Genome-wide association study of body composition traits ... · PDF filecomposition traits in chicken ... feet, blood, feather, leg, abdominal fat (ABFat), viscera (liver, gizzard

285 J. Chin. Soc. Anim. Sci. 45(4): 285~299, 2016

Genome-wide association study of body composition traits in chicken

Ching-Yi Lien(1)(2)(3), Michèle Tixier-Boichard(1), Shih-Wen Wu(4), Fa-Jui Tan(2) and Chih-Feng Chen(2)(5)(6)

ABSTRACT

Meat yield is an important economic trait for poultry production. The present study aims at the iden-

tification of significant single nucleotide polymorphism (SNP) effect associated with body composition

traits in chickens. An F2 population was produced by crossing the Taiwan Country Chicken L2 line (se-

lected for body weight, size of comb area and egg production) with the experimental line of Rhode Island

Red layer R- (selected for low residual feed consumption). A total of 157 F2 males were genotyped with

the 60K Illumina iSelect SNP chip. Genome-wide association study (GWAS) was performed for 21 body

composition traits measured at 23 weeks of age. Furthermore, functional annotation of causative genes was

used to identify relevant genes and corresponding SNPs within chromosomal regions. Whole genome link-

age analysis led to identifying 23 SNP effects for 7 carcass traits (abdominal fat, feather, feet, gizzard, in-

testine, breast skin, and testis weight) with 5% Bonferroni genome-wide significance (P < 6.20×10-6), and

a total of 225 SNP effects reached suggestive significance (P < 1.24×10-4). Possible candidate genes such

as SOX10 for body composition traits were identified. Genome-wide association study made it possible to

identify amounts of SNPs associated with relevant genes for recorded traits. Quantitative trait locus (QTL)

mapping should be applied for following analysis to confirm the association between QTLs and measured

traits in chicken.

(Key Words: Body composition, Chicken, Genome-wide association study, Single nucleotide

polymorphism)

(1) GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France.(2) Department of Animal Science, National Chung Hsing University, 145 Xingda Rd., South Dist., 40227 Taichung,

Taiwan.(3) Livestock Research Institute, Council of Agriculture, Executive Yuan, 112 Muchang, Xinhua Dist., 71246 Tainan,

Taiwan.(4) Fonghuanggu Bird and Ecology Park, National Museum of Natural Science, 1-9 Renyi Rd., Lugu Township,

55841 Nantou County, Taiwan. (5) Center for the Integrative and Evolutionary Galliformes Genomics, National Chung Hsing University , No. 250,

Guoguang Rd., South Dist., 40227 Taichung, Taiwan.(6) Corresponding author, E-mail: [email protected]

286 中國畜牧學會會誌　第四十五卷　第四期

INTRODUCTION

Traditional selection for phenotype had made large improvement in poultry growth and meat yield be-

cause of the high heritabilities of growth and body composition traits (Jego et al., 1995; Le Bihan-Duval et

al., 1998). Negative correlations between chicken production and fitness traits challenged the selection for

rapid growth (Martin et al., 1990; Pinard-van der Laan et al., 1998), which resulted in physiological disor-

ders such as obesity, ascites, and a reduction in immunocompetence (Dunnington and Siegel, 1996; Deeb

and Lamont, 2002). Previous study demonstrated that chicken growth and fitness traits are controlled by

multiple genes (Deeb and Lamont, 2002), so that understanding the genetic variation of growth in chickens

is another solution to provide the opportunity for genetic enhancement of production performance. Genetic

markers linked with chromosomal regions allow for direct selection on genotype (Lamont et al., 1996) and

suggest to involve in breeding program.

Genome-wide association study (GWAS) is a powerful approach for investigating the genetic archi-

tecture of quantitative trait, which focuses on many genetic variants such as SNPs in different individuals

to see if any variants are associated with the traits across a set of individuals. GWAS was made possible by

the availability of array technology for assaying SNPs, which are typically used as genetic markers of a ge-

nomic region and are by far the most abundant form of genetic variation in chicken genome. GWAS using

the SNP array covering whole genome improves to a great mapping accuracy. The SNPs which were used

for GWAS strategy to identify the effects with important traits could be further predicted for the functions

of relevant gene by bioinformatics tools in order to prepare further studies of functional genomics (Tranch-

event et al., 2011 and Patnala et al., 2013).

