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Mammalian Genome

, Volume 29, Issue 9–10, pp 632–655 | Cite as

In silico mapping of quantitative trait loci (QTL) regulating the milk ionome in mice identifies a milk iron locus on chromosome 1

  • Darryl L. Hadsell
  • Louise A. Hadsell
  • Monique Rijnkels
  • Yareli Carcamo-Bahena
  • Jerry Wei
  • Peter Williamson
  • Michael A. Grusak
Article

Abstract

The breast-feeding neonate depends on mother’s milk for both macronutrients and micronutrients including minerals. The goals of the present study were to document the effects of genetic background in mice on milk concentrations of select minerals and to use genome-wide association study (GWAS) to identify quantitative trait loci (QTL) regulating milk mineral concentrations. Milk samples from lactating mice in each of 31 different inbred strains of the mouse diversity panel (MDP) were analyzed by inductively coupled plasma—optical emission spectroscopy to determine the concentrations of calcium (Ca), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), sodium (Na), phosphorus (P), sulfur (S), and zinc (Zn). GWAS identified a single pleiotropic milk mineral concentration QTL (Mmcq) on chromosome 3 for Ca, Mg, and P. For the remaining minerals, six QTL were detected for Fe, four for K, three for Zn, and one for S. Intersecting the Mmcq with published chromatin immunoprecipitation sequence data identified 15 out of 4633 high-linkage disequilibrium single-nucleotide polymorphisms that resided in signal transducer and activation of transcription 5 (STAT5) binding regions. A milk Fe-associated locus (Mmcq9) on chromosome 1 contained an SNP that localized to a STAT5 binding region and intersected with a HOMER motif predicted to bind the transcriptional regulator E74-Like ETS transcription factor 5. This locus also contained the genes for solute carrier family (Slc) members Slc9a2, Slc9a4, Slc39a10, and Slc40a1. Expression analysis of these transporters supports the conclusion that Slc9a2 and Slc40a1 within the mammary gland could mediate the effect of Mmcq9 on milk Fe concentration.

Notes

Acknowledgements

The authors thank Walter Olea for technical assistance. Thanks also to Mr. David Dworak for his assistance in the mineral analysis, Ms. Clarissa Aguilar for help with immunohistochemistry, Ms. Jessica Elswood for help with the EMSA, and Dr. Karen Triff for assistance with SNP and genomic feature analysis. This project was funded by USDA/ARS cooperative agreement # 250-51000-052. The contents of this publication do not necessarily reflect the views or policies of the U.S. Department of Agriculture, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Supplementary material

335_2018_9762_MOESM1_ESM.pdf (40 kb)
Supplementary material 1 (PDF 39 KB)
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Supplementary material 2 (PDF 43 KB)
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Supplementary material 3 (PDF 58 KB)
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Supplementary material 4 (PDF 63 KB)
335_2018_9762_MOESM5_ESM.xlsx (213 kb)
Online Resource 5. Mammary Expression of Genes underlying milk mineral QTL detected in the mouse diversity panel. (XLSX 212 KB)
335_2018_9762_MOESM6_ESM.tif (543 kb)
