Effects Of Genetic Risk Variants Research Articles

In memory of Professor Iain Wilkinson: cognitive and neuroimaging endophenotypes in a consanguineous schizophrenia multiplex family.

Schizophrenia endophenotypes may help elucidate functional effects of genetic risk variants in multiply affected consanguineous families that segregate recessive risk alleles of large effect size. We studied the association between a schizophrenia risk locus involving a 6.1Mb homozygous region on chromosome 13q22-31 in a consanguineous multiplex family and cognitive functioning, haemodynamic response and white matter integrity using neuroimaging. We performed CANTAB neuropsychological testing on four affected family members (all homozygous for the risk locus), ten unaffected family members (seven homozygous and three heterozygous) and ten healthy volunteers, and tested neuronal responses on fMRI during an n-back working memory task, and white matter integrity on diffusion tensor imaging (DTI) on four affected and six unaffected family members (four homozygous and two heterozygous) and three healthy volunteers. For cognitive comparisons we used a linear mixed model (Kruskal-Wallis) test, followed by posthoc Dunn's pairwise tests with a Bonferroni adjustment. For fMRI analysis, we counted voxels exceeding the p < 0.05 corrected threshold. DTI analysis was observational. Family members with schizophrenia and unaffected family members homozygous for the risk haplotype showed attention (p < 0.01) and working memory deficits (p < 0.01) compared with healthy controls; a neural activation laterality bias towards the right prefrontal cortex (voxels reaching p < 0.05, corrected) and observed lower fractional anisotropy in the anterior cingulate cortex and left dorsolateral prefrontal cortex. In this family, homozygosity at the 13q risk locus was associated with impaired cognition, white matter integrity, and altered laterality of neural activation.

M99 - CELL-SPECIFIC HISTONE MODIFICATION MAPS LINK SCHIZOPHRENIA RISK TO THE NEURONAL EPIGENOME

Background Recent progress in understanding the genetic basis of many psychiatric diseases has identified rare and common variants responsible for genetic risk. Integrating epigenomics data from disease-relevant cell types and tissues promises to enhance interpretation of these risk variants and the mechanisms by which they confer disease liability. A better understanding of the role of non-coding regulatory DNA and variation in histone modification in mediating the effects of genetic risk variants promises to give insights into the molecular mechanisms underlying psychiatric disease. With this goal in mind, the PsychENCODE Consortium generated a large-scale epigenomics resource for the human brain to serve as a foundation for integrative genomics in psychiatric research. Methods We performed ChIP-Seq on 17 subjects, 2 brain regions, 2 cell types and 2 histone marks that yielded 136 total ChIP-Seq samples. Of these 129 (n=63 H3K4me3; n=66 H3K27ac) passed quality control. For each combination of brain region, cell type and histone mark, reads from corresponding samples were combined into a consolidated subset of the data. This produced 8 consolidated subsets, corresponding to: ACC-neuronal-H3K4me3, PFC-neuronal-H3K4me3, etc. The entire data set was then processed and analyzed through the annotation, peak calling, covariate and pathway analyses pipeline. Results We have generated the largest cell type-specific dataset of histone-modification in the human brain to date across two brain cortical regions, composed of reference maps from 129 samples collected from 17 healthy subjects. These reference maps consist of ChIP-Seq for two histone marks associated with promoters and active enhancers, H3-trimethyl-lysine 4 (H3K4me3) and H3-acetyl-lysine 27 (H3K27ac) from either neurons, or surrounding non-neuronal cells. Analysis of this data has revealed unexplored insights into cell-, brain region- and to a lesser extent, subject- and sex-specific histone methylation and acetylation landscapes shaping the epigenome of the adult human cerebral cortex. We identify multiple loci with robust region-specific differences separating prefrontal from cingulate neurons, and numerous genome loci with extraordinary high inter-individual variability in H3K27ac and H3K4me3. Finally, we found a significant enrichment of risk variants for schizophrenia, educational attainment and depressive symptoms only in neuronal markers, further indicating the importance of conducting cell type-specific epigenome studies. Discussion Risk variants for schizophrenia, depressive symptoms, neuroticism and educational years are significantly enriched in genomic loci marked specifically in neuronal cells. The difference between neuronal and non-neuronal cells is the major axis of variation in histone modification, yet many genomic loci show substantial epigenetic variability across subjects and brain regions. These findings highlight the utility of this NIMH PsychENCODE-sponsored resource to elucidate epigenomic architectures of regulatory and disease-associated non-coding DNA in the human brain.

