Significant Genetic Variants Research Articles

The role of linoleic acid in asthma and inflammatory markers: a Mendelian randomization study

Asthma is a common respiratory disease, possibly caused by autoimmunity. Linoleic acid (LA), the main n-6 (ω-6) PUFA from widely used vegetable oils, is thought to suppress immune responses that might have benefits for asthma. However, this question has not been examined in randomized controlled trials. To obtain unconfounded estimates, we assessed how genetically predicted LA affected asthma using 2-sample Mendelian randomization. We also examined its role in white blood cell traits (eosinophil, neutrophil, and low monocyte counts) identified as potential causal factors in asthma. We used 18 uncorrelated, genome-wide significant genetic variants to predict LA, which we applied to a large genetic case (n=19,954)-control (n=107,715) study of asthma, to the UK Biobank (408,961 people of European ancestry with 26,332 asthma cases), and for white blood cell traits to the UK Biobank. We also repeated the analysis on asthma using 29 replicated, functionally relevant genetic variants. In addition, we examined the role of asthma in LA to assess reverse causality. Genetically predicted LA was associated with lower risk of asthma (OR: 0.89 per SD increase in LA; 95% CI: 0.85, 0.93), with no association of asthma with LA. Genetically predicted LA was associated with lower eosinophil count (-0.03; 95% CI: -0.061, -0.004) and lower neutrophil count (-0.04; 95% CI: -0.057, -0.023). These estimates were robust to different selections of genetic variants and sensitivity analyses. LA might protect against asthma possibly via white blood cell traits, with relevance to the identification of effective new interventions for asthma.

Association between rs2303861 polymorphism in CD82 gene and non-alcoholic fatty liver disease: a preliminary case-control study

AimTo investigate the genetic factors involved in the development of non-alcoholic fatty liver disease (NAFLD) and its sequelae in a Middle Eastern population.MethodsThis genetic case-control association study, conducted in 2018, enrolled 30 patients with NAFLD and 30 control individuals matched for age, sex, and body mass index. After quality control measures, entire exonic regions of 3654 genes associated with human diseases were sequenced. Allelic association test and enrichment analysis of the significant genetic variants were performed.ResultsThe association analysis was conducted on 27 NAFLD patients and 28 controls. When Bonferroni correction was applied, NAFLD was significantly associated with rs2303861, a variant located in the CD82 gene (P = 2.49 × 10−7, adjusted P = 0.0059). When we used Benjamini-Hochberg adjustment for correction, NAFLD was significantly associated with six more variants. Enrichment analysis of the genes corresponding to all the seven variants showed significant enrichment for miR-193b-5p (P = 0.00004, adjusted P = 0.00922).ConclusionA variant on CD82 gene and a miR-193b expression dysregulation may have a role in the development and progression of NAFLD and its sequelae.

Role of Four ABC Transporter Genes in Pharmacogenetic Susceptibility to Breast Cancer in Jordanian Patients.

Breast cancer pharmacogenetics is increasingly being explored due to chemotherapy resistance among certain classes of patients. The ATP binding cassette (ABC) transporter genes have been previously implicated in breast cancer progression and drug response. In the present study, single nucleotide polymorphisms (SNPs) from the ABCC1, ABCC2, ABCB1, and ABCG2 genes were screened in breast cancer patients and healthy volunteers from the Jordanian-Arab population. Only the ABCB1 SNPs showed a significant association with BC in Jordanian-Arab patients, and the ABCB1 SNP rs2032582 exhibited a strong genotypic association with BC. With regard to the clinical characteristics of BC, the ABCC2 SNPs rs2273697 and rs717620 were found to be significantly associated with age at breast cancer diagnosis and breastfeeding status, while the ABCB1 SNP rs1045642 was significantly associated with age at breast cancer diagnosis. In terms of pathological characteristics, the ABCC1 SNP rs35628 and the ABCB1 SNP rs2032582 were significantly associated with tumor size, the ABCC2 SNP rs2273697 was significantly associated with estrogen receptor status, and the ABCG2 SNP rs2231142 was significantly associated with axillary lymph node status. In this current study, we assume that significant genetic variants within the ABC superfamily may increase the risk of breast cancer among Jordanian women. Furthermore, these variants might be responsible for worse BC prognosis.

