ArticleBMC genomics2010
Data-driven assessment of eQTL mapping methods.
Article in BMC genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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
30 citing papers in PubMed.
- Genetic and environmental determinants of multicellular-like phenotypes in fission yeast.Genetics · 2026Article
- Genetic effects on molecular network states explain complex traits.Molecular systems biology · 2023Article
- Functional characterization of human genomic variation linked to polygenic diseases.Trends in genetics : TIG · 2023Review
- Real age prediction from the transcriptome with RAPToR.Nature methods · 2022Article
- The impact of genomic variation on protein phosphorylation states and regulatory networks.Molecular systems biology · 2022Article
- Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment.Brain sciences · 2021Article
- Multi-tissue transcriptome-wide association studies.Genetic epidemiology · 2021Article
- An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments.Genetic epidemiology · 2020Article
- A framework for genomics-informed ecophysiological modeling in plants.Journal of experimental botany · 2019Article
- A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.Nature genetics · 2018Article
- A deep auto-encoder model for gene expression prediction.BMC genomics · 2017Article
- Prospects for Genomic Selection in Cassava Breeding.The plant genome · 2017Article
- Prior knowledge guided eQTL mapping for identifying candidate genes.BMC bioinformatics · 2016Article
- Testing and Validation of Computational Methods for Mass Spectrometry.Journal of proteome research · 2016Article
- Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression.Genome biology · 2016Article
- Common Genetic Variants in FOXP2 Are Not Associated with Individual Differences in Language Development.PloS one · 2016Article
- Multiple-Line Inference of Selection on Quantitative Traits.Genetics · 2015Article
- A random forest approach to capture genetic effects in the presence of population structure.Nature communications · 2015Article
- Article
- Mapping eQTL networks with mixed graphical Markov models.Genetics · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis.
resultsHere we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods.
conclusionsBenchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.