Genome-wide Association Studies Catalog Research Articles

Genome-wide analysis revealed sex-specific gene expression in asthmatics.

Global gene-expression analysis has shown remarkable difference between males and females in response to exposure to many diseases. Nevertheless, gene expression studies in asthmatics have so far focused on sex-combined analysis, ignoring inherent variabilities between the sexes, which potentially drive disparities in asthma prevalence. The objectives of this study were to identify (1) sex-specific differentially expressed genes (DEGs), (2) genes that show sex-interaction effects and (3) sex-specific pathways and networks enriched in asthma risk. We analyzed 711 males and 689 females and more than 2.8 million transcripts covering 20 000 genes leveraged from five different tissues and cell types (i.e. epithelial, blood, induced sputum, T cells and lymphoblastoids). Using tissue-specific meta-analysis, we identified 439 male- and 297 female-specific DEGs in all cell types, with 32 genes in common. By linking DEGs to the genome-wide association study (GWAS) catalog and the lung and blood eQTL annotation data from GTEx, we identified four male-specific genes (FBXL7, ITPR3 and RAD51B from epithelial tissue and ALOX15 from blood) and one female-specific gene (HLA-DQA1 from epithelial tissue) that are disregulated during asthma. The hypoxia-inducible factor 1 signaling pathway was enriched only in males, and IL-17 and chemokine signaling pathways were enriched in females. The cytokine-cytokine signaling pathway was enriched in both sexes. The presence of sex-specific genes and pathways demonstrates that sex-combined analysis does not identify genes preferentially expressed in each sex in response to diseases. Linking DEG and molecular eQTLs to GWAS catalog represents an important avenue for identifying biologically and clinically relevant genes.

Association of rs10830962 polymorphism with gestational diabetes mellitus risk in a Chinese population

To date, only three polymorphisms (rs10830962, rs7754840 and rs1470579) are included in the genome-wide association study Catalog (www.ebi.ac.uk/gwas). However, the available evidence is limited in pregnant Chinese women. We aimed to explore the associations of three polymorphisms (rs10830962, rs7754840 and rs1470579) with GDM risk in a Chinese population. We conducted a case-control study (964 GDM cases and 1,021 controls) to evaluate the associations of these polymorphisms with GDM risk. A logistic regression model was used to calculate odds ratios (ORs) and their confidence intervals (CIs). After adjustment for age, prepregnancy BMI, parity, abnormal pregnancy history and family history of diabetes, the minor allele of rs10830962 (C > G) demonstrated a significant association with an increased risk of GDM (OR = 1.16, 95% CI = 1.02–1.31, P = 0.029 in the additive model). However, no significant association was observed between the other two polymorphisms and GDM. Subsequent functional annotation shows that rs10830962 is located in the regulatory elements of pancreatic islets, alters the binding affinity of motifs and regulates SNORA8 expression. Our findings demonstrate that rs10830962 is associated with an increased risk of GDM in the Chinese population. Further functional characterization is warranted to uncover the mechanism of the genotype-phenotype association.

Meta-Analysis of Genome-Wide Association Studies Identifies Three Loci Associated With Stiffness Index of the Calcaneus.

The stiffness index (SI) from quantitative ultrasound measurements is a good indicator of BMD and may be used to predict the risk of osteoporotic fracture. We conducted a genomewide association study (GWAS) for SI using 7742 individuals from the Taiwan Biobank, followed by a replication study in a Korean population (n = 2955). Approximately 6.1 million SNPs were subjected to association analysis, and SI-associated variants were identified. We further conducted a meta-analysis of Taiwan Biobank significant SNPs with a Korean population-based cohort. Candidate genes were prioritized according to epigenetic annotations, gene ontology, protein-protein interaction, GWAS catalog, and expression quantitative trait loci analyses. Our results revealed seven significant single-nucleotide polymorphisms (SNPs) within three loci: 7q31.31, 17p13.3, and 11q14.2. Conditional analysis showed that three SNPs, rs2536195 (CPED1/WNT16), rs1231207 (SMG6), and rs4944661 (LOC10050636/TMEM135), were the most important signals within these regions. The associations for the three SNPs were confirmed in a UK Biobank estimated BMD GWAS; these three cytobands were replicated successfully after a meta-analysis with a Korean population cohort as well. However, two SNPs were not replicated. After prioritization, we identified two novel genes, RAB15 and FNTB, as strong candidates for association with SI. Our study identified three SI-associated SNPs and two novel SI-related genes. Overall, these results provide further insight into the genetic architecture of osteoporosis. Further studies in larger East Asian populations are needed. © 2019 American Society for Bone and Mineral Research.

