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A multiancestry polygenic risk score for Alzheimer's disease is associated with cognitive decline and neuropathological hallmarks in diverse populations.

Code ↔ Paper

8 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.

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  1. [1] § Methods › Statistical analysis ↔ README.Rmd, lines 415–488 · score 0.95 · 10–50 %, 50–90 %, 60–80, generalized additive mixed, mixed models, cognitive decline
  2. [2] § Results › AD PRS predicts age-related cognitive decline ↔ README.Rmd, lines 415–488 · score 0.92 · 10–50 %, 50–90 %, generalized additive mixed, PRS strata, age related, smooth
  3. [3] § Results › Overall study design for developing and evaluating a multiancestry AD PRS ↔ README.Rmd, lines 67–123 · score 0.74 · Bayesian shrinkage, LDpred2, PRS CSx, weighted summation, PRSice2, PRS weights
  4. [4] § Results › AD PRS predicts age-related cognitive decline ↔ README.Rmd, lines 491–561 · score 0.72 · predicted cognitive domain, PRS strata, way interactions, AD status, ribbons, baseline
  5. [5] § Methods › Training of PRS summation weights using ADGC data ↔ README.Rmd, lines 67–123 · score 0.71 · LDpred2, PRS CSx, FinnGen, PRS summation, PRSice2, PRS weights
  6. [6] § Methods › Statistical analysis ↔ README.Rmd, lines 491–561 · score 0.68 · cognitive domain score, lme4, unrelated individuals, probabilities, predictor, traits
  7. [7] § Methods › Training of PRS summation weights using ADGC data ↔ README.Rmd, lines 12–86 · score 0.55 · FinnGen, PRS CS, PRS summation, PRSice2, PRS weights, regression
  8. [8] § Methods › PRS development › PRS-CS ↔ README.Rmd, lines 12–86 · score 0.53 · polygenic risk scoring, PRS CS, PRSice2, clumping, LD, SNPs

