Person: Turman, Constance
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Publication Genome-Wide Association Studies of Multiple Keratinocyte Cancers
(Public Library of Science, 2017) Pardo, Luba M.; Li, Wen-Qing; Hwang, Shih-Jen; Verkouteren, Joris A. C.; Hofman, Albert; Uitterlinden, André G.; Kraft, Phillip; Turman, Constance; Han, Jiali; Cho, Eunyoung; Murabito, Joanne M.; Levy, Daniel; Qureshi, Abrar A.; Nijsten, TamarThere is strong evidence for a role of environmental risk factors involved in susceptibility to develop multiple keratinocyte cancers (mKCs), but whether genes are also involved in mKCs susceptibility has not been thoroughly investigated. We investigated whether single nucleotide polymorphisms (SNPs) are associated with susceptibility for mKCs. A genome-wide association study (GWAS) of 1,666 cases with mKCs and 1,950 cases with single KC (sKCs; controls) from Harvard cohorts (the Nurses' Health Study [NHS], NHS II, and the Health Professionals Follow-Up Study) and the Framingham Heart Study was carried-out using over 8 million SNPs (stage-1). We sought to replicate the most significant statistical associations (p-value≤ 5.5x10-6) in an independent cohort of 574 mKCs and 872 sKCs from the Rotterdam Study. In the discovery stage, 40 SNPs with suggestive associations (p-value ≤5.5x10-6) were identified, with eight independent SNPs tagging all 40 SNPs. The most significant SNP was located at chromosome 9 (rs7468390; p-value = 3.92x10-7). In stage-2, none of these SNPs replicated and only two of them were associated with mKCs in the same direction in the combined meta-analysis. We tested the associations for 19 previously reported basal cell carcinoma-related SNPs (candidate gene association analysis), and found that rs1805007 (MC1R locus) was significantly associated with risk of mKCs (p-value = 2.80x10-4). Although the suggestive SNPs with susceptibility for mKCs were not replicated, we found that previously identified BCC variants may also be associated with mKC, which the most significant association (rs1805007) located at the MC1R gene.
Publication A comprehensive survey of genetic variation in 20,691 subjects from four large cohorts
(Public Library of Science, 2017) Lindström, Sara; Loomis, Stephanie; Turman, Constance; Huang, Hongyan; Huang, Jinyan; Aschard, Hugues; Chan, Andrew; Choi, Hyon; Cornelis, Marilyn; Curhan, Gary; De Vivo, Immaculata; Eliassen, A; Fuchs, Charles; Gaziano, Michael; Hankinson, Susan; Hu, Frank; Jensen, Majken; Kang, Jae Hee; Kabrhel, Christopher; Liang, Liming; Pasquale, Louis; Rimm, Eric; Stampfer, Meir; Tamimi, Rulla; Tworoger, Shelley; Wiggs, Janey; Hunter, David; Kraft, PhillipThe Nurses’ Health Study (NHS), Nurses’ Health Study II (NHSII), Health Professionals Follow Up Study (HPFS) and the Physicians Health Study (PHS) have collected detailed longitudinal data on multiple exposures and traits for approximately 310,000 study participants over the last 35 years. Over 160,000 study participants across the cohorts have donated a DNA sample and to date, 20,691 subjects have been genotyped as part of genome-wide association studies (GWAS) of twelve primary outcomes. However, these studies utilized six different GWAS arrays making it difficult to conduct analyses of secondary phenotypes or share controls across studies. To allow for secondary analyses of these data, we have created three new datasets merged by platform family and performed imputation using a common reference panel, the 1,000 Genomes Phase I release. Here, we describe the methodology behind the data merging and imputation and present imputation quality statistics and association results from two GWAS of secondary phenotypes (body mass index (BMI) and venous thromboembolism (VTE)). We observed the strongest BMI association for the FTO SNP rs55872725 (β = 0.45, p = 3.48x10-22), and using a significance level of p = 0.05, we replicated 19 out of 32 known BMI SNPs. For VTE, we observed the strongest association for the rs2040445 SNP (OR = 2.17, 95% CI: 1.79–2.63, p = 2.70x10-15), located downstream of F5 and also observed significant associations for the known ABO and F11 regions. This pooled resource can be used to maximize power in GWAS of phenotypes collected across the cohorts and for studying gene-environment interactions as well as rare phenotypes and genotypes.