Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance

A Corrigendum to this article was published on 27 May 2015

This article has been updated

Abstract

Complex human traits are influenced by variation in regulatory DNA through mechanisms that are not fully understood. Because regulatory elements are conserved between humans and mice, a thorough annotation of cis regulatory variants in mice could aid in further characterizing these mechanisms. Here we provide a detailed portrait of mouse gene expression across multiple tissues in a three-way diallel. Greater than 80% of mouse genes have cis regulatory variation. Effects from these variants influence complex traits and usually extend to the human ortholog. Further, we estimate that at least one in every thousand SNPs creates a cis regulatory effect. We also observe two types of parent-of-origin effects, including classical imprinting and a new global allelic imbalance in expression favoring the paternal allele. We conclude that, as with humans, pervasive regulatory variation influences complex genetic traits in mice and provide a new resource toward understanding the genetic control of transcription in mammals.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Diallel crossing scheme and sample sizes.
Figure 2: Principal components (PCs) of brain RNA-seq and microarray expression levels across four tissues.
Figure 3: Balanced contribution of different subspecies to the identification of cis-regulated genes.
Figure 4: Differential gene expression is positively correlated with sequence diversity at multiple evolutionary scales.
Figure 5: Imprinted genes in mouse brain.
Figure 6: Global allelic imbalance in favor of the paternal allele.

Accession codes

Primary accessions

Gene Expression Omnibus

Sequence Read Archive

Referenced accessions

Gene Expression Omnibus

Change history

  • 16 April 2015

    In the version of this article initially published, an accession number was not provided for RNA-seq data sets. The RNA-seq data sets that passed quality control are available at the Sequence Read Archive (SRA) under accession SRP056236. The error has been corrected in the HTML and PDF versions of the article.

References

  1. 1

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  2. 2

    King, M.C. & Wilson, A.C. Evolution at two levels in humans and chimpanzees. Science 188, 107–116 (1975).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3

    Gan, X. et al. Multiple reference genomes and transcriptomes for Arabidopsis thaliana. Nature 477, 419–423 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4

    Keane, T.M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5

    Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6

    Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).

    CAS  Article  Google Scholar 

  7. 7

    Schaub, M.A., Boyle, A.P., Kundaje, A., Batzoglou, S. & Snyder, M. Linking disease associations with regulatory information in the human genome. Genome Res. 22, 1748–1759 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8

    Nicolae, D.L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. 9

    Wang, X. et al. Transcriptome-wide identification of novel imprinted genes in neonatal mouse brain. PLoS ONE 3, e3839 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  10. 10

    Gregg, C. et al. High-resolution analysis of parent-of-origin allelic expression in the mouse brain. Science 329, 643–648 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11

    Gregg, C., Zhang, J., Butler, J.E., Haig, D. & Dulac, C. Sex-specific parent-of-origin allelic expression in the mouse brain. Science 329, 682–685 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12

    DeVeale, B., van der Kooy, D. & Babak, T. Critical evaluation of imprinted gene expression by RNA-Seq: a new perspective. PLoS Genet. 8, e1002600 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13

    Wang, X., Soloway, P.D. & Clark, A.G. A survey for novel imprinted genes in the mouse placenta by mRNA-seq. Genetics 189, 109–122 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14

    Okae, H. et al. Re-investigation and RNA sequencing-based identification of genes with placenta-specific imprinted expression. Hum. Mol. Genet. 21, 548–558 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  15. 15

    Goncalves, A. et al. Extensive compensatory cis-trans regulation in the evolution of mouse gene expression. Genome Res. 22, 2376–2384 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16

    Babak, T. et al. Global survey of genomic imprinting by transcriptome sequencing. Curr. Biol. 18, 1735–1741 (2008).

    CAS  Article  Google Scholar 

  17. 17

    Hayden, E.C. RNA studies under fire. Nature 484, 428 (2012).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  18. 18

    Barlow, D.P. Gametic imprinting in mammals. Science 270, 1610–1613 (1995).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  19. 19

    Skarnes, W.C. et al. A conditional knockout resource for the genome-wide study of mouse gene function. Nature 474, 337–342 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20

    Collaborative Cross Consortium. The genome architecture of the Collaborative Cross mouse genetic reference population. Genetics 190, 389–401 (2012).

