Gottesman, Omri

Background The incidence of VTE in African-Americans (AAs) is similar to or higher than in Americans of European ancestry. However, the carrier frequencies of inherited thrombophilias common in whites (i.e., Factor V Leiden, Prothrombin G20210A) are very low in AAs, suggesting that other inherited thrombophilias may be associated with VTE in AAs. Objective To identify potentially novel non-hypothesis-driven single nucleotide polymorphisms (SNPs) associated with VTE in AAs. Methods We used the resources of the eMERGE Network to perform a GWAS of VTE in AAs. The eMERGE Network (funded by NHGRI) is comprised of nine sites each with DNA biobanks linked to electronic health records (EHRs). Approximately 39,206 unique DNA samples have been genotyped using either Affymetrix or Illumina genome-wide SNP arrays. Led by the Coordinating Center and eMERGE genomics workgroup, an imputation pipeline was developed for merging genomic data across the different SNP arrays used by the eMERGE sites, to maximize sample size and the power to detect associations. The imputation was performed using the 1000 Genomes Cosmopolitan reference panel which includes 1092 individuals and over 36 million SNPs. From our previously-identified cohort of Olmsted County, MN residents with incident or recurrent VTE, 1996-2005, we derived and validated an EHR-driven phenotype extraction algorithm that leveraged structured data based on ICD-9-CM codes and unstructured data from clinical notes via natural language processing to identify VTE cases and controls with 100% and 94% positive and negative predictive values, respectively. We tested for an association between each SNP and VTE among AAs using unconditional logistic regression, adjusting for age, sex and eMERGE site. Results Among 294 AA VTE cases and 3,661 AA controls (total n=3,955; females, n=2,512), the Factor V Leiden (F5 rs6025) was not analyzed due to a very low minor allele frequency (MAF=0.0036). The prothrombin G20210A (F2 rs1799963) and ABO blood type O (ABO rs8176719) SNPs were not genotyped in any of the arrays and could not be imputed. Among SNPs with an imputation score >0.8, the most significant SNPs associated with VTE were ITPR3 (inositol 1,4,5-triphosphate receptor type 3) rs2229637 (OR=1.65; p=3.61E-07; MAF=0.19) and CLEC7A (C-type lectin domain family 7, member A) rs59819090 (OR=2.16; p=1.06E-06; MAF=0.056). ITPR3 SNPs have been associated with coronary artery aneurysm in Kawasaki disease, type 1 diabetes mellitus and other autoimmune disorders. CLEC7A rs59819090 encodes for a serine82 to leucine nonsynonymous amino acid change in dectin-1. Dectin-1 is a transmembrane innate immune pattern recognition receptor on myeloid cells that upon binding it’s agonist, β-glucans, stimulates cytokine production and the respiratory burst, a prerequisite for formation of neutrophil extracelluar traps (NETs). NETs have been associated with VTE (van Montfoort, et al. Ateriosclero Thromb Vasc Biol 2013;33:147-51). Dectin-1 serine82 is not evolutionarily conserved and the serine82 to leucine amino acid change is predicted to be tolerated, with a moderate effect on protein function. Conclusions ITPR3 rs2229637 and CLAC7A rs59819090 may be associated with VTE in African-Americans. These observations require replication and functional studies toward understanding how they may lie on the causal pathway to VTE. Disclosures: No relevant conflicts of interest to declare.

Genome-wide association studies have identified >50 common variants associated with kidney function, but these variants do not fully explain the variation in eGFR. We performed a two-stage meta-analysis of associations between genotypes from the Illumina exome array and eGFR on the basis of serum creatinine (eGFRcrea) among participants of European ancestry from the CKDGen Consortium ( n Stage1 : 111,666; n Stage2 : 48,343). In single-variant analyses, we identified single nucleotide polymorphisms at seven new loci associated with eGFRcrea ( PPM1J , EDEM3, ACP1, SPEG, EYA4, CYP1A1 , and ATXN2L ; P Stage1 <3.7×10 −7 ), of which most were common and annotated as nonsynonymous variants. Gene-based analysis identified associations of functional rare variants in three genes with eGFRcrea, including a novel association with the SOS Ras/Rho guanine nucleotide exchange factor 2 gene, SOS2 ( P =5.4×10 −8 by sequence kernel association test). Experimental follow-up in zebrafish embryos revealed changes in glomerular gene expression and renal tubule morphology in the embryonic kidney of acp1- and sos2 -knockdowns. These developmental abnormalities associated with altered blood clearance rate and heightened prevalence of edema. This study expands the number of loci associated with kidney function and identifies novel genes with potential roles in kidney formation.

Background Intracranial aneurysms ( IAs ), abdominal aortic aneurysms ( AAAs ), and thoracic aortic aneurysms ( TAAs ) all have a familial predisposition. Given that aneurysm types are known to co‐occur, we hypothesized that there may be shared genetic risk factors for IAs , AAAs, and TAAs . Methods and Results We performed a mega‐analysis of 1000 Genomes Project‐imputed genome‐wide association study ( GWAS ) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA , AAA , and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA ‐, AAA ‐, and TAA ‐associated SNP s and tested these scores for association to case‐control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium–score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single‐nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls ( IA ) and 4391 cases and 37 904 controls ( AAA ), and found nominally significant associations for IA risk locus 18q11 near RBBP 8 to AAA (odds ratio [ OR ]=1.11; P =4.1×10 −5 ) and for TAA risk locus 15q21 near FBN 1 to AAA ( OR =1.07; P =1.1×10 −3 ). Conclusions Although there was no evidence for polygenic overlap between IAs , AAAs , and TAAs , we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs . These two loci will require further replication.

Abstract Objective Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems. Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org ), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites. Results As of June 2015, PheKB contained 30 finalized phenotype algorithms and 62 algorithms in development spanning a range of traits and diseases. Phenotypes have had over 3500 unique views in a 6-month period and have been reused by other institutions. International Classification of Disease codes were the most frequently used component, followed by medications and natural language processing. Among algorithms with published performance data, the median PPV was nearly identical when evaluated at the authoring institutions (n = 44; case 96.0%, control 100%) compared to implementation sites (n = 40; case 97.5%, control 100%). Discussion These results demonstrate that a broad range of algorithms to mine electronic health record data from different health systems can be developed with high PPV, and algorithms developed at one site are generally transportable to others. Conclusion By providing a central repository, PheKB enables improved development, transportability, and validity of algorithms for research-grade phenotypes using health care generated data.