In the present study, an F2 population was generated by crossing the Taiwan Country chicken L2 line

(selected for body weight, the size of comb area, and egg production) to the experimental line of Rhode

Island Red layer R- (selected for low residual feed consumption). GWAS was conducted on the body com-

position traits in an F2 population of birds at 23 weeks old to document the associated genomic loci and

relevant genes that might contribute to the phenotype. Therefore, functional annotation was applied in the

study to identify relevant genes and corresponding SNPs.

MATERIALS AND METHODS

1. Experimental population

An F2 cross design was produced by crossing the two parental lines L2 and R- at the experimental

farm of National Chung Hsing University (NCHU). The L2 line is a meat-type Taiwan Country chicken

selected for the body weight at 12 and 14 weeks of age, the size of comb area and egg production at 40

weeks of age (Chen et al., 1994; Lee et al., 1997). The R- line is a line of Rhode Island Red layer selected

for low value of residual feed consumption (RFC) at National Institute of Agricultural Research (INRA)

(Bordas and Mérat, 1984). Because the body composition traits of two parental lines were not available,

the variance analysis of L2 and R- for growth related traits were done before producing the F2 population.

Highly significant differences (P < 0.01) were found between L2 and R- lines for body weight at 0, 4, 8,

287Genome-wide association study of body composition traits in chicken

12, 16 weeks of age, and the size of comb area at 16 weeks of age. The results showed that the R- line was

lighter than the L2 line with a much smaller comb area. The 24th and 34th generation of the L2 and R-

lines were respectively used to set up an F2 population. Two F1 mating types, i.e. LR (L2 male mated to R-

female) and RL (R- male mated to L2 female), were produced from a total of 46 F0 parents by reciprocal

cross (6 L2 males mated to 15 R- females and 7 R- males mated to 18 L2 females). Then, the same mating

procedure was applied to create two F2 mating types XL (4 LR males mated to 32 RL females) and XR

(2 RL males mated to 19 LR females). A total of 157 F2 males were produced in 2 batches with the birth

dates: 31 Jan. 2011 and 18 Feb. 2011 were used in the study.

2. Husbandry

All chickens were reared on the floor in an open-sided building, with a temporary fence to close the

rooms and additional heating (24 hour/day) for the first two weeks. Fences were removed at three weeks

of age. Chicks were fed according to recommended nutrition standards, with a starter diet (metabolizable

energy: 2,830 kcal ME/kg and crude protein: 19.14%) from hatch to 4 weeks of age, a grower diet (me-

tabolizable energy: 2,818 kcal ME/kg and crude protein: 16.11%) from 5 weeks to 16 weeks of age, and

a breeder diet (metabolizable energy: 2,747 kcal ME/kg and crude protein: 18.18%) from 17 weeks to

23 weeks of age. Natural light was supplied during the rearing period. The vaccination plan set up by the

experimental farm of NCHU was applied to all birds. All the animals used in this study were processed

following the approved protocol of Institutional Animal Care and Use Committees of NCHU (Taichung,

Taiwan; IACUC No. 97-99).

3. Phenotypic measurements

The F2 chickens were fasted overnight, and were weighed before slaughtering. Then killed by manual

neck cut at the 23 weeks of age. After slaughtering, the birds were bled for 90 seconds, scalded at 55 to

60 °C for 50 seconds then put in a rotary drum picker to pluck feathers. The weight of carcass (CW), head

and neck, tenderloin, wing, back, feet, blood, feather, leg, abdominal fat (ABFat), viscera (liver, gizzard,

spleen, intestine, heart, and testis), and leg length (LegL) were measured and recorded (Lee and Chen.,

1984; Chen and Liu., 1992). The following parameters were taken:

(1)Head and neck obtained by cutting off the head to the last cervical vertebrae.

(2)Tenderloin obtained from the sternum, the pectoral major muscle and the pectoral minor muscle.

(3)Wing obtained by cutting through the humerus to the phalanx of front wings.

(4) Back: obtained by cutting from the part within scapula and the coracoid to the part within the rib-

bon and sternum.

(5)Leg obtained by cutting from the femur to the fibula (along the tibia).

(6)Foot obtained by cutting off the metatarsus and the phalanx.


