Candidate Causal Single Nucleotide Polymorphisms Research Articles

Using Functional Genomic Data in Monocytes/Macrophages and Genotyping to Nominate Disease-Driving Single Nucleotide Polymorphisms and Target Genes in Juvenile Idiopathic Arthritis.

GWAS have identified multiple regions that confer risk for juvenile idiopathic arthritis (JIA). However, identifying the single nucleotide polymorphisms (SNPs) that drive disease risk is impeded by the SNPs' that identify risk loci being in linkage disequilibrium (LD) with hundreds of other SNPs. Since the causal SNPs remain unknown, it is difficult to identify target genes and use genetic information to inform patient care. We used genotyping and functional data in primary human monocytes/macrophages to nominate disease-driving SNPs on JIA risk haplotypes and identify their likely target genes. We identified JIA risk haplotypes using Immunochip data from Hinks et al (Nature Gen 2013) and the meta-analysis from McIntosh et al (Arthritis Rheum 2017). We used genotyping data from 3,939 children with JIA and 14,412 healthy controls to identify SNPs that: (1) were situated within open chromatin in multiple immune cell types and (2) were more common in children with JIA than the controls (p< 0.05). We intersected the chosen SNPs (n=846) with regions of bi-directional transcription initiation characteristic of non-coding regulatory regions detected using dREG to analyze GRO-seq data. Finally, we used MicroC data to identify gene promoters interacting with the regulatory regions harboring the candidate causal SNPs. We identified 190 SNPs that overlap with dREG peaks in monocytes and126 SNPs that overlap with dREG peaks in macrophages. Of these SNPs, 101 were situated within dREG peaks in both monocytes and macrophages, suggesting that these SNPs exert their effects independent of the cellular activation state. MicroC data in monocytes identified 20 genes/transcripts whose promoters interact with the enhancers harboring the SNPs of interest. SNPs in JIA risk regions that are candidate causal variants can be further screened using functional data such as GRO-seq. This process identifies a finite number of candidate causal SNPs, the majority of which are likely to exert their biological effects independent of cellular activation state in monocytes. Three-dimensional chromatin data generated with MicroC identifies genes likely to be influenced by these SNPs. These studies demonstrate the importance of investigations into the role of innate immunity in JIA.

Functional analysis of low-grade glioma genetic variants predicts key target genes and transcription factors.

Large-scale genome-wide association studies (GWAS) have implicated thousands of germline genetic variants in modulating individuals' risk to various diseases, including cancer. At least 25 risk loci have been identified for low-grade gliomas (LGGs), but their molecular functions remain largely unknown. We hypothesized that GWAS loci contain causal single nucleotide polymorphisms (SNPs) that reside in accessible open chromatin regions and modulate the expression of target genes by perturbing the binding affinity of transcription factors (TFs). We performed an integrative analysis of genomic and epigenomic data from The Cancer Genome Atlas and other public repositories to identify candidate causal SNPs within linkage disequilibrium blocks of LGG GWAS loci. We assessed their potential regulatory role via in silico TF binding sequence perturbations, convolutional neural network trained on TF binding data, and simulated annealing-based interpretation methods. We built an interactive website (http://education.knoweng.org/alg3/) summarizing the functional footprinting of 280 variants in 25 LGG GWAS regions, providing rich information for further computational and experimental scrutiny. We identified as case studies PHLDB1 and SLC25A26 as candidate target genes of rs12803321 and rs11706832, respectively, and predicted the GWAS variant rs648044 to be the causal SNP modulating ZBTB16, a known tumor suppressor in multiple cancers. We showed that rs648044 likely perturbed the binding affinity of the TF MAFF, as supported by RNA interference and in vitro MAFF binding experiments. The identified candidate (causal SNP, target gene, TF) triplets and the accompanying resource will help accelerate our understanding of the molecular mechanisms underlying genetic risk factors for gliomas.

Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics.

SummaryHere, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole‐genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single‐nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1‐Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene‐centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss‐of‐function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.

Bayesian variable selection using partially observed categorical prior information in fine-mapping association studies.

