This work was supported by the NIH grant R00MH113823 (to H.W.) and R35GM128645 (to D.H.P.), NARSAD Young Investigator Award (to H.W.), and SPARK grant from the Simons Foundation Autism Research Initiative (SFARI, to N.M. and H.W.).

1. Workstation Setup
<ol>
	<li>Install R (version 3.5.0) and RStudio Desktop. Open RStudio.</li>
	<li>Install the following libraries in R by typing the following code into the console window in RStudio. 
	if (!&#34;BiocManager&#34; %in% rownames(installed.packages())) 
	install.packages(&#34;BiocManager&#34;, repos=&#34;https://cran.r-project.org&#34;) 
	BiocManager::install(&#34;GenomicRanges&#34;) 
	BiocManager::install(&#34;biomaRt&#34;) 
	BiocManager::install(&#34;WGCNA&#34;) 
	install.packages(&#34;reshape&#34;) 
	install.packages(&#34;ggplot2&#34;) 
	install.packages(&#34;corrplot&#34;) 
	install.packages(&#34;gProfileR&#34;) 
	install.packages(&#34;tidyverse&#34;) 
	install.packages(&#34;ggpubr&#34;)</li>
	<li>Download files. 
	NOTE: In this protocol, all files are required to be downloaded to ~/work directory.
	<ol>
		<li>Download the following files by clicking the links provided in Table of Materials.
		<ol>
			<li>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.</li>
			<li>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&#8722;20 kb).</li>
			<li>Download single cell expression datasets from the PsychENCODE. 
			NOTE: These are from neurotypical control samples.</li>
			<li>Download developmental expression datasets from the BrainSpan (described as devExpr.rda below). 
			NOTE: 267666527 is a zipped file, so unzip the 267666527 to extract &#34;columns_metadata.csv&#34;, &#34;expression_matrix.csv&#34;, and &#34;rows_metadata.csv&#34; to generate devExpr.rda (see section 3).</li>
		</ol>
		</li>
		<li>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.</li>
		<li>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.</li>
	</ol>
	</li>
</ol>
2. Generation of a GRanges Object for Credible SNPs
<ol>
	<li>Set up in R by typing the following code into the console window in RStudio. 
	library(GenomicRanges) 
	options(stringsAsFactors = F) 
	setwd(&#34;~/work&#34;) # This is the path to the working directory. 
	credSNP = read.delim(&#34;Supplementary_Table_8_Jansen.txt&#34;, header=T) 
	credSNP = credSNP[credSNP$Credible.Causal==&#34;Yes&#34;,]</li>
	<li>Make a GRanges object by typing the following code into the console window in RStudio. 
	credranges = GRanges(credSNP$Chr, IRanges(credSNP$bp, credSNP$bp), rsid=credSNP$SNP, P=credSNP$P) 
	save(credranges, file=&#34;AD_credibleSNP.rda&#34;)</li>
</ol>
3. Positional Mapping
NOTE: For each step, type the corresponding code into the console window in RStudio.
<ol>
	<li>Set up in R. 
	options(stringsAsFactors=F) 
	library(GenomicRanges) 
	load(&#34;AD_credibleSNP.rda&#34;) # (see 2)</li>
	<li>Positional mapping of promoter/exonic SNPs to genes
	<ol>
		<li>Load promoter and exonic region and generate a GRange object. 
		exon = read.table(&#34;Gencode19_exon.bed&#34;) 
		exonranges = GRanges(exon[,1],IRanges(exon[,2],exon[,3]),gene=exon[,4]) 
		promoter = read.table(&#34;Gencode19_promoter.bed&#34;) 
		promoterranges = GRanges(promoter[,1], IRanges(promoter[,2], promoter[,3]), gene=promoter[,4])</li>
		<li>Overlap credible SNPs with exonic regions. 
		olap = findOverlaps(credranges, exonranges) 
		credexon = credranges[queryHits(olap)] 
		mcols(credexon) = cbind(mcols(credexon), mcols(exonranges[subjectHits(olap)]))</li>
		<li>Overlap credible SNPs with promoter regions. 
		olap = findOverlaps(credranges, promoterranges) 
