Browsing by Author "Ritchie, Marylyn D."

Now showing 1 - 4 of 4
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    Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals
    (BioMed Central, 2017) Holzinger, Emily R.; Verma, Shefali S.; Moore, Carrie B.; Hall, Molly; De, Rishika; Gilbert-Diamond, Diane; Lanktree, Matthew B.; Pankratz, Nathan; Amuzu, Antoinette; Burt, Amber; Dale, Caroline; Dudek, Scott; Furlong, Clement E.; Gaunt, Tom R.; Kim, Daniel Seung; Riess, Helene; Sivapalaratnam, Suthesh; Tragante, Vinicius; Van Iperen, Erik P. A.; Brautba, Ariel; Carrell, David S.; Crosslin, David R.; Jarvik, Gail P.; Kuivaniemi, Helena; Kullo, Iftikhar J.; Larson, Eric B.; Rasmussen-Torvik, Laura J.; Tromp, Gerard; Baumert, Jens; Cruickshanks, Karen J.; Farrall, Martin; Hingorani, Aroon D.; Hovingh, G. K.; Kleber, Marcus E.; Klein, Barbara E.; Klein, Ronald; Koenig, Wolfgang; Lange, Leslie A.; Mӓrz, Winfried; North, Kari E.; Onland-Moret, N. Charlotte; Reiner, Alex P.; Talmud, Philippa J.; Van Der Schouw, Yvonne T.; Wilson, James G.; Kivimaki, Mika; Kumari, Meena; Moore, Jason H.; Drenos, Fotios; Asselbergs, Folkert W.; Keating, Brendan J.; Ritchie, Marylyn D.
    Background: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). Results: Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. Conclusions: These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.
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    eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants
    (BioMed Central, 2016) Verma, Anurag; Verma, Shefali S.; Pendergrass, Sarah A.; Crawford, Dana C.; Crosslin, David R.; Kuivaniemi, Helena; Bush, William S.; Bradford, Yuki; Kullo, Iftikhar; Bielinski, Suzette J.; Li, Rongling; Denny, Joshua C.; Peissig, Peggy; Hebbring, Scott; De Andrade, Mariza; Ritchie, Marylyn D.; Tromp, Gerard
    Background: We explored premature stop-gain variants to test the hypothesis that variants, which are likely to have a consequence on protein structure and function, will reveal important insights with respect to the phenotypes associated with them. We performed a phenome-wide association study (PheWAS) exploring the association between a selected list of functional stop-gain genetic variants (variation resulting in truncated proteins or in nonsense-mediated decay) and an extensive group of diagnoses to identify novel associations and uncover potential pleiotropy. Results: In this study, we selected 25 stop-gain variants: 5 stop-gain variants with previously reported phenotypic associations, and a set of 20 putative stop-gain variants identified using dbSNP. For the PheWAS, we used data from the electronic MEdical Records and GEnomics (eMERGE) Network across 9 sites with a total of 41,057 unrelated patients. We divided all these samples into two datasets by equal proportion of eMERGE site, sex, race, and genotyping platform. We calculated single effect associations between these 25 stop-gain variants and ICD-9 defined case-control diagnoses. We also performed stratified analyses for samples of European and African ancestry. Associations were adjusted for sex, site, genotyping platform and the first three principal components to account for global ancestry. We identified previously known associations, such as variants in LPL associated with hyperglyceridemia indicating that our approach was robust. We also found a total of three significant associations with p < 0.01 in both datasets, with the most significant replicating result being LPL SNP rs328 and ICD-9 code 272. 1 “Disorder of Lipoid metabolism” (pdiscovery = 2.59x10-6, preplicating = 2.7x10-4). The other two significant replicated associations identified by this study are: variant rs1137617 in KCNH2 gene associated with ICD-9 code category 244 “Acquired Hypothyroidism” (pdiscovery = 5.31x103, preplicating = 1.15x10-3) and variant rs12060879 in DPT gene associated with ICD-9 code category 996 “Complications peculiar to certain specified procedures” (pdiscovery = 8. 65x103, preplicating = 4.16x10-3).
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    Epistatic gene-based interaction analyses for glaucoma in eMERGE and NEIGHBOR Consortium
    (Public Library of Science, 2016) Verma, Shefali Setia; Bailey, Jessica N. Cooke; Lucas, Anastasia; Bradford, Yuki; Linneman, James G.; Hauser, Michael A.; Pasquale, Louis R.; Peissig, Peggy L.; Brilliant, Murray H.; McCarty, Catherine A.; Haines, Jonathan L.; Wiggs, Janey L.; Vrabec, Tamara R.; Tromp, Gerard; Ritchie, Marylyn D.; eMERGE Network; NEIGHBOR Consortium
    Primary open angle glaucoma (POAG) is a complex disease and is one of the major leading causes of blindness worldwide. Genome-wide association studies have successfully identified several common variants associated with glaucoma; however, most of these variants only explain a small proportion of the genetic risk. Apart from the standard approach to identify main effects of variants across the genome, it is believed that gene-gene interactions can help elucidate part of the missing heritability by allowing for the test of interactions between genetic variants to mimic the complex nature of biology. To explain the etiology of glaucoma, we first performed a genome-wide association study (GWAS) on glaucoma case-control samples obtained from electronic medical records (EMR) to establish the utility of EMR data in detecting non-spurious and relevant associations; this analysis was aimed at confirming already known associations with glaucoma and validating the EMR derived glaucoma phenotype. Our findings from GWAS suggest consistent evidence of several known associations in POAG. We then performed an interaction analysis for variants found to be marginally associated with glaucoma (SNPs with main effect p-value <0.01) and observed interesting findings in the electronic MEdical Records and GEnomics Network (eMERGE) network dataset. Genes from the top epistatic interactions from eMERGE data (Likelihood Ratio Test i.e. LRT p-value <1e-05) were then tested for replication in the NEIGHBOR consortium dataset. To replicate our findings, we performed a gene-based SNP-SNP interaction analysis in NEIGHBOR and observed significant gene-gene interactions (p-value <0.001) among the top 17 gene-gene models identified in the discovery phase. Variants from gene-gene interaction analysis that we found to be associated with POAG explain 3.5% of additional genetic variance in eMERGE dataset above what is explained by the SNPs in genes that are replicated from previous GWAS studies (which was only 2.1% variance explained in eMERGE dataset); in the NEIGHBOR dataset, adding replicated SNPs from gene-gene interaction analysis explain 3.4% of total variance whereas GWAS SNPs alone explain only 2.8% of variance. Exploring gene-gene interactions may provide additional insights into many complex traits when explored in properly designed and powered association studies.
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    Phenome-wide association study to explore relationships between immune system related genetic loci and complex traits and diseases
    (Public Library of Science, 2016) Verma, Anurag; Basile, Anna O.; Bradford, Yuki; Kuivaniemi, Helena; Tromp, Gerard; Carey, David; Gerhard, Glenn S.; Crowe, James E.; Ritchie, Marylyn D.; Pendergrass, Sarah A.
    This study highlights the utility of using PheWAS in conjunction with EHRs to discover new genotypic-phenotypic associations for immune-system related genetic loci.