Identify Risk Genes Research Articles

A convergent molecular network underlying autism and congenital heart disease.

Patients with neurodevelopmental disorders, including autism, have an elevated incidence of congenital heart disease, but the extent to which these conditions share molecular mechanisms remains unknown. Here, we use network genetics to identify a convergent molecular network underlying autism and congenital heart disease. This network is impacted by damaging genetic variants from both disorders in multiple independent cohorts of patients, pinpointing 101 genes with shared genetic risk. Network analysis also implicates risk genes for each disorder separately, including 27 previously unidentified genes for autism and 46 for congenital heart disease. For 7 genes with shared risk, we create engineered disruptions in Xenopus tropicalis, confirming both heart and brain developmental abnormalities. The network includes a family of ion channels, such as the sodium transporter SCN2A, linking these functions to early heart and brain development. This study provides a road map for identifying risk genes and pathways involved in co-morbid conditions.

Genome-wide association study followed by trans-ancestry meta-analysis identify 17 new risk loci for schizophrenia

BackgroundOver 200 schizophrenia risk loci have been identified by genome-wide association studies (GWASs). However, the majority of risk loci were identified in populations of European ancestry (EUR), potentially missing important biological insights. It is important to perform 5 GWASs in non-European populations.MethodsTo identify novel schizophrenia risk loci, we conducted a GWAS in Han Chinese population (3493 cases and 4709 controls). We then performed a large-scale meta-analysis (a total of 143,438 subjects) through combining our results with previous GWASs conducted in EAS and EUR. In addition, we also carried out comprehensive post-GWAS analysis, including heritability partitioning, enrichment of schizophrenia associations in tissues and cell types, trancscriptome-wide association study (TWAS), expression quantitative trait loci (eQTL) and differential expression analysis.ResultsWe identified two new schizophrenia risk loci, including associations in SHISA9 (rs7192086, P = 4.92 × 10-08) and PES1 (rs57016637, P = 2.33 × 10−11) in Han Chinese population. A fixed-effect meta-analysis (a total of 143,438 subjects) with summary statistics from EAS and EUR identifies 15 novel genome-wide significant risk loci. Heritability partitioning with linkage disequilibrium score regression (LDSC) reveals a significant enrichment of schizophrenia heritability in conserved genomic regions, promoters, and enhancers. Tissue and cell-type enrichment analyses show that schizophrenia associations are significantly enriched in human brain tissues and several types of neurons, including cerebellum neurons, telencephalon inhibitory, and excitatory neurons. Polygenic risk score profiling reveals that GWAS summary statistics from trans-ancestry meta-analysis (EAS + EUR) improves prediction performance in predicting the case/control status of our sample. Finally, transcriptome-wide association study (TWAS) identifies risk genes whose cis-regulated expression change may have a role in schizophrenia.ConclusionsOur study identifies 17 novel schizophrenia risk loci and highlights the importance and necessity of conducting genetic study in different populations. These findings not only provide new insights into genetic etiology of schizophrenia, but also facilitate to delineate the pathophysiology of schizophrenia and develop new therapeutic targets.

PCLasso: a protein complex-based, group lasso-Cox model for accurate prognosis and risk protein complex discovery.

For high-dimensional expression data, most prognostic models perform feature selection based on individual genes, which usually lead to unstable prognosis, and the identified risk genes are inherently insufficient in revealing complex molecular mechanisms. Since most genes carry out cellular functions by forming protein complexes-basic representatives of functional modules, identifying risk protein complexes may greatly improve our understanding of disease biology. Coupled with the fact that protein complexes have been shown to have innate resistance to batch effects and are effective predictors of disease phenotypes, constructing prognostic models and selecting features with protein complexes as the basic unit should improve the robustness and biological interpretability of the model. Here, we propose a protein complex-based, group lasso-Cox model (PCLasso) to predict patient prognosis and identify risk protein complexes. Experiments on three cancer types have proved that PCLasso has better prognostic performance than prognostic models based on individual genes. The resulting risk protein complexes not only contain individual risk genes but also incorporate close partners that synergize with them, which may promote the revealing of molecular mechanisms related to cancer progression from a comprehensive perspective. Furthermore, a pan-cancer prognostic analysis was performed to identify risk protein complexes of 19 cancer types, which may provide novel potential targets for cancer research.

