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Causal phenotypic networks for egg traits in an F2 chicken population

Goto Tatsuhiko Fernandes Arthur F. A. Tsudzuki Masaoki Rosa Guilherme J. M. 帯広畜産大学

2020.12.01

概要

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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参考文献

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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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

...

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