Causal Single Nucleotide Polymorphisms Research Articles

Warfarin Dose and CYP2C Gene Cluster: An African Ancestral-Specific Variant Is a Strong Predictor of Dose in Black South African Patients.

Warfarin is a widely prescribed anticoagulant with a narrow therapeutic index. The rs12777823G>A single-nucleotide polymorphism (SNP) in the CYP2C gene cluster has been shown to influence optimal warfarin doses in African Americans. We report here effects of rs12777823G>A SNP on warfarin dose requirements in two South African population groups, black Africans (BA) and mixed ancestry (MA). A total of 425 participants on warfarin treatment were enrolled in the study. The age group of the studied population ranged between 44 and 66 years, with 69% females enrolled. Genetic characterization of the rs12777823G>A was done using the TaqMan SNP genotyping assay. To further compare effects of rs12777823G>A to those of other SNPs, VKORC1 g.-1639G>A and 4 SNPs in CYP2C9 gene (i.e., CYP2C9 c.430C>T, c.1075A>C, c.449G>A, and c.1003C>T) were analyzed. The rs12777823A variant allele frequencies were 0.28 and 0.25 in the BA and MA, respectively. The rs12777823A/A genotype was associated with significantly (p = 0.002) reduced mean warfarin dosage (27 ± 5.3 mg/week) compared with the G/G genotype (45 ± 16.1 mg/week) among BA, but not among the MA. The rs12777823G>A is located in a nongenomic region, suggesting that this SNP might be in linkage disequilibrium with another, likely causal SNP that is present in BA only. Given ongoing worldwide efforts to identify clinically relevant human genetic variation impacting on optimal warfarin dose selection, the African ancestry-specific genetic variant in the CYP2C cluster and others warrant further research and consideration in development of future warfarin dosing algorithms for precision medicine guidelines.

Genetics of Factor VIII Inhibitor Development in Hemophilia Patients: Novel Statistical Approaches in the PATH Study

Here we apply state-of-the-art statistical genetic approaches toward investigating the genetic architecture of factor VIII (FVIII) inhibitor (FEI) development in Hemophilia A (HA). A total of 442 North American HA patients (237 Whites and 205 Blacks; 88% severely affected) enrolled in the PATH Study were: 1) ImmunoChip genotyped at ~167,000 single nucleotide polymorphisms (SNPs) in genes previously implicated in autoimmune disease risk; 2) Evaluated by DNA sequencing and assays for the recurrent intron (I)1 and I22 inversions to identify their causative F8 mutations; and 3) Tested with the Bethesda assay to determine their FEI status. The ImmunoChip genotypes were used to construct a genetic relationship matrix (GRM), denoted by K, following our previously published method,1 and the F8 sequence data along with results from the I1 and I22 inversion assays were used to construct a shared F8-mutation matrix, denoted by F. We analyzed a dichotomous FEI variable under the statistical genetic threshold/liability model (a probit regression in the fixed effects) in conjunction with a variance components model for the FEI liability phenotypic covariance matrix, denoted by P, to model potentially important random effects. For the latter, we specifically assumed independent additive genetic, F8-mutation, and residual environmental random effects. By the independence assumption, the covariance matrix is then decomposable as a sum of the additive genetic (Va), F8-mutation (Vf), and residual environmental (Ve) variances respectively structured by K, F, and the identity matrix I. The variance component model is given as: P = K*Va + F*Vf + I*Ve. Heritability, denoted by h2, is defined as the ratio of Va to the total phenotypic variance (Vp): h2 = Va / Vp. We can further speak of the total heritability given as: h2t = h2r + h2f + h2snp, where the subscripts t, r, f, and snp respectively denote total, residual additive genetic, F8-mutation-specific, and SNP heritabilities. Using eigenstructure methods,2 we can compute power under a simpler model in which Va and Vf are combined as a single variance component. We computed power to detect genetic association as measured by SNP-specific heritability for a set of 403 SNPs in or near 14 candidate immune response genes previously implicated in FEI risk. To account for multiple hypothesis testing, power was computed at the Bonferroni-adjusted significance level of 0.05/403 = 1.2 × 10-4. Under the simplified model, we computed the statistical power to detect causal SNPs for our sample and study design for the sample FEI prevalences, denoted by Kp, for Whites (22.5%) and Blacks (45%), across a range of total heritabilities, h2t = 15%, 35%, and 55%, where the lattermost total heritability was observed for FEI liability in the current study (Figure 1). It should be noted that because the liability heritability is known to be biased upward, we applied the Dempster-Lerner correction to both the total and SNP-specific heritabilities.3 Close inspection of Figure 1 reveals that varying h2t from 15% to 35% to 55% results in slight decreases in power due to the decreasing ratio of the SNP-specific heritability to the total heritability. However, as seen in all three panels, the more important determinant of power is clearly the FEI prevalence in that the power curve for a Kp of 45% is associated with greater power than the power curve for a Kp of 22.5% across the range of total heritabilities examined. As seen in Figure 1, we have adequate power to detect SNP heritabilities as low as 5% and 6%, respectively, for a Kp of 45% and 22.5%. As noted above, we observed a FEI liability total heritability of 55% consisting of a 47% residual additive genetic heritability (p = 0.019) and 8% F8-mutation specific heritability (p = 0.005). This is the first study to use a GRM based on genotype data and a shared causal F8 mutation matrix to model additive genetic and F8-mutation specific effects.Almeida M, Peralta J, Farook V, …, Blangero J. Pedigree-based random effect tests to screen gene pathways. BMC Proc. 2014; 8(Suppl 1 Genetic Analysis Workshop): S100.Blangero J, Diego VP, Dyer T, …, Göring H. A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees. Adv Genetics. 2013; 81: 1-31.Glahn D, Williams J, McKay D, …, Blangero J. Discovering schizophrenia endophenotypes in randomly ascertained pedigrees. Biol Psychiatry. 2015; 77(1): 75-83. [Display omitted] DisclosuresChitlur:Baxter, Bayer, Biogen Idec, and Pfizer: Honoraria; Novo Nordisk Inc: Consultancy. Dinh:Haplomics Biotechnology Corporation: Employment, Equity Ownership. Howard:Haplomics Biotechnology Corporation: Equity Ownership, Other: Chief Scientific Officer, Patents & Royalties: Patent applications and provisional patent applications ; CSL Behring: Research Funding.

