Genome-wide association study of body composition traits in chicken

Transcription

1 J. Chin. Soc. Anim. Sci. 45(4): 285~299, 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 identification 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 (selected 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 linkage analysis led to identifying 23 SNP effects for 7 carcass traits (abdominal fat, feather, feet, gizzard, intestine, breast skin, and testis weight) with 5% Bonferroni genome-wide significance (P < ), and a total of 225 SNP effects reached suggestive significance (P < ). 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, Jouy-en-Josas, France. (2) Department of Animal Science, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung, Taiwan. (3) Livestock Research Institute, Council of Agriculture, Executive Yuan, 112 Muchang, Xinhua Dist., Tainan, Taiwan. (4) Fonghuanggu Bird and Ecology Park, National Museum of Natural Science, 1-9 Renyi Rd., Lugu Township, Nantou County, Taiwan. (5) Center for the Integrative and Evolutionary Galliformes Genomics, National Chung Hsing University, No. 250, Guoguang Rd., South Dist., Taichung, Taiwan. (6) Corresponding author, cfchen@dragon.nchu.edu.tw

2 286 INTRODUCTION Traditional selection for phenotype had made large improvement in poultry growth and meat yield because 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 disorders 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 architecture 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 genomic 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 (Tranchevent 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 composition 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,

3 Genome-wide association study of body composition traits in chicken , 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 and 18 Feb 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 (metabolizable 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 ). 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 ribbon 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. Approximately 38.3% (22,059) SNPs were removed for failing to meet at least one of the following requirements: 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 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 r 2 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) distances 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 r 2 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 statistical 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 (0.05/8059) for genome-wide significance, and at (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 produced with qqman package available from R (Version 3.1.2).

5 Genome-wide association study of body composition traits in chicken 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) , , , , , , , , , , , E22C19W28_E50C E Z , Total 1, ,

6 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 inhouse Perl script. Several public databases, i.e. PANTHER and DAVID databases, which provide the comprehensive 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 databases 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 < ), 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 < ). 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.

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

8 292 中國畜牧學會會誌 第四十五卷 第四期 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 , and the blue line indicates the threshold for a suggestive linkage association (P < )

9 Genome-wide association study of body composition traits in chicken 293 Figure 2 Number of SNPs reaching significant level (P < ) for recorded traits. Table 2 Body composition traits in F2 male progeny at 23 weeks of age Trait XL XR Variance analysis Mean SD a Mean SD a Mating type Batch Sire b Abdominal Fat, g NS * ** Back, g ** ** ** Blood, g NS ** NS Breast length, cm NS ** NS Breast thickness, cm * ** NS Breast width, cm * ** NS Carcass, g ** ** ** Feather, g NS NS NS Feet, g NS ** NS Gizzard, g NS ** NS Heart, g * ** NS Head and neck, g ** ** ** Intestine, g * ** ** Leg length, cm NS ** NS Leg, g ** ** NS Liver, g NS NS * Breast skin, g ** ** ** Spleen, g ** ** ** Tenderloin, g * ** ** Testis, g NS ** NS Wing, g ** ** ** a standard deviation sire family *P < 0.05, ** P< 0.01, NS: no significance

10 294 Table 3 Results of the GWAS: the list of SNPs showing 5% Bonferroni genome-wide significance for the recorded traits a b c d e Trait GGA a Pos (bp) b SNP Poly-Type c Fun-Conseq d P-value Emp P e Effect f Nearest gene Abdominal Fat rs T/C Intergenic rs A/G Intergenic LOC rs A/G Intergenic rs C/T Intergenic rs G/A Intergenic LOC rs G/A Intergenic rs G/A Intergenic rs C/T Intron FHOD rs T/C Intron FHOD rs C/A Intron FHOD GGaluGA G/A Intergenic LOC GGaluGA G/A Intergenic rs C/A Intergenic LOC rs T/G Intergenic GGaluGA C/A Intron HPCAL rs G/A Upstream SPO rs A/G Intergenic SPO11 Feather rs C/T Intergenic Feet rs T/C Intron DSC GGaluGA A/G 3 prime UTR CALCOCO2 Gizzard rs G/A Intergenic rs G/A Intergenic LOC rs T/C Upstream GGaluGA C/T Intergenic rs A/G Intergenic rs C/T Upstream CDKN2C rs G/A Intergenic CASZ1 Intestine GGaluGA C/T Intron LOC GGaluGA G/A Intergenic GGaluGA C/T Intron NBAS rs C/T Intergenic rs C/T Intron GPATCH rs C/T 5 prime UTR rs T/C Intron GPI GGaluGA G/A intron DPY19L GGaluGA C/T intron DPY19L3 Breast skin rs G/T Intergenic LOC rs A/G Intron CNKSR2 Testis GGaluGA C/T intron GLIS GGaluGA T/G Intergenic GGA: gallus gallus chromosome Pos (bp): SNP position (base pair) Poly-Type: polymorphism type, first allele is favorable allele Fun-Conseq: functional consequence Emp P: empirical genome-wide P-value Effect: SNP effect f