Online Resource 6. The figure shows an ideogram of the 19 autosomes along with X and Y chromosomes for the mouse. Colored symbols and bars on each chromosome show the location of each of the 15 murine Mmcq along with the locations of milk mineral QTL that were identified in dairy cows. To map cattle milk mineral QTL (MM-QTL) to the mouse genome, bovine coordinates were obtained from the Animal QTL db (https://www.animal genome.org/cgi-bin/QTLdb/BT/index). These intervals were extended 10 kb on each side and resulting overlapping MM-QTL regions were merged for each mineral. The resulting bovine genome (Btau6) intervals were then lifted over to mm10 to be visualized alongside the mouse Mmcq loci using Liftover tool on the UCSC Genome Browser (http://genome-test.cse.ucsc.edu/). The ideogram was constructed using Ideographica (http://rtools.cbrc.jp/idiographica/). (TIF 543 KB)
335_2018_9762_MOESM7_ESM.pdf (13 kb)
Supplementary material 7 (PDF 12 KB)

References

  1. Abrams SA (2011) What are the risks and benefits to increasing dietary bone minerals and vitamin D intake in infants and small children? Annu Rev Nutr 31:285–297CrossRefGoogle Scholar
  2. Ackert-Bicknell CL, Karasik D, Li Q, Smith RV, Hsu YH, Churchill GA, Paigen BJ, Tsaih SW (2010) Mouse BMD quantitative trait loci show improved concordance with human genome-wide association loci when recalculated on a new, common mouse genetic map. J Bone Miner Res 25:1808–1820CrossRefGoogle Scholar
  3. Acosta D, Bagchi S, Broin PO, Hollern D, Racedo SE, Morrow B, Sellers RS, Greally JM, Golden A, Andrechek E, Wood T, Montagna C (2016) LPA receptor activity is basal specific and coincident with early pregnancy and involution during mammary gland postnatal development. Sci Rep 6:35810CrossRefGoogle Scholar
  4. Afgan E, Baker D, van den Beek M, Blankenberg D, Bouvier D, Cech M, Chilton J, Clements D, Coraor N, Eberhard C, Gruning B, Guerler A, Hillman-Jackson J, Von Kuster G, Rasche E, Soranzo N, Turaga N, Taylor J, Nekrutenko A, Goecks J (2016) The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res 44:W3–W10CrossRefGoogle Scholar
  5. Alam S, Hennigar SR, Gallagher C, Soybel DI, Kelleher SL (2015) Exome sequencing of SLC30A2 identifies novel loss- and gain-of-function variants associated with breast cell dysfunction. J Mamm Gland Biol Neoplasia 20:159–172CrossRefGoogle Scholar
  6. Alkaissi H, Ekstrand J, Jawad A, Nielsen JB, Havarinasab S, Soderkvist P, Hultman P (2016) Genome-wide association study to identify genes related to renal mercury concentrations in mice. Environ Health Perspect 124:920–926CrossRefGoogle Scholar
  7. Anders S, Pyl PT, Huber W (2015) HTSeq–a python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169CrossRefGoogle Scholar
  8. Beamer WG, Shultz KL, Donahue LR, Churchill GA, Sen S, Wergedal JR, Baylink DJ, Rosen CJ (2001) Quantitative trait loci for femoral and lumbar vertebral bone mineral density in C57BL/6J and C3H/HeJ inbred strains of mice. J Bone Miner Res 16:1195–1206CrossRefGoogle Scholar
  9. Beamer WG, Shultz KL, Ackert-Bicknell CL, Horton LG, Delahunty KM, Coombs HF 3rd, Donahue LR, Canalis E, Rosen CJ (2007) Genetic dissection of mouse distal chromosome 1 reveals three linked BMD QTLs with sex-dependent regulation of bone phenotypes. J Bone Miner Res 22:1187–1196CrossRefGoogle Scholar