  21. 21

    Churchill, G.A., Gatti, D.M., Munger, S.C. & Svenson, K.L. The Diversity Outbred mouse population. Mamm. Genome 23, 713–718 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  22. 22

    Huang, S., Holt, J., Kao, C.Y., McMillan, L. & Wang, W. A novel multi-alignment pipeline for high-throughput sequencing data. Database (Oxford) 2014, bau057 (2014).

    Article  CAS  Google Scholar 

  23. 23

    Zhang, Z. et al. GeneScissors: a comprehensive approach to detecting and correcting spurious transcriptome inference due to RNAseq reads misalignment. Bioinformatics 29, 291–299 (2013).

    Article  CAS  Google Scholar 

  24. 24

    Zou, F. et al. A novel statistical approach for jointly analyzing RNA-Seq data from F1 reciprocal crosses and inbred lines. Genetics 197, 389–399 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  25. 25

    Wright, F.A. et al. Heritability and genomics of gene expression in peripheral blood. Nat. Genet. 46, 430–437 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26

    Kim, Y. et al. A meta-analysis of gene expression quantitative trait loci in brain. Transl. Psychiatry 4, e459 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27

    Yang, H. et al. Subspecific origin and haplotype diversity in the laboratory mouse. Nat. Genet. 43, 648–655 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28

    Xie, W. et al. Base-resolution analyses of sequence and parent-of-origin dependent DNA methylation in the mouse genome. Cell 148, 816–831 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Ohno, S., Kaplan, W.D. & Kinosita, R. Formation of the sex chromatin by a single X-chromosome in liver cells of Rattus norvegicus. Exp. Cell Res. 18, 415–418 (1959).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. 30

    Lyon, M.F. Gene action in the X-chromosome of the mouse (Mus musculus L.). Nature 190, 372–373 (1961).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31

    Ohno, S. Sex Chromosomes and Sex Linked Genes (Springer Verlag, 1967).

  32. 32

    Cattanach, B.M. Controlling elements in the mouse X-chromosome. 3. Influence upon both parts of an X divided by rearrangement. Genet. Res. 16, 293–301 (1970).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. 33

    Calaway, J.D. et al. Genetic architecture of skewed X inactivation in the laboratory mouse. PLoS Genet. 9, e1003853 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  34. 34

    Aylor, D.L. et al. Genetic analysis of complex traits in the emerging Collaborative Cross. Genome Res. 21, 1213–1222 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35

    Cui, X., Affourtit, J., Shockley, K.R., Woo, Y. & Churchill, G.A. Inheritance patterns of transcript levels in F1 hybrid mice. Genetics 174, 627–637 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36

    Price, A.L. et al. Single-tissue and cross-tissue heritability of gene expression via identity-by-descent in related or unrelated individuals. PLoS Genet. 7, e1001317 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37

    Schadt, E.E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003).

    CAS  Article  Google Scholar 

  38. 38

    Kong, A. et al. Rate of de novo mutations and the importance of father's age to disease risk. Nature 488, 471–475 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39

    Drost, J.B. & Lee, W.R. Biological basis of germline mutation: comparisons of spontaneous germline mutation rates among Drosophila, mouse, and human. Environ. Mol. Mutagen. 25 (suppl. 26), 48–64 (1995).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40

    Lin, H. et al. Dosage compensation in the mouse balances up-regulation and silencing of X-linked genes. PLoS Biol. 5, e326 (2007).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. 41

    Johnston, C.M. et al. Large-scale population study of human cell lines indicates that dosage compensation is virtually complete. PLoS Genet. 4, e9 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. 42

    Yang, F., Babak, T., Shendure, J. & Disteche, C.M. Global survey of escape from X inactivation by RNA-sequencing in mouse. Genome Res. 20, 614–622 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43

    Li, N. & Carrel, L. Escape from X chromosome inactivation is an intrinsic property of the Jarid1c locus. Proc. Natl. Acad. Sci. USA 105, 17055–17060 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  44. 44

    Lopes, A.M. et al. Transcriptional changes in response to X chromosome dosage in the mouse: implications for X inactivation and the molecular basis of Turner Syndrome. BMC Genomics 11, 82 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  45. 45

    Carrel, L. & Willard, H.F. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature 434, 400–404 (2005).

