Dataset Information

Genetic Mapping of Behavioral Traits Using the Collaborative Cross Resource.

ABSTRACT: The complicated interactions between genetic background, environment and lifestyle factors make it difficult to study the genetic basis of complex phenotypes, such as cognition and anxiety levels, in humans. However, environmental and other factors can be tightly controlled in mouse studies. The Collaborative Cross (CC) is a mouse genetic reference population whose common genetic and phenotypic diversity is on par with that of humans. Therefore, we leveraged the power of the CC to assess 52 behavioral measures associated with locomotor activity, anxiety level, learning and memory. This is the first application of the CC in novel object recognition tests, Morris water maze tasks, and fear conditioning tests. We found substantial continuous behavioral variations across the CC strains tested, and mapped six quantitative trait loci (QTLs) which influenced these traits, defining candidate genetic variants underlying these QTLs. Overall, our findings highlight the potential of the CC population in behavioral genetic research, while the identified genomic loci and genes driving the variation of relevant behavioral traits provide a foundation for further studies.

SUBMITTER: Xuan W

PROVIDER: S-EPMC9821145 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Genetic Mapping of Behavioral Traits Using the Collaborative Cross Resource.

Xuan Wei W Zhang Ling L Zhang Yu Y Sun Xiuping X Wang Jue J Li Xianglei X Zhang Lingyan L Wang Xinpei X Morahan Grant G Qin Chuan C

International journal of molecular sciences 20221230 1

The complicated interactions between genetic background, environment and lifestyle factors make it difficult to study the genetic basis of complex phenotypes, such as cognition and anxiety levels, in humans. However, environmental and other factors can be tightly controlled in mouse studies. The Collaborative Cross (CC) is a mouse genetic reference population whose common genetic and phenotypic diversity is on par with that of humans. Therefore, we leveraged the power of the CC to assess 52 beha ...[more]

PMID: 36614124

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Project description:Purpose: To investigate the diet- or strain-dependence of liver transcriptome in eight Diversity Outbred (DO) founder strains [A/J, C57BL/6J (B6), 129S1/SvImJ (129), NOD/ShiLtJ (NOD), NZO/HILtJ (NZO), and three wild-derived strains CAST/EiJ (CAST), PWK/PhJ (PWK), and WSB/EiJ (WSB)] Methods: Total RNA from eight DO founder strains was extracted from snap frozen liver using Maxwell® 16 LEV simplyRNA Tissue Kit (Promega) according to the manufacturer’s protocol. The quality and amount of liver RNA was evaluated using a qubit RNA assay (Invitrogen R11490) and run on an Biorad Bioanalyzer Chip to verify RNA integrity. RNASeq library was prepared by using Illumina TruSeq RNA library construction protocol. RNA samples from 24 mice-fed AIN-93 M diet and 24 mice-fed HFCA diet were submitted to the David H. Murdock Research Institute (DHMRI) for sequencing. The RNA-seq libraries were constructed from 1 µg total RNA after poly-A library preparation. The pooled libraries were sequenced on two lanes of the Illumina HiSeq2500 Instrument. Results: Our liver transcriptomic analysis of eight mouse strains shows that diet and genetic background have a strong effect on the liver transcriptome, which may be related to differences in cardo-metabolic traits. We calculated a gene module consisting of highly correlated transcripts that enriched the biological pathway and provided a searchable database of liver transcript profiles. To further investigate the association of hepatic gene modules with cardio-metabolic traits, Fmo3, a key enzyme in the production of trimethylamine N-oxide (TMAO), and Nox4, which showed strong association with plasma TMAO and liver triglyceride, were identified as the highest expressed in the laboratory-like inbred strains (B6, 129 and NZO) and the lowest expressed transcripts in the CAST strain. We found differences in the production of TMAO and liver triglyceride, which contributes to metabolic syndrome, depending on genetic backgrounds. Conclusions: Our study provide a valuable data resource to the research community and show that liver transcriptomic analysis identified diet- or strain-specific pathways to pathogenesis of metabolic syndrome.

Dataset Information

Genetic Mapping of Behavioral Traits Using the Collaborative Cross Resource.

Publications

Genetic Mapping of Behavioral Traits Using the Collaborative Cross Resource.

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