Abstract 49: Adipose-Specific Knockout of
            Trib1
            Reduces Plasma Lipids and Diet-Induced Insulin Resistance, and Increases Circulating Adiponectin

Tribbles-1 (TRIB1) was recently identified through genome-wide association studies as a novel mediator of plasma lipids and coronary artery disease in humans. While subsequent in vivo mouse work confirmed a role for hepatic TRIB1 in these associations, little is known about metabolic roles for extra-hepatic Trib1. Interestingly, SNPs near the TRIB1 gene are significantly associated with circulating adiponectin levels in humans, suggesting a metabolic role for adipose TRIB1 . To further investigate this, we generated adipose-specific Trib1 KO mice (Trib1_ASKO) by crossing Trib1 cKO mice to transgenic Adiponectin-Cre mice. Chow-fed Trib1_ASKO mice exhibited no differences in adipose tissue mass and overall body mass as compared to control littermates (N=8/group). However, Trib1_ASKO mice had reduced total (-16.9%, p <0.01), HDL (-16.7%, p <0.01), and non-HDL cholesterol (-17.3%, p =0.068), as well as plasma triglycerides (-28.6%, p <0.001) as compared to WT mice. Trib1_ASKO mice also had increased plasma adiponectin levels, a finding more pronounced in female mice (+33.3%, p <0.001) than in males (+16.4%, p =0.072). Despite this increase, transcript levels of adipoQ were moderately decreased in Trib1_ASKO mice, suggesting a post-transcriptional mode of regulation. Transcript and protein levels of C/EBPα, the best described target of Trib1 and a key regulator of adipogenesis, remained unchanged. To further investigate the metabolic consequences of adipose-specific KO of Trib1 , WT and Trib1_ASKO mice were fed high-fat diet (HFD, 45% kCal fat) for 12 weeks to induce obesity. HFD-fed Trib1_ASKO mice had reduced fasting plasma glucose (-22.3%, p <0.05), insulin (-38.2%, p <0.05), and glucose tolerance (-19.8% AUC, p <0.05) compared to control mice. Body mass and fat mass of HFD-fed Trib1_ASKO mice remained unchanged from WT, and the reductions in plasma lipids and increase in plasma adiponectin persisted in the HFD-fed state. In summary, we present here the first in vivo validation of the human genetic association between TRIB1 and plasma adiponectin, and provide evidence suggesting that adipose TRIB1 contributes to the genetic associations observed in humans between TRIB1 and multiple metabolic parameters.

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Abstract 75: Somatic Overexpression and Knockdown in Mice to Identify Causal Genes Underlying 26 Lipid-associated Loci

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvb.33.suppl_1.a75 ◽

2013 ◽

Vol 33 (suppl_1) ◽

Author(s):

Evanthia Pashos ◽

Ioannis M Stylianou ◽

Dawn Marchadier ◽

Antonino Picataggi ◽

Valeska S Redon ◽

...

Keyword(s):

Lipid Metabolism ◽

Plasma Lipids ◽

Association Studies ◽

Plasma Lipid ◽

Genome Wide Association Studies ◽

Humanized Mouse Model ◽

Human Apolipoprotein ◽

Virus Serotype ◽

Causal Genes

Genome-wide association studies (GWAS) have identified 95 loci in the human genome that harbor common variants associated with plasma lipid traits. Of the 95 loci, 17 harbor genes known to cause monogenic lipid disorders and collectively a third of them contain genes with characterized roles in lipid metabolism. Therefore in the majority of loci the causal genes are unknown. We selected 32 genes, not previously implicated in lipid metabolism and representing a total of 26 loci, to test for their ability to modify plasma lipid concentrations upon somatic overexpression in vivo. We utilized adeno-associated virus serotype 8 (AAV8) to overexpress the selected genes specifically in the livers of both C57BL/6 mice and in an appropriate humanized mouse model (either mice expressing human apolipoprotein A-I for HDL loci or Apobec1-knockout, Ldlr haploinsufficient mice expressing human apolipoprotein B-100 for triglyceride and LDL loci). Approximately half of the genes tested reproducibly affected plasma lipids. For 13 of the interrogated loci the lipid-associated variants also correlated with expression variations of the respective genes in liver (liver expression quantitative trait loci-eQTLs). We demonstrate a causal role for 7 of these 13 genes. The overexpression of these 7 genes not only affected the predicted lipid class, but additionally exerted its effect in the predicted direction in 6 of 7 cases (Tmem57, Slc39a8, Ppp1r3b, Vkorc1, Tbkbp1 and Ube2l3). Additionally for a subset of the examined genes we proceeded to develop small interfering RNA (siRNA) nanoparticles that were particularly targeted to the liver. We were able to obtain robust knockdown for a significant number of genes and, in several cases, observe reciprocal effects on plasma lipids from our overexpression and knockdown studies. This work has identified several novel lipid regulators, whose further investigation can uncover novel mechanisms and pathways controlling plasma lipids.