Several methods have been proposed to allow functional genomic information to inform prior distributions in Bayesian fine-mapping case-control association studies. None of these methods allow the inclusion of partially observed functional genomic information. We use functional significance (FS) scores that combine information across multiple bioinformatics sources to inform our effect size prior distributions. These scores are not available for all single-nucleotide polymorphisms (SNPs) but by partitioning SNPs into naturally occurring FS score groups, we show how missing FS scores can easily be accommodated via finite mixtures of elicited priors. Most current approaches adopt a formal Bayesian variable selection approach and either limit the number of causal SNPs allowed or use approximations to avoid the need to explore the vast parameter space. We focus instead on achieving differential shrinkage of the effect sizes through prior scale mixtures of normals and use marginal posterior probability intervals to select candidate causal SNPs. We show via a simulation study how this approach can improve localisation of the causal SNPs compared to existing mutli-SNP fine-mapping methods. We also apply our approach to fine-mapping a region around the CASP8 gene using the iCOGS consortium breast cancer SNP data.

1697-P: Functional Impact of Type 1 Diabetes Risk Variants Identified in African- and European-Ancestry Populations

Genetic risk is the earliest measurable contributor to diseases such as type 1 diabetes (T1D). Multiple groups have demonstrated that genetic (or polygenic) risk scores can quantify individual disease risks in a clinically significant way for complex diseases such as T1D. To date, genetic studies of T1D have been conducted primarily in European-ancestry populations and have identified over 40 susceptibility regions. Genetic risk estimates for T1D have been predominantly calculated from these findings. At the same time, there is also strong evidence that there are limitations in transferability of risk estimates from European-ancestry populations to African ancestry populations. Thus, establishing a better understanding of the genetic architecture of T1D in different populations is critical for appropriate applications of risk scores and eliminating health disparities. Aiming to close this gap in knowledge, we conducted the largest T1D association study in African-ancestry participants to date. Single nucleotide polymorphisms (SNPs) in the 12q13.2 region were associated with T1D in European- and African-ancestry populations. We used comparative mapping in the 12q13.2 region to fine-map this physically large and gene-rich T1D risk locus coupled with functional studies. Twelve SNPs (of 22 previously identified European credible SNPs) were nominally (P &amp;lt; 10-4) associated with T1D in African-ancestry populations. Using bioinformatics and ENCODE data, we identified four putative candidate causal SNPs. Using functional assays, we confirmed allele-specific protein binding for two of the 12q13.2 SNPs near RPS26 and IKZF4. While biological influence and detailed functional role of each risk variant will need to be characterized in future functional studies, incorporation of SNPs that capture genetic risk in genetically diverse populations provides insights into causality. Disclosure S. Onengut: None. H. Yang: None. R.R. Pickin: None. S.S. Rich: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases

Genome-wide association pathway analysis to identify candidate single nucleotide polymorphisms and molecular pathways associated with TP53 expression status in HBV-related hepatocellular carcinoma.

BackgroundThe aim of this investigation was to identify candidate single nucleotide polymorphisms (SNPs) and molecular pathways associated with tumor protein p53 (TP53) expression status in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), clarify their potential mechanisms, and generate SNP-to-gene to pathway hypothesis.Materials and methodsIdentify candidate Causal SNPs and Pathways (ICSNPathway) was used to perform pathway analysis based on the results of our previous genome-wide association study of TP53 expression status in 387 HBV-related HCC patients.ResultsThrough the ICSNPathway analysis, we identified 18 candidate SNPs and 10 candidate pathways that are associated with TP53 expression status in HBV-related HCC. The strongest mechanism involved the modulation of major histocompatibility complex, class II, DP beta 1 (human leukocyte antigen [HLA]-DPB1-rs1042153), major histocompatibility complex, class II, DQ beta 1 (HLA-DQB1-rs1130399, HLA-DQB1-rs1049056, HLA-DQB1-rs1049059, and HLA-DQB1-rs1049060), and major histocompatibility complex, class II, DR beta 1 (HLA-DRB1-rs35445101). SNPs consequently affected regulatory roles in all the candidate pathways except hematopoietic cell lineage pathways. Association analysis using the GSE14520 data set, Gene Multiple Association Network Integration Algorithm, and Search Tool for the Retrieval of Interacting Genes/Proteins suggests that all genes of the candidate SNPs were associated with TP53. Survival analysis showed that the collagen type VI alpha 3 chain (COL6A3) rs111231885 and COL6A3-rs113155945 and COL6A3 block 4 CC haplotypes with TP53 negative status may have protective effects in HBV-related HCC patients after hepatectomy.ConclusionOur pathway analysis identified 18 candidate SNPs and 10 candidate pathways that were associated with TP53 expression status in HBV-related HCC. Among these candidate SNPs, the genetic variation of COL6A3 may be a potential prognostic biomarker of HBV-related HCC.