11 Genome-wide association study of body composition traits in chicken 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 spanning 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 (LOC ), 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 < ) 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 (rs , GGaluGA017598, and GGaluGA017646) and 2 genes (SOX10 and PLA2G6) was associated with LegL. The SRY (sex determining region Y)-box 10 gene (SOX10) significantly decreased body weight was observed in mouse research (Eppig et al., 2015). This gene also functions 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 factor 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 chromosomal regions identified in present study, and some of them harbored interesting relevant genes corresponding 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 uncharacterized 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 chromosomal region was identified on GGA 7 region (24.0 Mb 28.2 Mb) which was detected the suggestive association 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 selection process. Finally, QTL mapping should be applied for the next step in order to make the further confirmation for the relation between QTLs and measured traits.

12 296 ACKNOWLEDGEMENTS This study was supported by a grant of the Ministry of Science and Technology, Taiwan (grant number NSC B MY3), the fellowship from the French Institute of Taipei, the Ministry of Education, 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. REFERENCES Blagojević, M., Z. Pavlovski, Z. Škrbić, M. Lukić, N. Milosević, and L. Perić The effect of genoptype of broiler chicken on carcass quality in extensive rearing system. Acta. Vet-Beograd. 59: Bordas, A., and P. Mérat Correlated responses in a selection experiment on residual feed intake of adult Rhode-Island Red cocks and hens. Ann. Agric. Fenn. 23: Box, G. E., P. and D. R. Cox An analysis of transformation. J. R. Stat. Soc. 26: Chen, C. F., Y. P. Lee, Y. K. Fan, S. Y. Huang, and H. H. Huang The conservation of Taiwan s Local chickens. J. Chin. Soc. Anim. Sci. 23: Chen, M. T., and D. C. Liu Poultry specifications manual. Chimal-Monroy, J., J. Rodriguez-Leon, J. A. Montero, Y. Ganan, D. Macias, R. Merino, and J. M. Hurle Analysis of the molecular cascade responsible for mesodermal limb chondrogenesis: Sox genes and BMP signaling. Dev. Biol. 257: Deeb, N., and S. J. Lamont Genetic architecture of growth and body composition in unique chicken populations. J. Hered. 93: Dunnington, E. A., and P. B. Siegel Long-term divergent selection for eight-week body weight in White Plymouth rock chickens. Poult. Sci. 75: Eppig, J. T., J. A. Blake, C. J. Bult, J. A. Kadin, and J. E. Richardson The Mouse Genome Database Group. The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease. Nucleic Acids Res. 43:D Gao, Y., C. G. Feng, C. Song, Z. Q. Du, X. M. Deng, N. Li, and X.-X. Hu Mapping quantitative trait loci affecting chicken body size traits via genome scanning. Anim. Genet. 42: Gao, Y., Z. Q. Du, C. G. Feng, X. M. Deng, N. Li, Y. Da, and X. X. Hu Identification of quantitative trait loci for shank length and growth at different development stages in chicken. Anim. Genet. 41: Hu, Z. L., C. A. Park, and J. M. Reecy Developmental progress and current status of the Animal QTLdb. Nucleic Acids Res. 44:D Huang, D. W., B. T. Sherman, and R. A. Lempicki Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc. 4: Jego, Y., B. Besbes, and J. L. Donal Analyse de la variabilité génétique et de la réponse à la sélection

