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.
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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.
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.
Chaibub Neto E, Ferrara CT, Attie AD, Yandell BS (2008) Inferring causal phenotype networks from segregating populations. Genetics 179:1089–1100CrossRefGoogle Scholar
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
Ellegren H (2010) Evolutionary stasis: the stable chromosomes of birds. Trends Ecol Evol 25:283–291CrossRefGoogle Scholar
FAO (2013) Poultry development review. The United Nations, RomeGoogle Scholar
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
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
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
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
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
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
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
Hu ZL, Park CA, Reecy JM (2016) Developmental progress and current status of the Animal QTLdb. Nucleic Acids Res 44:D827–D833CrossRefGoogle Scholar
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
Kim YA, Przytycka TM (2013) Bridging the gap between genotype and phenotype via network approaches. Front Genet 3:227CrossRefGoogle Scholar
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
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
Mackay TFC (2014) Epistasis and quantitative traits: using model organisms to study gene-gene interactions. Nat Rev Genet 15:22–33CrossRefGoogle Scholar
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
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
R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Reynaud CA, Anquez V, Grimal H, Weill JC (1987) A hyperconversion mechanism generates the chicken light chain preimmune repertoire. Cell 48:379–388CrossRefGoogle Scholar
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
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
Scutari M, Howell P, Balding DJ, Mackay I (2014) Multiple quantitative trait analysis using Bayesian networks. Genetics 198:129–137CrossRefGoogle Scholar
Spirtes P, Glymour C, Scheines R (2000) Causation, prediction, and search adaptive computation and machine learning, 2nd edn. MIT Press, CambridgeGoogle Scholar
Wang H, van Eeuwijk FA (2014) A new method to infer causal phenotype networks using QTL and phenotypic information. PLoS ONE 9:e103997CrossRefGoogle Scholar
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
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
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