Analysis of whole genome sequenced cases and controls shows that the association of variants in TOMM40, BCAM, NECTIN2 and APOC1 with late onset Alzheimer’s disease is driven by linkage disequilibrium with APOE ε2/ε3/ε4 alleles

Variants in APOE are associated with risk of late onset Alzheimer's disease (LOAD) but the magnitude of the effect has been reported to vary across ancestries. Also, other variants in the region have been reported to show association though it has been unclear whether this was secondary to their linkage disequilibrium with the APOE variants rs429358 and rs7412. Previous analyses of exome-sequenced samples have identified other genes in which rare variants impact risk of disease. In this study 2000 whole genome sequenced cases and controls with different ancestries were subjected to gene-based weighted burden analysis to identify risk genes. Additionally, individual variants in the APOE region were tested for association with LOAD. When using the APOE variants as covariates no individual genes showed statistically significant evidence for association after Bonferroni correction for multiple testing, which may well be a consequence of the modest sample size. Likewise, for those variants initially showing evidence of association with LOAD incorporating the APOE variants as covariates dramatically reduced the strength of association. These results demonstrate that the differential association of APOE across ancestries does not appear to be driven by another variant in the region. It seems likely that no other genes in the region have a direct effect on LOAD risk.

Genetic and epigenetic interplay fine-tunes expression of TGFB1, encoding TGF-Β1, contributing to osteoarthritis risk

Genetic and epigenetic interplay fine-tunes expression of TGFB1, encoding TGF-Β1, contributing to osteoarthritis risk

DECO: a framework for jointly analyzing de novo and rare case/control variants, and biological pathways.

Rare variant-based analyses are beginning to identify risk genes for neuropsychiatric disorders and other diseases. However, the identified genes only account for a fraction of predicted causal genes. Recent studies have shown that rare damaging variants are significantly enriched in specific gene-sets. Methods which are able to jointly model rare variants and gene-sets to identify enriched gene-sets and use these enriched gene-sets to prioritize additional risk genes could improve understanding of the genetic architecture of diseases. We propose DECO (Integrated analysis of de novo mutations, rare case/control variants and omics information via gene-sets), an integrated method for rare-variant and gene-set analysis. The method can (i) test the enrichment of gene-sets directly within the statistical model, and (ii) use enriched gene-sets to rank existing genes and prioritize additional risk genes for tested disorders. In simulations, DECO performs better than a homologous method that uses only variant data. To demonstrate the application of the proposed protocol, we have applied this approach to rare-variant datasets of schizophrenia. Compared with a method which only uses variant information, DECO is able to prioritize additional risk genes. DECO can be used to analyze rare-variants and biological pathways or cell types for any disease. The package is available on Github https://github.com/hoangtn/DECO.

E-MAGMA: an eQTL-informed method to identify risk genes using genome-wide association study summary statistics.

Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with human traits and diseases, but the exact causal genes are largely unknown. Common genetic risk variants are enriched in non-protein-coding regions of the genome and often affect gene expression (expression quantitative trait loci, eQTL) in a tissue-specific manner. To address this challenge, we developed a methodological framework, E-MAGMA, which converts genome-wide association summary statistics into gene-level statistics by assigning risk variants to their putative genes based on tissue-specific eQTL information. We compared E-MAGMA to three eQTL informed gene-based approaches using simulated phenotype data. Phenotypes were simulated based on eQTL reference data using GCTA for all genes with at least one eQTL at chromosome 1. We performed 10 simulations per gene. The eQTL-h2 (i.e. the proportion of variation explained by the eQTLs) was set at 1%, 2% and 5%. We found E-MAGMA outperforms other gene-based approaches across a range of simulated parameters (e.g. the number of identified causal genes). When applied to genome-wide association summary statistics for five neuropsychiatric disorders, E-MAGMA identified more putative candidate causal genes compared to other eQTL-based approaches. By integrating tissue-specific eQTL information, these results show E-MAGMA will help to identify novel candidate causal genes from genome-wide association summary statistics and thereby improve the understanding of the biological basis of complex disorders. A tutorial and input files are made available in a github repository: https://github.com/eskederks/eMAGMA-tutorial. Supplementary data are available at Bioinformatics online.

Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation.

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the 'Rare Variant Genome Wide Association Study' (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.

Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders

Most genes associated with neurodevelopmental disorders (NDDs) were identified with an excess of de novo mutations (DNMs) but the significance in case–control mutation burden analysis is unestablished. Here, we sequence 63 genes in 16,294 NDD cases and an additional 62 genes in 6,211 NDD cases. By combining these with published data, we assess a total of 125 genes in over 16,000 NDD cases and compare the mutation burden to nonpsychiatric controls from ExAC. We identify 48 genes (25 newly reported) showing significant burden of ultra-rare (MAF < 0.01%) gene-disruptive mutations (FDR 5%), six of which reach family-wise error rate (FWER) significance (p < 1.25E−06). Among these 125 targeted genes, we also reevaluate DNM excess in 17,426 NDD trios with 6,499 new autism trios. We identify 90 genes enriched for DNMs (FDR 5%; e.g., GABRG2 and UIMC1); of which, 61 reach FWER significance (p < 3.64E−07; e.g., CASZ1). In addition to doubling the number of patients for many NDD risk genes, we present phenotype–genotype correlations for seven risk genes (CTCF, HNRNPU, KCNQ3, ZBTB18, TCF12, SPEN, and LEO1) based on this large-scale targeted sequencing effort.

Identifying Risk Genes and Interpreting Pathogenesis for Parkinson's Disease by a Multiomics Analysis.

Genome-wide association studies (GWAS) have identified tens of genetic variants associated with Parkinson’s disease (PD). Nevertheless, the genes or DNA elements that affect traits through these genetic variations are usually undiscovered. This study was the first to combine meta-analysis GWAS data and expression data to identify PD risk genes. Four known genes, CRHR1, KANSL1, NSF and LRRC37A, and two new risk genes, STX4 and BST1, were identified. Among them, CRHR1 is a known drug target, indicating that hydrocortisone may become a potential drug for the treatment of PD. Furthermore, the potential pathogenesis of CRHR1 and LRRC37A was explored by applying DNA methylation (DNAm) data, indicating a pathogenesis whereby the effect of a genetic variant on PD is mediated by genetic regulation of transcription through DNAm. Overall, this research identified the risk genes and pathogenesis that affect PD through genetic variants, which has significance for the diagnosis and treatment of PD.

Integration of genetic variants and gene network for drug repurposing in colorectal cancer