Paper

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The authors' code

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  1. ---
  2. title: "Alzheimer PRS"
  3. author: "Nuzulul Kurniansyah"
  4. date: "09/20/2024"
  5. output: md_document
  6. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(echo = TRUE)
  10. ```
  12. ## Introduction
  14. This repository provides information regarding the construction of a polygenic risk score (PRS) for Alzheimer Dieseses (AD) that we developed in manuscript titled: **A multi-ancestry polygenic risk score for Alzheimer disease is associated with cognitive decline, hippocampal atrophy and neuropathological hallmarks in diverse populations**. \
  15. This repository contains scripts and documentation for constructing the AD-PRS and performing downstream analyses. The final PRS weights, scaling information, and PRS summation weights will be made available here and in the PGS Catalog upon paper acceptance. All analyses were conducted using publicly available tools for PRS construction and downstream analyses.
  17. ## System requirements
  19. * Operating systems tested: Linux (CentOS 7, Boston University Shared Computing Cluster) and macOS Ventura 13.4 or later
  20. * R version: 4.0.3 or above
  21. * Python version: 3.6.10
  22. * Software dependencies to develop the PRS: \
  23. *Bayesian shrinkage models*: \
  24. PRS-CS (https://github.com/getian107/PRScs) \
  25. PRS-CSx (https://github.com/getian107/PRScsx) \
  26. LDpred2 (via the R package bigsnpr version 1.9.11 ) \
  27. *Clumping and thresholding*:\
  28. \
  29. PRSice-2 version 2.3.1.e(https://github.com/choishingwan/PRSice) \
  30. PLINK (version 1.9, https://www.cog-genomics.org/plink/1.9/) \
  31. \
  32. The final PRS was computed by applying the derived SNP weights or selected SNP sets to individual-level genotype data using PRSice-2 or PLINK v1.9. \
  33. \
  34. To construct multi weighted and un-weighted PRS summation (PRSsum), we followed the approach and code available at https://github.com/nkurniansyah/PRSsum_Simple. \
  36. * Software dependencies for downstream analyses:\
  37. To perform downstream analyses, we used R with the following packages: \
  38. \
  39. CRAN: dplyr version 1.1.3, tidyverse version 1.3.1, data.table version 1.17.2, pROC version 1.18.5, lme4 version 1.1-29, gamm4 version 0.2-6, ggeffect version 2.0.0, ggplot2 version 3.5.2, lmerTest version 3.1-3, MASS version 7.3-60 \
  40. Bioconductor: GENESIS version 2.16.1, GWASTools version 1.42\
  42. ## Installation guide
  44. Basic installation commands for the PRS software are provided below. For detailed setup options and additional dependencies, please refer to each software’s official documentation.
  45. ```{bash eval=FALSE, echo=TRUE}
  47. git clone https://github.com/getian107/PRScs.git
  48. git clone https://github.com/getian107/PRScsx.git
  49. git clone https://github.com/choishingwan/PRSice.git
  50. ```
  52. LDpred2 can be installed through CRAN via the bigsnpr package:
  54. ```{bash eval=FALSE, echo=TRUE}
  56. install.packages("bigsnpr")
  57. ```
  59. All required R packages for downstream analyses (e.g., dplyr, tidyverse, GENESIS) can be installed in the same way using standard CRAN or Bioconductor commands:
  61. ```{bash eval=FALSE, echo=TRUE}
  63. install.packages("dplyr")
  64. BiocManager::install(c("GENESIS", "GWASTools"))
  65. ```
  67. ## Instructions for use
  69. We developed PRS for AD by integrating genome-wide association study (GWAS) summary statistics from multiple ancestral populations, including European (EADB Stage II excluding UK Biobank and FinnGen), African (MVP), and East Asian (Japanese and South Korean) cohorts.
