Browsing by Author "Verma, Shefali S."

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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).