    CAS  Article  Google Scholar 

  46. 46

    Berletch, J.B., Yang, F. & Disteche, C.M. Escape from X inactivation in mice and humans. Genome Biol. 11, 213 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. 47

    Nguyen, D.K. & Disteche, C.M. Dosage compensation of the active X chromosome in mammals. Nat. Genet. 38, 47–53 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. 48

    Gupta, V. et al. Global analysis of X-chromosome dosage compensation. J. Biol. 5, 3 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. 49

    Xiong, Y. et al. RNA sequencing shows no dosage compensation of the active X-chromosome. Nat. Genet. 42, 1043–1047 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  50. 50

    Deng, X. et al. Evidence for compensatory upregulation of expressed X-linked genes in mammals, Caenorhabditis elegans and Drosophila melanogaster. Nat. Genet. 43, 1179–1185 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51

    Kharchenko, P.V., Xi, R. & Park, P.J. Evidence for dosage compensation between the X chromosome and autosomes in mammals. Nat. Genet. 43, 1167–1169 author reply 1171–1172 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  52. 52

    Lin, H. et al. Relative overexpression of X-linked genes in mouse embryonic stem cells is consistent with Ohno's hypothesis. Nat. Genet. 43, 1169–1170 author reply 1171–1172 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  53. 53

    Yildirim, E., Sadreyev, R.I., Pinter, S.F. & Lee, J.T. X-chromosome hyperactivation in mammals via nonlinear relationships between chromatin states and transcription. Nat. Struct. Mol. Biol. 19, 56–61 (2012).

    CAS  Article  Google Scholar 

  54. 54

    He, X. et al. He et al. reply. Nat. Genet. 43, 1171–1172 (2011).

    CAS  Article  Google Scholar 

  55. 55

    Lin, F., Xing, K., Zhang, J. & He, X. Expression reduction in mammalian X chromosome evolution refutes Ohno's hypothesis of dosage compensation. Proc. Natl. Acad. Sci. USA 109, 11752–11757 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  56. 56

    Brawand, D. et al. The evolution of gene expression levels in mammalian organs. Nature 478, 343–348 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57

    Disteche, C.M. Dosage compensation of the sex chromosomes. Annu. Rev. Genet. 46, 537–560 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58

    Jue, N.K. et al. Determination of dosage compensation of the mammalian X chromosome by RNA-seq is dependent on analytical approach. BMC Genomics 14, 150 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank P. Mieczkowski, A. Brandt, E. Malc, M. Vernon, J. Brennan and M. Calabrese for helpful discussions. Major funding was provided by National Institute of Mental Health/National Human Genome Research Institute Center of Excellence for Genome Sciences grants (P50MH090338 and P50HG006582, co-principal investigators F.P.-M.d.V. and P.F.S.). This work was also supported by grants R01GM074175 (principal investigator F.Z.) from the National Institute of General Medical Sciences and K01MH094406 (principal investigator J.J.C.) from the National Institute of Mental Health.

Author information

Affiliations

Authors

Contributions

F.P.-M.d.V., J.J.C., L.M., F.Z., W.S., V.Z. and P.F.S. designed the study, and J.J.C. managed the project. J.J.C. and F.P.-M.d.V. drafted the manuscript, and all authors edited it. D.R.M., G.D.S., T.A.B., R.J.B., M.E.C., S.D.H., N.N.R., J.S.S., R.J.N., C.R.Q. and Y.X. bred the mice and collected tissues. J.D.C., C.J.B., Z.Y. and T.J.G. prepared samples for expression profiling. W.S., F.Z., V.Z., Y.K. and W.W. developed statistical models and conducted analyses. W.V., A.B.L., D.W.T., L.M.T., K.K., J.X., J.P.D., A.P.M. and D.L.A. contributed to data analysis and interpretation. S.H., I.K.P., J.R.W., C.E.W., C.-P.F., Z.Z., J.H., Z.G. and L.M. contributed to pseudogenome construction and RNA-seq read alignment.

Corresponding author

Correspondence to Fernando Pardo-Manuel de Villena.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Detection of genes with cis regulatory variants.

Allele-specific expression of a gene requires the presence of a genetic or epigenetic variant in cis. This is demonstrated above, where trans-acting factors have equal opportunity to affect both alleles.

Supplementary Figure 2 Joint analysis of total and allele-specific read counts improves power.