  10. Beamer WG, Shultz KL, Coombs HF 3rd, DeMambro VE, Reinholdt LG, Ackert-Bicknell CL, Canalis E, Rosen CJ, Donahue LR (2011) BMD regulation on mouse distal chromosome 1, candidate genes, and response to ovariectomy or dietary fat. J Bone Miner Res 26:88–99CrossRefGoogle Scholar
  11. Brunschwig H, Levi L, Ben-David E, Williams RW, Yakir B, Shifman S (2012) Fine-scale maps of recombination rates and hotspots in the mouse genome. Genetics 191:757–764CrossRefGoogle Scholar
  12. Buitenhuis B, Poulsen NA, Larsen LB, Sehested J (2015) Estimation of genetic parameters and detection of quantitative trait loci for minerals in Danish Holstein and Danish Jersey milk. BMC Genet 16:52CrossRefGoogle Scholar
  13. Burgess-Herbert SL, Tsaih SW, Stylianou IM, Walsh K, Cox AJ, Paigen B (2009) An experimental assessment of in silico haplotype association mapping in laboratory mice. BMC Genet 10:81CrossRefGoogle Scholar
  14. Cervino AC, Li G, Edwards S, Zhu J, Laurie C, Tokiwa G, Lum PY, Wang S, Castellini LW, Lusis AJ, Carlson S, Sachs AB, Schadt EE (2005) Integrating QTL and high-density SNP analyses in mice to identify Insig2 as a susceptibility gene for plasma cholesterol levels. Genomics 86:505–517CrossRefGoogle Scholar
  15. Chowanadisai W, Lonnerdal B, Kelleher SL (2006) Identification of a mutation in SLC30A2 (ZnT-2) in women with low milk zinc concentration that results in transient neonatal zinc deficiency. J Biol Chem 281:39699–39707CrossRefGoogle Scholar
  16. Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971PubMedPubMedCentralGoogle Scholar
  17. Davis RC, van Nas A, Bennett B, Orozco L, Pan C, Rau CD, Eskin E, Lusis AJ (2013) Genome-wide association mapping of blood cell traits in mice. Mamm Genome 24:105–118CrossRefGoogle Scholar
  18. DePeters EJ, Hovey RC (2009) Methods for collecting milk from mice. J Mamm Gland Biol Neoplasia 14:397–400CrossRefGoogle Scholar
  19. Donovan A, Lima CA, Pinkus JL, Pinkus GS, Zon LI, Robine S, Andrews NC (2005) The iron exporter ferroportin/Slc40a1 is essential for iron homeostasis. Cell Metabol 1:191–200CrossRefGoogle Scholar
  20. Dos Santos CO, Dolzhenko E, Hodges E, Smith AD, Hannon GJ (2015) An epigenetic memory of pregnancy in the mouse mammary gland. Cell Rep 11:1102–1109CrossRefGoogle Scholar
  21. Eide DJ, Clark S, Nair TM, Gehl M, Gribskov M, Guerinot ML, Harper JF (2005) Characterization of the yeast ionome: a genome-wide analysis of nutrient mineral and trace element homeostasis in Saccharomyces cerevisiae. Genome Biol 6:R77CrossRefGoogle Scholar
  22. Frazer KA, Eskin E, Kang HM, Bogue MA, Hinds DA, Beilharz EJ, Gupta RV, Montgomery J, Morenzoni MM, Nilsen GB, Pethiyagoda CL, Stuve LL, Johnson FM, Daly MJ, Wade CM, Cox DR (2007) A sequence-based variation map of 8.27 million SNPs in inbred mouse strains. Nature 448:1050–1053CrossRefGoogle Scholar
  23. Gaucheron F (2005) The minerals of milk. Reprod Nutr Dev 45:473–483CrossRefGoogle Scholar
  24. Ghazalpour A, Rau CD, Farber CR, Bennett BJ, Orozco LD, van Nas A, Pan C, Allayee H, Beaven SW, Civelek M, Davis RC, Drake TA, Friedman RA, Furlotte N, Hui ST, Jentsch JD, Kostem E, Kang HM, Kang EY, Joo JW, Korshunov VA, Laughlin RE, Martin LJ, Ohmen JD, Parks BW, Pellegrini M, Reue K, Smith DJ, Tetradis S, Wang J, Wang Y, Weiss JN, Kirchgessner T, Gargalovic PS, Eskin E, Lusis AJ, LeBoeuf RC (2012) Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits. Mamm Genome 23:680–692CrossRefGoogle Scholar