Computational Generalization of Mixed Models on Large-Scale Data with Applications to Genetic Studies


 
 
 
 Aims: To discuss different LMM-based approaches applied in GWAS and software packages implementation and Classify different computational tools that applies LMM approaches according to their applicability and performance. To identify possible SNPs associated to a particular disease using different computational tools based on LMM approaches. To estimate genetic and residual variance parameters that account phenotypic variation of the disease.
 Study Design: Case control study
 Place and Duration of Study: The research was carried out in Tanzania at African Institute of Mathematical Science for six months.
 Methodology: Linear Mixed Models (LMMs) are widely applied in genomic wide associations studies (GWAS) owing to their effectiveness of correcting hidden relationship, population structure and family structure. This essay is aimed at exploring different mathematical approaches of LMMs in GWAS. These approaches are linear mixed model with inclusion of all markers (LMMi) and linear mixed model with exclusion of all markers (LMMe) when calculating genetic relationship matrix. LMMi is more efficient as compared to LMMe when applied in studies of randomly ascertained quantitative traits. The LMM approaches are classified based on their applicability and performance. Two computational GWAS tools namely, PLINK and EMMAX were used which were based on LMM approaches to analyze unpublished real data from West Africa (Gambia and Ghana). Genetic and residual variance parameters were estimated that accounted for the phenotypic variation of the disease to be 0.0594 and 0.0723. A total of 338408 variants and 959 people (484 males, 405 females and 70 missing phenotypes) pass filters and quality control using PLINK was used in the study. Among the remaining phenotypes, 864 are cases and 95 are controls. The performance of different mathematical approaches of LMMs and their software implementation, including EMMAX and Plink via the application to a GWAS of tuberculosis (TB) in 959 individuals in West Africa (Ghana and Gambia) was compared. Of these 864 cases of TB and 95 healthy individuals retained after quality control (QC) using Plink, and 329601 autosome single nucleotide polymorphisms (from chromosome 1 to chromosome 22) included in the analysis after 288 duplicands ID individuals removed after QC. The LMM approaches are classified based on their applicability and performance. Two computational GWAS tools, namely Plink and EMMAX were used in the analysis of data. Genetic and residual variance parameters were estimated that accounted for the phenotypic variation of the disease to be 0.0594 and 0.0723.
 Results: Result showed that SNPs associated with tuberculosis were on chromosome 17 and SNP on chromosome 13 with both having false discovery rate with step up significance value. Plink failed to correct hidden relatedness. Although EMMAX reduced the false positive rate, it still exhibited very low presence of stratification.
 Conclusion: This study aimed at understanding and exploring different approaches of mixed models as applied in genetic studies. Overview of genetic variation, advantages, successes and application of mixed models and current challenges of mixed models in GWAS were discussed. Moreover, the study showed that SNPs was associated with a particular disease using computational tools that applies LMM approaches. The summary statistics from PLINK and EMMAX found two causal SNPs associated with the TB. These SNPs were rs7225581 on chromosome 17 and SNP rs4941412 on chromosome 13 with both having 0.69% FDR H. However, PLINK failed to correct hidden relatedness. This phenotypic variation showed that all common single nucleotide polymorphisms (SNPs) expressed approximately 18.52% of phenotypic variation of the disease.
 
 
 
 

Fine mapping of 2q35 high-risk neuroblastoma locus reveals independent functional risk variants and suggests full-length BARD1 as tumor-suppressor.

A previous genome-wide association study (GWAS) identified common variation at the BARD1 locus as being highly associated with susceptibility to high-risk neuroblastoma, but the mechanisms underlying this association have been not extensively investigated. Here, we performed a fine mapping analysis of BARD1 locus (2q35) using GWAS data from 556 high-risk neuroblastoma patients and 2,575 controls of European-American ancestry, and identified two independent genome-wide neuroblastoma-associated loci. Functional single-nucleotide polymorphism (SNP) prioritization identified two causative variants that independently contributed to neuroblastoma risk, and each replicated robustly in multiple independent cohorts comprising 445 high-risk cases and 3,170 controls (rs17489363: combined p = 1.07 × 10-31 , OR:1.79, 95% CI:1.62-1.98 and rs1048108: combined p = 7.27 × 10-14 , OR:0.65, 95% CI:0.58-0.73). Particularly, the T risk allele of rs17489363 in the canonical promoter region of full-length BARD1 altered binding site of the transcription factor HSF1 and correlated with low expression of full-length BARD1 mRNA and protein. Low-level expression of full-length BARD1 associated with advanced neuroblastoma. In human neuroblastoma cells, attenuating full-length BARD1 increased proliferation and invasion capacity. In conclusion, we have identified two potentially causative SNPs at the BARD1 locus associated with predisposition to high-risk neuroblastoma, and have shown that full-length BARD1 may act as tumor suppressor.

Improving Imputation Accuracy by Inferring Causal Variants in Genetic Studies.

Genotype imputation has been widely utilized for two reasons in the analysis of genome-wide association studies (GWAS). One reason is to increase the power for association studies when causal single nucleotide polymorphisms are not collected in the GWAS. The second reason is to aid the interpretation of a GWAS result by predicting the association statistics at untyped variants. In this article, we show that prediction of association statistics at untyped variants that have an influence on the trait produces is overly conservative. Current imputation methods assume that none of the variants in a region (locus consists of multiple variants) affect the trait, which is often inconsistent with the observed data. In this article, we propose a new method, CAUSAL-Imp, which can impute the association statistics at untyped variants while taking into account variants in the region that may affect the trait. Our method builds on recent methods that impute the marginal statistics for GWAS by utilizing the fact that marginal statistics follow a multivariate normal distribution. We utilize both simulated and real data sets to assess the performance of our method. We show that traditional imputation approaches underestimate the association statistics for variants involved in the trait, and our results demonstrate that our approach provides less biased estimates of these association statistics.

A novel SNP-set analytical method without distinguishing common variants or rare variants in genome-wide association study

Single nucleotide polymorphism (SNP)-set analysis in genome-wide association studies (GWASs) has become a hot topic. Most existing SNP-set analystic methods are designed and work well according to the different natures of common or rare variants and associated diseases. But the information that the disease associated variants are common or rare cannot be gained in advance. Therefore, in this research, we proposed a new and powerful weighted function method without distinguishing common or rare variants to select tagging SNP-set. We applied our selection method to sequence kernel association test (SKAT) and compared the power with some existing methods. The simulation results showed that our method has higher power not only than SKAT in un-weighted case, but also than SKAT in other weighted functions. Moreover, the power is improved significantly when the minor allele frequency (MAF) of causal SNP is relatively small.

Type 2 Diabetes-Associated Genetic Variants Regulate Chromatin Accessibility in Human Islets.

Type 2 diabetes (T2D) is a complex disorder in which both genetic and environmental risk factors contribute to islet dysfunction and failure. Genome-wide association studies (GWAS) have linked single nucleotide polymorphisms (SNPs), most of which are noncoding, in >200 loci to islet dysfunction and T2D. Identification of the putative causal variants and their target genes and whether they lead to gain or loss of function remains challenging. Here, we profiled chromatin accessibility in pancreatic islet samples from 19 genotyped individuals and identified 2,949 SNPs associated with in vivo cis-regulatory element use (i.e., chromatin accessibility quantitative trait loci [caQTL]). Among the caQTLs tested (n = 13) using luciferase reporter assays in MIN6 β-cells, more than half exhibited effects on enhancer activity that were consistent with in vivo chromatin accessibility changes. Importantly, islet caQTL analysis nominated putative causal SNPs in 13 T2D-associated GWAS loci, linking 7 and 6 T2D risk alleles, respectively, to gain or loss of in vivo chromatin accessibility. By investigating the effect of genetic variants on chromatin accessibility in islets, this study is an important step forward in translating T2D-associated GWAS SNP into functional molecular consequences.

Application of Bayesian networks to GAW20 genetic and blood lipid data

BackgroundBayesian networks have been proposed as a way to identify possible causal relationships between measured variables based on their conditional dependencies and independencies. We explored the use of Bayesian network analyses applied to the GAW20 data to identify possible causal relationships between differential methylation of cytosine-phosphate-guanine dinucleotides (CpGs), single-nucleotide polymorphisms (SNPs), and blood lipid trait (triglycerides [TGs]).MethodsAfter initial exploratory linear regression analyses, 2 Bayesian networks analyses were performed. First, we used the real data and modeled the effects of 4 CpGs previously found to be associated with TGs in the Genetics of Lipid Lowering Drugs and Diet Network Study (GOLDN). Second, we used the simulated data and modeled the effect of a fictional lipid modifying drug with 5 known causal SNPs and 5 corresponding CpGs.ResultsIn the real data we show that relationships are present between the CpGs, TGs, and other variables—age, sex, and center. In the simulated data, we show, using linear regression, that no CpGs and only 1 SNP were associated with a change in TG levels, and, using Bayesian network analysis, that relationships are present between the change in TG levels and most SNPs, but not with CpGs.ConclusionsEven when the causal relationships between variables are known, as with the simulated data, if the relationships are not strong then it is challenging to reproduce them in a Bayesian network.

The challenge of detecting genotype-by-methylation interaction: GAW20

BackgroundGAW20 working group 5 brought together researchers who contributed 7 papers with the aim of evaluating methods to detect genetic by epigenetic interactions. GAW20 distributed real data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, including single-nucleotide polymorphism (SNP) markers, methylation (cytosine-phosphate-guanine [CpG]) markers, and phenotype information on up to 995 individuals. In addition, a simulated data set based on the real data was provided.ResultsThe 7 contributed papers analyzed these data sets with a number of different statistical methods, including generalized linear mixed models, mediation analysis, machine learning, W-test, and sparsity-inducing regularized regression. These methods generally appeared to perform well. Several papers confirmed a number of causative SNPs in either the large number of simulation sets or the real data on chromosome 11. Findings were also reported for different SNPs, CpG sites, and SNP–CpG site interaction pairs.ConclusionsIn the simulation (200 replications), power appeared generally good for large interaction effects, but smaller effects will require larger studies or consortium collaboration for realizing a sufficient power.

STAYGREEN, STAY HEALTHY: a loss-of-susceptibility mutation in the STAYGREEN gene provides durable, broad-spectrum disease resistances for over 50years of US cucumber production.

The Gy14 cucumber (Cucumis sativus) is resistant to oomyceteous downy mildew (DM), bacterial angular leaf spot (ALS) and fungal anthracnose (AR) pathogens, but the underlying molecular mechanisms are unknown. Quantitative trait locus (QTL) mapping for the disease resistances in Gy14 and further map-based cloning identified a candidate gene for the resistant loci, which was validated and functionally characterized by spatial-temporal gene expression profiling, allelic diversity and phylogenetic analysis, as well as local association studies. We showed that the triple-disease resistances in Gy14 were controlled by the cucumber STAYGREEN (CsSGR) gene. A single nucleotide polymorphism (SNP) in the coding region resulted in a nonsynonymous amino acid substitution in the CsSGR protein, and thus disease resistance. Genes in the chlorophyll degradation pathway showed differential expression between resistant and susceptible lines in response to pathogen inoculation. The causal SNP was significantly associated with disease resistances in natural and breeding populations. The resistance allele has undergone selection in cucumber breeding. The durable, broad-spectrum disease resistance is caused by a loss-of-susceptibility mutation of CsSGR. Probably, this is achieved through the inhibition of reactive oxygen species over-accumulation and phytotoxic catabolite over-buildup in the chlorophyll degradation pathway. The CsSGR-mediated host resistance represents a novel function of this highly conserved gene in plants.

Abstract 236: Identification of novel cancer target genes by combining data from the cancer genome-wide association studies (GWAS), regulatory DNA elements and The Cancer Genome Atlas (TCGA)

Abstract Unlike tumors that develop because of somatic genetic alterations such as frequent coding mutations or chromosomal deletions, amplifications or translocations, some tumors are addicted to expression of certain genes for their sustained proliferation and survival - for example, LIM domain only 1 (LMO1) expression in neuroblastoma (NB). Numerous genome-wide association studies (GWAS) have identified significant associations between germline single-nucleotide polymorphisms (SNPs), many in the non-coding genome, and cancer. A robust genome-wide association has been reported previously between an intronic LMO1 SNP and NB susceptibility. By further sequencing and epigenetic fine mapping of the SNP locus, it was demonstrated that the causal SNP is part of a transcription factor binding site within an enhancer element, regulates expression of LMO1 and in turn increases the tumorigenic potential. To identify additional examples of regulatory SNPs as cancer drivers, we overlaid published genome-wide significant cancer associations with active chromatin marks from Encyclopedia of DNA Elements and searched for SNPs that resided within gene regulatory elements. To map these SNPs to candidate genes and determine direction of effect, we co-localized GWAS signals with expression quantitative trait (eQTL) signals from the Genotype-Tissue Expression (GTEx) Consortium database. Lastly, we checked for gene amplification and/or overexpression of the mapped genes in the Cancer Genome Atlas (TCGA) data to identify a set of target genes that not only exhibit significant cancer association in GWAS, but also have evidence for epigenetic regulation and propensity for amplification and/or overexpression in tumors. We identified more than 25 novel cancer-target pairs with strong germline, regulatory and somatic evidence. A look up through synthetic lethality screen data available in-house suggested that several of these targets are self-lethal, further underscoring their importance for cancer cell proliferation and survival. Additional in vitro experiments are being planned to further validate the targets. Citation Format: Diptee A. Kulkarni, Karl Guo, Junping Jing, Mugdha Khaladkar, Kijoung Song, Coco Dong, David Cooper, Benjamin Schwartz. Identification of novel cancer target genes by combining data from the cancer genome-wide association studies (GWAS), regulatory DNA elements and The Cancer Genome Atlas (TCGA) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 236.

Genetic Effects on Enhancer Activity in Human Pancreatic Islets

Genome-wide association studies (GWAS) have identified >100 single nucleotide polymorphisms (SNPs) that modulate risk for type 2 diabetes (T2D) and related traits. Using functional genomic profiling, we and others have shown that T2D GWAS SNPs are significantly enriched to overlap pancreatic islet stretch enhancer chromatin states, implicating these regulatory elements as biological mediators of disease risk. Recent studies that integrate genetic variation and islet gene expression have identified expression quantitative trait loci (eQTL) that nominate target effector transcripts for T2D GWAS SNPs. However, the subset of causal regulatory SNPs that mediate genetic control of gene expression are unknown. To help close this gap in knowledge, we have performed capped analysis of gene expression (CAGE) on RNA enriched for active enhancers (eRNA-CAGE) across 40 human islet samples. Comparison of these profiles to previously published islet chromatin maps revealed significant enrichment for active chromatin states (P<0.001). Interestingly, we observe unbalanced bidirectional transcription at chromatin-defined intronic enhancer regions, where the predominant direction of enhancer transcription is concordant with the transcribed direction of the endogenous gene, suggesting interactions between gene and enhancer activity. One such example is an ABCC8 intronic enhancer that we previously functionally validated using a mouse transgenic reporter assay. We further performed genotyping and imputation, which allowed us to run an eRNA-QTL scan. We identified 4associations (P<0.for beta distribution adjusted P-value; QTLtools package), which are significantly enriched (P=7.3x10-7) to occur in islet active enhancer chromatin states. Notably, we found islet eRNA-QTL that overlap T2D GWAS SNPs and islet eQTL. Together, these results form a genetic map of human islet enhancer activity that is a foundation for interpreting regulatory mechanisms at T2D GWAS loci. Disclosure A. Varshney: None. S. Parker: None.

