我们提出了一种利用三维染色质相互作用来鉴定全基因组关联研究（GWAS）所识别的非编码变异功能意义的实验方案。
方法文章
利用染色质构象的计算分析将阿尔茨海默病相关变异映射至其靶基因
* These authors contributed equally
本文内容
摘要
摘要
全基因组关联研究（GWAS）已成功鉴定了数百个与人类性状和疾病相关的基因组位点。然而，由于大多数全基因组显著性（GWS）位点位于非编码基因组区域，其功能影响仍不明确。通过Hi-C或其衍生技术鉴定出的三维染色质相互作用，可作为有效工具，通过将非编码变异与可作用基因关联，实现对这些位点的功能注释。本文概述了一种利用阿尔茨海默病（AD）GWAS数据及来自人类成年脑组织的Hi-C数据集，将GWAS非编码变异映射至其潜在靶基因的实验方案。首先通过精细定位算法识别潜在的因果单核苷酸多态性（SNPs），随后基于Hi-C数据中的增强子-启动子相互作用，将SNPs映射至其潜在的靶基因。所得基因集合代表了AD风险基因，因其表达可能受到AD风险变异的调控。为进一步揭示AD潜在的分子机制，我们利用发育期脑组织表达数据及脑组织单细胞表达谱对AD风险基因进行功能表征。该方案可推广至任何GWAS与Hi-C数据集，用于鉴定各类人类性状和疾病相关的潜在靶基因及其分子机制。
引言
全基因组关联研究（GWAS）在揭示多种人类性状和疾病的遗传基础方面发挥了关键作用。这种大规模基因分型已发现了数千个与表型相关的基因组变异，这些表型范围从身高到精神分裂症风险。然而，尽管GWAS在识别与疾病和性状相关的位点方面取得了巨大成功，但要理解这些变异如何影响表型的分子机制仍具挑战性，因为大多数与表型相关的变异位于人类基因组的非编码区域。由于这些变异通常与预测的调控元件重叠，它们可能改变邻近基因的转录调控。然而，非编码位点可影响线性距离超过一兆碱基（megabase）的基因转录，使得每个变异所影响的基因难以确定。三维（3D）染色质结构在远距离调控位点与基因启动子之间的相互作用中起着重要作用，可用于识别受表型相关单核苷酸多态性（SNPs）影响的基因。
基因调控由一个复杂的过程介导，该过程涉及增强子的激活以及染色质环的形成，从而在物理上将增强子与基因启动子连接起来，使转录机制能够被引导至启动子区域1,2,3。由于染色质环通常跨越数十万碱基对（kb），因此需要详细的三维染色质结构图谱来解析基因调控机制。为鉴定三维染色质结构，已开发出多种染色质构象捕获技术4。在这些技术中，Hi-C 能提供最全面的染色质结构信息，因为它可捕获全基因组范围内的三维染色质相互作用图谱。Hi-C 数据集已被迅速应用于解读非编码区全基因组显著性（GWS）位点5,6,7,8,9,10,11,12,13，因为它能够基于染色质相互作用图谱将非编码区变异与潜在的靶基因关联起来。
本文介绍了一种利用染色质相互作用谱通过计算方法预测全基因组关联研究（GWAS）风险变异位点潜在靶基因的实验方案。我们应用该方案，结合成人人类大脑的Hi-C数据集9，将阿尔茨海默病（AD）的GWAS位点14映射到其靶基因。所得的AD风险基因进一步通过其他功能基因组学数据集进行特征分析，包括单细胞转录组数据和发育阶段基因表达谱。
方案
1. Workstation Setup
- Install R (version 3.5.0) and RStudio Desktop. Open RStudio.
- Install the following libraries in R by typing the following code into the console window in RStudio.
if (!"BiocManager" %in% rownames(installed.packages()))
install.packages("BiocManager", repos="https://cran.r-project.org")
BiocManager::install("GenomicRanges")
BiocManager::install("biomaRt")
BiocManager::install("WGCNA")
install.packages("reshape")
install.packages("ggplot2")
install.packages("corrplot")
install.packages("gProfileR")
install.packages("tidyverse")
install.packages("ggpubr") - Download files.
NOTE: In this protocol, all files are required to be downloaded to ~/work directory.- Download the following files by clicking the links provided in Table of Materials.
- Download fine-mapped credible SNPs for AD (Supplementary Table 8 from Jansen et al.14).
NOTE: Before analysis, open sheet eight in 41588_2018_311_MOESM3_ESM.xlsx, remove the first three rows and save the sheet as Supplementary_Table_8_Jansen.txt with tab separated format. - Download 10 kb resolution Hi-C interaction profiles in the adult brain from psychencode (described as Promoter-anchored_chromatin_loops.bed below).
NOTE: This file has the following format: chromosome, TSS_start, TSS_end, Enhancer_start, and Enhancer_end. In case other Hi-C datasets are used, this protocol requires Hi-C datasets processed at high resolution (5−20 kb). - Download single cell expression datasets from the PsychENCODE.
NOTE: These are from neurotypical control samples. - Download developmental expression datasets from the BrainSpan (described as devExpr.rda below).
NOTE: 267666527 is a zipped file, so unzip the 267666527 to extract "columns_metadata.csv", "expression_matrix.csv", and "rows_metadata.csv" to generate devExpr.rda (see section 3).
- Download fine-mapped credible SNPs for AD (Supplementary Table 8 from Jansen et al.14).
- Download exonic coordinates (see Supplementary Files, described as Gencode19_exon.bed and Gencode19_promoter.bed below) from Gencode version 19.
NOTE: Promoters are defined as 2 kb upstream of transcription start site (TSS). These files have the following format: chromosome, start, end, and gene. - Download gene annotation file (see Supplementary Files, described as geneAnno.rda below) from biomart.
NOTE: This file can be used to match genes based on Ensembl gene IDs and HUGO Gene Nomenclature Committee (HGNC) symbol.
- Download the following files by clicking the links provided in Table of Materials.
2. Generation of a GRanges Object for Credible SNPs
- Set up in R by typing the following code into the console window in RStudio.
library(GenomicRanges)
options(stringsAsFactors = F)
setwd("~/work") # This is the path to the working directory.
credSNP = read.delim("Supplementary_Table_8_Jansen.txt", header=T)
credSNP = credSNP[credSNP$Credible.Causal=="Yes",] - Make a GRanges object by typing the following code into the console window in RStudio.
credranges = GRanges(credSNP$Chr, IRanges(credSNP$bp, credSNP$bp), rsid=credSNP$SNP, P=credSNP$P)
save(credranges, file="AD_credibleSNP.rda")
3. Positional Mapping
NOTE: For each step, type the corresponding code into the console window in RStudio.
- Set up in R.
options(stringsAsFactors=F)
library(GenomicRanges)
load("AD_credibleSNP.rda") # (see 2) - Positional mapping of promoter/exonic SNPs to genes
- Load promoter and exonic region and generate a GRange object.
exon = read.table("Gencode19_exon.bed")
exonranges = GRanges(exon[,1],IRanges(exon[,2],exon[,3]),gene=exon[,4])
promoter = read.table("Gencode19_promoter.bed")
promoterranges = GRanges(promoter[,1], IRanges(promoter[,2], promoter[,3]), gene=promoter[,4]) - Overlap credible SNPs with exonic regions.
olap = findOverlaps(credranges, exonranges)
credexon = credranges[queryHits(olap)]
mcols(credexon) = cbind(mcols(credexon), mcols(exonranges[subjectHits(olap)])) - Overlap credible SNPs with promoter regions.
olap = findOverlaps(credranges, promoterranges)
credpromoter = credranges[queryHits(olap)]
mcols(credpromoter) = cbind(mcols(credpromoter), mcols(promoterranges[subjectHits(olap)]))
- Load promoter and exonic region and generate a GRange object.
- Link SNPs to their putative target genes using chromatin interactions.
- Load Hi-C dataset and generate a GRange object.
hic = read.table("Promoter-anchored_chromatin_loops.bed ", skip=1)
colnames(hic) = c("chr", "TSS_start", "TSS_end", "Enhancer_start", "Enhancer_end")
hicranges = GRanges(hic$chr, IRanges(hic$TSS_start, hic$TSS_end), enhancer=hic$Enhancer_start)
olap = findOverlaps(hicranges, promoterranges)
hicpromoter = hicranges[queryHits(olap)]
mcols(hicpromoter) = cbind(mcols(hicpromoter), mcols(promoterranges[subjectHits(olap)]))
hicenhancer = GRanges(seqnames(hicpromoter), IRanges(hicpromoter$enhancer, hicpromoter$enhancer+10000), gene=hicpromoter$gene) - Overlap credible SNPs with Hi-C GRange object.
olap = findOverlaps(credranges, hicenhancer)
credhic = credranges[queryHits(olap)]
mcols(credhic) = cbind(mcols(credhic), mcols(hicenhancer[subjectHits(olap)]))
- Load Hi-C dataset and generate a GRange object.
- Compile AD candidate genes defined by positional mapping and chromatin interaction profiles.
### The resulting candidate genes for AD:
ADgenes = Reduce(union, list(credhic$gene, credexon$gene, credpromoter$gene))
### to convert Ensembl Gene ID to HGNC symbol
load("geneAnno.rda")
ADhgnc = geneAnno1[match(ADgenes, geneAnno1$ensembl_gene_id), "hgnc_symbol"]
ADhgnc = ADhgnc[ADhgnc!=""]
save(ADgenes, ADhgnc, file="ADgenes.rda")
write.table(ADhgnc, file="ADgenes.txt", row.names=F, col.names=F, quote=F, sep="\t")
4. Developmental Expression Trajectories
NOTE: For each step, type the corresponding code into the console window in RStudio.
- Set up in R.
library(reshape); library(ggplot2); library(GenomicRanges); library(biomaRt)
library("WGCNA")
options(stringsAsFactors=F) - Process expression and meta data.
datExpr = read.csv("expression_matrix.csv", header = FALSE)
datExpr = datExpr[,-1]
datMeta = read.csv("columns_metadata.csv")
datProbes = read.csv("rows_metadata.csv")
datExpr = datExpr[datProbes$ensembl_gene_id!="",]
datProbes = datProbes[datProbes$ensembl_gene_id!="",]
datExpr.cr= collapseRows(datExpr, rowGroup = datProbes$ensembl_gene_id, rowID= rownames(datExpr))
datExpr = datExpr.cr$datETcollapsed
gename = data.frame(datExpr.cr$group2row)
rownames(datExpr) = gename$group- Specify developmental stages.
datMeta$Unit = "Postnatal"
idx = grep("pcw", datMeta$age)
datMeta$Unit[idx] = "Prenatal"
idx = grep("yrs", datMeta$age)
datMeta$Unit[idx] = "Postnatal"
datMeta$Unit = factor(datMeta$Unit, levels=c("Prenatal", "Postnatal")) - Select cortical regions.
datMeta$Region = "SubCTX"
r = c("A1C", "STC", "ITC", "TCx", "OFC", "DFC", "VFC", "MFC", "M1C", "S1C", "IPC", "M1C-S1C", "PCx", "V1C", "Ocx")
datMeta$Region[datMeta$structure_acronym %in% r] = "CTX"
datExpr = datExpr[,which(datMeta$Region=="CTX")]
datMeta = datMeta[which(datMeta$Region=="CTX"),]
save(datExpr, datMeta, file="devExpr.rda")
- Specify developmental stages.
- Extract developmental expression profiles of AD risk genes.
load("ADgenes.rda")
exprdat = apply(datExpr[match(ADgenes, rownames(datExpr)),],2,mean,na.rm=T)
dat = data.frame(Region=datMeta$Region, Unit=datMeta$Unit, Expr=exprdat) - Compare prenatal versus postnatal expression levels of AD risk genes.
pdf(file="developmental_expression.pdf")
ggplot(dat,aes(x=Unit, y=Expr, fill=Unit, alpha=Unit)) + ylab("Normalized expression") + geom_boxplot(outlier.size = NA) + ggtitle("Brain Expression") + xlab("") + scale_alpha_manual(values=c(0.2, 1)) + theme_classic() + theme(legend.position="na")
dev.off()
5. Cell-type Expression Profiles
NOTE: For each step, type the corresponding code into the console window in RStudio.
- Set up in R.
options(stringsAsFactors=F)
load("ADgenes.rda")
load("geneAnno.rda")
targetname = "AD"
targetgene = ADhgnc
cellexp = read.table("DER-20_Single_cell_expression_processed_TPM_backup.tsv",header=T,fill=T)
cellexp[1121,1] = cellexp[1120,1]
cellexp = cellexp[-1120,]
rownames(cellexp) = cellexp[,1]
cellexp = cellexp[,-1]
datExpr = scale(cellexp,center=T, scale=F)
datExpr = datExpr[,789:ncol(datExpr)] - Extract cellular expression profiles of AD risk genes.
exprdat = apply(datExpr[match(targetgene, rownames(datExpr)),],2,mean,na.rm=T)
dat = data.frame(Group=targetname, cell=names(exprdat), Expr=exprdat)
dat$celltype = unlist(lapply(strsplit(dat$cell, split="[.]"),'[[',1))
dat = dat[-grep("Ex|In",dat$celltype),]
dat$celltype = gsub("Dev","Fetal",dat$celltype)
dat$celltype = factor(dat$celltype, levels=c("Neurons","Astrocytes","Microglia","Endothelial",
Oligodendrocytes","OPC","Fetal"))
pdf(file="singlecell_expression_ADgenes.pdf")
ggplot(dat,aes(x=celltype, y=Expr, fill=celltype)) +
ylab("Normalized expression") + xlab("") + geom_violin() + theme(axis.text.x=element_text(angle = 90, hjust=1)) + theme(legend.position="none") +
ggtitle(paste0("Cellular expression profiles of AD risk genes"))
dev.off()
6. Gene Annotation Enrichment Analysis of AD Risk Genes
- Download and configure HOMER by typing the commands below in terminal.
mkdir homer
cd homer
wget http://homer.ucsd.edu/homer/configureHomer.pl
perl ./configureHomer.pl -install
perl ./configureHomer.pl -install human-p
perl ./configureHomer.pl -install human-o - Run HOMER by typing the commands below in terminal.
export PATH=$PATH:~/work/homer/bin
findMotifs.pl ~/work/ADgenes.txt human ~/work/ - Plot the enriched terms by typing the following code into the console window in RStudio.
library(ggpubr)
options(stringsAsFactors=F)
pdf("GO_enrichment.pdf",width=15,height=8)
plot_barplot = function(dbname,name,color){
input = read.delim(paste0(dbname,".txt"),header=T)
input = input[,c(-1,-10,-11)]
input = unique(input)
input$FDR = p.adjust(exp(input$logP))
input_sig = input[input$FDR < 0.1,]
input_sig$FDR = -log10(input_sig$FDR)
input_sig = input_sig[order(input_sig$FDR),]
p = ggbarplot(input_sig, x = "Term", y = "FDR", fill = color, color = "white", sort.val = "asc", ylab = expression(-log[10](italic(FDR))), xlab = paste0(name," Terms"), rotate = TRUE, label = paste0(input_sig$Target.Genes.in.Term,"/",input_sig$Genes.in.Term), font.label = list(color = "white", size = 9), lab.vjust = 0.5, lab.hjust = 1)
p = p+geom_hline(yintercept = -log10(0.05), linetype = 2, color = "lightgray")
return(p)
}
p1 = plot_barplot("biological_process","GO Biological Process","#00AFBB")
p2 = plot_barplot("kegg","KEGG","#E7B800")
p3 = plot_barplot("reactome","Reactome","#FC4E07")
ggarrange(p1, p2, p3, labels = c("A", "B", "C"), ncol = 2, nrow = 2)
dev.off()
结果
本研究中所描述的方法应用于一项原始研究定义的800个可信的SNP位点14。位置比对分析显示，其中103个SNP位于启动子区域（对应43个独特基因），42个SNP位于外显子区域（对应27个独特基因）。在完成位置比对后，仍有84%（669个）SNP未被注释。利用成人脑组织的Hi-C数据集，我们根据物理邻近性将额外的208个SNP与64个基因关联起来。总计，我们将284个阿尔茨海默病（AD）可信SNP映射到112个AD风险基因（图1A）。这些AD风险基因与淀粉样前体蛋白、淀粉样β蛋白的形成以及免疫应答相关，反映了已知的AD生物学机制15,16,17,18（图1B-D）。AD风险基因的发育期表达谱显示其在出生后显著富集，提示其与年龄相关的AD风险升高有关（图2A）。最后，AD风险基因在小胶质细胞——脑内主要的免疫细胞中高度表达（图2B）。这一发现与反复报道的结果一致，即AD具有显著的免疫基础，而小胶质细胞在AD发病机制中起核心作用14,19,20。
图 1：定义阿尔茨海默病（AD）全基因组显著（GWS）位点的推定靶基因。（A）来自前 29 个 AD 位点的可信 SNP 被分为启动子区 SNP、外显子区 SNP 和未注释的非编码区 SNP。通过位置映射将启动子区和外显子区 SNP 直接分配给其靶基因，同时利用成人大脑中的染色质相互作用谱，基于物理相互作用进一步映射 SNP。（B-D）使用 HOMER 对 AD 风险基因中的 GO（B）、KEGG（C）和 Reactome（D）术语进行富集分析，方法见方案第 6 节。横轴表示经假发现率（FDR）校正后的 -log10(P 值)。仅绘制 FDR < 0.1 的富集术语。灰色垂直线表示 FDR = 0.05。APP 为淀粉样前体蛋白。分子表示每个术语中包含的 AD 风险基因数量；分母表示每个术语中的总基因数量。请点击此处查看该图的放大版本。
图 2：AD 风险基因的表征。 （A）与产前皮层相比，AD 风险基因在产后皮层中高度表达。（B）小提琴图显示皮层中不同细胞类型基因表达值（标准化表达）的分布情况。这些结果表明，AD 风险基因在小胶质细胞中高度表达，与先前的研究结果一致14。 请点击此处查看该图的放大版本。
补充文件 1. 请点击此处查看该文件（右键点击可下载）。
补充文件 2. 请点击此处查看该文件（右键点击可下载）。
补充文件 3。 请点击此处查看该文件（右键单击以下载）。
讨论
本文描述了一种基于位置映射和染色质相互作用的功能性注释全基因组显著性（GWS）位点的分析框架。该过程包含多个步骤（更多细节请参见此综述13）。首先，由于染色质相互作用谱具有高度的细胞类型特异性，因此必须使用能够最好地反映疾病潜在生物学机制的相应细胞/组织类型的Hi-C数据。鉴于阿尔茨海默病（AD）是一种神经退行性疾病，我们采用了成人脑组织的Hi-C数据9来注释GWS位点。其次，由于连锁不平衡（LD）的存在，每个GWS位点通常包含多达数百个与性状相关的单核苷酸多态性（SNP），因此有必要通过精细定位算法进行计算预测21,22，或利用高通量方法（如大规模并行报告基因检测（MPRA）23或自转录活性调控区域测序（STARR-seq）24）进行实验验证，以确定潜在的因果性（“可信”）SNP。在本研究中，我们采用了Jansen等人14报告的可信SNP。第三，启动子区和外显子区的SNP通过位置映射进行注释。我们采用了一种简单的定位映射策略，即将与启动子区（定义为转录起始位点上游2 kb区域）或外显子区重叠的SNP映射到相应基因。然而，该方法还可进一步扩展，例如评估外显子区SNP的功能影响，如是否引起无义介导的mRNA降解、错义变异或无义变异。第四，可利用相应组织/细胞类型的染色质相互作用谱，根据物理邻近性将SNP分配至其潜在的靶基因。我们使用了以启动子为锚定点的相互作用谱，但也可进一步优化或扩展这些相互作用谱，例如结合增强子活性（由组蛋白H3K27乙酰化或染色质可及性指导）或外显子间相互作用信息。此过程中的一个重要考虑因素是使用一致的人类基因组版本。例如，如果汇总统计的基因组位置并非基于hg19（如hg18或hg38），则应获取相应版本的参考基因组，或使用liftover工具将汇总统计信息转换至hg1925。
我们应用该框架识别阿尔茨海默病全基因组关联研究（AD GWAS）的潜在靶基因，将284个SNP分配至112个AD风险基因。利用发育阶段的表达谱26和细胞类型特异性表达谱9，我们进一步证明该基因集合与已知的AD病理特征一致，揭示了相关细胞类型（小胶质细胞）、生物学功能（免疫反应和淀粉样蛋白β）以及随年龄增长而升高的风险。
尽管我们提出了一个用于界定阿尔茨海默病潜在靶基因及其潜在生物学机制的框架，但需要注意的是，基于Hi-C的注释方法可进一步扩展，用于注释任何非编码区的遗传变异。随着全基因组测序数据的不断积累以及对非编码区罕见变异认知的深入，Hi-C技术将为疾病相关遗传变异的解读提供关键资源。因此，整合来自多种组织和细胞类型的Hi-C资源文库，对于广泛推广应用该框架以深入解析各类人类性状及疾病的生物学机制至关重要。
披露
作者无任何利益冲突需要披露。
致谢
本工作由美国国立卫生研究院（NIH）资助项目R00MH113823（授予H.W.）和R35GM128645（授予D.H.P.）、NARSAD青年研究者奖（授予H.W.）以及西蒙斯基金会自闭症研究计划（SFARI）的SPARK资助项目（授予N.M.和H.W.）支持。
材料
| 姓名 | 公司 | 目录编号 | 评论 |
|---|---|---|---|
| 来自psychencode的成年大脑中10 kb分辨率的Hi-C相互作用图谱 | http://adult.psychencode.org/ | ||
| 发育阶段基因表达数据集 | http://www.brainspan.org/ | ||
| 阿尔茨海默病精细定位的可信SNP（来自Jansen等14的补充表8） | https://static-content.springer.com/ | ||
| HOMER | http://homer.ucsd.edu/ | ||
| R（版本3.5.0） | https://www.r-project.org/ | ||
| RStudio Desktop | https://www.rstudio.com/ | ||
| 单细胞表达数据集 | http://adult.psychencode.org/ |
参考文献
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