Pathway-based analysis of genome-wide association study of circadian phenotypes.

Sleepiness affects normal social life, which attracts more and more attention. Circadian phenotypes contribute to obvious individual differences in susceptibility to sleepiness. We aimed to identify candidate single nucleotide polymorphisms (SNPs) which may cause circadian phenotypes, elucidate the potential mechanisms, and generate corresponding SNP-gene-pathways. A genome-wide association studies (GWAS) dataset of circadian phenotypes was utilized in the study. Then, the Identify Candidate Causal SNPs and Pathways analysis was employed to the GWAS dataset after quality control filters. Furthermore, genotype-phenotype association analysis was performed with HapMap database. Four SNPs in three different genes were determined to correlate with usual weekday bedtime, totally providing seven hypothetical mechanisms. Eleven SNPs in six genes were identified to correlate with usual weekday sleep duration, which provided six hypothetical pathways. Our results demonstrated that fifteen candidate SNPs in eight genes played vital roles in six hypothetical pathways implicated in usual weekday bedtime and six potential pathways involved in usual weekday sleep duration.

Identification of breast cancer associated variants that modulate transcription factor binding.

Genome-wide association studies (GWAS) have discovered thousands loci associated with disease risk and quantitative traits, yet most of the variants responsible for risk remain uncharacterized. The majority of GWAS-identified loci are enriched for non-coding single-nucleotide polymorphisms (SNPs) and defining the molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter transcription factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approach to identify candidate TF binding motifs that confer breast cancer-specific phenotypes identified by GWAS. We performed de novo motif analysis of regulatory elements, analyzed evolutionary conservation of identified motifs, and assayed TF footprinting data to identify sequence elements that recruit TFs and maintain chromatin landscape in breast cancer-relevant tissue and cell lines. We identified candidate causal SNPs that are predicted to alter TF binding within breast cancer-relevant regulatory regions that are in strong linkage disequilibrium with significantly associated GWAS SNPs. We confirm that the TFs bind with predicted allele-specific preferences using CTCF ChIP-seq data. We used The Cancer Genome Atlas breast cancer patient data to identify ANKLE1 and ZNF404 as the target genes of candidate TF binding site SNPs in the 19p13.11 and 19q13.31 GWAS-identified loci. These SNPs are associated with the expression of ZNF404 and ANKLE1 in breast tissue. This integrative analysis pipeline is a general framework to identify candidate causal variants within regulatory regions and TF binding sites that confer phenotypic variation and disease risk.

Genetic variants and risk of prostate cancer using pathway analysis of a genome-wide association study.

This study explored candidate causal single nucleotide polymorphisms (SNPs) to clarify the biological mechanism of prostate cancer (PCa). Identify candidate Causal SNPs and Pathways (ICSNPathway) analysis was applied using a PCa genome-wide association study (GWAS) dataset that included 473,736 SNPs in 1151 cases of PCa and 1156 controls of European ancestry. Five candidate causal SNPs, three candidate causal genes, and two candidate causal pathways were identified using integrating linkage disequilibrium analysis, functional SNP annotation, and pathway-based analysis. The ICSNPathway analysis suggested three hypothetical mechanisms of PCa. The first was rs13112390, rs13112358, rs2048074 to nei-like DNA glycosylase 3 (NEIL3) gene to damaged DNA binding. The second was rs3087386 to REV1, DNA directed polymerase (REV1) gene to damaged DNA binding. The third was rs1063134 to potassium channel, inwardly rectifying subfamily J, member 4 (KCNJ4) gene to inward rectifier potassium channel activity.

Common variants at the CHEK2 gene locus and risk of epithelial ovarian cancer.