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RETRACTION: Illuminating the dark road from schizophrenia genetic associations to disease mechanisms

National Science Review ◽

10.1093/nsr/nww065 ◽

2016 ◽

Vol 4 (2) ◽

pp. 240-251 ◽

Cited By ~ 2

Author(s):

Ming Li ◽

Daniel R Weinberger

Keyword(s):

Large Scale ◽

Molecular Mechanisms ◽

Association Studies ◽

Human Populations ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Molecular Approaches ◽

Disease Biology

Abstract Recent large-scale genome-wide association studies (GWAS) have enabled the discovery of common genetic variations contributing to risk architectures of schizophrenia in human populations; however, the majority of GWAS-identified variants are located in large genomic regions spanning multiple genes, and recognizing the precise targets and mechanisms of these clinical associations is now the major challenge. Here, we review recent progress in schizophrenia genetics, functional genomics and related neuroscience research, and propose a functional pipeline to translate schizophrenia GWAS risk loci into disease biology and information for drug discovery. The pipeline includes identification of underlying molecular mechanisms using transcriptomic data in human brain, prioritization of putative functional causative variants by the integration of genetic epidemiological and bioinformatics methods as well as molecular approaches, and in vitro and in vivo experimental characterizations of the identified targeted species and causative variants to dissect the relevant disease biology. These approaches will accelerate progress from schizophrenia genetic studies to biological mechanisms and ultimately guide the development of prognostic, preventive and therapeutic measures.

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Penalized partial least squares for pleiotropy

BMC Bioinformatics ◽

10.1186/s12859-021-03968-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Camilo Broc ◽

Therese Truong ◽

Benoit Liquet

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Association Studies ◽

A Priori ◽

Simulated Data ◽

Real Data ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Multiple Traits ◽

Application Fields

Abstract Background The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene- and a pathway-level approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called joint-sgPLS, is able to convincingly detect signal at the variable level and at the group level. Results Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers. Conclusion The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.

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Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

Scientific Reports ◽

10.1038/s41598-021-94252-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nadav Brandes ◽

Nathan Linial ◽

Michal Linial

Keyword(s):

Cancer Risk ◽

Protein Function ◽

Association Studies ◽

Genetic Effects ◽

Cancer Predisposition ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Functional Interpretation ◽

Gene Damage ◽

Genomic Regions

AbstractThe characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting.

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A Variant Noncoding Region Regulates Prrx1 and Predisposes to Atrial Arrhythmias

Circulation Research ◽

10.1161/circresaha.121.319146 ◽

2021 ◽

Author(s):

Fernanda M Bosada ◽

Mathilde R Rivaud ◽

Jae-Sun Uhm ◽

Sander Verheule ◽

Karel van Duijvenboden ◽

...

Keyword(s):

Sodium Current ◽

Association Studies ◽

Noncoding Region ◽

Specific Gene ◽

Genome Wide Association Studies ◽

Enhancer Activity ◽

Atrial Cardiomyocytes ◽

The Impact ◽

Lineage Specific Gene

Rationale: Atrial Fibrillation (AF) is the most common cardiac arrhythmia diagnosed in clinical practice. Genome-wide association studies have identified AF-associated common variants across 100+ genomic loci, but the mechanism underlying the impact of these variant loci on AF susceptibility in vivo has remained largely undefined. One such variant region, highly associated with AF, is found at 1q24, close to PRRX1, encoding the Paired Related Homeobox 1 transcription factor. Objective: To identify the mechanistic link between the variant region at 1q24 and AF predisposition. Methods and Results: The mouse orthologue of the noncoding variant genomic region (R1A) at 1q24 was deleted using CRISPR genome editing. Among the genes sharing the topologically associated domain with the deleted R1A region (Kifap3, Prrx1, Fmo2, Prrc2c), only the broadly expressed gene Prrx1 was downregulated in mutants, and only in cardiomyocytes. Expression and epigenetic profiling revealed that a cardiomyocyte lineage-specific gene program (Mhrt, Myh6, Rbm20, Tnnt2, Ttn, Ckm) was upregulated in R1A-/- atrial cardiomyocytes, and that Mef2 binding motifs were significantly enriched at differentially accessible chromatin sites. Consistently, Prrx1 suppressed Mef2-activated enhancer activity in HL-1 cells. Mice heterozygous or homozygous for the R1A deletion were susceptible to atrial arrhythmia induction, had atrial conduction slowing and more irregular RR intervals. Isolated R1A-/- mouse left atrial cardiomyocytes showed lower action potential upstroke velocities and sodium current, as well as increased systolic and diastolic calcium concentrations compared to controls. Conclusions: The noncoding AF variant region at 1q24 modulates Prrx1 expression in cardiomyocytes. Cardiomyocyte-specific reduction of Prrx1 expression upon deletion of the noncoding region leads to a profound induction of a cardiac lineage-specific gene program and to propensity for AF. These data indicate that AF-associated variants in humans may exert AF predisposition through reduced PRRX1 expression in cardiomyocytes.

