Joint analysis of genome-wide genetic variants associated with gene expression and disease susceptibility

AbstractA large number of genetic variants associated with human longevity have been reported but how they play their functions remains elusive. We performed an integrative analysis on 113 genome-wide significant longevity and 14,529 age-related disease variants in the context of putative gene expression regulation. We found that most of the longevity allele types were different from the genotype of disease alleles when they were localized at the same chromosomal positions. Longevity variants were about eight times more likely to be associated with gene expression than randomly selected variants. The directions of the gene expression association were more likely to be opposite between longevity and disease variants when the association occurred to the same gene. Many longevity variants likely function through down-regulating inflammatory response and up-regulating healthy lipid metabolisms. In conclusion, this work helps to elucidate the potential mechanisms of longevity variants for follow-up studies to discover methods to extend human healthspan.

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A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

10.1101/563379 ◽

2019 ◽

Cited By ~ 2

Author(s):

Tom G Richardson ◽

Gibran Hemani ◽

Tom R Gaunt ◽

Caroline L Relton ◽

George Davey Smith

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

Complex Traits ◽

Mendelian Randomization ◽

Drug Repositioning ◽

Association Studies ◽

Thyroid Tissue ◽

Genome Wide Association Studies ◽

Tissue Specific ◽

Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

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A Genome-Wide mQTL Analysis in Human Adipose Tissue Identifies Genetic Variants Associated with DNA Methylation, Gene Expression and Metabolic Traits

PLoS ONE ◽

10.1371/journal.pone.0157776 ◽

2016 ◽

Vol 11 (6) ◽

pp. e0157776 ◽

Cited By ~ 50

Author(s):

Petr Volkov ◽

Anders H. Olsson ◽

Linn Gillberg ◽

Sine W. Jørgensen ◽

Charlotte Brøns ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Adipose Tissue ◽

Genetic Variants ◽

Human Adipose Tissue ◽

Metabolic Traits ◽

Genome Wide ◽

A Genome

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Gene-based association analysis identified 190 genes with polymorphisms affecting neuroticism

10.1101/2020.08.06.240721 ◽

2020 ◽

Author(s):

Nadezhda M. Belonogova ◽

Irina V. Zorkoltseva ◽

Yakov A. Tsepilov ◽

Tatiana I. Axenovich

Keyword(s):

Gene Expression ◽

Detailed Analysis ◽

Association Analysis ◽

Genetic Variants ◽

Bioinformatics Analysis ◽

Summary Statistics ◽

Uk Biobank ◽

Protein Coding ◽

Pathogenic Variants ◽

Genome Wide

AbstractRecent genome-wide studies have reported about 600 genes potentially influencing neuroticism. Little is known about the mechanisms of their action. Here, we aimed to conduct a more detailed analysis of genes whose polymorphisms can regulate the level of neuroticism. Using UK Biobank-based GWAS summary statistics, we performed a gene-based association analysis using four sets of genetic variants within a gene differing in their protein coding properties. To guard against the influence of strong GWAS signals outside the gene, we used the specially designed procedure. As a result, we identified 190 genes associated with neuroticism due to their polymorphisms. Thirty eight of these genes were novel. Within all genes identified, we distinguished two slightly overlapping groups comprising genes that demonstrated association when using protein-coding and non-coding SNPs. Many genes from the first group included potentially pathogenic variants. For some genes from the second group, we found evidence of pleiotropy with gene expression. We demonstrated that the association of almost two hundred known genes could be inflated by the GWAS signals outside the gene. Using bioinformatics analysis, we prioritized the neuroticism genes and showed that the genes influencing the trait by their polymorphisms are the most appropriate candidate genes.

