Albert FW, Kruglyak L (2015) The role of regulatory variation in complex traits and
disease. Nat Rev Genet 16: 197-212
Andersson L, Georges M (2004) Domestic-animal genomics: deciphering the genetics
of complex traits. Nat Rev Genet 5: 202-212
Broman KW, Sen S (2009) A guide to QTL mapping with R/qtl. Springer, New York
Chaibub Neto E, Ferrara CT, Attie AD, Yandell BS (2008) Inferring causal phenotype
10
networks from segregating populations. Genetics 179: 1089-1100
Ellegren H (2010) Evolutionary stasis: the stable chromosomes of birds. Trends Ecol
Evol 25: 283-291
11
FAO (2013) Poultry Development Review. The United Nations, Rome
12
Felipe VP, Silva MA, Valente BD, Rosa GJ (2015) Using multiple regression, Bayesian
13
networks and artificial neural networks for prediction of total egg production in
14
European quails based on earlier expressed phenotypes. Poult Sci 94: 772-780
15
Goto T, Ishikawa A, Onitsuka S, Goto N, Fujikawa Y et al. (2011) Mapping
16
quantitative trait loci for egg production traits in an F2 intercross of Oh-Shamo and
17
White Leghorn chickens. Anim Genet 42: 634–641
18
19
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: 375–386
17
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: 118–129
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: 81-87
10
11
12
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: 1-12
Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait
loci in line crosses using flanking markers. Heredity 69: 315-324
Hu ZL, Park CA, Reecy JM (2016) Developmental progress and current status of the
Animal QTLdb. Nucleic Acids Res 44: D827–D833
13
Ishishita S, Kinoshita K, Nakano M, Matsuda Y (2016) Embryonic development and
14
inviability phenotype of chicken-Japanese quail F1 hybrids. Sci Rep 20: 26369
15
Kim YA, Przytycka TM (2013) Bridging the gap between genotype and phenotype via
16
17
18
network approaches. Front Genet 3: 227
Li R, Tsaih SW, Shockley K, Stylianou IM, Wergedal J et al. (2006) Structural model
analysis of multiple quantitative traits. PLoS Genet 2: e114
18
Liao B, Qiao HG, Zhao XY, Bao M, Liu L et al. (2013) Influence of eggshell
ultrastructural organization on hatchability. Poult Sci 92: 2236-2239
Mackay TFC (2014) Epistasis and quantitative traits: using model organisms to study
gene-gene interactions. Nat Rev Genet 15: 22–33
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: 58
10
11
12
13
14
R Core Team (2017) R: A language and environment for statistical computing. R
Foundation
for
Statistical
Computing,
Vienna,
Austria.
URL
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: 379-388
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: 6
15
Schreiweis MA, Hester PY, Settar P, Moody DE (2006) Identification of quantitative
16
trait loci associated with egg quality, egg production, and body weight in an F2
17
resource population of chickens. Anim Genet 37: 106-112
18
Scutari M, Howell P, Balding DJ, Mackay I (2014) Multiple quantitative trait analysis
19
using Bayesian networks. Genetics 198: 129-137
Spirtes P, Glymour C, Scheines R (2000) Causation, Prediction, and Search. Adaptive
Computation and Machine Learning, 2nd edition. MIT Press, Cambridge
Wilson PB (2017) Recent advances in avian egg science: A review. Poult Sci 96:
3747-3754
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: 2334-2340
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
10
traits in pigs. J Anim Breed Genet 128: 329-343
11
Zhang LC, Ning ZH, Xu GY, Hou ZC, Yang N (2005) Heritabilities and genetic and
12
phenotypic correlations of egg quality traits in brown-egg dwarf layers. Poult Sci
13
84: 1209-1213
14
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Figure Captions
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17
Fig. 1
18
Directed acyclic graph (DAG), with variables (nodes) connected by directed edges
19
(arrows). Trait and QTL nodes are indicated by white and gray circles, respectively.
20
Traits abbreviations are shown in the footnote of Table 1.
