Bivariate Genome-Wide Association Scan Identifies 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease

Pleiotropic influence of DNA methylation QTLs on physiological and ageing traits

DNA methylation is influenced by genetic and non-genetic factors. Here, we chart quantitative trait loci (QTLs) that modulate levels of methylation at highly conserved CpGs using liver methylome data from mouse strains belonging to the BXD family. A regulatory hotspot on chromosome 5 had the highest density of trans-acting methylation QTLs (trans-meQTLs) associated with multiple distant CpGs. We refer to this locus as meQTL.5a. Trans-modulated CpGs showed age-dependent changes and were enriched in developmental genes, including several members of the MODY pathway (maturity onset diabetes of the young). The joint modulation by genotype and ageing resulted in a more 'aged methylome' for BXD strains that inherited the DBA/2J parental allele at meQTL.5a. Further, several gene expression traits, body weight, and lipid levels mapped to meQTL.5a, and there was a modest linkage with lifespan. DNA binding motif and protein-protein interaction enrichment analyses identified the hepatic nuclear factor, Hnf1a (MODY3 gene in humans), as a strong candidate. The pleiotropic effects of meQTL.5a could contribute to variations in body size and metabolic traits, and influence CpG methylation and epigenetic ageing that could have an impact on lifespan.

Genetic Factors for Coronary Heart Disease and Their Mechanisms: A Meta-Analysis and Comprehensive Review of Common Variants from Genome-Wide Association Studies

Genome-wide association studies (GWAS) have discovered 163 loci related to coronary heart disease (CHD). Most GWAS have emphasized pathways related to single-nucleotide polymorphisms (SNPs) that reached genome-wide significance in their reports, while identification of CHD pathways based on the combination of all published GWAS involving various ethnicities has yet to be performed. We conducted a systematic search for articles with comprehensive GWAS data in the GWAS Catalog and PubMed, followed by a meta-analysis of the top recurring SNPs from ≥2 different articles using random or fixed-effect models according to Cochran Q and I2 statistics, and pathway enrichment analysis. Meta-analyses showed significance for 265 of 309 recurring SNPs. Enrichment analysis returned 107 significant pathways, including lipoprotein and lipid metabolisms (rs7412, rs6511720, rs11591147, rs1412444, rs11172113, rs11057830, rs4299376), atherogenesis (rs7500448, rs6504218, rs3918226, rs7623687), shared cardiovascular pathways (rs72689147, rs1800449, rs7568458), diabetes-related pathways (rs200787930, rs12146487, rs6129767), hepatitis C virus infection/hepatocellular carcinoma (rs73045269/rs8108632, rs56062135, rs188378669, rs4845625, rs11838776), and miR-29b-3p pathways (rs116843064, rs11617955, rs146092501, rs11838776, rs73045269/rs8108632). In this meta-analysis, the identification of various genetic factors and their associated pathways associated with CHD denotes the complexity of the disease. This provides an opportunity for the future development of novel CHD genetic risk scores relevant to personalized and precision medicine.

Meta-analysis of sub-Saharan African studies provides insights into genetic architecture of lipid traits

Genetic associations for lipid traits have identified hundreds of variants with clear differences across European, Asian and African studies. Based on a sub-Saharan-African GWAS for lipid traits in the population cross-sectional AWI-Gen cohort (N = 10,603) we report a novel LDL-C association in the GATB region (P-value=1.56 × 10-8). Meta-analysis with four other African cohorts (N = 23,718) provides supporting evidence for the LDL-C association with the GATB/FHIP1A region and identifies a novel triglyceride association signal close to the FHIT gene (P-value =2.66 × 10-8). Our data enable fine-mapping of several well-known lipid-trait loci including LDLR, PMFBP1 and LPA. The transferability of signals detected in two large global studies (GLGC and PAGE) consistently improves with an increase in the size of the African replication cohort. Polygenic risk score analysis shows increased predictive accuracy for LDL-C levels with the narrowing of genetic distance between the discovery dataset and our cohort. Novel discovery is enhanced with the inclusion of African data.

Accounting for age of onset and family history improves power in genome-wide association studies

Genome-wide association studies (GWASs) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age of onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here, we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), which jointly accounts for age of onset and sex as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields statistically significant power gains over LT-FH and large power gains over genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data and to mortality in the UK Biobank and found 20 genome-wide significant associations with LT-FH++, compared to ten for LT-FH and eight for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

Weighted Co-Expression Network Analysis Identifies RNF181 as a Causal Gene of Coronary Artery Disease

Background: Coronary artery disease (CAD) exerts a global challenge to public health. Genetic heritability is one of the most vital contributing factors in the pathophysiology of CAD. Co-expression network analysis is an applicable and robust method for the interpretation of biological interaction from microarray data. Previous CAD studies have focused on peripheral blood samples since the processes of CAD may vary from tissue to blood. It is therefore necessary to find biomarkers for CAD in heart tissues; their association also requires further illustration.

Materials and Methods: To filter for causal genes, an analysis of microarray expression profiles, GSE12504 and GSE22253, was performed with weighted gene co-expression network analysis (WGCNA). Co-expression modules were constructed after batch effect removal and data normalization. The results showed that 7 co-expression modules with 8,525 genes and 1,210 differentially expressed genes (DEGs) were identified. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Four major pathways in CAD tissue and hub genes were addressed in the Hybrid Mouse Diversity Panel (HMDP) and Human Protein Atlas (HPA), and isoproterenol (ISO)/doxycycline (DOX)-induced heart toxicity models were used to validate the hub genes. Lastly, the hub genes and risk variants were verified in the CAD cohort and in genome-wide association studies (GWAS).

Results: The results showed that RNF181 and eight other hub genes are perturbed during CAD in heart tissues. Additionally, the expression of RNF181 was validated using RT-PCR and immunohistochemistry (IHC) staining in two cardiotoxicity mouse models. The association was further verified in the CAD patient cohort and in GWAS.

Conclusion: Our findings illustrated for the first time that the E3 ubiquitination ligase protein RNF181 may serve as a potential biomarker in CAD, but further in vivo validation is warranted.

Multi-Trait Genome-Wide Association Study of Atherosclerosis Detects Novel Pleiotropic Loci

Although affecting different arterial territories, the related atherosclerotic vascular diseases coronary artery disease (CAD) and peripheral artery disease (PAD) share similar risk factors and have shared pathobiology. To identify novel pleiotropic loci associated with atherosclerosis, we performed a joint analysis of their shared genetic architecture, along with that of common risk factors. Using summary statistics from genome-wide association studies of nine known atherosclerotic (CAD, PAD) and atherosclerosis risk factors (body mass index, smoking initiation, type 2 diabetes, low density lipoprotein, high density lipoprotein, total cholesterol, and triglycerides), we perform 15 separate multi-trait genetic association scans which resulted in 25 novel pleiotropic loci not yet reported as genome-wide significant for their respective traits. Colocalization with single-tissue eQTLs identified candidate causal genes at 14 of the detected signals. Notably, the signal between PAD and LDL-C at the PCSK6 locus affects PCSK6 splicing in human liver tissue and induced pluripotent derived hepatocyte-like cells. These results show that joint analysis of related atherosclerotic disease traits and their risk factors allowed identification of unified biology that may offer the opportunity for therapeutic manipulation. The signal at PCSK6 represent possible shared causal biology where existing inhibitors may be able to be leveraged for novel therapies.

Combining multivariate genomic approaches to elucidate the comorbidity between autism spectrum disorder and attention deficit hyperactivity disorder

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are two highly heritable neurodevelopmental disorders. Several lines of evidence point towards the presence of shared genetic factors underlying ASD and ADHD. We conducted genomic analyses of common risk variants (i.e. single nucleotide polymorphisms, SNPs) shared by ASD and ADHD, and those specific to each disorder.

METHODS

With the summary data from two GWAS, one on ASD (N = 46,350) and another on ADHD (N = 55,374) individuals, we used genomic structural equation modelling and colocalization analysis to identify SNPs shared by ASD and ADHD and SNPs specific to each disorder. Functional genomic analyses were then conducted on shared and specific common genetic variants. Finally, we performed a bidirectional Mendelian randomization analysis to test whether the shared genetic risk between ASD and ADHD was interpretable in terms of reciprocal relationships between ASD and ADHD.

RESULTS

We found that 37.5% of the SNPs associated with ASD (at p < 1e-6) colocalized with ADHD SNPs and that 19.6% of the SNPs associated with ADHD colocalized with ASD SNPs. We identified genes mapped to SNPs that are specific to ASD or ADHD and that are shared by ASD and ADHD, including two novel genes INSM1 and PAX1. Our bidirectional Mendelian randomization analyses indicated that the risk of ASD was associated with an increased risk of ADHD and vice versa.

CONCLUSIONS

Using multivariate genomic analyses, the present study uncovers shared and specific genetic variants associated with ASD and ADHD. Further functional investigation of genes mapped to those shared variants may help identify pathophysiological pathways and new targets for treatment.

Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.

Genome-Wide Identification of Rare and Common Variants Driving Triglyceride Levels in a Nevada Population

Clinical conditions correlated with elevated triglyceride levels are well-known: coronary heart disease, hypertension, and diabetes. Underlying genetic and phenotypic mechanisms are not fully understood, partially due to lack of coordinated genotypic-phenotypic data. Here we use a subset of the Healthy Nevada Project, a population of 9,183 sequenced participants with longitudinal electronic health records to examine consequences of altered triglyceride levels. Specifically, Healthy Nevada Project participants sequenced by the Helix Exome+ platform were cross-referenced to their electronic medical records to identify: (1) rare and common single-variant genome-wide associations; (2) gene-based associations using a Sequence Kernel Association Test; (3) phenome-wide associations with triglyceride levels; and (4) pleiotropic variants linked to triglyceride levels. The study identified 549 significant single-variant associations (p < 8.75 × 10-9), many in chromosome 11's triglyceride hotspot: ZPR1, BUD13, APOC3, APOA5. A well-known protective loss-of-function variant in APOC3 (R19X) was associated with a 51% decrease in triglyceride levels in the cohort. Sixteen gene-based triglyceride associations were identified; six of these genes surprisingly did not include a single variant with significant associations. Results at the variant and gene level were validated with the UK Biobank. The combination of a single-variant genome-wide association, a gene-based association method, and phenome wide-association studies identified rare and common variants, genes, and phenotypes associated with elevated triglyceride levels, some of which may have been overlooked with standard approaches.

Evaluating the cardiovascular safety of sclerostin inhibition using evidence from meta-analysis of clinical trials and human genetics

Inhibition of sclerostin is a therapeutic approach to lowering fracture risk in patients with osteoporosis. However, data from phase 3 randomized controlled trials (RCTs) of romosozumab, a first-in-class monoclonal antibody that inhibits sclerostin, suggest an imbalance of serious cardiovascular events, and regulatory agencies have issued marketing authorizations with warnings of cardiovascular disease. Here, we meta-analyze published and unpublished cardiovascular outcome trial data of romosozumab and investigate whether genetic variants that mimic therapeutic inhibition of sclerostin are associated with higher risk of cardiovascular disease. Meta-analysis of up to three RCTs indicated a probable higher risk of cardiovascular events with romosozumab. Scaled to the equivalent dose of romosozumab (210 milligrams per month; 0.09 grams per square centimeter of higher bone mineral density), the SOST genetic variants were associated with lower risk of fracture and osteoporosis (commensurate with the therapeutic effect of romosozumab) and with a higher risk of myocardial infarction and/or coronary revascularization and major adverse cardiovascular events. The same variants were also associated with increased risk of type 2 diabetes mellitus and higher systolic blood pressure and central adiposity. Together, our findings indicate that inhibition of sclerostin may elevate cardiovascular risk, warranting a rigorous evaluation of the cardiovascular safety of romosozumab and other sclerostin inhibitors.

Candidate Regulators of Dyslipidemia in Chromosome 1 Substitution Lines Using Liver Co-Expression Profiling Analysis

Dyslipidemia is a major risk factor for cardiovascular disease. Although many genetic factors have been unveiled, a large fraction of the phenotypic variance still needs further investigation. Chromosome 1 (Chr 1) harbors multiple gene loci that regulate blood lipid levels, and identifying functional genes in these loci has proved challenging. We constructed a mouse population, Chr 1 substitution lines (C1SLs), where only Chr 1 differs from the recipient strain C57BL/6J (B6), while the remaining chromosomes are unchanged. Therefore, any phenotypic variance between C1SLs and B6 can be attributed to the differences in Chr 1. In this study, we assayed plasma lipid and glucose levels in 13 C1SLs and their recipient strain B6. Through weighted gene co-expression network analysis of liver transcriptome and “guilty-by-association” study, eight associated modules of plasma lipid and glucose were identified. Further joint analysis of human genome wide association studies revealed 48 candidate genes. In addition, 38 genes located on Chr 1 were also uncovered, and 13 of which have been functionally validated in mouse models. These results suggest that C1SLs are ideal mouse models to identify functional genes on Chr 1 associated with complex traits, like dyslipidemia, by using gene co-expression network analysis.

Effect of non-normality and low count variants on cross-phenotype association tests in GWAS

Many complex human diseases, such as type 2 diabetes, are characterized by multiple underlying traits/phenotypes that have substantially shared genetic architecture. Multivariate analysis of correlated traits has the potential to increase the power of detecting underlying common genetic loci. Several cross-phenotype association methods have been proposed-some require individual-level data on traits and genotypes, while the others require only summary-level data. In this article, we explore whether non-normality of multivariate trait distribution affects the inference from some of the existing multi-trait methods and how that effect is dependent on the allele count of the genetic variant being tested. We find that most of these tests are susceptible to biases that lead to spurious association signals. Even after controlling for confounders that may contribute to non-normality and then applying inverse normal transformation on the residuals of each trait, these tests may have inflated type I errors for variants with low minor allele counts (MACs). A likelihood ratio test of association based on the ordinal regression of individual-level genotype conditional on the traits seems to be the least biased and can maintain type I error when the MAC is reasonably large (e.g., MAC > 30). Application of these methods to publicly available summary statistics of eight amino acid traits on European samples seem to exhibit systematic inflation (especially for variants with low MAC), which is consistent with our findings from simulation experiments.

Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network

The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype. Marker-phenotype association approaches can be grouped into univariate, bivariate, and multivariate categories based on the number of phenotypes considered at one time. Here we applied one statistical method per category followed by an eQTL colocalization analysis to identify potential pleiotropic variants that contribute to the link between cardiovascular and neurological diseases. We performed our analyses on ~530,000 common SNPs coupled with 65 electronic health record (EHR)-based phenotypes in 43,870 unrelated European adults from the Electronic Medical Records and Genomics (eMERGE) network. There were 31 variants identified by all three methods that showed significant associations across late onset cardiac- and neurologic- diseases. We further investigated functional implications of gene expression on the detected "lead SNPs" via colocalization analysis, providing a deeper understanding of the discovered associations. In summary, we present the framework and landscape for detecting potential pleiotropy using univariate, bivariate, multivariate, and colocalization methods. Further exploration of these potentially pleiotropic genetic variants will work toward understanding disease causing mechanisms across cardiovascular and neurological diseases and may assist in considering disease prevention as well as drug repositioning in future research.