Molecular Genetics and Genomics

, Volume 294, Issue 6, pp 1455–1462 | Cite as

Causal phenotypic networks for egg traits in an F2 chicken population

  • Tatsuhiko GotoEmail author
  • Arthur F. A. Fernandes
  • Masaoki TsudzukiEmail author
  • Guilherme J. M. Rosa
Original Article


Traditional single-trait genetic analyses, such as quantitative trait locus (QTL) and genome-wide association studies (GWAS), have been used to understand genotype–phenotype relationships for egg traits in chickens. Even though these techniques can detect potential genes of major effect, they cannot reveal cryptic causal relationships among QTLs and phenotypes. Thus, to better understand the relationships involving multiple genes and phenotypes of interest, other data analysis techniques must be used. Here, we utilized a QTL-directed dependency graph (QDG) mapping approach for a joint analysis of chicken egg traits, so that functional relationships and potential causal effects between them could be investigated. The QDG mapping identified a total of 17 QTLs affecting 24 egg traits that formed three independent networks of phenotypic trait groups (eggshell color, egg production, and size and weight of egg components), clearly distinguishing direct and indirect effects of QTLs towards correlated traits. For example, the network of size and weight of egg components contained 13 QTLs and 18 traits that are densely connected to each other. This indicates complex relationships between genotype and phenotype involving both direct and indirect effects of QTLs on the studied traits. Most of the QTLs were commonly identified by both the traditional (single-trait) mapping and the QDG approach. The network analysis, however, offers additional insight regarding the source and characterization of pleiotropy affecting egg traits. As such, the QDG analysis provides a substantial step forward, revealing cryptic relationships among QTLs and phenotypes, especially regarding direct and indirect QTL effects as well as potential causal relationships between traits, which can be used, for example, to optimize management practices and breeding strategies for the improvement of the traits.


Causal network Chicken Egg traits Genetic architecture Pleiotropy 



We thank Dr. Toshihiro Okamura for analytic support.


This work was supported in part by Research Center for Global Agromedicine in Obihiro University of Agriculture and Veterinary Medicine to T.G.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. This article does not contain any studies with human participants performed by any of the authors.

Supplementary material

438_2019_1588_MOESM1_ESM.doc (62 kb)
Supplementary material 1 (DOC 61 kb)


  1. Albert FW, Kruglyak L (2015) The role of regulatory variation in complex traits and disease. Nat Rev Genet 16:197–212CrossRefGoogle Scholar
  2. Andersson L, Georges M (2004) Domestic-animal genomics: deciphering the genetics of complex traits. Nat Rev Genet 5:202–212CrossRefGoogle Scholar
  3. Broman KW, Sen S (2009) A guide to QTL mapping with R/qtl. Springer, New YorkCrossRefGoogle Scholar
  4. Chaibub Neto E, Ferrara CT, Attie AD, Yandell BS (2008) Inferring causal phenotype networks from segregating populations. Genetics 179:1089–1100CrossRefGoogle Scholar
  5. Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010) Causal graphical models in systems genetics: A unified framework for joint inference of causal network and genetic architecture for correlated phenotypes. Ann Appl Stat 4:320–339CrossRefGoogle Scholar
  6. Ellegren H (2010) Evolutionary stasis: the stable chromosomes of birds. Trends Ecol Evol 25:283–291CrossRefGoogle Scholar
  7. FAO (2013) Poultry development review. The United Nations, RomeGoogle Scholar
  8. Felipe VP, Silva MA, Valente BD, Rosa GJ (2015) Using multiple regression, Bayesian networks and artificial neural networks for prediction of total egg production in European quails based on earlier expressed phenotypes. Poult Sci 94:772–780CrossRefGoogle Scholar
  9. Goto T, Tsudzuki M (2017) Genetic mapping of quantitative trait loci for egg production and egg quality traits in chickens: a review. J Poult Sci 54:1–12CrossRefGoogle Scholar
  10. Goto T, Ishikawa A, Onitsuka S, Goto N, Fujikawa Y et al (2011) Mapping quantitative trait loci for egg production traits in an F2 intercross of Oh-Shamo and White Leghorn chickens. Anim Genet 42:634–641CrossRefGoogle Scholar
  11. Goto T, Ishikawa A, Yoshida M, Goto N, Umino T et al (2014a) Quantitative trait loci mapping for external egg traits in F2 chickens. J Poult Sci 51:375–386Google Scholar
  12. Goto T, Ishikawa A, Goto N, Nishibori M, Umino T et al (2014b) Mapping of main-effect and epistatic quantitative trait loci for internal egg traits in an F2 resource population of chickens. J Poult Sci 51:118–129Google Scholar
  13. Goto T, Shiraishi J-i, Bungo T, Tsudzuki M (2015) Characteristics of egg-related traits in the Onagadori (Japanese Extremely Long Tail) breed of chickens. J Poult Sci 52:81–87CrossRefGoogle Scholar
  14. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324CrossRefGoogle Scholar
  15. Hu ZL, Park CA, Reecy JM (2016) Developmental progress and current status of the Animal QTLdb. Nucleic Acids Res 44:D827–D833CrossRefGoogle Scholar
  16. Ishishita S, Kinoshita K, Nakano M, Matsuda Y (2016) Embryonic development and inviability phenotype of chicken-Japanese quail F1 hybrids. Sci Rep 20:26369CrossRefGoogle Scholar
  17. Kim YA, Przytycka TM (2013) Bridging the gap between genotype and phenotype via network approaches. Front Genet 3:227CrossRefGoogle Scholar
  18. Li R, Tsaih SW, Shockley K, Stylianou IM, Wergedal J et al (2006) Structural model analysis of multiple quantitative traits. PLoS Genet 2:e114CrossRefGoogle Scholar
  19. Liao B, Qiao HG, Zhao XY, Bao M, Liu L et al (2013) Influence of eggshell ultrastructural organization on hatchability. Poult Sci 92:2236–2239CrossRefGoogle Scholar
  20. Mackay TFC (2014) Epistasis and quantitative traits: using model organisms to study gene-gene interactions. Nat Rev Genet 15:22–33CrossRefGoogle Scholar
  21. Osman SAM, Sekino M, Nishihata A, Kobayashi Y, Takenaka W et al (2006) The genetic variability and relationships of Japanese and foreign chickens assessed by microsatellite DNA profiling. Asian-Aust J Anim Sci 19:1369–1378CrossRefGoogle Scholar
  22. Penagaricano F, Valente BD, Steibel JP, Bates RO, Ernst CW et al (2015) Exploring causal networks underlying fat deposition and muscularity in pigs through the integration of phenotypic, genotypic and transcriptomic data. BMC Syst Biol 9:58CrossRefGoogle Scholar
  23. R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  24. Reynaud CA, Anquez V, Grimal H, Weill JC (1987) A hyperconversion mechanism generates the chicken light chain preimmune repertoire. Cell 48:379–388CrossRefGoogle Scholar
  25. Rosa GJ, Valente BD, de los Campos G, Wu XL, Gianola D et al (2011) Inferring causal phenotype networks using structural equation models. Genet Sel Evol 43:6CrossRefGoogle Scholar
  26. Schreiweis MA, Hester PY, Settar P, Moody DE (2006) Identification of quantitative trait loci associated with egg quality, egg production, and body weight in an F2 resource population of chickens. Anim Genet 37:106–112CrossRefGoogle Scholar
  27. Scutari M, Howell P, Balding DJ, Mackay I (2014) Multiple quantitative trait analysis using Bayesian networks. Genetics 198:129–137CrossRefGoogle Scholar
  28. Spirtes P, Glymour C, Scheines R (2000) Causation, prediction, and search adaptive computation and machine learning, 2nd edn. MIT Press, CambridgeGoogle Scholar
  29. Wang H, van Eeuwijk FA (2014) A new method to infer causal phenotype networks using QTL and phenotypic information. PLoS ONE 9:e103997CrossRefGoogle Scholar
  30. Wilson PB (2017) Recent advances in avian egg science: a review. Poult Sci 96:3747–3754CrossRefGoogle Scholar
  31. Wolc A, White IMS, Hill WG, Olori VE (2010) Inheritance of hatchability in broiler chickens and its relationship to egg quality traits. Poult Sci 89:2334–2340CrossRefGoogle Scholar
  32. Yang B, Navarro N, Noguera JL, Munoz M, Guo TF et al (2011) Building phenotype networks to improve QTL detection: a comparative analysis of fatty acid and fat traits in pigs. J Anim Breed Genet 128:329–343CrossRefGoogle Scholar
  33. Zhang LC, Ning ZH, Xu GY, Hou ZC, Yang N (2005) Heritabilities and genetic and phenotypic correlations of egg quality traits in brown-egg dwarf layers. Poult Sci 84:1209–1213CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Animal SciencesUniversity of Wisconsin-MadisonMadisonUSA
  2. 2.Research Center for Global Agromedicine, Obihiro University of Agriculture and Veterinary MedicineObihiroJapan
  3. 3.Department of Life and Food SciencesObihiro University of Agriculture and Veterinary MedicineObihiroJapan
  4. 4.Japanese Avian Bioresource Project Research Center, Hiroshima UniversityHigashihiroshimaJapan
  5. 5.Graduate School of Biosphere ScienceHiroshima UniversityHigashihiroshimaJapan
  6. 6.Department of Biostatistics and Medical InformaticsUniversity of Wisconsin-MadisonMadisonUSA

Personalised recommendations