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>

<ol>
	<li>Dekker, J., Misteli, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Long-Range+Chromatin+Interactions.">Long-Range Chromatin Interactions.</a> Cold Spring Harbor Perspectives in Biology. 7 (10), a019356 (2015).</li><li>Sanyal, A., Lajoie, B. R., Jain, G., Dekker, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+long-range+interaction+landscape+of+gene+promoters.">The long-range interaction landscape of gene promoters.</a> Nature. 489 (7414), 109-113 (2012).</li><li>Plank, J. L., Dean, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhancer+function:+mechanistic+and+genome-wide+insights+come+together.">Enhancer function: mechanistic and genome-wide insights come together.</a> Molecular Cell. 55 (1), 5-14 (2014).</li><li>Dekker, J., Marti-Renom, M. A., Mirny, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploring+the+three-dimensional+organization+of+genomes:+interpreting+chromatin+interaction+data.">Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.</a> Nature Reviews Genetics. 14 (6), 390-403 (2013).</li><li>Martin, P., 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=Capture+Hi-C+reveals+novel+candidate+genes+and+complex+long-range+interactions+with+related+autoimmune+risk+loci.">Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci.</a> Nature Communications. 6, 10069 (2015).</li><li>Won, H., 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=Chromosome+conformation+elucidates+regulatory+relationships+in+developing+human+brain.">Chromosome conformation elucidates regulatory relationships in developing human brain.</a> Nature. 538 (7626), 523-527 (2016).</li><li>J&#228;ger, R., 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=Capture+Hi-C+identifies+the+chromatin+interactome+of+colorectal+cancer+risk+loci.">Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci.</a> Nature Communications. 6, 6178 (2015).</li><li>Chen, J. A. A., 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=Joint+genome-wide+association+study+of+progressive+supranuclear+palsy+identifies+novel+susceptibility+loci+and+genetic+correlation+to+neurodegenerative+diseases.">Joint genome-wide association study of progressive supranuclear palsy identifies novel susceptibility loci and genetic correlation to neurodegenerative diseases.</a> Molecular Neurodegeneration. 13 (1), 41 (2018).</li><li>Wang, 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=Comprehensive+functional+genomic+resource+and+integrative+model+for+the+adult+brain.">Comprehensive functional genomic resource and integrative model for the adult brain.</a> Science. 362 (6420), eaat8464 (2018).</li><li>Demontis, 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=Discovery+of+the+first+genome-wide+significant+risk+loci+for+attention+deficit/hyperactivity+disorder.">Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder.</a> Nature Genetics. 51 (1), 63-75 (2019).</li><li>Grove, 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=Identification+of+common+genetic+risk+variants+for+autism+spectrum+disorder.">Identification of common genetic risk variants for autism spectrum disorder.</a> Nature Genetics. 51 (3), 431-444 (2019).</li><li>Lee, P. H., 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+meta-analysis+identifies+genomic+relationships,+novel+loci,+and+pleiotropic+mechanisms+across+eight+psychiatric+disorders.">Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders.</a> bioRxiv. , 528117 (2019).</li><li>Mah, W., Won, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+three-dimensional+landscape+of+the+genome+in+human+brain+tissue+unveils+regulatory+mechanisms+leading+to+schizophrenia+risk.">The three-dimensional landscape of the genome in human brain tissue unveils regulatory mechanisms leading to schizophrenia risk.</a> Schizophrenia Research. , (2019).</li><li>Jansen, I. E., 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+meta-analysis+identifies+new+loci+and+functional+pathways+influencing+Alzheimer's+disease+risk.">Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk.</a> Nature Genetics. 51 (3), 404-413 (2019).</li><li>Viola, K. L., Klein, W. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Amyloid+&#946;+oligomers+in+Alzheimer's+disease+pathogenesis,+treatment,+and+diagnosis.">Amyloid &#946; oligomers in Alzheimer's disease pathogenesis, treatment, and diagnosis.