  25. Ghishan FK, Knobel SM, Summar M (1995) Molecular cloning, sequencing, chromosomal localization, and tissue distribution of the human Na+/H+ exchanger (SLC9A2). Genomics 30:25–30CrossRefGoogle Scholar
  26. Gibson JN, Jellen LC, Unger EL, Morahan G, Mehta M, Earley CJ, Allen RP, Lu L, Jones BC (2011) Genetic analysis of iron-deficiency effects on the mouse spleen. Mamm Genome 22:556–562CrossRefGoogle Scholar
  27. Griffin IJ, Abrams SA (2001) Iron and breastfeeding. Pediatr Clin N Am 48:401–413CrossRefGoogle Scholar
  28. Grupe A, Germer S, Usuka J, Aud D, Belknap JK, Klein RF, Ahluwalia MK, Higuchi R, Peltz G (2001) In silico mapping of complex disease-related traits in mice. Science 292:1915–1918CrossRefGoogle Scholar
  29. Hadsell DL, Wei J, Olea W, Hadsell LA, Renwick A, Thomson PC, Shariflou M, Williamson P (2012) In silico QTL mapping of maternal nurturing ability using the mouse diversity panel. Physiol Genom 44:787–798CrossRefGoogle Scholar
  30. Hadsell DL, Hadsell LA, Olea W, Rijnkels M, Creighton CJ, Smyth I, Short KM, Cox LL, Cox TC (2015) In-silico QTL mapping of postpubertal mammary ductal development in the mouse uncovers potential human breast cancer risk loci. Mamm Genome 26:57–79CrossRefGoogle Scholar
  31. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38:576–589CrossRefGoogle Scholar
  32. Heinz RE, Rudolph MC, Ramanathan P, Spoelstra NS, Butterfield KT, Webb PG, Babbs BL, Gao H, Chen S, Gordon MA, Anderson SM, Neville MC, Gu H, Richer JK (2016) Constitutive expression of microRNA-150 in mammary epithelium suppresses secretory activation and impairs de novo lipogenesis. Development 143:4236–4248CrossRefGoogle Scholar
  33. Hojsgaard SH, Halekoh U, Robinson-Cox J, Leidi AA (2013) doBy—groupwise summary statistics, general linerar constrasts, population means (least-squares-means), and other utilitiesGoogle Scholar
  34. Huang L, Gitschier J (1997) A novel gene involved in zinc transport is deficient in the lethal milk mouse. Nat Genet 17:292–297CrossRefGoogle Scholar
  35. Itsumura N, Inamo Y, Okazaki F, Teranishi F, Narita H, Kambe T, Kodama H (2013) Compound heterozygous mutations in SLC30A2/ZnT2 results in low milk zinc concentrations: a novel mechanism for zinc deficiency in a breast-fed infant. PLoS ONE 8:e64045CrossRefGoogle Scholar
  36. Jellen LC, Beard JL, Jones BC (2009) Systems genetics analysis of iron regulation in the brain. Biochimie 91:1255–1259CrossRefGoogle Scholar
  37. Jellen LC, Unger EL, Lu L, Williams RW, Rousseau S, Wang X, Earley CJ, Allen RP, Miles MF, Jones BC (2012) Systems genetic analysis of the effects of iron deficiency in mouse brain. Neurogenetics 13:147–157CrossRefGoogle Scholar
  38. Jin F, Ji C, Liu L, Dai J, Gu S, Sun X, Xie Y, Mao Y (2004) Molecular cloning and characterization of a novel human protein phosphatase 2C cDNA (PP2C epsilon*). Mol Biol Rep 31:197–202CrossRefGoogle Scholar
  39. Jones LC, McCarthy KA, Beard JL, Keen CL, Jones BC (2006) Quantitative genetic analysis of brain copper and zinc in BXD recombinant inbred mice. Nutr Neurosci 9:81–92CrossRefGoogle Scholar
  40. Jones LC, Beard JL, Jones BC (2008) Genetic analysis reveals polygenic influences on iron, copper, and zinc in mouse hippocampus with neurobiological implications. Hippocampus 18:398–410CrossRefGoogle Scholar
