The tissue-specific effects of glucose-lowering drug targets on aging mediated through DNA methylation: a multi-omics genetic study.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches

[1] § Methods › Selection of genetic instruments for glucose-lowering drug targets ↔ R/ld.R, lines 1–25 · score 0.63 · clumping window, clumping r2, kb, LD, variants, SNP
[2] § Methods › Statistical analysis › Genetic correlation analysis ↔ README.Rmd, lines 18–45 · score 0.60 · LD score regression, genetic correlations, LD reference, ancestry, heritability, traits
[3] § Methods › Statistical analysis › Mendelian randomization analyses ↔ R/format_mr_results2.R, lines 137–214 · score 0.59 · inverse variance weighted, weighted median, Wald, ratio, exposures, MR
[4] § Methods › Selection of genetic instruments for glucose-lowering drug targets ↔ R/multivariable_mr.R, lines 118–152 · score 0.58 · clumping r2, allele frequency, kb, trait, SNP, gene
[5] § Methods › Statistical analysis › Mendelian randomization analyses ↔ R/forest_plot2.R, lines 1–53 · score 0.58 · inverse variance weighted, outcome GWAS, Wald, ratio, exposures, MR
[6] § Methods › Statistical analysis › Genetic correlation analysis ↔ R/ldsc_h2.R, lines 1–41 · score 0.55 · LD score regression, heritability, ancestry, allele, traits, SNP

Paper

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The authors' code

R · 106 lines · 5 KB · other · 1 match

#' Perform LD clumping on SNP data
#'
#' Uses PLINK clumping method, where SNPs in LD within a particular window will be pruned. The SNP with the lowest p-value is retained.
#'
#' @details
#' This function interacts with the OpenGWAS API, which houses LD reference panels for the 5 super-populations in the 1000 genomes reference panel.
#' It includes only bi-allelic SNPs with MAF > 0.01, so it's quite possible that a variant you want to include in the clumping process will be absent.
#' If it is absent, it will be automatically excluded from the results.
#'
#' You can check if your variants are present in the LD reference panel using [ieugwasr::ld_reflookup()].
#'
#' This function does put load on the OpenGWAS servers, which makes life more difficult for other users.
#' We have implemented a method and made available the LD reference panels to perform clumping locally, see [ieugwasr::ld_clump()] and related vignettes for details.
#'
#' @param dat Output from [format_data()]. Must have a SNP name column (`SNP`), SNP chromosome column (`chr_name`), SNP position column (`chrom_start`). If `id.exposure` or `pval.exposure` not present they will be generated.
#' @param clump_kb Clumping window, default is `10000`.
#' @param clump_r2 Clumping r2 cutoff. Note that this default value has recently changed from `0.01` to `0.001`.
#' @param clump_p1 Clumping sig level for index SNPs, default is `1`.
#' @param pop Super-population to use as reference panel. Default = `"EUR"`. Options are `"EUR"`, `"SAS"`, `"EAS"`, `"AFR"`, `"AMR"`. `'legacy'` also available - which is a previously used version of the EUR panel with a slightly different set of markers
#' @param bfile If this is provided then will use the API. Default = `NULL`
#' @param plink_bin If `NULL` and `bfile` is not `NULL` then will detect packaged plink binary for specific OS. Otherwise specify path to plink binary. Default = `NULL`
#'
#' @export
#' @return Data frame
clump_data <- function(
dat,
clump_kb = 10000,
clump_r2 = 0.001,
clump_p1 = 1,
pop = "EUR",
bfile = NULL,
plink_bin = NULL
) {
# .Deprecated("ieugwasr::ld_clump()")
pval_column <- "pval.exposure"
if (!is.data.frame(dat)) {
stop("Expecting data frame returned from format_data().")
}
if ("pval.exposure" %in% names(dat) && "pval.outcome" %in% names(dat)) {
message("pval.exposure and pval.outcome columns present. Using pval.exposure for clumping.")
} else if (!"pval.exposure" %in% names(dat) && "pval.outcome" %in% names(dat)) {
message("pval.exposure column not present, using pval.outcome column for clumping.")
pval_column <- "pval.outcome"
} else if (!"pval.exposure" %in% names(dat)) {
message("pval.exposure not present, setting clumping p-value to 0.99 for all variants")
dat$pval.exposure <- 0.99
} else {
pval_column <- "pval.exposure"
}
if (!"id.exposure" %in% names(dat)) {
dat$id.exposure <- random_string(1)
}
d <- data.frame(rsid = dat$SNP, pval = dat[[pval_column]], id = dat$id.exposure)
out <- ieugwasr::ld_clump(
d,
clump_kb = clump_kb,
clump_r2 = clump_r2,
clump_p = clump_p1,
pop = pop,
bfile = bfile,
plink_bin = plink_bin
)
keep <- paste(dat$SNP, dat$id.exposure) %in% paste(out$rsid, out$id)
return(dat[keep, ])
}
#' Get LD matrix for list of SNPs
#'
#' This function takes a list of SNPs and searches for them in a specified super-population in the 1000 Genomes phase 3 reference panel.
#' It then creates an LD matrix of r values (signed, and not squared).
#' All LD values are with respect to the major alleles in the 1000G dataset.
#' You can specify whether the allele names are displayed.
#'
#' @details
#' The data used for generating the LD matrix includes only bi-allelic SNPs with MAF > 0.01,
#' so it's quite possible that a variant you want to include will be absent.
#' If it is absent, it will be automatically excluded from the results.
#'
#' You can check if your variants are present in the LD reference panel using [ieugwasr::ld_reflookup()].
#'
#' This function does put load on the OpenGWAS servers, which makes life more difficult for other users,
#' and has been limited to analyse only up to 500 variants at a time.
#' We have implemented a method and made available the LD reference panels to perform the operation locally,
#' see [ieugwasr::ld_matrix()] and related vignettes for details.
#'
#' @param snps List of SNPs.
#' @param with_alleles Whether to append the allele names to the SNP names. The default is `TRUE`.
#' @param pop Super-population to use as reference panel. Default = `"EUR"`. Options are `"EUR"`, `"SAS"`, `"EAS"`, `"AFR"`, `"AMR"`. `'legacy'` also available - which is a previously used version of the EUR panel with a slightly different set of markers.
#'
#' @export
#' @return Matrix of LD r values
ld_matrix <- function(snps, with_alleles = TRUE, pop = "EUR") {
# .Deprecated("ieugwasr::ld_matrix()")
ieugwasr::ld_matrix(
variants = snps,
with_alleles = with_alleles,
pop = pop,
x_api_source = x_api_source_header()
)
}

