Genome-wide association study of primary tooth eruption identifies pleiotropic loci associated with height and craniofacial distances

Twin and family studies indicate that the timing of primary tooth eruption is highly heritable, with estimates typically exceeding 80%. To identify variants involved in primary tooth eruption we performed a population based genome-wide association study of ‘age at first tooth’ and ‘number of teeth’ using 5998 and 6609 individuals respectively from the Avon Longitudinal Study of Parents and Children (ALSPAC) and 5403 individuals from the 1966 Northern Finland Birth Cohort (NFBC1966). We tested 2,446,724 SNPs imputed in both studies. Analyses were controlled for the effect of gestational age, sex and age of measurement. Results from the two studies were combined using fixed effects inverse variance meta-analysis. We identified a total of fifteen independent loci, with ten loci reaching genome-wide significance (p<5x10−8) for ‘age at first tooth’ and eleven loci for ‘number of teeth’. Together these associations explain 6.06% of the variation in ‘age of first tooth’ and 4.76% of the variation in ‘number of teeth’. The identified loci included eight previously unidentified loci, some containing genes known to play a role in tooth and other developmental pathways, including a SNP in the protein-coding region of BMP4 (rs17563, P= 9.080x10−17). Three of these loci, containing the genes HMGA2, AJUBA and ADK, also showed evidence of association with craniofacial distances, particularly those indexing facial width. Our results suggest that the genome-wide association approach is a powerful strategy for detecting variants involved in tooth eruption, and potentially craniofacial growth and more generally organ development

Diagnosis of Kawasaki disease using a minimal whole blood gene expression signature

Importance There is no diagnostic test for Kawasaki disease (KD). Diagnosis is based on clinical features shared with other febrile conditions, frequently resulting in delayed or missed treatment and an increased risk of coronary artery aneurysms. Objective To identify a whole blood gene expression signature that distinguishes children with KD in the first week of illness from other febrile conditions. Design Case-control discovery study groups comprising training, test, and validation groups of children with KD or comparator febrile illness. Setting Hospitals in the UK, Spain, Netherlands and USA. Participants The training and test discovery group comprised 404 children with infectious and inflammatory conditions (78 KD, 84 other inflammatory diseases, 242 bacterial or viral infections) and 55 healthy controls. The independent validation group included 130 febrile children and 102 KD patients, including 72 in the first 7 days of illness. Exposures Whole blood gene expression was evaluated using microarrays, and minimal transcript sets distinguishing KD were identified using a novel variable selection method (Parallel Deterministic Model Search). Main outcomes and measures The ability of transcript signatures - implemented as Disease Risk Scores - to discriminate KD cases from controls, was assessed by Area Under the Curve (AUC), sensitivity, and specificity at the optimal cut-point according to Youden’s index. Results A 13-transcript signature identified in the discovery training set distinguished KD from other infectious and inflammatory conditions in the discovery test set with AUC, sensitivity, and specificity (95% confidence intervals (CI)) of 96.2% (92.5-99.9), 81.7% (60.0-94.8), and 92.1% (84.0-97.0), respectively. In the validation set, the signature distinguished KD from febrile controls with AUC, sensitivity, and specificity (95% CI) of 94.6% (91.3-98.0), 85.9% (76.8-92.6), and 89.1% (83.0-93.7) respectively. The signature was applied to clinically defined categories of Definite, Highly Probable and Possible KD resulting in AUCs of 98.1%, 96.3% and 70.0% respectively, mirroring clinical certainty. Conclusions and relevance A 13-transcript blood gene expression signature distinguished KD from other febrile conditions. Diagnostic accuracy increased with certainty of clinical diagnosis. A test incorporating the 13-transcript Disease Risk Score may enable earlier diagnosis and treatment of KD, and reduce inappropriate treatment in those with other diagnoses

