scholarly journals LIN28B affects gene expression at the hypothalamic-pituitary axis and serum testosterone levels

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jaakko T. Leinonen ◽  
Yu-Chia Chen ◽  
Jana Pennonen ◽  
Leevi Lehtonen ◽  
Nella Junna ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pubertal Timing ◽  
Association Studies ◽  
Serum Testosterone ◽  
Special Focus ◽  
Genome Wide Association Studies ◽  
Adult Size ◽  
Testosterone Levels ◽  
Genome Wide ◽  
The Uk

AbstractGenome-wide association studies (GWAS) have recurrently associated sequence variation nearby LIN28B with pubertal timing, growth and disease. However, the biology linking LIN28B with these traits is still poorly understood. With our study, we sought to elucidate the mechanisms behind the LIN28B associations, with a special focus on studying LIN28B function at the hypothalamic-pituitary (HP) axis that is ultimately responsible for pubertal onset. Using CRISPR-Cas9 technology, we first generated lin28b knockout (KO) zebrafish. Compared to controls, the lin28b KO fish showed both accelerated growth tempo, reduced adult size and increased expression of mitochondrial genes during larval development. Importantly, data from the knockout zebrafish models and adult humans imply that LIN28B expression has potential to affect gene expression in the HP axis. Specifically, our results suggest that LIN28B expression correlates positively with the expression of ESR1 in the hypothalamus and POMC in the pituitary. Moreover, we show how the pubertal timing advancing allele (T) for rs7759938 at the LIN28B locus associates with higher testosterone levels in the UK Biobank data. Overall, we provide novel evidence that LIN28B contributes to the regulation of sex hormone pathways, which might help explain why the gene associates with several distinct traits.

scholarly journals A novel quantile regression approach for eQTL discovery

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.

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 Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

2020 ◽  
Author(s):  
Jack W. O’Sullivan ◽  
John P. A. Ioannidis
Keyword(s):  
Effect Size ◽  
Association Studies ◽  
Genome Wide Association ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Single Nucleotide ◽  
Genome Wide ◽  
The Uk ◽  
Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

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 Gene expression levels as endophenotypes in genome-wide association studies of Alzheimer disease

Neurology ◽  
2010 ◽  
Vol 74 (6) ◽  
pp. 480-486 ◽  
Author(s):  
F. Zou ◽  
M. M. Carrasquillo ◽  
V. S. Pankratz ◽  
O. Belbin ◽  
K. Morgan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer Disease ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Expression Levels ◽  
Genome Wide ◽  
Gene Expression Levels

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 Dissecting meningococcal disease and carriage traits using high throughput phenotypic testing

2020 ◽  
Vol 2 (7A) ◽  
Author(s):  
Megan De Ste Croix ◽  
Dave Neelam ◽  
Neil Oldfield ◽  
Jay Lucidarme ◽  
David Turner ◽  
...  
Keyword(s):  
High Throughput ◽  
Meningococcal Disease ◽  
Association Studies ◽  
Phase Variation ◽  
Genome Wide Association Studies ◽  
Current Policy ◽  
Genome Sequences ◽  
Phenotypic Differences ◽  
Genome Wide ◽  
The Uk

Despite on-going vaccination programmes, Neisseria meningitidis causes over 700 cases of invasive meningococcal disease (IMD) in the UK each year. In 2017-18, the MenW and MenY capsular groups caused 38% of all IMD cases. Current policy is to generate genome sequences of all meningococcal disease isolates. Using this resource, we aim to understand how genetic variation contributes to phenotypic differences between carriage and disease isolates. We are adapting a variety of assays, designed to mimic carriage and disease behaviours, for high throughput phenotypic testing of multiple meningococcal isolates from carriage and cases of IMD. We have selected 335 MenW cc11 and MenY cc23 isolates and are currently testing subsets of isolates in cell culture (CaLu3), growth and biofilm assays. Phenotypic differences will be utilised as input data for Genome Wide Association Studies that aim to identify the specific genomic variants, or combinations of variants, determining observed differences. Genomic data will include whole genome sequences and repeat-mediated phase variation states. Our preliminary data has detected variation in the ability of cc11 and cc23 isolates to disrupt monolayers of CaLu3 cells, indicating that minor genetic differences in phylogentically similar organisms may be physiologically important for both carriage and disease. We will also discuss progress in establishing successful, high-throughput assays for testing multiple isolates.

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