ld.R at commit d4df219, under other · at the source

Overview

Authors: Yuqi Sun^1,2,3, Haonan Zheng^1,2,4, Lanhui Huang^1,2, Min Ma^1,2, Rongrong Gu^1,2,3, Manqing Wang^1,2,3, Si Fang^5,6, Yangbo Sun⁷, Qian Yang^1,2,5, Yufang Bi^1,2, Jie Zheng^1,2,5

Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine,Shanghai, China
Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Lifecycle Health Management Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine,Shanghai, China
College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine,Shanghai, China
College of Basic Medical Science, Shanghai Jiao Tong University School of Medicine,Shanghai, China
MRC Integrative Epidemiology Unit, University of Bristol,Bristol, UK
Population Health Sciences, Bristol Medical School, University of Bristol,Bristol, UK
Department of Preventive Medicine, The University of Tennessee Health Science Center,Memphis, TN USA

Institutions: Shanghai Jiao Tong University (China); University of Bristol (United Kingdom); University of Tennessee Health Science Center (United States)

Journal: BMC medicine, volume 24, issue 1, article 442

Dates: received 7 October 2025; accepted 9 April 2026; published online 21 May 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1186/s12916-026-04869-x · PMID 42163256 · PMCID PMC13471528 · OpenAlex W4415155464

Open access: gold, a free copy (OpenAlex)

Preprint: doi:10.1101/2025.10.13.25337858

Status: code verified

Categories: genetics / omics (modality), human (organism), other condition (population)

Methods: Statistics, Smoothing, state filtering, decompositions

Keywords: Aging, DNA methylation, Anti-aging drugs, Anti-diabetic drugs, Mendelian randomization

MeSH: Aging*, Diabetes Mellitus, Type 2*, DNA Methylation*, Hypoglycemic Agents*, Epigenesis, Genetic, Humans, Mendelian Randomization Analysis, Multiomics, Organ Specificity, Quantitative Trait Loci (* major topic)

Topic: Epigenetics and DNA Methylation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: Roche University Research Fund (20240513); Students’ Innovation Training Program, Shanghai Jiao Tong University, School of Medicine, China (1824955Y); National Key Research and Development Program of China (2022YFC2505203); Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0531500, 2024ZD0531502); National Natural Science Foundation of China (32500519, 32570728); Shanghai Municipal Education Commission–Gaofeng Clinical Medicine Grant Support (20161307 and 20152508 Round 2)

Citations: not cited yet (Europe PMC); 80 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

MRCIEU/TwoSampleMR

License: other

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: d4df21929fdabeb2f89686ffaad0db5f56973d64, 25 September 2026

Languages: R (68)

Size: 238 files, 68 scripts

Software Heritage: not archived

Found in: “Data availability”

