Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets.

Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes.

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.

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.

Biliary atresia is associated with polygenic susceptibility in ciliogenesis and planar polarity effector genes.

BACKGROUND & AIMS: Biliary atresia (BA) is poorly understood and leads to liver transplantation (LT), with the requirement for and associated risks of lifelong immunosuppression, in most children. We performed a genome-wide association study (GWAS) to determine the genetic basis of BA. METHODS: We performed a GWAS in 811 European BA cases treated with LT in US, Canadian and UK centers, and 4,654 genetically matched controls. Whole-genome sequencing of 100 cases evaluated synthetic association with rare variants. Functional studies included whole liver transcriptome analysis of 64 BA cases and perturbations in experimental models. RESULTS: A GWAS of common single nucleotide polymorphisms (SNPs), i.e. allele frequencies >1%, identified intronic SNPs rs6446628 in AFAP1 with genome-wide significance (p = 3.93E-8) and rs34599046 in TUSC3 at sub-threshold genome-wide significance (p = 1.34E-7), both supported by credible peaks of neighboring SNPs. Like other previously reported BA-associated genes, AFAP1 and TUSC3 are ciliogenesis and planar polarity effectors (CPLANE). In gene-set-based GWAS, BA was associated with 6,005 SNPs in 102 CPLANE genes (p = 5.84E-15). Compared with non-CPLANE genes, more CPLANE genes harbored rare variants (allele frequency <1%) that were assigned Human Phenotype Ontology terms related to hepatobiliary anomalies by predictive algorithms, 87% vs. 40%, p <0.0001. Rare variants were present in multiple genes distinct from those with BA-associated common variants in most BA cases. AFAP1 and TUSC3 knockdown blocked ciliogenesis in mouse tracheal cells. Inhibition of ciliogenesis caused biliary dysgenesis in zebrafish. AFAP1 and TUSC3 were expressed in fetal liver organoids, as well as fetal and BA livers, but not in normal or disease-control livers. Integrative analysis of BA-associated variants and liver transcripts revealed abnormal vasculogenesis and epithelial tube formation, explaining portal vein anomalies that co-exist with BA. CONCLUSIONS: BA is associated with polygenic susceptibility in CPLANE genes. Rare variants contribute to polygenic risk in vulnerable pathways via unique genes. IMPACT AND IMPLICATIONS: Liver transplantation is needed to cure most children born with biliary atresia, a poorly understood rare disease. Transplant immunosuppression increases the likelihood of life-threatening infections and cancers. To improve care by preventing this disease and its progression to transplantation, we examined its genetic basis. We find that this disease is associated with both common and rare mutations in highly specialized genes which maintain normal communication and movement of cells, and their organization into bile ducts and blood vessels during early development of the human embryo. Because defects in these genes also cause other birth defects, our findings could lead to preventive strategies to lower the incidence of biliary atresia and potentially other birth defects.

DeepCNV: a deep learning approach for authenticating copy number variations.

Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably high false positive rate. A common practice is to have human experts manually review original CNV calls for filtering false positives before further downstream analysis or experimental validation. Here, we propose DeepCNV, a deep learning-based tool, intended to replace human experts when validating CNV calls, focusing on the calls made by one of the most accurate CNV callers, PennCNV. The sophistication of the deep neural network algorithm is enriched with over 10 000 expert-scored samples that are split into training and testing sets. Variant confidence, especially for CNVs, is a main roadblock impeding the progress of linking CNVs with the disease. We show that DeepCNV adds to the confidence of the CNV calls with an optimal area under the receiver operating characteristic curve of 0.909, exceeding other machine learning methods. The superiority of DeepCNV was also benchmarked and confirmed using an experimental wet-lab validation dataset. We conclude that the improvement obtained by DeepCNV results in significantly fewer false positive results and failures to replicate the CNV association results.

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.

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.

Rare neurological manifestations in a Saudi Arabian patient with Ehlers-Danlos syndrome and a novel homozygous variant in the TNXB gene.

We report a 38-year-old Saudi male with Ehlers-Danlos Syndrome (EDS). The patient presented with rare and unusual neurological manifestations, including but not limited to ophthalmoplegia and myopathic pattern on his electromyography. In addition to hand weakness, there was skin hyperextensibility, joint hyperflexibility, and frontal baldness. Next-generation sequencing was performed on target exon sequences, using whole exome sequencing and Burrows-Wheeler Aligner for alignment/base calling. Genome Analysis Toolkit and reference genome Homo sapiens (UCSC hg19) were used for sequence processing and analysis. Variant classification was done according to standard international recommendations. A novel homozygous variant, NM_019105.6: c.8488C>T p.(Gln2830*), was detected in the TNXB gene. This variant is not reported in the literature nor dbSNP or gnomAD databases. Additionally, this variant is predicted to create a premature stop codon and produce a truncated protein or nonsense-mediated mRNA decay. Hence, it is classified as a likely pathogenic variant. The same point variant was found in a heterozygous state in the patient's father and sister. Both presented with milder symptoms associated with Ehlers-Danlos syndromes and heritable connective tissue disorders. Therefore, the patient was diagnosed as a tenascin-X (TNX) deficient type of EDS known as classical-like Ehlers-Danlos syndrome. TNX deficient patients may present with clinical and electrophysiological manifestations that are unusual in EDS like frontal baldness, ophthalmoplegia, and myotonia, which mimic myotonic dystrophy type I. Clinicians should be aware of the potential overlap of symptoms among these two diseases to ensure correct diagnosis is made.

MONTAGE: a new tool for high-throughput detection of mosaic copy number variation.

BACKGROUND: Not all cells in a given individual are identical in their genomic makeup. Mosaicism describes such a phenomenon where a mixture of genotypic states in certain genomic segments exists within the same individual. Mosaicism is a prevalent and impactful class of non-integer state copy number variation (CNV). Mosaicism implies that certain cell types or subset of cells contain a CNV in a segment of the genome while other cells in the same individual do not. Several studies have investigated the impact of mosaicism in single patients or small cohorts but no comprehensive scan of mosaic CNVs has been undertaken to accurately detect such variants and interpret their impact on human health and disease. RESULTS: We developed a tool called Montage to improve the accuracy of detection of mosaic copy number variants in a high throughput fashion. Montage directly interfaces with ParseCNV2 algorithm to establish disease phenotype genome-wide association and determine which genomic ranges had more or less than expected frequency of mosaic events. We screened for mosaic events in over 350,000 samples using 1% allele frequency as the detection limit. Additionally, we uncovered disease associations of multiple phenotypes with mosaic CNVs at several genomic loci. We additionally investigated the allele imbalance observations genome-wide to define non-diploid and non-integer copy number states. CONCLUSIONS: Our novel algorithm presents an efficient tool with fast computational runtime and high levels of accuracy of mosaic CNV detection. A curated mosaic CNV callset of 3716 events in 2269 samples is presented with comparability to previous reports and disease phenotype associations. The new algorithm can be freely accessed via: https://github.com/CAG-CNV/MONTAGE .

Association of novel rare coding variants with juvenile idiopathic arthritis.

OBJECTIVE: Juvenile idiopathic arthritis (JIA) is the most common type of arthritis among children, but a few studies have investigated the contribution of rare variants to JIA. In this study, we aimed to identify rare coding variants associated with JIA for the genome-wide landscape. METHODS: We established a rare variant calling and filtering pipeline and performed rare coding variant and gene-based association analyses on three RNA-seq datasets composed of 228 JIA patients in the Gene Expression Omnibus against different sets of controls, and further conducted replication in our whole-exome sequencing (WES) data of 56 JIA patients. Then we conducted differential gene expression analysis and assessed the impact of recurrent functional coding variants on gene expression and signalling pathway. RESULTS: By the RNA-seq data, we identified variants in two genes reported in literature as JIA causal variants, as well as additional 63 recurrent rare coding variants seen only in JIA patients. Among the 44 recurrent rare variants found in polyarticular patients, 10 were replicated by our WES of patients with the same JIA subtype. Several genes with recurrent functional rare coding variants have also common variants associated with autoimmune diseases. We observed immune pathways enriched for the genes with rare coding variants and differentially expressed genes. CONCLUSION: This study elucidated a novel landscape of recurrent rare coding variants in JIA patients and uncovered significant associations with JIA at the gene pathway level. The convergence of common variants and rare variants for autoimmune diseases is also highlighted in this study.

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters.

RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. This study used machine learning to reduce gene/non-coding RNA features. Dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 individuals was obtained from the CommonMind consortium. The average predictive accuracy for SCZ patients was 67% based on coding genes, and 96% based on long non-coding RNAs (lncRNAs). Machine learning is a powerful algorithm to reduce functional biomarkers in SCZ patients. The lncRNAs capture the characteristics of SCZ tissue more accurately than mRNA as the former regulate every level of gene expression, not limited to mRNA levels.

Common variants at 5q33.1 predispose to migraine in African-American children.

BACKGROUND: Genome-wide association studies (GWASs) have identified multiple susceptibility loci for migraine in European adults. However, no large-scale genetic studies have been performed in children or African Americans with migraine. METHODS: We conducted a GWAS of 380 African-American children and 2129 ancestry-matched controls to identify variants associated with migraine. We then attempted to replicate our primary analysis in an independent cohort of 233 African-American patients and 4038 non-migraine control subjects. RESULTS: The results of this study indicate that common variants at 5q33.1 associated with migraine risk in African-American children (rs72793414, p=1.94×10-9). The association was validated in an independent study (p=3.87×10-3) for an overall meta-analysis p value of 3.81×10-10. eQTL (Expression quantitative trait loci) analysis of the Genotype-Tissue Expression data also shows the genotypes of rs72793414 were strongly correlated with the mRNA expression levels of NMUR2 at 5q33.1. NMUR2 encodes a G protein-coupled receptor of neuromedin-U (NMU). NMU, a highly conserved neuropeptide, participates in diverse physiological processes of the central nervous system. CONCLUSIONS: This study provides new insights into the genetic basis of childhood migraine and allow for precision therapeutic development strategies targeting migraine patients of African-American ancestry.

Leveraging electronic health records to assess the role of ADRB2 single nucleotide polymorphisms in predicting exacerbation frequency in asthma patients.

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 ß-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.

Identification of Four Novel Loci in Asthma in European American and African American Populations.

RATIONALE: Despite significant advances in knowledge of the genetic architecture of asthma, specific contributors to the variability in the burden between populations remain uncovered. OBJECTIVES: To identify additional genetic susceptibility factors of asthma in European American and African American populations. METHODS: A phenotyping algorithm mining electronic medical records was developed and validated to recruit cases with asthma and control subjects from the Electronic Medical Records and Genomics network. Genome-wide association analyses were performed in pediatric and adult asthma cases and control subjects with European American and African American ancestry followed by metaanalysis. Nominally significant results were reanalyzed conditioning on allergy status. MEASUREMENTS AND MAIN RESULTS: The validation of the algorithm yielded an average of 95.8% positive predictive values for both cases and control subjects. The algorithm accrued 21,644 subjects (65.83% European American and 34.17% African American). We identified four novel population-specific associations with asthma after metaanalyses: loci 6p21.31, 9p21.2, and 10q21.3 in the European American population, and the PTGES gene in African Americans. TEK at 9p21.2, which encodes TIE2, has been shown to be involved in remodeling the airway wall in asthma, and the association remained significant after conditioning by allergy. PTGES, which encodes the prostaglandin E synthase, has also been linked to asthma, where deficient prostaglandin E2 synthesis has been associated with airway remodeling. CONCLUSIONS: This study adds to understanding of the genetic architecture of asthma in European Americans and African Americans and reinforces the need to study populations of diverse ethnic backgrounds to identify shared and unique genetic predictors of asthma.

Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data.

Individuals with psychiatric disorders have elevated rates of autoimmune comorbidity and altered immune signaling. It is unclear whether these altered immunological states have a shared genetic basis with those psychiatric disorders. The present study sought to use existing summary-level data from previous genome-wide association studies to determine if commonly varying single nucleotide polymorphisms are shared between psychiatric and immune-related phenotypes. We estimated heritability and examined pair-wise genetic correlations using the linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics methods. Using LDSC, we observed significant genetic correlations between immune-related disorders and several psychiatric disorders, including anorexia nervosa, attention deficit-hyperactivity disorder, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia, smoking behavior, and Tourette syndrome. Loci significantly mediating genetic correlations were identified for schizophrenia when analytically paired with Crohn's disease, primary biliary cirrhosis, systemic lupus erythematosus, and ulcerative colitis. We report significantly correlated loci and highlight those containing genome-wide associations and candidate genes for respective disorders. We also used the LDSC method to characterize genetic correlations among the immune-related phenotypes. We discuss our findings in the context of relevant genetic and epidemiological literature, as well as the limitations and caveats of the study.

Loss-of-Function Mutations in KIF15 Underlying a Braddock-Carey Genocopy.

Braddock-Carey Syndrome (BCS) is characterized by microcephaly, congenital thrombocytopenia, Pierre-Robin sequence (PRS), and agenesis of the corpus callosum. BCS has been shown to be caused by a 21q22.11 microdeletion that encompasses multiple genes. Here, we report a BCS genocopy characterized by congenital thrombocytopenia and PRS that is caused by a loss-of-function mutation in KIF15 in a consanguineous Saudi Arabian family. Mutations of mitotic kinesins are a well-established cause of microcephaly. To our knowledge, KIF15 is the first kinesin to be associated with congenital thrombocytopenia.

Copy Number Variations in CTNNA3 and RBFOX1 Associate with Pediatric Food Allergy.