  41. Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723CrossRefGoogle Scholar
  42. Kelleher SL, Lonnerdal B (2003) Marginal maternal Zn intake in rats alters mammary gland Cu transporter levels and milk Cu concentration and affects neonatal Cu metabolism. J Nutr 133:2141–2148CrossRefGoogle Scholar
  43. Kelleher SL, Lonnerdal B (2005) Low vitamin a intake affects milk iron level and iron transporters in rat mammary gland and liver. J Nutr 135:27–32CrossRefGoogle Scholar
  44. Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357–360CrossRefGoogle Scholar
  45. Kin T, Ono Y (2007) Idiographica: a general-purpose web application to build idiograms on-demand for human, mouse and rat. Bioinformatics 23:2945–2946CrossRefGoogle Scholar
  46. Kirby A, Kang HM, Wade CM, Cotsapas CJ, Kostem E, Han B, Furlotte N, Kang EY, Rivas M, Bogue MA, Frazer KA, Johnson FM, Beilharz EJ, Cox DR, Eskin E, Daly MJ (2010) Fine mapping in 94 inbred mouse strains using a high-density haplotype resource. Genetics 185:1081–1095CrossRefGoogle Scholar
  47. Kohl M (2013) MKmisc: miscellaneous functions from M. KohlGoogle Scholar
  48. Krebs NF (1999) Zinc transfer to the breastfed infant. J Mammary Gland Biol Neoplasia 4:259–268CrossRefGoogle Scholar
  49. Krebs NF, Reidinger CJ, Hartley S, Robertson AD, Hambidge KM (1995) Zinc supplementation during lactation: effects on maternal status and milk zinc concentrations. Am J Clin Nutr 61:1030–1036CrossRefGoogle Scholar
  50. Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4:1073–1081CrossRefGoogle Scholar
  51. Kumar L, Michalczyk A, McKay J, Ford D, Kambe T, Hudek L, Varigios G, Taylor PE, Ackland ML (2015) Altered expression of two zinc transporters, SLC30A5 and SLC30A6, underlies a mammary gland disorder of reduced zinc secretion into milk. Genes Nutr 10:487CrossRefGoogle Scholar
  52. Lahner B, Gong J, Mahmoudian M, Smith EL, Abid KB, Rogers EE, Guerinot ML, Harper JF, Ward JM, McIntyre L, Schroeder JI, Salt DE (2003) Genomic scale profiling of nutrient and trace elements in Arabidopsis thaliana. Nat Biotechnol 21:1215–1221CrossRefGoogle Scholar
  53. Lee HJ, Ormandy CJ (2012) Elf5, hormones and cell fate. Trends Endocrinol Metabol 23:292–298CrossRefGoogle Scholar
  54. Lonnerdal B (1998) Copper nutrition during infancy and childhood. Am J Clin Nutr 67:1046S–1053SCrossRefGoogle Scholar
  55. Lonnerdal B (2007) Trace element transport in the mammary gland. Annu Rev Nutr 27:165–177CrossRefGoogle Scholar
  56. Lymboussaki A, Pignatti E, Montosi G, Garuti C, Haile DJ, Pietrangelo A (2003) The role of the iron responsive element in the control of ferroportin1/IREG1/MTP1 gene expression. J Hepatol 39:710–715CrossRefGoogle Scholar
  57. Ma S, Lee SG, Kim EB, Park TJ, Seluanov A, Gorbunova V, Buffenstein R, Seravalli J, Gladyshev VN (2015) Organization of the mammalian ionome according to organ origin, lineage specialization, and longevity. Cell Rep 13:1319–1326CrossRefGoogle Scholar
  58. McLachlan S, Lee SM, Steele TM, Hawthorne PL, Zapala MA, Eskin E, Schork NJ, Anderson GJ, Vulpe CD (2011) In silico QTL mapping of basal liver iron levels in inbred mouse strains. Physiol Genom 43:136–147CrossRefGoogle Scholar