Abstract 230: Joint genome-wide association study of endometrial cancer and ovarian cancer identifies a novel genetic risk region at 14q23.3

Abstract Ovarian cancer is the most lethal gynecologic malignancy and the sixth most common cause of death from all cancer in women, with an estimated 140,000 deaths per year worldwide. Endometrial cancer (cancer of the uterine lining) is the most commonly diagnosed gynecologic cancer, ranking sixth in incident cancer in women. Ovarian and endometrial cancer share many epidemiologic, histopathologic, and tumor genetic characteristics. Meta-analyses of genome-wide association study (GWAS) datasets across etiologically related diseases have successfully been used to increase statistical power and identify novel genetic risk regions. We hypothesised that joint meta-analysis of ovarian and endometrial cancer GWAS datasets would identify novel genetic loci for both cancers. Following quality control, summary statistics for &amp;gt;11 million genetic variants were available from the largest genome-wide association studies performed by the Ovarian Cancer Association Consortium (OCAC, Phelan et al., Nature Genetics 2017) and the Endometrial Cancer Association Consortium (ECAC, unpublished). A total of 35,312 cancer cases from all histologic subtypes (22,406 ovarian and 12,906 endometrial cancer cases) and 149,920 controls were included in the analysis. Summary statistics were combined using an inverse-variance, fixed-effects model in METAL and identified ten loci at genome-wide significance (P &amp;lt; 5 x 10-8), of which one locus at 14q23.3 (rs4072776 combined OR 0.94; 95% CI 0.92-0.96; P = 8.0 x 10-9) had not been previously associated with the risk of either cancer. Integration with chromatin conformation data (RNAPolII HiChIP) from the Ishikawa endometrial cancer cell line suggests interaction between the risk signal and the promoter of FUT8. FUT8 encodes a fucosyltransferase and its expression has been implicated in breast, stomach, and melanoma and lung cancer. Interestingly, FUT8 is a downstream target regulated by the loss of PAX2 and mutated p53, which is the earliest known molecular aberration in the progression of the fallopian tissue epithelial to serous ovarian cancer. Further functional evaluation of the 14q23.3 risk region is required to determine the regulatory effect of genetic risk variants on FUT8. Future meta-analyses of datasets, stratifying results by histologic subtypes, will be undertaken. Citation Format: Tracy A. O'Mara, Dylan M. Glubb, Daniel D. Buchanan, Diether Lambrechts, Per Hall, Emma Tham, Jone Trovik, Ellen L. Goode, Peter Fasching, Thilo Dörk, Rodney J. Scott, Paul L. Auer, Roger L. Milne, Graham G. Giles, John Perry, Immaculata de Vivo, Ian Tomlinson, Douglas F. Easton, Deborah J. Thompson, Amanda B. Spurdle, E2C2, BCAC, OCAC, ECAC. Joint genome-wide association study of endometrial cancer and ovarian cancer identifies a novel genetic risk region at 14q23.3 [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 230.

Neuroepigenetics of Schizophrenia.

Schizophrenia is a complex disorder of the brain, where genetic variants explain only a portion of risk. Neuroepigenetic mechanisms may explain the remaining share of risk, as well as the transition from susceptibility to the actual disease. Here, we discuss the most recent findings in the field of brain epigenetics applied to the study of schizophrenia. Methylome studies have found several candidates exhibiting methylation modifications in association with the disorder, but genes affected do not always overlap. Notably, these studies converge in that genes within the schizophrenia risk loci or genes differentially methylated in patients affected with the disorder are dynamically regulated during early life. They also imply that schizophrenia-associated genetic variation may affect DNA methylation in fetal and adult brains. Histone modifications may help mediating the effect of genetic risk variants associated with schizophrenia, and regulating chromatin higher-order structure. The 3D-organization of chromatin in the brain creates physical interactions within chromosomes, so that schizophrenia-associated genetic variants can be linked with genes distant from their loci; this suggests that chromatin conformation matters in the mechanism of risk for the disorder. Non-coding RNAs provide a novel and complex mechanism of gene regulation potentially significant for schizophrenia, as proposed by research on specific microRNAs and long non-coding RNAs (lncRNAs). Finally, a recent study in epitranscriptomics identifies RNA methylation as a further epigenetic mechanism active in human brain and specifically in a portion of the transcriptome associated with schizophrenia susceptibility. These findings indicate that, as expected from the complexity of the brain and its development, several epigenetic mechanisms may intervene in the etiopathogenesis of schizophrenia. An understanding of their roles calls for research approaches integrating the investigation of different epigenetic mechanisms and of environmental and genetic risk, in the context of development.

