This readme file was generated on [2022-07-22] by [Peng-Peng Niu] GENERAL INFORMATION Title of Dataset: Supplementary data for paper entitled Maternal Testosterone and Offspring Birth Weight: A Mendelian Randomization Study Author Information Name: Bing-Kun Zheng Institution: First Affiliated Hospital of Zhengzhou University Address: First Affiliated Hospital of Zhengzhou University, Jianshe Road 1#, ZhengZhou, China, 450000 Email: fcczhengbk@zzu.edu.cn Name: Peng-Peng Niu ORCID: https://orcid.org/0000-0002-5943-9654 Institution: First Affiliated Hospital of Zhengzhou University Address: First Affiliated Hospital of Zhengzhou University, Jianshe Road 1#, ZhengZhou, China, 450000 Email: fccniupp@zzu.edu.cn SHARING/ACCESS INFORMATION Publications that cite or use the data: Bing-Kun Zheng et al. Maternal Testosterone and Offspring Birth Weight: A Mendelian Randomization Study. J Clin Endocrinol Metab. 2022 Jun 27;dgac389. doi: 10.1210/clinem/dgac389 Was data derived from another source? If yes, list source(s): Ruth KS et al. Using human genetics to understand the disease impacts of testosterone in men and women. Nat Med 2020;26(2):252-258. DOI: 10.1038/s41591-020-0751-5 Recommended citation for this dataset: Zheng, Bing-Kun; Sun, Xue-Yi; Xian, Jie; Niu, Peng-Peng (2022), Supplementary data for paper entitled Maternal Testosterone and Offspring Birth Weight: A Mendelian Randomization Study, Dryad, Dataset, https://doi.org/10.5061/dryad.79cnp5hxx DATA & FILE OVERVIEW File List: File 1 name: TableS1.csv File 1 Description: Genetic instruments and their effects on exposures and offspring's birth weight File 2 name: TableS2.csv File 2 Description: Genetic instruments and their effects on exposures and premature delivery File 3 name: TableS3.csv File 3 Description: Pleiotropic genetic instruments METHODOLOGICAL INFORMATION We obtained independent genetic instruments from a sex-specific genome wide association study with up to 230,454 females of European descent from UK biobank. This study was performed by by Ruth et al (Nat Med. 2020 Feb;26(2):252-258. doi: 10.1038/s41591-020-0751-5). The age at recruitment ranges from 40 to 69 years. For total testosterone (n = 230,454), fasting time, age, centre and chip/release of genetic data were adjusted. For bioavailable testosterone (n = 188,507), age, dilution, batch, mins since blood draw, time of blood draw, menopause and operation status were adjusted. The first 10 genetically derived principal components were also adjusted. Bioavailable testosterone was calculated from testosterone, accounting for concentration of sex-hormone binding globulin (SHBG) and albumin using Vermeulen equation. The authors reported statistically independent single nucleotide polymorphisms (SNPs) using a physical distance of 1Mb and a linkage disequilibrium r^2 of 0.05. All imputed variants with P < 5×10^8, an imputation quality score > 0.5 and minor allele frequency > 0.1% were included to perform the clumping procedure. Because there were many shared signals between different sex hormone traits, the authors performed a cluster analysis to identify signals with primary effects on individual sex hormone traits. A cluster ("female specific testosterone cluster") representing genetic instruments (i.e., SNPs) with consistent testosterone effects (both total and bioavailable testosterone) but no aggregate effect on sex-hormone binding SHBG was used as the main exposure in our study. A total of 241 SNPs were reported in the "female specific testosterone cluster". Each SNP in this cluster was weighted by its effect on total testosterone. The other two group of SNPs that associated with total testosterone (n = 254) and bioavailable testosterone (n = 180) were also reported. Because maternal traits of type 2 diabetes, systolic blood pressure, smoking, height, body mass index are associated with offspring's birth weight, we excluded SNPs (i.e., pleiotropic genetic instruments) associated with (P < 5×10^-8) these traits from the analysis. SNPs' effects on these traits were obtained from the MRC IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/). File 3 shows the pleiotropic genetic instruments. The SNPs included in our MR analysis were provided in File 1 and File 2. Genetic instruments and their effects on both exposures (i.e., specific testosterone cluster, total testosterone, and bioavailable testosterone) and offspring's birth weight were reported in File 1. Genetic instruments and their effects on both exposures (i.e., specific testosterone cluster, total testosterone, and bioavailable testosterone) and premature delivery were reported in File 2. Pleiotropic genetic instruments (i.e., SNPs in File 3) and genetic instruments that not found in outcomes (i.e., offspring's birth weight and premature delivery) were not included in File 1/2. SNPs in File 1/2 and SNPs' effects on female specific testosterone cluster, total testosterone, and bioavailable testosterone were obtained from the supplementary Table of the study by Ruth et al (Nat Med. 2020 Feb;26(2):252-258. doi: 10.1038/s41591-020-0751-5). In File 1, SNPs' effects on offspring's birth weight were obtained from a study that has been contributed by the Early Growth Genetics (EGG) Consortium using data mainly from the UK Biobank (Warrington NM et al, Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat Genet 2019;51(5):804-814. doi: 10.1038/s41588-019-0403-1). In File 2, SNPs' effects on preterm delivery were obtained from the lasted release (release 6) from FinnGen study (FinnGen. Documentation of R6 release. https://finngen.gitbook.io/documentation/ 2022;accessed date: 2022-02-03). DATA-SPECIFIC INFORMATION FOR: File 1 Number of column: 17 Heading row: Yes Number of rows including heading row: 717 Column name List: SNP, chr, pos, EA, OA, Exposure, Exposure.Beta, Exposure.SE, Exposure.p value, Exposure.eaf, Exposure.R^2, Exposure.F statistic, Outcome.Beta, Outcome.SE, Outcome.p value, Outcome.eaf Outcome indicates offspring's birth weight. Missing data codes: NA F statistic was caculated using (N – K – 1) R^2/ (K (1– R^2)), where N denotes the number of sample size. K denotes the number of genetic instruments. R^2 denotes the proportion of variance explained by all genetic instruments. R^2 for each genetic instrument was calculated using the following formula: 2 Beta^2 eaf (1–eaf). eaf denotes effect allele frequency. Abbreviations: chr, chromosome; EA, effect allele; eaf, effect allele frequency; OA other allele; pos, position (GRCh37); SHBG, sex hormone binding globulin; R^2, proportion of variance explained; SNP, single nucleotide polymorphism; T, testosterone. DATA-SPECIFIC INFORMATION FOR: File 2 Number of column: 17 Heading row: Yes Number of rows including heading row: 701 Column name List: SNP, chr, pos, EA, OA, Exposure, Exposure.Beta, Exposure.SE, Exposure.p value, Exposure.eaf, Exposure.R^2, Exposure.F statistic, Outcome.Beta, Outcome.SE, Outcome.p value, Outcome.eaf Outcome indicates preterm delivery. Missing data codes: NA F statistic was caculated using (N – K – 1) R^2/ (K (1– R^2)), where N denotes the number of sample size. K denotes the number of genetic instruments. R^2 denotes the proportion of variance explained by all genetic instruments. R^2 for each genetic instrument was calculated using the following formula: 2 Beta^2 eaf (1–eaf). eaf denotes effect allele frequency. Abbreviations: chr, chromosome; EA, effect allele; eaf, effect allele frequency; OA other allele; pos, position (GRCh37); SHBG, sex hormone binding globulin; SNP, single nucleotide polymorphism; R^2, proportion of variance explained; T, testosterone. DATA-SPECIFIC INFORMATION FOR: File 3 Number of column: 7 Heading row: Yes Number of rows including heading row: 420 Column name List: SNP, chr, pos, Exposure, Confounder, Data source of confounder*, p value for confounder Abbreviations: chr, chromosome; pos, position (GRCh37); SHBG, sex hormone binding globulin; SNP, single nucleotide polymorphism; T, testosterone. *ID in the MRC IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/).