Gene Genome-wide Association Studies Research Articles

Abstract 606: Reciprocal Gene-Gene Regulation Between GWAS Genes ADTRP and MIA3 Contributing to Susceptibility of Coronary Artery Disease

Genome-wide association studies (GWAS) have identified >60 genomic loci for coronary artery disease (CAD), including ADTRP (regulator of TFPI and coagulation) and MIA3 (involved in ER trafficking of collagens). In this study, we hypothesized that some GWAS genes form a molecular regulatory network involved in the pathogenesis of CAD. Global microarray analysis showed that ADTRP regulated expression of MIA3 and PIK3R3 encoding the regulatory subunit 3 of PI3K. ADTRP -mediated up-regulation of PIK3R3 activates AKT, resulting in up-regulation of MIA3 . Knockdown of ADTRP expression promoted oxidized-LDL-mediated monocyte adhesion to endothelial cell (EC) and transendothelial migration of monocytes, inhibited EC proliferation and migration, and increased apoptosis, which was reversed by expression of constitutively active AKT1, while the over-expression of ADTRP in ECs blunted these processes. Knockdown of MIA3 expression also promoted monocyte adhesion to ECs and transendothelial migration of monocytes. However, knockdown of MIA3 increased ADTRP expression, suggesting that MIA3 negatively regulates ADTRP expression. Genetically, no significant interaction between ADTRP SNP rs6903956 and MIA3 SNP rs17465637 was detected in 2,185 CAD patients and 2,156 non-CAD controls by classical two-locus genotypic analysis, relative excess risk due to interaction or INTERSNP programs. We have recently found that one molecular mechanism for gene-gene interaction is positive cyclic cross-regulation of gene expression: Gene A positively regulates gene B, whereas gene B also positively regulates gene A. Here we have identified a different scenario in which gene A positively regulates gene B, but gene B negatively regulates gene A, thereby resulting in lack of gene-gene interaction. In conclusion, we have uncovered a novel molecular signaling pathway involving ADTRP and MIA3 for the pathogenesis of CAD. We show that ADTRP positively regulates PIK3R3 expression, which leads to activation of AKT and up-regulation of MIA3 , thereby regulating endothelial cell functions directly relevant to atherosclerosis. These results further support that positive cyclic cross-regulation of gene expression is a molecular mechanism for gene-gene interaction.

Functional interactors of three genome-wide association study genes are differentially expressed in severe chronic obstructive pulmonary disease lung tissue

In comparison to genome-wide association studies (GWAS), there has been poor replication of gene expression studies in chronic obstructive pulmonary disease (COPD). We performed microarray gene expression profiling on a large sample of resected lung tissues from subjects with severe COPD. Comparing 111 COPD cases and 40 control smokers, 204 genes were differentially expressed; none were at significant GWAS loci. The top differentially expressed gene was HMGB1, which interacts with AGER, a known COPD GWAS gene. Differentially expressed genes showed enrichment for putative interactors of the first three identified COPD GWAS genes IREB2, HHIP, and FAM13A, based on gene sets derived from protein and RNA binding studies, RNA-interference, a murine smoking model, and expression quantitative trait locus analyses. The gene module most highly associated for COPD in Weighted Gene Co-Expression Network Analysis (WGCNA) was enriched for B cell pathways, and shared seventeen genes with a mouse smoking model and twenty genes with previous emphysema studies. As in other common diseases, genes at COPD GWAS loci were not differentially expressed; however, using a combination of network methods, experimental studies and careful phenotype definition, we found differential expression of putative interactors of these genes, and we replicated previous human and mouse microarray results.

Experimental Lung Injury Reduces Krüppel-like Factor 2 to Increase Endothelial Permeability via Regulation of RAPGEF3-Rac1 Signaling.

Acute respiratory distress syndrome (ARDS) is caused by widespread endothelial barrier disruption and uncontrolled cytokine storm. Genome-wide association studies (GWAS) have linked multiple genes to ARDS. Although mechanosensitive transcription factor Krüppel-like factor 2 (KLF2) is a major regulator of endothelial function, its role in regulating pulmonary vascular integrity in lung injury and ARDS-associated GWAS genes remains poorly understood. To examine KLF2 expression in multiple animal models of acute lung injury and further elucidate the KLF2-mediated pathways involved in endothelial barrier disruption and cytokine storm in experimental lung injury. Animal and in vitro models of acute lung injury were used to characterize KLF2 expression and its downstream effects responding to influenza A virus (A/WSN/33 [H1N1]), tumor necrosis factor-α, LPS, mechanical stretch/ventilation, or microvascular flow. KLF2 manipulation, permeability measurements, small GTPase activity, luciferase assays, chromatin immunoprecipitation assays, and network analyses were used to determine the mechanistic roles of KLF2 in regulating endothelial monolayer integrity, ARDS-associated GWAS genes, and lung pathophysiology. KLF2 is significantly reduced in several animal models of acute lung injury. Microvascular endothelial KLF2 is significantly induced by capillary flow but reduced by pathologic cyclic stretch and inflammatory stimuli. KLF2 is a novel activator of small GTPase Ras-related C3 botulinum toxin substrate 1 by transcriptionally controlling Rap guanine nucleotide exchange factor 3/exchange factor directly activated by cyclic adenosine monophosphate, which maintains vascular integrity. KLF2 regulates multiple ARDS GWAS genes related to cytokine storm, oxidation, and coagulation in lung microvascular endothelium. KLF2 overexpression ameliorates LPS-induced lung injury in mice. Disruption of endothelial KLF2 results in dysregulation of lung microvascular homeostasis and contributes to lung pathology in ARDS.

SigMod: an exact and efficient method to identify a strongly interconnected disease-associated module in a gene network.

Apart from single marker-based tests classically used in genome-wide association studies (GWAS), network-assisted analysis has become a promising approach to identify a set of genes associated with disease. To date, most network-assisted methods aim at finding genes connected in a background network, whatever the density or strength of their connections. This can hamper the findings as sparse connections are non-robust against noise from either the GWAS results or the network resource. We present SigMod, a novel and efficient method integrating GWAS results and gene network to identify a strongly interconnected gene module enriched in high association signals. Our method is formulated as a binary quadratic optimization problem, which can be solved exactly through graph min-cut algorithms. Compared to existing methods, SigMod has several desirable properties: (i) edge weights quantifying confidence of connections between genes are taken into account, (ii) the selection path can be computed rapidly, (iii) the identified gene module is strongly interconnected, hence includes genes of high functional relevance, and (iv) the method is robust against noise from either the GWAS results or the network resource. We applied SigMod to both simulated and real data. It was found to outperform state-of-the-art network-assisted methods in identifying disease-associated genes. When SigMod was applied to childhood-onset asthma GWAS results, it successfully identified a gene module enriched in consistently high association signals and made of functionally related genes that are biologically relevant for asthma. An R package SigMod is available at: https://github.com/YuanlongLiu/SigMod. yuanlong.liu@inserm.fr. Supplementary data are available at Bioinformatics online.

Rare variants in optic disc area gene CARD10 enriched in primary open-angle glaucoma.

BackgroundGenome‐wide association studies (GWAS) have identified association of common alleles with primary open‐angle glaucoma (POAG) and its quantitative endophenotypes near numerous genes. This study aims to determine whether rare pathogenic variants in these disease‐associated genes contribute to POAG.MethodsParticipants fulfilled strict inclusion criteria of advanced POAG at a young age of diagnosis. Myocilin mutation carriers were excluded using direct sequencing. Whole exome sequencing was performed on 187 glaucoma cases and 103 local screened nonglaucoma controls then joint‐called with exomes of 993 previously sequenced Australian controls. GWAS‐associated genes were assessed for enrichment of rare predicted pathogenic variants in POAG. Significantly enriched genes were compared against Exome Aggregation Consortium (ExAC) public control.ResultsEighty‐six GWAS disease or trait‐associated glaucoma genes were captured and sequenced. CARD10 showed enrichment after Bonferroni correction for rare variants in glaucoma cases (OR = 13.2, P = 6.94 × 10−5) with mutations identified in 4.28% of our POAG cohort compared to 0.27% in controls. CARD10 was significantly associated with optic disc parameters in previous GWAS. The whole GWAS gene set showed no enrichment in POAG overall (OR = 1.12, P = 0.51).ConclusionWe report here an enrichment of rare predicted pathogenic coding variants within a GWAS‐associated locus in POAG (CARD10). These findings indicate that both common and rare pathogenic coding variants in CARD10 may contribute to POAG pathogenesis.

Finding lost genes in GWAS via integrative-omics analysis reveals novel sub-networks associated with preterm birth.

Maternal genome influences associate with up to 40% of spontaneous preterm births (PTB). Multiple genome wide association studies (GWAS) have been completed to identify genetic variants associated with PTB. Disappointingly, no highly significant SNPs have replicated in independent cohorts so far. We developed an approach combining protein-protein interaction (PPI) network data with tissue specific gene expression data to "find" SNPs of modest significance to identify candidate genes of functional importance that would otherwise be overlooked. This approach is based on the assumption that "high-ranking" SNPs falling short of genome wide significance may nevertheless indicate genes that have substantial biological value in understanding PTB. We mapped highly-ranked candidate SNPs from a meta-analysis of PTB-GWAS to coding genes and developed a PPI network enriched with PTB-SNP carrying genes. This network was scored with gene expression data from term and preterm myometrium to identify subnetworks of PTB-SNP associated genes coordinately expressed with labour onset in myometrial tissue. Our analysis consistently identified significant sub-networks associated with the interacting transcription factors MEF2C and TWIST1, genes not previously associated with PTB, both of which regulate processes clearly relevant to birth timing. Other genes in the significant sub-networks were also associated with inflammatory pathways, as well as muscle function and ion channels. Gene expression level dysregulation was confirmed for eight of these networks by qRT-PCR in an independent set of term and pre-term subjects. Our method identifies novel genes dysregulated in PTB and provides a generalized framework to identify GWAS SNPs that would otherwise be overlooked.

Resequencing analysis of five Mendelian genes and the top genes from genome-wide association studies in Parkinson's Disease.

BackgroundMost sequencing studies in Parkinson’s disease (PD) have focused on either a particular gene, primarily in familial and early onset PD samples, or on screening single variants in sporadic PD cases. To date, there is no systematic study that sequences the most common PD causing genes with Mendelian inheritance [α-synuclein (SNCA), leucine-rich repeat kinase 2 (LRRK2), PARKIN, PTEN-induced putative kinase 1 (PINK1) and DJ-1 (Daisuke-Junko-1)] and susceptibility genes [glucocerebrosidase beta acid (GBA) and microtubule-associated protein tau (MAPT)] identified through genome-wide association studies (GWAS) in a European-American case-control sample (n=815).ResultsDisease-causing variants in the SNCA,LRRK2 and PARK2 genes were found in 2 % of PD patients. The LRRK2, p.G2019S mutation was found in 0.6 % of sporadic PD and 4.8 % of familial PD cases. Gene-based analysis suggests that additional variants in the LRRK2 gene also contribute to PD risk. The SNCA duplication was found in 0.8 % of familial PD patients. Novel variants were found in 0.8 % of PD cases and 0.6 % of controls. Heterozygous Gaucher disease-causing mutations in the GBA gene were found in 7.1 % of PD patients. Here, we established that the GBA variant (p.T408M) is associated with PD risk and age at onset. Additionally, gene-based and single-variant analyses demostrated that GBA gene variants (p.L483P, p.R83C, p.N409S, p.H294Q and p.E365K) increase PD risk.ConclusionsOur data suggest that the impact of additional untested coding variants in the GBA and LRRK2 genes is higher than previously estimated. Our data also provide compelling evidence of the existence of additional untested variants in the primary Mendelian and PD GWAS genes that contribute to the genetic etiology of sporadic PD.

Alzheimer's Disease Risk Polymorphisms Regulate Gene Expression in the ZCWPW1 and the CELF1 Loci.

Late onset Alzheimer’s disease (LOAD) is a genetically complex and clinically heterogeneous disease. Recent large-scale genome wide association studies (GWAS) have identified more than twenty loci that modify risk for AD. Despite the identification of these loci, little progress has been made in identifying the functional variants that explain the association with AD risk. Thus, we sought to determine whether the novel LOAD GWAS single nucleotide polymorphisms (SNPs) alter expression of LOAD GWAS genes and whether expression of these genes is altered in AD brains. The majority of LOAD GWAS SNPs occur in gene dense regions under large linkage disequilibrium (LD) blocks, making it unclear which gene(s) are modified by the SNP. Thus, we tested for brain expression quantitative trait loci (eQTLs) between LOAD GWAS SNPs and SNPs in high LD with the LOAD GWAS SNPs in all of the genes within the GWAS loci. We found a significant eQTL between rs1476679 and PILRB and GATS, which occurs within the ZCWPW1 locus. PILRB and GATS expression levels, within the ZCWPW1 locus, were also associated with AD status. Rs7120548 was associated with MTCH2 expression, which occurs within the CELF1 locus. Additionally, expression of several genes within the CELF1 locus, including MTCH2, were highly correlated with one another and were associated with AD status. We further demonstrate that PILRB, as well as other genes within the GWAS loci, are most highly expressed in microglia. These findings together with the function of PILRB as a DAP12 receptor supports the critical role of microglia and neuroinflammation in AD risk.

Genome-wide association study of 25(OH) Vitamin D concentrations in Punjabi Sikhs: Results of the Asian Indian diabetic heart study

Vitamin D deficiency is implicated in multiple disease conditions and accumulating evidence supports that the variation in serum vitamin D (25(OH)D) levels, including deficiency, is under strong genetic control. However, the underlying genetic mechanism associated with vitamin 25(OH)D concentrations is poorly understood. We earlier reported a very high prevalence of vitamin D deficiency associated with an increased risk for type 2 diabetes and obesity in a Punjabi Sikh diabetic cohort as part of the Asian Indian diabetic heart study (AIDHS). Here we have performed the first genome-wide association study (GWAS) of serum 25(OH)D on 3538 individuals from this Punjabi Sikh population. Our discovery GWAS comprised of 1387 subjects followed by validation of 24 putative SNPs (P<10−4) using an independent replication sample (n=2151) from the same population by direct genotyping. A novel locus at chromosome 20p11.21 represented by rs2207173 with minor allele frequency (MAF) 0.29, [β=−0.13, p=4.47×10−9] between FOXA2 and SSTR4 was identified to be associated with 25(OH)D levels. Another suggestive association signal at rs11586313 (MAF 0.54) [β=0.90; p=1.36×10−6] was found within the regulatory region of the IVL gene on chromosome 1q21.3. Additionally, our study replicated 3 of 5 known GWAS genes associated with 25(OH)D concentrations including GC (p=0.007) and CYP2R1 (p=0.019) reported in Europeans and the DAB1 (p=0.003), reported in Hispanics. Identification of novel association signals in biologically plausible regions with 25(OH)D metabolism will provide new molecular insights on genetic drivers of vitamin D status and its implications in health disparities.

Translational utility of experimental autoimmune encephalomyelitis: recent developments.

Multiple sclerosis (MS) is a complex autoimmune condition with firmly established genetic and environmental components. Genome-wide association studies (GWAS) have revealed a large number of genetic polymorphisms in the vicinity of, and within, genes that associate to disease. However, the significance of these single-nucleotide polymorphisms in disease and possible mechanisms of action remain, with a few exceptions, to be established. While the animal model for MS, experimental autoimmune encephalomyelitis (EAE), has been instrumental in understanding immunity in general and mechanisms of MS disease in particular, much of the translational information gathered from the model in terms of treatment development (glatiramer acetate and natalizumab) has been extensively summarized. In this review, we would thus like to cover the work done in EAE from a GWAS perspective, highlighting the research that has addressed the role of different GWAS genes and their pathways in EAE pathogenesis. Understanding the contribution of these pathways to disease might allow for the stratification of disease subphenotypes in patients and in turn open the possibility for new and individualized treatment approaches in the future.

Placental genome and maternal-placental genetic interactions: a genome-wide and candidate gene association study of placental abruption.

While available evidence supports the role of genetics in the pathogenesis of placental abruption (PA), PA-related placental genome variations and maternal-placental genetic interactions have not been investigated. Maternal blood and placental samples collected from participants in the Peruvian Abruptio Placentae Epidemiology study were genotyped using Illumina’s Cardio-Metabochip platform. We examined 118,782 genome-wide SNPs and 333 SNPs in 32 candidate genes from mitochondrial biogenesis and oxidative phosphorylation pathways in placental DNA from 280 PA cases and 244 controls. We assessed maternal-placental interactions in the candidate gene SNPS and two imprinted regions (IGF2/H19 and C19MC). Univariate and penalized logistic regression models were fit to estimate odds ratios. We examined the combined effect of multiple SNPs on PA risk using weighted genetic risk scores (WGRS) with repeated ten-fold cross-validations. A multinomial model was used to investigate maternal-placental genetic interactions. In placental genome-wide and candidate gene analyses, no SNP was significant after false discovery rate correction. The top genome-wide association study (GWAS) hits were rs544201, rs1484464 (CTNNA2), rs4149570 (TNFRSF1A) and rs13055470 (ZNRF3) (p-values: 1.11e-05 to 3.54e-05). The top 200 SNPs of the GWAS overrepresented genes involved in cell cycle, growth and proliferation. The top candidate gene hits were rs16949118 (COX10) and rs7609948 (THRB) (p-values: 6.00e-03 and 8.19e-03). Participants in the highest quartile of WGRS based on cross-validations using SNPs selected from the GWAS and candidate gene analyses had a 8.40-fold (95% CI: 5.8–12.56) and a 4.46-fold (95% CI: 2.94–6.72) higher odds of PA compared to participants in the lowest quartile. We found maternal-placental genetic interactions on PA risk for two SNPs in PPARG (chr3∶12313450 and chr3∶12412978) and maternal imprinting effects for multiple SNPs in the C19MC and IGF2/H19 regions. Variations in the placental genome and interactions between maternal-placental genetic variations may contribute to PA risk. Larger studies may help advance our understanding of PA pathogenesis.

Hope for GWAS: relevant risk genes uncovered from GWAS statistical noise.

Hundreds of genetic variants have been associated to common diseases through genome-wide association studies (GWAS), yet there are limits to current approaches in detecting true small effect risk variants against a background of false positive findings. Here we addressed the missing heritability problem, aiming to test whether there are indeed risk variants within GWAS statistical noise and to develop a systematic strategy to retrieve these hidden variants. Employing an integrative approach, which combines protein-protein interactions with association data from GWAS for 6 common diseases, we found that associated-genes at less stringent significance levels (p < 0.1) with any of these diseases are functionally connected beyond noise expectation. This functional coherence was used to identify disease-relevant subnetworks, which were shown to be enriched in known genes, outperforming the selection of top GWAS genes. As a proof of principle, we applied this approach to breast cancer, supporting well-known breast cancer genes, while pinpointing novel susceptibility genes for experimental validation. This study reinforces the idea that GWAS are under-analyzed and that missing heritability is rather hidden. It extends the use of protein networks to reveal this missing heritability, thus leveraging the large investment in GWAS that produced so far little tangible gain.

8.2 Cardiac Ion Channelopathies in Unexplained Stillbirths

BackgroundAlthough cardiac ion channelopathies are reported in up to 15% of sudden infant deaths, the prevalence in unexplained stillbirths is not known.ObjectiveTo determine the prevalence of genetic mutations in cardiac...

GWAS and drug targets.