For this study, we developed a likelihood-based method to jointly analyze both total read counts (TReC) and allele-specific read counts (ASReC) from inbred and F1 mice. (a) A negative binomial distribution was generated for TReC, and a beta-binomial distribution was used for ASReC. (b) Simulation and real data support the increased power of this joint model (simulation and real data analyses use the same sample size: 24 = 6(AA) + 6(BB) + 6(AB) + 6(BA).

Supplementary Figure 3 Clustering of expression microarray data and shared strain effects across tissues.

(a) All 384 microarrays run across 4 different tissues were entered into a single cluster analysis. Microarray samples cluster first by tissue, followed by cross, parent of origin and sex. (b) Venn diagram showing the number of genes with significant strain effects within at least one cross and the degree to which these strain effects are shared across multiple tissues.

Supplementary Figure 4 Additive expression of Mad1l1.

Mad1l1 (mitotic arrest deficient 1–like 1) is one of several thousand genes identified with statistically significant allelic imbalance. (a) For each animal, two points are plotted to reflect the proportion of allele-specific Mad1l1 reads aligned to each parent (colored by genotype). The F1 allele-specific data indicate that, at the cis level, the PWK/PhJ Mad1l1 allele is stronger than the WSB/EiJ allele, which is stronger than the CAST/EiJ allele. (b) For each animal, one point is plotted to reflect the total number of reads aligned to Mad1l1, regardless of whether they are allele specific or not. The points for F1 mice are colored with the maternal strain on the outside and the paternal strain on the inside. This pattern indicates that the cis effect identified in the top panel is consistent with total expression in the parental strains. Furthermore, the expression levels in the F1 samples are intermediate to those of their parents, indicating an additive effect.

Supplementary Figure 5 Overdominant expression of Fos.

Fos (FBJ osteosarcoma oncogene) overdominance effect. (a) The F1 allele-specific data indicate that, at the cis level, the PWK/PhJ allele is stronger than the WSB/EiJ allele, which is stronger than the CAST/EiJ allele. (b) This cis effect is consistent with total expression in the parental strains, but the expression levels in the F1 animals show a dominance effect. (c) The dominance effect replicated in the brain microarray data.

Supplementary Figure 6 Microarray strain effects across four tissues.

Balanced contribution of different subspecies to the identification of genes with additive strain effects using microarray data. For each tissue, a Venn diagram shows the degree to which additive strain effects are shared by multiple crosses. For example, in brain, 74% of all expressed genes (9,701/13,162) showed a strain effect in at least one cross (FDR < 0.05), with the majority identified in at least 2 crosses. Also shown for each tissue is a distribution of the effect size (a positive value indicates higher expression from the alphabetically second strain for each cross) for all genes with a strain effect, separated by cross.

Supplementary Figure 7 Sequence diversity and magnitude of differential expression (RNA-seq).

Sequence divergence is correlated to the number and magnitude of differentially expressed genes. Plotted for each reciprocal cross is the relationship between the number of SNPs in a 10-kb window (x axis), the proportion of expressed genes that are differentially expressed (y axis) and the P value associated with differential expression (z axis). The widths of the colored and shadow lines are proportional to the number of expressed genes in a given SNP density bin. The y and z axes are loess-smoothed curves, and this plot excludes genomic regions in which strains share subspecific origin (13% of the genome).

Supplementary Figure 8 Proportion of SNPs creating a cis regulatory effect.

Estimation of the proportion of SNPs creating cis regulatory effects. As shown at the top of this figure, WSB/EiJ is of M. m. domesticus origin and CAST/EiJ is of M. m. castaneous origin for 91% of the genome. For such regions, we found 5,045 genes with a cis eQTL between WSB/EiJ and CAST/EiJ out of a total of 10,011 possible genes (50%), defined as having at least one strain-informative expressed SNP. These 10,011 genes are known to possess a total of 6,167,501 SNPs between WSB/EiJ and CAST/EiJ, considering the entire gene body with 10 kb upstream and downstream. Because each cis eQTL must result from at least one regulatory variant, we can estimate the minimum proportion of SNPs creating a cis regulatory effect as 0.082% of all new variants (5,045 cis eQTLs/6,167,501 SNPs). The same logic follows for every other cross and genomic regions with different phylogenies. Overall, the mean percentage of SNPs creating a cis eQTL is 0.10% (±0.02%, 95% confidence interval).