Pleiotropic mapping for genome-wide association studies using group variable selection

Introduction Results from genome-wide association studies (GWAS) suggest that complex diseases are often affected by many variants with small effects, known as polygenicity. Bayesian methods provide attractive tools for identifying signal in data where the effects are small but clustered. For example, by incorporating biological pathway membership in the prior they are able to integrate the ideas of gene set enrichment to identify groups of biologically significant genetic variants. Accumulating evidence suggests that genetic variants may affect multiple different complex diseases, a phenomenon known as pleiotropy. Method In this work we propose frequenstist and Bayesian statistical method to leverage pleiotropic effects and incorporate prior pathway knowledge to increase statistical power and identify important risk variants. We offer novel feature selection methods for the group variable selection in multi-task regression problem. We develop methods using both penalised likelihood methods and Bayesian spike and slab priors to induce structured sparsity at a gene and SNP level. We implement Gibbs sampling algorithms for the Bayesian analysis and an alternating direction method of multipliers (ADMM) algorithm for the penalised regression methods. The performances of the proposed approaches are compared to state-of-the-art variable selection strategies on simulated data sets. Results The penalised likelihood approaches are computationally efficient using alternating direction method of multipliers algorithm. These approaches perform reasonably well in variable selection but the reconstructed signal is underestimated. The multivariate Bayesian sparse group selection with spike and slab prior performed the best in terms of signal recovery. The Bayesian method provides a natural method for quantifying the variability of the estimated coefficients. Simulation results suggest that when computationally possible the Bayesian estimators should be used. Conclusion The developed statistical approaches is applied for enriching our insights about the genetic mechanisms of thyroid and breast cancer types. The analysed data come from case-control studies including 6677 SNPs from 618 genes from 10 non-overlapping gene pathways. The thyroid cancer data set includes 482 cases and 463 controls. The breast cancer data set includes 1172 cases and 1125 controls.

A Multi-Trait Approach Identified Genetic Variants Including a Rare Mutation in RGS3 with Impact on Abnormalities of Cardiac Structure/Function

Heart failure is a major cause for premature death. Given the heterogeneity of the heart failure syndrome, identifying genetic determinants of cardiac function and structure may provide greater insights into heart failure. Despite progress in understanding the genetic basis of heart failure through genome wide association studies, the heritability of heart failure is not well understood. Gaining further insights into mechanisms that contribute to heart failure requires systematic approaches that go beyond single trait analysis. We integrated a Bayesian multi-trait approach and a Bayesian networks for the analysis of 10 correlated traits of cardiac structure and function measured across 3387 individuals with whole exome sequence data. While using single-trait based approaches did not find any significant genetic variant, applying the integrative Bayesian multi-trait approach, we identified 3 novel variants located in genes, RGS3, CHD3, and MRPL38 with significant impact on the cardiac traits such as left ventricular volume index, parasternal long axis interventricular septum thickness, and mean left ventricular wall thickness. Among these, the rare variant NC_000009.11:g.116346115C > A (rs144636307) in RGS3 showed pleiotropic effect on left ventricular mass index, left ventricular volume index and maximal left atrial anterior-posterior diameter while RGS3 can inhibit TGF-beta signaling associated with left ventricle dilation and systolic dysfunction.

Refractive Error Has Minimal Influence on the Risk of Age-Related Macular Degeneration: A Mendelian Randomization Study

To test the hypothesis that refractive errors such as myopia and hyperopia cause an increased risk of age-related macular degeneration (AMD) and to quantify the degree of risk. Two-sample Mendelian randomization analysis of data from a genome-wide association study. As instrumental variables for refractive error, 126 genome-wide significant genetic variants identified by the Consortium for Refractive Error and Myopia and 23andMe Inc. were chosen. The association with refractive error for the 126 variants was obtained from a published study for a sample of 95,505 European ancestry participants from UK Biobank. Association with AMD for the 126 genetic variants was determined from a genome-wide association study (GWAS) published by the International Age-related Macular Degeneration Genomics consortium of 33,526 (16,144 cases and 17,832 controls) European ancestry participants. Two-sample Mendelian randomization (MR) analysis was used to assess the causal role of refractive error on AMD risk, using the 126 genetic variants associated with refractive error as instrumental variables, under the assumption that the relationship between refractive error and AMD risk is linear. the risk AMD was caused by a 1-diopter (D) change in refractive error. MR analysis suggested that refractive error had very limited influence on the risk of AMD. Specifically, 1 D more hyperopic refractive error was associated with an odds ratio (OR) of 1.080 (95% confidence interval [CI], 1.021-1.142; P= 0.007) increased risk of AMD. MR-Egger, MR pleiotropy residual sum and outlier, weighted median, and Phenoscanner-based sensitivity analyses detected minimal evidence to suggest that this result was biased by horizontal pleiotropy. Under the assumption of a linear relationship between refractive error and the risk of AMD, myopia and hyperopia only minimally influence the causal risk for AMD. Thus, inconsistently reported strong associations between refractive error and AMD are likely to be the result of noncausal factors such as stochastic variation, confounding, or selection bias.

