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Khan AH, Bagley JR, LaPierre N, Gonzalez-Figueroa C, Spencer TC, Choudhury M, Xiao X, Eskin E, Jentsch JD, Smith DJ. Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice. Cell Rep 2023; 42:112856. [PMID: 37481717 PMCID: PMC10530068 DOI: 10.1016/j.celrep.2023.112856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 06/06/2023] [Accepted: 07/10/2023] [Indexed: 07/25/2023] Open
Abstract
To identify addiction genes, we evaluate intravenous self-administration of cocaine or saline in 84 inbred and recombinant inbred mouse strains over 10 days. We integrate the behavior data with brain RNA-seq data from 41 strains. The self-administration of cocaine and that of saline are genetically distinct. We maximize power to map loci for cocaine intake by using a linear mixed model to account for this longitudinal phenotype while correcting for population structure. A total of 15 unique significant loci are identified in the genome-wide association study. A transcriptome-wide association study highlights the Trpv2 ion channel as a key locus for cocaine self-administration as well as identifying 17 additional genes, including Arhgef26, Slc18b1, and Slco5a1. We find numerous instances where alternate splice site selection or RNA editing altered transcript abundance. Our work emphasizes the importance of Trpv2, an ionotropic cannabinoid receptor, for the response to cocaine.
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Affiliation(s)
- Arshad H Khan
- Department of Molecular and Medical Pharmacology, Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Jared R Bagley
- Department of Psychology, Binghamton University, Binghamton, NY, USA
| | - Nathan LaPierre
- Department of Computer Science, UCLA, Los Angeles, CA 90095, USA
| | | | - Tadeo C Spencer
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Mudra Choudhury
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Xinshu Xiao
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Eleazar Eskin
- Department of Computational Medicine, UCLA, Los Angeles, CA 90095, USA
| | - James D Jentsch
- Department of Psychology, Binghamton University, Binghamton, NY, USA
| | - Desmond J Smith
- Department of Molecular and Medical Pharmacology, Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA.
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Perez BC, Bink MCAM, Svenson KL, Churchill GA, Calus MPL. Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence. G3 (BETHESDA, MD.) 2022; 12:jkac258. [PMID: 36161485 PMCID: PMC9635642 DOI: 10.1093/g3journal/jkac258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Recent developments allowed generating multiple high-quality 'omics' data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.
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Affiliation(s)
- Bruno C Perez
- Hendrix Genetics B.V., Research and Technology Center (RTC), 5830 AC Boxmeer, The Netherlands
| | - Marco C A M Bink
- Hendrix Genetics B.V., Research and Technology Center (RTC), 5830 AC Boxmeer, The Netherlands
| | | | | | - Mario P L Calus
- Corresponding author: Animal Breeding and Genomics, Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands.
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