Detection of quantitative trait loci from RNA-seq data with or without genotypes using BaseQTL.

Autor: Vigorito E; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK., Lin WY; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK., Starr C; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK., Kirk PDW; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.; Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK., White SR; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.; Department of Psychiatry, University of Cambridge, Cambridge, UK., Wallace C; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.; Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK.
Jazyk: angličtina
Zdroj: Nature computational science [Nat Comput Sci] 2021 Jun; Vol. 1, pp. 421-432. Date of Electronic Publication: 2021 Jun 24.
DOI: 10.1038/s43588-021-00087-y
Abstrakt: Detecting genetic variants associated with traits (quantitative trait loci, QTL) requires genotyped study individuals. Here we describe BaseQTL, a Bayesian method that exploits allele-specific expression to map molecular QTL from sequencing reads (eQTL for gene expression) even when no genotypes are available. When used with genotypes to map eQTL, BaseQTL has lower error rates and increased power compared with existing QTL mapping methods. Running without genotypes limits how many tests can be performed, but due to the proximity of QTL variants to gene bodies, the 2.8% of variants within a 100 kB window that could be tested contained 26% of eQTL detectable with genotypes. eQTL effect estimates were invariably consistent between analyses performed with and without genotypes. Often, sequencing data may be generated in the absence of genotypes on patients and controls in differential expression studies, and we identified an apparent psoriasis-specific eQTL for GSTP1 in one such dataset, providing new insights into disease-dependent gene regulation.
Competing Interests: Competing interests The authors declare no competing interests.
Databáze: MEDLINE
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