Leveraging Human Genetics to Guide Cancer Drug Development.

The high attrition rate of cancer drug development programs is a barrier to realizing the promise of precision oncology. We have examined whether the genetic insights from genome-wide association studies of cancer can guide drug development and repurposing in oncology. Across 37 cancers, we identified 955 genetic risk variants from the National Human Genome Research Institute-European Bioinformatics Institute genome-wide association study catalog. We linked these variants to target genes using strategies that were based on linkage disequilibrium, DNA three-dimensional structure, and integration of predicted gene function and expression. With the use of the Informa Pharmaprojects database, we identified genes that are targets of unique drugs and assessed the level of enrichment that would be afforded by incorporation of genetic information in preclinical and phase II studies. For targets not under development, we implemented machine learning approaches to assess druggability. For all preclinical targets incorporating genetic information, a 2.00-fold enrichment of a drug being successfully approved could be achieved (95% CI, 1.14- to 3.48-fold; P = .02). For phase II targets, a 2.75-fold enrichment could be achieved (95% CI, 1.42- to 5.35-fold; P < .001). Application of genetic information suggests potential repurposing of 15 approved nononcology drugs. The findings illustrate the value of using insights from the genetics of inherited cancer susceptibility discovery projects as part of a data-driven strategy to inform drug discovery. Support for cancer germline genetic information for prospective targets is available online from the Institute of Cancer Research.

Polygenic Risk Score in Follicular Lymphoma Risk and Prognosis in a Population-Based Case-Control Study in Los Angeles County

Background: Although several prognostic factors are regularly utilized for follicular lymphoma (FL) - e.g., extent of disease, presence of B-symptoms, and the FL international prognostic index (IPI) - they do not fully account for the heterogeneity in patient outcomes. Etiologic risk factors may influence the heterogeneity of prognostic outcomes, but relatively few risk factors for FL have been identified and subsequently confirmed. Recent epidemiologic studies have uncovered genetic risk loci associated with FL risk. To date, the association between these risk alleles with FL prognosis remains unknown. We therefore sought to evaluate whether identified genetic risk loci specific to FL also play a role in FL prognosis.Methods: We previously conducted a population-based case-control study of primary, incident Non-Hodgkin Lymphoma (NHL) among women in Los Angeles County diagnosed from 2006 to 2009. A total of 230 FL cases were enrolled in the study along with 246 age- and race- matched controls. To ascertain treatment and follow-up information, medical records were retrieved and abstracted, and data linkages to the California hospitalization discharge records and SEER-Medicare were conducted. Based on abstracted data, we constructed a surrogate to the FL IPI. Genotyping for FL genetic risk alleles identified in the National Human Genome Research Institute-European Bioinformatics Institute genome wide association study catalog (rs12195582, rs13254990, rs17749561, rs4245081, and rs4938573) was conducted and used to construct a polygenic risk score (PRS). The PRS was computed by taking a weighted average of the five alleles and the log of their reported odds ratio and creating tertiles based on the values of our control. To confirm the risk association, we first evaluated the association between our PRS and FL risk, adjusted for demographic characteristics and potential confounders (e.g., smoking status, BMI, and family history of hematologic malignancies). We subsequently confirmed the prognostic performance of our reconstructed IPI and then evaluated the association between the PRS and FL outcomes, including overall survival (OS), defined as date of initial diagnosis to date of death or last known follow-up; and event-free survival at 12 months (EFS12) and 24 months (EFS24), where events consisted of progression, refractory disease, or death.Results: In case-control analysis, we confirmed an increased FL risk associated with the third tertile PRS (OR=2.19, 95% CI=1.22-3.94), compared to the first tertile. The median follow-up time among FL cases was 8.5 years (IQR: 7.1-10.1) after initial diagnosis: 50 (22%) FL cases had died, 198 (86%) achieved EFS12 and 186 (81%) achieved EFS24. The re-constructed FL-IPI in our case population was statistically significantly associated with overall survival (HR=4.00, 95% CI=1.32-12.16). In our multivariate model that included the PRS, we observed a marginally significant risk for longer overall survival (HR=0.39, 95% CI=0.15, 1.01), but no association with EFS12 or EFS24. No statistically significant associations of individual risk alleles and prognostic outcomes were observed. Race-specific results and evaluation of demographic and other risk factors on risk and survival will also be presented in relation to the PRS.Conclusion: In our population sample of FL cases identified from the Los Angeles County Cancer Registry and initially recruited for a case-control study, we confirmed the association between a PRS and FL risk. We further report a potential association between the PRS and improved overall survival, suggesting an opposite effect for the PRS on risk versus survival. Larger studies on FL with genetic data and prognostic outcomes are warranted to replicate this finding. DisclosuresChao:Seattle Genetics: Research Funding.

Comprehensive identification of pleiotropic loci for body fat distribution using the NHGRI-EBI Catalog of published genome-wide association studies.

We conducted a hypothesis-free cross-trait analysis for waist-to-hip ratio adjusted for body mass index (WHRadjBMI ) loci derived through genome-wide association studies (GWAS). Summary statistics from published GWAS were used to capture all WHRadjBMI single-nucleotide polymorphisms (SNPs), and their proxy SNPs were identified. These SNPs were used to extract cross-trait associations between WHRadjBMI SNPs and other traits through the NHGRI-EBI GWAS Catalog. Pathway analysis was conducted for pleiotropic WHRadjBMI SNPs. We found 160 WHRadjBMI SNPs and 3675 proxy SNPs. Cross-trait analysis identified 239 associations, of which 100 were for obesity traits. The remaining 139 associations were filtered down to 101 unique linkage disequilibrium block associations, which were grouped into 13 categories: lipids, red blood cell traits, white blood cell counts, inflammatory markers and autoimmune diseases, type 2 diabetes-related traits, adiponectin, cancers, blood pressure, height, neuropsychiatric disorders, electrocardiography changes, urea measurement, and others. The highest number of cross-trait associations were found for triglycerides (n=10), high-density lipoprotein cholesterol (n=9), and reticulocyte counts (n=8). Pathway analysis for WHRadjBMI pleiotropic SNPs found immune function pathways as the top canonical pathways. Results from our original methodology indicate a novel genetic association between WHRadjBMI and reticulocyte counts and highlight the pleiotropy between abdominal obesity, immune pathways, and other traits.

Genetics of Obesity in Diverse Populations.

The prevalence of obesity continues to rise, fueling a global public health crisis characterized by dramatic increases in type 2 diabetes, cardiovascular disease, and many cancers. In the USA, several minority populations, who bear much of the obesity burden (47% in African Americans and Hispanic/Latinos, compared to 38% in European descent groups), are particularly at risk of downstream chronic disease. Compounding these disparities, most genome-wide association studies (GWAS)-including those of obesity-have largely been conducted in populations of European or East Asian ancestry. In fact, analysis of the GWAS Catalog found that while the proportion of participants of non-European or non-Asian descent had risen from 4% in 2009 to 19% in 2016, African-ancestry participants are still just 3% of GWAS, Hispanic/Latinos are < 0.5%, and other ancestries are < 0.3% or not represented at all. This review summarizes recent developments in obesity genomics in US minority populations, with the goal of reducing obesity health disparities and improving public health programs and access to precision medicine. GWAS of populations with the highest burden of obesity are essential to narrow candidate variants for functional follow-up, to identify additional ancestry-specific variants that contribute to individual genetic susceptibility, and to advance both public health and precision medicine approaches to obesity. Given the global public health burden posed by obesity and downstream chronic conditions which disproportionately affect non-European populations, GWAS of obesity-related traits in diverse populations is essential to (1) locate causal variants in GWAS-identified regions through fine mapping, (2) identify variants which influence obesity across ancestries through generalization, and (3) discover novel ancestry-specific variants which may be low frequency in European populations but common in other groups. Recent efforts to expand obesity genomic studies to understudied and underserved populations, including AAAGC, PAGE, and HISLA, are working to reduce obesity health disparities, improve public health, and bring the promise of precision medicine to all.

Bayesian statistical methods in genetic association studies: Empirical examination of statistically non-significant Genome Wide Association Study (GWAS) meta-analyses in cancers: A systematic review

A Bayesian statistical method was developed to assess the noteworthiness of a single nucleotide polymorphism (SNP)-phenotype association that shows statistical significance in various observational studies, but it has seldom been applied to GWAS meta-analyses in cancers. Data (i.e. allelic frequency, odds ratio, 95% confidence interval, etc.) on various SNP-cancer associations were extracted from meta-analysis of GWAS and the National Human Genome Research Institute (NHGRI) Catalog of Published GWAS and were used to compute the false positive report probability (FPRP) and Bayesian false discovery probability (BFDP) to evaluate the noteworthiness of SNP-cancer associations. Independent paired t-tests showed a direct relationship between SNP-cancer P-values and both FPRP and BFDP estimates. However, a discrepancy in the number of noteworthy associations between P-value comparison and either FPRP or BFDP was found using data extracted from meta-analyses of GWAS and the GWAS Catalog. Most P-values of associations with nonsignificant P-values but with noteworthy FPRP and BFDP estimates were within the range of 10−6 to 5 × 10−8. A poorly selected genome-wide significance threshold and inclusion of a nonsignificant SNP-phenotype association into the noteworthy test can, with either noteworthy FPRP or BFDP computation, give a false impression of noteworthiness for a nonsignificant association.

An efficient analytic approach in genome-wide identification of methylation quantitative trait loci response to fenofibrate treatment

BackgroundThe study of DNA methylation quantitative trait loci (meQTLs) helps dissect regulatory mechanisms underlying genetic associations of human diseases. In this study, we conducted the first genome-wide examination of genetic drivers of methylation variation in response to a triglyceride-lowering treatment with fenofibrate (response-meQTL) by using an efficient analytic approach.MethodsSubjects (n = 429) from the GAW20 real data set with genotype and both pre- (visit 2) and post- (visit 4) fenofibrate treatment methylation measurements were included. Following the quality control steps of removing certain cytosine-phosphate-guanine (CpG) probes, the post−/premethylation changes (post/pre) were log transformed and the association was performed on 208,449 CpG sites. An additive linear mixed-effects model was used to test the association between each CpG probe and single nucleotide polymorphisms (SNPs) around ±1 Mb region, with age, sex, smoke, batch effect, and principal components included as covariates. Bonferroni correction was applied to define the significance threshold (p < 5.6 × 10− 10, given a total of 89,217,303 tests). Finally, we integrated our response-meQTL (re-meQTL) findings with the published genome-wide association study (GWAS) catalog of human diseases/traits.ResultsWe identified 1087 SNPs as cis re-meQTLs associated with 610 CpG probes/sites located in 351 unique gene loci. Among these 1087 cis re-meQTL SNPs, 229 were unique and 6 were co-localized at 8 unique disease/trait loci reported in the GWAS catalog (enrichment p = 1.51 × 10− 23). Specifically, a lipid SNP, rs10903129, located in intron regions of gene TMEM57, was a re-meQTL (p = 3.12 × 10− 36) associated with the CpG probe cg09222892, which is in the upstream region of the gene RHCE, indicating a new target gene for rs10903129. In addition, we found that SNP rs12710728 has a suggestive association with cg17097782 (p = 1.77 × 10− 4), and that this SNP is in high linkage disequilibrium (LD) (R2 > 0.8) with rs7443270, which was previously reported to be associated with fenofibrate response (p = 5.00 × 10− 6).ConclusionsBy using a novel analytic approach, we efficiently identified thousands of cis re-meQTLs that provide a unique resource for further characterizing functional roles and gene targets of the SNPs that are most responsive to fenofibrate treatment. Our efficient analytic approach can be extended to large response quantitative trait locus studies with large sample sizes and multiple time points data.

Insights and Implications of Genome-Wide Association Studies of Height.

ContextIn the last decade, genome-wide association studies (GWASs) have catalyzed our understanding of the genetics of height and have identified hundreds of regions of the genome associated with adult height and other height-related body measurements.Evidence AcquisitionGWASs related to height were identified via PubMed search and a review of the GWAS catalog.Evidence SynthesisThe GWAS results demonstrate that height is highly polygenic: that is, many thousands of genetic variants distributed across the genome each contribute to an individual’s height. These height-associated regions of the genome are enriched for genes in known biological pathways involved in growth, such as fibroblast growth factor signaling, as well as for genes expressed in relevant tissues, such as the growth plate. GWASs can also uncover previously unappreciated biological pathways, such as theSTC2/PAPPA/IGFBP4 pathway. The genes implicated by GWASs are often the same genes that are the genetic causes of Mendelian growth disorders or skeletal dysplasias, and GWAS results can provide complementary information about these disorders.ConclusionsHere, we review the rationale behind GWASs and what we have learned from GWASs for height, including how it has enhanced our understanding of the underlying biology of human growth. We also highlight the implications of GWASs in terms of prediction of adult height and our understanding of Mendelian growth disorders.

A decade in psychiatric GWAS research.

After more than ten years of accumulated efforts, genome-wide association studies (GWAS) have led to many findings, most of which have been deposited into the GWAS Catalog. Between GWAS’s inception and March 2017, the GWAS Catalog has collected 2,430 studies, 1,818 phenotypes, and 28,462 associated SNPs. We reclassified the psychology-related phenotypes into 198 reclassified phenotypes, which accounted for 472 studies and 6,632 SNPs. In total, 1,109 of the SNPs reached genome-wide significance. Of these, 133 were replicated for the same psychological trait in different studies. Another 379 SNPs were replicated within one original study. The SNPs rs2075650 and rs4420638 were linked to the most replications within a single reclassified phenotype; both were associated with Alzheimer’s disease (AD). Schizophrenia was associated with 76 SNPs. Alzheimer’s disease and schizophrenia were linked to many physical phenotypes, including cholesterol and body mass index, through common GWAS signals. Alzheimer’s disease also shared risk SNPs with age-related phenotypes such as age-related macular degeneration and longevity. Smoking-related SNPs were linked to lung cancer and respiratory function. Alcohol-related SNPs were associated with cardiovascular and digestive system phenotypes and disorders. Two separate studies also identified a shared risk SNP for bipolar disorder and educational attainment. This review revealed a list of reproducible SNPs worthy of future functional investigation. Additionally, by identifying SNPs associated with multiple phenotypes, we illustrated the importance of studying the relationships among phenotypes to resolve the nature of their causal links. The insights within this review will hopefully pave the way for future evidence-based genetic studies.

Replication of Previously Type 2 Diabetes Associated Loci in 35,000 Samples of Korean Population

In the last decade, genome-wide association studies (GWAS) have revealed about hundreds of loci associated with type 2 diabetes(T2D) and related traits. However, most of loci have been discovered based on Europeans and a few GWAS conducted in populations with East Asian ancestry. To identify genetic variants responsible for complex traits such as T2D, obesity, and blood biochemical traits in Koreans, Korea Biobank Array(KBA) project was initiated in 2014. KBA is designed to contain about 830K tagging and functional variants optimized for Korean genome. In this study, quality controlled genotype data of about 35,000 samples was used. Among them, we conducted genome-wide association scan in 3,022 T2D case subjects and 27,195 control subjects. As a result, we replicated five previously known loci (CDKAL1, SLC30A8, CDKN2A, KCNQ1, and HNF1B) at genome-wide significance (P &amp;lt;= 5x10-8). We further compared genetic effects in Koreans with those of previously reported loci. As of March 2018, there were 373 unique variants of 50 T2D GWAS papers listed in GWAS catalogue. Among 373 variants, 359 variants were available after excluding variants with low imputation quality (&amp;lt;0.3). We compared odds ratios of 359 variants between KBA T2D GWAS and previously reported results. In overall, Pearson correlation coefficient of odds ratios was -0.04. For replicated results (130 variants with P&amp;lt;0.05 in KBA T2D GWAS), however, we observed high correlation (r&amp;lt;0.58). Our study identified five known T2D loci and confirmed consistent genetic effects across populations for 130 previously reported T2D associated variants. Further study is warranted to examine genetic diversity and lack of statistical power due to insufficient sample size in non-replicated loci. These results will be valuable scientific evidence in T2D precision medicine considering genetic effects across populations. Disclosure Y. Kim: None. M. Hwang: None. D. Shin: None.

Lack Of Diversity In Genomic Databases Is A Barrier To Translating Precision Medicine Research Into Practice.

Precision medicine is predicted to revolutionize the clinical practice of medicine, in part by using molecular biomarkers to assess patients' risk, prognosis, and therapeutic response more precisely. However, reliance on biomarkers could present challenges for diverse populations that are not equitably represented in precision medicine research. We examined the populations included in genomic studies whose data were available in the following two public databases: the Genome-Wide Association Study Catalog and the database of Genotypes and Phenotypes. We found significantly fewer studies of African, Latin American, and Asian ancestral populations in comparison to European populations. These patterns were consistent across both data types and disease areas. While the number of genomic research studies that include non-European populations is modestly improving, the overall numbers are still low, and decisive action is needed now to implement the changes necessary for realizing the promise of precision medicine for all.

Correlation between prostate stem cell antigen gene expression and oral squamous cell carcinoma.

The aetiology of oral squamous cell carcinoma (OSCC) remains unclear. Numerous single nucleotide polymorphisms (SNPs) associated with cancer have been identified using genome-wide association studies (GWAS). The present study was designed to identify common SNPs associated with cancer susceptibility and to evaluate their involvement in OSCC. Susceptible loci were identified by analysing a cancer GWAS catalogue. A multicentre case-control study using an OSCC and control population was performed for selected SNPs. The function of the selected locus and its associated gene was explored using a reverse transcription-polymerase chain reaction, enzyme linked immunosorbent assay and immunohistochemistry. The association between genotypes and clinical parameters was assessed in 76 patients with OSCC. Rs2294008 located in the prostate stem cell antigen gene (PSCA) was selected. It was identified that the rs2294008 polymorphism was associated with OSCC susceptibility and PSCA may be involved in the development, progression and prognosis of OSCC.

An integrative functional genomics framework for effective identification of novel regulatory variants in genome\u2013phenome studies

BackgroundGenome–phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases.MethodsIn this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx.ResultsWe found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer’s disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1).ConclusionsThis study offers powerful tools for exploring the functional consequences of variants generated from genome–phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.

Using postgenome-wide association study analysis; Vars2-Pic3ca-AKT is novel putative interactive pathway associated with conotruncal heart defects

Background: Genome-wide association studies (GWASs) have identified 43-associated variants with conotruncal heart defects (CTD) at P ≤ 5E-7, P ≤ 5E-6, and P ≤ 5E-5 till January 2018. Despite the GWASs still provide a growing number of associated genetic variants, the identification of their pathogenic mechanisms remains a major challenge in human genetics requiring data mining efforts and predictive models for accurate interpretation of noncoding variants especially. Hence, I applied a post-GWAS analysis approach to identify the possible mechanism of action of noncoding variants associated with CTD according to the latest update of the GWAS catalog. Method: I aimed in this study to conduct a post-GWAS analysis of the most associated noncoding variants with CTD to elucidate its probable pathogenic mechanisms. Through three GWAS traits belong to CTDs, it was used in a variety of integrated and computational algorithms to collect the available information about the selected noncoding variants and its associated partners. Results: It was found differential histone modification, motif binding, and gene expression among the CTD-associated single nucleotide polymorphisms. Through intensive analysis, it was also found that both the linked rs2517582 and rs17189763 haplotype might lead to alteration in the Pic3ca-AKT signal pathway due to a change of CTCF-binding affinity and upregulation of VARS2 gene. Conclusion: It was concluded that the indirect effect of the upregulation VARS2 gene might associates with CTD. Consequently, phosphoinositide 3-kinases/AKT (pic3ca/AKT) pathway has a pivotal role in the signal system of heart morphogenesis.

Bayesian Network Construction and Genotype-Phenotype Inference Using GWAS Statistics.

Genome-wide association studies (GWASs) have received increasing attention to understand how genetic variation affects different human traits. In this paper, we study whether and to what extent exploiting the GWAS statistics can be used for inferring private information about a human individual. We first provide a method to construct a three-layered Bayesian network explicitly revealing the conditional dependency between single-nucleotide polymorphisms (SNPs) and traits from public GWAS catalog. The key challenge in building a Bayesian network from GWAS statistics is the specification of the conditional probability table of a variable with multiple parent variables. We employ the models of independence of causal influences which assume that the causal mechanism of each parent variable is mutually independent. We then formulate three inference problems based on the dependency relationship captured in the Bayesian network, namely trait inference given SNP genotype, genotype inference given trait, and trait inference given known traits, and develop efficient formulas and algorithms. Different from previous work, the possible target of these inference problems we study may be any individual, not limited to GWAS participants. Empirical evaluations show the effectiveness of our proposed methods. In summary, our work implies that meaningful information can be inferred from modeling GWAS statistics, and appropriate privacy protection mechanisms need to be developed to protect genetic privacy not only of GWAS participants but also regular individuals.