  59. Metser G, Shin HY, Wang C, Yoo KH, Oh S, Villarino AV, O’Shea JJ, Kang K, Hennighausen L (2016) An autoregulatory enhancer controls mammary-specific STAT5 functions. Nucleic Acids Res 44:1052–1063CrossRefGoogle Scholar
  60. Michalczyk AA, Rieger J, Allen KJ, Mercer JF, Ackland ML (2000) Defective localization of the Wilson disease protein (ATP7B) in the mammary gland of the toxic milk mouse and the effects of copper supplementation. Biochem J 352(Pt 2):565–571CrossRefGoogle Scholar
  61. Miller BH, Schultz LE, Gulati A, Su AI, Pletcher MT (2010) Phenotypic characterization of a genetically diverse panel of mice for behavioral despair and anxiety. PLoS ONE 5, e14458CrossRefGoogle Scholar
  62. Muslimatun S, Schmidt MK, West CE, Schultink W, Hautvast JG, Karyadi D (2001) Weekly vitamin A and iron supplementation during pregnancy increases vitamin A concentration of breast milk but not iron status in Indonesian lactating women. J Nutr 131:2664–2669CrossRefGoogle Scholar
  63. Neuwirth E (2007) RColorBrewer: ColorBrewer palettes. R package version 1.0–2Google Scholar
  64. Neville AM, Zhang P, Allen JC (1995) Minerals, ions, and trace elements in milk. In: Jensen RG (ed) Handbook of milk composition. Academic Press, San DiegoGoogle Scholar
  65. Oftedal OT (2012) The evolution of milk secretion and its ancient origins. Animal 6:355–368CrossRefGoogle Scholar
  66. Orlowski J, Kandasamy RA, Shull GE (1992) Molecular cloning of putative members of the Na/H exchanger gene family. cDNA cloning, deduced amino acid sequence, and mRNA tissue expression of the rat Na/H exchanger NHE-1 and two structurally related proteins. J Biol Chem 267:9331–9339PubMedGoogle Scholar
  67. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP (2004) Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise correlations. Biotechnol Lett 26:509–515CrossRefGoogle Scholar
  68. Piletz JE, Ganschow RE (1978) Zinc deficiency in murine milk underlies expression of the lethal milk (lm) mutation. Science 199:181–183CrossRefGoogle Scholar
  69. Pletcher MT, McClurg P, Batalov S, Su AI, Barnes SW, Lagler E, Korstanje R, Wang X, Nusskern D, Bogue MA, Mural RJ, Paigen B, Wiltshire T (2004) Use of a dense single nucleotide polymorphism map for in silico mapping in the mouse. PLoS Biol 2, e393CrossRefGoogle Scholar
  70. Przybylkowski A, Gromadzka G, Wawer A, Bulska E, Jablonka-Salach K, Grygorowicz T, Schnejder-Pacholek A, Czlonkowski A (2013) Neurochemical and behavioral characteristics of toxic milk mice: an animal model of Wilson’s disease. Neurochem Res 38:2037–2045CrossRefGoogle Scholar
  71. Qian L, Wang B, Tang N, Zhang W, Cai W (2012) Polymorphisms of SLC30A2 and selected perinatal factors associated with low milk zinc in Chinese breastfeeding women. Early Hum Dev 88:663–668CrossRefGoogle Scholar
  72. Ramakrishnan SK, Anderson ER, Martin A, Centofanti B, Shah YM (2015) Maternal intestinal HIF-2alpha is necessary for sensing iron demands of lactation in mice. Proc Natl Acad Sci USA 112, E3738–E3747CrossRefGoogle Scholar
  73. Rauch H (1983) Toxic milk, a new mutation affecting cooper metabolism in the mouse. J Hered 74:141–144CrossRefGoogle Scholar
  74. Rauch H, Wells AJ (1995) The toxic milk mutation, tx, which results in a condition resembling Wilson disease in humans, is linked to mouse chromosome 8. Genomics 29:551–552CrossRefGoogle Scholar
