ArticleGenetics2019
Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples.
Article in Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis.Genome research · 2024Pooled it
- Genetic diversity promotes resilience in a mouse model of Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice.Cell reports · 2023Article
- Gene expression genetics of the striatum of Diversity Outbred mice.Scientific data · 2023Article
- Gene expression genetics of the striatum of Diversity Outbred mice.bioRxiv : the preprint server for biology · 2023Article
- The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats.Nucleic acids research · 2022Article
- Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits.BMC genomics · 2022Article
- The dihydropyrimidine dehydrogenase gene contributes to heritable differences in sleep in mice.Current biology : CB · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript-trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative "reference" traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (
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