A Two-Stage Random Forest-Based Pathway Analysis Method

Pathway analysis provides a powerful approach for identifying the joint effect of genes grouped into biologically-based pathways on disease. Pathway analysis is also an attractive approach for a secondary analysis of genome-wide association study (GWAS) data that may still yield new results from these valuable datasets. Most of the current pathway analysis methods focused on testing the cumulative main effects of genes in a pathway. However, for complex diseases, gene-gene interactions are expected to play a critical role in disease etiology. We extended a random forest-based method for pathway analysis by incorporating a two-stage design. We used simulations to verify that the proposed method has the correct type I error rates. We also used simulations to show that the method is more powerful than the original random forest-based pathway approach and the set-based test implemented in PLINK in the presence of gene-gene interactions. Finally, we applied the method to a breast cancer GWAS dataset and a lung cancer GWAS dataset and interesting pathways were identified that have implications for breast and lung cancers

Diagnostic Test Accuracy of a 2-Transcript Host RNA Signature for Discriminating Bacterial vs Viral Infection in Febrile Children.

IMPORTANCE: Because clinical features do not reliably distinguish bacterial from viral infection, many children worldwide receive unnecessary antibiotic treatment, while bacterial infection is missed in others. OBJECTIVE: To identify a blood RNA expression signature that distinguishes bacterial from viral infection in febrile children. DESIGN, SETTING, AND PARTICIPANTS: Febrile children presenting to participating hospitals in the United Kingdom, Spain, the Netherlands, and the United States between 2009-2013 were prospectively recruited, comprising a discovery group and validation group. Each group was classified after microbiological investigation as having definite bacterial infection, definite viral infection, or indeterminate infection. RNA expression signatures distinguishing definite bacterial from viral infection were identified in the discovery group and diagnostic performance assessed in the validation group. Additional validation was undertaken in separate studies of children with meningococcal disease (n = 24) and inflammatory diseases (n = 48) and on published gene expression datasets. EXPOSURES: A 2-transcript RNA expression signature distinguishing bacterial infection from viral infection was evaluated against clinical and microbiological diagnosis. MAIN OUTCOMES AND MEASURES: Definite bacterial and viral infection was confirmed by culture or molecular detection of the pathogens. Performance of the RNA signature was evaluated in the definite bacterial and viral group and in the indeterminate infection group. RESULTS: The discovery group of 240 children (median age, 19 months; 62% male) included 52 with definite bacterial infection, of whom 36 (69%) required intensive care, and 92 with definite viral infection, of whom 32 (35%) required intensive care. Ninety-six children had indeterminate infection. Analysis of RNA expression data identified a 38-transcript signature distinguishing bacterial from viral infection. A smaller (2-transcript) signature (FAM89A and IFI44L) was identified by removing highly correlated transcripts. When this 2-transcript signature was implemented as a disease risk score in the validation group (130 children, with 23 definite bacterial, 28 definite viral, and 79 indeterminate infections; median age, 17 months; 57% male), all 23 patients with microbiologically confirmed definite bacterial infection were classified as bacterial (sensitivity, 100% [95% CI, 100%-100%]) and 27 of 28 patients with definite viral infection were classified as viral (specificity, 96.4% [95% CI, 89.3%-100%]). When applied to additional validation datasets from patients with meningococcal and inflammatory diseases, bacterial infection was identified with a sensitivity of 91.7% (95% CI, 79.2%-100%) and 90.0% (95% CI, 70.0%-100%), respectively, and with specificity of 96.0% (95% CI, 88.0%-100%) and 95.8% (95% CI, 89.6%-100%). Of the children in the indeterminate groups, 46.3% (63/136) were classified as having bacterial infection, although 94.9% (129/136) received antibiotic treatment. CONCLUSIONS AND RELEVANCE: This study provides preliminary data regarding test accuracy of a 2-transcript host RNA signature discriminating bacterial from viral infection in febrile children. Further studies are needed in diverse groups of patients to assess accuracy and clinical utility of this test in different clinical settings

Genetic Variation in the SLC8A1 Calcium Signaling Pathway Is Associated With Susceptibility to Kawasaki Disease and Coronary Artery Abnormalities.