</a> Acta Neuropathologica. 129 (2), 183-206 (2015).</li><li>Mroczko, B., Groblewska, M., Litman-Zawadzka, A., Kornhuber, J., Lewczuk, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Amyloid+&#946;+oligomers+(A&#946;Os)+in+Alzheimer's+disease.">Amyloid &#946; oligomers (A&#946;Os) in Alzheimer's disease.</a> Journal of Neural Transmission. 125 (2), 177-191 (2018).</li><li>Heneka, M. T., 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=Neuroinflammation+in+Alzheimer's+disease.">Neuroinflammation in Alzheimer's disease.</a> Lancet Neurology. 14 (4), 388-405 (2015).</li><li>Minter, M. R., Taylor, J. M., Crack, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+contribution+of+neuroinflammation+to+amyloid+toxicity+in+Alzheimer's+disease.">The contribution of neuroinflammation to amyloid toxicity in Alzheimer's disease.</a> Journal of Neurochemistry. 136 (3), 457-474 (2016).</li><li>Hansen, D. V., Hanson, J. E., Sheng, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microglia+in+Alzheimer's+disease.">Microglia in Alzheimer's disease.</a> The Journal of Cell Biology. 217 (2), 459-472 (2018).</li><li>Gjoneska, E., 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=Conserved+epigenomic+signals+in+mice+and+humans+reveal+immune+basis+of+Alzheimer's+disease.">Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease.</a> Nature. 518 (7539), 365-369 (2015).</li><li>Benner, C., 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=FINEMAP:+efficient+variable+selection+using+summary+data+from+genome-wide+association+studies.">FINEMAP: efficient variable selection using summary data from genome-wide association studies.</a> Bioinformatics. 32 (10), 1493-1501 (2016).</li><li>Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B., Eskin, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identifying+causal+variants+at+loci+with+multiple+signals+of+association.">Identifying causal variants at loci with multiple signals of association.</a> Genetics. 198 (2), 497-508 (2014).</li><li>Tewhey, R., 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=Direct+Identification+of+Hundreds+of+Expression-Modulating+Variants+using+a+Multiplexed+Reporter+Assay.">Direct Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter Assay.</a> Cell. 165 (6), 1519-1529 (2016).</li><li>Arnold, C. 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><tbody><tr><td>10 kb resolution Hi-C interaction profiles in the adult brain from psychencode</td><td></td><td></td><td>&lt;a target=&quot;_blank&quot; href=&quot;http://adult.psychencode.org/Datasets/Integrative/Promoter-anchored_chromatin_loops.bed&quot;&gt;http://adult.psychencode.org/&lt;/a&gt;</td></tr><tr><td>Developmental expression datasets</td><td></td><td></td><td>&lt;a target=&quot;_blank&quot; href=&quot;http://www.brainspan.org/api/v2/well_known_file_download/267666527&quot;&gt;http://www.brainspan.org/&lt;/a&gt;</td></tr><tr><td>Fine-mapped credible SNPs for AD (Supplementary Table 8 from Jansen et al.&lt;sup&gt;14&lt;/sup&gt;)</td><td></td><td></td><td>&lt;a target=&quot;_blank&quot; href=&quot;https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0311-9/MediaObjects/41588_2018_311_MOESM3_ESM.xlsx&quot;&gt;https://static-content.springer.com/&lt;/a&gt;</td></tr><tr><td>HOMER</td><td></td><td></td><td>&lt;a target=&quot;_blank&quot; href=&quot;http://homer.ucsd.edu/homer/configureHomer.pl&quot;&gt;http://homer.ucsd.edu/&lt;/a&gt;</td></tr><tr><td>R (version 3.5.0)</td><td></td><td></td><td>&lt;a target=&quot;_blank&quot; href=&quot;https://www.r-project.org/&quot;&gt;https://www.r-project.org/&lt;/a&gt;</td></tr><tr><td>RStudio Desktop</td><td></td><td></td><td>&lt;a target=&quot;_blank&quot; href=&quot;https://www.rstudio.com/products/rstudio/download/&quot;&gt;https://www.rstudio.com/&lt;/a&gt;</td></tr><tr><td>Single cell expression datasets</td><td></td><td></td><td>&lt;a target=&quot;_blank&quot; href=&quot;http://adult.psychencode.org/Datasets/Derived/SC_Decomp/DER-20_Single_cell_expression_processed_TPM_backup.tsv&quot;&gt;http://adult.psychencode.org/&lt;/a&gt;</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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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

Nanyang Technological University

Research

JoVE Journal

Genetics

18.9K Views.  University of North Carolina. Hoewel GWAS met succes genomische regio's heeft geïdentificeerd die verband houden met menselijke eigenschappen en ziekten, is de biologische impact van deze risicovarianten onduidelijk. Hier schetsen we een protocol om computationeel putatieve doelgenen van GWAS-risicovarianten te voorspellen met chromatine interactieprofielen. Vaak is de identificatie van risicogenen een eerste stap om ziektemechanismen te begrijpen en normale therapeutische benaderingen mogelijk te maken.We hopen d...

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

High-resolution Melting PCR for Complement Receptor 1 Length Polymorphism Genotyping: An Innovative Tool for Alzheimer's Disease Gene Susceptibility Assessment

Complement receptor 1 (CR1), a transmembrane glycoprotein that plays a key role in the innate immune system, is expressed on many cell types, but especially on red blood cells (RBCs). As a receptor for the complement components C3b and C4b, CR1 regulates the activation of the complement cascade and promotes the phagocytosis of immune complexes and cellular debris, as well as the amyloid-beta (A&#946;) peptide in Alzheimer's disease (AD). Several studies have confirmed AD-associated single nucleotide polymorphisms (SNPs), as well as a copy-number variation (CNV) in the CR1 gene. Here, we describe an innovative method for determining the length polymorphism of the CR1 receptor. The receptor includes three domains, called long homologous repeats (LHR)-LHR-A, LHR-C, and LHR-D-and an n domain, LHR-B, where n is an integer between 0 and 3. Using a single pair of specific primers, the genetic material is used to amplify a first fragment of the LHR-B domain (the variant amplicon B) and a second fragment of the LHR-C domain (the invariant amplicon). The variant amplicon B and the invariant amplicon display differences at five nucleotides outside of the hybridization areas of said primers. The numbers of variant amplicons B and of invariant amplicons is deduced using a quantitative tool (high-resolution melting (HRM) curves), and the ratio of the variant amplicon B to the invariant amplicon differs according to the CR1 length polymorphism. This method provides several advantages over the canonical phenotype method, as it does not require fresh material and is cheaper, faster, and therefore applicable to larger populations. Thus, the use of this method should be helpful to better understand the role of CR1 isoforms in the pathogenesis of diseases such as AD.

Here, we describe an innovative method to determine complement receptor 1 (CR1) length polymorphisms for use in several applications, particularly the assessment of susceptibility to diseases such as Alzheimer&#39;s disease (AD). This method could be useful to better understand the role of CR1 isoforms in the pathogenesis of AD.

Complement receptor 1 (CR1), a transmembrane glycoprotein that plays a key role in the innate immune system, is expressed on many cell types, but ...

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 ...

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of associated loci. In addition, the presence of specific modifications has been used to determine the position and identity non-coding functional elements such as enhancers. In recent years, chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) has become a powerful tool in determining the genome-wide profiles of individual histone modifications. However, it has become increasingly clear that the combinatorial patterns of chromatin modifications, referred to as Chromatin States, determine the identity and nature of the associated genomic locus. Therefore, workflows consisting of robust high-throughput (HT) methodologies for profiling a number of histone modification marks, as well as computational analyses pipelines capable of handling myriads of ChIP-Seq profiling datasets, are needed for comprehensive determination of epigenomic states in large number of samples. The HT-ChIP-Seq workflow presented here consists of two modules: 1) an experimental protocol for profiling several histone modifications from small amounts of tumor samples and cell lines in a 96-well format; and 2) a computational data analysis pipeline that combines existing tools to compute both individual mark occupancy and combinatorial chromatin state patterns. Together, these two modules facilitate easy processing of hundreds of ChIP-Seq samples in a fast and efficient manner. The workflow presented here is used to derive chromatin state patterns from 6 histone mark profiles in melanoma tumors and cell lines. Overall, we present a comprehensive ChIP-seq workflow that can be applied to dozens of human tumor samples and cancer cell lines to determine epigenomic aberrations in various malignancies.

Here, we describe an optimized high-throughput ChIP-sequencing protocol and computational analyses pipeline for the determination of genome-wide chromatin state patterns from frozen tumor tissues and cell lines.

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of ...

Cancer Research

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted next-generation sequencing is a time- and cost-efficient approach that is becoming increasingly popular in both disease research and clinical diagnostics. The protocol described here presents the complex workflow required for sequencing and the bioinformatics process used to identify genetic variants that contribute to disease.

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

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 ...

Genome-Scale Screening to Identify and Compare Therapeutic Targets in Tumors

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome neurofibromatosis type 1 (NF1), MPNSTs are the most common malignancy and the leading cause of death. These rare and aggressive soft-tissue sarcomas offer a stark future, with 5-year disease-free survival rates of 34-60%. Treatment options for individuals with MPNSTs are disappointingly limited, with disfiguring surgery being the foremost treatment option. Many once-promising therapies such as tipifarnib, an inhibitor of Ras signaling, have failed clinically. Likewise, phase II clinical trials with erlotinib, which targets the epidermal growth factor (EFGR), and sorafenib, which targets the vascular endothelial growth factor receptor (VEGF), platelet-derived growth factor receptor (PDGF), and Raf, in combination with standard chemotherapy, have also failed to produce a response in patients. 
In recent years, functional genomic screening methods combined with genetic profiling of cancer cell lines have proven useful for identifying essential cytoplasmic signaling pathways and the development of target-specific therapies. In the case of rare tumor types, a variation of this approach known as cross-species comparative oncogenomics is increasingly being used to identify novel therapeutic targets. In cross-species comparative oncogenomics, genetic profiling and functional genomics are performed in genetically engineered mouse (GEM) models and the results are then validated in the rare human specimens and cell lines that are available. 
This paper describes how to identify candidate driver gene mutations in human and mouse MPNST cells using whole exome sequencing (WES). We then describe how to perform genome-scale shRNA screens to identify and compare critical signaling pathways in mouse and human MPNST cells and identify druggable targets in these pathways. These methodologies provide an effective approach to identifying new therapeutic targets in a variety of human cancer types.

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens 

We have developed a cross-species comparative oncogenomics approach utilizing genomic analyses and functional genomic screens to identify and compare therapeutic targets in tumors arising in genetically engineered mouse models and the corresponding human tumor type.

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome ...

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

Biology

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Population and family-based genetic studies typically result in the identification of genetic variants that are statistically associated with a clinical disease or phenotype. For many diseases and traits, most variants are non-coding, and are thus likely to act by impacting subtle, comparatively hard to predict mechanisms controlling gene expression. Here, we describe a general strategic approach to prioritize non-coding variants, and screen them for their function. This approach involves computational prioritization using functional genomic databases followed by experimental analysis of differential binding of transcription factors (TFs) to risk and non-risk alleles. For both electrophoretic mobility shift assay (EMSA) and DNA affinity precipitation assay (DAPA) analysis of genetic variants, a synthetic DNA oligonucleotide (oligo) is used to identify factors in the nuclear lysate of disease or phenotype-relevant cells. For EMSA, the oligonucleotides with or without bound nuclear factors (often TFs) are analyzed by non-denaturing electrophoresis on a tris-borate-EDTA (TBE) polyacrylamide gel. For DAPA, the oligonucleotides are bound to a magnetic column and the nuclear factors that specifically bind the DNA sequence are eluted and analyzed through mass spectrometry or with a reducing sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) followed by Western blot analysis. This general approach can be widely used to study the function of non-coding genetic variants associated with any disease, trait, or phenotype.

