Detection of expression quantitative trait loci in complex mouse crosses

Impact and alleviation of data quality and complex population substructure

Ovidiu Iancu, Priscila Darakjian, Sunita Kawane, Daniel Bottomly, Robert Hitzemann, Shannon McWeeney

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Complex Musmusculus crosses, e.g., heterogeneous stock (HS), 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, HS 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 HS formed by crossing four inbred strains (HS4), and a HS (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.

Original languageEnglish (US)
Article numberArticle 157
JournalFrontiers in Genetics
Volume3
Issue numberAUG
DOIs
StatePublished - 2012

Fingerprint

Quantitative Trait Loci
Chromosomes
Population
Genome
Gene Expression
Genotype
Data Accuracy
Brain

Keywords

  • Collaborative cross
  • eQTL detection
  • Gene expression
  • Mouse genetics
  • Population substructure

ASJC Scopus subject areas

  • Genetics
  • Molecular Medicine
  • Genetics(clinical)

Cite this

Detection of expression quantitative trait loci in complex mouse crosses : Impact and alleviation of data quality and complex population substructure. / Iancu, Ovidiu; Darakjian, Priscila; Kawane, Sunita; Bottomly, Daniel; Hitzemann, Robert; McWeeney, Shannon.

In: Frontiers in Genetics, Vol. 3, No. AUG, Article 157, 2012.

Research output: Contribution to journalArticle

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