A multi-dimensional integrative scoring framework for predicting functional variants in the human genome.

Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics and estimates the joint posterior functional probabilities of each genomic position. This estimate offers more comprehensive and interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping and heritability enrichment analysis by using the lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.

A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

FAVOR: functional annotation of variants online resource and annotator for variation across the human genome.

Large biobank-scale whole genome sequencing (WGS) studies are rapidly identifying a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries and functionally annotate the genotype data of large biobank-scale WGS studies. We develop the Functional Annotation of Variants Online Resources (FAVOR) to meet these pressing needs. FAVOR provides a comprehensive multi-faceted variant functional annotation online portal that summarizes and visualizes findings of all possible nine billion single nucleotide variants (SNVs) across the genome. It allows for rapid variant-, gene- and region-level queries of variant functional annotations. FAVOR integrates variant functional information from multiple sources to describe the functional characteristics of variants and facilitates prioritizing plausible causal variants influencing human phenotypes. Furthermore, we provide a scalable annotation tool, FAVORannotator, to functionally annotate large-scale WGS studies and efficiently store the genotype and their variant functional annotation data in a single file using the annotated Genomic Data Structure (aGDS) format, making downstream analysis more convenient. FAVOR and FAVORannotator are available at https://favor.genohub.org.

Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer.

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1ß pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies.

Whole-genome sequencing (WGS) studies are being widely conducted in order to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set-based analyses are commonly used by researchers for analyzing rare variants. However, existing variant-set-based approaches need to pre-specify genetic regions for analysis; hence, they are not directly applicable to WGS data because of the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding-window method requires the pre-specification of fixed window sizes, which are often unknown as a priori, are difficult to specify in practice, and are subject to limitations given that the sizes of genetic-association regions are likely to vary across the genome and phenotypes. We propose a computationally efficient and dynamic scan-statistic method (Scan the Genome [SCANG]) for analyzing WGS data; this method flexibly detects the sizes and the locations of rare-variant association regions without the need to specify a prior, fixed window size. The proposed method controls for the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected sizes of rare-variant association regions to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative methods for detecting rare-variant-associations while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.

Genome-wide association study of neck circumference identifies sex-specific loci independent of generalized adiposity.

BACKGROUND/OBJECTIVES: Neck circumference, an index of upper airway fat, has been suggested to be an important measure of body-fat distribution with unique associations with health outcomes such as obstructive sleep apnea and metabolic disease. This study aims to study the genetic bases of neck circumference. METHODS: We conducted a multi-ethnic genome-wide association study of neck circumference, adjusted and unadjusted for BMI, in up to 15,090 European Ancestry (EA) and African American (AA) individuals. Because sexually dimorphic associations have been observed for anthropometric traits, we conducted both sex-combined and sex-specific analysis. RESULTS: We identified rs227724 near the Noggin (NOG) gene as a possible quantitative locus for neck circumference in men (N = 8831, P = 1.74 × 10-9) but not in women (P = 0.08). The association was replicated in men (N = 1554, P = 0.045) in an independent dataset. This locus was previously reported to be associated with human height and with self-reported snoring. We also identified rs13087058 on chromosome 3 as a suggestive locus in sex-combined analysis (N = 15090, P = 2.94 × 10-7; replication P =0.049). This locus was also associated with electrocardiogram-assessed PR interval and is a cis-expression quantitative locus for the PDZ Domain-containing ring finger 2 (PDZRN3) gene. Both NOG and PDZRN3 interact with members of transforming growth factor-beta superfamily signaling proteins. CONCLUSIONS: Our study suggests that neck circumference may have unique genetic basis independent of BMI.

Epstein-Barr Virus Episome Physically Interacts with Active Regions of the Host Genome in Lymphoblastoid Cells.