The Importance of Context: Uncovering Species- and Tissue-Specific Effects of Genetic Risk Variants for Type 2 Diabetes.

Genome-wide association studies (GWAS) have been highly successful in identifying genetic variation associated with type 2 diabetes (T2D) risk and related quantitative traits (1–3). The vast majority of association signals are located in non-coding regions of the genome, influencing nearby genes through regulation of transcriptional, translational, or splicing activity (4). Due to the highly context-dependent nature of gene expression, the effects of many risk variants are restricted to specific cell types and produce more subtle effects than those observed in organism-wide (or “global”) knockouts. In addition, identification of the underlying causal genes and target tissues is often a major challenge, hindering translation into disease mechanisms. Recent studies have shown that the intersection of genetic data and genomic annotations can be used to produce a cellular atlas with which to understand the phenotypes of GWAS signals. Through the generation of directed hypotheses, this integrated framework has the potential to bridge the gap between association signals and disease biology.

Validation of Type 2 Diabetes Risk Variants Identified by Genome-Wide Association Studies in Northern Han Chinese.

Background: More than 60 genetic susceptibility loci associated with type 2 diabetes mellitus (T2DM) have been established in populations of Asian and European ancestry. Given ethnic differences and environmental factors, validation of the effects of genetic risk variants with reported associations identified by Genome-Wide Association Studies (GWASs) is essential. The study aims at evaluating the associations of T2DM with 29 single nucleotide polymorphisms (SNPs) from 19 candidate genes derived from GWASs in a northern Han Chinese population. Method: In this case-control study, 461 T2DM-diagnosed patients and 434 controls were recruited at the Jidong oil field hospital (Hebei, China) from January 2009 to October 2013. A cumulative genetic risk score (cGRS) was calculated by summation of the number of risk alleles, and a weight GRS (wGRS) was calculated as the sum of risk alleles at each locus multiplied by their effect sizes for T2DM, using the independent variants selected. Result: The allelic frequency of the “A” allele at rs17106184 (Fas-associated factor 1, FAF1) was significantly higher in the T2DM patients than that of the healthy controls (11.7% vs. 6.4%, p < 0.001). Individuals in the highestquartile of wGRS had an over three-fold increased risk for developing T2DM compared with those in the lowest quartile (odds ratio = 3.06, 95% CI = 1.92–4.88, p < 0.001) adjusted for age, sex, BMI, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP) and diastolic blood pressure (DBP). The results were similar when analyzed with the cGRS. Conclusions: We confirmed the association between rs17106184 (FAF1) and T2DM in a northern Han Chinese population. The GRS calculated based on T2DM susceptibility variants may be a useful tool for predicting the T2DM susceptibility.

Parkinson-associated risk variant in distal enhancer of α-synuclein modulates target gene expression.

Genome-wide association studies (GWAS) have identified numerous genetic variants associated with complex diseases but mechanistic insights are impeded by the lack of understanding of how specific risk variants functionally contribute to the underlying pathogenesis1. It has been proposed that cis-acting effects of non-coding risk variants on gene expression are a major factor for phenotypic variation of complex traits and disease susceptibility. Recent genome-scale chromatin mapping studies have highlighted the enrichment of GWAS variants in regulatory DNA elements of disease-relevant cell types2–6. Furthermore, single nucleotide polymorphism (SNP)-specific changes in transcription factor (TF) binding are correlated with heritable alterations in chromatin state and considered a major mediator of sequence-dependent regulation of gene expression7–10. Here we describe a novel strategy to functionally dissect the cis-acting effect of genetic risk variants in regulatory elements on gene expression by combining genome-wide epigenetic information with clustered regularly-interspaced short palindromic repeats (CRISPR)/Cas9 genome editing in human pluripotent stem cells (hPSCs). By generating a genetically precisely controlled experimental system we identify a common Parkinson’s disease (PD)-associated risk variant in a non-coding distal enhancer element that regulates the expression of alpha-synuclein (SNCA), a key gene implicated in the pathogenesis of PD. Our data suggest that the transcriptional deregulation of SNCA is associated with sequence-dependent binding of the brain-specific TFs EMX2 and NKX6-1. This work establishes an experimental paradigm to functionally connect genetic variation with disease relevant phenotypes.