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A novel quantile regression approach for eQTL discovery

10.1101/070052 ◽

2016 ◽

Author(s):

Xiaoyu Song ◽

Gen Li ◽

Iuliana Ionita-Laza ◽

Ying Wei

Keyword(s):

Gene Expression ◽

Genetic Variation ◽

Linear Regression ◽

Genetic Variants ◽

Molecular Mechanisms ◽

Association Studies ◽

Expression Level ◽

Special Focus ◽

Genome Wide Association Studies ◽

Genome Wide

AbstractOver the past decade, there has been a remarkable improvement in our understanding of the role of genetic variation in complex human diseases, especially via genome-wide association studies. However, the underlying molecular mechanisms are still poorly characterized, impending the development of therapeutic interventions. Identifying genetic variants that influence the expression level of a gene, i.e. expression quantitative trait loci (eQTLs), can help us understand how genetic variants influence traits at the molecular level. While most eQTL studies focus on identifying mean effects on gene expression using linear regression, evidence suggests that genetic variation can impact the entire distribution of the expression level. Indeed, several studies have already investigated higher order associations with a special focus on detecting heteroskedasticity. In this paper, we develop a Quantile Rank-score Based Test (QRBT) to identify eQTLs that are associated with the conditional quantile functions of gene expression. We have applied the proposed QRBT to the Genotype-Tissue Expression project, an international tissue bank for studying the relationship between genetic variation and gene expression in human tissues, and found that the proposed QRBT complements the existing methods, and identifies new eQTLs with heterogeneous effects genome-wideacross different quantile levels. Notably, we show that the eQTLs identified by QRBT but missed by linear regression are more likely to be tissue specific, and also associated with greater enrichment in genome-wide significant SNPs from the GWAS catalog. An R package implementing QRBT is available on our website.

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JASS: command line and web interface for the joint analysis of GWAS results

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqaa003 ◽

2020 ◽

Vol 2 (1) ◽

Cited By ~ 1

Author(s):

Hanna Julienne ◽

Pierre Lechat ◽

Vincent Guillemot ◽

Carla Lasry ◽

Chunzi Yao ◽

...

Keyword(s):

Genetic Variants ◽

Large Scale ◽

Genome Wide Association Study ◽

Real Data ◽

Joint Analysis ◽

Summary Statistics ◽

Web Interface ◽

Multiple Phenotypes ◽

Genome Wide ◽

Secondary Analyses

Abstract Genome-wide association study (GWAS) has been the driving force for identifying association between genetic variants and human phenotypes. Thousands of GWAS summary statistics covering a broad range of human traits and diseases are now publicly available. These GWAS have proven their utility for a range of secondary analyses, including in particular the joint analysis of multiple phenotypes to identify new associated genetic variants. However, although several methods have been proposed, there are very few large-scale applications published so far because of challenges in implementing these methods on real data. Here, we present JASS (Joint Analysis of Summary Statistics), a polyvalent Python package that addresses this need. Our package incorporates recently developed joint tests such as the omnibus approach and various weighted sum of Z-score tests while solving all practical and computational barriers for large-scale multivariate analysis of GWAS summary statistics. This includes data cleaning and harmonization tools, an efficient algorithm for fast derivation of joint statistics, an optimized data management process and a web interface for exploration purposes. Both benchmark analyses and real data applications demonstrated the robustness and strong potential of JASS for the detection of new associated genetic variants. Our package is freely available at https://gitlab.pasteur.fr/statistical-genetics/jass.

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OR09-04 Common Genetic Variants Associated with SERPINA6 Expression in Liver Influence Cortisol-Responsive Transcriptional Networks in Human Adipose Tissue

Journal of the Endocrine Society ◽

10.1210/jendso/bvaa046.317 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

Author(s):

Sean A Bankier ◽

Andrew A Crawford ◽

Lingfei Wang ◽

Katyayani Sukhavasi ◽

Raili Ermel ◽

...

