Detection of Expression Quantitative Trait Loci in Complex Mouse Crosses: Impact and Alleviation of Data Quality and Complex Population Substructure
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2012-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2012.00157/full |
| _version_ | 1818721160277262336 |
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| author | Ovidiu Dan Iancu Priscila eDarakjian Sunita eKawane Daniel eBottomly Robert eHitzemann Robert eHitzemann Shannon K McWeeney Shannon K McWeeney |
| author_facet | Ovidiu Dan Iancu Priscila eDarakjian Sunita eKawane Daniel eBottomly Robert eHitzemann Robert eHitzemann Shannon K McWeeney Shannon K McWeeney |
| author_sort | Ovidiu Dan Iancu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T20:34:19Z |
| format | Article |
| id | doaj.art-c4f97dad8d604c659362880726ae5a43 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-17T20:34:19Z |
| publishDate | 2012-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-c4f97dad8d604c659362880726ae5a432022-12-21T21:33:30ZengFrontiers Media S.A.Frontiers in Genetics1664-80212012-08-01310.3389/fgene.2012.0015727116Detection of Expression Quantitative Trait Loci in Complex Mouse Crosses: Impact and Alleviation of Data Quality and Complex Population SubstructureOvidiu Dan Iancu0Priscila eDarakjian1Sunita eKawane2Daniel eBottomly3Robert eHitzemann4Robert eHitzemann5Shannon K McWeeney6Shannon K McWeeney7Oregon Health & Science UniversityOregon Health & Science UniversityOregon Health & Science UniversityOregon Health & Science UniversityOregon Health & Science UniversityVeterans Affairs Medical CenterOregon Health & Science UniversityOregon Health & Science UniversityComplex 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.http://journal.frontiersin.org/Journal/10.3389/fgene.2012.00157/fullGene ExpressionCollaborative Crossmouse geneticseQTL detectionpopulation substructure |
| spellingShingle | Ovidiu Dan Iancu Priscila eDarakjian Sunita eKawane Daniel eBottomly Robert eHitzemann Robert eHitzemann Shannon K McWeeney Shannon K McWeeney Detection of Expression Quantitative Trait Loci in Complex Mouse Crosses: Impact and Alleviation of Data Quality and Complex Population Substructure Frontiers in Genetics Gene Expression Collaborative Cross mouse genetics eQTL detection population substructure |
| title | Detection of Expression Quantitative Trait Loci in Complex Mouse Crosses: Impact and Alleviation of Data Quality and Complex Population Substructure |
| title_full | Detection of Expression Quantitative Trait Loci in Complex Mouse Crosses: Impact and Alleviation of Data Quality and Complex Population Substructure |
| title_fullStr | Detection of Expression Quantitative Trait Loci in Complex Mouse Crosses: Impact and Alleviation of Data Quality and Complex Population Substructure |
| title_full_unstemmed | Detection of Expression Quantitative Trait Loci in Complex Mouse Crosses: Impact and Alleviation of Data Quality and Complex Population Substructure |
| title_short | Detection of Expression Quantitative Trait Loci in Complex Mouse Crosses: Impact and Alleviation of Data Quality and Complex Population Substructure |
| title_sort | detection of expression quantitative trait loci in complex mouse crosses impact and alleviation of data quality and complex population substructure |
| topic | Gene Expression Collaborative Cross mouse genetics eQTL detection population substructure |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2012.00157/full |
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