Large-scale Genome-wide Association Analysis Research Articles

Bayesian hierarchical hypothesis testing in large-scale genome-wide association analysis.

Variable selection and large-scale hypothesis testing are techniques commonly used to analyze high-dimensional genomic data. Despite recent advances in theory and methodology, variable selection and inference with highly collinear features remain challenging. For instance, collinearity poses a great challenge in genome-wide association studies involving millions of variants, many of which may be in high linkage disequilibrium. In such settings, collinearity can significantly reduce the power of variable selection methods to identify individual variants associated with an outcome. To address such challenges, we developed a Bayesian hierarchical hypothesis testing (BHHT)-a novel multiresolution testing procedure that offers high power with adequate error control and fine-mapping resolution. We demonstrate through simulations that the proposed methodology has a power-FDR performance that is competitive with (and in many scenarios better than) state-of-the-art methods. Finally, we demonstrate the feasibility of using BHHT with large sample size (n∼ 300,000) and ultra dimensional genotypes (∼ 15 million single-nucleotide polymorphisms or SNPs) by applying it to eight complex traits using data from the UK-Biobank. Our results show that the proposed methodology leads to many more discoveries than those obtained using traditional SNP-centered inference procedures. The article is accompanied by open-source software that implements the methods described in this study using algorithms that scale to biobank-size ultra-high-dimensional data.

Hypnotic use and the risk of cardiovascular diseases in insomnia patients

Abstract Background Insomnia, commonly treated with hypnotic agents, is an independent risk factor of cardiovascular diseases. Previous research demonstrated the detrimental effect of hypnotics on the prognosis of cardiovascular patients. Purpose We aimed to examine the association between hypnotic agents and cardiovascular outcomes in general individuals with insomnia. Methods In a propensity score matched cohort of UK Biobank (UKB) participants with insomnia, Cox proportional hazard model was used to estimate the association between regular use of hypnotic agents and predetermined cardiovascular outcomes including incident coronary heart diseases (CHD), heart failure (HF), stroke, and cardiovascular death. Inverse probability of treatment weighting and competing risk models were further performed during sensitivity analysis. Drug-target Mendelian randomization (MR) analyses were employed for further evaluation of hypnotics-related genes and cardiovascular disease association. The genome-wide association data of CHD, HF, and stroke were there large-scale genome-wide association analyses. Tissue-specified expression quantitative trait loci (eQTL) data was derived from BrainSeq phase II. Results During a median follow-up of 14.3 years, the matched cohort documented a total of 1,019 CHD cases, 406 HF cases, 285 stroke cases, and 197 cardiovascular deaths. No significant association was detected between Z-meds and CHD, stroke, and cardiovascular mortality. Benzodiazepine use was significantly associated with the increased risk of CHD, HF, and cardiovascular mortality (Figure 1). The inverse probability of treatment weighting and competing risk models didn’t alter the above associations. Moreover, drug-target MR analyses corroborated the safety of Z-meds in the general population regarding cardiovascular health. However, GABRG1—a drug target for benzodiazepines—was associated with heightened risks for heart failure, and ischemic stroke (Figure 2). Conclusions Our findings suggested the heterogeneous associations between different categories of hypnotics and incident cardiovascular events in individuals with insomnia. Z-meds are safe regarding the risk of cardiovascular outcomes in the UKB population with insomnia.Figure 1Figure 2

Large scale genome-wide association analyses identify novel genetic loci and mechanisms in hypertrophic cardiomyopathy

Abstract Background Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality with both monogenic and polygenic components. Prior genome-wide association studies (GWAS) identified few genomic loci and disease genes due to limited sample size. Purpose To discover novel genetic loci, genes and mechanisms implicated in HCM using a large scale GWAS and multi-trait analysis of GWAS (MTAG). Methods and results We performed the largest HCM GWAS meta-analysis and MTAG to date including 5,900 HCM cases, 68,359 controls, and 36,083 UK Biobank (UKB) participants with cardiac magnetic resonance (CMR) imaging. We estimated the heritability of HCM attributable to common genetic variation (h2SNP) to be 0.25±0.02 using genome-based restricted maximum likelihood (GREML), with higher h2SNP in non-sarcomeric (0.29±0.02) compared to sarcomeric HCM (0.16±0.04). We identified a total of 70 loci (50 novel) associated with HCM (Figure 1), and 62 loci (32 novel) associated with relevant left ventricular (LV) structural or functional traits. Amongst the common variant HCM loci, we identify a novel HCM disease gene, SVIL, which encodes the actin-binding protein supervillin. We performed rare variant burden analysis including 1,845 clinically-diagnosed unrelated HCM cases and 37,481 controls and demonstrated a 10.5-fold (95% CI: 4.1-26.8; P=0.0000002) excess burden of SVIL loss of function (LoF) variants in HCM cases. Two-sample mendelian randomization analyses using LV contractility as exposure and obstructive (oHCM) and non-obstructive HCM (nHCM) as outcomes support a causal role of increased LV contractility in both oHCM and nHCM (Figure 2), suggesting common disease mechanisms and anticipating shared response to therapy. Conclusion We identify 50 novel genomic loci associated with HCM. Our data suggest that LoF variants in SVIL are a cause of HCM, and that increased contractility mediate both nHCM and oHCM. Taken together, the findings significantly increase our understanding of the genetic basis and molecular mechanisms of HCM, with potential implications for disease management.Figure 1Figure 2

Progress in genetics of type 2 diabetes and diabetic complications.

Type 2 diabetes results from a complex interaction between genetic and environmental factors. Precision medicine for type 2 diabetes using genetic data is expected to predict the risk of developing diabetes and complications and to predict the effects of medications and life-style intervention more accurately for individuals. Genome-wide association studies (GWAS) have been conducted in European and Asian populations and new genetic loci have been identified that modulate the risk of developing type 2 diabetes. Novel loci were discovered by GWAS in diabetic complications with increasing sample sizes. Large-scale genome-wide association analysis and polygenic risk scores using biobank information is making it possible to predict the development of type 2 diabetes. In the ADVANCE clinical trial of type 2 diabetes, a multi-polygenic risk score was useful to predict diabetic complications and their response to treatment. Proteomics and metabolomics studies have been conducted and have revealed the associations between type 2 diabetes and inflammatory signals and amino acid synthesis. Using multi-omics analysis, comprehensive molecular mechanisms have been elucidated to guide the development of targeted therapy for type 2 diabetes and diabetic complications.

STS-BN: An efficient Bayesian network method for detecting causal SNPs.

Background: The identification of the causal SNPs of complex diseases in large-scale genome-wide association analysis is beneficial to the studies of pathogenesis, prevention, diagnosis and treatment of these diseases. However, existing applicable methods for large-scale data suffer from low accuracy. Developing powerful and accurate methods for detecting SNPs associated with complex diseases is highly desired. Results: We propose a score-based two-stage Bayesian network method to identify causal SNPs of complex diseases for case-control designs. This method combines the ideas of constraint-based methods and score-and-search methods to learn the structure of the disease-centered local Bayesian network. Simulation experiments are conducted to compare this new algorithm with several common methods that can achieve the same function. The results show that our method improves the accuracy and stability compared to several common methods. Our method based on Bayesian network theory results in lower false-positive rates when all correct loci are detected. Besides, real-world data application suggests that our algorithm has good performance when handling genome-wide association data. Conclusion: The proposed method is designed to identify the SNPs related to complex diseases, and is more accurate than other methods which can also be adapted to large-scale genome-wide analysis studies data.

Detection of Parent-of-Origin Effects for the Variants Associated With Behavioral Disinhibition in the MCTFR Data.