  70. We develop the PRS using following method PRS-CS, PRS-CSx, and LDpred2 (Bayesian shrinkage models), as well as PRSice-2 (clumping and thresholding). Instructions for using these tools are available in their respective software documentation. \
  72. Weighted and unweighted PRS summations were then constructed using the PRSsum Simple framework (https://github.com/nkurniansyah/PRSsum_Simple), with weighted summation derived from the Alzheimer’s Disease Genetics Consortium (ADGC) dataset to optimize multi-ancestry performance.
  73. Further details on the weighted summation procedure are provided in the Methods section of the manuscript.\
  75. The final PRS weights will be made publicly available upon manuscript acceptance in this repository and deposited in the PGS Catalog to facilitate community access and reuse.
  77. ### PRS construction
  79. The final PRSsum Simple output file provides the SNP-level weights required to compute individual PRS. The file includes the following columns:
  81. CHR: chromosome \
  82. rsID: rsID based on dbSNP \
  83. POS: Position variants in Hg 38 \
  84. A1 : effect allele \
  85. A2 : minor allele \
  86. BETA: weighted sum BETA \
  87. P : mock Pvalue (we need this colum to run PRSice2) \
  89. We provide the option to cosntruct the PRS using PRSice2 or Using PLINK. \
  91. ### PRSice command for PRS construction
  93. This command is to construct PRS using the summary statistics that we provide. No clumping is needed and no selection of SNPs. The summary statistics are already based on the weighted sum from multiple GWAS. Note that genetic data files need to be specified in the –target argument and please add command **--score sum**
  95. ```{bash eval=FALSE, echo=TRUE}
  98. Rscript ./PRSice.R \
  99. --dir ./PRS_Output \
  100. --prsice ./PRSice_linux/PRSice_linux \
  101. --base ./Summary_Statistics/. \
  102. --target ./Genotype \
  103. --thread 2 \
  104. --chr CHR \
  105. --bp POS \
  106. --A1 A1 \
  107. --A2 A2 \
  108. --pvalue P \
  109. --bar-levels 1 \
  110. --stat BETA
  111. --all-score T \
  112. --out ./out_prs \
  113. --no-clump T
  114. --print-snp T \
  115. --ignore-fid T \
  116. --no-regress T \
  117. --fastscore T \
  118. --score sum \
  119. --no-full T \
  120. --chr-id c:l
  123. ```
  126. ### PLINK command for PRS construction
  128. This command is to construct PRS using the summary statistics that we provide, by using PLINK command below:
  132. ```{bash eval=FALSE, echo=TRUE}
  134. plink --bfile ./Genotype --score ./Summary_Statistics/. 2 4 6 sum --out ./out_prs
  135. ```
  138. ## Standardize the PRSsum
  140. After constructing the PRSsum, For scaling, we use the ADSP mean and SD values for final scaling. Using the same scaling throughout guarantees that effect size estimates are similarly interpreted across all datasets and individuals who use this PRS. Scaling information files are provided in this repository in ../PRSsum_Scaling/* (will provide upon acceptance)
  141. Below are the code to standardize the PRSsum.
  143. ```{r eval=FALSE, echo=TRUE}
  144. library(data.table)
  145. library(dplyr)
  147. scaling_files<-paste0("PRSsum_Scaling/20240820_Final_scaling_AD_PRSsum.csv")
  148. scaling<- read.csv(scaling_files)
  150. #if you using PRSice2
  152. prs_file<- paste0("../PRS/AD_PRS.all_score")
  154. #if you using plink
  155. prs_file<- paste0("../PRS/AD_PRS.profile")
  158. ### comment either PRSice2 or PLINK
  161. prs_df<- fread(prs_file, data.table=F)
  163. head(prs_df)
  165. # this contain IDs and PRS score, if you generated PRS using PRSice2, youll get Pt_1 column name as PRS score, if you using plink you will SCORESUM colum as PRS