Keyword(s):

Gene Expression ◽

Adipose Tissue ◽

Coronary Artery ◽

Genetic Variants ◽

Plasma Cortisol ◽

Transcriptional Networks ◽

Genome Wide ◽

Common Genetic Variants ◽

Regulated Gene Expression ◽

Genome Wide Significance

Abstract A genome wide meta-analysis by the CORtisol NETwork (CORNET) consortium(1) has identified genetic variants spanning the SERPINA6/SERPINA1 locus on chromosome 14, associated with morning plasma cortisol and predictive of cardiovascular disease (Crawford et al, Unpublished). SERPINA6 encodes Corticosteroid Binding Globulin (CBG), responsible for binding most cortisol in blood and putatively mediating delivery of cortisol to target tissues. We hypothesised that genetic variants in SERPINA6 influence CBG expression in liver and cortisol delivery to extra-hepatic tissues, influencing cortisol-regulated gene expression. The Stockholm Tartu Atherosclerosis Reverse Networks Engineering Task study (STARNET)(2) provides RNA sequencing data in 9 vascular and metabolic tissues from 600 genotyped individuals (mean age 65.8, 70.3% male) undergoing coronary artery bypass grafting. We used STARNET to identify SNPs associated with plasma cortisol at genome wide significance in CORNET as cis-eQTLs for SERPINA6 in liver and as trans-eQTLs for the expression of genes across STARNET tissues. Causal inference methodologies(3) were then employed for the network reconstruction of these trans-genes and their downstream targets. We identified 21 SNPs that both were associated with cortisol at genome wide significance in CORNET (p ≤ 5x10-8) and were cis-eQTLs for SERPINA6 expression in liver (q ≤ 0.05). Moreover, these SNPs were trans-eQTLs for sets of genes in liver, subcutaneous and visceral abdominal adipose tissue, with over-representation of known glucocorticoid-regulated genes in adipose. The highest confidence gene network identified was specific to subcutaneous adipose, with the interferon regulatory trans-gene, IRF2, controlling a putative glucocorticoid-regulated network. Targets in this network include LDB2 and LIPA, both associated with coronary artery disease. We conclude that variants in the SERPINA6/SERPINA1 locus mediate their effect on plasma cortisol through variation in SERPINA6 expression in liver, and in turn affect gene expression in extra-hepatic tissues through modulating cortisol delivery. This supports a dynamic role for CBG in modulating cortisol delivery to tissues. The cortisol-responsive gene networks identified here represent candidate pathways to mediate cardiovascular risk attributable to elevated cortisol. (1) Bolton, et al. (2014) PLOS Genet. 10:e1004474., (2) Franzén et al. (2016). Science 353:827., (3) Wang and Michoel. (2017). PLOS Comput. Biol. 13:e1005703.

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Genetic factors contributing to extensive variability of sex-specific hepatic gene expression in Diversity Outbred mice

10.1101/2020.06.29.177311 ◽

2020 ◽

Author(s):

Tisha Melia ◽

David J. Waxman

Keyword(s):

Gene Expression ◽

Growth Hormone ◽

Sex Differences ◽

Genetic Variants ◽

Genetic Factors ◽

Disease Susceptibility ◽

Gene Clusters ◽

Individual Variability ◽

Diversity Outbred ◽

The Individual

AbstractSex-specific transcription characterizes hundreds of genes in mouse liver, many implicated in sex-differential drug and lipid metabolism and disease susceptibility. While the regulation of liver sex differences by growth hormone-activated STAT5 is well established, little is known about autosomal genetic factors regulating the sex-specific liver transcriptome. Here we show, using genotyping and expression data from a large population of Diversity Outbred mice, that genetic factors work in tandem with growth hormone to control the individual variability of hundreds of sex-biased genes, including many lncRNA genes. Significant associations between single nucleotide polymorphisms and sex-specific gene expression were identified as expression quantitative trait loci (eQTLs), many of which showed strong sex-dependent associations. Remarkably, autosomal genetic modifiers of sex-specific genes were found to account for more than 200 instances of gain or loss of sex-specificity across eight Diversity Outbred mouse founder strains. Sex-biased STAT5 binding sites and open chromatin regions with strain-specific variants were significantly enriched at eQTL regions regulating correspondingly sex-specific genes, supporting the proposed functional regulatory nature of the eQTL regions identified. Binding of the male-biased, growth hormone-regulated repressor BCL6 was most highly enriched at trans-eQTL regions controlling female-specific genes. Co-regulated gene clusters defined by overlapping eQTLs included sets of highly correlated genes from different chromosomes, further supporting trans-eQTL action. These findings elucidate how an unexpectedly large number of autosomal factors work in tandem with growth hormone signaling pathways to regulate the individual variability associated with sex differences in liver metabolism and disease.Author summaryMale-female differences in liver gene expression confer sex differences in many biological processes relevant to health and disease, including lipid and drug metabolism and liver disease susceptibility. While the role of hormonal factors, most notably growth hormone, in regulating hepatic sex differences is well established, little is known about how autosomal genetic factors impact sex differences on an individual basis. Here, we harness the power of mouse genetics provided by the Diversity Outbred mouse model to discover significant genome-wide associations between genetic variants and sex-specific liver gene expression. Remarkably, we found that autosomal expression quantitative trait loci with a strong sex-bias account for the loss or gain of sex-specific expression of more than 200 autosomal genes seen across eight founder mice strains. Genetic associations with sex-specific genes were enriched for sex-biased and growth hormone-dependent regulatory regions harboring strain-specific genetic variants. Co-regulated gene clusters identified by overlapping regulatory regions included highly correlated genes from different chromosomes. These findings reveal the extensive regulatory role played by autosomal genetic variants, working in tandem with growth hormone signaling pathways, in the transcriptional control of sex-biased genes, many of which have been implicated in sex differential outcomes in liver metabolism and disease susceptibility.