Pleiotropic effects of genetic risk variants for other cancers on colorectal cancer risk: PAGE, GECCO and CCFR consortia

Pleiotropic effects of genetic risk variants for other cancers on colorectal cancer risk: PAGE, GECCO and CCFR consortia

Pleiotropic effects of genetic risk variants for other cancers on colorectal cancer risk: PAGE, GECCO and CCFR consortia

ObjectiveGenome-wide association studies have identified a large number of single nucleotide polymorphisms (SNPs) associated with a wide array of cancer sites. Several of these variants demonstrate associations with multiple cancers,...

Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes

Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic β cells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology. We used a systems biology approach to unravel genome to phenome correlation in T2D. We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance. Genes at lower significance threshold showed enrichment of insulin secretion related pathway. Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion. The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs. The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways. Next, we generated genome-wide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients. Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology. Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling. Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed β cell dysfunction to precipitate diabetes.

Effects of a mis‐sense DISC1 variant on brain activation in two cohorts at high risk of bipolar disorder or schizophrenia

Bipolar disorder and schizophrenia share a number of clinical features and genetic risk variants of small effect, suggesting overlapping pathogenic mechanisms. The effect of single genetic risk variants on brain function is likely to differ in people at high familial risk versus controls as these individuals have a higher overall genetic loading and are therefore closer to crossing a threshold of disease liability. Therefore, whilst the effects of genetic risk variants on brain function may be similar across individuals at risk of both disorders, they are hypothesized to differ compared to that seen in control subjects. We sought to examine the effects of the DISC1 Leu(607) Phe polymorphism on brain activation in young healthy individuals at familial risk of bipolar disorder (n = 84), in a group of controls (n = 78), and in a group at familial risk of schizophrenia (n = 47), performing a language task. We assessed whether genotype effects on brain activation differed according to risk status. There was a significant genotype × group interaction in a cluster centered on the left pre/postcentral gyrus, extending to the inferior frontal gyrus. The origin of this genotype × group effect originated from a significant effect of the presumed risk variant (Phe) on brain activation in the control group, which was absent in both high-risk groups. Differential effects of this polymorphism in controls compared to the two familial groups suggests a commonality of effect across individuals at high-risk of the disorders, which is likely to be dependant upon existing genetic background.

Estimating Causal Effects of Genetic Risk Variants for Breast Cancer Using Marker Data from Bilateral and Familial Cases

Cases with a family history are enriched for genetic risk variants, and the power of association studies can be improved by selecting cases with a family history of disease. However, in recent genome-wide association scans utilizing familial sampling, the excess relative risk for familial cases is less than predicted when compared with unselected cases. This can be explained by incomplete linkage disequilibrium between the tested marker and the underlying causal variant. We show that the allele frequency and effect size of the underlying causal variant can be estimated by combining marker data from studies that ascertain cases based on different family histories. This allows us to learn about the genetic architecture of a complex trait, without having identified any causal variants. We consider several validated common marker alleles for breast cancer, using our own study of high risk, predominantly bilateral cases, cases preferentially selected to have at least two affected first- or second-degree relatives, and published estimates of relative risk from standard case-control studies. To obtain realistic estimates and to accommodate some prior beliefs, we use Bayesian estimation to infer that the causal variants are probably common, with minor allele frequency >5%, and have small effects, with relative risk around 1.2. These results strongly support the common disease common variant hypothesis for these specific loci associated with breast cancer. Our results agree with recent assertions that synthetic associations of rare variants are unlikely to account for most associations seen in genome-wide studies.

Cumulative effects of genetic risk variants on the development of common atherosclerosis

Evaluation of: Rasmussen-Torvik LJ, Li M, Kao WH et al. Association of a fasting glucose genetic risk score with subclinical atherosclerosis: the Atherosclerosis Risk in Communities (ARIC) study. Diabetes 60(1), 331–335 (2011).Rapid advances in microarray technology have made it possible to quickly retrieve comprehensive information about a genomic region using thousands of samples. Genome-wide association studies have successfully identified a large number of genetic variants strongly related to complex phenotypes and diseases. Previous studies have suggested that the predictive value could be improved by combining multiple genetic variants. The study under evaluation in this article described a new model in the prediction of a complex phenotype using multiple genetic factors with well-replicated associations. The findings of this study may indicate a new biochemical pathway involved in the development of atherosclerosis.