ArticleProceedings of the National Academy of Sciences of the United States of America2010
Correction for hidden confounders in the genetic analysis of gene expression.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.
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Who cites it
76 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Review
- Article
- Article
- Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2.iScience · 2022Article
- Navigating the pitfalls of applying machine learning in genomics.Nature reviews. Genetics · 2022Review
- Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders.G3 (Bethesda, Md.) · 2022Article
- UNIFYING AND GENERALIZING METHODS FOR REMOVING UNWANTED VARIATION BASED ON NEGATIVE CONTROLS.Statistica Sinica · 2021Article
- DataRemix: a universal data transformation for optimal inference from gene expression datasets.Bioinformatics (Oxford, England) · 2021Article
- A general approach to sensitivity analysis for Mendelian randomization.Statistics in biosciences · 2021Article
- LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression.Frontiers in genetics · 2021Article
- Fully automated web-based tool for identifying regulatory hotspots.BMC genomics · 2020Article
- Effective SNP ranking improves the performance of eQTL mapping.Genetic epidemiology · 2020Article
- Controlling technical variation amongst 6693 patient microarrays of the randomized MINDACT trial.Communications biology · 2020Article
- Data-based RNA-seq simulations by binomial thinning.BMC bioinformatics · 2020Article
- Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives.Genetics · 2019Article
- BAYESIAN LATENT HIERARCHICAL MODEL FOR TRANSCRIPTOMIC META-ANALYSIS TO DETECT BIOMARKERS WITH CLUSTERED META-PATTERNS OF DIFFERENTIAL EXPRESSION SIGNALS.The annals of applied statistics · 2019Article
- Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis.Genetics, selection, evolution : GSE · 2018Article
- Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data.Journal of proteome research · 2018Article
16 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the genetic underpinnings of disease is important for screening, treatment, drug development, and basic biological insight. One way of getting at such an understanding is to find out which parts of our DNA, such as single-nucleotide polymorphisms, affect particular intermediary processes such as gene expression. Naively, such associations can be identified using a simple statistical test on all paired combinations of genetic variants and gene transcripts. However, a wide variety of confounders lie hidden in the data, leading to both spurious associations and missed associations if not properly addressed. We present a statistical model that jointly corrects for two particular kinds of hidden structure--population structure (e.g., race, family-relatedness), and microarray expression artifacts (e.g., batch effects), when these confounders are unknown. Applying our method to both real and synthetic, human and mouse data, we demonstrate the need for such a joint correction of confounders, and also the disadvantages of other possible approaches based on those in the current literature. In particular, we show that our class of models has maximum power to detect eQTL on synthetic data, and has the best performance on a bronze standard applied to real data. Lastly, our software and the associations we found with it are available at http://www.microsoft.com/science.
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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.