		credpromoter = credranges[queryHits(olap)] 
		mcols(credpromoter) = cbind(mcols(credpromoter), mcols(promoterranges[subjectHits(olap)]))</li>
	</ol>
	</li>
	<li>Link SNPs to their putative target genes using chromatin interactions.
	<ol>
		<li>Load Hi-C dataset and generate a GRange object. 
		hic = read.table(&#34;Promoter-anchored_chromatin_loops.bed &#34;, skip=1) 
		colnames(hic) = c(&#34;chr&#34;, &#34;TSS_start&#34;, &#34;TSS_end&#34;, &#34;Enhancer_start&#34;, &#34;Enhancer_end&#34;) 
		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$gene)</li>
		<li>Overlap credible SNPs with Hi-C GRange object. 
		olap = findOverlaps(credranges, hicenhancer) 
		credhic = credranges[queryHits(olap)] 
		mcols(credhic) = cbind(mcols(credhic), mcols(hicenhancer[subjectHits(olap)]))</li>
	</ol>
	</li>
	<li>Compile AD candidate genes defined by positional mapping and chromatin interaction profiles. 
	### The resulting candidate genes for AD: 
	ADgenes = Reduce(union, list(credhic$gene, credexon$gene, credpromoter$gene)) 
	### to convert Ensembl Gene ID to HGNC symbol 
	load(&#34;geneAnno.rda&#34;) 
	ADhgnc = geneAnno1[match(ADgenes, geneAnno1$ensembl_gene_id), &#34;hgnc_symbol&#34;] 
	ADhgnc = ADhgnc[ADhgnc!=&#34;&#34;] 
	save(ADgenes, ADhgnc, file=&#34;ADgenes.rda&#34;) 
	write.table(ADhgnc, file=&#34;ADgenes.txt&#34;, row.names=F, col.names=F, quote=F, sep=&#34;\t&#34;)</li>
</ol>
4. Developmental Expression Trajectories
NOTE: For each step, type the corresponding code into the console window in RStudio.
<ol>
	<li>Set up in R. 
	library(reshape); library(ggplot2); library(GenomicRanges); library(biomaRt) 
	library(&#34;WGCNA&#34;) 
	options(stringsAsFactors=F)</li>
	<li>Process expression and meta data. 
	datExpr = read.csv(&#34;expression_matrix.csv&#34;, header = FALSE) 
	datExpr = datExpr[,-1] 
	datMeta = read.csv(&#34;columns_metadata.csv&#34;) 
	datProbes = read.csv(&#34;rows_metadata.csv&#34;) 
	datExpr = datExpr[datProbes$ensembl_gene_id!=&#34;&#34;,] 
	datProbes = datProbes[datProbes$ensembl_gene_id!=&#34;&#34;,] 
	datExpr.cr= collapseRows(datExpr, rowGroup = datProbes$ensembl_gene_id, rowID= rownames(datExpr)) 
	datExpr = datExpr.cr$datETcollapsed 
	gename = data.frame(datExpr.cr$group2row) 
	rownames(datExpr) = gename$group
	<ol>
		<li>Specify developmental stages. 
		datMeta$Unit = &#34;Postnatal&#34; 
		idx = grep(&#34;pcw&#34;, datMeta$age) 
		datMeta$Unit[idx] = &#34;Prenatal&#34; 
		idx = grep(&#34;yrs&#34;, datMeta$age) 
		datMeta$Unit[idx] = &#34;Postnatal&#34; 
		datMeta$Unit = factor(datMeta$Unit, levels=c(&#34;Prenatal&#34;, &#34;Postnatal&#34;))</li>
		<li>Select cortical regions. 
		datMeta$Region = &#34;SubCTX&#34; 
		r = c(&#34;A1C&#34;, &#34;STC&#34;, &#34;ITC&#34;, &#34;TCx&#34;, &#34;OFC&#34;, &#34;DFC&#34;, &#34;VFC&#34;, &#34;MFC&#34;, &#34;M1C&#34;, &#34;S1C&#34;, &#34;IPC&#34;, &#34;M1C-S1C&#34;, &#34;PCx&#34;, &#34;V1C&#34;, &#34;Ocx&#34;) 
		datMeta$Region[datMeta$structure_acronym %in% r] = &#34;CTX&#34; 
		datExpr = datExpr[,which(datMeta$Region==&#34;CTX&#34;)] 
		datMeta = datMeta[which(datMeta$Region==&#34;CTX&#34;),] 
		save(datExpr, datMeta, file=&#34;devExpr.rda&#34;)</li>
	</ol>
	</li>
	<li>Extract developmental expression profiles of AD risk genes. 
	load(&#34;ADgenes.rda&#34;) 
	exprdat = apply(datExpr[match(ADgenes, rownames(datExpr)),],2,mean,na.rm=T) 
	dat = data.frame(Region=datMeta$Region, Unit=datMeta$Unit, Expr=exprdat)</li>
	<li>Compare prenatal versus postnatal expression levels of AD risk genes. 
	pdf(file=&#34;developmental_expression.pdf&#34;) 
