Skip to main content

Multiparental Population in Crops: Methods of Development and Dissection of Genetic Traits

  • Protocol
  • First Online:
Crop Breeding

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2264))

Abstract

Multiparental populations are located midway between association mapping that relies on germplasm collections and classic linkage analysis, based upon biparental populations. They provide several key advantages such as the possibility to include a higher number of alleles and increased level of recombination with respect to biparental populations, and more equilibrated allelic frequencies than association mapping panels. Moreover, in these populations new allele’s combinations arise from recombination that may reveal transgressive phenotypes and make them a useful pre-breeding material. Here we describe the strategies for working with multiparental populations, focusing on nested association mapping populations (NAM) and multiparent advanced generation intercross populations (MAGIC). We provide details from the selection of founders, population development, and characterization to the statistical methods for genetic mapping and quantitative trait detection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Morrell PL, Buckler ES, Ross-Ibarra J (2012) Crop genomics: advances and applications. Nat Rev Genet 13:85–96

    Article  CAS  Google Scholar 

  2. Price AH (2006) Believe it or not, QTLs are accurate! Trends Plant Sci 11:213–216

    Article  CAS  PubMed  Google Scholar 

  3. Korte A, Farlow A (2013) The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 9:29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Visscher PM, Brown MA, McCarthy MI, Yang J (2012) Five years of GWAS discovery. Am J Hum Genet 90:7–24

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Churchill G, Airey DC, Allayee H, Angel JM, Attie AD, Beatty J et al (2004) The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat Genet 36:1133–1137

    Article  CAS  PubMed  Google Scholar 

  6. Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551

    Article  PubMed  PubMed Central  Google Scholar 

  7. Mackay I, Powell W (2007) Methods for linkage disequilibrium mapping in crops. Trends Plant Sci 12:57–63

    Article  CAS  PubMed  Google Scholar 

  8. Cavanagh C, Morell M, Mackay I, Powell W (2008) From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Curr Opin Plant Biol 11:215–221

    Article  CAS  PubMed  Google Scholar 

  9. Huang BE, Verbyla KL, Verbyla AP, Raghavan C, Singh VK, Gaur P et al (2015) MAGIC populations in crops: current status and future prospects. Theor Appl Genet 128:999–1017

    Article  PubMed  Google Scholar 

  10. Pascual L, Albert E, Sauvage C, Duangjit J, Bouchet J-P, Bitton F et al (2016) Dissecting quantitative trait variation in the resequencing era: complementarity of bi-parental, multi-parental and association panels. Plant Sci 242:120–130

    Article  CAS  PubMed  Google Scholar 

  11. Stich B (2009) Comparison of mating designs for establishing nested association mapping populations in maize and Arabidopsis thaliana. Genetics 183:1525–1534

    Article  PubMed  PubMed Central  Google Scholar 

  12. Bauer E, Falque M, Walter H et al (2013) Intraspecific variation of recombination rate in maize. Genome Biol 14:R103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Maurer A, Draba V, Jiang Y, Schnaithmann F, Sharma R, Schumann E et al (2015) Modelling the genetic architecture of flowering time control in barley through nested association mapping. BMC Genomics 16(1):290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Nice LM, Steffenson BJ, Brown-Guedira GL, Akhunov ED, Liu C, Kono TJY et al (2016) Development and genetic characterization of an advanced backcross-nested association mapping (AB-NAM) population of wild × cultivated barley. Genetics 203:1453

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Bajgain P, Rouse MN, Tsilo TJ, Macharia GK, Bhavani S, Jin Y et al (2016) Nested association mapping of stem rust resistance in wheat using genotyping by sequencing. PLoS One 11:e0155760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Fragoso CA, Moreno M, Wang Z, Heffelfinger C, Arbelaez LJ, Aguirre JA et al (2017) Genetic architecture of a rice nested association mapping population. G3 7:1913–1926