Behavioral disinhibition is one of the important characteristics of many mental diseases. It has been reported in literature that serious behavioral disinhibition will affect people’s health and greatly reduce people’s quality of life. Meanwhile, behavioral disinhibition can easily lead to illegal drug abuse and violent crimes, etc., which will bring great harm to the society. At present, large-scale genome-wide association analysis has identified many loci associated with behavioral disinhibition. However, these studies have not incorporated the parent-of-origin effects (POE) into analysis, which may ignore or underestimate the genetic effects of loci on behavioral disinhibition. Therefore, in this article, we analyzed the five phenotypes related to behavioral disinhibition in the Minnesota Center for Twin and Family Research data (nicotine, alcohol consumption, alcohol dependence, illicit drugs, and non-substance use related behavioral disinhibition), to further explore the POE of variants on behavioral disinhibition. We applied a linear mixed model to test for the POE at a genome-wide scale on five transformed phenotypes, and found nine SNPs with statistically significant POE at the significance level of 5 × 10−8. Among them, SNPs rs4141854, rs9394515, and rs4711553 have been reported to be associated with two neurological disorders (restless legs syndrome and Tourette’s syndrome) which are related to behavioral disinhibition; SNPs rs12960235 and rs715351 have been found to be associated with head and neck squamous cell carcinoma, skin cancer and type I diabetes, while both SNPs have not been identified to be related to behavioral disinhibition in literature; SNPs rs704833, rs6837925, rs1863548, and rs11067062 are novel loci identified in this article, and their function annotations have not been reported in literature. Follow-up study in molecular genetics is needed to verify whether they are surely related to behavioral disinhibition.

Identify known and novel candidate genes associated with backfat thickness in Duroc pigs by large-scale genome-wide association analysis.

Backfat thickness (BFT) is complex and economically important traits in the pig industry, since it reflects fat deposition and can be used to measure the carcass lean meat percentage in pigs. In this study, all 6,550 pigs were genotyped using the Geneseek Porcine 50K SNP Chip to identify SNPs related to BFT and to search for candidate genes through genome-wide association analysis in two Duroc populations. In total, 80 SNPs, including 39 significant and 41 suggestive SNPs, and 6 QTLs were identified significantly associated with the BFT. In addition, 9 candidate genes, including a proven major gene MC4R, 3 important candidate genes (RYR1, HMGA1, and NUDT3) which were previously described as related to BFT, and 5 novel candidate genes (SIRT2, NKAIN2, AMH, SORCS1, and SORCS3) were found based on their potential functional roles in BFT. The functions of candidate genes and gene set enrichment analysis indicate that most important pathways are related to energy homeostasis and adipogenesis. Finally, our data suggest that most of the candidate genes can be directly used for genetic improvement through molecular markers, except that the MC4R gene has an antagonistic effect on growth rate and carcass lean meat percentage in breeding. Our results will advance our understanding of the complex genetic architecture of BFT traits and laid the foundation for additional genetic studies to increase carcass lean meat percentage of pig through marker-assisted selection and/or genomic selection.

A large-scale genome-wide association analysis of lung function in the Chinese population identifies novel loci and highlights shared genetic aetiology with obesity.

BackgroundLung function is a heritable complex phenotype with obesity being one of its important risk factors. However, knowledge of their shared genetic basis is limited. Most genome-wide association studies (GWASs) for lung function have been based on European populations, limiting the generalisability across populations. Large-scale lung function GWASs in other populations are lacking.MethodsWe included 100 285 subjects from the China Kadoorie Biobank (CKB). To identify novel loci for lung function, single-trait GWAS analyses were performed on forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC in the CKB. We then performed genome-wide cross-trait analysis between lung function and obesity traits (body mass index (BMI), BMI-adjusted waist-to-hip ratio and BMI-adjusted waist circumference) to investigate the shared genetic effects in the CKB. Finally, polygenic risk scores (PRSs) of lung function were developed in the CKB and their interaction with BMI's association on lung function were examined. We also conducted cross-trait analysis in parallel with the CKB using up to 457 756 subjects from the UK Biobank (UKB) for replication and investigation of ancestry-specific effects.ResultsWe identified nine genome-wide significant novel loci for FEV1, six for FVC and three for FEV1/FVC in the CKB. FEV1 and FVC showed significant negative genetic correlation with obesity traits in both the CKB and UKB. Genetic loci shared between lung function and obesity traits highlighted important biological pathways, including cell proliferation, embryo, skeletal and tissue development, and regulation of gene expression. Mendelian randomisation analysis suggested significant negative causal effects of BMI on FEV1 and on FVC in both the CKB and UKB. Lung function PRSs significantly modified the effect of change in BMI on change in lung function during an average follow-up of 8 years.ConclusionThis large-scale GWAS of lung function identified novel loci and shared genetic aetiology between lung function and obesity. Change in BMI might affect change in lung function differently according to a subject's polygenic background. These findings may open new avenues for the development of molecular-targeted therapies for obesity and lung function improvement.

IMIX: a multivariate mixture model approach to association analysis through multi-omics data integration.

Integrative genomic analysis is a powerful tool used to study the biological mechanisms underlying a complex disease or trait across multiplatform high-dimensional data, such as DNA methylation, copy number variation and gene expression. It is common to perform large-scale genome-wide association analysis of an outcome for each data type separately and combine the results ad hoc, leading to loss of statistical power and uncontrolled overall false discovery rate (FDR). We propose a multivariate mixture model (IMIX) framework that integrates multiple types of genomic data and allows modeling of inter-data-type correlations. We investigated the across-data-type FDR control in IMIX and demonstrated lower misclassification rates at controlled overall FDR than established individual data type analysis strategies, such as the Benjamini-Hochberg FDR control, the q-value and the local FDR control by extensive simulations. IMIX features statistically principled model selection, FDR control and computational efficiency. Applications to The Cancer Genome Atlas data provided novel multi-omics insights into the genes and mechanisms associated with the luminal and basal subtypes of bladder cancer and the prognosis of pancreatic cancer. We have implemented our method in R package 'IMIX' available at https://github.com/ziqiaow/IMIX, as well as CRAN soon. Supplementary data are available at Bioinformatics online.

The Role of Chromosome X in Intraocular Pressure Variation and Sex-Specific Effects.

PurposeThe purpose of this study was to identify genetic variants on chromosome X associated with intraocular pressure (IOP) and determine if they possess any sex-specific effects.MethodsAssociation analyses were performed across chromosome X using 102,407 participants from the UK Biobank. Replication and validation analyses were conducted in an additional 6599 participants from the EPIC-Norfolk cohort, and an independent 331,682 participants from the UK Biobank.ResultsWe identified three loci associated with IOP at genomewide significance (P < 5 × 10−8), located within or near the following genes: MXRA5 (rs2107482, P = 7.1 × 10−11), GPM6B (rs66819623, P = 6.9 × 10−10), NDP, and EFHC2 (rs12558081, P = 4.9 × 10−11). Alleles associated with increased IOP were also associated with increased risk for primary open-angle glaucoma in an independent sample. Finally, our results indicate that chromosome X genetics most likely do not illicit sex-specific effects on IOP.ConclusionsIn this study, we report the results of genomewide levels of association of three loci on chromosome X with IOP, and provide a framework to include chromosome X in large-scale genomewide association analyses for complex phenotypes.

Genetic variants of ADAMTS7 confer risk for ischaemic stroke in the Chinese population.

Large-scale genome-wide association analyses show an association between ADAMTS7 variations and coronary risk. However, the link between ADAMTS7 variability and ischaemic stroke (IS) has yet to be determined. This study evaluated ADAMTS7 variants with respect to the risk of IS. Genetic association analyses were performed in two independent case-control cohorts with 1279 patients with IS and 1268 age-matched healthy controls. Four variant genotypes of the ADAMTS7 gene were identified using the Multiplex SNaPshot assay. The rs3825807, rs11634042, and rs7173743 variants of ADAMTS7 were related to lower IS risk in both initial and replication cohort. The G-T-T-C and G-T-C-C haplotypes are significantly less prevalent in the IS group than in the control group. Further stratification according to IS subtypes indicated that carriers with the variant alleles of the rs3825807, rs11634042 and rs7173743 variants of ADAMTS7conferred a lower risk of developing large-artery atherosclerosis stroke subtype. Also, the mutated rs3825807 G allele, as well as the mutated rs11634042 T allele of ADAMTS7, are linked to a significant reduction of ADAMTS7 in patients with IS. Our findings confirm the role of ADAMTS7 in the pathophysiology of IS, with potentially significant implications for the prevention, treatment, and development of novel therapies for IS.