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Combined knockout of Lrrk2 and Rab29 does not result in behavioral abnormalities in vivo

10.1101/2020.05.13.093708 ◽

2020 ◽

Cited By ~ 2

Author(s):

Melissa Conti Mazza ◽

Victoria Nguyen ◽

Alexandra Beilina ◽

Jinhui Ding ◽

Mark R. Cookson

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Kinase Activity ◽

Association Studies ◽

Dopamine Neurons ◽

Substantia Nigra Pars Compacta ◽

Genome Wide Association Studies ◽

Double Knockout ◽

Knockout Mouse Model

AbstractCoding mutations in the LRRK2 gene, encoding for a large protein kinase, have been shown to cause familial Parkinson’s disease (PD). The immediate biological consequence of LRRK2 mutations is to increase kinase activity, leading to the suggestion that inhibition of this enzyme might be useful therapeutically to slow disease progression. Genome-wide association studies have identified the chromosomal loci around LRRK2 and one of its proposed substrates, RAB29, as contributors towards the lifetime risk of sporadic PD. Considering the evidence for interactions between LRRK2 and RAB29 on the genetic and protein levels, here we generated a double knockout mouse model and determined whether there are any consequences on brain function with aging. From a battery of motor and non-motor behavioral tests, we noted only that 18-24 month Rab29-/- and double (Lrrk2-/-/Rab29-/-) knockout mice had diminished locomotor behavior in open field compared to wildtype mice. However, no genotype differences were seen in number of substantia nigra pars compacta (SNc) dopamine neurons or in tyrosine hydroxylase levels in the SNc and striatum, which might reflect a PD-like pathology. These results suggest that depletion of both Lrrk2 and Rab29 is tolerated, at least in mice, and support that this pathway might be able to be safely targeted for therapeutics in humans.Significance statementGenetic variation in LRRK2 that result in elevated kinase activity can cause Parkinson’s disease (PD), suggesting LRRK2 inhibition as a therapeutic strategy. RAB29, a substrate of LRRK2, has also been associated with increased PD risk. Evidence exists for an interactive relationship between LRRK2 and RAB29. Mouse models lacking either LRRK2 or RAB29 do not show brain pathologies. We hypothesized that the loss of both targets would result in additive effects across in vivo and post-mortem assessments in aging mice. We found that loss of both LRRK2 and RAB29 did not result in significant behavioral deficits or dopamine neuron loss. This evidence suggests that chronic inhibition of this pathway should be tolerated clinically.

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Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

10.1101/011569 ◽

2014 ◽

Cited By ~ 1

Author(s):

Daniel S Himmelstein ◽

Sergio E Baranzini

Keyword(s):

Data Integration ◽

Protein Interactions ◽

Heterogeneous Network ◽

Association Studies ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Protein Coding ◽

Multiple Node ◽

Pathogenic Variants ◽

Disease Associated Genes

The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants, and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks—graphs with multiple node and edge types—for accomplishing both tasks. First we constructed a network with 18 node types—genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database)collections—and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as fundamental mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains.

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Perspective of the GEMSTONE Consortium on Current and Future Approaches to Functional Validation for Skeletal Genetic Disease Using Cellular, Molecular and Animal-Modeling Techniques

Frontiers in Endocrinology ◽

10.3389/fendo.2021.731217 ◽

2021 ◽

Vol 12 ◽

Author(s):

Martina Rauner ◽

Ines Foessl ◽

Melissa M. Formosa ◽

Erika Kague ◽

Vid Prijatelj ◽

...