Even though many genetic risk loci for human diseases have been identified and comprehensively cataloged, strategies to guide clinical research by integrating the extensive results of genetic studies and biological resources are still limited. Moreover, integrative analyses that provide novel insights into disease biology are expected to be especially useful for drug discovery. Herein, we used text mining of genetic studies on colorectal cancer (CRC) and assigned biological annotations to identified risk genes in order to discover novel drug targets and potential drugs for repurposing. Risk genes for CRC were obtained from PubMed text mining, and for each gene, six functional and bioinformatic annotations were analyzed. The annotations include missense mutations, cis-expression quantitative trait loci (cis-eQTL), molecular pathway analyses, protein-protein interactions (PPIs), a genetic overlap with knockout mouse phenotypes, and primary immunodeficiency (PID). We then prioritized the biological risk candidate genes according to a scoring system of the six functional annotations. Each functional annotation was assigned one point, and those genes with a score ≥2 were designated “biological CRC risk genes”. Using this method, we revealed 82 biological CRC risk genes, which were mapped to 128 genes in an expanded PPI network. Further utilizing DrugBank and the Therapeutic Target Database, we found 21 genes in our list that are targeted by 166 candidate drugs. Based on data from ClinicalTrials.gov and literature review, we found four known target genes with six drugs for clinical treatment in CRC, and three target genes with nine drugs supported by previous preclinical results in CRC. Additionally, 12 genes are targeted by 32 drugs approved for other indications, which can possibly be repurposed for CRC treatment. Finally, analysis from Connectivity Map (CMap) showed that 18 drugs have a high potential for CRC.

Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma

BackgroundChildhood-onset asthma is highly affected by genetic components. In recent years, many genome-wide association studies (GWAS) have reported a large group of genetic variants and susceptible genes associated with asthma-related phenotypes including childhood-onset asthma. However, the regulatory mechanisms of these genetic variants for childhood-onset asthma susceptibility remain largely unknown.MethodsIn the current investigation, we conducted a two-stage designed Sherlock-based integrative genomics analysis to explore the cis- and/or trans-regulatory effects of genome-wide SNPs on gene expression as well as childhood-onset asthma risk through incorporating a large-scale GWAS data (N = 314,633) and two independent expression quantitative trait loci (eQTL) datasets (N = 1890). Furthermore, we applied various bioinformatics analyses, including MAGMA gene-based analysis, pathway enrichment analysis, drug/disease-based enrichment analysis, computer-based permutation analysis, PPI network analysis, gene co-expression analysis and differential gene expression analysis, to prioritize susceptible genes associated with childhood-onset asthma.ResultsBased on comprehensive genomics analyses, we found 31 genes with multiple eSNPs to be convincing candidates for childhood-onset asthma risk; such as, PSMB9 (cis-rs4148882 and cis-rs2071534) and TAP2 (cis-rs9267798, cis-rs4148882, cis-rs241456, and trans-10,447,456). These 31 genes were functionally interacted with each other in our PPI network analysis. Our pathway enrichment analysis showed that numerous KEGG pathways including antigen processing and presentation, type I diabetes mellitus, and asthma were significantly enriched to involve in childhood-onset asthma risk. The co-expression patterns among 31 genes were remarkably altered according to asthma status, and 25 of 31 genes (25/31 = 80.65%) showed significantly or suggestively differential expression between asthma group and control group.ConclusionsWe provide strong evidence to highlight 31 candidate genes for childhood-onset asthma risk, and offer a new insight into the genetic pathogenesis of childhood-onset asthma.

Leveraging genetic data to investigate molecular targets and drug repurposing candidates for treating alcohol use disorder and hepatotoxicity

BackgroundNovel treatments for alcohol use disorder (AUD) and alcohol-related liver disease (ALD) are greatly needed. Genetic information can improve drug discovery rates by facilitating the identification of novel biological targets and potential drugs for repurposing. MethodsThe present study utilized a recently developed Bayesian approach, Integrative Risk Gene Selector (iRIGS), to identify additional risk genes for alcohol consumption using SNPs from the largest alcohol consumption GWAS to date (N = 941,280). iRIGS incorporates several genomic features and closeness of these genes in network space to compute a posterior probability for protein coding genes near each SNP. We subsequently used the Target Central Resource Database to search for drug-protein interactions for these newly identified genes and previously identified risk genes for alcohol consumption. ResultsWe identified several genes that are novel contributions to the previously published alcohol consumption GWAS. Namely, ACVR2A, which is critical for liver function and linked to anxiety and cocaine self-administration, and PRKCE, which has been linked to alcohol self-administration. Notably, only a minority of the SNPs (18.4 %) were linked to genes with confidence (>0.75), underscoring the need to apply multiple methods to assign function to loci. Finally, some previously identified risk genes for alcohol consumption code for proteins that are implicated in liver function and are targeted by drugs, some of which are candidates for managing hepatotoxicity. ConclusionsThis study demonstrates the value of incorporating regulatory information and drug-protein interaction data to highlight additional molecular targets and drug repurposing candidates for treating AUD and ALD.

MTADA is a framework for identifying risk genes from de novo mutations in multiple traits

Joint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. Studies of neuropsychiatric disorders and congenital heart disease (CHD) which use de novo mutations (DNMs) from parent-offspring trios have reported multiple putatively causal genes. However, a joint analysis method designed to integrate DNMs from multiple studies has yet to be implemented. We here introduce multiple-trait TADA (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. We first demonstrate that mTADA is able to leverage genetic overlaps to increase the statistical power of risk-gene identification. We then apply mTADA to large datasets of >13,000 trios for five neuropsychiatric disorders and CHD. We report additional risk genes for schizophrenia, epileptic encephalopathies and CHD. We outline some shared and specific biological information of intellectual disability and CHD by conducting systems biology analyses of genes prioritized by mTADA.

Group Sparse Joint Non-Negative Matrix Factorization on Orthogonal Subspace for Multi-Modal Imaging Genetics Data Analysis.

With the development of multi-model neuroimaging technology and gene detection technology, the efforts of integrating multi-model imaging genetics data to explore the virulence factors of schizophrenia (SZ) are still limited. To address this issue, we propose a novel algorithm called group sparse of joint non-negative matrix factorization on orthogonal subspace (GJNMFO). Our algorithm fuses single nucleotide polymorphism (SNP) data, function magnetic resonance imaging (fMRI) data and epigenetic factors (DNA methylation) by projecting three-model data into a common basis matrix and three different coefficient matrices to identify risk genes, epigenetic factors and abnormal brain regions associated with SZ. Specifically, we introduce orthogonal constraints on the basis matrix to discard unimportant features in the row of coefficient matrices. Since imaging genetics data have rich group information, we draw into group sparse on three coefficient matrices to make the extracted features more accurate. Both the simulated and real Mind Clinical Imaging Consortium (MCIC) datasets are performed to validate our approach. Simulation results show that our algorithm works better than other competing methods. Through the experiments of MCIC datasets, GJNMFO reveals a set of risk genes, epigenetic factors and abnormal brain functional regions, which have been verified to be both statistically and biologically significant.

De novo variants in exomes of congenital heart disease patients identify risk genes and pathways

BackgroundCongenital heart disease (CHD) affects ~ 1% of live births and is the most common birth defect. Although the genetic contribution to the CHD has been long suspected, it has only been well established recently. De novo variants are estimated to contribute to approximately 8% of sporadic CHD.MethodsCHD is genetically heterogeneous, making pathway enrichment analysis an effective approach to explore and statistically validate CHD-associated genes. In this study, we performed novel gene and pathway enrichment analyses of high-impact de novo variants in the recently published whole-exome sequencing (WES) data generated from a cohort of CHD 2645 parent-offspring trios to identify new CHD-causing candidate genes and mutations. We performed rigorous variant- and gene-level filtrations to identify potentially damaging variants, followed by enrichment analyses and gene prioritization.ResultsOur analyses revealed 23 novel genes that are likely to cause CHD, including HSP90AA1, ROCK2, IQGAP1, and CHD4, and sharing biological functions, pathways, molecular interactions, and properties with known CHD-causing genes.ConclusionsUltimately, these findings suggest novel genes that are likely to be contributing to CHD pathogenesis.

Transcriptomic analyses of humans and mice provide insights into depression.

Accumulating studies have been conducted to identify risk genes and relevant biological mechanisms underlying major depressive disorder (MDD). In particular, transcriptomic analyses in brain regions engaged in cognitive and emotional processes, e.g., the dorsolateral prefrontal cortex (DLPFC), have provided essential insights. Based on three independent DLPFC RNA-seq datasets of 79 MDD patients and 75 healthy controls, we performed differential expression analyses using two alternative approaches for cross-validation. We also conducted transcriptomic analyses in mice undergoing chronic variable stress (CVS) and chronic social defeat stress (CSDS). We identified 12 differentially expressed genes (DEGs) through both analytical methods in MDD patients, the majority of which were also dysregulated in stressed mice. Notably, the mRNA level of the immediate early gene FOS (Fos proto-oncogene) was significantly decreased in both MDD patients and CVS-exposed mice, and CSDS-susceptible mice exhibited a greater reduction in Fos expression compared to resilient mice. These findings suggest the potential key roles of this gene in the pathogenesis of MDD related to stress exposure. Altered transcriptomes in the DLPFC of MDD patients might be, at least partially, the result of stress exposure, supporting that stress is a primary risk factor for MDD.

Development and verification of a nomogram for prediction of recurrence-free survival in clear cell renal cell carcinoma.

Nowadays, gene expression profiling has been widely used in screening out prognostic biomarkers in numerous kinds of carcinoma. Our studies attempt to construct a clinical nomogram which combines risk gene signature and clinical features for individual recurrent risk assessment and offer personalized managements for clear cell renal cell carcinoma. A total of 580 differentially expressed genes (DEGs) were identified via microarray. Functional analysis revealed that DEGs are of fundamental importance in ccRCC progression and metastasis. In our study, 338 ccRCC patients were retrospectively analysed and a risk gene signature which composed of 5 genes was obtained from a LASSO Cox regression model. Further analysis revealed that identified risk gene signature could usefully distinguish the patients with poor prognosis in training cohort (hazard ratio [HR] = 3.554, 95% confidence interval [CI] 2.261‐7.472, P < .0001, n = 107). Moreover, the prognostic value of this gene‐signature was independent of clinical features (P = .002). The efficacy of risk gene signature was verified in both internal and external cohorts. The area under receiver operating characteristic curve of this signature was 0.770, 0.765 and 0.774 in the training, testing and external validation cohorts, respectively. Finally, a nomogram was developed for clinicians and did well in the calibration plots. This nomogram based on risk gene signature and clinical features might provide a practical way for recurrence prediction and facilitating personalized managements of ccRCC patients after surgery.

Identification of New Risk Loci and Regulatory Mechanisms Influencing Genetic Susceptibility to Acute Lymphoblastic Leukaemia

There is increasing evidence for an inherited genetic basis of susceptibility to acute lymphoblastic leukaemia (ALL) in children. In particular, genome-wide association studies (GWAS) have identified nine genomic regions with common polymorphisms associated with ALL risk. However, these loci cumulatively only explain a minority of the genetic risk of childhood ALL, and differences in the etiology amongst molelcular subtypes of ALL remain poorly understood. In this study, we performed one of the largest ALL susceptibility GWAS, totalling 5,321 cases and 16,666 controls of European descent, to comprehensively map ALL risk loci. With this large sample size, we also sought to identify risk genes for two major subtypes of ALL, namely those with ETV6-RUNX1 fusion and hyperdiploid ALL. This meta-analysis included four previously published GWAS datasets with European cases from the UK ALL cohorts and the German Berlin-Frankfurt-Münster ALL trials, and the US cases enrolled on ALL trials in Children's Oncology Group and at St. Jude Children's Research Hospital (Nat Genet2009, Blood 2012, Nat Genet 2013, Blood 2013, Nat Commun 2018). The replication series included 2,237 ALL cases of non-European ancestry from the COG and St. Jude GWAS cohorts. For each GWAS dataset, the genotypes of ~10 million SNPs in each study were imputed. After filtering out SNPs on the basis of minor allele frequency and imputation quality, we assessed associations between ALL status and SNP genotype in each study using logistic regression. Risk estimates were combined through an inverse variance weighted fixed-effects meta-analysis. The meta-analysis identified 16 risk loci above genome-wide significance (P &amp;lt; 5 x 10-8), of which ten confirmed previously published associations. Of the six new candidate risk loci, four were validated in the replication series (P &amp;lt; 0.05): for all B-ALL at 9q21.3 (nearest gene TLE1), for hyperdiploid ALL at 5q31.1 (C5orf56) and 6p21.31 (BAK1), and for ETV6-RUNX1 positive ALL at 17q21.32 (IGF2BP1). As well as providing further evidence for the 21q22.3 (ERG) association for all B-ALL, we also identified novel risk variants with the hyperdiploid subtype-specific association. To gain insight into the biological basis of association signals, we examined the epigenetic landscape of risk regions in B-cells. Of particular note, hyperdiploid ALL-specific risk variant at 6p21 is located within an intron 1kb downstream of the BAK1 transcription start site and possess histone marks characteristic of active promoter activity and open chromatin accessibility. The top SNP at this locus falls within a transcription factor binding cluster, and the C-risk allele is associated with reduced BAK1 expression (P&amp;lt;2×10-16). Mendelian randomization analysis confirmed a significant association with BAK1 expression and ALL consistent with a causal relationship. Similarly, the ETV6-RUNX1-specific risk variant at 17q12 is located in the second intron of IGF2BP1 and is predicted to influence topological associated domain structure in cis. Interestingly, ETV6-RUNX1 positive ALL leukemia cells showed a dramatic overexpression of IGF2BP1 compared to those without this fusion gene (P=3.7×10-23), suggesting its potential role in the pathogenesis of this ALL subtype. Analysis of disruption of transcription factor binding sites at ALL risk loci genome wide identified over-representation of PBX1 (P=0.007), TCF3 (P=0.007), ETS1 (P=0.009), RUNX1 (P=0.012) and ERG (P=0.030), BRD4 (P=0.007), and NR3C1 (P=0.009). Many of these transcription factors are also somatically mutated in ALL, pointing to multiple mechanisms by which they can contribute to leukemogenesis. Using the Linkage Disequilibrium Adjusted Kinships method, we estimated that ALL risk loci identified so far accounted for 31% of the total variance in genetic risk of this cancer. Polygenic risk score analysis indicates that an individual in the top 1% of genetic risk would have a 4.7-fold increased risk of ALL when compared to an individual with median genetic risk. In summary, our study provides further evidence for inherited susceptibility to ALL and support for subtype specificity at risk loci. Our findings also highlight a model of ALL pathogenesis relying on transcriptional deregulation, particularly of genes involved in B cell differentiation. Disclosures Raetz: Pfizer: Research Funding. Mullighan:Pfizer: Honoraria, Other: speaker, sponsored travel, Research Funding; AbbVie: Research Funding; Loxo Oncology: Research Funding; Amgen: Honoraria, Other: speaker, sponsored travel; Illumina: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: sponsored travel. Hunger:Amgen: Consultancy, Equity Ownership; Bristol Myers Squibb: Consultancy; Jazz: Honoraria; Novartis: Consultancy. Schrappe:Together with study group from SHIRE, JazzPharma, Servier, SigmaTau, Amgen, and Novartis.: Research Funding; SHIRE, Servier, and JazzPharma: Honoraria. Relling:Servier Pharmaceuticals: Research Funding. Loh:Medisix Therapeutics, Inc.: Membership on an entity's Board of Directors or advisory committees.

Scientists Identify Risk Genes that Regulate Peripheral Immune Cells and Microglia in Multiple Sclerosis

Scientists Identify Risk Genes that Regulate Peripheral Immune Cells and Microglia in Multiple Sclerosis