Expression QTLs Mapping and Analysis: A Bayesian Perspective.

The aim of expression Quantitative Trait Locus (eQTL) mapping is the identification of DNA sequence variants that explain variation in gene expression. Given the recent yield of trait-associated genetic variants identified by large-scale genome-wide association analyses (GWAS), eQTL mapping has become a useful tool to understand the functional context where these variants operate and eventually narrow down functional gene targets for disease. Despite its extensive application to complex (polygenic) traits and disease, the majority of eQTL studies still rely on univariate data modeling strategies, i.e., testing for association of all transcript-marker pairs. However these "one at-a-time" strategies are (1) unable to control the number of false-positives when an intricate Linkage Disequilibrium structure is present and (2) are often underpowered to detect the full spectrum of trans-acting regulatory effects. Here we present our viewpoint on the most recent advances on eQTL mapping approaches, with a focus on Bayesian methodology. We review the advantages of the Bayesian approach over frequentist methods and provide an empirical example of polygenic eQTL mapping to illustrate the different properties of frequentist and Bayesian methods. Finally, we discuss how multivariate eQTL mapping approaches have distinctive features with respect to detection of polygenic effects, accuracy, and interpretability of the results.

The Effects of Genetic Background on Psychiatric Behaviors in CACNA1C +/‐ Mice

Bipolar disorder and schizophrenia are genetic‐based psychiatric disorders. The heritability of bipolar disorder and schizophrenia, around 70% ‐ 90% and 83%, respectively, shows that the genetic variation in certain genes help leads to the development of these psychiatric disorders and that studying these genetic variations will help us understand the physiological mechanisms that lead to these psychiatric disorders. Among other genes, CACNA1C was identified as a gene, via large‐scale genome‐wide association analyses, that contributes to a person's risk for these disorders. However, the effects of the genes found only account for a small percentage of the heritable risk of these psychiatric diseases. There has been evidence that the interactions between genes, or epistasis, may account for a significant amount of the heritability. We identified different genetic interactions with CACNA1C by utilizing mouse genetics. The behavioral phenotypes of both wild‐type and CACNA1C +/‐ mice of 19 different genetic backgrounds were observed via a prepulse inhibition test. Each test measures specific behaviors that correlate to neurobiological characteristics of bipolar disorder and schizophrenia. Our data suggests that the CACNA1C +/‐ mutation has different effects on the mouse's prepulse inhibition, depending on the genetic background. The data also suggests that the CACNA1C +/‐ mutation trends toward a decreased startle response in females, while not in males. This allows us to suggest that the CACNA1C gene does undergo epistasis. In the future, we will conduct a genome‐wide association study for both trait markers and modifier markers. We will then identify high‐priority candidate modifier genes that we may be able to confirm in human data sets.

Up-Regulation of NOTCH4 Gene Expression in Bipolar Disorder: Future Studies

Back to table of contents Previous article Next article Communications and UpdatesFull AccessUp-Regulation of NOTCH4 Gene Expression in Bipolar Disorder: Future StudiesMing Li, Ph.D., and Bing Su, Ph.D.Ming LiSearch for more papers by this author, Ph.D., and Bing SuSearch for more papers by this author, Ph.D.Published Online:1 May 2013https://doi.org/10.1176/appi.ajp.2013.12121529AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To the Editor: In the December 2012 issue, Dieset et al. (1) reported significant up-regulation of NOTCH4 gene expression in whole blood in patients with bipolar disorder relative to healthy comparison subjects, and they identified several single-nucleotide polymorphisms (SNPs) that were significantly associated with NOTCH4 expression. This is a nice piece of research, and their findings have encouraged future research on the molecular mechanisms of NOTCH4 in bipolar disorder. However, several lines of their study data await further validation.First, we are curious about whether the altered NOTCH4 expression that was observed in patients was caused by changes in the genetic background in bipolar disorder (related to the pathogenesis of the illness) or was just an outcome of the patients' physiological conditions. This is an important issue, but was inconclusive in the article, although the authors conducted the analyses adjusting for a range of confounders. A plausible solution to this problem is the use of an intermediate group: the healthy siblings of patients. These populations shared numerous genetic risk factors with the clinical patients but did not have any psychiatric illnesses. If the NOTCH4 expression was also elevated in the healthy siblings of bipolar patients relative to healthy comparison subjects, we could conclude that up-regulation of NOTCH4 expression may be related to the genetic mechanism in bipolar disorder and is likely involved in the pathophysiology of bipolar illness. However, because such data were absent in the Dieset et al. study, we are cautious about drawing any conclusions before further investigations.Another issue we are concerned with is the analyses of SNPs in or around NOTCH4 and their associations with gene expression. Dieset et al. observed significant effects of the SNPs on NOTCH4 expression only in healthy individuals but not in bipolar patients, although the effect went in the same direction. Are these significantly associated SNPs authentic genetic risk factors for bipolar disorder? We examined their associations with bipolar disorder in the case-control samples of European ancestries published by the Psychiatric GWAS Consortium (2), which are the largest samples so far. However, none of these SNPs were significant (Table 1), suggesting that they are not risk variants for bipolar disorder even if they show strong associations with NOTCH4 expression in healthy individuals. On the other hand, if the increased NOTCH4 expression in patients was related to genetic mechanisms in bipolar disorder, the gene may harbor unidentified SNPs that are significantly associated with up-regulated NOTCH4 expression in patients. We think these SNPs may be the authentic risk genetic variants for bipolar disorder, and we call for future studies.TABLE 1. Association of the NOTCH4 Single-Nucleotide Polymorphisms (SNPs) With Bipolar Disorder in Samples Reported by the Psychiatric GWAS Consortium (7,481 Case Subjects and 8,250 Comparison Subjects)AnalysisSNPPositionAlleleFrequencypOdds RatioSErs51032132302370A0.18850.0771.0550.030rs38970332307217C0.81150.0830.9540.027rs36505332303966G0.19670.0661.0520.028rs40489032306845A0.29510.9530.9990.024rs313492632308125C0.78690.0500.9500.026rs41592932297010C0.28690.7500.9920.026rs926787332307330C0.57380.1960.9690.025TABLE 1. Association of the NOTCH4 Single-Nucleotide Polymorphisms (SNPs) With Bipolar Disorder in Samples Reported by the Psychiatric GWAS Consortium (7,481 Case Subjects and 8,250 Comparison Subjects)Enlarge tableFrom the State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.The authors report no financial relationships with commercial interests.References1 Dieset I, Djurovic S, Tesli M, Hope S, Mattingsdal M, Michelsen A, Joa I, Larsen TK, Agartz I, Melle I, Røssberg JI, Aukrust P, Andreassen OA, Ueland T: Up-regulation of NOTCH4 gene expression in bipolar disorder. Am J Psychiatry 2012; 169:1292–1300Link, Google Scholar2 Psychiatric GWAS Consortium Bipolar Disorder Working Group: Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet 2011; 43:977–983Crossref, Medline, Google Scholar FiguresReferencesCited byDetailsCited ByNone Volume 170Issue 5 May 2013Pages 560a-561 Metrics PDF download History Accepted 1 January 2013 Published online 1 May 2013 Published in print 1 May 2013

Erratum: Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4

Erratum: Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4

Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4

New from NPG: Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4

Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4

We conducted a combined genome-wide association study (GWAS) of 7,481 individuals with bipolar disorder (cases) and 9,250 controls as part of the Psychiatric GWAS Consortium. Our replication study tested 34 SNPs in 4,496 independent cases with bipolar disorder and 42,422 independent controls and found that 18 of 34 SNPs had P < 0.05, with 31 of 34 SNPs having signals with the same direction of effect (P = 3.8 × 10(-7)). An analysis of all 11,974 bipolar disorder cases and 51,792 controls confirmed genome-wide significant evidence of association for CACNA1C and identified a new intronic variant in ODZ4. We identified a pathway comprised of subunits of calcium channels enriched in bipolar disorder association intervals. Finally, a combined GWAS analysis of schizophrenia and bipolar disorder yielded strong association evidence for SNPs in CACNA1C and in the region of NEK4-ITIH1-ITIH3-ITIH4. Our replication results imply that increasing sample sizes in bipolar disorder will confirm many additional loci.

Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4

Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4

Genetics of the ceramide/sphingosine-1-phosphate rheostat in blood pressure regulation and hypertension

BackgroundSeveral attempts to decipher the genetics of hypertension of unknown causes have been made including large-scale genome-wide association analysis (GWA), but only a few genes have been identified. Unsolved heterogeneity of the regulation of blood pressure and the shortcomings of the prevailing monogenic approach to capture genetic effects in a polygenic condition are the main reasons for the modest results. The level of the blood pressure is the consequence of the genotypic state of the presumably vast network of genes involved in regulating the vascular tonus and hence the blood pressure. Recently it has been suggested that components of the sphingolipid metabolism pathways may be of importance in vascular physiology. The basic metabolic network of sphingolipids has been established, but the influence of genetic variations on the blood pressure is not known. In the approach presented here the impact of genetic variations in the sphingolipid metabolism is elucidated by a two-step procedure. First, the physiological heterogeneity of the blood pressure is resolved by a latent class/structural equation modelling to obtain homogenous subpopulations. Second, the genetic effects of the sphingolipid metabolism with focus on de novo synthesis of ceramide are analysed. The model does not assume a particular genetic model, but assumes that genes operate in networks.ResultsThe stratification of the study population revealed that (at least) 14 distinct subpopulations are present with different propensity to develop hypertension. Main effects of genes in the de novo synthesis of ceramides were rare (0.14% of all possible). However, epistasis was highly significant and prevalent amounting to approximately 70% of all possible two-gene interactions. The phenotypic variance explained by the ceramide synthesis network were substantial in 4 of the subpopulations amounting to more than 50% in the subpopulation in which all subjects were hypertensive. Construction of the network using the epistatic values revealed that only 17% of the interactions detected were in the direct metabolic pathway, the remaining jumping one or more intermediates.ConclusionsThis study established the components of the ceramide/sphingosine-1-phosphate rheostat as central to blood pressure regulation. The results in addition confirm that epistasis is of paramount importance and is most conspicuous in the regulation of the rheostat network. Finally, it is shown that applying a simple case-control approach with single gene association analysis is bound to fail, short of identifying a few potential genes with small effects.

CANu1, a novel nucleolar protein, accumulated on centromere in response to DNA damage

Single nucleotide polymorphism is known to be an ideal marker to detect human diseases. We isolated a novel human gene, to be called as CANu1, by the large-scale genome-wide association analysis to screen specific Single nucleotide polymorphisms in colon cancer. It is mapped to chromosome 14q11.2 and its transcript contains a 948-nt open reading frame encoding a protein of 315 aa. Here, we observed that green fluorescence protein (GFP)-fused CANu1 protein was localized to nucleoli and the C-termini of CANu1 protein were essential for its localization. Moreover, the silencing of the CANu1 gene by siRNA caused ribosomal stress leading to G1 cell cycle arrest, the induction of p53 protein, and the translocation of B23 protein. In addition, CANu1 protein was translocated from nucleolus to nuclear foci in response to UV damage. Interestingly, the mobility of a GFP-CANu1 protein in the UV damaged cells was two times faster than non-irradiated cells. Taken together, we report that a novel nucleolar protein, CANu1, is essential to maintain ribosomal structure and responsive upon UV damage.