4. Statistical analysis

The distributions of measured traits were checked by the SAS® UNIVARIATE procedure (Statistical

Analysis System, Version 9.3, SAS, Institute Inc., Cary, NC, USA). Box-Cox transformation was applied

when the recorded traits were not in normal distribution. Variance analysis was performed with the SAS®

GLM procedure to estimate the fixed effects of dam and batch, taking into account CW as a covariate for

all recorded variables (exclude CW).

5. Genotyping and quality control

Genomic DNA was extracted from the venous blood using a commercial DNA extraction kit (DNeasy®

Blood kit) and diluted to 50 ng/µl. After DNA quality check, each chicken was genotyped using Illumina

60 K Chicken iSelect SNP chip. The SNP set used in present study consisted in 57,636 SNP markers. Ap-

proximately 38.3% (22,059) SNPs were removed for failing to meet at least one of the following require-

ments: low call rate of the sample or SNP (< 95%), low minor allele frequency (< 0.05), Hardy-Weinberg

equilibrium test P <1×10-6, or SNP located at unknown chromosome. Finally, marker data were validated

for 157 F2 individuals and 35,577 SNP markers distributed on 28 autosomes and Z chromosome were used

in the study. The marker information on each chromosome is summarized in Table 1.

6. Genome-wide association study

The F2 population stratification was assessed by multidimensional scaling (MDS) analysis available

from PLINK (Version 1.0.7) (Purcell et. al., 2007). The indep-pairwise option with a window size 25

SNPs, a step of 5 SNPs, and r2 threshold of 0.2 which represents the pairwise SNP-SNP metric based on

the genotypic correlation was used to obtain the independent SNPs. Pairwise identity-by-state (IBS) dis-

tances were calculated between all the individuals using 2,813 independent SNPs, and MDS components

were estimated by the mds-plot option based on the IBS matrix. Linkage disequilibrium (LD) blocks were

defined as a set of contiguous SNPs with pairwise r2 values exceeding 0.4, resulting in 5,246 LD blocks for

body composition traits. GWAS was carried out between phenotypic variables and SNP markers with the

linear regression analysis available from PLINK. A linear model was applied for each autosome, with batch

and the first MDS component for fixed effects, and CW as a covariate (excluded CW). While the statisti-

cal model for CW included the first MDS component and batch as fixed effects. Measures of SNP effects

were calculated by the GCTA package (Yang et al., 2011). The P-value threshold of the 5% Bonferroni

genome-wide significance and the significance of suggestive linkage were computed based on the number

of independent SNPs and LD blocks (Nicodemus et al., 2005; Lander and Kruglyak, 1995). Therefore, the

P-value threshold of 5% Bonferroni was set at 6.20×10-6 (0.05/8059) for genome-wide significance, and at

1.24×10-4 (1/8059) for suggestive significance. In addition, empirical genome-wide P-values were obtained

by the maxT option with 25,000 permutations. Manhattan plots of GWAS results for each trait were pro-

duced with qqman package available from R (Version 3.1.2).

289

Table 1 Basic information of SNP markers on physical map in chicken in this study

Chromosome Physical Map (Mb) No. of SNP Marker Density (Kb/SNP)

1 199.4 5,395 37.0

2 154.4 4,248 36.3

3 113.6 3,490 32.6

4 94.0 2,636 35.7

5 62.0 1,554 39.9

6 37.4 1,275 29.3

7 38.4 1,313 29.2

8 30.5 1,083 28.2

9 25.4 992 25.6

10 22.4 1,031 21.7

11 21.9 963 22.7

12 20.4 1,111 18.4

13 18.4 904 20.4

14 15.8 748 21.1

15 13.0 847 15.3

16 0.43 17 25.3

17 11.2 693 16.2

18 10.9 689 15.8

19 9.8 639 15.3

20 13.9 1,123 12.4

21 6.7 584 11.5

22 3.8 230 16.5

23 6.0 473 12.7

24 2.0 572 3.5

25 6.4 127 50.4

26 5.1 464 11.0

27 4.6 419 11.0

28 4.4 472 9.3

E22C19W28_E50C23 0.89 71 12.5

E64 0.049 3 16.3

Z 74.6 1,411 52.9

Total 1,027.8 35,577 22.8



7. Gene annotation

A SNP set (included the information of SNP ID and position) which reached the significant level and

showed the association with measured traits were automatically used for searching the information of

potential candidate genes in NCBI and Ensembl database (Pruitt et al., 2014; Yates et al., 2016) by an in-

house Perl script. Several public databases, i.e. PANTHER and DAVID databases, which provide the com-

prehensive set of functional annotation to understand biological meaning behind a list of given genes were

widely used for gene annotation and integrated discovery. Investigation of PANTHER and DAVID databas-

es for those possible candidate genes associated with significant SNPs was performed to make hypothesis

about the biological processes and molecular functions likely to influence the trait of interest (Thomas et

al., 2003; Huang et al., 2009).

