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12863_2017_519_MOESM5_ESM.rdata (3.76 MB)

Additional file 5: ExampleData_GxE_1.05_210corr_840noise. of Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression

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posted on 2017-06-12, 05:00 authored by Anke Hüls, Katja Ickstadt, Tamara Schikowski, Ursula Krämer
Available at https://figshare.com/s/bca99fc248b678adbb07. Contains a data frame called “dat” with 1067 columns and 10,000 rows. The dataset consists of 10,000 subjects and contains six independent genetic risk factors (CFHrs1061170, LOCrs10490924, CFHrs1410996, C2rs9332739, CFBrs641153, CFHrs2230199) and eight non-genetic covariables (age, sex, education, disease status at baseline, smoking, BMI, antioxidant group, zinc group) for the development of an AMD (binary health outcome), which is the main cause for blindness of people older than 50 years of age. PM10: continuous environmental risk factor that has an adverse effect on AMD and further interacts positively with the six risk SNPs. In this dataset, the mean OR for GxE of the 6 risk SNPs is 1.05 (compare Additional file 1, Table S3). 210 SNPs correlated with the 6 risk SNPs, e.g. CFHrs1061170_100 is in high LD with CFHrs1061170 (same variable, but 100 entries are randomized), 840 noise SNPs (CFHrs1061170_rand1 to CFHrs2230199_rand140). (RData 3 mb)

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