	ggplot(dat,aes(x=Unit, y=Expr, fill=Unit, alpha=Unit)) + ylab(&#34;Normalized expression&#34;) + geom_boxplot(outlier.size = NA) + ggtitle(&#34;Brain Expression&#34;) + xlab(&#34;&#34;) + scale_alpha_manual(values=c(0.2, 1)) + theme_classic() + theme(legend.position=&#34;na&#34;) 
	dev.off()</li>
</ol>
5. Cell-type Expression Profiles
NOTE: For each step, type the corresponding code into the console window in RStudio.
<ol>
	<li>Set up in R. 
	options(stringsAsFactors=F) 
	load(&#34;ADgenes.rda&#34;) 
	load(&#34;geneAnno.rda&#34;) 
	targetname = &#34;AD&#34; 
	targetgene = ADhgnc 
	cellexp = read.table(&#34;DER-20_Single_cell_expression_processed_TPM_backup.tsv&#34;,header=T,fill=T) 
	cellexp[1121,1] = cellexp[1120,1] 
	cellexp = cellexp[-1120,] 
	rownames(cellexp) = cellexp[,1] 
	cellexp = cellexp[,-1] 
	datExpr = scale(cellexp,center=T, scale=F) 
	datExpr = datExpr[,789:ncol(datExpr)]</li>
	<li>Extract cellular expression profiles of AD risk genes. 
	exprdat = apply(datExpr[match(targetgene, rownames(datExpr)),],2,mean,na.rm=T) 
	dat = data.frame(Group=targetname, cell=names(exprdat), Expr=exprdat) 
	dat$celltype = unlist(lapply(strsplit(dat$cell, split=&#34;[.]&#34;),&#39;[[&#39;,1)) 
	dat = dat[-grep(&#34;Ex|In&#34;,dat$celltype),] 
	dat$celltype = gsub(&#34;Dev&#34;,&#34;Fetal&#34;,dat$celltype) 
	dat$celltype = factor(dat$celltype, levels=c(&#34;Neurons&#34;,&#34;Astrocytes&#34;,&#34;Microglia&#34;,&#34;Endothelial&#34;, 
	Oligodendrocytes&#34;,&#34;OPC&#34;,&#34;Fetal&#34;)) 
	pdf(file=&#34;singlecell_expression_ADgenes.pdf&#34;) 
	ggplot(dat,aes(x=celltype, y=Expr, fill=celltype)) + 
	ylab(&#34;Normalized expression&#34;) + xlab(&#34;&#34;) + geom_violin() + theme(axis.text.x=element_text(angle = 90, hjust=1)) + theme(legend.position=&#34;none&#34;) + 
	ggtitle(paste0(&#34;Cellular expression profiles of AD risk genes&#34;)) 
	dev.off()</li>
</ol>
6. Gene Annotation Enrichment Analysis of AD Risk Genes
<ol>
	<li>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</li>
	<li>Run HOMER by typing the commands below in terminal. 
	export PATH=$PATH:~/work/homer/bin 
	findMotifs.pl ~/work/ADgenes.txt human ~/work/</li>
	<li>Plot the enriched terms by typing the following code into the console window in RStudio. 
	library(ggpubr) 
	options(stringsAsFactors=F) 
	pdf(&#34;GO_enrichment.pdf&#34;,width=15,height=8) 
	plot_barplot = function(dbname,name,color){ 
	input = read.delim(paste0(dbname,&#34;.txt&#34;),header=T) 
	input = input[,c(-1,-10,-11)] 
	input = unique(input) 
	input$FDR = p.adjust(exp(input$logP)) 
	input_sig = input[input$FDR &#60; 0.1,] 
	input_sig$FDR = -log10(input_sig$FDR) 
	input_sig = input_sig[order(input_sig$FDR),] 
	p = ggbarplot(input_sig, x = &#34;Term&#34;, y = &#34;FDR&#34;, fill = color, color = &#34;white&#34;, sort.val = &#34;asc&#34;, ylab = expression(-log[10](italic(FDR))), xlab = paste0(name,&#34; Terms&#34;), rotate = TRUE, label = paste0(input_sig$Target.Genes.in.Term,&#34;/&#34;,input_sig$Genes.in.Term), font.label = list(color = &#34;white&#34;, size = 9), lab.vjust = 0.5, lab.hjust = 1) 
	p = p+geom_hline(yintercept = -log10(0.05), linetype = 2, color = &#34;lightgray&#34;) 
	return(p) 
	} 
	p1 = plot_barplot(&#34;biological_process&#34;,&#34;GO Biological Process&#34;,&#34;#00AFBB&#34;) 
	p2 = plot_barplot(&#34;kegg&#34;,&#34;KEGG&#34;,&#34;#E7B800&#34;) 
	p3 = plot_barplot(&#34;reactome&#34;,&#34;Reactome&#34;,&#34;#FC4E07&#34;) 
	ggarrange(p1, p2, p3, labels = c(&#34;A&#34;, &#34;B&#34;, &#34;C&#34;), ncol = 2, nrow = 2) 
	dev.off()</li>
</ol>