The Epstein-Barr virus (EBV) episome is known to interact with the three-dimensional structure of the human genome in infected cells. However, the exact locations of these interactions and their potential functional consequences remain unclear. Recently, high-resolution chromatin conformation capture (Hi-C) assays in lymphoblastoid cells have become available, enabling us to precisely map the contacts between the EBV episome(s) and the human host genome. Using available Hi-C data at a 10-kb resolution, we have identified 15,000 reproducible contacts between EBV episome(s) and the human genome. These contacts are highly enriched in chromatin regions denoted by typical or super enhancers and active markers, including histone H3K27ac and H3K4me1. Additionally, these contacts are highly enriched at loci bound by host transcription factors that regulate B cell growth (e.g., IKZF1 and RUNX3), factors that enhance cell proliferation (e.g., HDGF), or factors that promote viral replication (e.g., NBS1 and NFIC). EBV contacts show nearly 2-fold enrichment in host regions bound by EBV nuclear antigen 2 (EBNA2) and EBNA3 transcription factors. Circular chromosome conformation capture followed by sequencing (4C-seq) using the EBV origin of plasmid replication (oriP) as a "bait" in lymphoblastoid cells further confirmed contacts with active chromatin regions. Collectively, our analysis supports interactions between EBV episome(s) and active regions of the human genome in lymphoblastoid cells.IMPORTANCE EBV is associated with ∼200,000 cancers each year. In vitro, EBV can transform primary human B lymphocytes into immortalized cell lines. EBV-encoded proteins, along with noncoding RNAs and microRNAs, hijack cellular proteins and pathways to control cell growth. EBV nuclear proteins usurp normal transcriptional programs to activate the expression of key oncogenes, including MYC, to provide a proliferation signal. EBV nuclear antigens also repress CDKN2A to suppress senescence. EBV membrane protein activates NF-κB to provide survival signals. EBV genomes are maintained by EBNA1, which tethers EBV episomes to the host chromosomes during mitosis. However, little is known about where EBV episomes are located in interphase cells. In interphase cells, EBV promoters drive the expression of latency genes, while oriP functions as an enhancer for these promoters. In this study, integrative analyses of published lymphoblastoid cell line (LCL) Hi-C data and our 4C-seq experiments position EBV episomes to host genomes with active epigenetic marks. These contact points were significantly enriched for super enhancers. The close proximity of EBV episomes and the super enhancers that are enriched for transcription cofactors or mediators in lymphoblasts may benefit EBV gene expression, suggesting a novel mechanism of transcriptional activation.

Nasopharyngeal carcinoma super-enhancer-driven ETV6 correlates with prognosis.

Nasopharyngeal carcinoma (NPC) most frequently occurs in southern China and southeast Asia. Epidemiology studies link NPC to genetic predisposition, Epstein-Barr virus (EBV) infection, and environmental factors. Genetic studies indicate that mutations in chromatin-modifying enzymes are the most frequent genetic alterations in NPC. Here, we used H3K27ac chromatin immune precipitation followed by deep sequencing (ChIP-seq) to define the NPC epigenome in primary NPC biopsies, NPC xenografts, and an NPC cell line, and compared them to immortalized normal nasopharyngeal or oral epithelial cells. We identified NPC-specific enhancers and found these enhancers were enriched with nuclear factor κB (NF-κB), IFN-responsive factor 1 (IRF1) and IRF2, and ETS family members ETS1 motifs. Normal cell-specific enhancers were enriched with basic leucine zipper family members and TP53 motifs. NPC super-enhancers with extraordinarily broad and high H3K27ac signals were also identified, and they were linked to genes important for oncogenesis including ETV6. ETV6 was also highly expressed in NPC biopsies by immunohistochemistry. High ETV6 expression correlated with a poor prognosis. Furthermore, we defined the EBV episome epigenetic landscapes in primary NPC tissue.

Mouse model of Epstein-Barr virus LMP1- and LMP2A-driven germinal center B-cell lymphoproliferative disease.

Epstein-Barr virus (EBV) is a major cause of immunosuppression-related B-cell lymphomas and Hodgkin lymphoma (HL). In these malignancies, EBV latent membrane protein 1 (LMP1) and LMP2A provide infected B cells with surrogate CD40 and B-cell receptor growth and survival signals. To gain insights into their synergistic in vivo roles in germinal center (GC) B cells, from which most EBV-driven lymphomas arise, we generated a mouse model with conditional GC B-cell LMP1 and LMP2A coexpression. LMP1 and LMP2A had limited effects in immunocompetent mice. However, upon T- and NK-cell depletion, LMP1/2A caused massive plasmablast outgrowth, organ damage, and death. RNA-sequencing analyses identified EBV oncoprotein effects on GC B-cell target genes, including up-regulation of multiple proinflammatory chemokines and master regulators of plasma cell differentiation. LMP1/2A coexpression also up-regulated key HL markers, including CD30 and mixed hematopoietic lineage markers. Collectively, our results highlight synergistic EBV membrane oncoprotein effects on GC B cells and provide a model for studies of their roles in immunosuppression-related lymphoproliferative diseases.

Epstein-Barr Virus Nuclear Antigen Leader Protein Coactivates EP300.

Epstein-Barr virus nuclear antigen (EBNA) leader protein (EBNALP) is one of the first viral genes expressed upon B-cell infection. EBNALP is essential for EBV-mediated B-cell immortalization. EBNALP is thought to function primarily by coactivating EBNA2-mediated transcription. Chromatin immune precipitation followed by deep sequencing (ChIP-seq) studies highlight that EBNALP frequently cooccupies DNA sites with host cell transcription factors (TFs), in particular, EP300, implicating a broader role in transcription regulation. In this study, we investigated the mechanisms of EBNALP transcription coactivation through EP300. EBNALP greatly enhanced EP300 transcription activation when EP300 was tethered to a promoter. EBNALP coimmunoprecipitated endogenous EP300 from lymphoblastoid cell lines (LCLs). EBNALP W repeat serine residues 34, 36, and 63 were required for EP300 association and coactivation. Deletion of the EP300 histone acetyltransferase (HAT) domain greatly reduced EBNALP coactivation and abolished the EBNALP association. An EP300 bromodomain inhibitor also abolished EBNALP coactivation and blocked the EP300 association with EBNALP. EBNALP sites cooccupied by EP300 had significantly higher ChIP-seq signals for sequence-specific TFs, including SPI1, RelA, EBF1, IRF4, BATF, and PAX5. EBNALP- and EP300-cooccurring sites also had much higher H3K4me1 and H3K27ac signals, indicative of activated enhancers. EBNALP-only sites had much higher signals for DNA looping factors, including CTCF and RAD21. EBNALP coactivated reporters under the control of NF-κB or SPI1. EP300 inhibition abolished EBNALP coactivation of these reporters. Clustered regularly interspaced short palindromic repeat interference targeting of EBNALP enhancer sites significantly reduced target gene expression, including that of EP300 itself. These data suggest a previously unrecognized mechanism by which EBNALP coactivates transcription through subverting of EP300 and thus affects the expression of LCL genes regulated by a broad range of host TFs.IMPORTANCE Epstein-Barr virus was the first human DNA tumor virus discovered over 50 years ago. EBV is causally linked to ∼200,000 human malignancies annually. These cancers include endemic Burkitt lymphoma, Hodgkin lymphoma, lymphoma/lymphoproliferative disease in transplant recipients or HIV-infected people, nasopharyngeal carcinoma, and ∼10% of gastric carcinoma cases. EBV-immortalized human B cells faithfully model key aspects of EBV lymphoproliferative diseases and are useful models of EBV oncogenesis. EBNALP is essential for EBV to transform B cells and transcriptionally coactivates EBNA2 by removing repressors from EBNA2-bound DNA sites. Here, we found that EBNALP can also modulate the activity of the key transcription activator EP300, an acetyltransferase that activates a broad range of transcription factors. Our data suggest that EBNALP regulates a much broader range of host genes than was previously appreciated. A small-molecule inhibitor of EP300 abolished EBNALP coactivation of multiple target genes. These findings suggest novel therapeutic approaches to control EBV-associated lymphoproliferative diseases.

Epstein-Barr virus super-enhancer eRNAs are essential for MYC oncogene expression and lymphoblast proliferation.

Epstein-Barr virus (EBV) super-enhancers (ESEs) are essential for lymphoblastoid cell (LCL) growth and survival. Reanalyses of LCL global run-on sequencing (Gro-seq) data found abundant enhancer RNAs (eRNAs) being transcribed at ESEs. Inactivation of ESE components, EBV nuclear antigen 2 (EBNA2) and bromodomain-containing protein 4 (BRD4), significantly decreased eRNAs at ESEs -428 and -525 kb upstream of the MYC oncogene transcription start site (TSS). shRNA knockdown of the MYC -428 and -525 ESE eRNA caused LCL growth arrest and reduced cell growth. Furthermore, MYC ESE eRNA knockdown also significantly reduced MYC expression, ESE H3K27ac signals, and MYC ESEs looping to MYC TSS. These data indicate that ESE eRNAs strongly affect cell gene expression and enable LCL growth.

Evasion of affinity-based selection in germinal centers by Epstein-Barr virus LMP2A.

Epstein-Barr virus (EBV) infects germinal center (GC) B cells and establishes persistent infection in memory B cells. EBV-infected B cells can cause B-cell malignancies in humans with T- or natural killer-cell deficiency. We now find that EBV-encoded latent membrane protein 2A (LMP2A) mimics B-cell antigen receptor (BCR) signaling in murine GC B cells, causing altered humoral immune responses and autoimmune diseases. Investigation of the impact of LMP2A on B-cell differentiation in mice that conditionally express LMP2A in GC B cells or all B-lineage cells found LMP2A expression enhanced not only BCR signals but also plasma cell differentiation in vitro and in vivo. Conditional LMP2A expression in GC B cells resulted in preferential selection of low-affinity antibody-producing B cells despite apparently normal GC formation. GC B-cell-specific LMP2A expression led to systemic lupus erythematosus-like autoimmune phenotypes in an age-dependent manner. Epigenetic profiling of LMP2A B cells found increased H3K27ac and H3K4me1 signals at the zinc finger and bric-a-brac, tramtrack domain-containing protein 20 locus. We conclude that LMP2A reduces the stringency of GC B-cell selection and may contribute to persistent EBV infection and pathogenesis by providing GC B cells with excessive prosurvival effects.

Epstein-Barr virus nuclear antigen 3A partially coincides with EBNA3C genome-wide and is tethered to DNA through BATF complexes.

Epstein-Barr Virus (EBV) conversion of B-lymphocytes to Lymphoblastoid Cell Lines (LCLs) requires four EBV nuclear antigen (EBNA) oncoproteins: EBNA2, EBNALP, EBNA3A, and EBNA3C. EBNA2 and EBNALP associate with EBV and cell enhancers, up-regulate the EBNA promoter, MYC, and EBV Latent infection Membrane Proteins (LMPs), which up-regulate BCL2 to protect EBV-infected B-cells from MYC proliferation-induced cell death. LCL proliferation induces p16(INK4A) and p14(ARF)-mediated cell senescence. EBNA3A and EBNA3C jointly suppress p16(INK4A) and p14(ARF), enabling continuous cell proliferation. Analyses of the EBNA3A human genome-wide ChIP-seq landscape revealed 37% of 10,000 EBNA3A sites to be at strong enhancers; 28% to be at weak enhancers; 4.4% to be at active promoters; and 6.9% to be at weak and poised promoters. EBNA3A colocalized with BATF-IRF4, ETS-IRF4, RUNX3, and other B-cell Transcription Factors (TFs). EBNA3A sites clustered into seven unique groups, with differing B-cell TFs and epigenetic marks. EBNA3A coincidence with BATF-IRF4 or RUNX3 was associated with stronger EBNA3A ChIP-Seq signals. EBNA3A was at MYC, CDKN2A/B, CCND2, CXCL9/10, and BCL2, together with RUNX3, BATF, IRF4, and SPI1. ChIP-re-ChIP revealed complexes of EBNA3A on DNA with BATF. These data strongly support a model in which EBNA3A is tethered to DNA through a BATF-containing protein complexes to enable continuous cell proliferation.

TRAF1 Coordinates Polyubiquitin Signaling to Enhance Epstein-Barr Virus LMP1-Mediated Growth and Survival Pathway Activation.

The Epstein-Barr virus (EBV) encoded oncoprotein Latent Membrane Protein 1 (LMP1) signals through two C-terminal tail domains to drive cell growth, survival and transformation. The LMP1 membrane-proximal TES1/CTAR1 domain recruits TRAFs to activate MAP kinase, non-canonical and canonical NF-kB pathways, and is critical for EBV-mediated B-cell transformation. TRAF1 is amongst the most highly TES1-induced target genes and is abundantly expressed in EBV-associated lymphoproliferative disorders. We found that TRAF1 expression enhanced LMP1 TES1 domain-mediated activation of the p38, JNK, ERK and canonical NF-kB pathways, but not non-canonical NF-kB pathway activity. To gain insights into how TRAF1 amplifies LMP1 TES1 MAP kinase and canonical NF-kB pathways, we performed proteomic analysis of TRAF1 complexes immuno-purified from cells uninduced or induced for LMP1 TES1 signaling. Unexpectedly, we found that LMP1 TES1 domain signaling induced an association between TRAF1 and the linear ubiquitin chain assembly complex (LUBAC), and stimulated linear (M1)-linked polyubiquitin chain attachment to TRAF1 complexes. LMP1 or TRAF1 complexes isolated from EBV-transformed lymphoblastoid B cell lines (LCLs) were highly modified by M1-linked polyubiqutin chains. The M1-ubiquitin binding proteins IKK-gamma/NEMO, A20 and ABIN1 each associate with TRAF1 in cells that express LMP1. TRAF2, but not the cIAP1 or cIAP2 ubiquitin ligases, plays a key role in LUBAC recruitment and M1-chain attachment to TRAF1 complexes, implicating the TRAF1:TRAF2 heterotrimer in LMP1 TES1-dependent LUBAC activation. Depletion of either TRAF1, or the LUBAC ubiquitin E3 ligase subunit HOIP, markedly impaired LCL growth. Likewise, LMP1 or TRAF1 complexes purified from LCLs were decorated by lysine 63 (K63)-linked polyubiqutin chains. LMP1 TES1 signaling induced K63-polyubiquitin chain attachment to TRAF1 complexes, and TRAF2 was identified as K63-Ub chain target. Co-localization of M1- and K63-linked polyubiquitin chains on LMP1 complexes may facilitate downstream canonical NF-kB pathway activation. Our results highlight LUBAC as a novel potential therapeutic target in EBV-associated lymphoproliferative disorders.

Epstein-Barr virus nuclear antigen 3C binds to BATF/IRF4 or SPI1/IRF4 composite sites and recruits Sin3A to repress CDKN2A.

Epstein-Barr virus nuclear antigen 3C (EBNA3C) repression of CDKN2A p14(ARF) and p16(INK4A) is essential for immortal human B-lymphoblastoid cell line (LCL) growth. EBNA3C ChIP-sequencing identified >13,000 EBNA3C sites in LCL DNA. Most EBNA3C sites were associated with active transcription; 64% were strong H3K4me1- and H3K27ac-marked enhancers and 16% were active promoters marked by H3K4me3 and H3K9ac. Using ENCODE LCL transcription factor ChIP-sequencing data, EBNA3C sites coincided (±250 bp) with RUNX3 (64%), BATF (55%), ATF2 (51%), IRF4 (41%), MEF2A (35%), PAX5 (34%), SPI1 (29%), BCL11a (28%), SP1 (26%), TCF12 (23%), NF-κB (23%), POU2F2 (23%), and RBPJ (16%). EBNA3C sites separated into five distinct clusters: (i) Sin3A, (ii) EBNA2/RBPJ, (iii) SPI1, and (iv) strong or (v) weak BATF/IRF4. EBNA3C signals were positively affected by RUNX3, BATF/IRF4 (AICE) and SPI1/IRF4 (EICE) cooccupancy. Gene set enrichment analyses correlated EBNA3C/Sin3A promoter sites with transcription down-regulation (P < 1.6 × 10(-4)). EBNA3C signals were strongest at BATF/IRF4 and SPI1/IRF4 composite sites. EBNA3C bound strongly to the p14(ARF) promoter through SPI1/IRF4/BATF/RUNX3, establishing RBPJ-, Sin3A-, and REST-mediated repression. EBNA3C immune precipitated with Sin3A and conditional EBNA3C inactivation significantly decreased Sin3A binding at the p14(ARF) promoter (P < 0.05). These data support a model in which EBNA3C binds strongly to BATF/IRF4/SPI1/RUNX3 sites to enhance transcription and recruits RBPJ/Sin3A- and REST/NRSF-repressive complexes to repress p14(ARF) and p16(INK4A) expression.

Epstein-Barr virus nuclear antigen leader protein localizes to promoters and enhancers with cell transcription factors and EBNA2.

Epstein-Barr virus (EBV) nuclear antigens EBNALP (LP) and EBNA2 (E2) are coexpressed in EBV-infected B lymphocytes and are critical for lymphoblastoid cell line outgrowth. LP removes NCOR and RBPJ repressive complexes from promoters, enhancers, and matrix-associated deacetylase bodies, whereas E2 activates transcription from distal enhancers. LP ChIP-seq analyses identified 19,224 LP sites of which ~50% were ± 2 kb of a transcriptional start site. LP sites were enriched for B-cell transcription factors (TFs), YY1, SP1, PAX5, BATF, IRF4, ETS1, RAD21, PU.1, CTCF, RBPJ, ZNF143, SMC3, NFκB, TBLR, and EBF. E2 sites were also highly enriched for LP-associated cell TFs and were more highly occupied by RBPJ and EBF. LP sites were highly marked by H3K4me3, H3K27ac, H2Az, H3K9ac, RNAPII, and P300, indicative of activated transcription. LP sites were 29% colocalized with E2 (LP/E2). LP/E2 sites were more similar to LP than to E2 sites in associated cell TFs, RNAPII, P300, and histone H3K4me3, H3K9ac, H3K27ac, and H2Az occupancy, and were more highly transcribed than LP or E2 sites. Gene affected by CTCF and LP cooccupancy were more highly expressed than genes affected by CTCF alone. LP was at myc enhancers and promoters and of MYC regulated ccnd2, 23 med complex components, and MYC regulated cell survival genes, igf2r and bcl2. These data implicate LP and associated TFs and DNA looping factors CTCF, RAD21, SMC3, and YY1/INO80 chromatin-remodeling complexes in repressor depletion and gene activation necessary for lymphoblastoid cell line growth and survival.

LipidGO: database for lipid-related GO terms and applications.

MOTIVATION: Lipid, an essential class of biomolecules, is receiving increasing attention in the research community, especially with the development of analytical technique like mass spectrometry. Gene Ontology (GO) is the de facto standard function annotation scheme for gene products. Identification of both explicit and implicit lipid-related GO terms will help lipid research in many ways, e.g. assigning lipid function in protein function prediction. RESULTS: We have constructed a Web site 'LipidGO' that facilitates browsing and searching lipid-related GO terms. An expandable hierarchical GO tree is constructed that allows users to find lipid-related GO terms easily. To support large-scale analysis, a user is able to upload a list of gene products or a list of GO terms to find out which of them is lipid related. Finally, we demonstrate the usefulness of 'LipidGO' by two applications: (i) identifying lipid-related gene products in model organisms and (ii) discovering potential novel lipid-related molecular functions AVAILABILITY AND IMPLEMENTATION: LipidGO is available at http://compbio.ddns.comp.nus.edu.sg/%7elipidgo/index.php.

Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

AFA: Ancestry-specific allele frequency estimation in admixed populations: The Hispanic Community Health Study/Study of Latinos.

Allele frequency estimates in admixed populations, such as Hispanics and Latinos, rely on the sample's specific admixture composition and thus may differ between two seemingly similar populations. However, ancestry-specific allele frequencies, i.e., pertaining to the ancestral populations of an admixed group, may be particularly useful for prioritizing genetic variants for genetic discovery and personalized genomic health. We developed a method, ancestry-specific allele frequency estimation in admixed populations (AFA), to estimate the frequencies of biallelic variants in admixed populations with an unlimited number of ancestries. AFA uses maximum-likelihood estimation by modeling the conditional probability of having an allele given proportions of genetic ancestries. It can be applied using either local ancestry interval proportions encompassing the variant (local-ancestry-specific allele frequency estimations in admixed populations [LAFAs]) or global proportions of genetic ancestries (global-ancestry-specific allele frequency estimations in admixed populations [GAFAs]), which are easier to compute and are more widely available. Simulations and comparisons to existing software demonstrated the high accuracy of the method. We implemented AFA on high-quality imputed data of ∼9,000 Hispanics and Latinos from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), an understudied, admixed population with three predominant continental ancestries: Amerindian, European, and African. Comparison of the European and African estimated frequencies to the respective gnomAD frequencies demonstrated high correlations (Pearson R2 = 0.97-0.99). We provide a genome-wide dataset of the estimated ancestry-specific allele frequencies for available variants with allele frequency between 5% and 95% in at least one of the three ancestral populations. Association analysis of Amerindian-enriched variants with cardiometabolic traits identified five loci associated with lipid traits in Hispanics and Latinos, demonstrating the utility of ancestry-specific allele frequencies in admixed populations.