  166. prs_df<-prs_df[,c(2:3)]
  168. # change the name
  169. colnames(prs_df)<-c("sample.id", "prs")
  172. mean_adsp<- as.numeric(scaling$Mean)
  173. sd_adsp<- as.numeric(scaling$SD)
  175. prs_df[,"prs"]<-(prs_df[,"prs"]-mean_adsp)/sd_adsp
  178. write.csv(prs_df, file = "save/your/prs/",type,"_wPRSsum.csv", row.names = F)
  181. ```
  184. ### Example code for association analysis AD PRS with AD and related traits
  186. Here we provide an example of how to run the association analysis using a set of unrelated individuals with linear regression,linear model or ordinal regression. This example utilizes a function provided in the 'Code' folder.
  189. ```{r eval=FALSE, echo=TRUE}
  191. library(GENESIS)
  192. library(GWASTools)
  193. library(pROC)
  196. source("./Code/run_linear_regression.R")
  198. source("./Code/auc.R")
  201. #User can startified the pheno type here based on rancestry, or Sex
  204. #phenotype only unrelated individual
  206. pheno<- fread(phenotype_file, data.table=F)
  209. #open the weighted PRSsum from previous step
  211. wPRSsum<- read.csv("../PRS/",type,"_wPRSsum.csv")
  212. # merge PRSsum with phenotype
  214. pheno_df<-left_join(pheno,wPRSsum, by="sample.id" )
  216. # adjust the the name variable.
  218. sexes<-c("Sex_combined","Female","Male")
  220. out<-list()
  222. for(sex in sexes ){
  225. if(sex=="Sex_combined"){
  226. pheno_sex<-pheno_df
  227. cov_prs<- c("age_on_set","sex","apoe4","apoe2","education" ,paste0("pc_",1:11), "prs")
  229. }else{
  230. pheno_sex<-pheno_df[which(pheno_df$Sex==sex),]
  231. cov_prs<- c("age_on_set","apoe4","apoe2","education" ,paste0("pc_",1:11), "prs")
  233. }
  236. assoc<-run_linear_regression(pheno=pheno_sex, covars_prs=cov_prs, exposure="prs", outcome="AD")
  240. if(all(pheno_sex[,outcome] %in% c(0,1,NA))){
  241. auc<- generate_auc(pheno=pheno_df,
  242. outcome=outcome,
  243. covars_prs=cov_prs)
  244. final_assoc<-c(assoc,auc)
  247. }else{
  248. #if outcome gaussian or ordninal, we don need AUC
  249. final_assoc<-assoc
  250. }
  253. final_assoc$Sex<-sex
  255. out[[sex]]<- final_assoc
  256. }
  258. res<-do.call(rbind, out)
  259. res
  262. ```
  265. ### Example code for association analysis AD PRS with AD and related traits using mix model
  267. We provide an example to perform the association analysis using all individuals. If you include all individuals, you must provide a GRM or kinship matrix to account for relatedness. However, for calculating the AUC , only unrelated individuals should be used. Follow the code provided below: \
  269. Note: \
  271. You need to specifiy, ID name that match with kinship or GRM, e.g: "framid" \
  274. ```{r eval=FALSE, echo=TRUE}
  276. library(GWASTools)
  277. library(GENESIS)
  278. library(dplyr)
  279. library(data.table)
  280. library(pROC)
  282. source("./Code/run_mix_model.R")
  284. source("./Code/auc.R")
  287. #phenotype all individual
  289. pheno<- fread(phenotype_file, data.table=F)
  292. #open the weighted PRSsum from previous step
  294. wPRSsum<- read.csv("../PRS/",type,"_wPRSsum.csv")
  295. # merge PRSsum with phenotype
  297. pheno_df<-left_join(pheno,wPRSsum, by="sample.id" )
  299. # adjust the the name variable.
  301. #open kinsip matrix
  303. covmat<- getobj("load/your/kinship/matrix.RData")
  305. sexes<-c("Sex_combined","Female","Male")
  307. out<-list()
  308. for(sex in sexes ){
  311. if(sex=="Sex_combined"){
  312. pheno_sex<-pheno_df
  313. cov_prs<- c("age_on_set","sex","apoe4","apoe2","education" ,paste0("pc_",1:11), "prs")
  315. }else{
  316. pheno_sex<-pheno_df[which(pheno_df$Sex==sex),]
