Detection of Expression Quantitative Trait Loci in Complex Mouse Crosses: Impact and Alleviation of Data Quality and Complex Population Substructure

Frontiers in Genetics

Robert eHitzemann¹  Shannon K McWeeney¹  Daniel eBottomly¹  Priscila eDarakjian¹  Ovidiu Dan Iancu²  Sunita eKawane² 

[1] Science University;;Oregon Health &

关键词: Gene Expression;    Collaborative Cross;    mouse genetics;    eQTL detection;    population substructure;   

DOI  :  10.3389/fgene.2012.00157

来源: DOAJ

【 摘 要 】

Complex Mus Musculus crosses e.g. heterogeneous stock, provide increased resolution for quantitative trait loci detection. However, increased genetic complexity challenges detection methods, with discordant results due to low data quality or complex genetic architecture. We quantified the impact of theses factors across three mouse crosses and two different detection methods, identifying procedures that greatly improve detection quality. Importantly, heterogeneous stock populations have complex genetic architectures not fully captured by the whole-genome kinship matrix, calling for incorporating chromosome specific relatedness information. We analyze three increasingly complex crosses, using gene expression levels as quantitative traits. The three crosses were an F2 intercross, a heterogeneous stock formed by crossing four inbred strains (HS4), and a heterogeneous stock (HS-CC) derived from the eight lines found in the collaborative cross. Brain (striatum) gene expression and genotype data were obtained using the Illumina platform. We found large disparities between methods, with concordance varying as genetic complexity increased; this problem was more acute for probes with distant regulatory elements (trans). A suite of data filtering steps resulted in substantial increases in reproducibility. Genetic relatedness between samples generated overabundance of detected eQTLs; an adjustment procedure that includes the kinship matrix attenuates this problem. However, we find that relatedness between individuals is not evenly distributed across the genome; information from distinct chromosomes results in relatedness structure different from the whole genome kinship matrix. Shared polymorphisms from distinct chromosomes collectively affect expression levels, confounding eQTL detection. We suggest that considering chromosome-specific relatedness can result in improved eQTL detection.

【 授权许可】

Unknown