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

We present a strategic plan and protocol for identifying non-coding genetic variants affecting transcription factor (TF) DNA binding. A detailed experimental protocol is provided for electrophoretic mobility shift assay (EMSA) and DNA affinity precipitation assay (DAPA) analysis of genotype-dependent TF DNA binding.

Population and family-based genetic studies typically result in the identification of genetic variants that are statistically associated with a clinical ...

Sex-Specific Glial Biomarkers in Alzheimer&#39;s Disease: A Transcriptome Analysis

Biomarker Identification for Gender Specificity of Alzheimer's Disease Based on the Glial Transcriptome Profiles

Many sex-specific biomarkers have been recently revealed in Alzheimer&#39;s disease (AD); however, cerebral glial cells were rarely reported. This study analyzed 220,095 single-nuclei transcriptomes from the frontal cortex of thirty-three AD individuals in the GEO database. Sex-specific Differentially Expressed Genes (DEGs) were identified in glial cells, including 243 in astrocytes, 1,154 in microglia, and 572 in oligodendrocytes. Gene Ontology (GO) functional annotation analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed functional concentration in synaptic, neural, and hormone-related pathways. Protein-protein interaction network (PPI) identified MT3, CALM2, DLG2, KCND2, PAKACB, CAMK2D, and NLGN4Y in astrocytes, TREM2, FOS, APOE, APP, and NLGN4Y in microglia, and GRIN2A, ITPR2, GNAS, and NLGN4Y in oligodendrocytes as key genes. NLGN4Y was the only gene shared by the three glia and was identified as the biomarker for the gender specificity of AD. Gene-transcription factor (TF)-miRNA coregulatory network identified key regulators for NLGN4Y and its target TCMs. Ecklonia kurome Okam (Kunbu) and Herba Ephedrae (Mahuang) were identified, and the effects of the active ingredients on AD were displayed. Finally, enrichment analysis of Kunbu and Mahuang suggested that they might act as therapeutic candidates for gender specificity of AD.

Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer&#39;s Disease

This study analyzed single-nuclei transcriptomes of thirty-three individuals with Alzheimer's disease (AD), revealing sex-specific DEGs in glial cells. Functional enrichment analysis highlighted synaptic, neural, and hormone-related pathways. Key genes, namely NLGN4Y and its regulators, were identified, and potential therapeutic candidates for gender-specific AD were proposed.

Many sex-specific biomarkers have been recently revealed in Alzheimer&#39;s disease (AD); however, cerebral glial cells were rarely reported. This study ...

Neuroscience

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

The spatial organization of the genome contributes to its function and regulation in many contexts, including transcription, replication, recombination, and repair. Understanding the exact causality between genome topology and function is therefore crucial and increasingly the subject of intensive research. Chromosome conformation capture technologies (3C) allow inferring the 3D structure of chromatin by measuring the frequency of interactions between any region of the genome. Here we describe a fast and simple protocol to perform Capture Hi-C, a 3C-based target enrichment method that characterizes the allele-specific 3D organization of megabased-sized genomic targets at high-resolution. In Capture Hi-C, target regions are captured by an array of biotinylated probes before downstream high-throughput sequencing. Thus, higher resolution and allele-specificity are achieved while improving the time-effectiveness and affordability of the technology. To demonstrate its strengths, the Capture Hi-C protocol was applied to the mouse X-inactivation center (Xic), the master regulatory locus of X-chromosome inactivation (XCI).

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

This protocol describes the Capture Hi-C method used to characterize the 3D organization of megabased-sized targeted genomic regions at high-resolution, including boundaries of topologically associating domains (TADs) and long-range chromatin interactions between regulatory and other DNA sequence elements.

The spatial organization of the genome contributes to its function and regulation in many contexts, including transcription, replication, recombination, ...

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

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