  317. cov_prs<- c("age_on_set","apoe4","apoe2","education" ,paste0("pc_",1:11), "prs")
  319. }
  320. assoc<-run_assoc_mixmodel(pheno = pheno_sex, outcome = "AD",covars_prs = cov_prs, covmat = covmat,group.var = NULL,col_id_name = "framid")
  323. ### need to remove related individual
  326. if(all(pheno[,outcome] %in% c(0,1,NA))){
  327. auc<- generate_auc(pheno=pheno_df,
  328. outcome=outcome,
  329. covars_prs=cov_prs)
  330. final_assoc<-c(assoc_df,auc)
  333. }else{
  334. #if outcome gaussian, we don need AUC
  335. final_assoc<-assoc
  336. }
  342. final_assoc$Sex<-sex
  344. out[[sex]]<- final_assoc
  345. }
  348. res<-do.call(rbind, out)
  349. res
  352. ```
  355. ### Example code for association analysis AD PRS with longitudinal cognitive perfomance and hippocampal atrophy
  356. We provide example R scripts to examine the association between AD PRS and longitudinal datasets, such as cognitive performance and brain MRI measures. Analyses were conducted among unrelated individuals. In the Framingham Heart Study (FHS), all participants were included, and family IDs were modeled as random effects to account for relatedness.
  358. ```{r eval=FALSE, echo=TRUE}
  361. library(dplyr)
  362. library(data.table)
  363. library(lme4)
  364. library(lmerTest)
  366. source("./Code/run_longutudinal_mix_model.R")
  369. #phenotype unrelated individual for longutudinal data
  371. pheno<- fread(phenotype_file, data.table=F)
  374. #open the weighted PRSsum from previous step
  376. wPRSsum<- read.csv("../PRS/",type,"_wPRSsum.csv")
  377. # merge PRSsum with phenotype
  379. pheno_df<-left_join(pheno,wPRSsum, by="sample.id" )
  381. # adjust the the name variable.
  384. sexes<-c("Sex_combined","Female","Male")
  386. out<-list()
  387. for(sex in sexes ){
  390. if(sex=="Sex_combined"){
  391. pheno_sex<-pheno_df
  392. cov_prs<- c("age_at_exam","sex","apoe4","apoe2","education" ,paste0("pc_",1:11), "prs")
  394. }else{
  395. pheno_sex<-pheno_df[which(pheno_df$Sex==sex),]
  396. cov_prs<- c("age_at_exam","apoe4","apoe2","education" ,paste0("pc_",1:11), "prs")
  398. }
  400. assoc<- run_assoc_longutudinal_mixmodel(pheno=pheno_sex, outcome="memory", covars_prs=cov_prs,random_effect=c("IDs"), exposure=c("prs")){
  402. assoc$Sex<-sex
  404. out[[sex]]<- assoc
  405. }
  408. res<-do.call(rbind, out)
  409. res
  412. ```
  415. ### Example code for AD PRS predicts age-related cognitive decline analyis
  417. Here, we provide code to run the analysis of predicted age-related cognitive decline using a Generalized Additive Mixed Model (GAMM). This approach models non-linear age trajectories of cognitive performance while accounting for within-subject correlations through random effects. Further details about this analysis are provided in the manuscript.
  421. ```{r eval=FALSE, echo=TRUE}
  424. library(ggeffects)
  425. library(gamm4)
  426. library(dplyr)
  427. library(data.table)
  428. library(ggplot2)
  431. #phenotype unrelated individual for longutudinal data
  433. pheno<- fread(phenotype_file, data.table=F)
  436. #open the weighted PRSsum from previous step
  438. wPRSsum<- read.csv("../PRS/",type,"_wPRSsum.csv")
  439. # merge PRSsum with phenotype
  441. pheno_df<-left_join(pheno,wPRSsum, by="sample.id" )
  443. covariates<-c("sex", "education", "apoe2dos", "apoe4dos",paste0("pc_",1:10))
  445. pheno_df$prs_qtile<- cut(pheno_df$prs,
  446. breaks = quantile(pheno_df$prs, probs = c(0, 0.10, 0.50, 0.90, 1), na.rm = TRUE),
  447. include.lowest = TRUE,
  448. labels = c("<10%", "10–50%", "50–90%", ">90%"))
  450. age_seq <- seq(60, 80, by = 0.5)
  452. trait<-c("Memory")
  453. fixed_effect <- paste(covariates, collapse = " + ")