    Article  PubMed  PubMed Central  Google Scholar 

  17. Bouchet S, Olatoye MO, Marla SR, Perumal R, Tesso T, Yu J et al (2017) Increased power to dissect adaptive traits in global Sorghum diversity using a nested association mapping population. Genetics 206:573–585

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hu J, Guo C, Wang B, Ye J, Liu M, Wu Z et al (2018) Genetic properties of a nested association mapping population constructed with semi-winter and spring oilseed rapes. Front Plant Sci 9:1740

    Article  PubMed  PubMed Central  Google Scholar 

  19. Diers BW, Specht J, Rainey KM, Cregan P, Song Q, Ramasubramanian V et al (2018) Genetic architecture of soybean yield and agronomic traits. G3 8:3367–3375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Jordan KW, Wang S, He F, Chao S, Lun Y, Paux E et al (2018) The genetic architecture of genome-wide recombination rate variation in allopolyploid wheat revealed by nested association mapping. Plant J 95:1039–1054

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chen Q, Yang CJ, York AM, Xue W, Daskalska LL, DeValk CA et al (2019) TeoNAM: a nested association mapping population for domestication and agronomic trait analysis in maize. Genetics 213:1065–1078

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hemshrot A, Poets AM, Tyagi P, Lei L, Carter CK, Hirsch CN et al (2019) Development of a multiparent population for genetic mapping and allele discovery in six-row barley. Genetics 213:595–613

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Marla SR, Burow G, Chopra R, Hayes C, Olatoye MO, Felderhoff T et al (2019) Genetic architecture of chilling tolerance in Sorghum dissected with a nested association mapping population. G3 9:4045–4057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kidane YG, Gesesse CA, Hailemariam BN, Desta EA, Mengistu DK, Fadda C et al (2019) A large nested association mapping population for breeding and quantitative trait locus mapping in Ethiopian durum wheat. Plant Biotechnol J 17:1380–1393

    Article  PubMed  PubMed Central  Google Scholar 

  25. Thachuk C, Crossa J, Franco J, Dreisigacker S, Warburton M, Davenport GF (2009) Core Hunter: an algorithm for sampling genetic resources based on multiple genetic measure. BMC Bioinformatics 10:243

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Knott DR, Kumar J (1975) Comparison of early generation yield testing and a single seed descent procedure in wheat breeding. Crop Sci 15:295–299

    Article  Google Scholar 

  27. Guo B, Sleper DA, Beavis WD (2010) Nested association mapping for identification of functional markers. Genetics 186:373–383

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Klasen JR, Piepho HP, Stich B (2012) QTL detection power of multi-parental RIL populations in Arabidopsis thaliana. Heredity 108:626–632

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Li J, Bus A, Spamer V, Stich B (2016) Comparison of statistical models for nested association mapping in rapeseed (Brassica napus eL.) through computer simulations. BMC Plant Biol 16:26

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Griffing B (1956) Concept of general and specific combining ability in relation to diallel crossing systems. Aust J Biol Sci 9:463–493

    Article  Google Scholar 

  31. Poland JA, Brown PJ, Sorrells ME, Jannink J-L (2012) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS One 7:e32253

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sansaloni C, Petroli C, Jaccoud D et al (2011) Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high throughput, highly informative genotyping for molecular breeding of Eucalyptus. BMC Proc 5:P54

    Article  PubMed Central  Google Scholar 

  33. McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q et al (2009) Genetic properties of the maize nested association mapping population. Science 325:737–740

    Article  CAS  PubMed  Google Scholar 

  34. Guo B, Beavis WD (2011) In silico genotyping of the maize nested association mapping population. Mol Breed 27:107–113

    Article  PubMed  Google Scholar 

  35. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES et al (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6:e1937

    Article  CAS  Google Scholar 

  36. Zan Y, Payen T, Lillie M, Honaker CF, Siegel PB, Carlborg O (2019) Genotyping by low-coverage whole-genome sequencing in intercross pedigrees from outbred founders: a cost-efficient approach. Genet Select Evol 51:44

