scholarly journals Trans effects on gene expression can drive omnigenic inheritance

2018 ◽  
Author(s):  
Xuanyao Liu ◽  
Yang I Li ◽  
Jonathan K Pritchard
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Formal Model ◽  
Association Studies ◽  
Direct Consequence ◽  
Large Contribution ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genetic Contributions ◽  
Core Genes

Early genome-wide association studies (GWAS) led to the surprising discovery that, for typical complex traits, the most significant genetic variants contribute only a small fraction of the estimated heritability. Instead, it has become clear that a huge number of common variants, each with tiny effects, explain most of the heritability. Previously, we argued that these patterns conflict with standard conceptual models, and that new models are needed. Here we provide a formal model in which genetic contributions to complex traits can be partitioned into direct effects from core genes, and indirect effects from peripheral genes acting as trans-regulators. We argue that the central importance of peripheral genes is a direct consequence of the large contribution of trans-acting variation to gene expression variation. In particular, we propose that if the core genes for a trait are co-regulated – as seems likely – then the effects of peripheral variation can be amplified by these co-regulated networks such that nearly all of the genetic variance is driven by peripheral genes. Thus our model proposes a framework for understanding key features of the architecture of complex traits.

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

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

2019 ◽  
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/.

scholarly journals Transcriptome-wide association supplements genome-wide association in Zea mays

2018 ◽  
Author(s):  
Karl A. G. Kremling ◽  
Christine H. Diepenbrock ◽  
Michael A. Gore ◽  
Edward S. Buckler ◽  
Nonoy B. Bandillo
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
New Technologies ◽  
Seed Quality ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Organization ◽  
Genome Wide

AbstractModern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The potential of using endophenotypes for dissecting traits of interest remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299 genotype and 7 tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation for agronomic and seed quality (carotenoid, tocochromanol) traits is regulatory. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits, beating the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This improves not only the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.Author summaryWe examined the ability to associate variability in gene expression directly with terminal phenotypes of interest, as a supplement linking genotype to phenotype. We found that transcriptome-wide association studies (TWAS) are a useful accessory to genome-wide association studies (GWAS). In a combined test with GWAS results, TWAS improves the capacity to re-detect genes known to underlie quantitative trait loci for kernel and agronomic phenotypes. This improves not only the capacity to link genes to phenotypes, but also illustrates the widespread importance of regulation for phenotype.

scholarly journals Integrating gene expression with summary association statistics to identify susceptibility genes for 30 complex traits

2016 ◽  
Author(s):  
Nicholas Mancuso ◽  
Huwenbo Shi ◽  
Pagé Goddard ◽  
Gleb Kichaev ◽  
Alexander Gusev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Correlation ◽  
Complex Traits ◽  
Association Studies ◽  
Susceptibility Genes ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Causal Variants

AbstractAlthough genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. We leverage recently introduced methods to integrate gene expression measurements from 45 expression panels with summary GWAS data to perform 30 transcriptome-wide association studies (TWASs). We identify 1,196 susceptibility genes whose expression is associated with these traits; of these, 168 reside more than 0.5Mb away from any previously reported GWAS significant variant, thus providing new risk loci. Second, we find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, 8 are not found through genetic correlation at the SNP level. Third, we use bi-directional regression to find evidence for BMI causally influencing triglyceride levels, and triglyceride levels causally influencing LDL. Taken together, our results provide insights into the role of expression to susceptibility of complex traits and diseases.

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Core Gene ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Gene Sets ◽  
Genome Wide ◽  
Trait Variance ◽  
Core Genes

Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—with better-understood biology than most other complex traits. We find that many of the most significant hits are readily interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.

scholarly journals Mendelian Randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

2018 ◽  
Author(s):  
Eleonora Porcu ◽  
Sina Rüeger ◽  
Kaido Lepik ◽  
Federico A. Santoni ◽  
Alexandre Reymond ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Regulation Of Gene Expression ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Biological Interpretation ◽  
Independent Association

AbstractGenome-wide association studies (GWAS) identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs (eQTLs), indicating their potential involvement in the regulation of gene expression.Here, we propose an advanced transcriptome-wide summary statistics-based Mendelian Randomization approach (called TWMR) that uses multiple SNPs jointly as instruments and multiple gene expression traits as exposures, simultaneously.When applied to 43 human phenotypes it uncovered 2,369 genes whose blood expression is putatively associated with at least one phenotype resulting in 3,913 gene-trait associations; of note, 36% of them had no genome-wide significant SNP nearby in previous GWAS analysis. Using independent association summary statistics (UKBiobank), we confirmed that the majority of these loci were missed by conventional GWAS due to power issues. Noteworthy among these novel links is educational attainment-associated BSCL2, known to carry mutations leading to a mendelian form of encephalopathy. We similarly unraveled novel pleiotropic causal effects suggestive of mechanistic connections, e.g. the shared genetic effects of GSDMB in rheumatoid arthritis, ulcerative colitis and Crohn’s disease.Our advanced Mendelian Randomization unlocks hidden value from published GWAS through higher power in detecting associations. It better accounts for pleiotropy and unravels new biological mechanisms underlying complex and clinical traits.

scholarly journals Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jamie W. Robinson ◽  
Richard M. Martin ◽  
Spiridon Tsavachidis ◽  
Amy E. Howell ◽  
Caroline L. Relton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Tissue Expression ◽  
Tissue Type ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Causal Pathways ◽  
Genome Wide ◽  
Glioma Risk ◽  
Brain Tissues

AbstractGenome-wide association studies (GWAS) have discovered 27 loci associated with glioma risk. Whether these loci are causally implicated in glioma risk, and how risk differs across tissues, has yet to be systematically explored. We integrated multi-tissue expression quantitative trait loci (eQTLs) and glioma GWAS data using a combined Mendelian randomisation (MR) and colocalisation approach. We investigated how genetically predicted gene expression affects risk across tissue type (brain, estimated effective n = 1194 and whole blood, n = 31,684) and glioma subtype (all glioma (7400 cases, 8257 controls) glioblastoma (GBM, 3112 cases) and non-GBM gliomas (2411 cases)). We also leveraged tissue-specific eQTLs collected from 13 brain tissues (n = 114 to 209). The MR and colocalisation results suggested that genetically predicted increased gene expression of 12 genes were associated with glioma, GBM and/or non-GBM risk, three of which are novel glioma susceptibility genes (RETREG2/FAM134A, FAM178B and MVB12B/FAM125B). The effect of gene expression appears to be relatively consistent across glioma subtype diagnoses. Examining how risk differed across 13 brain tissues highlighted five candidate tissues (cerebellum, cortex, and the putamen, nucleus accumbens and caudate basal ganglia) and four previously implicated genes (JAK1, STMN3, PICK1 and EGFR). These analyses identified robust causal evidence for 12 genes and glioma risk, three of which are novel. The correlation of MR estimates in brain and blood are consistently low which suggested that tissue specificity needs to be carefully considered for glioma. Our results have implicated genes yet to be associated with glioma susceptibility and provided insight into putatively causal pathways for glioma risk.

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

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