  454. model_formula<- paste0(outcome ," ~ ", fixed_effect, " +s( age_centered, by=prs_qtile, k=4)")
  456. gamm_model <- gamm4(
  457. formula = as.formula(model_formula),
  458. random = ~(1 +age_centered | IDs),
  459. data = pheno_df)
  461. age_centered_seq <- age_seq - median_age
  463. #call ggpredict with numeric range
  464. pred_df <- ggpredict(gamm_model$gam,
  465. terms = c(paste0("age_centered [", min(age_centered_seq), ":", max(age_centered_seq), " by=0.5]"), "prs_qtile"))
  468. # Add actual age for plotting
  469. pred_df$age <- pred_df$x + median_age
  473. pred_pict <- ggplot(pred_df, aes(x = age, y = predicted, color = group, fill = group)) +
  474. geom_line(size = 1.2) + coord_cartesian(xlim = c(60, 80)) +
  475. geom_smooth(method = "loess", se = FALSE) +
  476. geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.2, color = NA) +
  477. labs(
  478. x = "Age",
  479. y = "Predicted cognitive domain score",
  480. color = "PRS strata",
  481. fill = "PRS strata"
  482. ) +
  483. theme_bw() +
  484. theme(legend.position = "top")
  486. print(pred_pict)
  488. ```
  491. We also provide the code to evaluate whether cognitive decline differed by PRS and AD status, we additionally fit LMMs, which allow for interpretable estimation of interaction effects and enable direct comparison of cognitive decline slopes across groups. Due to limited sample size among AD cases, AD PRS was modeled categorically using tertiles (low, moderate, high) to ensure stable group comparisons. The model included fixed effects for age at first visit, AD diagnosis, PRS group, time since baseline, and their two- and three-way interactions. see manuscrit for furthe detail.
  493. ```{r eval=FALSE, echo=TRUE}
  496. library(ggeffects)
  497. library(lme4)
  498. library(dplyr)
  499. library(data.table)
  500. library(ggplot2)
  504. #phenotype unrelated individual for longutudinal data
  506. pheno<- fread(phenotype_file, data.table=F)
  509. #open the weighted PRSsum from previous step
  511. wPRSsum<- read.csv("../PRS/",type,"_wPRSsum.csv")
  512. # merge PRSsum with phenotype
  514. pheno_df<-left_join(pheno,wPRSsum, by="sample.id" )
  516. median_age <- median(pheno_df$age, na.rm = TRUE)
  518. pheno_df$prs_group<- cut(pheno_df$prs,
  519. breaks = quantile(pheno_df$prs, probs = c(0, 1/3, 2/3, 1), na.rm = TRUE),
  520. include.lowest = TRUE,
  521. labels = cc("Low", "Medium", "High"))
  524. trait <- "Memory"
  525. fixed_covars <- c("age_at_first_visit","time","sex","education","apoe2dos","apoe4dos", paste0("pc_", 1:10))
  527. interaction_term <- "time*prs_group*AD_Diagnosis"
  529. # Build formula: put the interaction in the model formula
  530. fixed_effect <- paste(c(fixed_covars, interaction_term), collapse = " + ")
  531. model_formula <- as.formula(paste(outcome, "~", fixed_effect, "+ (1 + time | IDs)"))
  533. # Fit LMM
  534. model_fit <- lmer(model_formula, data = pheno_df)
  536. # Predictions for plotting
  538. pred <- ggpredict(model_fit, terms = c("time [0:20 by=1]", "prs_group", "AD_Diagnosis"))
  539. plot(pred)
  541. pred$facet<-factor(pred$facet, levels = c("Non-AD","AD"))
  543. #all_res$x
  544. pred_pict <- ggplot(pred, aes(x = x, y = predicted, color = group, fill = group)) +
  545. geom_line(size = 1.2) + coord_cartesian(xlim = c(0, 20)) +
  546. geom_smooth(method = "loess", se = FALSE) +
  547. geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.2, color = NA) +
  548. labs(
  549. x = "Years since baseline",
  550. y = "Predicted cognitive domain scores",
  551. color = "PRS strata",
  552. fill = "PRS strata"
  553. ) +
  554. theme_bw() +
  555. theme(legend.position = "top")
  557. print(pred_pict+facet_wrap(~facet))
  561. ```