Supplementary Figure 9 Genome-wide distribution of genes imprinted in mouse brain.

Distribution of genes imprinted in mouse brain. Plotted in red across each chromosome is the parent-of-origin effect P value for a combined analysis involving all three crosses. The positions of 128 known imprinted genes, and the clusters they form, are shown in green, and novel imprinted gene locations are shown in orange.

Supplementary Figure 10 Parent-of-origin methylation and paternal overexpression.

Genes with consistent overexpression from the paternal allele are closer to CpG islands that are preferentially methylated on the maternal allele. To create this plot, CpG islands were first divided into three categories on the basis of data from Xie et al. (Cell 148, 816–831, 2012): those preferentially methylated on the paternal allele (red) or maternal allele (blue) or with no preference (black). The y axis indicates the log of the ratio between two distances: (1) the distance between the TSS of consistently paternally expressed genes and the nearest CpG island and (2) the same measure for inconsistently expressed genes. The distribution of this ratio is plotted for all three categories of CpG islands. The blue line (CpG islands that are preferentially maternally methylated) shows a greater enrichment for negative values (greater area under the curve) than the red line, indicating that genes with consistent paternal expression are generally closer to maternally methylated CpG islands than paternally methylated CpG islands. The sharp downward spikes in each curve are due to the nature of the distance distributions, as many genes have a distance of zero (TSS overlaps a CpG island).

Supplementary Figure 11 Dosage compensation in mouse.

Dosage compensation in mouse. (a) Mean expression values for each gene on the X chromosome are plotted for males (n = 39) versus females (n = 51). The 1:1 linear relationship indicates that, as expected, inactivation of one X chromosome in females equalizes expression levels between the sexes, with the exception of Xist. (b) For each of 90 animals (52 female, 39 male), a distribution of gene expression levels was generated for autosomal and X-chromosome genes separately. These distributions were then plotted against each other, with each line representing a mouse. The result was a roughly 1:1 relationship in the levels of expression from the autosomes and X chromosome.

Supplementary Figure 12 Examples of skewed X inactivation.

Representative examples of skewed X inactivation for two female animals. Plotted is the proportion of allele-specific reads assigned to each parent over the entire length of the X chromosome. The animal plotted on the top, a (CAST/EiJ × WSB/EiJ)F1 female, showed on average 76% of allele-specific reads derived from the strain with the stronger Xce allele, CAST/EiJ. The animal on the bottom, a (WSB/EiJ × CAST/EiJ)F1, showed on average 55% of allele-specific reads derived from CAST/EiJ. These examples underscore the importance of calculating individual-level null hypotheses that factor in stochastic and genetic contribution to X-inactivation skewing.

Supplementary Figure 13 Copy number variant identified by RNA-seq data.

Unusual clustering of allele-specific reads from Vti1b led to identification of a 250-kb duplication. (a) Allele-specific and (b) total read counts for Vti1b. For two crosses (PWK/PhJ × CAST/EiJ and WSB/EiJ × CAST/EiJ), allele-specific read counts for biological replicates formed two clusters (red circles) for several consecutive genes on chromosome 12. The pattern was consistent across genes, with an overrepresentation of CAST/EiJ alleles in certain animals, coinciding with an overall higher level of total gene expression. This highly unusual pattern suggested a CAST/EiJ duplication in this region. (c) Further analysis of RNA-seq data suggested a CAST duplication affecting at least five genes (colored in red). A total of 12 DNA samples (n = 6 presumed three copy, n = 6 presumed two copy) were then examined with a high-density SNP array. Probe intensity data were used to identify duplication spanning at least 239 kb and encompassing the entire coding region of the five genes with unusual expression data. The flanking genes Pigh and Plekhh1 are expressed in the brain but do not show increased expression in mice with the duplication, suggesting that the duplication did not include regulatory sequences necessary for expression of these genes.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13, Supplementary Tables 1–5 and Supplementary Note. (PDF 6288 kb)

Supplementary Data Set

Detailed gene-level statistical effects and list of 95 imprinted genes. (XLSX 11859 kb)

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Crowley, J., Zhabotynsky, V., Sun, W. et al. Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance. Nat Genet 47, 353–360 (2015). https://doi.org/10.1038/ng.3222

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing