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Impacts of host genetics on gut microbiome composition in Alzheimer's disease.

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.

The 8 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Identification of microbial signatures at the community level and single taxon level ↔ Analysis/01_statistic_of_gut_microbiome.R, lines 127–215 · score 0.88 · B_Ent, E_Ent, PCoA, Bray, Escherichia, adonis
  2. [2] § Methods › Identification of microbial subgroup signatures using LDA topic model ↔ LDA/LDA.R, the whole file · a weak match · score 0.68 · selectK, integers, perplexity, LDA, cross, Gibbs
  3. [3] § Results › Complex genetic effects of ES-Ana relative abundance on cognitive disability ↔ Analysis/04_PRS_association.R, lines 42–100 · score 0.64 · linear regression, PRS association, MoCA_B, MMSE, ES Ana, beta
  4. [4] § Methods › PRS analysis ↔ Analysis/04_PRS_association.R, lines 42–100 · score 0.63 · stat_smooth, linear regression, PRS, r2, thresholds, ES Ana
  5. [5] § Methods › SNP-based PheWAS analysis ↔ Analysis/06_enrichment_analysis.R, lines 1–46 · score 0.61 · PheWAS, lead SNPs, enrichment, traits, domains, fold
  6. [6] § Results › Enriched biological functions and expression dynamics of ES-Ana-associated genes ↔ Analysis/06_enrichment_analysis.R, lines 101–142 · score 0.58 · sensory perception, biological functions, enrichment, Reactome, gene, AD
  7. [7] § Methods › Evaluating the performance of AD predictive classifiers based on ES-Ana ↔ Analysis/02_ES-Ana_associated_with_AD.R, lines 1–41 · score 0.54 · pROC, logistic, prediction, AUC, ES Ana, LDA
  8. [8] § Methods › Analyzing associations between microbial topic probability and host phenotypes ↔ LDA/Topic_distribution.R, lines 267–308 · score 0.51 · Dirichlet regression, MoCA_B, MMSE, Beta, NC, AD

Paper

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The authors' code

R · 100 lines · 4 KB · MIT · 2 matches

  1. #04_PRS_association.R
  3. rm(list=ls())
  5. #1.data preparations----
  6. PRS=read.table('./01_taxa/all/Topic/v6.all.score',header = T)
  7. datas=merge(PRS,meta,by=c('FID','IID'),sort = F)
  8. datas=datas%>%mutate(
  9. C1.sd = C1/sd(C1, na.rm = T),
  10. C2.sd = C2/sd(C2, na.rm = T),
  11. C3.sd = C3/sd(C3, na.rm = T),
  12. C4.sd = C4/sd(C4, na.rm = T),
  13. C5.sd = C5/sd(C5, na.rm = T),
  14. age.sd = Age/sd(Age, na.rm = T),
  15. gender=relevel(factor(gender),ref='female')
  16. )
  17. Topic=read.table('./01_taxa/all/Topic/pheno.txt',header = T)
  18. datas=merge(datas,Topic[,c(1,3)],by='FID',sort=F)
  20. #2.AD vs. NC----
  21. my_PRS=function(data=datas,phe=c('Topic_8','MMSE','MoCA_B')){
  22. temp <- data.frame(Set=NA, Threshold=NA, R2=NA, R2.adj=NA, P=NA, Coefficient=NA, Standard.Error=NA)
  23. for (j in phe) {
  24. k_test=intersect(c("X5e.08","X1e.05","X5e.05","X0.0001","X0.0005","X0.001","X0.005","X0.01","X0.05","X0.1","X0.5","X1"),colnames(data))
  25. for (i in k_test) {
  26. pheno.cov=data[,c("FID",i,j,paste0('C',1:5,'.sd'),'age.sd','gender')]
  28. colnames(pheno.cov)[2:3]=c('PRS','Pheno')
  29. pheno.cov$Pheno=resid(lm(Pheno ~ . , data=pheno.cov[,!colnames(pheno.cov)%in%c("FID", "PRS")]))
  30. pheno.merge <- pheno.cov[,colnames(pheno.cov)%in%c("FID", "Pheno", "PRS")]
  31. model=summary(lm(Pheno ~ . ,data=pheno.merge[,c("Pheno", "PRS")]))
  33. temp=rbind(temp,c(j,i,model$r.squared,model$adj.r.squared,
  34. model$coefficients[2,4],model$coefficients[2,1],model$coefficients[2,2]))
  35. }
  36. }
  38. return(temp)
  39. }
  42. datas2=subset(datas,group%in%c('NC','AD'))
  43. results=my_PRS(data=datas2,phe=c('MMSE','MoCA_B','Topic_8'))
  44. results=results[-1,]
  45. results2=results%>%group_by(Set)%>%arrange(desc(R2))
  46. results2=lapply(unique(results$Set),function(x){
  47. temp=subset(results,Set==x)
  48. temp=temp[which.max(temp$R2),]
  49. return(temp)
  50. })
  51. results2=do.call(rbind,results2)
  52. results2$Set[as.numeric(results2$P)<0.05]
  53. #"MMSE" "MoCA_B" "Topic_8"
  54. results2
  55. xx=results%>%filter(Threshold=='X0.5')
  57. #3.visualization----
  58. #(1) AD vs. NC boxlpot
  59. library(ggsignif)#test = "wilcox.test",
  60. P1=ggplot(data=datas2,aes(x=group,y=X0.5,color=group))+
  61. geom_jitter(alpha=0.2,
  62. position=position_jitterdodge(jitter.width = 0.35,
  63. jitter.height = 0,
  64. dodge.width = 0.8))+
  65. geom_boxplot(alpha=0.2,width=0.45,
  66. position=position_dodge(width=0.8),
  67. size=0.75,outlier.colour = NA)+
  68. geom_violin(alpha=0.2,width=0.9,
  69. position=position_dodge(width=0.8),
  70. size=0.75)+
  71. scale_color_manual(values = group_col)+
  72. theme_classic() + labs(x='',y='PRS_AD')+
  73. theme(legend.position="none") +
  74. theme(axis.text = element_text(face = 'bold',size = 10),
  75. text= element_text(face = 'bold',size = 10),
  76. axis.title.x = element_blank())+geom_signif(comparisons = list(c("NC", "AD")),
  77. y_position=c(2.8),
  78. map_signif_level=TRUE,color='black')
  80. #(2)linear regression----
  81. colnames(datas2)
  82. P1=ggplot(datas2)+geom_point(aes(x=X0.5,y=Topic_8,color=group))+
  83. scale_color_manual(values = c("#459943","#982b2b"),
  84. limits = c('NC','AD')) +
  85. labs(title ='beta=-0.022; P=0.003',x='PRS_AD',y='ES_Ana')+
  86. stat_smooth(mapping = aes(x=X0.5,y=Topic_8) ,method=lm)+ theme_classic()+
  87. theme(text=element_text(face='bold', size=10),
  88. axis.text = element_text(face = 'bold',size = 10),
  89. plot.title=element_text(hjust=0.5))
  91. P2=ggplot(datas2)+geom_point(aes(x=X0.5,y=MMSE,color=group))+
  92. scale_color_manual(values = c("#459943","#982b2b"),
  93. limits = c('NC','AD')) +
  94. labs(title ='beta=-2.187; P=2.768e-05',x='PRS_AD',y='MMSE')+
  95. stat_smooth(mapping = aes(x=X0.5,y=MMSE) ,method=lm)+ theme_classic()+
  96. theme(text=element_text(face='bold', size=10),
  97. axis.text = element_text(face = 'bold',size = 10),
  98. plot.title=element_text(hjust=0.5))
  100. P=cowplot::plot_grid(P1,P2,nrow = 2,align = "vh")

04_PRS_association.R at commit 1bbc7f3, under MIT · at the source

Overview

Authors: Jinxin Liu1,2,3, Jixin Cao2, Longhao Jia2,4, Ziquan Gan2, Xingzhong Zhao2, Anyi Yang2, Senying Lai2, Feng Chen5, Yucheng T. Yang3,6,7, Xing-Ming Zhao1,3,6,7,8
  1. Department of Neurology, Zhongshan Hospital, Fudan University,Shanghai, 200433 China
  2. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University,Shanghai, 200433 China
  3. Huzhou Central Hospital, Affiliated Central Hospital Huzhou University,Huzhou, 313000 China
  4. College of Life Science and Medicine, Zhejiang Sci-Tech University,Hangzhou, 310018 China
  5. Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University),Haikou, 570100 China
  6. College of Biomedical Engineering, Fudan University,Shanghai, 200438 China
  7. MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University,Shanghai, 200433 China
  8. State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University,Shanghai, 200433 China
Institutions: Zhongshan Hospital (China); Fudan University (China); Huzhou Central Hospital (China); Zhejiang Sci-Tech University (China); Hainan General Hospital (China)
Journal: Microbiome, volume 14, issue 1, article 115
Dates: received 14 October 2025; accepted 6 January 2026; published online 5 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s40168-026-02342-8 · PMID 41782023 · PMCID PMC13069797 · OpenAlex W7133517879
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population)
Methods: Connectivity, Statistics, Machine learning
Keywords: Alzheimer’s disease, Microbiome, Genetic variation, Polygenic risk scores, Mendelian randomization
MeSH: Alzheimer Disease*, Bacteria*, Gastrointestinal Microbiome*, Host Microbial Interactions*, Aged, Female, Genetic Risk Score, Genome-Wide Association Study, Genotype, Humans, Male, Whole Genome Sequencing (* major topic)
Topic: Gut microbiota and health (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Shanghai Science and Technology Commission Program (24JS2810100, 23JS1410100); Shanghai Municipal Education Commission (24KXZNA11); National Natural Science Foundation of China (National Science Foundation of China) (T2225015); Key Science and Technology Project of Hainan Province (ZDYF2024SHFZ058); Major Project of Guangzhou National Laboratory (GZNL2024A01003); National Key R&D Program of China (2023YFF1204800)
Citations: cited by 2 papers (Europe PMC); 172 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

ZhaoXM-Lab/microbiome-GWAS

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 1bbc7f3a7b048e1ec795e230ff0381ef9f884c20, 30 October 2025
Languages: R (8)
Size: 10 files, 8 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), ggplot2 (5 files), data.table (3 files), ggpubr (3 files), cowplot (2 files), reshape2 (2 files), caret (1 file), circlize (1 file), ComplexHeatmap (1 file), metafor (1 file), pROC (1 file), randomForest (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
10 files
At the source: github.com/ZhaoXM-Lab/microbiome-GWAS

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

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Read it in the paper: doi.org/10.1186/s40168-026-02342-8.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 5 keywords, 12 MeSH terms, 6 funders, 165 references.

Cite

This paper

Liu, J., Cao, J., Jia, L., Gan, Z., Zhao, X., Yang, A., Lai, S., Chen, F., Yang, Y. T., & Zhao, X.-M. (2026). Impacts of host genetics on gut microbiome composition in Alzheimer's disease. Microbiome, 14(1), 115. https://doi.org/10.1186/s40168-026-02342-8

BibTeX

@article{liu2026impacts,
author = {Liu, Jinxin and Cao, Jixin and Jia, Longhao and Gan, Ziquan and Zhao, Xingzhong and Yang, Anyi and Lai, Senying and Chen, Feng and Yang, Yucheng T. and Zhao, Xing-Ming},
title = {{Impacts of host genetics on gut microbiome composition in Alzheimer's disease}},
journal = {Microbiome},
year = {2026},
month = mar,
volume = {14},
number = {1},
pages = {115},
publisher = {BMC},
issn = {2049-2618},
doi = {10.1186/s40168-026-02342-8},
url = {https://doi.org/10.1186/s40168-026-02342-8},
pmid = {41782023},
pmcid = {PMC13069797}
}

RIS

TY - JOUR
AU - Liu, Jinxin
AU - Cao, Jixin
AU - Jia, Longhao
AU - Gan, Ziquan
AU - Zhao, Xingzhong
AU - Yang, Anyi
AU - Lai, Senying
AU - Chen, Feng
AU - Yang, Yucheng T.
AU - Zhao, Xing-Ming
TI - Impacts of host genetics on gut microbiome composition in Alzheimer's disease
T2 - Microbiome
J2 - Microbiome
PY - 2026
DA - 2026/03/05
VL - 14
IS - 1
SP - 115
SN - 2049-2618
PB - BMC
DO - 10.1186/s40168-026-02342-8
UR - https://doi.org/10.1186/s40168-026-02342-8
LA - en
ER -

CSL-JSON

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