README.Rmd, under CC-BY-4.0 · at the source

Overview

Authors: Nuzulul Kurniansyah1,2, Shinya Tasaki3, Habbibur Rehman2, Congcong Zhu2, John Farrell2, Richard Sherva2,4, Richard Hauger5,6,7,8, Victoria C Merritt5,6,7, Matthew Panizzon6,8, Rui Zhang4, J Michael Gaziano9,10, Jungsoo Gim11,12,13,14, Kunho Lee11,12,13,15, Dong Yong Lee16, Kwansik Nho17, Ricardo A Vialle3, Shubhabrata Mukherjee18, Emily H Trittschuh19,20, Annie J Lee21,22,23, Adam M Brickman24
and 22 other authorsCarlos Cruchaga25,26, Shannon Risacher17,27, Douglas N Greve28, Paul Crane18, Eden Martin29, William S Bush30, Richard Mayeux21,22,23,31, Jonathan L Haines30, Margaret A Pericak-Vance29, Mark Logue2,4,32,33, David A Bennett3, Lisa L Barnes3, Andrew Saykin17, Timothy Hohman34,35, Li-San Wang36, Gerard D Schellenberg36, Ting Fang Alvin Ang37,38, Rhoda Au2,37,38,39,40,41,42, Jesse Mez39,40,41, Kathryn L Lunetta33,39, Xiaoling Zhang1,2,33, Lindsay A Farrer1,2,33,39,40,41,42,43
ORCID iDs: Shinya Tasaki, John Farrell, Richard Sherva, Richard Hauger, Victoria C Merritt, Jungsoo Gim, Kunho Lee, Dong Yong Lee, Ricardo A Vialle, Shubhabrata Mukherjee, Carlos Cruchaga, William S Bush, Richard Mayeux, Mark Logue, Timothy Hohman, Ting Fang Alvin Ang, Rhoda Au, Jesse Mez, Kathryn L Lunetta, Xiaoling Zhang, Lindsay A Farrer
43 affiliations
  1. Bioinformatics Program, Boston University, Boston, MA USA
  2. Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
  3. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL USA
  4. National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA USA
  5. Research Service, VA San Diego Healthcare System, San Diego, CA USA
  6. Department of Psychiatry, University of California, San Diego, La Jolla, CA USA
  7. Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA USA
  8. Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA USA
  9. Million Veteran Program (MVP) Coordinating Center, VA Boston Healthcare System, Boston, MA USA
  10. Division of Aging, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA USA
  11. BK21 FOUR, Department of Integrative Biological Sciences, Chosun University, Gwangju, Republic of Korea
  12. Gwangju Alzheimer’s and Related Dementia (GARD) Cohort Research Center, Chosun University, Gwangju, Republic of South Korea
  13. Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
  14. Well-Ageing Medicare Institute, Chosun University, Gwangju, Republic of Korea
  15. Korea Brain Research Institute, Daegu, Republic of Korea
  16. Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
  17. Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
  18. Department of Medicine, School of Medicine, University of Washington, Seattle, WA USA
  19. Geriatric Research, Education, and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, WA USA
  20. Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA USA
  21. Department of Neurology, Columbia University Medical Center, New York, NY USA
  22. Taub Institute for Research on Alzheimer’s Disease and The Aging Brain, Columbia University Medical Center, New York, NY USA
  23. The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY USA
  24. Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia Hospital and the New York Presbyterian Hospital, New York, NY USA
  25. Department of Psychiatry, Washington University in St. Louis, St. Louis, MO USA
  26. NeuroGenomics and Informatics Center, Washington University in St. Louis, St. Louis, MO USA
  27. Department of Radiology and Imaging Sciences, Wake Forest University School of Medicine, Winston-Salem, NC USA
  28. Department of Radiology, Massachusetts General Hospital, Boston, MA USA
  29. John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL USA
  30. Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, School of Medicine, Case Western Reserve University, Cleveland, OH USA
  31. The Institute for Genomic Medicine, Columbia University Medical Center, New York, NY USA
  32. Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
  33. Department of Biostatistics, Boston University School of Public Health, Boston, MA USA
  34. Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN USA
  35. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
  36. Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
  37. Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
  38. Slone Epidemiology Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
  39. Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
  40. Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
  41. Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
  42. Department of Epidemiology, Boston University School of Public Health, Boston, MA USA
  43. Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
Institutions: Boston University (United States); Rush University Medical Center (United States); VA Boston Healthcare System (United States); University of California San Diego (United States); VA San Diego Healthcare System (United States); Center of Excellence for Stress and Mental Health (United States); Brigham and Women's Hospital (United States); Harvard University (United States); Chosun University (South Korea); Korea Brain Research Institute (South Korea); Seoul National University (South Korea); Indiana University School of Medicine; University of Washington (United States); VA Puget Sound Health Care System (United States); Columbia University Irving Medical Center (United States); Columbia University (United States); NewYork–Presbyterian Hospital (United States); Washington University in St. Louis (United States); Wake Forest University Health Sciences (United States); Wake Forest University (United States); Massachusetts General Hospital (United States); University of Miami (United States); Case Western Reserve University (United States); Vanderbilt University Medical Center (United States); University of Pennsylvania (United States); Framingham Heart Study (United States)
Journal: Nature genetics, volume 58, issue 9, pages 2141-2151
Dates: received 11 September 2025; accepted 21 July 2026; published online 27 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41588-026-02722-8 · PMID 42661068 · PMCID PMC13553296 · OpenAlex W7204465938
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), histology / microscopy (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning, Statistics, Preprocessing
Keywords: Alzheimer's disease, Genome-wide association studies
MeSH: Alzheimer Disease*, Cognitive Dysfunction*, Genetic Risk Score*, Aged, Amyloid beta-Peptides, Black or African American, Caribbean People, Cohort Studies, East Asian People, European People, Female, Genome-Wide Association Study, Hispanic or Latino, Humans, Male, Polymorphism, Single Nucleotide, tau Proteins, White People (* major topic)
Topic: Genetic Associations and Epidemiology (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIA NIH HHS (R01 AG048927, U01 AG082665, R01 AG080810, U01 AG072577, U01 AG062602, U01 AG058654, U19 AG068753); U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (U01-AG082665, R01-AG048927, U01-AG062602, U19-AG068753, U01-AG058654, U01-AG072577, R01-AG080810); NIGMS NIH HHS (T32 GM150533); U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) (T32-GM150533)
Citations: cited by 1 paper (Europe PMC); 118 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 8 matches between paragraphs and lines of code.

