Analysis provided in the "Studies of human twins reveal genetic variation that affects dietary fat perception" manuscript
This repository is meant to provide the source code for the analysis provided in the above manuscript by Cailu Lin, Lauren Colquitt, Paul Wise, Paul A. S. Breslin, Nancy E. Rawson, Federica Genovese, Ivy Maina, Paule Joseph, Lydia Formuso, Louise Slade, Dennis Brooks, Aurélie Miclo, John E. Hayes, Antonio Sullo, and Danielle R. Reed
Abstract: To learn more about the mechanisms of human dietary fat perception, 398 human twins rated fattiness and liking for six types of potato chips that differed in triglyceride content (2.5, 5, 10, and 15% corn oil); reliability estimates were obtained from a subset (n = 50) who did the task twice. Some chips also had a saturated long-chain fatty acid (hexadecanoic acid, 16:0) added (0.2%) to evaluate its effect on fattiness and liking. We computed the heritability of these measures and conducted a genome-wide association study (GWAS) to identify regions of the genome that co-segregate with fattiness and liking. Perceived fattiness and liking for the potato chips were reliable (r = 0.31-0.62, p < 0.05) and heritable (up to h2 = 0.29, p < 0.001, for liking). Adding hexadecanoic acid to the potato chips significantly increased ratings of fattiness but decreased liking. Twins with the G allele of rs263429 near GATA3-AS1 or the G allele of rs8103990 within ZNF729 reported more liking for potato chips than did twins with the other allele (multivariate GWAS, p < 1×10-5), with results reaching genome-wide suggestive (p = 1e-5) but not significance (p = 5.0e-8) criteria. Person-to-person variation in the perception and liking of dietary fat was (a) negatively affected by the addition of a saturated fatty acid and (b) related to inborn genetic variants. These data suggest liking for dietary fat is not due solely to fatty acid content and highlight new candidate genes and proteins within this sensory pathway.
Keywords: taste, genetics, sensory, fat perception, oleogustus, taste receptors
This source code was executed under the following session information: R version 3.5.3 (2019-03-11) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 7 x64 (build 7601) Service Pack 1
Matrix products: default
locale: [1] LC_COLLATE=Chinese (Simplified)_People's Republic of China.936 [2] LC_CTYPE=Chinese (Simplified)_People's Republic of China.936 [3] LC_MONETARY=Chinese (Simplified)_People's Republic of China.936 [4] LC_NUMERIC=C [5] LC_TIME=Chinese (Simplified)_People's Republic of China.936
attached base packages: [1] grid stats graphics grDevices utils datasets methods base
other attached packages: [1] nlme_3.1-137 sjPlot_2.8.1 mime_0.7 [4] later_1.0.0 plotly_4.9.1 moments_0.14 [7] Rmisc_1.5 lsmeans_2.30-0 emmeans_1.4.2 [10] multcompView_0.1-7 easyGgplot2_1.0.0.9000 lmerTest_3.1-0 [13] lme4_1.1-21 Matrix_1.2-15 gridExtra_2.3 [16] Zelig_5.1.6.1 optmatch_0.9-12 MatchIt_3.0.2 [19] forcats_0.4.0 stringr_1.4.0 purrr_0.3.3 [22] tidyr_1.0.0 tibble_2.1.3 tidyverse_1.2.1 [25] stargazer_5.2.2 Hmisc_4.3-0 Formula_1.2-3 [28] survival_2.43-3 lattice_0.20-38 GGally_1.4.0 [31] readr_1.3.1 ggpubr_0.2.4 magrittr_1.5 [34] cowplot_1.0.0 ggplot2_3.2.1 data.table_1.12.6 [37] reshape2_1.4.3 dplyr_0.8.3 plyr_1.8.4
loaded via a namespace (and not attached): [1] readxl_1.3.1 backports_1.1.5 miscTools_0.6-24 VGAM_1.1-1 [5] lazyeval_0.2.2 splines_3.5.3 TH.data_1.0-10 digest_0.6.22 [9] htmltools_0.4.0 svd_0.5 checkmate_1.9.4 cluster_2.0.7-1 [13] openxlsx_4.1.3 modelr_0.1.5 MCMCpack_1.4-4 sandwich_2.5-1 [17] colorspace_1.4-1 ggrepel_0.8.1 rvest_0.3.5 mitools_2.4 [21] haven_2.2.0 xfun_0.11 crayon_1.3.4 jsonlite_1.6 [25] AER_1.2-8 zeallot_0.1.0 zoo_1.8-6 glue_1.3.1 [29] geepack_1.2-1 gtable_0.3.0 MatrixModels_0.4-1 sjstats_0.17.7 [33] sjmisc_2.8.2 car_3.0-3 maxLik_1.3-6 abind_1.4-5 [37] SparseM_1.77 scales_1.1.0 mvtnorm_1.0-11 DBI_1.0.0 [41] ggeffects_0.13.0 Rcpp_1.0.3 performance_0.4.0 viridisLite_0.3.0 [45] xtable_1.8-4 htmlTable_1.13.2 foreign_0.8-71 stats4_3.5.3 [49] survey_3.36 htmlwidgets_1.5.1 httr_1.4.1 RColorBrewer_1.1-2 [53] ellipsis_0.3.0 acepack_1.4.1 farver_2.0.1 pkgconfig_2.0.3 [57] reshape_0.8.8 nnet_7.3-12 labeling_0.3 tidyselect_0.2.5 [61] rlang_0.4.1 effectsize_0.0.1 munsell_0.5.0 cellranger_1.1.0 [65] tools_3.5.3 cli_1.1.0 generics_0.0.2 pacman_0.5.1 [69] sjlabelled_1.1.1 broom_0.5.2 yaml_2.2.0 mcmc_0.9-6 [73] knitr_1.26 zip_2.0.4 quantreg_5.52 RItools_0.1-17 [77] xml2_1.2.2 pbkrtest_0.4-7 compiler_3.5.3 rstudioapi_0.10 [81] curl_4.2 ggsignif_0.6.0 stringi_1.4.3 parameters_0.2.0 [85] psych_1.8.12 Amelia_1.7.5 nloptr_1.2.1 vctrs_0.2.0 [89] pillar_1.4.2 lifecycle_0.1.0 lmtest_0.9-37 estimability_1.3 [93] insight_0.7.0 R6_2.4.1 latticeExtra_0.6-28 rio_0.5.16 [97] codetools_0.2-16 boot_1.3-23 MASS_7.3-51.1 assertthat_0.2.1 [101] withr_2.1.2 mnormt_1.5-5 multcomp_1.4-10 parallel_3.5.3 [105] bayestestR_0.4.0 hms_0.5.2 rpart_4.1-15 coda_0.19-3 [109] minqa_1.2.4 snakecase_0.11.0 carData_3.0-3 numDeriv_2016.8-1.1 [113] lubridate_1.7.4 base64enc_0.1-3