Screening the RFX6-DNA binding domain for potential genetic variants in patients with type 2 diabetes.

BACKGROUNDThe regulatory factor X6 (RFX6), a member of regulatory factor X family, is known to play a key role in the development and differentiation of pancreatic beta cells as well as insulin production and secretion. However, the potential role of RFX6 in type 2 diabetes (T2D) is still unclear.AIMRecent studies have indicated that RFX6 binding to DNA could be disrupted in diabetes. Therefore, in this study we investigated whether genetic mutations are present in the DNA binding domain of RFX6 gene that could abrogate its function in T2D.METHODSA cohort of T2D patients was enrolled in this study, and the gene encoding the DNA binding domain of RFX6 was amplified by polymerase chain reaction and then analysed by direct DNA sequencing.RESULTSThe DNA sequence analysis revealed the absence of any exonic mutation. However, we have identified a new heterozygous single nucleotide polymorphism (IVS6+31 C>T) in the intronic region of DNA binding domain gene that is present in 9.2% and 8.5% of diabetic and control people, respectively (P = 0.97).CONCLUSIONWe report the absence of any significant genetic variant that could affect the function of RFX6-DNA binding domain in T2D.

Investigating causality in the association between vitamin D status and self-reported tiredness

Self-reported tiredness or low energy, often referred to as fatigue, has been linked to low levels of circulating 25-hydroxyvitamin D (25OHD), a biomarker of vitamin D status. Although it is uncertain if the association is causal, fatigue is a common indication for testing, and correcting, low 25OHD-levels. We used two-sample Mendelian randomization to test for genetic evidence of a causal association between low 25OHD-levels and fatigue. Genetic-25OHD associations were estimated from the largest genome-wide association study of vitamin D to date, and genetic-fatigue associations were estimated in 327,478 individuals of European descent in UK Biobank, of whom 19,526 (5.96%) reported fatigue (tiredness or low energy nearly every day over the past two weeks). Using seven genome-wide significant 25OHD-reducing genetic variants, there was little evidence for a causal effect of 25OHD on fatigue [odds ratio for fatigue was 1.05 with 95% confidence interval (CI) of 0.87–1.27 per unit decrease in log-transformed 25OHD (1.02 with 95% CI of 0.99-1.06 per 1-SD decrease in log-transformed 25OHD)]. There was also little evidence of association between any individual 25OHD-reducing variant and fatigue. Our results suggest that a clinically relevant protective effect of 25OHD-levels on fatigue is unlikely. Therefore, vitamin D supplementation of the general population to raise 25OHD-levels is not likely to be useful in preventing fatigue.

A comprehensive gene-environment interaction analysis in Ovarian Cancer using genome-wide significant common variants.

As a follow-up to genome-wide association analysis of common variants associated with ovarian carcinoma (cancer), our study considers seven well-known ovarian cancer risk factors and their interactions with 28 genome-wide significant common genetic variants. The interaction analyses were based on data from 9971 ovarian cancer cases and 15,566 controls from 17 case-control studies. Likelihood ratio and Wald tests for multiplicative interaction and for relative excess risk due to additive interaction were used. The top multiplicative interaction was noted between oral contraceptive pill (OCP) use (ever vs. never) and rs13255292 (p value = 3.48 × 10-4 ). Among women with the TT genotype for this variant, the odds ratio for OCP use was 0.53 (95% CI = 0.46-0.60) compared to 0.71 (95%CI = 0.66-0.77) for women with the CC genotype. When stratified by duration of OCP use, women with 1-5 years of OCP use exhibited differential protective benefit across genotypes. However, no interaction on either the multiplicative or additive scale was found to be statistically significant after multiple testing correction. The results suggest that OCP use may offer increased benefit for women who are carriers of the T allele in rs13255292. On the other hand, for women carrying the C allele in this variant, longer (5+ years) use of OCP may reduce the impact of carrying the risk allele of this SNP. Replication of this finding is needed. The study presents a comprehensive analytic framework for conducting gene-environment analysis in ovarian cancer.