Genome-wide association studies have identified 20 genomic regions associated with risk of epithelial ovarian cancer (EOC), but many additional risk variants may exist. Here, we evaluated associations between common genetic variants [single nucleotide polymorphisms (SNPs) and indels] in DNA repair genes and EOC risk. We genotyped 2896 common variants at 143 gene loci in DNA samples from 15 397 patients with invasive EOC and controls. We found evidence of associations with EOC risk for variants at FANCA, EXO1, E2F4, E2F2, CREB5 and CHEK2 genes (P ≤ 0.001). The strongest risk association was for CHEK2 SNP rs17507066 with serous EOC (P = 4.74 x 10(-7)). Additional genotyping and imputation of genotypes from the 1000 genomes project identified a slightly more significant association for CHEK2 SNP rs6005807 (r (2) with rs17507066 = 0.84, odds ratio (OR) 1.17, 95% CI 1.11-1.24, P = 1.1×10(-7)). We identified 293 variants in the region with likelihood ratios of less than 1:100 for representing the causal variant. Functional annotation identified 25 candidate SNPs that alter transcription factor binding sites within regulatory elements active in EOC precursor tissues. In The Cancer Genome Atlas dataset, CHEK2 gene expression was significantly higher in primary EOCs compared to normal fallopian tube tissues (P = 3.72×10(-8)). We also identified an association between genotypes of the candidate causal SNP rs12166475 (r (2) = 0.99 with rs6005807) and CHEK2 expression (P = 2.70×10(-8)). These data suggest that common variants at 22q12.1 are associated with risk of serous EOC and CHEK2 as a plausible target susceptibility gene.

Genome-wide pathway analysis for diabetic nephropathy in type 1 diabetes

Objective: The aim of this study was to identify candidate single-nucleotide polymorphisms (SNPs) that might play a role in susceptibility to diabetic nephropathy (DN) in type 1 diabetes, elucidate their potential mechanisms, and generate SNP-to-gene-to-pathway hypotheses. Methods: A genome-wide association study (GWAS) dataset of DN in type 1 diabetes, which included 345,363 SNPs from a total of 1,705 samples (820 DN cases and 885 normoalbuminuric controls) of European ancestry, was used in this study. The Identify Candidate Causal SNPs and Pathways (ICSNPathway) analysis was applied to the GWAS dataset. Results: ICSNPathway analysis identified 14 candidate SNPs, 10 genes, and 19 pathways, which in turn revealed 10 hypothetical biological mechanisms. The strongest hypothetical biological mechanism was one in which rs4740 altered the role of EBI3 in various pathways and processes, including regulation of the cytokine biosynthetic process, cytokine metabolic process, positive regulation of the cytokine biosynthetic process, regulation of the interferon gamma biosynthetic process, and interferon gamma production (0.008 ≤ p < 0.001; 0.047 ≤ false discovery rate [FDR] ≤ 0.002). This next most strongly supported hypothesis was the modulation of NMUR2 by rs982715, rs4958531, 4958532, rs1895245, and rs4958535 to affect its role in various pathways and processes, including calcium-mediated signaling and peptide receptor activity, and G-protein activity (p < 0.001, 0.002; FDR = 0.005, 0.049, respectively). Conclusions: By using the ICSNPathway to analyze the DN GWAS data, we identified 14 candidate SNPs, 10 genes (including EBI3, NMUR2, and EFNA1), and 19 pathways that likely contribute to the susceptibility to DN in type 1 diabetes.

Genetic variants and risk of gastric cancer: a pathway analysis of a genome-wide association study

This study aimed to discover candidate single nucleotide polymorphisms (SNPs) for hypothesizing significant biological pathways of gastric cancer (GC). We performed an Identify Candidate Causal SNPs and Pathways (ICSNPathway) analysis using a GC genome-wide association study (GWAS) dataset, including 472,342 SNPs in 2,240 GC cases and 3,302 controls of Asian ethnicity. By integrating linkage disequilibrium analysis, functional SNP annotation, and pathway-based analysis, seven candidate SNPs, four genes and 12 pathways were selected. The ICSNPathway analysis produced 4 hypothetical mechanisms of GC: (1) rs4745 and rs12904 → EFNA1 → ephrin receptor binding; (2) rs1801019 → UMPS → drug and pyrimidine metabolism; (3) rs364897 → GBA → cyanoamino acid metabolism; and (4) rs11187870, rs2274223, and rs3765524 → PLCE1 → lipid biosynthetic process, regulation of cell growth, and cation homeostasis. This pathway analysis using GWAS dataset suggests that the 4 hypothetical biological mechanisms might contribute to GC susceptibility.