Holds: README, license file, CITATION.cff, environment (DESCRIPTION), tests, continuous integration, documentation, 8 notebooks

Tools: data.table (17 files), ggplot2 (10 files), tidyverse (5 files), cowplot (2 files), psych (2 files), car (1 file), glmnet (1 file), randomForest (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

71 files

R/TwoSampleMR-package.R, R, 6 lines
R/add_metadata.r, R, 82 lines
R/add_rsq.r, R, 391 lines
R/backward_compatibility.R, R, 24 lines
R/enrichment.R, R, 88 lines
R/eve.R, R, 339 lines
R/forest_plot.R, R, 384 lines
R/forest_plot2.R, R, 699 lines, 1 match
R/forest_plot_1-to-many.R, R, 922 lines
R/format_mr_results2.R, R, 460 lines, 1 match
R/globals.R, R, 116 lines
R/harmonise.R, R, 794 lines
R/heterogeneity.R, R, 99 lines
R/instruments.R, R, 73 lines
R/knit.R, R, 177 lines
R/ld.R, R, 106 lines, 1 match
R/ldsc.r, R, 232 lines
R/leaveoneout.R, R, 200 lines
R/make_dat.R, R, 19 lines
R/moe.R, R, 229 lines
R/mr-grip.R, R, 118 lines
R/mr.R, R, 1,239 lines
R/mr_mode.R, R, 401 lines
R/multivariable_mr.R, R, 890 lines, 1 match
R/other_formats.R, R, 305 lines
R/query.R, R, 332 lines
R/read_data.R, R, 831 lines
R/rucker.R, R, 607 lines
R/scatterplot.R, R, 178 lines
R/singlesnp.R, R, 415 lines
R/steiger.R, R, 285 lines
R/steiger_filtering.R, R, 59 lines
R/transform.R, R, 62 lines
R/utils-pipe.R, R, 11 lines
R/zzz.R, R, 57 lines
README.Rmd, R, 14 lines
inst/reports/mr_report.Rmd, R, 88 lines
tests/testthat.R, R, 4 lines
tests/testthat/helper-twosamplemr.R, R, 7 lines
tests/testthat/test_add_metadata.r, R, 107 lines
tests/testthat/test_create_test_data.r, R, 18 lines
tests/testthat/test_eve.R, R, 11 lines
tests/testthat/test_format_d.R, R, 26 lines
tests/testthat/test_format_data.R, R, 93 lines
tests/testthat/test_harmonise.R, R, 11 lines
tests/testthat/test_harmonise_edge_cases.R, R, 70 lines
tests/testthat/test_heterogeneity.R, R, 14 lines
tests/testthat/test_instruments.R, R, 87 lines
tests/testthat/test_ld.R, R, 58 lines
tests/testthat/test_ldsc.R, R, 18 lines
tests/testthat/test_leaveoneout.R, R, 15 lines
tests/testthat/test_mr.R, R, 92 lines
tests/testthat/test_mr_mode.R, R, 43 lines
tests/testthat/test_mvmr.R, R, 88 lines
tests/testthat/test_mvmr_local.R, R, 51 lines
tests/testthat/test_otherformats.R, R, 60 lines
tests/testthat/test_outcomes.R, R, 59 lines
tests/testthat/test_plots.R, R, 136 lines
tests/testthat/test_rsq.r, R, 148 lines
tests/testthat/test_rucker.R, R, 55 lines
tests/testthat/test_singlesnp.R, R, 75 lines
tests/testthat/test_steiger.R, R, 26 lines
vignettes/exposure.Rmd, R, 386 lines
vignettes/gwas2020.Rmd, R, 113 lines
vignettes/harmonise.Rmd, R, 180 lines
vignettes/introduction.Rmd, R, 129 lines
vignettes/outcome.Rmd, R, 199 lines
vignettes/perform_mr.Rmd, R, 1,017 lines
LICENSE, License, 2 lines
LICENSE.md, License, 21 lines
README.md, Text, 63 lines

At the source: github.com/MRCIEU/TwoSampleMR/

mglev1n/ldscr

License: other

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: d6b23694d8d4f6f2120c98a2c410cf1e455b9246, 18 August 2025

Languages: R (14), MATLAB (6)

Size: 67 files, 20 scripts

Software Heritage: archived

Found in: “Data availability”

Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 1 notebook

Not found: CITATION.cff

Tools: tidyverse (5 files), data.table (2 files), ggplot2 (2 files)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