Pharmacogenomics – From Large Collaborations To Implementation and Drug Discovery

Overall Abstract Introduction Recent genetic studies have identified variants in specific gene regions showing not only evidence of association but also biological mechanisms underlying the association. The goal of this symposium is to demonstrate how human genetic association findings can be translated into improved understanding of biological mechanism, disease prognosis, and treatment response using examples across different psychiatric disorders such as mood disorders, schizophrenia, eating disorder, and addiction. Methods A variety of methods are used including randomized treatment trials, large genome wide association studies or meta-analyses, bioinformatics, and epigenetics. Results The first talk by Dr. Schulze is on genotype-phenotype relationships in bipolar and related traits. He will demonstrate how applying the friendly data sharing approach spanning five continents has helped pave the way for a better understanding of the genetic basis of response to lithium treatment, serving as an example for successful precision medicine research. The 2nd talk by Dr. Muller is on current evidence, evidence adjudication, implementation challenges of pharmacogenetics testing for antidepressants and antipsychotics use. He will present a review of the evidence that was considered in formulating that guidance, along with a summary of the main recommendations and associated ethical issues. He will also discuss the kinds of evidence still needed to incorporate genetic testing in clinical decision-making. The 3rd talk by Dr. Breen is on translation of genetic findings from large GWAS studies in Psychiatric Genomics Consortium (PGC) into druggable genomes. He will discuss how discovery of the biological pathways can not only reveal etiological pathways but also lead to drug discovery. The 4th talk by Dr. Chen demonstrates the significant genetic variants that predict nicotine dependence, smoking cessation, and response to cessation pharmacotherapy. These data suggest that genetic risks may predict smoking cessation outcomes and moderate the effect of pharmacological treatments. Some pharmacogenetic findings have been replicated in meta-analyses of multiple smoking cessation trials. The variation in efficacy between smokers with different genetic markers suggests that personalized smoking cessation pharmacotherapy based upon genotype could maximize the efficiency of such treatment while minimizing side effects. As a result, these genetic markers are useful to predict clinical predictor such as the number needed to treat (NNT) and the number needed to harm. Conclusion Different psychiatric disorders serve as successful examples in how genetic findings in human association studies inform biological experiments to uncover the underlying mechanisms and treatments tailored to individual genetic background. Each presentation represents unique and complementary research paradigms in understanding how genomics inform personalized medicine by linking the biology and treatment in different psychiatric disorders. Learning Objectives 1) human genetic studies inform the biological mechanisms of psychiatric disorders, 2) pharmacogenetic studies inform personalized treatments with maximized efficacy and minimized risk of side effects.

Pathways To Precision Medicine In Smoking Cessation Treatments

Introduction: Cigarette smoking is highly addictive and prevalent among individuals with mental illness. Modern genetic research has identified robust genetic influences on nicotine dependence. An important step in translating these genetic findings is to identify the genetic factors affecting smoking cessation in order to enhance current smoking cessation treatments. MethodsWe investigated the significant genetic variants that predict nicotine dependence, smoking cessation, and response to cessation pharmacotherapy using both large cohort studies of smokers and randomized controlled trials. ResultsWe find that genetic risks may predict prognosis and response to pharmacological treatments. For prognosis, the nicotinic receptor genotypes predict delayed quitting and earlier lung cancer diagnoses after adjusting for smoking quantity in large meta-analyses of studies of lung cancer and nicotine dependence. For treatment response, some pharmacogenetic findings have been replicated in meta-analyses or multiple smoking cessation trials for both pharmacodynamic markers (e.g., CHRNA5) and pharmacokinetic marker (e.g., CYP2A6). The variation in efficacy between smokers with different genetic markers supports the notion that personalized smoking cessation intervention based upon genotype could maximize the efficiency of such treatment while minimizing side effects, thus influencing the number needed to treat (NNT) and the number needed to harm. ConclusionCurrent evidence strongly suggests that cessation failure and cessation pharmacotherapy effectiveness are modulated by biomarkers such as nicotinic receptor genotypes, nicotine metabolism ratio (NMR), or polygenic risk scores for nicotine dependence. These findings strengthen the case for further research on genetics of smoking cessation success, and the development and rigorous testing of treatments for smokers with different genetic risk profiles. Learning Objectives1) human genetic studies can inform the biological mechanisms of nicotine dependence and health consequences such as lung cancer, 2) pharmacogenetic studies may inform personalized treatments for tobacco use disorder, possibly increasing efficacy and reducing risk of side effects.