BackgroundGenome wide association studies (GWAS) have revealed a large number of links between genome variation and complex disease. Among other benefits, it is expected that these insights will lead to new therapeutic strategies, particularly the identification of new drug targets. In this paper, we evaluate the power of GWAS studies to find drug targets by examining how many existing drug targets have been directly 'rediscovered' by this technique, and the extent to which GWAS results may be leveraged by network information to discover known and new drug targets.ResultsWe find that only a very small fraction of drug targets are directly detected in the relevant GWAS studies. We investigate two possible explanations for this observation. First, we find evidence of negative selection acting on drug target genes as a consequence of strong coupling with the disease phenotype, so reducing the incidence of SNPs linked to the disease. Second, we find that GWAS genes are substantially longer on average than drug targets and than all genes, suggesting there is a length related bias in GWAS results. In spite of the low direct relationship between drug targets and GWAS reported genes, we found these two sets of genes are closely coupled in the human protein network. As a consequence, machine-learning methods are able to recover known drug targets based on network context and the set of GWAS reported genes for the same disease. We show the approach is potentially useful for identifying drug repurposing opportunities.ConclusionsAlthough GWA studies do not directly identify most existing drug targets, there are several reasons to expect that new targets will nevertheless be discovered using these data. Initial results on drug repurposing studies using network analysis are encouraging and suggest directions for future development.

Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions

BackgroundThe number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown.ResultsWe carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of ~58% and ~ 40% for localization and functions respectively of proteins were determined at a threshold of ~30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k-nearest neighbor classifier confirmed that our results compared favorably.ConclusionsThis approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in GWAS studies to be associated with diseases, which are of translational interest.

Genome-wide and candidate gene association studies of placental abruption.

Placental abruption (PA), a pregnancy-related vascular disorder, is a leading cause of maternal and perinatal morbidity and mortality. The success of identifying genetic susceptibility loci for PA, a multi-factorial heritable disorder, has been limited. We conducted a genome-wide association study (GWAS) and candidate gene association study using 470 PA cases and 473 controls from Lima, Peru. Genotyping for common genetic variations (single nucleotide polymorphisms, SNPs) was conducted using the Illumina Cardio-Metabo Chip platform. Common variations in 35 genes that participate in mitochondrial biogenesis (MB) and oxidative phosphorylation (OS) were selected for the candidate gene study. Regression models were fit to examine associations of each SNP with risk of PA. In pathway analyses, we examined functions and functional relationships of genes represented by the top GWAS hits. Genetic risk scores (GRS), based on top hits of the GWAS and candidate gene analyses, respectively, were computed using the risk allele counting method. The top hit in the GWAS analyses was rs1238566 (empirical P-value=1.04e-4 and FDR-adjusted P-value=5.65E-04) in FLI-1 gene, a megakaryocyte-specific transcription factor. Networks of genes involved in lipid metabolism and cell signaling were significantly enriched by the 51 genes whose SNPs were among the top 200 GWAS hits (P-value <2.1e-3). SNPs known to regulate MB (e.g. CAMK2B, NR1H3, PPARG, PRKCA, and THRB) and OP (e.g., COX5A, and NDUF family of genes) were associated with PA risk (P-value <0.05). GRS was significantly associated with PA risk (trend P-value <0.001 and 0.01 for GWAS and candidate gene based GRS, respectively). Our study suggests that integrating multiple analytical strategies in genetic association studies can provide opportunities for identifying genetic risk factors and novel molecular mechanisms that underlie PA.

Identifying multiple causative genes at a single GWAS locus.

Genome-wide association studies (GWAS) are useful for nominating candidate genes, but typically are unable to establish disease causality or differentiate between the effects of variants in linkage disequilibrium (LD). Additionally, some GWAS loci might contain multiple causative variants or genes that contribute to the overall disease susceptibility at a single locus. However, the majority of current GWAS lack the statistical power to test whether multiple causative genes underlie the same locus, prompting us to adopt an alternative approach to testing multiple GWAS genes empirically. We used gene targeting in a disease-susceptible rat model of genetic hypertension to test all six genes at the Agtrap-Plod1 locus (Agtrap, Mthfr, Clcn6, Nppa, Nppb, and Plod1) for blood pressure (BP) and renal phenotypes. This revealed that the majority of genes at this locus (five out of six) can impact hypertension by modifying BP and renal phenotypes. Mutations of Nppa, Plod1, and Mthfr increased disease susceptibility, whereas Agtrap and Clcn6 mutations decreased hypertension risk. Reanalysis of the human AGTRAP-PLOD1 locus also implied that disease-associated haplotype blocks with polygenic effects were not only possible, but rather were highly plausible. Combined, these data demonstrate for the first time that multiple modifiers of hypertension can cosegregate at a single GWAS locus.

