Haines, Jonathan L.

Abstract Introduction Genetic loci for Alzheimer's disease (AD) have been identified in whites of European ancestry, but the genetic architecture of AD among other populations is less understood. Methods We conducted a transethnic genome‐wide association study (GWAS) for late‐onset AD in Stage 1 sample including whites of European Ancestry, African‐Americans, Japanese, and Israeli‐Arabs assembled by the Alzheimer's Disease Genetics Consortium. Suggestive results from Stage 1 from novel loci were followed up using summarized results in the International Genomics Alzheimer's Project GWAS dataset. Results Genome‐wide significant (GWS) associations in single‐nucleotide polymorphism (SNP)–based tests ( P < 5 × 10 −8 ) were identified for SNPs in PFDN1/HBEGF , USP6NL/ECHDC3 , and BZRAP1‐AS1 and for the interaction of the (apolipoprotein E) APOE ε4 allele with NFIC SNP. We also obtained GWS evidence ( P < 2.7 × 10 −6 ) for gene‐based association in the total sample with a novel locus, TPBG ( P = 1.8 × 10 −6 ). Discussion Our findings highlight the value of transethnic studies for identifying novel AD susceptibility loci.

AbstractIntroductionAfrican Americans' (AAs) late‐onset Alzheimer's disease (LOAD) genetic risk profile is incompletely understood. Including clinical covariates in genetic analyses using informed conditioning might improve study power.MethodsWe conducted a genome‐wide association study (GWAS) in AAs employing informed conditioning in 1825 LOAD cases and 3784 cognitively normal controls. We derived a posterior liability conditioned on age, sex, diabetes status, current smoking status, educational attainment, and affection status, with parameters informed by external prevalence information. We assessed association between the posterior liability and a genome‐wide set of single‐nucleotide polymorphisms (SNPs), controlling for APOE and ABCA7, identified previously in a LOAD GWAS of AAs.ResultsTwo SNPs at novel loci, rs112404845 (P = 3.8 × 10−8), upstream of COBL, and rs16961023 (P = 4.6 × 10−8), downstream of SLC10A2, obtained genome‐wide significant evidence of association with the posterior liability.DiscussionAn informed conditioning approach can detect LOAD genetic associations in AAs not identified by traditional GWAS.

Abstract Objective: Several attributes of female reproductive history, including age at natural menopause (ANM), have been related to primary open-angle glaucoma (POAG). We assembled 18 previously reported common genetic variants that predict ANM to determine their association with ANM or POAG. Methods: Using data from the Nurses’ Health Study (7,143 women), we validated the ANM weighted genetic risk score in relation to self-reported ANM. Subsequently, to assess the relation with POAG, we used data from 2,160 female POAG cases and 29,110 controls in the National Eye Institute Glaucoma Human Genetics Collaboration Heritable Overall Operational Database (NEIGHBORHOOD), which consists of 8 datasets with imputed genotypes to 5.6+ million markers. Associations with POAG were assessed in each dataset, and site-specific results were meta-analyzed using the inverse weighted variance method. Results: The genetic risk score was associated with self-reported ANM (P = 2.2 × 10–77) and predicted 4.8% of the variance in ANM. The ANM genetic risk score was not associated with POAG (Odds Ratio (OR) = 1.002; 95% Confidence Interval (CI): 0.998, 1.007; P = 0.28). No single genetic variant in the panel achieved nominal association with POAG (P ≥0.20). Compared to the middle 80 percent, there was also no association with the lowest 10th percentile or highest 90th percentile of genetic risk score with POAG (OR = 0.75; 95% CI: 0.47, 1.21; P = 0.23 and OR = 1.10; 95% CI: 0.72, 1.69; P = 0.65, respectively). Conclusions: A genetic risk score predicting 4.8% of ANM variation was not related to POAG; thus, genetic determinants of ANM are unlikely to explain the previously reported association between the two phenotypes.

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.

AbstractIntroductionAfrican‐American (AA) individuals have a higher risk for late‐onset Alzheimer's disease (LOAD) than Americans of primarily European ancestry (EA). Recently, the largest genome‐wide association study in AAs to date confirmed that six of the Alzheimer's disease (AD)‐related genetic variants originally discovered in EA cohorts are also risk variants in AA; however, the risk attributable to many of the loci (e.g., APOE, ABCA7) differed substantially from previous studies in EA. There likely are risk variants of higher frequency in AAs that have not been discovered.MethodsWe performed a comprehensive analysis of genetically determined local and global ancestry in AAs with regard to LOAD status.ResultsCompared to controls, LOAD cases showed higher levels of African ancestry, both globally and at several LOAD relevant loci, which explained risk for AD beyond global differences.DiscussionExploratory post hoc analyses highlight regions with greatest differences in ancestry as potential candidate regions for future genetic analyses.

ABSTRACTBioinformatics approaches to examine gene‐gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge‐driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty‐three SNP‐SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell‐to‐cell adhesion signaling, cell‐cell junction organization, and cell‐cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP‐SNP models, which included signal transduction and PI3K‐Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology‐driven method, applicable for any genome‐wide association study dataset.