Genetic Health Economy

The article provides an overview of the social and economic effects of restructuring healthcare on the basis of regular use of genetic data. The review covers such problems as medical efficiency of the proposed model, its affordability and economic efficiency (micro and macro levels), and external (technical, behavioral, ethical, and legal) constraints. Empirical studies indicate low medical and economic efficiency of universal coverage of whole genome sequencing. For most diseases, genome analysis does not allow one to take adequate measures to prevent risk of suffering from it given the lack of information about its genetic nature, the limited role of genetic factors in onset and progression of diseases and the absence of effective low risk management practices. The combination of rarity of significant genetic variants and low efficiency of early prevention leads to worse efficiency of universal genetic screening compared with traditional preventive measures. Promising directions for genome medical technologies in the short and medium term are diagnosis of hereditary diseases and strong predisposition to certain diseases as well as drug therapy personalization. The rate of adoption of new technologies in regular clinical practice will be determined by the willingness of the state and private entities to invest in the required infrastructure, including creating national banks for genetic data, acquiring equipment for analysis and storing the data obtained, and training of qualified personnel. At the moment Russia has a backlog of several years in this area compared with advanced countries. The national program for the development of genetic technologies, capable of closing the gap, is expected to be approved in early 2019.

Integrative Next Generation Sequencing of Myeloproliferative Neoplasms and Correlation of Genetic Variations to Disease Severity

INTRODUCTIONMyeloproliferative neoplasms (MPNs) are clonal hematopoietic stem cell diseases characterized by the overproduction of differentiated myeloid cells. In classical BCR-ABL negative MPNs -essential thrombocythemia (ET), polycythemia vera (PV), prefibrotic primary myelofibrosis (prePMF) and overt myelofibrosis(MF) - driver mutations are described in JAK2, CALR and MPL genes. However, there is no compelling evidence to explain how each mutant gene can lead to phenotypically distinct and/or overlapping diseases, nor is it understood how additional gene aberrations modulate MPN phenotype. In an attempt to understand the molecular events that underlay clinical characteristics of MPNs, we sequenced hematopoietic cells of MPN patients with a focus on MF landscape. METHODSPatients were consented to MI-ONCOSEQ study approved by the University of Michigan IRB. MI-ONCOSEQ is a CLIA-certified Next Generation Sequencing (NGS) platform designed to identify genetic aberrations in a panel of 1,711 genes. MI-ONCOSEQ test can identify somatic point mutations, small insertions/deletions (indels), and copy number alterations by comparing the tumor sample (blood or bone marrow derived myeloid cells) to the matched normal sample (buccal swab or saliva derived normal cells). The assay also includes gene expression and gene fusion analysis of 24,774 capture targets based on transcriptome sequencing. Significant genetic variants were reported based on tumor burden in sample, as samples were collected at various stages of disease progression and treatment. Diagnoses of ET, PV, prePMF, or overt MF were confirmed by 2016 WHO classification as close to the time of sample collection as possible. RESULTSA cohort of 166 patients with suspected classical MPNs were enrolled, of which 32 were excluded from analysis due to diagnosis of other myeloid malignancy or a benign condition. Of the 134 evaluable patients, 97 (age range 33-84 yrs) were determined to have WHO-defined overt MF and 37 (age range 20-83 yrs) were determined to have WHO-defined ET, PV, or prePMF (14, 15, and 8, respectively). Most (95% of overt MF and 90% of ET/PV/prePMF) harbored driver mutations in JAK2, CALR, or MPL. In total, 167 genes were found to be mutated in 97 patients with overt MF (51 with primary MF, 46 with post-PV/ET MF) (Figure 1) as compared to only 37 genes in 37 patients with ET/PV/prePMF (Figure 2). There was a significant difference (p<0.0001) between the number of genetic variants in overt MF (4 per patient, range 0-9) as compared to ET/PV/prePMF (2 per patient, range 0-7). Of note, RAS pathway genes (NRAS, KRAS, and NF1) were found to be mutated only in overt MF (24% of cases) and not in ET/PV/prePMF. A comparison of genetic variant rate in ET to PV was also found to be significant (p<0.0007) 1 vs 3 variants per patient respectively, whereas prePMF presented a genetic variant landscape that was in between ET and PV. Both primary and post-PV/ET MF had the same average variant rate of 4 per patient. CONCLUSIONSTo our knowledge, this is the largest integrative (NGS, gene expression, and transcriptome) sequencing study in patients with MPNs to date. Our results illustrate the following: 1) MF is likely an evolutionary disease arising from ET/PV/PrePMF by accumulating mutations, 2) RAS pathway activation might play a role in the transformation of ET/PV/PrePMF to overt MF and contribute to the proliferative nature of the disease, 3) Contrary to prior reports we observed an increasing mutation rate from ET < prePMF < PV < overt MF, 4) The aggressive nature of PV might be partly explained by a higher mutation burden compared to ET, 5) PrePMF displays an intermediate number of mutations which correlates with the clinical phenotype. Gene expression analysis to determine activated pathways and multivariate analysis to correlate clinical phenotype and gene to gene relationships are in progress. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Integrating genetic, transcriptional, and biological information provides insights into obesity

