-wide significant locus associated with AOM.

More than a million childhood diarrhoeal episodes occur worldwide each year, and in developed countries a considerable part of them are caused by viral infections. In this study, we aimed to search for genetic variants associated with diarrhoeal disease in young children by meta-analyzing genome-wide association studies, and to elucidate plausible biological mechanisms. The study was conducted in the context of the Early Genetics and Lifecourse Epidemiology (EAGLE) consortium. Data about diarrhoeal disease in two time windows (around 1 year of age and around 2 years of age) was obtained via parental questionnaires, doctor interviews or medical records. Standard quality control and statistical tests were applied to the 1000 Genomes imputed genotypic data. The meta-analysis (N = 5758) followed by replication (N = 3784) identified a genome-wide significant association between rs8111874 and diarrhoea at age 1 year. Conditional analysis suggested that the causal variant could be rs601338 (W154X) in the FUT2 gene. Children with the A allele, which results in a truncated FUT2 protein, had lower risk of diarrhoea. FUT2 participates in the production of histo-blood group antigens and has previously been implicated in the susceptibility to infections, including Rotavirus and Norovirus Gene-set enrichment analysis suggested pathways related to the histo-blood group antigen production, and the regulation of ion transport and blood pressure. Among others, the gastrointestinal tract, and the immune and neuro-secretory systems were detected as relevant organs. In summary, this genome-wide association meta-analysis suggests the implication of the FUT2 gene in diarrhoeal disease in young children from the general population., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

A large number of genetic loci are associated with adult body mass index. However, the genetics of childhood body mass index are largely unknown. We performed a meta-analysis of genome-wide association studies of childhood body mass index, using sex- and age-adjusted standard deviation scores. We included 35 668 children from 20 studies in the discovery phase and 11 873 children from 13 studies in the replication phase. In total, 15 loci reached genome-wide significance (P-value < 5 × 10(-8)) in the joint discovery and replication analysis, of which 12 are previously identified loci in or close to ADCY3, GNPDA2, TMEM18, SEC16B, FAIM2, FTO, TFAP2B, TNNI3K, MC4R, GPR61, LMX1B and OLFM4 associated with adult body mass index or childhood obesity. We identified three novel loci: rs13253111 near ELP3, rs8092503 near RAB27B and rs13387838 near ADAM23. Per additional risk allele, body mass index increased 0.04 Standard Deviation Score (SDS) [Standard Error (SE) 0.007], 0.05 SDS (SE 0.008) and 0.14 SDS (SE 0.025), for rs13253111, rs8092503 and rs13387838, respectively. A genetic risk score combining all 15 SNPs showed that each additional average risk allele was associated with a 0.073 SDS (SE 0.011, P-value = 3.12 × 10(-10)) increase in childhood body mass index in a population of 1955 children. This risk score explained 2% of the variance in childhood body mass index. This study highlights the shared genetic background between childhood and adult body mass index and adds three novel loci. These loci likely represent age-related differences in strength of the associations with body mass index., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)