23 files

R/aaa.R, R, 45 lines
R/autoplot.R, R, 169 lines
R/example_data.R, R, 31 lines
R/helper.R, R, 160 lines
R/ldsc_h2.R, R, 193 lines, 1 match
R/ldsc_rg.R, R, 402 lines
R/ldscr-package.R, R, 22 lines
R/utils-pipe.R, R, 14 lines
README.Rmd, R, 59 lines, 1 match
inst/extdata/AFR/UKBB.AFR.l2.M, MATLAB, 1 line
inst/extdata/AMR/UKBB.AMR.l2.M, MATLAB, 1 line
inst/extdata/CSA/UKBB.CSA.l2.M, MATLAB, 1 line
inst/extdata/EAS/UKBB.EAS.l2.M, MATLAB, 1 line
inst/extdata/EUR/UKBB.EUR.l2.M, MATLAB, 1 line
inst/extdata/MID/UKBB.MID.l2.M, MATLAB, 1 line
tests/testthat.R, R, 12 lines
tests/testthat/test-autoplot.R, R, 15 lines
tests/testthat/test-helper.R, R, 27 lines
tests/testthat/test-ldsc_h2.R, R, 29 lines
tests/testthat/test-ldsc_rg.R, R, 26 lines
LICENSE, License, 2 lines
LICENSE.md, License, 595 lines
README.md, Text, 91 lines

At the source: github.com/mglev1n/ldscr/

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
88 scripts, each with its path and the digest of its content;
6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to the authors' code: mglev1n/ldscr, MRCIEU/TwoSampleMR
it says that the code is available on request

Read it in the paper: doi.org/10.1186/s12916-026-04869-x.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 10 MeSH terms, 6 funders, 79 references.

Cite

This paper

Sun, Y., Zheng, H., Huang, L., Ma, M., Gu, R., Wang, M., Fang, S., Sun, Y., Yang, Q., Bi, Y., & Zheng, J. (2026). The tissue-specific effects of glucose-lowering drug targets on aging mediated through DNA methylation: a multi-omics genetic study. BMC medicine, 24(1), 442. https://doi.org/10.1186/s12916-026-04869-x

BibTeX

@article{sun2026tissue, author = {Sun, Yuqi and Zheng, Haonan and Huang, Lanhui and Ma, Min and Gu, Rongrong and Wang, Manqing and Fang, Si and Sun, Yangbo and Yang, Qian and Bi, Yufang and Zheng, Jie}, title = {{The tissue-specific effects of glucose-lowering drug targets on aging mediated through DNA methylation: a multi-omics genetic study}}, journal = {BMC medicine}, year = {2026}, month = may, volume = {24}, number = {1}, pages = {442}, publisher = {BioMed Central}, issn = {1741-7015}, doi = {10.1186/s12916-026-04869-x}, url = {https://doi.org/10.1186/s12916-026-04869-x}, pmid = {42163256}, pmcid = {PMC13471528} }

RIS

TY - JOUR AU - Sun, Yuqi AU - Zheng, Haonan AU - Huang, Lanhui AU - Ma, Min AU - Gu, Rongrong AU - Wang, Manqing AU - Fang, Si AU - Sun, Yangbo AU - Yang, Qian AU - Bi, Yufang AU - Zheng, Jie TI - The tissue-specific effects of glucose-lowering drug targets on aging mediated through DNA methylation: a multi-omics genetic study T2 - BMC medicine J2 - BMC Med PY - 2026 DA - 2026/05/21 VL - 24 IS - 1 SP - 442 SN - 1741-7015 PB - BioMed Central DO - 10.1186/s12916-026-04869-x UR - https://doi.org/10.1186/s12916-026-04869-x LA - en ER -

CSL-JSON

{ "id": "10.1186/s12916-026-04869-x", "type": "article-journal", "title": "The tissue-specific effects of glucose-lowering drug targets on aging mediated through DNA methylation: a multi-omics genetic study", "container-title": "BMC medicine", "author": [ { "family": "Sun", "given": "Yuqi" }, { "family": "Zheng", "given": "Haonan" }, { "family": "Huang", "given": "Lanhui" }, { "family": "Ma", "given": "Min" }, { "family": "Gu", "given": "Rongrong" }, { "family": "Wang", "given": "Manqing" }, { "family": "Fang", "given": "Si" }, { "family": "Sun", "given": "Yangbo" }, { "family": "Yang", "given": "Qian" }, { "family": "Bi", "given": "Yufang" }, { "family": "Zheng", "given": "Jie" } ], "container-title-short": "BMC Med", "volume": "24", "issue": "1", "page": "442", "DOI": "10.1186/s12916-026-04869-x", "PMID": "42163256", "PMCID": "PMC13471528", "ISSN": "1741-7015", "publisher": "BioMed Central", "URL": "https://doi.org/10.1186/s12916-026-04869-x", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 21 ] ] } }

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