nkurniansyah/PRSsum_Simple

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0cbba34b85d6d9b5c19085e6a4ccaed4f4a3b801, 4 October 2023
Languages: R (3)
Size: 14 files, 3 scripts
Software Heritage: not archived
Found in: the text, “PRS evaluation in the ADSP dataset”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (2 files), data.table (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/nkurniansyah/PRSsum_Simple

nkurniansyah/Alzheimer_PRS

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State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 588cf099df008eb80872dea40a04a477a06510a2, 23 June 2026
Languages: R (5)
Size: 9 files, 5 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (1 file), lmerTest (1 file), pROC (1 file), survival (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/nkurniansyah/Alzheimer_PRS

nkurniansyah

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: github.com/nkurniansyah/

Zenodo 20818015

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (5)
Size: 8 files, 5 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: lmerTest (2 files), data.table (1 file), ggplot2 (1 file), lme4 (1 file), pROC (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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6 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41588-026-02722-8.

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 13 scripts, each with its path and the digest of its content;
  • 8 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:

Read it in the paper: doi.org/10.1038/s41588-026-02722-8.

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 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 42 authors, 2 keywords, 18 MeSH terms, 4 funders, 115 references.

Cite

This paper

Kurniansyah, N., Tasaki, S., Rehman, H., Zhu, C., Farrell, J., Sherva, R., Hauger, R., Merritt, V. C., Panizzon, M., Zhang, R., Gaziano, J. M., Gim, J., Lee, K., Lee, D. Y., Nho, K., Vialle, R. A., Mukherjee, S., Trittschuh, E. H., Lee, A. J., . . . Farrer, L. A. (2026). A multiancestry polygenic risk score for Alzheimer's disease is associated with cognitive decline and neuropathological hallmarks in diverse populations. Nature genetics, 58(9), 2141-2151. https://doi.org/10.1038/s41588-026-02722-8

BibTeX

@article{kurniansyah2026multiancestry,
author = {Kurniansyah, Nuzulul and Tasaki, Shinya and Rehman, Habbibur and Zhu, Congcong and Farrell, John and Sherva, Richard and Hauger, Richard and Merritt, Victoria C and Panizzon, Matthew and Zhang, Rui and Gaziano, J Michael and Gim, Jungsoo and Lee, Kunho and Lee, Dong Yong and Nho, Kwansik and Vialle, Ricardo A and Mukherjee, Shubhabrata and Trittschuh, Emily H and Lee, Annie J and Brickman, Adam M and Cruchaga, Carlos and Risacher, Shannon and Greve, Douglas N and Crane, Paul and Martin, Eden and Bush, William S and Mayeux, Richard and Haines, Jonathan L and Pericak-Vance, Margaret A and Logue, Mark and Bennett, David A and Barnes, Lisa L and Saykin, Andrew and Hohman, Timothy and Wang, Li-San and Schellenberg, Gerard D and Ang, Ting Fang Alvin and Au, Rhoda and Mez, Jesse and Lunetta, Kathryn L and Zhang, Xiaoling and Farrer, Lindsay A},
title = {{A multiancestry polygenic risk score for Alzheimer's disease is associated with cognitive decline and neuropathological hallmarks in diverse populations}},
journal = {Nature genetics},
year = {2026},
month = aug,
volume = {58},
number = {9},
pages = {2141--2151},
publisher = {Nature Portfolio},
issn = {1061-4036},
doi = {10.1038/s41588-026-02722-8},
url = {https://doi.org/10.1038/s41588-026-02722-8},
pmid = {42661068},
pmcid = {PMC13553296}
}

RIS

TY - JOUR
AU - Kurniansyah, Nuzulul
AU - Tasaki, Shinya
AU - Rehman, Habbibur
AU - Zhu, Congcong
AU - Farrell, John
AU - Sherva, Richard
AU - Hauger, Richard
AU - Merritt, Victoria C
AU - Panizzon, Matthew
AU - Zhang, Rui
AU - Gaziano, J Michael
AU - Gim, Jungsoo
AU - Lee, Kunho
AU - Lee, Dong Yong
AU - Nho, Kwansik
AU - Vialle, Ricardo A
AU - Mukherjee, Shubhabrata
AU - Trittschuh, Emily H
AU - Lee, Annie J
AU - Brickman, Adam M
AU - Cruchaga, Carlos
AU - Risacher, Shannon
AU - Greve, Douglas N
AU - Crane, Paul
AU - Martin, Eden
AU - Bush, William S
AU - Mayeux, Richard
AU - Haines, Jonathan L
AU - Pericak-Vance, Margaret A
AU - Logue, Mark
AU - Bennett, David A
AU - Barnes, Lisa L
AU - Saykin, Andrew
AU - Hohman, Timothy
AU - Wang, Li-San
AU - Schellenberg, Gerard D
AU - Ang, Ting Fang Alvin
AU - Au, Rhoda
AU - Mez, Jesse
AU - Lunetta, Kathryn L
AU - Zhang, Xiaoling
AU - Farrer, Lindsay A
TI - A multiancestry polygenic risk score for Alzheimer's disease is associated with cognitive decline and neuropathological hallmarks in diverse populations
T2 - Nature genetics
J2 - Nat Genet
PY - 2026
DA - 2026/08/27
VL - 58
IS - 9
SP - 2141
EP - 2151
SN - 1061-4036
PB - Nature Portfolio
DO - 10.1038/s41588-026-02722-8
UR - https://doi.org/10.1038/s41588-026-02722-8
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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