Causal phenotype networks for 24 egg traits in chickens
20
Table 1. Summary of differences of results between QDG and traditional QTL mapping
Trait1
AFE
EPR02
EPR03
EPR09
E_EW
E_LLE
E_LSE
E_SW
E_SS
E_STN
E_STB
E_STE
Chromosome
11
12
10
12
17
17
17
17
QTL position2
ADL0188
ADL0188
ADL0188
MCW0112
MCW0095
MCW0095
MCW0258
MCW0095
MCW0095
ABR0289
ABR0289
ABR0530
ABR0289
ABR0530
ABR0289
Indirect path3
Difference4
Non-effect
Non-effect
E-LSE <- E-EW <- E-LLE <- MCW0258
E-SW <- E-STE <- ABR0530
E-SW <- E-STE <- ABR0289
E-STN <- E-STE <- ABR0530
Direct QTL effect
Non-effect
Direct QTL effect
Direct QTL effect
Non-effect
Non-effect
Indirect QTL effect
Direct QTL effect
Indirect QTL effect
Indirect QTL effect
Non-effect
Direct QTL effect
Non-effect
Indirect QTL effect
Reference5
[1]
[1]
[1]
[1]
[1]
[2]
[2]
[2]
[2]
[2]
[2]
[2]
[2]
[2]
[2]
[2]
[2]
Direct QTL effect
[2]
[2]
[2]
21
Table 1
(continued)
E_SCR
E_SCY
E_AW
E_AH
E_LTA
E_STA
E_YW
E_YH
E_YL
E_YS
E_YCL
27
15
MCW0112
ABR0331
ADL0233
MCW0112
ABR0331
ADL0233
MCW0240
MCW0095
LEI0146
MCW0223
MCW0258
MCW0095
MCW0095
MCW0233
MCW0095
ABR0119
MCW0120
MCW0095
ADL0273
MCW0095
MCW0095
ADL0229
Non-effect
Non-effect
Non-effect
E-YH <- E-AH <- E-YCY <- ABR0622
Indirect QTL effect
Non-effect
22
[2]
[2]
[2]
[2]
[2]
[2]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
Table 1
(continued)
E_YCY
MCW0095
ABR0622
MCW0095
[3]
[3]
[3]
AFE = age at first egg, EPR02 = egg production rate during 26 to 30 weeks of age, EPR03 = egg production rate during 30 to 34
weeks of age, EPR09 = egg production rate during 54 to 58 weeks of age, E_: egg collected at early stage (first 10 eggs), EW= egg
weight, LLE = length of the long axis of the egg, LSE = length of the short axis of the egg, SW = eggshell weight, SS = eggshell strength,
STN = eggshell thickness at the narrow end of the egg, STB = eggshell thickness at the blunt end of the egg, STE = eggshell thickness
at the equator of the egg, SCR = redness of the eggshell color, SCY = yellowness of the eggshell color, AW = albumen weight, AH =
albumen height, LTA = length of the long axis of the thick albumen, STA = length of the short axis of the thick albumen, YW = yolk
weight, YH = yolk height, YL = length of the long axis of the yolk, YS = length of the short axis of the yolk, YCL = lightness of the yolk
color, YCY = yellowness of the yolk color.
QTL position was indicated by microsatellite marker name. Hyphen indicate no QTL was detected by QDG mapping.
Indirect path was detected by QDG mapping (Please see Fig. 1). QTL to the trait was indicated from right to left and was connected by
arrows.
4 Difference from previous reports (traditional QTL mappings) was listed. Direct QTL effect: direct effect was newly detected by QDG
mapping, Indirect QTL effect: indirect effect was newly detected by QDG mapping, Non-effect: no effect was detected by QDG mapping.
[1] Goto et al. 2011; [2] Goto et al. 2014a; [3] Goto et al. 2014b.
23
Fig. 1
24
Table S1. LOD score and direction of each edges.
No.
Node1
Direction
Node2
LOD
10
11
12
13
14
15
16
AFE
AFE
E_EW
E_EW
E_EW
E_LLE
E_SCR
E_SS
E_SW
E_SW
E_STN
E_STN
E_STB
E_AW
E_AH
E_AH
<--------->
<--------->
----->
<----<----<----<----<--------->
<--------->
----->
----->
----->
EPR02
EPR03
E_LLE
E_LSA
E_AW
E_LTA
E_SCY
E_STB
E_STE
E_YH
E_STB
E_STE
E_STE
E_STA
E_STA
E_YH
-2.83E-04
2.68E+01
-2.81E+01
8.18E-01
6.24E+01
-9.32E+00
-7.25E-02
-3.10E+01
-3.94E+01
-3.38E+01
4.25E+01
-4.38E+01
4.24E+01
5.55E+01
3.92E+01
1.76E+01
17
18
19
20
21
22
23
E_AH
E_LTA
E_YW
E_YW
E_YW
E_YL
E_YCL
<----<--------->
<--------->
<----<-----
E_YCY
E_STA
E_YH
E_YL
E_YS
E_YS
E_YCY
-6.41E-01
-4.98E+01
2.05E+01
-1.07E+02
1.09E+02
-1.09E+02
-1.20E+00
25
...