  75. Reinhardt TA, Lippolis JD, Shull GE, Horst RL (2004) Null mutation in the gene encoding plasma membrane Ca2+-ATPase isoform 2 impairs calcium transport into milk. J Biol Chem 279:42369–42373CrossRefGoogle Scholar
  76. Rijnkels M, Freeman-Zadrowski C, Hernandez J, Potluri V, Wang L, Li W, Lemay DG (2013) Epigenetic modifications unlock the milk protein gene loci during mouse mammary gland development and differentiation. PLoS ONE 8, e53270CrossRefGoogle Scholar
  77. Shawki A, Engevik MA, Kim RS, Knight PB, Baik RA, Anthony SR, Worrell RT, Shull GE, Mackenzie B (2016) Intestinal brush-border Na+/H+ exchanger-3 drives H+-coupled iron absorption in the mouse. Ame J Physiol Gastrointest Liver Physiol 311:G423–G430CrossRefGoogle Scholar
  78. Shin HY, Willi M, Yoo KH, Zeng X, Wang C, Metser G, Hennighausen L (2016) Hierarchy within the mammary STAT5-driven Wap super-enhancer. Nat Genet 48:904–911CrossRefGoogle Scholar
  79. Stevens GA, Finucane MM, De-Regil LM, Paciorek CJ, Flaxman SR, Branca F, Pena-Rosas JP, Bhutta ZA, Ezzati M, Nutrition Impact Model Study Group (2013) Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: a systematic analysis of population-representative data. Lancet Global Health 1:e16–e25CrossRefGoogle Scholar
  80. Tang PL, Cheung CL, Sham PC, McClurg P, Lee B, Chan SY, Smith DK, Tanner JA, Su AI, Cheah KS, Kung AW, Song YQ (2009) Genome-wide haplotype association mapping in mice identifies a genetic variant in CER1 associated with BMD and fracture in southern Chinese women. J Bone Miner Res 24:1013–1021CrossRefGoogle Scholar
  81. Taylor KM, Nicholson RI (2003) The LZT proteins; the LIV-1 subfamily of zinc transporters. Biochim Biophys Acta 1611:16–30CrossRefGoogle Scholar
  82. Team RDC (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  83. Thomas-Chollier M, Hufton A, Heinig M, O’Keeffe S, Masri NE, Roider HG, Manke T, Vingron M (2011) Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs. Nat Protoc 6:1860–1869CrossRefGoogle Scholar
  84. van Hulzen KJ, Sprong RC, van der Meer R, van Arendonk JA (2009) Genetic and nongenetic variation in concentration of selenium, calcium, potassium, zinc, magnesium, and phosphorus in milk of Dutch Holstein-Friesian cows. J Dairy Sci 92:5754–5759CrossRefGoogle Scholar
  85. Van Eenennaam AL, Weigel KA, Young AE, Cleveland MA, Dekkers JC (2014) Applied animal genomics: results from the field. Annu Rev Anim Biosci 2:105–139CrossRefGoogle Scholar
  86. Vuori E, Makinen SM, Kara R, Kuitunen P (1980) The effects of the dietary intakes of copper, iron, manganese, and zinc on the trace element content of human milk. Am J Clin Nutr 33:227–231CrossRefGoogle Scholar
  87. Warnes G, Bolker B, Lumley T (2007) gplots: various R programming tools for plotting data. R package version 2.6.0Google Scholar
  88. Weller JI, Ron M (2011) Invited review: quantitative trait nucleotide determination in the era of genomic selection. J Dairy Sci 94:1082–1090CrossRefGoogle Scholar
  89. Xiong Q, Jiao Y, Hasty KA, Canale ST, Stuart JM, Beamer WG, Deng HW, Baylink D, Gu W (2009) Quantitative trait loci, genes, and polymorphisms that regulate bone mineral density in mouse. Genomics 93:401–414CrossRefGoogle Scholar
