ABSTRACT Cystic kidney diseases (CyKD) are a diverse group of disorders affecting more than 1 in 1000 individuals. Over 120 genes are implicated, primarily encoding components of the primary cilium, transcription factors, and morphogens. Prognosis varies greatly by molecular diagnosis. Causal variants are not identified in 10%–60% of individuals due to our limited understanding of CyKD. To elucidate the molecular landscape of CyKD, we queried the CAG Biobank using the ICD10 codes N28.1, Q61.1, Q61.11, Q61.19, Q61.2, Q61.3, and Q61.8 to identify individuals with CyKD. One hundred eight individuals met clinical criteria for CyKD and underwent proband‐only exome sequencing. Causal variants were identified in 86/108 (80%) individuals. The most common molecular diagnoses were PKD1 ‐related autosomal dominant polycystic kidney disease (32/108; 30%) and autosomal recessive polycystic kidney disease (21/108; 19%). Other common molecular diagnoses were ciliopathy syndromes (7/108; 6.5%) and Tuberous Sclerosis (6/108; 5.6%). Seven individuals had variants in genes not previously associated with CyKD (7/108; 6.5%). Candidate genes were identified in five individuals (5/108; 4.5%). Discordance between molecular and clinical diagnosis was present in two individuals. We demonstrate a high molecular diagnosis rate in individuals with CyKD that can result in diagnostic reclassification, supporting a role for genetic testing in CyKD.
Despite experimental data linking HIF-1α dysfunction to inflammatory airway conditions, the effect of single nucleotide polymorphisms within the HIF1A gene on these conditions remains poorly understood. In the current study, we complete a phenotype wide association study to assess the link between SNPs with known disease associations and respiratory phenotypes. We report two SNPs of the HIF1A gene, the intronic rs79865957 and the missense rs41508050. In these positions the A and the T allele are significantly associated with allergic rhinitis and acute bronchitis and bronchiolitis, respectively. These findings further support the role of HIF-1α in inflammatory pulmonary conditions and may serve as a basis to refine our understanding of other HIF-1α associated phenotypes.
Complex vascular anomalies arise from perturbed development of blood or lymphatic vessels, often caused by germline or post-zygotic variants. Complex vascular anomalies give rise to complications including pain, coagulopathies, respiratory failure, and abnormal fluid accumulation in different body compartments, which are often intractable and can be life-threatening despite interventions such as sclerotherapy, embolization, or surgery. Recent work has demonstrated the importance of phenotype-genotype correlation to guide biologically based medical treatments.
Abstract Phenome-wide association studies (PheWAS), which assess whether a genetic variant is associated with multiple phenotypes across a phenotypic spectrum, have been proposed as a possible aid to drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we evaluate whether PheWAS can inform target validation during drug development. We selected 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease therapeutic indications. We independently interrogated these SNPs through PheWAS in four large “real-world data” cohorts (23andMe, UK Biobank, FINRISK, CHOP) for association with a total of 1,892 binary endpoints. We then conducted meta-analyses for 145 harmonized disease endpoints in up to 697,815 individuals and joined results with summary statistics from 57 published GWAS. Our analyses replicate 70% of known GWAS associations and identify 10 novel associations with study-wide significance after multiple test correction (P<1.8x10 -6 ; out of 72 novel associations with FDR<0.1). By leveraging directionality and point estimate of the effect sizes, we describe new associations that may predict ADEs, e.g., acne, high cholesterol, gout and gallstones for rs738409 (p.I148M) in PNPLA3 ; or asthma for rs1990760 (p.T946A) in IFIH1 . We further propose how quantitative estimates of genetic safety/efficacy profiles can be used to help prioritize candidate targets for a specific indication. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery. One Sentence Summary Matching genetics with phenotypes in 800,000 individuals predicts efficacy and on-target safety of future drugs.
The prevalence of Type II Diabetes (T2D) has been increasing and has become a disease of significant public health burden in Jordan.The genetic determinants of T2D in Middle-East populations have not been well studied by genome-wide association studies (GWAS).The Circassian and Chechen communities in Jordan represent unique populations that are genetically distinct from the Arab population and other populations in the Caucasus.Prevalence of T2D is very high in both the Circassian and Chechen communities in Jordan despite low obesity prevalence.We conducted GWAS on T2D in these two populations and further performed metaanalysis of the results.We identified a novel T2D locus at chr20p12.2 at genome-wide significance (rs6134031, P=1.12x10 -8 ) and we replicated the results in the Wellcome Trust Case Control Consortium (WTCCC) dataset.Another locus at chr12q24.31 is associated with T2D at a suggestive significance level (top SNP rs4758690, P= 4.20x10 -5 ).This SNP is a robust eQTL for the gene, MLXIP (P=1.10x10 -1 ), and is significantly associated with methylation level in MLXIP, the functions of which involves cellular glucose response.Therefore, in this first GWAS of T2D in Jordan subpopulations, we identified novel and unique susceptibility loci which may help inform the genetic underpinnings of T2D in other populations.
Additional file 2: Table S2. The 100 top-ranked differentially methylated CpGs associated with dimensional psychopathology using all the CpG sites of the EPIC array and included covariates for batch effects. Table S3. The differentially methylated CpGs associated with chronological age in the Philadelphia Neurodevelopmental Cohort (PNC). Table S4. The differentially methylated CpGs (DMPs) associated with the emergence of dimensional psychopathology using batch effects as covariates. Table S5. Overview of significant results from the enrichment analysis obtained using all genes for which the DMPs/DMRs were mapped to or near to the genes. Table S6. Statistics, beta fold change and adjusted p-values for the 663 differentially methylated CpGs (DMPs) associated with the emergence of dimensional psychopathology using batch effects and medication as covariates. Table S7. Gene expression results of genes for which the DMPs were mapped to or near to the genes. Table S8. DNA methylation correlation between brain and blood for DMPs mapped or near to differentially expressed genes. Correlation results are from IMAGE-CpG (Braun, P et al., 2019). Table S9. Information about correlations between: a) the variation of the DNA methylation of the differentially methylated positions (ΔDNAm) and the variation of the expression of genes that were differentially expressed in whole blood (ΔmRNA); b) the variation of the total score of CBCL (ΔCBCL) and ΔDNAm; and c) ΔCBCL and ΔmRNA. The variations were calculated subtracting wave 1 (W1) values from wave 0 (W0) values. Table S10. Differentially methylated regions from DMRCate analysis. Table S11. Gene expression results of genes for which the DMRs were mapped to or near to the genes. Table S12. DNA methytion correlation between brain and blood for CpG site from DMRs mapped or near to differentially expressed genes. Correlation results are from IMAGE-CpG (Braun, P et al., 2019).
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