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.

A Meta-Analysis of Genome-Wide Association Studies of Growth Differentiation Factor-15 Concentration in Blood

Blood levels of growth differentiation factor-15 (GDF-15), also known as macrophage inhibitory cytokine-1 (MIC-1), have been associated with various pathological processes and diseases, including cardiovascular disease and cancer. Prior studies suggest genetic factors play a role in regulating blood MIC-1/GDF-15 concentration. In the current study, we conducted the largest genome-wide association study (GWAS) to date using a sample of ∼5,400 community-based Caucasian participants, to determine the genetic variants associated with MIC-1/GDF-15 blood concentration. Conditional and joint (COJO), gene-based association, and gene-set enrichment analyses were also carried out to identify novel loci, genes, and pathways. Consistent with prior results, a locus on chromosome 19, which includes nine single nucleotide polymorphisms (SNPs) (top SNP, rs888663, p = 1.690 × 10-35), was significantly associated with blood MIC-1/GDF-15 concentration, and explained 21.47% of its variance. COJO analysis showed evidence for two independent signals within this locus. Gene-based analysis confirmed the chromosome 19 locus association and in addition, a putative locus on chromosome 1. Gene-set enrichment analyses showed that the“COPI-mediated anterograde transport” gene-set was associated with MIC-1/GDF15 blood concentration with marginal significance after FDR correction (p = 0.067). In conclusion, a locus on chromosome 19 was associated with MIC-1/GDF-15 blood concentration with genome-wide significance, with evidence for a new locus (chromosome 1). Future studies using independent cohorts are needed to confirm the observed associations especially for the chromosomes 1 locus, and to further investigate and identify the causal SNPs that contribute to MIC-1/GDF-15 levels.

SMMB: a stochastic Markov blanket framework strategy for epistasis detection in GWAS

Large scale genome-wide association studies (GWAS) are tools of choice for discovering associations between genotypes and phenotypes. To date, many studies rely on univariate statistical tests for association between the phenotype and each assayed single nucleotide polymorphism (SNP). However, interaction between SNPs, namely epistasis, must be considered when tackling the complexity of underlying biological mechanisms. Epistasis analysis at large scale entails a prohibitive computational burden when addressing the detection of more than two interacting SNPs. In this paper, we introduce a stochastic causal graph-based method, SMMB, to analyze epistatic patterns in GWAS data. We present Stochastic Multiple Markov Blanket algorithm (SMMB), which combines both ensemble stochastic strategy inspired from random forests and Bayesian Markov blanket-based methods. We compared SMMB with three other recent algorithms using both simulated and real datasets. Our method outperforms the other compared methods for a majority of simulated cases of 2-way and 3-way epistasis patterns (especially in scenarii where minor allele frequencies of causal SNPs are low). Our approach performs similarly as two other compared methods for large real datasets, in terms of power, and runs faster. Parallel version available on https://ls2n.fr/listelogicielsequipe/DUKe/128/. Supplementary data are available at Bioinformatics online.

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.

Abstract A34: The genetic etiology of esophageal cancer in South African Black populations

Abstract Esophageal squamous cell carcinoma (ESCC) is common in Black populations of the sub-Saharan Africa, with high incidence regions in East and South Africa. Clinical presentation in Africa is late, and treatment is mainly palliative with a very poor prognosis. Various environmental risk factors have been identified, but the possible contribution of genetics to disease risk is an important question, which is unresolved. The aim of this project is to test the hypothesis that genetic variation in the South African Black population contributes significantly to the risk of ESCC. We have begun by testing genetic risk factors for ESCC which were identified by genome-wide association scans in other populations for association with ESCC in the South African Black population. Cases with a histologically confirmed diagnosis of ESCC and population controls were recruited, with informed consent and institutional ethical approval. All individuals were from the Black population of the Western Cape, with 98.2% being from the Xhosa ethnic group. A total of 513 ESCC cases and 820 controls were genotyped for 15 single nucleotide polymorphisms (SNPs) by validated TaqMan assays on a 7900HT (Applied Biosystems). SNPs were tested for association using an allelic chi-square test; odds ratios (OR) and 95% confidence intervals (CI) used the common allele as reference. A significance threshold of p=0.0033 was applied, from a Bonferroni multiple testing correction for 15 SNPs. No significant evidence of association was observed for the index SNPs at the previously reported loci ATP1B2, CASP8, c20orf54, HEATR3, PDE4D, PLCE1, PTPN2, RUNX1, SMG6, ST6GAL1, TMEM173 and UNC5L loci. However, the SNP rs2239815 at XBP1 on chromosome 22q12 was associated with ESCC (OR =1.41, P = 0.00087), as were 2 SNPs in CHEK2, rs4822983 (OR = 1.31, P = 0.0013) and rs1033667 (OR = 1.29, P = 0.0025). These 3 SNPs are within 70kb of each other and are in partial linkage disequilibrium in African populations. Fine-mapping of this locus will be required to determine the causal gene and variant/s driving this association. The lack of association with ESCC in the Black South African population at most of the loci previously associated in Asian GWAS suggests that at least some of these may not contribute to susceptibility to ESCC in the South African population. However, expansion of samples sizes and thus statistical power is required for more definitive exclusions, and the lower level of linkage disequilibrium (LD) across African genomes may produce a negative result if the SNP which is tagging the causal variant in Asian or European populations is in weak LD with the causal SNP in African populations. In order to address these issues we are now expanding the power and density of this study by genotyping 36 SNPs from loci of interest in 1000 South African ESCC cases and 940 controls from the Johannesburg Cancer Study by mass spectrometry using the MassArray genotyping system (Agena Bioscience). Citation Format: Wenlong Carl Chen, Hannah Bye, Marco Matejcic, Robyn Kerr, Elvira Singh, Natalie J. Prescott, Cathryn M. Lewis, Chantal Babb de Villers, Iqbal Parker, Christopher G. Mathew. The genetic etiology of esophageal cancer in South African Black populations [abstract]. In: Proceedings of the AACR International Conference: New Frontiers in Cancer Research; 2017 Jan 18-22; Cape Town, South Africa. Philadelphia (PA): AACR; Cancer Res 2017;77(22 Suppl):Abstract nr A34.

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.

Embracing polygenicity: a review of methods and tools for psychiatric genetics research.

The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.

Genome re-sequencing to identify single nucleotide polymorphism markers for muscle color traits in broiler chickens.

ObjectiveMeat quality including muscle color in chickens is an important trait and continuous selective pressures for fast growth and high yield have negatively impacted this trait. This study was conducted to investigate genetic variations responsible for regulating muscle color.MethodsWhole genome re-sequencing analysis using Illumina HiSeq paired end read method was performed with pooled DNA samples isolated from two broiler chicken lines divergently selected for muscle color (high muscle color [HMC] and low muscle color [LMC]) along with their random bred control line (RAN). Sequencing read data was aligned to the chicken reference genome sequence for Red Jungle Fowl (Galgal4) using reference based genome alignment with NGen program of the Lasergene software package. The potential causal single nucleotide polymorphisms (SNPs) showing non-synonymous changes in coding DNA sequence regions were chosen in each line. Bioinformatic analyses to interpret functions of genes retaining SNPs were performed using the ingenuity pathways analysis (IPA).ResultsMillions of SNPs were identified and totally 2,884 SNPs (1,307 for HMC and 1,577 for LMC) showing >75% SNP rates could induce non-synonymous mutations in amino acid sequences. Of those, SNPs showing over 10 read depths yielded 15 more reliable SNPs including 1 for HMC and 14 for LMC. The IPA analyses suggested that meat color in chickens appeared to be associated with chromosomal DNA stability, the functions of ubiquitylation (UBC) and quality and quantity of various subtypes of collagens.ConclusionIn this study, various potential genetic markers showing amino acid changes were identified in differential meat color lines, that can be used for further animal selection strategy.