High Comorbidity of Pediatric Cancers in Patients with Birth Defects: Insights from Whole Genome Sequencing Analysis of Copy Number Variations.

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.

Genomic variants exclusively identified in children with birth defects and concurrent malignant tumors predispose to cancer development.

Children with birth defects (BD) express distinct clinical features that often have various medical consequences, one of which is predisposition to the development of cancers. Identification of the underlying genetic mechanisms related to the development of cancer in BD patients would allow for preventive measures. We performed a whole genome sequencing (WGS) study on blood-derived DNA samples from 1566 individuals without chromosomal anomalies, including 454 BD probands with at least one type of malignant tumors, 767 cancer-free BD probands, and 345 healthy individuals. Exclusive recurrent variants were identified in BD-cancer and BD-only patients and mapped to their corresponding genomic regions. We observed statistically significant overlaps for protein-coding/ncRNA with exclusive variants in exons, introns, ncRNAs, and 3'UTR regions. Exclusive exonic variants, especially synonymous variants, tend to occur in prior exons locus in BD-cancer children. Intronic variants close to splicing site (< 500 bp from exon) have little overlaps in BD-cancer and BD-only patients. Exonic variants in non-coding RNA (ncRNA) tend to occur in different ncRNAs exons regardless of the overlaps. Notably, genes with 5' UTR variants are almost mutually exclusive between the two phenotypes. In conclusion, we conducted the first genomic study to explore the impact of recurrent variants exclusive to the two distinguished clinical phenotypes under study, BD with or without cancer, demonstrating enrichment of selective protein-coding/ncRNAs differentially expressed between these two phenotypes, suggesting that selective genetic factors may underlie the molecular processes of pediatric cancer development in BD children.

Elevated Levels of the Cytokine LIGHT in Pediatric Crohn's Disease.

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 ß 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.

Identification of risk variants related to malignant tumors in children with birth defects by whole genome sequencing.

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.

Identification of novel loci in obstructive sleep apnea in European American and African American children.

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 1,486 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.

Mutation burden analysis of six common mental disorders in African Americans by whole genome sequencing.

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.

An electronic health record (EHR) phenotype algorithm to identify patients with attention deficit hyperactivity disorders (ADHD) and psychiatric comorbidities.

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.

Burden of rare coding variants reveals genetic heterogeneity between obese and non-obese asthma patients in the African American population.

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.

Multiancestral polygenic risk score for pediatric asthma.

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, PFDR = 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.

Contribution of Mendelian Disorders in a Population-Based Pediatric Neurodegeneration Cohort.

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.

Identification of Novel Loci Shared by Juvenile Idiopathic Arthritis Subtypes Through Integrative Genetic Analysis.

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.

Application of deep learning algorithm on whole genome sequencing data uncovers structural variants associated with multiple mental disorders in African American patients.

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.

Integrative analysis of genome-wide association studies identifies novel loci associated with neuropsychiatric disorders.

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.

Evaluation of the MC4R gene across eMERGE network identifies many unreported obesity-associated variants.

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/m2. 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.

Minority-centric meta-analyses of blood lipid levels identify novel loci in the Population Architecture using Genomics and Epidemiology (PAGE) study.

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.

A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies.

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.

Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network.

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 ±â€¯1.38. Specifically, the average knowledge (K) score is 0.64 ±â€¯0.66, interpretation (I) score is 0.33 ±â€¯0.55, and programming (P) score is 0.40 ±â€¯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.

Facilitating phenotype transfer using a common data model.

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.