  90. Yamaji D, Kang K, Robinson GW, Hennighausen L (2013) Sequential activation of genetic programs in mouse mammary epithelium during pregnancy depends on STAT5A/B concentration. Nucleic Acids Res 41:1622–1636CrossRefGoogle Scholar
  91. Yin L, Unger EL, Jellen LC, Earley CJ, Allen RP, Tomaszewicz A, Fleet JC, Jones BC (2012) Systems genetic analysis of multivariate response to iron deficiency in mice. Am J Physiol Regulat Integr Comp Physiol 302:R1282–R1296CrossRefGoogle Scholar
  92. Yue F, Cheng Y, Breschi A, Vierstra J, Wu W, Ryba T, Sandstrom R, Ma Z, Davis C, Pope BD, Shen Y, Pervouchine DD, Djebali S, Thurman RE, Kaul R, Rynes E, Kirilusha A, Marinov GK, Williams BA, Trout D, Amrhein H, Fisher-Aylor K, Antoshechkin I, DeSalvo G, See LH, Fastuca M, Drenkow J, Zaleski C, Dobin A, Prieto P, Lagarde J, Bussotti G, Tanzer A, Denas O, Li K, Bender MA, Zhang M, Byron R, Groudine MT, McCleary D, Pham L, Ye Z, Kuan S, Edsall L, Wu YC, Rasmussen MD, Bansal MS, Kellis M, Keller CA, Morrissey CS, Mishra T, Jain D, Dogan N, Harris RS, Cayting P, Kawli T, Boyle AP, Euskirchen G, Kundaje A, Lin S, Lin Y, Jansen C, Malladi VS, Cline MS, Erickson DT, Kirkup VM, Learned K, Sloan CA, Rosenbloom KR, Lacerda de Sousa B, Beal K, Pignatelli M, Flicek P, Lian J, Kahveci T, Lee D, Kent WJ, Ramalho Santos M, Herrero J, Notredame C, Johnson A, Vong S, Lee K, Bates D, Neri F, Diegel M, Canfield T, Sabo PJ, Wilken MS, Reh TA, Giste E, Shafer A, Kutyavin T, Haugen E, Dunn D, Reynolds AP, Neph S, Humbert R, Hansen RS, De Bruijn M, Selleri L, Rudensky A, Josefowicz S, Samstein R, Eichler EE, Orkin SH, Levasseur D, Papayannopoulou T, Chang KH, Skoultchi A, Gosh S, Disteche C, Treuting P, Wang Y, Weiss MJ, Blobel GA, Cao X, Zhong S, Wang T, Good PJ, Lowdon RF, Adams LB, Zhou XQ, Pazin MJ, Feingold EA, Wold B, Taylor J, Mortazavi A, Weissman SM, Stamatoyannopoulos JA, Snyder MP, Guigo R, Gingeras TR, Gilbert DM, Hardison RC, Beer MA, Ren B, Mouse EC (2014) A comparative encyclopedia of DNA elements in the mouse genome. Nature 515:355–364CrossRefGoogle Scholar
  93. Zhang DL, Hughes RM, Ollivierre-Wilson H, Ghosh MC, Rouault TA (2009) A ferroportin transcript that lacks an iron-responsive element enables duodenal and erythroid precursor cells to evade translational repression. Cell Metabol 9:461–473CrossRefGoogle Scholar
  94. Zhao A, Ning Y, Zhang Y, Yang X, Wang J, Li W, Wang P (2014) Mineral compositions in breast milk of healthy Chinese lactating women in urban areas and its associated factors. Chin Med J (Engl) 127:2643–2648Google Scholar

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Authors and Affiliations

  1. 1.Department of Pediatrics, USDA/ARS Children’s Nutrition Research CenterBaylor College of MedicineHoustonUSA
  2. 2.Department of Molecular and Cellular BiologyBaylor College of MedicineHoustonUSA
  3. 3.Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical SciencesTexas A&M UniversityCollege StationUSA
  4. 4.Department of Medical GenomicsRoyal Prince Alfred HospitalSydneyAustralia
  5. 5.School of Veterinary ScienceUniversity of SydneySydneyAustralia
  6. 6.School of life and Environmental SciencesUniversity of SydneySydneyAustralia
  7. 7.USDA/ARS Red River Valley Agricultural Research CenterFargoUSA
  8. 8.Department of PediatricsUSDA/ARS Children’s Nutrition Research CenterHoustonUSA

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