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A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data

Genetic Epidemiology ◽

10.1002/gepi.22058 ◽

2017 ◽

Vol 41 (7) ◽

pp. 620-635 ◽

Cited By ~ 2

Author(s):

Liang He ◽

Ilya Zhbannikov ◽

Konstantin G. Arbeev ◽

Anatoliy I. Yashin ◽

Alexander M. Kulminski

Keyword(s):

Stochastic Process ◽

Longitudinal Data ◽

Process Model ◽

Disease Susceptibility ◽

Joint Analysis ◽

Genome Wide

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Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma

BMC Medical Genomics ◽

10.1186/s12920-020-00768-z ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Xiuqing Ma ◽

Peilan Wang ◽

Guobing Xu ◽

Fang Yu ◽

Yunlong Ma

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Integrative Genomics ◽

Ppi Network ◽

Childhood Onset ◽

Pathway Enrichment ◽

Asthma Risk ◽

Genome Wide

Abstract Background Childhood-onset asthma is highly affected by genetic components. In recent years, many genome-wide association studies (GWAS) have reported a large group of genetic variants and susceptible genes associated with asthma-related phenotypes including childhood-onset asthma. However, the regulatory mechanisms of these genetic variants for childhood-onset asthma susceptibility remain largely unknown. Methods In the current investigation, we conducted a two-stage designed Sherlock-based integrative genomics analysis to explore the cis- and/or trans-regulatory effects of genome-wide SNPs on gene expression as well as childhood-onset asthma risk through incorporating a large-scale GWAS data (N = 314,633) and two independent expression quantitative trait loci (eQTL) datasets (N = 1890). Furthermore, we applied various bioinformatics analyses, including MAGMA gene-based analysis, pathway enrichment analysis, drug/disease-based enrichment analysis, computer-based permutation analysis, PPI network analysis, gene co-expression analysis and differential gene expression analysis, to prioritize susceptible genes associated with childhood-onset asthma. Results Based on comprehensive genomics analyses, we found 31 genes with multiple eSNPs to be convincing candidates for childhood-onset asthma risk; such as, PSMB9 (cis-rs4148882 and cis-rs2071534) and TAP2 (cis-rs9267798, cis-rs4148882, cis-rs241456, and trans-10,447,456). These 31 genes were functionally interacted with each other in our PPI network analysis. Our pathway enrichment analysis showed that numerous KEGG pathways including antigen processing and presentation, type I diabetes mellitus, and asthma were significantly enriched to involve in childhood-onset asthma risk. The co-expression patterns among 31 genes were remarkably altered according to asthma status, and 25 of 31 genes (25/31 = 80.65%) showed significantly or suggestively differential expression between asthma group and control group. Conclusions We provide strong evidence to highlight 31 candidate genes for childhood-onset asthma risk, and offer a new insight into the genetic pathogenesis of childhood-onset asthma.

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