BACKGROUND: Kawasaki disease (KD) is an acute pediatric vasculitis in which host genetics influence both susceptibility to KD and the formation of coronary artery aneurysms. Variants discovered by genome-wide association studies and linkage studies only partially explain the influence of genetics on KD susceptibility. METHODS AND RESULTS: To search for additional functional genetic variation, we performed pathway and gene stability analysis on a genome-wide association study data set. Pathway analysis using European genome-wide association study data identified 100 significantly associated pathways (P<5×10-4). Gene stability selection identified 116 single nucleotide polymorphisms in 26 genes that were responsible for driving the pathway associations, and gene ontology analysis demonstrated enrichment for calcium transport (P=1.05×10-4). Three single nucleotide polymorphisms in solute carrier family 8, member 1 (SLC8A1), a sodium/calcium exchanger encoding NCX1, were validated in an independent Japanese genome-wide association study data set (meta-analysis P=0.0001). Patients homozygous for the A (risk) allele of rs13017968 had higher rates of coronary artery abnormalities (P=0.029). NCX1, the protein encoded by SLC8A1, was expressed in spindle-shaped and inflammatory cells in the aneurysm wall. Increased intracellular calcium mobilization was observed in B cell lines from healthy controls carrying the risk allele. CONCLUSIONS: Pathway-based association analysis followed by gene stability selection proved to be a valuable tool for identifying risk alleles in a rare disease with complex genetics. The role of SLC8A1 polymorphisms in altering calcium flux in cells that mediate coronary artery damage in KD suggests that this pathway may be a therapeutic target and supports the study of calcineurin inhibitors in acute KD

Comparisons of seven algorithms for pathway analysis using the WTCCC Crohn's Disease dataset

<p>Abstract</p> <p>Background</p> <p>Though rooted in genomic expression studies, pathway analysis for genome-wide association studies (GWAS) has gained increasing popularity, since it has the potential to discover hidden disease pathogenic mechanisms by combining statistical methods with biological knowledge. Generally, algorithms or programs proposed recently can be categorized by different types of input data, null hypothesis or counts of analysis stages. Due to complexity caused by SNP, gene and pathway relationships, re-sampling strategies like permutation are always utilized to derive an empirical distribution for test statistics for evaluating the significance of candidate pathways. However, evaluation of these algorithms on real GWAS datasets and real biological pathway databases needs to be addressed before we apply them widely with confidence.</p> <p>Findings</p> <p>Two algorithms which use summary statistics from GWAS as input were implemented in KGG, a novel and user-friendly software tool for GWAS pathway analysis. Comparisons of these two algorithms as well as the other five selected algorithms were conducted by analyzing the WTCCC Crohn's Disease dataset utilizing the MsigDB canonical pathways. As a result of using permutation to obtain empirical p-value, most of these methods could control Type I error rate well, although some are conservative. However, the methods varied greatly in terms of power and running time, with the PLINK truncated set-based test being the most powerful and KGG being the fastest.</p> <p>Conclusions</p> <p>Raw data-based algorithms, such as those implemented in PLINK, are preferable for GWAS pathway analysis as long as computational capacity is available. It may be worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset, since the methods differ greatly in their outputs and might provide complementary findings for the studied complex disease.</p

Diagnostic Test Accuracy of a 2-Transcript Host RNA Signature for Discriminating Bacterial vs Viral Infection in Febrile Children

Importance: Because clinical features do not reliably distinguish bacterial from viral infection, many children worldwide receive unnecessary antibiotic treatment, while bacterial infection is missed in others. Objective: To identify a blood RNA expression signature that distinguishes bacterial from viral infection in febrile children. Design, Setting, and Participants: Febrile children presenting to participating hospitals in the United Kingdom, Spain, the Netherlands, and the United States between 2009-2013 were prospectively recruited, comprising a discovery group and validation group. Each group was classified after microbiological investigation as having definite bacterial infection, definite viral infection, or indeterminate infection. RNA expression signatures distinguishing definite bacterial from viral infection were identified in the discovery group and diagnostic performance assessed in the validation group. Additional validation was undertaken in separate studies of children with meningococcal disease (n = 24) and inflammatory diseases (n = 48) and on published gene expression datasets. Exposures: A 2-transcript RNA expression signature distinguishing bacterial infection from viral infection was evaluated against clinical and microbiological diagnosis. Main Outcomes and Measures: Definite bacterial and viral infection was confirmed by culture or molecular detection of the pathogens. Performance of the RNA signature was evaluated in the definite bacterial and viral group and in the indeterminate infection group. Results: The discovery group of 240 children (median age, 19 months; 62% male) included 52 with definite bacterial infection, of whom 36 (69%) required intensive care, and 92 with definite viral infection, of whom 32 (35%) required intensive care. Ninety-six children had indeterminate infection. Analysis of RNA expression data identified a 38-transcript signature distinguishing bacterial from viral infection. A smaller (2-transcript) signature (FAM89A and IFI44L) was identified by removing highly correlated transcripts. When this 2-transcript signature was implemented as a disease risk score in the validation group (130 children, with 23 definite bacterial, 28 definite viral, and 79 indeterminate infections; median age, 17 months; 57% male), all 23 patients with microbiologically confirmed definite bacterial infection were classified as bacterial (sensitivity, 100% [95% CI, 85%-100%]) and 27 of 28 patients with definite viral infection were classified as viral (specificity, 96.4% [95% CI, 89.3%-100%]). When applied to additional validation datasets from patients with meningococcal and inflammatory diseases, bacterial infection was identified with a sensitivity of 91.7% (95% CI, 79.2%-100%) and 90.0% (95% CI, 70.0%-100%), respectively, and with specificity of 96.0% (95% CI, 88.0%-100%) and 95.8% (95% CI, 89.6%-100%). Of the children in the indeterminate groups, 46.3% (63/136) were classified as having bacterial infection, although 94.9% (129/136) received antibiotic treatment. Conclusions and Relevance: This study provides preliminary data regarding test accuracy of a 2-transcript host RNA signature discriminating bacterial from viral infection in febrile children. Further studies are needed in diverse groups of patients to assess accuracy and clinical utility of this test in different clinical settings.This work was supported by the Imperial College Comprehensive Biomedical Research Centre (DMPED P26077); National Institute of Health Research (NIHR) Senior Investigator award (Dr Levin); Great Ormond St Hospital Charity (V1401) (Dr Wright); European Union’s Seventh Framework Program (EC-GA 279185) (EUCLIDS) (Dr Herberg); Imperial College-Wellcome Trust Antimicrobial Research Collaborative (ARC) Early Career Fellowship (RSRO 54990) (Dr Kaforou); Spanish Research Program (FIS; PI10/00540 and Intensificación actividad investigadora of National Plan I + D + I and FEDER funds) and Regional Galician funds (Promotion of Research Project 10 PXIB 918 184 PR) (Dr Martinón-Torres); Southampton NIHR Wellcome Trust Clinical Research Facility and NIHR Wessex Local Clinical Research Network; and Academic Medical Centre Amsterdam MD/PhD program 2013 (Ms Barendregt). The UK meningococcal disease cohort was established with grant support from the Meningitis Research Foundation (United Kingdom); the inflammatory disease cohort was supported by a Macklin Foundation grant (Dr Burns), National Institutes of Health grant U54-HL108460 (Dr Burns); and The Hartwell Foundation and The Harold Amos Medical Faculty Development Program/Robert Wood Johnson Foundation (Dr Tremoulet)

Adrenergic Alpha-1 Pathway Is Associated with Hypertension among Nigerians in a Pathway-focused Analysis