    Article  CAS  Google Scholar 

  37. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635

    Article  CAS  PubMed  Google Scholar 

  38. Islam MS, Thyssen GN, Jenkins JN, Zeng L, Delhom CD, McCarty JC et al (2016) A MAGIC population-based genome-wide association study reveals functional association of GhRBB1_A07 gene with superior fiber quality in cotton. BMC Genomics 17. https://doi.org/10.1186/s12864-016-3249-2

  39. Bandillo N, Raghavan C, Muyco PA, Sevilla MAL, Lobina IT, Dilla-Ermita CJ et al (2013) Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice 6:11

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ongom PO, Ejeta G (2018) Mating design and genetic structure of a multi-parent advanced generation intercross (MAGIC) population of Sorghum (Sorghum bicolor (L.) Moench). G3 8:331–341

    Article  CAS  PubMed  Google Scholar 

  41. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ et al (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397–2399

    Article  CAS  PubMed  Google Scholar 

  42. Naoumkina M, Thyssen GN, Fang DD, Jenkins JN, McCarty JC, Florane CB (2019) Genetic and transcriptomic dissection of the fiber length trait from a cotton (Gossypium hirsutum L) MAGIC population. BMC Genomics 20:112

    Article  PubMed  PubMed Central  Google Scholar 

  43. Butler D, Cullis B, Gilmour A, Gogel B (2007) ASRemlR reference manual. State of Queensland Department of Primary Industries and Fisheries

    Google Scholar 

  44. Giraud H, Bauland C, Falque M, Madur D, Combes V, Jamin P et al (2017) Linkage analysis and association mapping QTL detection models for hybrids between multiparental population from two heterotic groups: application to biomass production in maize (Zea mays L.). G3 7:3649–3657

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Giraud H, Bauland C, Falque M, Madur D, Combes V, Jamin P et al (2017) Reciprocal genetics: identifying QTL for general and specific combining abilities in hybrids between multiparental populations from two maize (Zea mays L.) heterotic groups. Genetics 207:1167–1180

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Mott R, Talbot CJ, Turri MG, Collins AC, Flint J (2000) A method for fine mapping quantitative trait loci in outbred animal stocks. Proc Natl Acad Sci U S A 97:12649–12654

    Article  PubMed  PubMed Central  Google Scholar 

  47. Huang BE, George AW, Forrest KL, Kilian A, Hayden MJ, Morell MK et al (2012) A multiparent advanced generation inter-cross population for genetic analysis in wheat. Plant Biotechnol J 10:826–839

    Article  CAS  PubMed  Google Scholar 

  48. Gnan S, Priest A, Kover PX (2014) The genetic basis of natural variation in seed size and seed number and their trade-off using Arabidopsis thaliana MAGIC lines. Genetics 198:1751

    Article  PubMed  PubMed Central  Google Scholar 

  49. Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MD et al (2009) A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet 5:e1000551

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Huang BE, George AW (2011) R/mpMap: a computational platform for the genetic analysis of multiparent recombinant inbred lines. Bioinformatics 27:727–729

    Article  CAS  PubMed  Google Scholar 

  51. Sannemann W, Huang BE, Mathew B, Leon J (2015) Multi-parent advanced generation inter-cross in barley: high-resolution quantitative trait locus mapping for flowering time as a proof of concept. Mol Breed 35:86

    Article  CAS  Google Scholar 

  52. Stadlmeier M, Hartl L, Mohler V (2018) Usefulness of a multiparent advanced generation intercross population with a greatly reduced mating design for genetic studies in winter wheat. Front Plant Sci 9:1825

    Article  PubMed  PubMed Central  Google Scholar 

  53. Pascual L, Desplat N, Huang BE, Desgroux A, Bruguier L, Bouchet J-P et al (2015) Potential of a tomato MAGIC population to decipher the genetic control of quantitative traits and detect causal variants in the resequencing era. Plant Biotechnol J 13:565–577

    Article  CAS  PubMed  Google Scholar 

  54. Huynh B-L, Ehlers JD, Huang BE, Munoz-Amatriain M, Lonardi S, Santos JRP et al (2018) A multi-parent advanced generation inter-cross (MAGIC) population for genetic analysis and improvement of cowpea (Vigna unguiculata L. Walp.). Plant J 93:1129–1142

    Article  CAS  PubMed  Google Scholar 

  55. Broman KW, Gatti DM, Simecek P, Furlotte NA, Prins P, Sen S et al (2019) R/qtl2: software for mapping quantitative trait loci with high-dimensional data and multiparent populations. Genetics 211:495–502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. de Jong M, Tavares H, Pasam RK, Butler R, Ward S, George G et al (2019) Natural variation in Arabidopsis shoot branching plasticity in response to nitrate supply affects fitness. PLoS Genet 15:e100836

    Google Scholar 

  57. Wei J, Xu S (2016) A random-model approach to QTL mapping in multiparent advanced generation intercross (MAGIC) populations. Genetics 202:471

    Article  CAS  PubMed  Google Scholar 

  58. Verbyla AP, George AW, Cavanagh CR, Verbyla KL (2014) Whole-genome QTL analysis for MAGIC. Theor Appl Genet 127:1753–1770

    Article  PubMed  Google Scholar 

  59. Verbyla AP, Cavanagh CR, Verbyla KL (2014) Whole-genome analysis of multienvironment or multitrait QTL in MAGIC. G3 4:1569–1584

    Article  PubMed  PubMed Central  Google Scholar 

  60. Zhang L, Meng L, Wang J (2019) Linkage analysis and integrated software GAPL for pure-line populations derived from four-way and eight-way crosses. Crop J 7:283–293

    Article  Google Scholar 

  61. Shi J, Wang J, Zhang L (2019) Genetic mapping with background control for quantitative trait locus (QTL) in 8-parental pure-line populations. J Hered 110:880–891

    Article  PubMed  PubMed Central  Google Scholar 

  62. Liu X, Huang M, Fan B, Buckler ES, Zhang Z (2016) Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet 12:e1005767

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Butron A, Santiago R, Cao A, Samayoa LF, Malvar RA (2019) QTLs for resistance to Fusarium ear rot in a multiparent advanced generation intercross (MAGIC) maize population. Plant Dis 103:897–904

    Article  CAS  PubMed  Google Scholar 

  64. Mackay IJ, Bansept-Basler P, Barber T, Bentley AR, Cockram J, Gosman N et al (2014) An eight-parent multiparent advanced generation inter-cross population for winter-sown wheat: creation, properties, and validation. G3 4:1603–1610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Sallam A, Martsch R (2015) Association mapping for frost tolerance using multi-parent advanced generation inter-cross (MAGIC) population in faba bean (Vicia faba L.). Genetica 143:501–514

    Article  PubMed  Google Scholar 

  66. Campanelli G, Sestili S, Acciarri N, Montemurro F, Palma D, Leteo F et al (2019) Multi-parental advances generation inter-cross population, to develop organic tomato genotypes by participatory plant breeding. Agronomy 9:119

    Article  Google Scholar 

  67. Meng L, Zhao X, Ponce K, Ye G, Leung H (2016) QTL mapping for agronomic traits using multi-parent advanced generation inter-cross (MAGIC) populations derived from diverse elite indica rice lines. Field Crops Res 189:19–42

    Article  Google Scholar 

  68. Tuinstra MR, Ejeta G, Goldsbrough PB (1997) Heterogeneous inbred family (HIF) analysis: a method for developing near-isogenic lines that differ at quantitative trait loci. Theor Appl Genet 95:1005–1011

    Article  CAS  Google Scholar 

  69. Goodwin S, McPherson JD, McCombie WR (2016) Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 17:333–351

    Article  CAS  PubMed  Google Scholar 

  70. Diouf IA, Derivot L, Bitton F, Pascual L, Causse M (2018) Water deficit and salinity stress reveal many specific QTL for plant growth and fruit quality traits in tomato. Front Plant Sci 9:279

    Article  PubMed  PubMed Central  Google Scholar 

  71. Ponce KS, Ye G, Zhao X (2018) QTL identification for cooking and eating quality in indica rice using multi-parent advanced generation intercross (MAGIC) population. Front Plant Sci 9:868

    Article  PubMed  PubMed Central  Google Scholar 

  72. Valente F, Gauthier F, Bardol N, Blanc G, Joets J, Charcosset A et al (2014) OptiMAS: a decision support tool to conduct marker-assisted selection programs. Crop Breed Methods Protoc 1145:97–116

    Article  CAS  Google Scholar 

  73. R Core Team (2019) R: a language and environment for statistical computing. In: R Foundation for Statistical Computing. Available via DIALOG. https://www.R-project.org/. Accessed 31 Jan 2020

  74. Gardner KA, Wittern LM, Mackay IJ (2016) A highly recombined, high-density, eight-founder wheat MAGIC map reveals extensive segregation distortion and genomic locations of introgression segments. Plant Biotechnol J 14:1406–1417

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Shah R, Huang E (2019) Map construction using multi-parent populations (Version v0.0.6). In: Zenodo. Available via DIALOG. https://doi.org/10.5281/zenodo.2613114. Accessed 31 Jan 2020

  76. Zheng C, Boer MP, van Eeuwijk FA (2019) Construction of genetic linkage maps in multiparental populations. Genetics 212:1031–1044

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Ogawa D, Yamamoto E, Ohtani T, Kanno N, Tsunematsu H, Nonoue Y et al (2018) Haplotype-based allele mining in the Japan-MAGIC rice population. Sci Rep 8:4379

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324

    Article  CAS  PubMed  Google Scholar 

  79. Li H, Bradbury P, Ersoz E, Buckler ES, Wang J (2011) Joint QTL linkage mapping for multiple-cross mating design sharing one common parent. PLoS One 6:e17573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S-Y, Freimer NB et al (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42:348–U110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Xavier A, Xu S, Muir WM, Rainey KM (2015) NAM: association studies in multiple populations. Bioinformatics 31:3862–3864

    CAS  PubMed  Google Scholar 

  82. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C et al (2009) The genetic architecture of maize flowering time. Science 325:714–718

    Article  CAS  PubMed  Google Scholar 

  83. Li H, Ye G, Wang J (2007) A modified algorithm for the improvement of composite interval mapping. Genetics 175:361–374

    Article  PubMed  PubMed Central  Google Scholar 

  84. Meng L, Li H, Zhang L, Wang J (2015) QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J 3:269–283

    Article  Google Scholar 

  85. SAS Institute (2011) SAS/STAT 9.3 user’s guide. SAS Institute Inc, Cary, NC, USA. Available via DIALOG. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.226.6407&rep=rep1&type=pdf. Accessed 31 Jan 2020

  86. Bian Y, Holland JB (2015) Ensemble learning of QTL models improves prediction of complex traits. G3 5:2073–2084

    Article  PubMed  PubMed Central  Google Scholar 

  87. Dietterich TG (2000) Ensemble methods in machine learning. In: International workshop on multiple classifier systems. Springer, Berlin, pp 1–15

    Google Scholar 

  88. Lehermeier C, Kramer N, Bauer E et al (2014) Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction. Genetics 198:3–16

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Laura Pascual .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Diouf, I., Pascual, L. (2021). Multiparental Population in Crops: Methods of Development and Dissection of Genetic Traits. In: Tripodi, P. (eds) Crop Breeding. Methods in Molecular Biology, vol 2264. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1201-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1201-9_2

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1200-2

  • Online ISBN: 978-1-0716-1201-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics