In-silico QTL mapping of postpubertal mammary ductal development in the mouse uncovers potential human breast cancer risk loci


Genetic background plays a dominant role in mammary gland development and breast cancer (BrCa). Despite this, the role of genetics is only partially understood. This study used strain-dependent variation in an inbred mouse mapping panel, to identify quantitative trait loci (QTL) underlying structural variation in mammary ductal development, and determined if these QTL correlated with genomic intervals conferring BrCa susceptibility in humans. For about half of the traits, developmental variation among the complete set of strains in this study was greater (P < 0.05) than that of previously studied strains, or strains in current common use for mammary gland biology. Correlations were also detected with previously reported variation in mammary tumor latency and metastasis. In-silico genome-wide association identified 20 mammary development QTL (Mdq). Of these, five were syntenic with previously reported human BrCa loci. The most significant (P = 1 × 10−11) association of the study was on MMU6 and contained the genes Plxna4, Plxna4os1, and Chchd3. On MMU5, a QTL was detected (P = 8 × 10−7) that was syntenic to a human BrCa locus on h12q24.5 containing the genes Tbx3 and Tbx5. Intersection of linked SNP (r2 > 0.8) with genomic and epigenomic features, and intersection of candidate genes with gene expression and survival data from human BrCa highlighted several for further study. These results support the conclusion that mammary tumorigenesis and normal ductal development are influenced by common genetic factors and that further studies of genetically diverse mice can improve our understanding of BrCa in humans.

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The authors thank Ms. Elizabeth Lessels for her contribution to the initial phenotyping. Thanks also to Dr. Daniel Gatti for providing advice on the association analysis and for providing R scripts used in processing the SNP dataset in this analysis. Thanks also to Mathew LaCourse for technical assistance related to optical projection tomography, to Dr. John Belmont for providing computing resources for the permutation analysis, and to Fengju Chen for technical assistance related to the mining the human tumor gene expression data. Thanks also to Dr. Jeff Rosen for helpful discussion involving the interpretation of the results, and for valuable suggestions on the manuscript. This Project was supported by NICHD Grant Number 5R21HD059746 (Darryl Hadsell), by USDA/ARS Cooperative Agreement No. 6250-51000-052 (Darryl Hadsell), and by NCI Grant Number P30 CA125123 (Chad Creighton). Ian Smyth holds a Future Fellowship from the Australian Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Correspondence to Darryl L. Hadsell.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Video S1. Mammary ductal reconstruction in 3D illustrating local patterning in the CZECHII/EiJ stain at 6 week of age. This video is representative of independent samples from at least 6 animals. (MOV 5418 kb)

Video S2. Mammary ductal reconstruction in 3D illustrating local patterning in the KK/HlJ stain at 6 week of age. This video is representative of independent samples from at least 6 animals. (MOV 5878 kb)

Figure S1. Image processing steps used in the measurement of quantitative ductal development traits. Wholemount images (A) are processed through a series of steps that allow for the creation of background image (B), a background-subtracted image (C), a manually trimmed, background-subtracted image (D), and a binary image used for detection and measurement of the ductal tree (E). The binary image was then skeletonized (F) so that all ductal segments and branch points could be counted and measured. (TIFF 4767 kb)

Figure S2. Measuring quantitative traits for mammary ductal development in mouse mammary gland wholemounts. Mammary wholemounts are stained with hematoxylin and imaged producing images of (A) a fat pad containing a lymph node (purple oval), ductal epithelium (thick black lines). These images are then processed to segment out all components but the ductal tree (B). Ductal area is measured in square millimeters by measuring the number of pixels that occupy the ductal tree and the using a distance conversion based on a size reference. Ductal Perimeter (C) is measured in millimeters and represents the length (orange) of the line that completely surrounds the ductal tree. The ductal tree is then eroded to a single pixel width skeleton (D, orange) and branch points are identified (black squares). This skeleton was used to count total branches and to measure total ductal length. Branch density was then calculated as the ratio of branch counts to total ductal length. (TIFF 279 kb)

Table S1. Descriptive statistics, broad-sense heritabilities, and statistical comparisons among individual strains. This table provides an indication of the variability among and within strains and provides a means for differentiating the strains from the population mean through a one-sample T test. (PDF 60 kb)

Table S2. Comparison of variance estimates for mammary gland development among strains sets. This table compares the variance estimates for 15 ductal development traits among the complete set of 43 strains and subset of these strains that would be consider either classical strains or common current strains in mammary gland biology and indicates significant differences (P < 0.05) among the three sets of strains. (PDF 45 kb)

Table S3. Analysis of long-range LD between 20 QTL regions associated with mammary ductal development in the MDP. This table gives the r2 for the lead SNP presented in Table 1. (PDF 48 kb)

Table S4. Amino acid substitutions resulting from high-LD SNP associated with mammary ductal development QTL. A total of 9 high-LD SNP were found to produce non-synonymous substitutions in the coding regions of 5 genes. This table shows the predicted consequences of these substitutions. (PDF 89 kb)

Table S5. Overlap of high-LD SNP in the 3′ UTR with miR target sites. This table shows miR targets sites that overlap in the 3′UTR of candidate genes. (PDF 93 kb)

Table S6. Overlap of high-LD SNPs with H3K4me2, STAT5, PgR, and HOMER motifs. This table shows the presence of overlaps between STAT5 and PgR binding sites and HOMER Motifs. (PDF 47 kb)

Video S1. Mammary ductal reconstruction in 3D illustrating local patterning in the CZECHII/EiJ stain at 6 week of age. This video is representative of independent samples from at least 6 animals. (MOV 5418 kb)

Video S2. Mammary ductal reconstruction in 3D illustrating local patterning in the KK/HlJ stain at 6 week of age. This video is representative of independent samples from at least 6 animals. (MOV 5878 kb)

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Hadsell, D.L., Hadsell, L.A., Olea, W. et al. In-silico QTL mapping of postpubertal mammary ductal development in the mouse uncovers potential human breast cancer risk loci. Mamm Genome 26, 57–79 (2015).

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  • Quantitative Trait Locus
  • Mammary Gland
  • Mouse Mammary Tumor Virus
  • Linkage Disequilibrium Block
  • Inbred Mouse Strain