The HDAC9-associated risk locus promotes coronary artery disease by governing TWIST1.

Genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of common disorders. However, since the large majority of these risk SNPs reside outside gene-coding regions, GWAS generally provide no information about causal mechanisms regarding the specific gene(s) that are affected or the tissue(s) in which these candidate gene(s) exert their effect. The 'gold standard' method for understanding causal genes and their mechanisms of action are laborious basic science studies often involving sophisticated knockin or knockout mouse lines, however, these types of studies are impractical as a high-throughput means to understand the many risk variants that cause complex diseases like coronary artery disease (CAD). As a solution, we developed a streamlined, data-driven informatics pipeline to gain mechanistic insights on complex genetic loci. The pipeline begins by understanding the SNPs in a given locus in terms of their relative location and linkage disequilibrium relationships, and then identifies nearby expression quantitative trait loci (eQTLs) to determine their relative independence and the likely tissues that mediate their disease-causal effects. The pipeline then seeks to understand associations with other disease-relevant genes, disease sub-phenotypes, potential causality (Mendelian randomization), and the regulatory and functional involvement of these genes in gene regulatory co-expression networks (GRNs). Here, we applied this pipeline to understand a cluster of SNPs associated with CAD within and immediately adjacent to the gene encoding HDAC9. Our pipeline demonstrated, and validated, that this locus is causal for CAD by modulation of TWIST1 expression levels in the arterial wall, and by also governing a GRN related to metabolic function in skeletal muscle. Our results reconciled numerous prior studies, and also provided clear evidence that this locus does not govern HDAC9 expression, structure or function. This pipeline should be considered as a powerful and efficient way to understand GWAS risk loci in a manner that better reflects the highly complex nature of genetic risk associated with common disorders.

Transcription Factor MAFF (MAF Basic Leucine Zipper Transcription Factor F) Regulates an Atherosclerosis Relevant Network Connecting Inflammation and Cholesterol Metabolism.

BACKGROUND: Coronary artery disease (CAD) is a multifactorial condition with both genetic and exogenous causes. The contribution of tissue-specific functional networks to the development of atherosclerosis remains largely unclear. The aim of this study was to identify and characterize central regulators and networks leading to atherosclerosis. METHODS: Based on several hundred genes known to affect atherosclerosis risk in mouse (as demonstrated in knockout models) and human (as shown by genome-wide association studies), liver gene regulatory networks were modeled. The hierarchical order and regulatory directions of genes within the network were based on Bayesian prediction models, as well as experimental studies including chromatin immunoprecipitation DNA-sequencing, chromatin immunoprecipitation mass spectrometry, overexpression, small interfering RNA knockdown in mouse and human liver cells, and knockout mouse experiments. Bioinformatics and correlation analyses were used to clarify associations between central genes and CAD phenotypes in both human and mouse. RESULTS: The transcription factor MAFF (MAF basic leucine zipper transcription factor F) interacted as a key driver of a liver network with 3 human genes at CAD genome-wide association studies loci and 11 atherosclerotic murine genes. Most importantly, expression levels of the low-density lipoprotein receptor (LDLR) gene correlated with MAFF in 600 CAD patients undergoing bypass surgery (STARNET [Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task]) and a hybrid mouse diversity panel involving 105 different inbred mouse strains. Molecular mechanisms of MAFF were tested in noninflammatory conditions and showed positive correlation between MAFF and LDLR in vitro and in vivo. Interestingly, after lipopolysaccharide stimulation (inflammatory conditions), an inverse correlation between MAFF and LDLR in vitro and in vivo was observed. Chromatin immunoprecipitation mass spectrometry revealed that the human CAD genome-wide association studies candidate BACH1 (BTB domain and CNC homolog 1) assists MAFF in the presence of lipopolysaccharide stimulation with respective heterodimers binding at the MAF recognition element of the LDLR promoter to transcriptionally downregulate LDLR expression. CONCLUSIONS: The transcription factor MAFF was identified as a novel central regulator of an atherosclerosis/CAD-relevant liver network. MAFF triggered context-specific expression of LDLR and other genes known to affect CAD risk. Our results suggest that MAFF is a missing link between inflammation, lipid and lipoprotein metabolism, and a possible treatment target.

alona: a web server for single-cell RNA-seq analysis.

SUMMARY: Single-cell RNA sequencing (scRNA-seq) is a technology to measure gene expression in single cells. It has enabled discovery of new cell types and established cell type atlases of tissues and organs. The widespread adoption of scRNA-seq has created a need for user-friendly software for data analysis. We have developed a web server, alona that incorporates several of the most popular single-cell analysis algorithms into a flexible pipeline. alona can perform quality filtering, normalization, batch correction, clustering, cell type annotation and differential gene expression analysis. Data are visualized in the web browser using an interface based on JavaScript, allowing the user to query genes of interest and visualize the cluster structure. alona accepts a compressed gene expression matrix and identifies cell clusters with a graph-based clustering strategy. Cell types are identified from a comprehensive collection of marker genes or by specifying a custom set of marker genes. AVAILABILITY AND IMPLEMENTATION: The service runs at https://alona.panglaodb.se and the Python package can be downloaded from https://oscar-franzen.github.io/adobo/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data.

The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose a novel ensemble learning framework, ensembleCNV, to detect and genotype CNVs using single nucleotide polymorphism (SNP) array data. EnsembleCNV (a) identifies and eliminates batch effects at raw data level; (b) assembles individual CNV calls into CNV regions (CNVRs) from multiple existing callers with complementary strengths by a heuristic algorithm; (c) re-genotypes each CNVR with local likelihood model adjusted by global information across multiple CNVRs; (d) refines CNVR boundaries by local correlation structure in copy number intensities; (e) provides direct CNV genotyping accompanied with confidence score, directly accessible for downstream quality control and association analysis. Benchmarked on two large datasets, ensembleCNV outperformed competing methods and achieved a high call rate (93.3%) and reproducibility (98.6%), while concurrently achieving high sensitivity by capturing 85% of common CNVs documented in the 1000 Genomes Project. Given this CNV call rate and accuracy, which are comparable to SNP genotyping, we suggest ensembleCNV holds significant promise for performing genome-wide CNV association studies and investigating how CNVs predispose to human diseases.

Functional regulatory mechanism of smooth muscle cell-restricted LMOD1 coronary artery disease locus.

Recent genome-wide association studies (GWAS) have identified multiple new loci which appear to alter coronary artery disease (CAD) risk via arterial wall-specific mechanisms. One of the annotated genes encodes LMOD1 (Leiomodin 1), a member of the actin filament nucleator family that is highly enriched in smooth muscle-containing tissues such as the artery wall. However, it is still unknown whether LMOD1 is the causal gene at this locus and also how the associated variants alter LMOD1 expression/function and CAD risk. Using epigenomic profiling we recently identified a non-coding regulatory variant, rs34091558, which is in tight linkage disequilibrium (LD) with the lead CAD GWAS variant, rs2820315. Herein we demonstrate through expression quantitative trait loci (eQTL) and statistical fine-mapping in GTEx, STARNET, and human coronary artery smooth muscle cell (HCASMC) datasets, rs34091558 is the top regulatory variant for LMOD1 in vascular tissues. Position weight matrix (PWM) analyses identify the protective allele rs34091558-TA to form a conserved Forkhead box O3 (FOXO3) binding motif, which is disrupted by the risk allele rs34091558-A. FOXO3 chromatin immunoprecipitation and reporter assays show reduced FOXO3 binding and LMOD1 transcriptional activity by the risk allele, consistent with effects of FOXO3 downregulation on LMOD1. LMOD1 knockdown results in increased proliferation and migration and decreased cell contraction in HCASMC, and immunostaining in atherosclerotic lesions in the SMC lineage tracing reporter mouse support a key role for LMOD1 in maintaining the differentiated SMC phenotype. These results provide compelling functional evidence that genetic variation is associated with dysregulated LMOD1 expression/function in SMCs, together contributing to the heritable risk for CAD.

Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression.

Genome-wide association studies (GWASs) have identified a multitude of genetic loci involved with traits and diseases. However, it is often unclear which genes are affected in such loci and whether the associated genetic variants lead to increased or decreased gene function. To mitigate this, we integrated associations of common genetic variants in 57 GWASs with 24 studies of expression quantitative trait loci (eQTLs) from a broad range of tissues by using a Mendelian randomization approach. We discovered a total of 3,484 instances of gene-trait-associated changes in expression at a false-discovery rate < 0.05. These genes were often not closest to the genetic variant and were primarily identified in eQTLs derived from pathophysiologically relevant tissues. For instance, genes with expression changes associated with lipid traits were mostly identified in the liver, and those associated with cardiovascular disease were identified in arterial tissue. The affected genes additionally point to biological processes implicated in the interrogated traits, such as the interleukin-27 pathway in rheumatoid arthritis. Further, comparing trait-associated gene expression changes across traits suggests that pleiotropy is a widespread phenomenon and points to specific instances of both agonistic and antagonistic pleiotropy. For instance, expression of SNX19 and ABCB9 is positively correlated with both the risk of schizophrenia and educational attainment. To facilitate interpretation, we provide this lexicon of how common trait-associated genetic variants alter gene expression in various tissues as the online database GWAS2Genes.

Assembly and diploid architecture of an individual human genome via single-molecule technologies.

We present the first comprehensive analysis of a diploid human genome that combines single-molecule sequencing with single-molecule genome maps. Our hybrid assembly markedly improves upon the contiguity observed from traditional shotgun sequencing approaches, with scaffold N50 values approaching 30 Mb, and we identified complex structural variants (SVs) missed by other high-throughput approaches. Furthermore, by combining Illumina short-read data with long reads, we phased both single-nucleotide variants and SVs, generating haplotypes with over 99% consistency with previous trio-based studies. Our work shows that it is now possible to integrate single-molecule and high-throughput sequence data to generate de novo assembled genomes that approach reference quality.

Poliovirus Receptor-Related 2: A Cholesterol-Responsive Gene Affecting Atherosclerosis Development by Modulating Leukocyte Migration.

OBJECTIVE: Recently, poliovirus receptor-related 2 (Pvrl2) emerged as a top gene in a global gene expression study aiming to detect plasma cholesterol-responsive genes causally related to atherosclerosis regression in hypercholesterolemic mice. PVRL2 is an adherens junction protein implied to play a role in transendothelial migration of leukocytes, a key feature in atherosclerosis development. In this study, we investigated the effect of Pvrl2 deficiency on atherosclerosis development and transendothelial migration of leukocytes activity. APPROACH AND RESULTS: Pvrl2-deficient mice bred onto an atherosclerosis-prone background (Pvrl2-/-Ldlr-/-Apob100/100) had less atherosclerotic lesions and more stable plaques compared with littermate controls (Pvrl2+/+Ldlr-/-Apob100/100). Pvrl2-/-Ldlr-/-Apob100/100 mice also showed a 49% decrease in transendothelial migration of leukocytes activity observed using the in vivo air pouch model. In accordance, augmented arterial wall expression of Pvrl2 during atherosclerosis progression coincided with an increased gene expression of migrating leukocytes into the vessel wall. Both in human and mice, gene and protein expression of PVRL2 was predominantly observed in the vascular endothelium according to the immunohistochemical and gene expression data. In addition, the cholesterol responsiveness of PVRL2 was also observed in humans. CONCLUSIONS: PVRL2 is a plasma cholesterol-responsive gene acting at endothelial sites of vascular inflammation that could potentially be a new therapeutic target for atherosclerosis prevention through its suggested transendothelial migration of leukocytes modulating activity.

Comparative genomic analyses of freshly isolated Giardia intestinalis assemblage A isolates.

BACKGROUND: The diarrhea-causing protozoan Giardia intestinalis makes up a species complex of eight different assemblages (A-H), where assemblage A and B infect humans. Comparative whole-genome analyses of three of these assemblages have shown that there is significant divergence at the inter-assemblage level, however little is currently known regarding variation at the intra-assemblage level. We have performed whole genome sequencing of two sub-assemblage AII isolates, recently axenized from symptomatic human patients, to study the biological and genetic diversity within assemblage A isolates. RESULTS: Several biological differences between the new and earlier characterized assemblage A isolates were identified, including a difference in growth medium preference. The two AII isolates were of different sub-assemblage types (AII-1 [AS175] and AII-2 [AS98]) and showed size differences in the smallest chromosomes. The amount of genetic diversity was characterized in relation to the genome of the Giardia reference isolate WB, an assemblage AI isolate. Our analyses indicate that the divergence between AI and AII is approximately 1 %, represented by ~100,000 single nucleotide polymorphisms (SNP) distributed over the chromosomes with enrichment in variable genomic regions containing surface antigens. The level of allelic sequence heterozygosity (ASH) in the two AII isolates was found to be 0.25-0.35 %, which is 25-30 fold higher than in the WB isolate and 10 fold higher than the assemblage AII isolate DH (0.037 %). 35 protein-encoding genes, not found in the WB genome, were identified in the two AII genomes. The large gene families of variant-specific surface proteins (VSPs) and high cysteine membrane proteins (HCMPs) showed isolate-specific divergences of the gene repertoires. Certain genes, often in small gene families with 2 to 8 members, localize to the variable regions of the genomes and show high sequence diversity between the assemblage A isolates. One of the families, Bactericidal/Permeability Increasing-like protein (BPIL), with eight members was characterized further and the proteins were shown to localize to the ER in trophozoites. CONCLUSIONS: Giardia genomes are modular with highly conserved core regions mixed up by variable regions containing high levels of ASH, SNPs and variable surface antigens. There are significant genomic variations in assemblage A isolates, in terms of chromosome size, gene content, surface protein repertoire and gene polymorphisms and these differences mainly localize to the variable regions of the genomes. The large genetic differences within one assemblage of G. intestinalis strengthen the argument that the assemblages represent different Giardia species.

Intraclonal variations among Streptococcus pneumoniae isolates influence the likelihood of invasive disease in children.

BACKGROUND: Pneumococcal serotypes are represented by a varying number of clonal lineages with different genetic contents, potentially affecting invasiveness. However, genetic variation within the same genetic lineage may be larger than anticipated. METHODS: A total of 715 invasive and carriage isolates from children in the same region and during the same period were compared using pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing. Bacterial genome sequencing, functional assays, and in vivo virulence mice studies were performed. RESULTS: Clonal types of the same serotype but also intraclonal variants within clonal complexes (CCs) showed differences in invasive-disease potential. CC138, a common CC, was divided into several PFGE patterns, partly explained by number, location, and type of temperate bacteriophages. Whole-genome sequencing of 4 CC138 isolates representing PFGE clones with different invasive-disease potentials revealed intraclonal sequence variations of the virulence-associated proteins pneumococcal surface protein A (PspA) and pneumococcal choline-binding protein C (PspC). A carrier isolate lacking PcpA exhibited decreased virulence in mice, and there was a differential binding of human factor H, depending on invasiveness. CONCLUSIONS: Pneumococcal clonal types but also intraclonal variants exhibited different invasive-disease potentials in children. Intraclonal variants, reflecting different prophage contents, showed differences in major surface antigens. This suggests ongoing immune selection, such as that due to PspC-mediated complement resistance through varied human factor H binding, that may affect invasiveness in children.

Complex polymorphisms in the Plasmodium falciparum multidrug resistance protein 2 gene and its contribution to antimalarial response.

Plasmodium falciparum has the capacity to escape the actions of essentially all antimalarial drugs. ATP-binding cassette (ABC) transporter proteins are known to cause multidrug resistance in a large range of organisms, including the Apicomplexa parasites. P. falciparum genome analysis has revealed two genes coding for the multidrug resistance protein (MRP) type of ABC transporters: Pfmrp1, previously associated with decreased parasite drug susceptibility, and the poorly studied Pfmrp2. The role of Pfmrp2 polymorphisms in modulating sensitivity to antimalarial drugs has not been established. We herein report a comprehensive account of the Pfmrp2 genetic variability in 46 isolates from Thailand. A notably high frequency of 2.8 single nucleotide polymorphisms (SNPs)/kb was identified for this gene, including some novel SNPs. Additionally, we found that Pfmrp2 harbors a significant number of microindels, some previously not reported. We also investigated the potential association of the identified Pfmrp2 polymorphisms with altered in vitro susceptibility to several antimalarials used in artemisinin-based combination therapy and with parasite clearance time. Association analysis suggested Pfmrp2 polymorphisms modulate the parasite's in vitro response to quinoline antimalarials, including chloroquine, piperaquine, and mefloquine, and association with in vivo parasite clearance. In conclusion, our study reveals that the Pfmrp2 gene is the most diverse ABC transporter known in P. falciparum with a potential role in antimalarial drug resistance.

Transcriptome profiling of Giardia intestinalis using strand-specific RNA-seq.

Giardia intestinalis is a common cause of diarrheal disease and it consists of eight genetically distinct genotypes or assemblages (A-H). Only assemblages A and B infect humans and are suggested to represent two different Giardia species. Correlations exist between assemblage type and host-specificity and to some extent symptoms. Phenotypical differences have been documented between assemblages and genome sequences are available for A, B and E. We have characterized and compared the polyadenylated transcriptomes of assemblages A, B and E. Four genetically different isolates were studied (WB (AI), AS175 (AII), P15 (E) and GS (B)) using paired-end, strand-specific RNA-seq. Most of the genome was transcribed in trophozoites grown in vitro, but at vastly different levels. RNA-seq confirmed many of the present annotations and refined the current genome annotation. Gene expression divergence was found to recapitulate the known phylogeny, and uncovered lineage-specific differences in expression. Polyadenylation sites were mapped for over 70% of the genes and revealed many examples of conserved and unexpectedly long 3' UTRs. 28 open reading frames were found in a non-transcribed gene cluster on chromosome 5 of the WB isolate. Analysis of allele-specific expression revealed a correlation between allele-dosage and allele expression in the GS isolate. Previously reported cis-splicing events were confirmed and global mapping of cis-splicing identified only one novel intron. These observations can possibly explain differences in host-preference and symptoms, and it will be the basis for further studies of Giardia pathogenesis and biology.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse.

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse.

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Comparative genomic analysis of human infective Trypanosoma cruzi lineages with the bat-restricted subspecies T. cruzi marinkellei.

BACKGROUND: Trypanosoma cruzi marinkellei is a bat-associated parasite of the subgenus Schizotrypanum and it is regarded as a T. cruzi subspecies. Here we report a draft genome sequence of T. c. marinkellei and comparison with T. c. cruzi. Our aims were to identify unique sequences and genomic features, which may relate to their distinct niches. RESULTS: The T. c. marinkellei genome was found to be ~11% smaller than that of the human-derived parasite T. c. cruzi Sylvio X10. The genome size difference was attributed to copy number variation of coding and non-coding sequences. The sequence divergence in coding regions was ~7.5% between T. c. marinkellei and T. c. cruzi Sylvio X10. A unique acetyltransferase gene was identified in T. c. marinkellei, representing an example of a horizontal gene transfer from eukaryote to eukaryote. Six of eight examined gene families were expanded in T. c. cruzi Sylvio X10. The DGF gene family was expanded in T. c. marinkellei. T. c. cruzi Sylvio X10 contained ~1.5 fold more sequences related to VIPER and L1Tc elements. Experimental infections of mammalian cell lines indicated that T. c. marinkellei has the capacity to invade non-bat cells and undergo intracellular replication. CONCLUSIONS: Several unique sequences were identified in the comparison, including a potential subspecies-specific gene acquisition in T. c. marinkellei. The identified differences reflect the distinct evolutionary trajectories of these parasites and represent targets for functional investigation.

Integrative Prioritization of Causal Genes for Coronary Artery Disease.

BACKGROUND: Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets. METHODS: We aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task). RESULTS: We identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks. CONCLUSIONS: We identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.

A mechanistic framework for cardiometabolic and coronary artery diseases.

Coronary atherosclerosis results from the delicate interplay of genetic and exogenous risk factors, principally taking place in metabolic organs and the arterial wall. Here we show that 224 gene-regulatory coexpression networks (GRNs) identified by integrating genetic and clinical data from patients with (n = 600) and without (n = 250) coronary artery disease (CAD) with RNA-seq data from seven disease-relevant tissues in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study largely capture this delicate interplay, explaining >54% of CAD heritability. Within 89 cross-tissue GRNs associated with clinical severity of CAD, 374 endocrine factors facilitated inter-organ interactions, primarily along an axis from adipose tissue to the liver (n = 152). This axis was independently replicated in genetically diverse mouse strains and by injection of recombinant forms of adipose endocrine factors (EPDR1, FCN2, FSTL3 and LBP) that markedly altered blood lipid and glucose levels in mice. Altogether, the STARNET database and the associated GRN browser (http://starnet.mssm.edu) provide a multiorgan framework for exploration of the molecular interplay between cardiometabolic disorders and CAD.

Cis-epistasis at the LPA locus and risk of cardiovascular diseases.

AIMS: Coronary artery disease (CAD) has a strong genetic predisposition. However, despite substantial discoveries made by genome-wide association studies (GWAS), a large proportion of heritability awaits identification. Non-additive genetic effects might be responsible for part of the unaccounted genetic variance. Here, we attempted a proof-of-concept study to identify non-additive genetic effects, namely epistatic interactions, associated with CAD. METHODS AND RESULTS: We tested for epistatic interactions in 10 CAD case-control studies and UK Biobank with focus on 8068 SNPs at 56 loci with known associations with CAD risk. We identified a SNP pair located in cis at the LPA locus, rs1800769 and rs9458001, to be jointly associated with risk for CAD [odds ratio (OR) = 1.37, P = 1.07 × 10-11], peripheral arterial disease (OR = 1.22, P = 2.32 × 10-4), aortic stenosis (OR = 1.47, P = 6.95 × 10-7), hepatic lipoprotein(a) (Lp(a)) transcript levels (beta = 0.39, P = 1.41 × 10-8), and Lp(a) serum levels (beta = 0.58, P = 8.7 × 10-32), while individual SNPs displayed no association. Further exploration of the LPA locus revealed a strong dependency of these associations on a rare variant, rs140570886, that was previously associated with Lp(a) levels. We confirmed increased CAD risk for heterozygous (relative OR = 1.46, P = 9.97 × 10-32) and individuals homozygous for the minor allele (relative OR = 1.77, P = 0.09) of rs140570886. Using forward model selection, we also show that epistatic interactions between rs140570886, rs9458001, and rs1800769 modulate the effects of the rs140570886 risk allele. CONCLUSIONS: These results demonstrate the feasibility of a large-scale knowledge-based epistasis scan and provide rare evidence of an epistatic interaction in a complex human disease. We were directed to a variant (rs140570886) influencing risk through additive genetic as well as epistatic effects. In summary, this study provides deeper insights into the genetic architecture of a locus important for cardiovascular diseases.

Draft genome sequencing of giardia intestinalis assemblage B isolate GS: is human giardiasis caused by two different species?

Giardia intestinalis is a major cause of diarrheal disease worldwide and two major Giardia genotypes, assemblages A and B, infect humans. The genome of assemblage A parasite WB was recently sequenced, and the structurally compact 11.7 Mbp genome contains simplified basic cellular machineries and metabolism. We here performed 454 sequencing to 16x coverage of the assemblage B isolate GS, the only Giardia isolate successfully used to experimentally infect animals and humans. The two genomes show 77% nucleotide and 78% amino-acid identity in protein coding regions. Comparative analysis identified 28 unique GS and 3 unique WB protein coding genes, and the variable surface protein (VSP) repertoires of the two isolates are completely different. The promoters of several enzymes involved in the synthesis of the cyst-wall lack binding sites for encystation-specific transcription factors in GS. Several synteny-breaks were detected and verified. The tetraploid GS genome shows higher levels of overall allelic sequence polymorphism (0.5 versus <0.01% in WB). The genomic differences between WB and GS may explain some of the observed biological and clinical differences between the two isolates, and it suggests that assemblage A and B Giardia can be two different species.