Figure S5: Characteristics of lncMod analysis and results

Table S1: TARGET clinical sample and RNA-sequencing characteristics, Table S2: Genomic loci for lncRNA and protein coding genes in this study, Table S3: Number and types of genes expressed per cancer

Table S13: lncRNAs associated with CRC of NBL, Table S14: Differentially expressed lncRNAs between major subtypes in NBL

Figure S3: Regions of somatic copy number aberration across cancers and genes dysregulated due to copy number

Genome-wide association studies (GWASs) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping chromatin interactions. We performed a melanoma GWAS region-focused capture-HiC assay in human primary melanocytes to identify physical interactions between fine-mapped risk variants and potential causal melanoma-susceptibility genes. Overall, chromatin-interaction data alone nominated potential causal genes for 61 of the 68 melanoma risk signals, identifying many candidates beyond those reported by previous studies. We further integrated these data with epigenomic (chromatin state, accessibility), gene expression (expression quantitative trait locus [eQTL]/transcriptome-wide association study [TWAS]), DNA methylation (methylation QTL [meQTL]/methylome-wide association study [MWAS]), and massively parallel reporter assay (MPRA) data generated from melanoma-relevant cell types to prioritize potentially cis-regulatory variants and their respective candidate gene targets. From the set of fine-mapped variants across these loci, we identified 140 prioritized credible causal variants linked to 195 candidate genes at 42 risk signals. In addition, we developed an integrative scoring system to facilitate candidate gene prioritization, integrating melanocyte and melanoma datasets. Notably, at several GWAS risk signals, we observed long-range chromatin connections (500 kb to >1 Mb) with distant candidate target genes. We validated several such cis-regulatory interactions using CRISPR inhibition, providing evidence for known cancer driver genes MDM4 and CBL, as well as the SRY-box transcription factor SOX4, as likely melanoma risk genes.

Table S15: Data integration using multi-dimensional analysis to prioritize functional lncRNAs in each cancer, Table S16: Prioritized lncRNAs and predicted lncRNA target genes and pathways in NBL, Table S17: GSEA Analysis: MSigDB Hallmarks enriched across genes impacted by siTBX2-AS1 or siTBX2 treatment in NLF

Supplementary Methods, Index for Tables S1-S17, Index for Figures S1-S8

Figure S2: lncRNA expression varies across pediatric cancers

Figure S6: Defining mesenchymal vs adrenergic lncRNAs in two NBL cohorts

Table S4: Top 10 expressed lncRNAs across TARGET cancers and GTEx tissues, Table S5: Tissue specificity index (tau score) annotation per gene, Table S6: Validation of tissue specific lncRNAs based on tau score analysis in alternate NBL datasets

Table S10: Statistics for input and output variables of lncMod analysis (xls file), Table S11: Significantly dysregulated lncMod triplets, Table S12: lncRNA TF associations ranked by # target genes

Figure S1: Workflow for RNA-seq gene mapping and quantification

Figure S7: Validation of TBX2-AS1 expression in NBL cell lines and impact on NBL cell growth

Figure S4: Structural variants impact lncRNAs in pediatric cancers

Table S7: Number of TARGET samples with WGS and SCNA events per cancer identified by GISTIC, Table S8: Differential expression of genes in samples with and without SCNA, Table S9: Number of samples with SVs in lncRNAs

Figure S8: RNA expression profiling following TBX2-AS1 and TBX2 knockdown in NLF

Late-onset Alzheimer's disease (LOAD) research has principally focused on neurons over the years due to their known role in the production of amyloid beta plaques and neurofibrillary tangles. In contrast, recent genomic studies of LOAD have implicated microglia as culprits of the prolonged inflammation exacerbating the neurodegeneration observed in patient brains. Indeed, recent LOAD genome-wide association studies (GWAS) have reported multiple loci near genes related to microglial function, including TREM2, ABI3, and CR1. However, GWAS alone cannot pinpoint underlying causal variants or effector genes at such loci, as most signals reside in non-coding regions of the genome and could presumably confer their influence frequently via long-range regulatory interactions. We elected to carry out a combination of ATAC-seq and high-resolution promoter-focused Capture-C in two human microglial cell models (iPSC-derived microglia and HMC3) in order to physically map interactions between LOAD GWAS-implicated candidate causal variants and their corresponding putative effector genes. Notably, we observed consistent evidence that rs6024870 at the GWAS CASS4 locus contacted the promoter of nearby gene, RTFDC1. We subsequently observed a directionallly consistent decrease in RTFDC1 expression with the the protective minor A allele of rs6024870 via both luciferase assays in HMC3 cells and expression studies in primary human microglia. Through CRISPR-Cas9-mediated deletion of the putative regulatory region harboring rs6024870 in HMC3 cells, we observed increased pro-inflammatory cytokine secretion and decreased DNA double strand break repair related, at least in part, to RTFDC1 expression levels. Our variant-to-function approach therefore reveals that the rs6024870-harboring regulatory element at the LOAD 'CASS4' GWAS locus influences both microglial inflammatory capacity and DNA damage resolution, along with cumulative evidence implicating RTFDC1 as a novel candidate effector gene.

Genome-wide association studies (GWAS) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping cell-type specific chromatin interactions. We performed a melanoma GWAS region-focused capture-HiC assay in human primary melanocytes to identify physical interactions between fine-mapped risk variants and potential causal melanoma susceptibility genes. Overall, chromatin interaction data alone nominated potential causal genes for 61 of the 68 melanoma risk signals, identifying many candidates beyond those reported by previous studies. We further integrated these data with cell-type specific epigenomic (chromatin state, accessibility), gene expression (eQTL/TWAS), DNA methylation (meQTL/MWAS), and massively parallel reporter assay (MPRA) data to prioritize potentially cis-regulatory variants and their respective candidate gene targets. From the set of fine-mapped variants across these loci, we identified 140 prioritized candidate causal variants linked to 195 candidate genes at 42 risk signals. In addition, we developed an integrative scoring system to facilitate candidate gene prioritization, integrating melanocyte and melanoma datasets. Notably, at several GWAS risk signals we observed long-range chromatin connections (500 kb to >1 Mb) with distant candidate target genes. We validated several such cis-regulatory interactions using CRISPR inhibition, providing evidence for known cancer driver genes MDM4 and CBL, as well as the SRY-box transcription factor SOX4, as likely melanoma risk genes.

Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same conditions. We observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we find that functionally important players lack associations but are prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies. PhenoPLIER integrates genetic studies with gene modules expressed in specific contexts. This aids in extracting mechanistic insight from statistical associations to enhance our understanding of complex diseases and their therapeutic modalities.

Abstract Long noncoding RNAs (lncRNA) play an important role in gene regulation and contribute to tumorigenesis. While pan-cancer studies of lncRNA expression have been performed for adult malignancies, the lncRNA landscape across pediatric cancers remains largely uncharted. Here, we curated RNA sequencing data for 1,044 pediatric leukemia and extracranial solid tumors and integrated paired tumor whole genome sequencing and epigenetic data in relevant cell line models to explore lncRNA expression, regulation, and association with cancer. A total of 2,657 lncRNAs were robustly expressed across six pediatric cancers, including 1,142 exhibiting histotype-elevated expression. DNA copy number alterations contributed to lncRNA dysregulation at a proportion comparable to protein coding genes. Application of a multidimensional framework to identify and prioritize lncRNAs impacting gene networks revealed that lncRNAs dysregulated in pediatric cancer are associated with proliferation, metabolism, and DNA damage hallmarks. Analysis of upstream regulation via cell type–specific transcription factors further implicated distinct histotype-elevated and developmental lncRNAs. Integration of these analyses prioritized lncRNAs for experimental validation, and silencing of TBX2-AS1, the top-prioritized neuroblastoma-specific lncRNA, resulted in significant growth inhibition of neuroblastoma cells, confirming the computational predictions. Taken together, these data provide a comprehensive characterization of lncRNA regulation and function in pediatric cancers and pave the way for future mechanistic studies. Significance: Comprehensive characterization of lncRNAs in pediatric cancer leads to the identification of highly expressed lncRNAs across childhood cancers, annotation of lncRNAs showing histotype-specific elevated expression, and prediction of lncRNA gene regulatory networks.