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D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+quantitative+enhancer+activity+maps+identified+by+STARR-seq.">Genome-wide quantitative enhancer activity maps identified by STARR-seq.</a> Science. 339 (6123), 1074-1077 (2013).</li><li>Kent, W. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+human+genome+browser+at+UCSC.">The human genome browser at UCSC.</a> Genome Research. 12 (6), 996-1006 (2002).</li><li>Kang, H. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatio-temporal+transcriptome+of+the+human+brain.">Spatio-temporal transcriptome of the human brain.</a> Nature. 478 (7370), 483-489 (2011).</li></ol>

The authors have nothing to disclose.

Here we describe an analytic framework that can be used to functionally annotate GWS loci based on positional mapping and chromatin interactions. This process involves multiple steps (for more details see this review13). First, given that chromatin interaction profiles are highly cell-type specific, Hi-C data obtained from the appropriate cell/tissue types that best capture underlying biology of the disorder needs to be used. Given that AD is a neurodegenerative disorder, we used adult brain Hi-C data9 to annotate GWS loci. Second, each GWS locus often has up to hundreds of SNPs that are associated with the trait because of linkage disequilibrium (LD), so it is important to obtain putative causal (&#39;credible&#39;) SNPs by computationally predicting the causality through the use of fine-mapping algorithms21,22 or experimentally testing regulatory activities using high-throughput approaches such as massively parallel reporter assays (MPRA)23 or self-transcribing active regulatory region sequencing (STARR-seq)24. For the work described here, we used credible SNPs reported in Jansen et al.14. Third, promoter and exonic SNPs are annotated based on positional mapping. We used a simple positional mapping strategy in which SNPs were mapped to the genes when they overlapped with promoters (defined as 2 kb upstream of transcription start site) or exons. However, this approach can be further elaborated by assessing the functional consequences of exonic SNPs, such as whether the SNP induces nonsense mediated decay, missense variation, or nonsense variation. Fourth, chromatin interaction profiles from the appropriate tissue/cell type can be used to assign SNPs to their putative target genes based on physical proximity. We used interaction profiles anchored to promoters, but we can further refine or expand the interaction profiles by taking enhancer activities (guided by histone H3 K27 acetylation or chromatin accessibility) or exonic interactions into account. One important consideration in this process is to use consistent human genome build. For example, if genomic positions of summary statistics are not based on hg19 (i.e., hg18 or hg38), an appropriate version of the reference genome should be obtained or the summary statistics need to be converted to hg19 using liftover25.
We applied this framework to identify putative target genes for AD GWAS, assigning 284 SNPs to 112 AD risk genes. Using developmental expression profiles26 and cell-type specific expression profiles9, we then demonstrated that this gene set was consistent with what is known about AD pathology, revealing the cell types (microglia), biological functions (immune response and amyloid beta), and elevated risk upon age.
While we presented a framework that delineates potential target genes of AD and its underlying biology, it is of note that Hi-C based annotation can be expanded to annotate any non-coding variation. As more whole-genome sequencing data becomes available and our understanding about the non-coding rare variation grows, Hi-C will provide a key resource for interpretation of disease-associated genetic variants. A compendium of Hi-C resources obtained from multiple tissue and cell types will be therefore critical to facilitating a wide application of this framework to garner biological insights into various human traits and disease.

Genome-wide association studies (GWAS) have played a pivotal role in revealing the genetic basis of a range of human traits and diseases. This large-scale genotyping has uncovered thousands of genomic variants associated with phenotypes ranging from height to schizophrenia risk. However, despite the enormous success of GWAS in identifying disease and trait associated loci, a mechanistic understanding of how these variants contribute to phenotype has been challenging because most phenotype associated variants reside in the non-coding fraction of the human genome. Since these variants often overlap with predicted regulatory elements, they are likely to alter transcriptional control of a nearby gene. However, non-coding loci can influence transcription of genes at linear distances exceeding one megabase, making the genes affected by each variant difficult to identify. Three-dimensional (3D) chromatin structure plays an important role in mediating connections between distant regulatory loci and gene promoters and can be used to identify genes affected by phenotype associated single-nucleotide polymorphisms (SNPs).
Gene regulation is mediated by a complex process, which involves enhancer activation and chromatin loop formation that physically connect enhancers to gene promoters to which the transcriptional machinery can be directed1,2,3. Because chromatin loops often span several hundred kilobases (kb), detailed maps of 3D chromatin architecture are required to decipher gene regulatory mechanisms. Multiple chromatin conformation capture technologies have been invented to identify the 3D chromatin architecture4. Among these technologies, Hi-C provides the most comprehensive architecture, as it captures genome-wide 3D chromatin interaction profiles. Hi-C datasets have been quickly adapted to interpret non-coding genome-wide significant (GWS) loci5,6,7,8,9,10,11,12,13, as it can link non-coding variants to their putative target genes based on chromatin interaction profiles.
In this article, we outline a protocol to computationally predict putative target genes of GWAS risk variants using chromatin interaction profiles. We apply this protocol to map AD GWS loci14 to their target genes using Hi-C datasets in the adult human brain9. The resulting AD risk genes are characterized by other functional genomic datasets that include single cell transcriptomic and developmental expression profiles.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>10 kb resolution Hi-C interaction profiles in the adult brain from psychencode</td>
			<td></td>
			<td></td>
			<td><a target="_blank" href="http://adult.psychencode.org/Datasets/Integrative/Promoter-anchored_chromatin_loops.bed">http://adult.psychencode.org/</a></td>
		</tr>
		<tr>
			<td>Developmental expression datasets</td>
			<td></td>
			<td></td>
			<td><a target="_blank" href="http://www.brainspan.org/api/v2/well_known_file_download/267666527">http://www.brainspan.org/</a></td>
		</tr>
		<tr>
			<td>Fine-mapped credible SNPs for AD (Supplementary Table 8 from Jansen et al.14)</td>
			<td></td>
			<td></td>
			<td><a target="_blank" href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0311-9/MediaObjects/41588_2018_311_MOESM3_ESM.xlsx">https://static-content.springer.com/</a></td>
		</tr>
		<tr>
			<td>HOMER</td>
			<td></td>
			<td></td>
			<td><a target="_blank" href="http://homer.ucsd.edu/homer/configureHomer.pl">http://homer.ucsd.edu/</a></td>
		</tr>
		<tr>
			<td>R (version 3.5.0)</td>
			<td></td>
			<td></td>
			<td><a target="_blank" href="https://www.r-project.org/">https://www.r-project.org/</a></td>
		</tr>
		<tr>
			<td>RStudio Desktop</td>
			<td></td>
			<td></td>
			<td><a target="_blank" href="https://www.rstudio.com/products/rstudio/download/">https://www.rstudio.com/</a></td>
		</tr>
		<tr>
			<td>Single cell expression datasets</td>
			<td></td>
			<td></td>
			<td><a target="_blank" href="http://adult.psychencode.org/Datasets/Derived/SC_Decomp/DER-20_Single_cell_expression_processed_TPM_backup.tsv">http://adult.psychencode.org/</a></td>
		</tr>
	</tbody>
</table>

mapping alzheimer's disease variants to their target genes using computational analysis of chromatin configuration