Objective:Indices of body fat distribution are heritable, but few genetic signals have been reported from genome-wide association studies (GWAS) of computed tomography (CT) imaging measurements of body fat distribution. We aimed to identify genes associated with adiposity traits and the key drivers that are central to adipose regulatory networks.Subjects:We analyzed gene transcript expression data in blood from participants in the Framingham Heart Study, a large community-based cohort (n up to 4,303), as well as implemented an integrative analysis of these data and existing biological information.Results:Our association analyses identified unique and common gene expression signatures across several adiposity traits, including body mass index, waist-hip ratio, waist circumference, and CT-measured indices, including volume and quality of visceral and subcutaneous adipose tissues. We identified six enriched KEGG pathways and two co-expression modules for further exploration of adipose regulatory networks. The integrative analysis revealed four gene sets (Apoptosis, p53 signaling pathway, Proteasome, Ubiquitin mediated proteolysis) and two co-expression modules with significant genetic variants and 94 key drivers/genes whose local networks were enriched with adiposity-associated genes, suggesting that these enriched pathways or modules have genetic effects on adiposity. Most identified key driver genes are involved in essential biological processes such as controlling cell cycle, DNA repair and degradation of regulatory proteins and are cancer related.Conclusion:Our integrative analysis of genetic, transcriptional and biological information provides a list of compelling candidates for further follow-up functional studies to uncover the biological mechanisms underlying obesity. These candidates highlight the value of examining CT-derived and central adiposity traits.

Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women

Genome-Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. The main objective is to find single nucleotide polymorphisms (SNPs) that influence a particular phenotype (i.e., disease trait). GWAS typically use a p-value threshold of 5*10-8 to identify highly ranked SNPs. While this approach has proven useful for detecting disease-susceptible SNPs, evidence has shown that many of these are, in fact, false positives. Consequently, there is some ambiguity about the most suitable threshold for claiming genome-wide significance. Many believe that using lower p-values will allow us to investigate the joint epistatic interactions between SNPs and provide better insights into phenotype expression. One example that uses this approach is multifactor dimensionality reduction (MDR), which identifies combinations of SNPs that interact to influence a particular outcome. However, computational complexity is increased exponentially as a function of higher-order combinations making approaches like MDR difficult to implement. Even so, understanding epistatic interactions in complex diseases is a fundamental component for robust genotype-phenotype mapping. In this paper, we propose a novel framework that combines GWAS quality control and logistic regression with deep learning stacked autoencoders to abstract higher-order SNP interactions from large, complex genotyped data for case-control classification tasks in GWAS analysis. We focus on the challenging problem of classifying preterm births which has a strong genetic component with unexplained heritability reportedly between 20-40 percent. A GWAS data set, obtained from dbGap is utilised, which contains predominantly urban low-income African-American women who had normal and preterm deliveries. Epistatic interactions from original SNP sequences were extracted through a deep learning stacked autoencoder model and used to fine-tune a classifier for discriminating between term and preterm births observations. All models are evaluated using standard binary classifier performance metrics. The findings show that important information pertaining to SNPs and epistasis can be extracted from 4,666 raw SNPs generated using logistic regression (p-value = 5*10-3) and used to fit a highly accurate classifier model. The following results (Sen = 0.9562, Spec = 0.8780, Gini = 0.9490, Logloss = 0.5901, AUC = 0.9745, and MSE = 0.2010) were obtained using 50 hidden nodes and (Sen = 0.9289, Spec = 0.9591, Gini = 0.9651, Logloss = 0.3080, AUC = 0.9825, and MSE = 0.0942) using 500 hidden nodes. The results were compared with a Support Vector Machine (SVM), a Random Forest (RF), and a Fishers Linear Discriminant Analysis classifier, which all failed to improve on the deep learning approach.

Investigation of parent-of-origin effects induced by fenofibrate treatment on triglycerides levels

BackgroundGenome-wide association studies performed on triglycerides (TGs) have not accounted for epigenetic mechanisms that may partially explain trait heritability.ResultsParent-of-origin (POO) effect association analyses using an agnostic approach or a candidate approach were performed for pretreatment TG levels, posttreatment TG levels, and pre- and posttreatment TG-level differences in the real GAW20 family data set. We detected 22 genetic variants with suggestive POO effects with at least 1 phenotype (P ≤ 10− 5). We evaluated the association of these 22 significant genetic variants showing POO effects with close DNA methylation probes associated with TGs. A total of 18 DNA methylation probes located in the vicinity of the 22 SNPs were associated with at least 1 phenotype and 6 SNP-probe pairs were associated with DNA methylation probes at the nominal level of P < 0.05, among which 1 pair presented evidence of POO effect. Our analyses identified a paternal effect of SNP rs301621 on the difference between pre- and posttreatment TG levels (P = 1.2 × 10− 5). This same SNP showed evidence for a maternal effect on methylation levels of a nearby probe (cg10206250; P = 0.01). Using a causal inference test we established that the observed POO effect of rs301621 was not mediated by DNA methylation at cg10206250.ConclusionsWe performed POO effect association analyses of SNPs with TGs, as well as association analyses of SNPs with DNA methylation probes. These analyses, which were followed by a causal inference test, established that the paternal effect at the SNP rs301621 is induced by treatment and is not mediated by methylation level at cg10206250.

Evaluating the effect of multiple genetic risk score models on colorectal cancer risk prediction

Currently, genetic risk score (GRS) model has been a widely used method to evaluate the genetic effect of cancer risk prediction, but seldom studies investigated their discriminatory power, especially for colorectal cancer (CRC) risk prediction. In this study, we applied both simulation and real data to comprehensively compare the discriminability of different GRS models. The GRS models were fitted by logistic regression with three scenarios, including simple count GRS (SC-GRS), logistic regression weighted GRS (LR-GRS, including DL-GRS and OR-GRS) and explained variance weighted GRS (EV-GRS, including EV_DL-GRS and EV_OR-GRS) models. The model performance was evaluated by receiver operating characteristic (ROC) curves and area under curves (AUC) metric, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In real data analysis, as DL-GRS and EV_DL-GRS models were carried with serious over-fitting, the other three models were kept for further comparison. Compared to unweighted SC-GRS model, reclassification was significantly decreased in OR-GRS model (NRI = −0.082, IDI = −0.002, P < 0.05), while EV_OR-GRS model showed negative NRI and IDI (NRI = −0.077, IDI = −5.54E−04, P < 0.05) compared to OR-GRS model. Besides, traditional model with smoking status (AUC = 0.523) performed lower discriminability compared to the combined model (AUC = 0.607) including genetic (i.e., SC-GRS) and smoking factors. Similarly, the findings from simulation were all consistent to real data results. It is plausible that SC-GRS model could be optimal for predicting genetic risk of CRC. Moreover, the addition of more significant genetic variants to traditional model could further improve predictive power on CRC risk prediction.

