Supplemental Material for Feofanova et al., 2018
datasetposted on 02.04.2018 by Elena V. Feofanova, Bing Yu, Ginger A. Metcalf, Xiaoming Liu, Donna Muzny, Jennifer E. Below, Lynne E. Wagenknecht, Richard A. Gibbs, Alanna C. Morrison, Eric Boerwinkle
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Figure S1 shows proportion of single nucleotide variants by minor allele frequency for sequencing data. Figure S2 contains Manhattan plots and QQ-plots for the statistically significant replicated traits. Figure S3 demonstrates distribution of rare variants in EAs and AAs. Figure S4 pictures predicted levels of FADS1 and FADS2 in skeletal muscle by rs174564 genotypes in EAs. Table S1 contains basic characteristics of the 102 lipid-related traits. Common variants significantly associated with lipid-related traits in EAs, AAs, or in meta-analysis can be found in Table S2, with sentinel common variant-region pairs significantly associated in meta-analysis available in Table S3. Association of predicted gene transcription levels with lipid-related phenotypes in EAs is presented in Table S4. Table S5 contains Whole Genome T5 results significantly associated in meta-analysis. Rare nonsynonimous exonic variants of SLC10A1 – glycocholate are listed in Table S6. Table S7 contains regulatory domain T5 results significantly associated in meta-analysis. Rare variants and conditional analyses for the regulatory element of CYP3A43 for 3 traits are available in Tables S8 and S9, respectively. Table S10 has sliding windows T5 results significantly associated in meta-analysis, with rare variants of the significant and replicated sliding windows listed in Table S11. Table S12 contains variants used for gene transcription levels imputation in EAs.