The pathway-focused association approach offers a hypothesis driven alternative to the agnostic genome-wide association study. Here we apply the pathway-focused approach to an association study of hypertension, systolic blood pressure (SBP), and diastolic blood pressure (DBP) in 1614 Nigerians with genome-wide data.Testing of 28 pathways with biological relevance to hypertension, selected a priori, containing a total of 101 unique genes and 4,349 unique single-nucleotide polymorphisms (SNPs) showed an association for the adrenergic alpha 1 (ADRA1) receptor pathway with hypertension (p<0.0009) and diastolic blood pressure (p<0.0007). Within the ADRA1 pathway, the genes PNMT (hypertension P(gene)<0.004, DBP P(gene)<0.004, and SBP P(gene)<0.009, and ADRA1B (hypertension P(gene)<0.005, DBP P(gene)<0.02, and SBP P(gene)<0.02) displayed the strongest associations. Neither ADRA1B nor PNMT could be the sole mediator of the observed pathway association as the ADRA1 pathway remained significant after removing ADRA1B, and other pathways involving PNMT did not reach pathway significance.We conclude that multiple variants in several genes in the ADRA1 pathway led to associations with hypertension and DBP. SNPs in ADRA1B and PNMT have not previously been linked to hypertension in a genome-wide association study, but both genes have shown associations with hypertension through linkage or model organism studies. The identification of moderately significant (10(-2)>p>10(-5)) SNPs offers a novel method for detecting the "missing heritability" of hypertension. These findings warrant further studies in similar and other populations to assess the generalizability of our results, and illustrate the potential of the pathway-focused approach to investigate genetic variation in hypertension

Common Inherited Variation in Mitochondrial Genes Is Not Enriched for Associations with Type 2 Diabetes or Related Glycemic Traits

Mitochondrial dysfunction has been observed in skeletal muscle of people with diabetes and insulin-resistant individuals. Furthermore, inherited mutations in mitochondrial DNA can cause a rare form of diabetes. However, it is unclear whether mitochondrial dysfunction is a primary cause of the common form of diabetes. To date, common genetic variants robustly associated with type 2 diabetes (T2D) are not known to affect mitochondrial function. One possibility is that multiple mitochondrial genes contain modest genetic effects that collectively influence T2D risk. To test this hypothesis we developed a method named Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA; http://www.broadinstitute.org/mpg/magenta). MAGENTA, in analogy to Gene Set Enrichment Analysis, tests whether sets of functionally related genes are enriched for associations with a polygenic disease or trait. MAGENTA was specifically designed to exploit the statistical power of large genome-wide association (GWA) study meta-analyses whose individual genotypes are not available. This is achieved by combining variant association p-values into gene scores and then correcting for confounders, such as gene size, variant number, and linkage disequilibrium properties. Using simulations, we determined the range of parameters for which MAGENTA can detect associations likely missed by single-marker analysis. We verified MAGENTA's performance on empirical data by identifying known relevant pathways in lipid and lipoprotein GWA meta-analyses. We then tested our mitochondrial hypothesis by applying MAGENTA to three gene sets: nuclear regulators of mitochondrial genes, oxidative phosphorylation genes, and ∼1,000 nuclear-encoded mitochondrial genes. The analysis was performed using the most recent T2D GWA meta-analysis of 47,117 people and meta-analyses of seven diabetes-related glycemic traits (up to 46,186 non-diabetic individuals). This well-powered analysis found no significant enrichment of associations to T2D or any of the glycemic traits in any of the gene sets tested. These results suggest that common variants affecting nuclear-encoded mitochondrial genes have at most a small genetic contribution to T2D susceptibility

Patterns of Ancestry, Signatures of Natural Selection, and Genetic Association with Stature in Western African Pygmies

African Pygmy groups show a distinctive pattern of phenotypic variation, including short stature, which is thought to reflect past adaptation to a tropical environment. Here, we analyze Illumina 1M SNP array data in three Western Pygmy populations from Cameroon and three neighboring Bantu-speaking agricultural populations with whom they have admixed. We infer genome-wide ancestry, scan for signals of positive selection, and perform targeted genetic association with measured height variation. We identify multiple regions throughout the genome that may have played a role in adaptive evolution, many of which contain loci with roles in growth hormone, insulin, and insulin-like growth factor signaling pathways, as well as immunity and neuroendocrine signaling involved in reproduction and metabolism. The most striking results are found on chromosome 3, which harbors a cluster of selection and association signals between approximately 45 and 60 Mb. This region also includes the positional candidate genes DOCK3, which is known to be associated with height variation in Europeans, and CISH, a negative regulator of cytokine signaling known to inhibit growth hormone-stimulated STAT5 signaling. Finally, pathway analysis for genes near the strongest signals of association with height indicates enrichment for loci involved in insulin and insulin-like growth factor signaling