Genome-wide pathway analysis in amyotrophic lateral sclerosis.

The aims of this study were to identify candidate single-nucleotide polymorphisms (SNPs) and mechanisms of amyotrophic lateral sclerosis (ALS) and to generate SNP-to-gene-to-pathway hypotheses. An ALS genome-wide association study (GWAS) dataset that included 483,051 SNPs in 276 patients with ALS and 271 controls of European descent was used in this study. Identify Candidate Causal SNPs and Pathway (ICSNPathway) analysis was applied to the GWAS dataset. ICSNPathway analysis identified 19 candidate SNPs, 8 genes, and 9 pathways, which provided 8 hypothetical biological mechanisms. The strongest hypothetical biological mechanism was that rs9352 alters the role of chromatin assembly factor 1 subunit A in the context of the pathways of chromatin and nucleosome assembly (nominal P < 0.001, false discovery rate (FDR) ≤ 0.001, 0.018, respectively). The second strongest was rs1046329 → HILS1 → chromatin assembly (nominal P < 0.001, FDR = 0.018). The third was rs11100790 → SMARCA5 → chromatin and nucleosome assembly (nominal P < 0.001, FDR ≤ 0.001, 0.018, respectively). The application of ICSNPathway analysis to the ALS GWAS dataset resulted in the identification of candidate SNPs, pathways, and biological mechanisms that might contribute to ALS susceptibility.

Genome-wide pathway analysis in glioma.

The aim of this study was to identify candidate single-nucleotide polymorphisms (SNPs) that may play arole in the susceptibility to glioma, to elucidate their potential mechanisms, and to generate SNP-to-gene-to-pathway hypotheses.A genome-wide association study (GWAS) dataset of glioma including 509,345 SNPs from 1,856 glioma patients and 4,955 control subjects of European descent was used in this study. Identify candidate Causal SNPs and Pathways (ICSNPathway) analysis was applied to the GWAS dataset.ICSNPathway analysis identified 6 candidate SNPs, 5 genes, and 9 pathways, which revealed 5 hypothetical biological mechanisms. The hypothetical mechanisms, beginning with the strongest, are summarized as follows: (i) rs667128 alters the role of taste receptor, type 2, member 8 (TAS2R8) in taste receptor activity and taste transduction pathways (p < 0.001, false discovery rate (FDR) < 0.001; p= 0.001, FDR = 0.012, respectively), (ii) rs619381 modulates the effect of taste receptor, type 2, member 7 (TAS2R7) on taste receptor activity and taste transduction (p < 0.001, FDR < 0.001; p= 0.001, FDR = 0.012), (iii) rs1033583 modulates delta-like protein 1 (DLL1), regulating cell adhesion and segment specification (p < 0.001, FDR = 0.011; p= 0.001, FDR = 0.032), (iv) rs2232580 affects the role of lipopolysaccharide binding protein (LBP) in the response to lipopolysaccharide, positive regulation of interleukin-6 production, acute inflammatory response, and in macrophage activation (0.002 ≤ p≤ 0.013; 0.012 ≤ FDR ≤ 0.030), and (v) rs4644 and rs4652 regulate lectin, galactoside-binding, soluble, 3 (LGALS3), affecting immunoglobulin binding (p = 0.010; FDR = 0.040).Using the ICSNPathway to analyze glioma GWAS data, 6 candidate SNPs, 5 genes (TAS2R8, TAS2R7, DLL1, LBP, and LGALS3), and 9 pathways that may contribute to the susceptibility of glioma were identified. glioma, genome-wide association study, pathway-based analysis.