Genetics of Coronary Artery Disease

Coronary heart disease affects ≈15.4 million individuals in the United States1 and is one of the main causes of mortality and morbidity.1 A familial component contributes to cardiovascular disease (CVD) susceptibility,2 but it was not until the emergence of genomewide association studies (GWAS) that genetic loci were identified that displayed consistent associations with coronary artery disease (CAD) across multiple cohorts. The present article describes recent advances in the understanding of the genetic basis of CAD. Family2 and twin3 studies provided convincing evidence that CAD clusters in families and has a heritable component2–5; however, the delineation of the specific genetic architecture that predisposes to CAD has been challenging, with initial candidate gene approaches yielding inconsistent results.6 In contrast to candidate gene approaches that focus on genetic variation in genes whose gene products are known to play an important role in cardiovascular physiology, GWAS simultaneously assess the association with CAD of hundreds of thousands of genetic variants distributed across the whole genome. Therefore, GWAS represent an essentially unbiased approach that is not limited by the current (patho)physiological understanding of CVD and bears the potential of discovering completely new molecular mechanisms that predispose to CAD. In 2007, the first GWAS for CAD were published.7–9 The main finding was a locus on chromosome 9p21, which is still the most consistently associated CAD locus to date.10 Subsequent studies revealed that this locus is related to a broad spectrum of vascular phenotypes, including CAD and myocardial infarction,7–9 coronary artery calcification,11 peripheral artery disease,12,13 and abdominal aortic aneurysm.14 To increase power for the detection of genetic variants with smaller effect sizes, large consortia have been built that combine genetic-epidemiological data from multiple cohorts and tens …

A systematic review of cancer GWAS and candidate gene meta-analyses reveals limited overlap but similar effect sizes

Candidate gene and genome-wide association studies (GWAS) represent two complementary approaches to uncovering genetic contributions to common diseases. We systematically reviewed the contributions of these approaches to our knowledge of genetic associations with cancer risk by analyzing the data in the Cancer Genome-wide Association and Meta Analyses database (Cancer GAMAdb). The database catalogs studies published since January 1, 2000, by study and cancer type. In all, we found that meta-analyses and pooled analyses of candidate genes reported 349 statistically significant associations and GWAS reported 269, for a total of 577 unique associations. Only 41 (7.1%) associations were reported in both candidate gene meta-analyses and GWAS, usually with similar effect sizes. When considering only noteworthy associations (defined as those with false-positive report probabilities≤0.2) and accounting for indirect overlap, we found 202 associations, with 27 of those appearing in both meta-analyses and GWAS. Our findings suggest that meta-analyses of well-conducted candidate gene studies may continue to add to our understanding of the genetic associations in the post-GWAS era.

Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases

BackgroundWhile genome-wide association studies (GWAS) of complex traits have revealed thousands of reproducible genetic associations to date, these loci collectively confer very little of the heritability of their respective diseases and, in general, have contributed little to our understanding the underlying disease biology. Physical protein interactions have been utilized to increase our understanding of human Mendelian disease loci but have yet to be fully exploited for complex traits.MethodsWe hypothesized that protein interaction modeling of GWAS findings could highlight important disease-associated loci and unveil the role of their network topology in the genetic architecture of diseases with complex inheritance.ResultsNetwork modeling of proteins associated with the intragenic single nucleotide polymorphisms of the National Human Genome Research Institute catalog of complex trait GWAS revealed that complex trait associated loci are more likely to be hub and bottleneck genes in available, albeit incomplete, networks (OR=1.59, Fisher's exact test p<2.24×10−12). Network modeling also prioritized novel type 2 diabetes (T2D) genetic variations from the Finland–USA Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics and the Wellcome Trust GWAS data, and demonstrated the enrichment of hubs and bottlenecks in prioritized T2D GWAS genes. The potential biological relevance of the T2D hub and bottleneck genes was revealed by their increased number of first degree protein interactions with known T2D genes according to several independent sources (p<0.01, probability of being first interactors of known T2D genes).ConclusionVirtually all common diseases are complex human traits, and thus the topological centrality in protein networks of complex trait genes has implications in genetics, personal genomics, and therapy.