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Quantitative trait locus analysis identifies Gabra3 as a regulator of behavioral despair in mice.

ABSTRACT: The Tail Suspension Test (TST), which measures behavioral despair, is widely used as an animal model of human depressive disorders and antidepressant efficacy. In order to identify novel genes involved in the regulation of TST performance, we crossed an inbred strain exhibiting low immobility in the TST (RIIIS/J) with two high-immobility strains (C57BL/6J and NZB/BlNJ) to create two distinct F2 hybrid populations. All F2 offspring (n = 655) were genotyped at high density with a panel of SNP markers. Whole-genome interval mapping of the F2 populations identified statistically significant quantitative trait loci (QTLs) on mouse chromosomes (MMU) 4, 6, and X. Microarray analysis of hippocampal gene expression in the three parental strains was used to identify potential candidate genes within the MMUX QTLs identified in the NZB/BlNJ x RIIIS/J cross. Expression of Gabra3, which encodes the GABA(A) receptor alpha3 subunit, was robust in the hippocampus of B6 and RIIIS mice but absent from NZB hippocampal tissue. To verify the role of Gabra3 in regulating TST behavior in vivo, mice were treated with SB-205384, a positive modulator of the alpha3 subunit. SB-205384 significantly reduced TST immobility in B6 mice without affecting general activity, but it had no effect on behavior in NZB mice. This work suggests that GABRA3 regulates a behavioral endophenotype of depression and establishes this gene as a viable new target for the study and treatment of human depression.

SUBMITTER: Miller BH

PROVIDER: S-EPMC2890984 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Protein quantitative trait locus study in obesity during weight-loss identifies a leptin regulator.

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Automated quantitative trait locus analysis (AutoQTL).

Project description:BackgroundQuantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data.ResultsUsing a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL.ConclusionsThis proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies.

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2018-01-25 | E-MTAB-5572 | biostudies-arrayexpress

Expression quantitative trait locus analysis of chicken cerebrum

Project description:This data originates from an expression quantitative trait locus analysis of cerebrum in an advanced intercross of Red Jungefowl and White Leghorn chickens. The aim of the study was to map the genetic basis of cerebrum and body mass, and idenifiy transcriptional differences within the intercross to assess any candidate genes for cerebrum and body mass.

2020-07-07 | E-MTAB-9313 | biostudies-arrayexpress

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