Keyword(s):

Resource Sharing ◽

Complex Traits ◽

Cellular Localization ◽

Target Genes ◽

Mission Statement ◽

Association Studies ◽

Repetitive Sequences ◽

Genome Wide Association Studies ◽

Causal Genes

The availability of large human datasets for genome-wide association studies (GWAS) and the advancement of sequencing technologies have boosted the identification of genetic variants in complex and rare diseases in the skeletal field. Yet, interpreting results from human association studies remains a challenge. To bridge the gap between genetic association and causality, a systematic functional investigation is necessary. Multiple unknowns exist for putative causal genes, including cellular localization of the molecular function. Intermediate traits (“endophenotypes”), e.g. molecular quantitative trait loci (molQTLs), are needed to identify mechanisms of underlying associations. Furthermore, index variants often reside in non-coding regions of the genome, therefore challenging for interpretation. Knowledge of non-coding variance (e.g. ncRNAs), repetitive sequences, and regulatory interactions between enhancers and their target genes is central for understanding causal genes in skeletal conditions. Animal models with deep skeletal phenotyping and cell culture models have already facilitated fine mapping of some association signals, elucidated gene mechanisms, and revealed disease-relevant biology. However, to accelerate research towards bridging the current gap between association and causality in skeletal diseases, alternative in vivo platforms need to be used and developed in parallel with the current -omics and traditional in vivo resources. Therefore, we argue that as a field we need to establish resource-sharing standards to collectively address complex research questions. These standards will promote data integration from various -omics technologies and functional dissection of human complex traits. In this mission statement, we review the current available resources and as a group propose a consensus to facilitate resource sharing using existing and future resources. Such coordination efforts will maximize the acquisition of knowledge from different approaches and thus reduce redundancy and duplication of resources. These measures will help to understand the pathogenesis of osteoporosis and other skeletal diseases towards defining new and more efficient therapeutic targets.

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Asthma-associated genetic variants induce IL33 differential expression through an enhancer-blocking regulatory region

Nature Communications ◽

10.1038/s41467-021-26347-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ivy Aneas ◽

Donna C. Decker ◽

Chanie L. Howard ◽

Débora R. Sobreira ◽

Noboru J. Sakabe ◽

...

Keyword(s):

Association Studies ◽

Regulatory Region ◽

Airway Epithelial Cells ◽

Genome Wide Association Studies ◽

Airway Epithelial ◽

Allele Specific ◽

Enhancer Blocking ◽

Underlying Mechanisms

AbstractGenome-wide association studies (GWAS) have implicated the IL33 locus in asthma, but the underlying mechanisms remain unclear. Here, we identify a 5 kb region within the GWAS-defined segment that acts as an enhancer-blocking element in vivo and in vitro. Chromatin conformation capture showed that this 5 kb region loops to the IL33 promoter, potentially regulating its expression. We show that the asthma-associated single nucleotide polymorphism (SNP) rs1888909, located within the 5 kb region, is associated with IL33 gene expression in human airway epithelial cells and IL-33 protein expression in human plasma, potentially through differential binding of OCT-1 (POU2F1) to the asthma-risk allele. Our data demonstrate that asthma-associated variants at the IL33 locus mediate allele-specific regulatory activity and IL33 expression, providing a mechanism through which a regulatory SNP contributes to genetic risk of asthma.

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Novel recessive locus for body mass index in childhood asthma

Thorax ◽

10.1136/thoraxjnl-2020-215742 ◽

2021 ◽

pp. thoraxjnl-2020-215742

Author(s):

Sanghun Lee ◽

Jessica Lasky-Su ◽

Sungho Won ◽

Cecelia Laurie ◽

Juan Carlos Celedón ◽

...

Keyword(s):

Body Mass Index ◽

Body Mass ◽

Association Studies ◽

Additive Model ◽

Recessive Model ◽

Genome Wide Association Studies ◽

Association Testing ◽

Genome Wide ◽

Children With Asthma ◽

Recessive Locus

Most genome-wide association studies of obesity and body mass index (BMI) have so far assumed an additive mode of inheritance in their analysis, although association testing supports a recessive effect for some of the established loci, for example, rs1421085 in FTO. In two whole-genome sequencing (WGS) studies of children with asthma and their parents (892 Costa Rican trios and 286 North American trios), we discovered an association between a locus (rs9292139) in LOC102724122 and BMI that reaches genome-wide significance under a recessive model in the combined analysis. As the association does not achieve significance under an additive model, our finding illustrates the benefits of the recessive model in WGS analyses.

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