Genetic variants and risk of esophageal squamous cell carcinoma: A GWAS-based pathway analysis

This study was designed to identify candidate single-nucleotide polymorphisms (SNPs) that may affect the susceptibility to esophageal squamous cell carcinoma (ESCC) and elucidate their potential mechanisms to generate SNP-to-gene-to-pathway hypotheses. A genome-wide association study (GWAS) dataset for ESCC, which included 453,852 SNPs from 1898 ESCC patients and 2100 control subjects of Chinese population, was reviewed. The identify candidate causal SNPs and pathways (ICSNPathway) analysis identified seven candidate SNPs, five genes, and seven pathways, which together revealed seven hypothetical biological mechanisms. The three strongest hypothetical biological mechanisms were as follows: rs4135113→TDG→BASE EXCISION REPAIR; rs1800450→MBL2→MONOSACCHARIDE BINDING; and rs3769823→CASP8→d4gdiPathway. The GWAS dataset was evaluated using the ICSNPathway, which showed seven candidate SNPs, five genes, and seven pathways that may contribute to the susceptibility of patients to ESCC.

Genome-wide pathway analysis of a genome-wide association study on Alzheimer's disease.

The aims of this study were to identify candidate single nucleotide polymorphisms (SNPs) and mechanisms of Alzheimer's disease (AD) and to generate SNP to gene to pathway hypotheses. An AD genome-wide association study (GWAS) dataset that included 370,542 SNPs in 1,000 cases and 1,000 controls of European descent was used in this study. Identify Candidate Causal SNPs and Pathway (ICSNPathway) analysis was applied to the GWAS dataset. ICSNPathway analysis identified 3 candidate SNPs and 2 pathways, which provided 3 hypothetical biological mechanisms. The strongest hypothetical biological mechanism was rs8076604 [non-synonymous coding (deleterious)] to MYO18A to negative regulation of programmed cell death [nominal P<0.001, false discovery rate (FDR) <0.043]. The second was rs2811226 (regulatory region) to ANXA1 to negative regulation of programmed cell death (nominal P<0.001, FDR 0.043). The third was rs3734166 (non-synonymous coding) to CDC25C to M phase of the mitotic cell cycle (nominal P<0.001, FDR 0.049). By applying the ICSNPathway analysis to the AD GWAS meta-analysis data, three candidate SNPs, three genes (MYO18A, ANXA1, CDC25C), 2 pathways involving negative regulation of programmed cell death and 1 pathway involving the M phase of the mitotic cell cycle were identified, which may contribute to AD susceptibility.

Genome-wide pathway analysis of breast cancer.

The aim of this study was to identify candidate single-nucleotide polymorphisms (SNPs) that might affect susceptibility to breast cancer and then elucidate their potential mechanisms and generate SNP-to-gene-to-pathway hypotheses. A genome-wide association study (GWAS) dataset of breast cancer that included 453,852 SNPs from 1,145 breast cancer patients and 1,142 control subjects of European descent was used in this study. The identify candidate causal SNPs and pathways (ICSNPathway) method was applied to the GWAS dataset. ICSNPathway analysis identified 16 candidate SNPs, 13 genes, and 7 pathways, which together revealed 7 hypothetical biological mechanisms. The strongest hypothetical biological mechanism was that rs3168891 and rs2899849 alter the role of MBIP in the inactivation of mitogen-activated protein kinase (MAPK) (p < 0.001; false discovery rate (FDR) = 0.038). The second strongest mechanism was that rs2229714 modulates RPS6KA1 to affect its role in growth hormone signaling (p = 0.001; FDR = 0.039). The third strongest mechanism was that rs2230394 modulates ITGB1 to regulate the PTEN pathway and hsa04360 (axon guidance pathway) (p < 0.001; FDR = 0.039, 0.041). Use of the ICSNPathway to analyze breast cancer GWAS data identified 16 candidate SNPs, 13 genes (including MBIP, RPS6KA1, and ITGB1), and 7 pathways that might contribute to the susceptibility of patients to breast cancer.

Genome-wide pathway analysis in attention-deficit/hyperactivity disorder

This study aimed to (1) to identify candidate single-nucleotide polymorphisms (SNPs) and mechanisms of attention-deficit/hyperactivity disorder (ADHD) and (2) to generate SNP-to-gene-to-pathway hypotheses. An ADHD genome-wide association study (GWAS) dataset that included 428,074 SNPs in 924 trios (2,758 individuals) of European descent was used in this study. The Identify candidate Causal SNPs and Pathways (ICSNPathway) analysis was applied to the GWAS dataset. ICSNPathway analysis identified 11 candidate SNPs, 6 genes, and 6 pathways, which provided 6 hypothetical biological mechanisms. The strongest hypothetical biological mechanism was that rs2532502 alters the role of CD27 in the context of the pathways of positive regulation of nucleocytoplasmic transport [nominal p < 0.001; false discovery rate (FDR) = 0.028]. The second strongest mechanism was the rs1820204, rs1052571, rs1052576 → CASP9 → mitochondrial pathway (nominal p < 0.001; FDR = 0.032). The third mechanism was the rs1801516 → ATM → CD25 pathway (nominal p < 0.001; FDR = 0.034). By applying the ICSNPathway analysis to the ADHD GWAS data, 11 candidate SNPs, 6 genes that included CD27, CASP9, ATM, CD12orf65, OXER1, and ACRY, and 6 pathways were identified that may contribute to ADHD susceptibility.

Genome-wide pathway analysis in neuroblastoma

The aim of this study was to identify candidate single-nucleotide polymorphisms (SNPs) that might play a role in susceptibility to neuroblastoma, elucidate their potential mechanisms, and generate SNP-to-gene-to-pathway hypotheses. A genome-wide association study (GWAS) dataset of neuroblastoma that included 442,976 SNPs from 1,627 neuroblastoma patients and 3,254 control subjects of European descent was used in this study. The identify candidate causal SNPs and pathways (ICSNPathway) analysis was applied to the GWAS dataset. ICSNPathway analysis identified 15 candidate SNPs, 10 genes, and 31 pathways, which revealed 10 hypothetical biological mechanisms. The strongest hypothetical biological mechanism was one wherein SNPrs40401 modulates the role of IL3 in several pathways and conditions, including the stem pathway, asthma (hsa05310), the dendritic cell pathway, and development (0.001 < p < 0.004; 0.001 < FDR < 0.033). The second strongest mechanism identified was that in which rs1048108 and rs16852600 alter the function of BARD1, which negatively regulates developmental process and modulates processes including cell development and programmed cell death (0.001 < p < 0.004; 0.001 < FDR < 0.033). The third mechanism identified was one wherein rs1939212 modulated CFL1, resulting in negative regulation of development, cell death, neural crest cell migration, and apoptosis (0.001 < p < 0.004; 0.001 < FDR < 0.033). By using the ICSNPathway to analyze neuroblastoma GWAS data, 15 candidate SNPs, 10 genes including IL3, BARD1, and CFL, and 31 pathways were identified that might contribute to the susceptibility of patients to neuroblastoma.

Genome-Wide Pathway Analysis in Major Depressive Disorder

The aims of this study were: (1) to identify candidate single-nucleotide polymorphisms (SNPs) and mechanisms of major depressive disorder (MDD) and (2) to generate SNP-to-gene-to-pathway hypotheses. An MDD genome-wide association study (GWAS) data set that included 365,419 SNPs in 1,821 MDD cases and 1,822 controls of European descent was used in this study. Identify Candidate Causal SNPs and Pathway (ICSNPathway) analysis was applied to the GWAS dataset. ICSNPathway analysis identified 21 candidate SNPs, 16 genes, and 5 pathways, which provided 16 hypothetical biological mechanisms. The strongest hypothetical biological mechanism was that rs3213764 alters the role of ATF7IP in the context of the pathways of negative regulation of transcription, negative regulation of nucleobase, nucleoside, nucleotide, and nucleic acid metabolic processes and negative regulation of gene expression (nominal p < 0.001, FDR = 0.043, 0.044, and 0.046, respectively). Five of 16 candidate genes are known to be associated with inflammatory or immune response that may be associated with MDD: ANPEP, PRDM1, ZBTB32, MMP8, and ENPEP. By applying the ICSNPathway analysis to the MDD GWAS data, 21 candidate SNPs, 16 genes that included ATF7IP, ANPEP, PRDM1, ZBTB32, MMP8, and ENPEP, and 5 pathways that involved negative regulation of transcription and nucleic acid metabolism were identified that may contribute to MDD susceptibility.