RESULTS AND DISCUSSION

The distribution of each variables were checked. Three measured traits (abdominal fat, gizzard, and

spleen weight) did not comply with normal distribution were transformed by Box-Cox transformation (Box

and Cox, 1964). Means and standard deviations for F2 crosses are showed in Table 2. The fixed effects

(batch) were significant for each traits (except feather and liver weight). Highly significant differences

were found between 2 mating types (XL and XR) and 6 half-sib families for carcass, back, head and neck,

breast skin, spleen, and wing weight. All traits were not available in F0.

The distributions of P-value of SNP effects for each trait were illustrated by Manhattan plots (Figure

1). A total of 23 SNP effects were identified for 7 traits (abdominal fat, feather, feet, gizzard, intestine,

breast skin, and testis weight) with 5% Bonferroni genome-wide significance (P < 6.20×10-6), then all SNP

effects reach 5% empirical genome-wide significance from permutation test (Table 3). Furthermore, two

hundred and twenty-five SNP effects reached suggestive significance (P < 1.24×10-4). The largest number

of SNP effects (71 SNP effects) for a given trait was found for the intestine weight, followed by gizzard (38

SNP effects) and testis weight (37 effects) (Figure 2). At the contrary, no SNP effect was detected for the

back, leg, liver, and wing weight. Whereas the SNP effects associated with intestine weight were scattered

on 11 chromosomes, followed by gizzard weight (9 chromosomes). SNP effects for the other traits with a

minimum of 1 chromosome for BreastT, BreastW, LegL, tenderloin, and a maximum of 5 chromosomes for

CW.

291 Genome-wide association study of body composition traits in chicken


Figure 1 Manhattan plot of GWAS for the recorded traits. The red line indicates the threshold for a 5%

Bonferroni genome-wide significance with a P-value of 6.20×10-6, and the blue line indicates the

threshold for a suggestive linkage association (P < 1.24×10-4)

293

Table 2 Body composition traits in F2 male progeny at 23 weeks of age

TraitXL XR Variance analysis

Mean SDa Mean SDa Mating type Batch Sireb

Abdominal Fat, g 26.56 19.99 33.98 25.45 NS * **

Back, g 365.52 70.78 393.74 66.00 ** ** **

Blood, g 72.18 20.47 73.75 19.69 NS ** NS

Breast length, cm 18.09 1.29 18.13 1.28 NS ** NS

Breast thickness, cm 1.35 0.30 1.47 0.35 * ** NS

Breast width, cm 14.76 1.39 15.19 1.45 * ** NS

Carcass, g 2279.80 348.76 2449.55 342.38 ** ** **

Feather, g 158.84 26.73 154.34 23.51 NS NS NS

Feet, g 90.57 11.67 93.31 12.53 NS ** NS

Gizzard, g 32.93 6.07 31.65 3.77 NS ** NS

Heart, g 67.53 14.26 63.19 9.09 * ** NS

Head and neck, g 227.15 44.11 248.14 44.08 ** ** **

Intestine, g 28.38 6.41 30.70 7.97 * ** **

Leg length, cm 17.38 0.91 17.38 1.07 NS ** NS

Leg, g 566.93 101.95 626.02 101.84 ** ** NS

Liver, g 1.29 0.91 1.59 1.18 NS NS *

Breast skin, g 33.18 12.75 37.15 11.70 ** ** **

Spleen, g 12.91 2.80 14.88 2.91 ** ** **

Tenderloin, g 32.80 8.50 35.18 9.03 * ** **

Testis, g 4.34 1.99 4.00 1.42 NS ** NS

Wing, g 199.93 28.39 211.78 31.48 ** ** **a standard deviationb sire family*P < 0.05, ** P< 0.01, NS: no significance

Figure 2 Number of SNPs reaching significant level (P < 1.24×10-4) for recorded traits.



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295

Seven chromosomes (GGA 15, 16, 18, 19, 22, 24, and 26) did not harbor any significant SNP. The

chromosomes that were carrying genome-wide significant SNP effects for at least 2 traits were GGA 2 and

GGA 8. The strongest association across all chromosomes was found on GGA 27 where a region span-

ning 0.4 Mb (3.1 Mb – 3.5 Mb) harbored 1 genome-wide and 2 suggestive significant SNPs associated

with the feet weight. This region harbored 2 genes (FAM117A and CALCOCO2), 1 uncharacterized gene

(LOC101751129), and also the published QTL region associated with shank weight was located at this

chromosomal region (Park et al., 2006). There is no functional analysis of body composition trait for these

genes in chicken, even in mouse. Among these, the highest significance (P < 3.31×10-8) was obtained for

the calcium binding and coiled-coil domain 2 gene (CALCOCO2).

A chromosomal region located on GGA 1 where spanning 2.1 Mb (50.9 Mb – 53.0 Mb) harbored

3 suggestive SNPs (rs13865791, GGaluGA017598, and GGaluGA017646) and 2 genes (SOX10 and

PLA2G6) was associated with LegL. The SRY (sex determining region Y)-box 10 gene (SOX10) signifi-

cantly decreased body weight was observed in mouse research (Eppig et al., 2015). This gene also func-

tions to regulate chondrogenesis during limb development of the chicken embryo (Chimal-Monroy et al.,

2003). Moreover, this region on GGA 1 has also been previously associated with the QTLs for chicken

skeletal related traits, such as body slope length (Gao et al., 2011), femur and tibia weight (Sharman et al.,

2007), drumstick percentage (Li et al., 2005), shank growth (Gao et al., 2010), and insulin-like growth fac-

tor level (Park et al., 2006). The PLA2G6 gene was found the relation with the weight loss in mouse, so far,

there is no body composition related research in chicken (Eppig et al., 2015).

Previously researches showed that there were several published QTLs overlapped with the chromo-

somal regions identified in present study, and some of them harbored interesting relevant genes correspond-

ing to chicken body composition related traits. A region spanning 5.5 Mb (49.2 Mb – 54.7 Mb) on GGA

5 which covered by 12 SNPs (4 genome-wide and 8 suggestive SNPs), harbored 4 genes, and 2 uncharac-

terized genes showed the association with gizzard weight. This regions also overlapped with the published

QTL corresponded to chicken gizzard weight (Navarro et al., 2005). The TNF receptor associated factor

3 gene (TRAF3) is one of the genes harbored in this region and was showed the functional annotation for

spleen hyperplasia, decreasing body size, and body weight in mouse (Eppig et al., 2015). Another chromo-

somal region was identified on GGA 7 region (24.0 Mb – 28.2 Mb) which was detected the suggestive as-

sociation with CW. Several body weight related QTLs overlapped with this region included a genome-wide

significant QTL corresponding to CW (Nassar et al., 2012). Three genes (TTLL4, MYLK, and SEMA5B)

harbored in the region and 2 of 3 were involved in the function of body weight, body mass, fat amount, and

food intake decreasing (Eppig et al., 2015).

In conclusion, the present study has identified several SNP effects associated with body composition

traits for specific chicken male population in tropical climate condition. These results may be considered

for the future management of the L2 and R- lines. First, the segregation of SNPs for relevant genes remains

to be investigated in the F2 cross in order to confirm their effects on poultry male production performance.

Then, the frequency and the phenotypic consequence of the candidate SNPs need to be determined in both

parental lines, in order to decide whether these SNPs may be used for future breeding programs and selec-

tion process. Finally, QTL mapping should be applied for the next step in order to make the further confir-

mation for the relation between QTLs and measured traits.


Tabl

e 3

R

esul

ts o

f th

e G

WA

S: t

he li

st o

f S

NP

s sh

owin

g 5%

Bon

ferr

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ide

sign

ific

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for

the

reco

rded

trai

ts

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itG

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bp)b

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PP

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un-C

onse

qdP

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rest

gen

eA

bdom

inal

Fat

281

4913

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1421

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rgen

ic2.

94×

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37

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terg

enic

2.94

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0000

1714

0.37

LO

C10

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238

281

5569

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rgen

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40

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8158

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0000

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281

8614

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rgen

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95×

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0.00

0071

430.

44

LOC

1017

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82

8266

8398

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6361

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terg

enic

5.95

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0000

8857

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283

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0000

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0.40

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32

8317

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5.28

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0000

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0.61

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1.60

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0000

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0.40

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32

8367

4546

GG

aluG

A15

5388

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Inte

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ic2.

94×

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0.00

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37

LOC

1017

5019

12

8529

4913

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aluG

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5558

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28×

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290.

61

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1078

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1423

2014

C/A

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05×

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0.00

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31

LOC

1017

5180

13

9614

7570

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3280

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terg

enic

5.68

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0000

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0.82

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399

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2355

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0000

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0.82

H

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120

1199

2058

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pstr

eam

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64×

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290.

41

SPO

1120

1199

8949

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1788

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terg

enic

5.64

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0000

7429

0.41

SP

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ther

2821

2013

rs16

2087

61C

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terg

enic

3.20

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0000

2286

12.6

8 -

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4569

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27

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551

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70×

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4.69

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ACKNOWLEDGEMENTS

This study was supported by a grant of the Ministry of Science and Technology, Taiwan (grant number

NSC 99-2321-B-005-009-MY3), the fellowship from the French Institute of Taipei, the Ministry of Edu-

cation, and the Ministry of Science and Technology in Taiwan. The staff of the experimental farm of the

NCHU is gratefully acknowledged. This work was dedicated to André Bordas, INRA, who selected the R-

line and organized the shipment of a subset of the line to NCHU in 2003, Yen-Pai Lee, who preserved and

maintained the L2 line in NCHU, and Bing-Yen, Tsai, who made an in-house Perl script to combine the

database information between SNPs and relevant genes.

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299

雞體組成性狀全基因組關聯性分析

練慶儀(1)(2)(3) Michèle Tixier-Boichard(1) 吳詩雯(4)

譚發瑞(2) 陳志峰 (2)(5)(6)

摘要：產肉量為家禽產業的重要經濟性狀。本研究旨在於尋找與雞隻體組成性狀

顯著相關之單一核甘酸多型性（signal nuclide polymorphism, SNP）效應。動物族

群採用台灣土雞 L2 品系（選拔第 12、14 週齡雞冠面積及第 40 週齡產蛋量）與

洛島紅 R-試驗品系（選拔低飼料採實殘差）進行雜交之子二代進行試驗。157 隻

F2 代公雞於 23 週齡時屠宰，收集 21 個體組成表型性狀與由 Illumina 60K iSelect

SNP 晶片鑑定取得之 SNP 基因型資料後，利用全基因組關聯性分析（genome-wide

association study, GWAS）找尋性狀與 SNP 效應間的關聯性。此外，候選基因的

功能性解析運用於定義染色體區間內相關基因與其對應之 SNP的功能。全基因組

關聯性分析結果顯示，有 23 個 SNP 效應達 5% Bonferroni 基因組顯著水準（P <

6.2×10-6），其與腹脂、羽毛、腳、砂囊、腸、胸皮、及睪丸重量等體組成性狀具

關連性，另亦有 225 個 SNP 效應達建議顯著水準（P < 1.24×10-4）。此外，本研

究亦找出許多與雞隻體組成相關之潛在候選基因，如：SOX10。利用基因組關聯性

分析可尋找與性狀相關聯的 SNP 效應及候選基因。未來可利用數量性狀基因座定

位（quantitative trait locus mapping, QTL mapping）分析，以精確定義與雞隻體組成

性狀相關的 QTLs。

（關鍵語：體組成、雞、基因組關聯性分析、單一核苷酸多型性）

(1)法國國家農業科學研究院，巴黎農業學院，巴黎薩克雷大學，78350 法國茹伊昂若薩斯。(2)國立中興大學動物科學系，40227 臺中市興大路 145 號。(3)行政院農業委員會畜產試驗所 , 71246 臺南市新化區牧場 112 號。(4)國立自然科學博物館鳳凰谷鳥園生態園區，55841 南投縣鹿谷鄉鳳凰村仁義路 1-9 號。(5)國立中興大學鳥禽類演化與基因體研究中心，40227 臺中市南區國光路 250 號。(6)通訊作者，E-mail: [email protected]

中國畜牧學會會誌 45(4)：285~299, 2016

Documents

Genome-wide association study of body composition traits ... · PDF filecomposition traits in chicken ... feet, blood, feather, leg, abdominal fat (ABFat), viscera (liver, gizzard