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Additional file 3: Table S3. Replication of a previous GWAS of body height in the SNPs of the training group (313 of 1722 SNPs).

Additional file 4: Figure S1. The clumping and p-value threshold method identifies the “best-fit” SNP number for the polygenic risk score (PRS) calculation, according to the largest explainable phenotype correlation r2 using only PRS (PRS r2 and SNP number). The x-axis shows the p-value thresholds from the height of GWAS results. The y-axis represents the explainable phenotypic correlation r2 using only the PRS (PRS r2). The p-values above the bars show the statistical significance of the associations between genetically determined height (PRS) and measured height (phenotype). (A) 251 SNPs were obtained from 6,941 SNPs (novel and reported SNPs; PRS r2 = 0.0712, SNP number = 251). (B) 194 SNPs were obtained from 6,843 SNPs (novel SNPs; PRS r2 = 0.0622, SNP number = 194). (C) 154 SNPs were obtained from 313 SNPs (reported SNPs; PRS r2 = 0.0706, SNP number = 154). Figure S2. Scree plot identifying the number of principal component analyses (PCA) needed for the correction of population structure in the height GWAS study, using pcadapt (an R package used to determine the number of principal components). Figure S3. Association between genetically determined height (PRS237) and measured height (phenotype) in an independent cohort of the Big Data Center in China Medical University Hospital in Taiwan. The measured height (cm) and calculated polygenic risk score (PRS) for height were stratified by sex, mean-centered, and normalized to one standard deviation (SD; males, N = 46,310; females, N = 54,728). The normalized measured height is represented on the y-axis, and normalized genetically determined height (PRS237) is represented on the x-axis.

Additional file 1: Table S1. Basic characteristics of the study participants at enrollment.

Additional file 5: Table S4. Characteristics of 237 out of the 251 SNPs associated with height in the independent cohort of the Big Data Center at China Medical University Hospital in Taiwan.

Additional file 2: Table S2. The top lead SNPs in 89 genomic regions that were significantly associated with height (p < 5 × 10−8), using LD (r2 < 0.2), in individuals of Han Chinese ancestry.

There are limited population-based studies on the progress of oseltamivir therapy for influenza infection. Using insurance claims data of 2005, 2009, and 2010, the authors established an “in-time” cohort and a “lag-time” cohort representing influenza patients taking the medicine within and not within 1 week to examine the treatment progress. Incident outpatient visit, emergency care and hospitalization, and fatality were compared between the 2 cohorts in the first week and the second week of follow-up periods, after the oseltamivir therapy. A total of 112,492 subjects diagnosed with influenza on oseltamivir therapy in 2005, 2009, and 2010 were identified. The multivariate logistic regression analysis showed that the in-time treatment was superior to the lag-time treatment with less repeat outpatient visits, hospitalizations, and fatality. The overall corresponding in-time treatment to lag-time treatment odds ratios (OR) were 0.50, 0.54, and 0.71 (all P value