ReviewHuman molecular genetics2008
Using gene expression to investigate the genetic basis of complex disorders.
Review in Human molecular genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types.Frontiers in genetics · 2020Pooled it
- Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.PLoS genetics · 2014Pooled it
- Application of GWAS and mGWAS in Livestock and Poultry Breeding.Animals : an open access journal from MDPI · 2024Review
- Article
- Functional analysis of the 1p34.3 risk locus implicates GNL2 in high-grade serous ovarian cancer.American journal of human genetics · 2022Article
- Psychosocial experiences modulate asthma-associated genes through gene-environment interactions.eLife · 2021Article
- A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits.Nature communications · 2021Article
- Bivariate genome-wide association study (GWAS) of body mass index and blood pressure phenotypes in northern Chinese twins.PloS one · 2021Article
- Evaluation of Whole-Exome Enrichment Solutions: Lessons from the High-End of the Short-Read Sequencing Scale.Journal of clinical medicine · 2020Article
- Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution.Human molecular genetics · 2019Article
- Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis.Mathematical biosciences and engineering : MBE · 2019Article
- Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach.BMC bioinformatics · 2019Article
- Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives.Frontiers in genetics · 2019Review
- Colon-specific eQTL analysis to inform on functional SNPs.British journal of cancer · 2018Article
- JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts.Bioinformatics (Oxford, England) · 2018Article
- Estimating the causal tissues for complex traits and diseases.Nature genetics · 2017Article
- Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types.Nature genetics · 2017Article
- Article
- Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model.BMC medical genomics · 2014Article
- HEFT: eQTL analysis of many thousands of expressed genes while simultaneously controlling for hidden factors.Bioinformatics (Oxford, England) · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The identification of complex disease susceptibility loci through genome-wide association studies (GWAS) has recently become possible and is now a method of choice for investigating the genetic basis of complex traits. The number of results from such studies is constantly increasing but the challenge lying forward is to identify the biological context in which these statistically significant candidate variants act. Regulatory variation plays an important role in shaping phenotypic differences among individuals and thus is very likely to also influence disease susceptibility. As such, integrating gene expression data and other disease relevant intermediate phenotypes with GWAS results could potentially help prioritize fine-mapping efforts and provide a shortcut to disease biology. Combining these different levels of information in a meaningful way is however not trivial. In the present review, we outline the several approaches that have been explored so far in this sense and their achievements. We also discuss the limitations of the methods and how upcoming technological developments could help circumvent these limitations. Overall, such efforts will be very helpful in understanding initially regulatory effects on disease and disease etiology in general.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.