Food allergy is a significant public health concern, especially among children. Previous candidate gene studies suggested a few susceptibility loci for food allergy, but no study investigated the contribution of copy number variations (CNVs) to food allergy on a genome-wide scale. To investigate the genetics of food allergy, we performed CNV assessment using high-resolution genome-wide single nucleotide polymorphism arrays. CNV calls from a total of 357 cases with confirmed food allergy and 3980 controls were analyzed within a discovery cohort, followed by a replication analysis composed of 167 cases and 1573 controls. We identified that CNVs in CTNNA3 were significantly associated with food allergy in both the discovery cohort and the replication cohort. Of particular interest, CTNNA3 CNVs hit exons or intron regions rich in histone marker H3K4Me1. CNVs in a second gene (RBFOX1) showed a significant association (p = 7.35 × 10(-5)) with food allergy at the genome-wide level in our meta-analysis of the European ancestry cohorts. The presence of these CNVs was confirmed by quantitative PCR. Furthermore, knockdown of CTNNA3 resulted in upregulation of CD63 and CD203c in mononuclear cells upon PMA stimulation, suggesting a role in sensitization to allergen. We uncovered at least two plausible genes harboring CNV loci that are enriched in pediatric patients with food allergies. The novel gene candidates discovered in this study by genome-wide CNV analysis are compelling drug and diagnostic targets for food allergy.

The Philadelphia Neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth.

The Philadelphia Neurodevelopmental Cohort (PNC) is a large-scale study of child development that combines neuroimaging, diverse clinical and cognitive phenotypes, and genomics. Data from this rich resource is now publicly available through the Database of Genotypes and Phenotypes (dbGaP). Here we focus on the data from the PNC that is available through dbGaP and describe how users can access this data, which is evolving to be a significant resource for the broader neuroscience community for studies of normal and abnormal neurodevelopment.

GWAS of blood cell traits identifies novel associated loci and epistatic interactions in Caucasian and African-American children.

Hematological traits are important clinical indicators, the genetic determinants of which have not been fully investigated. Common measures of hematological traits include red blood cell (RBC) count, hemoglobin concentration (HGB), hematocrit (HCT), mean corpuscular hemoglobin (MCH), MCH concentration (MCHC), mean corpuscular volume (MCV), platelet count (PLT) and white blood cell (WBC) count. We carried out a genome-wide association study of the eight common hematological traits among 7943 African-American children and 6234 Caucasian children. In African Americans, we report five novel associations of HBE1 variants with HCT and MCHC, the alpha-globin gene cluster variants with RBC and MCHC, and a variant at the ARHGEF3 locus with PLT, as well as replication of four previously reported loci at genome-wide significance. In Caucasians, we report a novel association of variants at the COPZ1 locus with PLT as well as replication of four previously reported loci at genome-wide significance. Extended analysis of an association observed between MCH and the alpha-globin gene cluster variants demonstrated independent effects and epistatic interaction at the locus, impacting the risk of iron deficiency anemia in African Americans with specific genotype states. In summary, we extend the understanding of genetic variants underlying hematological traits based on analyses in African-American children.

Genome-wide association analysis of red blood cell traits in African Americans: the COGENT Network.

Laboratory red blood cell (RBC) measurements are clinically important, heritable and differ among ethnic groups. To identify genetic variants that contribute to RBC phenotypes in African Americans (AAs), we conducted a genome-wide association study in up to ~16 500 AAs. The alpha-globin locus on chromosome 16pter [lead SNP rs13335629 in ITFG3 gene; P < 1E-13 for hemoglobin (Hgb), RBC count, mean corpuscular volume (MCV), MCH and MCHC] and the G6PD locus on Xq28 [lead SNP rs1050828; P < 1E - 13 for Hgb, hematocrit (Hct), MCV, RBC count and red cell distribution width (RDW)] were each associated with multiple RBC traits. At the alpha-globin region, both the common African 3.7 kb deletion and common single nucleotide polymorphisms (SNPs) appear to contribute independently to RBC phenotypes among AAs. In the 2p21 region, we identified a novel variant of PRKCE distinctly associated with Hct in AAs. In a genome-wide admixture mapping scan, local European ancestry at the 6p22 region containing HFE and LRRC16A was associated with higher Hgb. LRRC16A has been previously associated with the platelet count and mean platelet volume in AAs, but not with Hgb. Finally, we extended to AAs the findings of association of erythrocyte traits with several loci previously reported in Europeans and/or Asians, including CD164 and HBS1L-MYB. In summary, this large-scale genome-wide analysis in AAs has extended the importance of several RBC-associated genetic loci to AAs and identified allelic heterogeneity and pleiotropy at several previously known genetic loci associated with blood cell traits in AAs.