Genome-wide association studies (GWAS) have successfully identified hundreds of genomic loci that are associated with human traits and disease. However, because the majority of the genome-wide significant (GWS) loci fall onto the non-coding genome, the functional impact of many remain unknown. Three-dimensional chromatin interactions identified by Hi-C or its derivatives can provide useful tools to annotate these loci by linking non-coding variants to their actionable genes. Here, we outline a protocol to map GWAS non-coding variants to their putative genes using Alzheimer&#39;s disease (AD) GWAS and Hi-C datasets from human adult brain tissue. Putative causal single-nucleotide polymorphisms (SNPs) are identified by application of fine-mapping algorithms. SNPs are then mapped to their putative target genes using enhancer-promoter interactions based on Hi-C. The resulting gene set represents AD risk genes, as they are potentially regulated by AD risk variants. To garner further biological insights into molecular mechanisms underlying AD, we characterize AD risk genes using developmental brain expression data and brain single-cell expression profiles. This protocol can be expanded to any GWAS and Hi-C datasets to identify putative target genes and molecular mechanisms underlying various human traits and diseases.

The process described here was applied to a set of 800 credible SNPs that were defined by the original study14. Positional mapping revealed that 103 SNPs overlapped with promoters (43 unique genes) and 42 SNPs overlapped with exons (27 unique genes). After positional mapping, 84% (669) SNPs remained unannotated. Using Hi-C datasets in the adult brain, we were able to link an additional 208 SNPs to 64 genes based on physical proximity. In total, we mapped 284 AD credible SNPs to 112 AD risk genes (Figure 1A). AD risk genes were associated with amyloid precursor proteins, amyloid-beta formation, and immune response, reflecting the known biology of AD15,16,17,18 (Figure 1B-D). Developmental expression profiles of AD risk genes showed marked postnatal enrichment, indicative of the age-associated elevated risk of AD (Figure 2A). Finally, AD risk genes were highly expressed in microglia, primary immune cells in the brain (Figure 2B). This is in agreement with the recurrent findings that AD has a strong immune basis and microglia are the central player in AD pathogenesis14,19,20.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60428/60428fig1b.jpg" /> 
Figure 1: Defining putative target genes of AD GWS loci. (A) Credible SNPs derived from the top 29 AD loci were categorized into promoter SNPs, exonic SNPs, and unannotated non-coding SNPs. Promoter and exonic SNPs were directly assigned to their target genes by positional mapping, while chromatin interaction profiles in the adult brain were additionally used to map SNPs based on physical interactions. (B-D) Enrichment of GO (B), KEGG (C), and Reactome (D) terms in AD risk genes was performed using HOMER as described in protocol section 6. The x axis represents the false discovery rate (FDR) corrected -log10 (P-value). Enriched terms with FDR &#60; 0.1 were plotted. Grey vertical lines represent FDR = 0.05. APP amyloid precursor protein. Numerator, the number of AD risk genes represented in each term; denominator, the number of genes in each term. <a href="https://www.jove.com/files/ftp_upload/60428/60428fig1alarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60428/60428fig2.jpg" /> 
Figure 2: Characterization of AD risk genes. (A) AD risk genes are highly expressed in the postnatal cortex compared to the prenatal cortex. (B) Violin plots depicting distributions of gene expression values (normalized expression) in different cell types from the cortex. These results show that AD risk genes are highly expressed in microglia, consistent with previous studies14. <a href="https://www.jove.com/files/ftp_upload/60428/60428fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary File 1. <a href="https://www.jove.com/files/ftp_upload/60428/Gencode19_exon.bed" target="_blank">Please click here to view this file (Right click to download).</a>
Supplementary File 2. <a href="https://www.jove.com/files/ftp_upload/60428/Gencode19_promoter.bed" target="_blank">Please click here to view this file (Right click to download).</a>
Supplementary File 3. <a href="https://www.jove.com/files/ftp_upload/60428/geneAnno2.rda" target="_blank">Please click here to view this file (Right click to download).</a>

Watch this Scientific Journal Video about Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration at JoVE.com

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

We present a protocol to identify functional implications of non-coding variants identified by genome-wide association studies (GWAS) using three-dimensional chromatin interactions.

Genome-wide association studies (GWAS) have successfully identified hundreds of genomic loci that are associated with human traits and disease. However, ...

mapping-alzheimer-s-disease-variants-to-their-target-genes-using

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Genetics

18.9K Views.  University of North Carolina. We present a protocol to identify functional implications of non-coding variants identified by genome-wide association studies (GWAS) using three-dimensional chromatin interactions.

Video: Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

Knowing how RNAs interact with themselves and with others is key to understanding RNA based gene regulation in the cell. While examples of RNA-RNA interactions such as microRNA-mRNA interactions have been shown to regulate gene expression, the full extent to which RNA interactions occur in the cell is still unknown. Previous methods to study RNA interactions have primarily focused on subsets of RNAs that are interacting with a particular protein or RNA species. Here, we detail a method named Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH) that allows genome-wide capture of RNA interactions in vivo in an unbiased manner. SPLASH utilizes in vivo crosslinking, proximity ligation, and high throughput sequencing to identify intramolecular and intermolecular RNA base-pairing partners globally. SPLASH can be applied to different organisms including bacteria, yeast and human cells, as well as diverse cellular conditions to facilitate the understanding of the dynamics of RNA organization under diverse cellular contexts. The entire experimental SPLASH protocol takes about 5 days to complete and the computational workflow takes about 7 days to complete.

Here, we detail the method of Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH), which enables genome-wide mapping of intramolecular and intermolecular RNA-RNA interactions in vivo. SPLASH can be applied to study RNA interactomes of organisms including yeast, bacteria and humans.

Knowing how RNAs interact with themselves and with others is key to understanding RNA based gene regulation in the cell. While examples of RNA-RNA ...

Sequential Salt Extractions for the Analysis of Bulk Chromatin Binding Properties of Chromatin Modifying Complexes

Elucidation of the binding properties of chromatin-targeting proteins can be very challenging due to the complex nature of chromatin and the heterogeneous nature of most mammalian chromatin-modifying complexes. In order to overcome these hurdles, we have adapted a sequential salt extraction (SSE) assay for evaluating the relative binding affinities of chromatin-bound complexes. This easy and straightforward assay can be used by non-experts to evaluate the relative difference in binding affinity of two related complexes, the changes in affinity of a complex when a subunit is lost or an individual domain is inactivated, and the change in binding affinity after alterations to the chromatin landscape. By sequentially re-suspending bulk chromatin in increasing amounts of salt, we are able to profile the elution of a particular protein from chromatin. Using these profiles, we are able to determine how alterations in a chromatin-modifying complex or alterations to the chromatin environment affect binding interactions. Coupling SSE with other in vitro and in vivo assays, we can determine the roles of individual domains and proteins on the functionality of a complex in a variety of chromatin environments.

Sequential salt extraction of chromatin bound proteins is a useful tool for determining the binding properties of large protein complexes. This method can be employed to evaluate the role of individual subunits or domains in the overall affinity of a protein complex to bulk chromatin.

Elucidation of the binding properties of chromatin-targeting proteins can be very challenging due to the complex nature of chromatin and the heterogeneous ...

Analysis of the c-KIT Ligand Promoter Using Chromatin Immunoprecipitation

Multiple cellular processes, including DNA replication and repair, DNA recombination, and gene expression, require interactions between proteins and DNA. Therefore, DNA-protein interactions regulate multiple physiological, pathophysiological, and biological functions, such as cell differentiation, cell proliferation, cell cycle control, chromosome stability, epigenetic gene regulation, and cell transformation. In eukaryotic cells, the DNA interacts with histone and nonhistone proteins and is condensed into chromatin. Several technical tools can be used to analyze DNA-protein interactions, such as the Electrophoresis (gel) Mobility Shift Assay (EMSA) and DNase I footprinting. However, these techniques analyze the protein-DNA interaction in vitro, not within the cellular context. Chromatin immunoprecipitation (ChIP) is a technique that captures proteins at their specific DNA binding sites, thereby allowing for the identification of DNA-protein interactions within their chromatin context. It is done by fixation&#160;of the DNA-protein interaction, followed by&#160;immunoprecipitation of the protein of interest. Subsequently, the genomic site that the protein was bound to is characterized. Here, we describe and discuss ChIP and demonstrate its analytical value for the identification of the Transforming Growth Factor-&#946; (TGF-&#946;)-induced binding of the transcription factor SMAD2 to SMAD Binding Elements (SBE) within the promoter region of the tyrosine-protein kinase Kit (c-KIT) receptor ligand Stem Cell Factor (SCF).

DNA-protein interactions are essential for multiple biological processes. During the evaluation of cellular functions, the analysis of DNA-protein interactions is indispensable for understanding gene regulation. Chromatin immunoprecipitation (ChIP) is a powerful tool to analyze such interactions in vivo.

Multiple cellular processes, including DNA replication and repair, DNA recombination, and gene expression, require interactions between proteins and DNA. ...

Retroviral Scanning: Mapping MLV Integration Sites to Define Cell-specific Regulatory Regions

Moloney murine leukemia (MLV) virus-based retroviral vectors integrate predominantly in acetylated enhancers and promoters. For this reason, mLV integration sites can be used as functional markers of active regulatory elements. Here, we present a retroviral scanning tool, which allows the genome-wide identification of cell-specific enhancers and promoters. Briefly, the target cell population is transduced with an mLV-derived vector and genomic DNA is digested with a frequently cutting restriction enzyme. After ligation of genomic fragments with a compatible DNA linker, linker-mediated polymerase chain reaction (LM-PCR) allows the amplification of the virus-host genome junctions. Massive sequencing of the amplicons is used to define the mLV integration profile genome-wide. Finally, clusters of recurrent integrations are defined to identify cell-specific regulatory regions, responsible for the activation of cell-type specific transcriptional programs.
The retroviral scanning tool allows the genome-wide identification of cell-specific promoters and enhancers in prospectively isolated target cell populations. Notably, retroviral scanning represents an instrumental technique for the retrospective identification of rare populations (e.g. somatic stem cells) that lack robust markers for prospective isolation.

Here, we describe a protocol for genome-wide mapping of the integration sites of Moloney murine leukemia virus-based retroviral vectors in human cells.

Moloney murine leukemia (MLV) virus-based retroviral vectors integrate predominantly in acetylated enhancers and promoters. For this reason, mLV ...

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method used for the identification of open (accessible) regions of chromatin. These regions represent regulatory DNA elements (e.g., promoters, enhancers, locus control regions, insulators) to which transcription factors bind. Mapping the accessible chromatin landscape is a powerful approach for uncovering active regulatory elements across the genome. This information serves as an unbiased approach for discovering the network of relevant transcription factors and mechanisms of chromatin structure that govern gene expression programs. ATAC-seq is a robust and sensitive alternative to DNase I hypersensitivity analysis coupled with next-generation sequencing (DNase-seq) and formaldehyde-assisted isolation of regulatory elements (FAIRE-seq) for genome-wide analysis of chromatin accessibility and to the sequencing of micrococcal nuclease-sensitive sites (MNase-seq) to determine nucleosome positioning. We present a detailed ATAC-seq protocol optimized for human primary immune cells i.e. CD4+ lymphocytes (T helper 1 (Th1) and Th2 cells). This comprehensive protocol begins with cell harvest, then describes the molecular procedure of chromatin tagmentation, sample preparation for next-generation sequencing, and also includes methods and considerations for the computational analyses used to interpret the results. Moreover, to save time and money, we introduced quality control measures to assess the ATAC-seq library prior to sequencing. Importantly, the principles presented in this protocol allow its adaptation to other human immune and non-immune primary cells and cell lines. These guidelines will also be useful for laboratories which are not proficient with next-generation sequencing methods.

Assay for Transposase-Accessible Chromatin coupled with high-throughput sequencing (ATAC-seq) is a genome-wide method to uncover accessible chromatin. This is a step-by-step ATAC-seq protocol, from molecular to the final computational analysis, optimized for human lymphocytes (Th1/Th2). This protocol can be adopted by researchers without prior experience in next-generation sequencing methods.

Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method used for the identification of open (accessible) regions ...

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

For several decades, 5-methylcytosine (5mC) has been thought to be the only DNA modification with a functional significance in metazoans. The discovery of enzymatic oxidation of 5mC to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) as well as detection of N6-methyladenine (6mA) in the DNA of multicellular organisms provided additional degrees of complexity to the epigenetic research. According to a growing body of experimental evidence, these novel DNA modifications may play specific roles in different cellular and developmental processes. Importantly, as some of these marks (e. g. 5hmC, 5fC and 5caC) exhibit tissue- and developmental stage-specific occurrence in vertebrates, immunochemistry represents an important tool allowing assessment of spatial distribution of DNA modifications in different biological contexts. Here the methods for computational analysis of DNA modifications visualized by immunostaining followed by confocal microscopy are described. Specifically, the generation of 2.5 dimension (2.5D) signal intensity plots, signal intensity profiles, quantification of staining intensity in multiple cells and determination of signal colocalization coefficients are shown. Collectively, these techniques may be operational in evaluating the levels and localization of these DNA modifications in the nucleus, contributing to elucidating their biological roles in metazoans.

Newly discovered oxidized forms of 5-methylcytosine (oxi-mCs), 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) may represent distinct DNA modifications with unique functional roles. Here a semi-quantitative workflow for visualization of oxi-mCs' spatial distribution, signal intensity profiling and colocalization is described.

For several decades, 5-methylcytosine (5mC) has been thought to be the only DNA modification with a functional significance in metazoans. The discovery of ...

Using Sniper-Cas9 to Minimize Off-target Effects of CRISPR-Cas9 Without the Loss of On-target Activity Via Directed Evolution

The development of clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) into therapeutic modalities requires the avoidance of its potentially deleterious off-target effects. Several methods have been devised to reduce such effects. Here, we present an Escherichia coli-based directed evolution method called Sniper-screen to obtain a Cas9 variant with optimized specificity and retained on-target activity, called Sniper-Cas9. Using Sniper-screen, positive and negative selection can be performed simultaneously. The screen can also be repeated with other single-guide RNA (sgRNA) sequences to enrich for the true positive hits. By using the CMV-PltetO1 dual promoter to express Cas9 variants, the performance of the pooled library can be quickly checked in mammalian cells. Methods to increase the specificity of Sniper-Cas9 are also described. First, the use of truncated sgRNAs has previously been shown to increase Cas9 specificity. Unlike other engineered Cas9s, Sniper-Cas9 retains a wild-type (WT) level of on-target activity when combined with truncated sgRNAs. Second, the delivery of Sniper-Cas9 in a ribonucleoprotein (RNP) format instead of a plasmid format is possible without affecting its on-target activity.

Here, we present a protocol to optimize CRISPR-Cas9 to achieve a higher specificity without the loss of on-target activity. We use a directed evolution approach called Sniper-screen to find a mutant Cas9 with the desired characteristics. Sniper-Cas9 is compatible with truncated single-guide RNAs and delivery in a ribonucleoprotein format, well-known strategies for achieving higher specificities.

The development of clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) into therapeutic modalities requires the ...

An In Vitro Protocol for Evaluating MicroRNA Levels, Functions, and Associated Target Genes in Tumor Cells

MicroRNAs (miRNAs) are small regulatory RNAs which are recognized to modulate numerous intracellular signaling pathways in several diseases including cancers. These small regulatory RNAs mainly interact with the 3&#8217; untranslated regions (3&#8217; UTR) of their target messenger RNAs (mRNAs) ultimately resulting in the inhibition of decoding processes of mRNAs and the augmentation of target mRNA degradations. Based on the expression levels and intracellular functions, miRNAs are able to serve as regulatory factors of oncogenic and tumor-suppressive mRNAs. Identification of bona fide target genes of a miRNA among hundreds or even thousands of computationally predicted targets is a crucial step to discern the roles and basic molecular mechanisms of a miRNA of interest. Various miRNA target prediction programs are available to search possible miRNA-mRNA interactions. However, the most challenging question is how to validate direct target genes of a miRNA of interest. This protocol describes a reproducible strategy of key methods on how to identify miRNA targets related to the function of a miRNA. This protocol presents a practical guide on step-by-step procedures to uncover miRNA levels, functions, and related target mRNAs using the probe-based real-time polymerase chain reaction (PCR), sulforhodamine B (SRB) assay following a miRNA mimic transfection, dose-response curve generation, and luciferase assay along with the cloning of 3&#8217; UTR of a gene, which is necessary for proper understanding of the roles of individual miRNAs.

This protocol uses a probe-based real-time polymerase chain reaction (PCR), a sulforhodamine B (SRB) assay, 3&#8217; untranslated regions (3&#8217; UTR) cloning, and a luciferase assay to verify the target genes of a miRNA of interest and to understand the functions of miRNAs.

MicroRNAs (miRNAs) are small regulatory RNAs which are recognized to modulate numerous intracellular signaling pathways in several diseases including ...

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Advances in sequencing technology have made whole-genome and whole-exome datasets more accessible for both clinical diagnosis and cutting-edge human genetics research. Although a number of in silico algorithms have been developed to predict the pathogenicity of variants identified in these datasets, functional studies are critical to determining how specific genomic variants affect protein function, especially for missense variants. In the Undiagnosed Diseases Network (UDN) and other rare disease research consortia, model organisms (MO) including Drosophila, C. elegans, zebrafish, and mice are actively used to assess the function of putative human disease-causing variants. This protocol describes a method for the functional assessment of rare human variants used in the Model Organisms Screening Center Drosophila Core of the UDN. The workflow begins with gathering human and MO information from multiple public databases, using the MARRVEL web resource to assess whether the variant is likely to contribute to a patient&#39;s condition as well as design effective experiments based on available knowledge and resources. Next, genetic tools (e.g., T2A-GAL4 and UAS-human cDNA lines) are generated to assess the functions of variants of interest in Drosophila. Upon development of these reagents, two-pronged functional assays based on rescue and overexpression experiments can be performed to assess variant function. In the rescue branch, the endogenous fly genes are &#34;humanized&#34;&#160;by replacing the orthologous Drosophila gene with reference or variant human transgenes. In the overexpression branch, the reference and variant human proteins are exogenously driven in a variety of tissues. In both cases, any scorable phenotype (e.g., lethality, eye morphology, electrophysiology) can be used as a read-out, irrespective of the disease of interest. Differences observed between reference and variant alleles suggest a variant-specific effect, and thus likely pathogenicity. This protocol allows rapid, in vivo assessments of putative human disease-causing variants of genes with known and unknown functions.

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

The goal of this protocol is to outline the design and performance of in vivo experiments in Drosophila melanogaster to assess the functional consequences of rare gene variants associated with human diseases.

Advances in sequencing technology have made whole-genome and whole-exome datasets more accessible for both clinical diagnosis and cutting-edge human ...

Identification of Circular RNAs using RNA Sequencing

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein interactions, and regulation of parental gene transcription. In classical next generation RNA sequencing (RNA-seq), circRNAs are typically overlooked as a result of poly-A selection during construction of mRNA libraries, or are found at very low abundance, and are therefore difficult to isolate and detect. Here, a circRNA library construction protocol was optimized by comparing library preparation kits, pre-treatment options and various total RNA input amounts. Two commercially available whole transcriptome library preparation kits, with and without RNase R pre-treatment, and using variable amounts of total RNA input (1 to 4 &#181;g), were tested. Lastly, multiple tissue types; including liver, lung, lymph node, and pancreas; as well as multiple brain regions; including the cerebellum, inferior parietal lobe, middle temporal gyrus, occipital cortex, and superior frontal gyrus; were compared to evaluate circRNA abundance across tissue types. Analysis of the generated RNA-seq data using six different circRNA detection tools (find_circ, CIRI, Mapsplice, KNIFE, DCC, and CIRCexplorer) revealed that a stranded total RNA library preparation kit with RNase R pre-treatment and 4 &#181;g RNA input is the optimal method for identifying the highest relative number of circRNAs. Consistent with previous findings, the highest enrichment of circRNAs was observed in brain tissues compared to other tissue types.

Circular RNAs (circRNAs) are non-coding RNAs that may have roles in transcriptional regulation and mediating interactions between proteins. Following assessment of different parameters for construction of circRNA sequencing libraries, a protocol was compiled utilizing stranded total RNA library preparation with RNase R pre-treatment and is presented here.

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Identification of Circular RNAs using RNA Sequencing