Convergence of placenta biology and genetic risk for schizophrenia

Defining the environmental context in which genes enhance disease susceptibility can provide insight into the pathogenesis of complex disorders. We report that the intra-uterine environment modulates the association of schizophrenia with genomic risk (in this study, genome-wide association study-derived polygenic risk scores (PRSs)). In independent samples from the United States, Italy, and Germany, the liability of schizophrenia explained by PRS is more than five times greater in the presence of early-life complications (ELCs) compared with their absence. Patients with ELC histories have significantly higher PRS than patients without ELC histories, which is confirmed in additional samples from Germany and Japan. The gene set composed of schizophrenia loci that interact with ELCs is highly expressed in placenta, is differentially expressed in placentae from complicated in comparison with normal pregnancies, and is differentially upregulated in placentae from male compared with female offspring. Pathway analyses reveal that genes driving the PRS-ELC interaction are involved in cellular stress response; genes that do not drive such interaction implicate orthogonal biological processes (for example, synaptic function). We conclude that a subset of the most significant genetic variants associated with schizophrenia converge on a developmental trajectory sensitive to events that affect the placental response to stress, which may offer insights into sex biases and primary prevention.

LEPR Deficiency: Prevalence and Importance of a Novel Mutation and Significant Genetic Variants, Usually Underestimated

LEPR Deficiency: Prevalence and Importance of a Novel Mutation and Significant Genetic Variants, Usually Underestimated

Spontaneous preterm birth: advances toward the discovery of genetic predisposition

Evidence from family and twin-based studies provide strong support for a significant contribution of maternal and fetal genetics to the timing of parturition and spontaneous preterm birth. However, there has been only modest success in the discovery of genes predisposing to preterm birth, despite increasing sophistication of genetic and genomic technology. In contrast, DNA variants associated with other traits/diseases have been identified. For example, there is overwhelming evidence that suggests that the nature and intensity of an inflammatory response in adults and children are under genetic control. Because inflammation is often invoked as an etiologic factor in spontaneous preterm birth, the question of whether spontaneous preterm birth has a genetic predisposition in the case of pathologic inflammation has been of long-standing interest to investigators. Here, we review various genetic approaches used for the discovery of preterm birth genetic variants in the context of inflammation-associated spontaneous preterm birth. Candidate gene studies have sought genetic variants that regulate inflammation in the mother and fetus; however, the promising findings have often not been replicated. Genome-wide association studies, an approach to the identification of chromosomal loci responsible for complex traits, have also not yielded compelling evidence for DNA variants predisposing to preterm birth. A recent genome-wide association study that included a large number of White women (>40,000) revealed that maternal loci contribute to preterm birth. Although none of these loci harbored genes directly related to innate immunity, the results were replicated. Another approach to identify DNA variants predisposing to preterm birth is whole exome sequencing, which examines the DNA sequence of protein-coding regions of the genome. A recent whole exome sequencing study identified rare mutations in genes encoding for proteins involved in the negative regulation (dampening) of the innate immune response (eg, CARD6, CARD8, NLRP10, NLRP12, NOD2, TLR10) and antimicrobial peptide/proteins (eg, DEFB1, MBL2). These findings support the concept that preterm labor, at least in part, has an inflammatory etiology, which can be induced by pathogens (ie, intraamniotic infection) or "danger signals" (alarmins) released during cellular stress or necrosis (ie, sterile intraamniotic inflammation). These findings support the notion that preterm birth has a polygenic basis that involves rare mutations or damaging variants in multiple genes involved in innate immunity and host defense mechanisms against microbes and their noxious products. An overlap among the whole exome sequencing-identified genes and other inflammatory conditions associated with preterm birth, such as periodontal disease and inflammatory bowel disease, was observed, which suggests a shared genetic substrate for these conditions. We propose that whole exome sequencing, as well as whole genome sequencing, is the most promising approach for the identification of functionally significant genetic variants responsible for spontaneous preterm birth, at least in the context of pathologic inflammation. The identification of genes that contribute to preterm birth by whole exome sequencing, or whole genome sequencing, promises to yield valuable population-specific biomarkers to identify the risk for spontaneous preterm birth and potential strategies to mitigate such a risk.