Genetic susceptibility to infectious diseases: Current status and future perspectives from genome-wide approaches

Genome-wide association studies (GWASs) have been widely applied to identify genetic factors that affect complex diseases or traits. Presently, the GWAS Catalog includes >2800 human studies. Of these, only a minority have investigated the susceptibility to infectious diseases or the response to therapies for the treatment or prevention of infections. Despite their limited application in the field, GWASs have provided valuable insights by pinpointing associations to both innate and adaptive immune response loci, as well as novel unexpected risk factors for infection susceptibility. Herein, we discuss some issues and caveats of GWASs for infectious diseases, we review the most recent findings ensuing from these studies, and we provide a brief summary of selected GWASs for infections in non-human mammals. We conclude that, although the general trend in the field of complex traits is to shift from GWAS to next-generation sequencing, important knowledge on infectious disease-related traits can be still gained by GWASs, especially for those conditions that have never been investigated using this approach. We suggest that future studies will benefit from the leveraging of information from the host's and pathogen's genomes, as well as from the exploration of models that incorporate heterogeneity across populations and phenotypes. Interactions within HLA genes or among HLA variants and polymorphisms located outside the major histocompatibility complex may also play an important role in shaping the susceptibility and response to invading pathogens.

Implicating candidate genes at GWAS signals by leveraging topologically associating domains.

Genome-wide association studies (GWAS) have contributed significantly to the understanding of complex disease genetics. However, GWAS only report association signals and do not necessarily identify culprit genes. As most signals occur in non-coding regions of the genome, it is often challenging to assign genomic variants to the underlying causal mechanism(s). Topologically associating domains (TADs) are primarily cell-type-independent genomic regions that define interactome boundaries and can aid in the designation of limits within which an association most likely impacts gene function. We describe and validate a computational method that uses the genic content of TADs to prioritize candidate genes. Our method, called 'TAD_Pathways', performs a Gene Ontology (GO) analysis over genes that reside within TAD boundaries corresponding to GWAS signals for a given trait or disease. Applying our pipeline to the bone mineral density (BMD) GWAS catalog, we identify 'Skeletal System Development' (Benjamini-Hochberg adjusted P=1.02x10-5) as the top-ranked pathway. In many cases, our method implicated a gene other than the nearest gene. Our molecular experiments describe a novel example: ACP2, implicated near the canonical 'ARHGAP1' locus. We found ACP2 to be an important regulator of osteoblast metabolism, whereas ARHGAP1 was not supported. Our results via BMD, for example, demonstrate how basic principles of three-dimensional genome organization can define biologically informed association windows.

Genome-Wide Association Studies of Chemotherapeutic Toxicities: Genomics of Inequality.

With an estimated global population of cancer survivors exceeding 32 million and growing, there is a heightened awareness of the long-term toxicities resulting from cancer treatments and their impact on quality of life. Unexplained heterogeneity in the persistence and development of toxicities, as well as an incomplete understanding of their mechanisms, have generated a growing need for the identification of predictive pharmacogenomic markers. Early studies addressing this need used a candidate gene approach; however, over the last decade, unbiased and comprehensive genome-wide association studies (GWAS) have provided markers of phenotypic risk and potential targets to explore the mechanistic and regulatory pathways of biological functions associated with chemotherapeutic toxicity. In this review, we provide the current status of GWAS of chemotherapeutic toxicities with an emphasis on examining the ancestral diversity of the representative cohorts within these studies. Persistent calls to incorporate both ancestrally diverse and/or admixed populations into genomic efforts resulted in a recent rise in the number of studies utilizing cohorts of East Asian descent; however, few pharmacogenomic studies to date include cohorts of African, Indigenous American, Southwest Asian, and admixed populations. Through comprehensively evaluating sample size, composition by ancestry, genome-wide significant variants, and population-specific minor allele frequencies as reported by HapMap/dbSNP using NCBI PubMed and the NHGRI-EBI GWAS Catalog, we illustrate how allele frequencies and effect sizes tend to vary among individuals of differing ancestries. In an era of personalized medicine, the lack of diversity in genome-wide studies of anticancer agent toxicity may contribute to the health disparity gap. Clin Cancer Res; 23(15); 4010-9. ©2017 AACR.