13 Genome-wide association study of body composition traits in chicken 297 dans deux lignées commerciales de poulets de chair. Page in Proceedings of the 1e`res Journées de la Recherche Avicole, Angers, France. ITAVI, Paris, France. Lamont, S. J., N. Lakshmanan, Y. Plotsky, M. G. Kaiser, M. Kuhn, J. A. Arthur, N. J. Beck, and N. P. O Sullivan Genetic markers linked to quantitative traits in poultry. Anim. Genet. 27:1-8. Lander, E., and L. Kruglyak Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat. Genet. 11: Le Bihan-Duval, E., S. Mignon-Grasteau, N. Millet, and C. Beaumont Genetic analysis of a selection experiment on increased body weight and breast muscle weight as well as on limited abdominal fat weight. Br. Poult. Sci. 39: Lee, Y. P., and M. T. Chen Growth and carcass part proportions of the three main varieties of meat-type chickens in Taiwan. J. Chin. Soc. Anim. Sci. 13: Lee, Y. P., L. T. Yeh, and H. H. Huang A study on the production system of Taiwan Country chicken: growth traits of three-strain cross country chickens. J. Chin. Soc. Anim. Sci. 26: Li, H., N. Deeb, H. Zhou, C. M. Ashwell, and S. J. Lamont Chicken quantitative trait loci for growth and body composition associated with the very low density apolipoprotein-ii gene. Poult. Sci. 84: Martin, A., E. A. Dunnington, W. B. Gross, W. E. Briles, R. W. Briles, and P. B. Siegel Production traits and alloantigen systems in lines of chickens selected for high or low antibody responses to sheep erythrocytes. Poult. Sci. 69: Nassar, M. K., Z. S. Goraga, and G. A. Brockmann Quantitative trait loci segregating in crosses between New Hampshire and White Leghorn chicken lines: II. Muscle weight and carcass composition. Anim. Genet. 43: Navarro, P. 1., P. M. Visscher, S. A. Knott, D. W. Burt, P. M. Hocking, and C. S. Haley Mapping of quantitative trait loci affecting organ weights and blood variables in a broiler layer cross. Br. Poult. Sci. 46: Nicodemus, K. K., W. Liu, G. A. Chase, Y. Y. Tsai, and M. D. Fallin Comparison of type I error for multiple test corrections in large single-nucleotide polymorphism studies using principal components versus haplotype blocking algorithms. BMC Genet. 6 (Suppl. 1):S78. Park, H. B., L. Jacobsson, P. Wahlberg, P. B. Siegel, and L. Andersson QTL analysis of body composition and metabolic traits in an intercross between chicken lines divergently selected for growth. Physiol. Genomics. 25: Patnala, R., J. Clements, and J. Batra Candidate gene association studies: a comprehensive guide to useful in silico tools. BMC Genet. 14:39. Pinard-van der Laan, M. H., P. B. Siegel, and S. J. Lamont Lessons from selection experiments on immune response in the chicken. Poult. Avian Biol. Rev. 9: Pruitt, K. D., G. R. Brown, S. M. Hiatt, F. Thibaud-Nissen, A. Astashyn, O. Ermolaeva, C. M. Farrell, J. Hart, M. J. Landrum, K. M. McGarvey, M. R. Murphy, N. A. O Leary, S. Pujar, B. Rajput, S. H. Rangwala, L. D. Riddick, A. Shkeda, H. Sun, P. Tamez, R. E. Tully, C. Wallin, D. Webb, J. Weber, W. Wu, M. DiCuccio, P. Kitts, D. R. Maglott, T. D. Murphy, and J. M. Ostell RefSeq: an update on mammalian reference

14 298 sequences. Nucleic Acids Res. 42:D Purcell, S., B. Neale, K. Todd-Brown, L. Thomas, M. A. Ferreira, D. Bender, and J. Maller, P. Sklar, P. I. de Bakker, M. J. Daly, and P. C. Sham PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81: Sharman. P. W. A., D. R. Morrice, A. S. Law, D. W. Burt, and P. M. Hocking Quantitative trait loci for bone traits segregating independently of those for growth in an F2 broiler layer cross. Cytogenet. Genome Res. 117: Thomas, P. D., M. J. Campbell, A. Kejariwal, H. Mi, B. Karlak, R. Daverman, K. Diemer, A. Muruganujan, and A. Narechania PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 13: Tranchevent, L.-C., F. B. Capdevila, D. Nitsch, B. De Moor, P. De Causmaecker, and Y. Moreau A guide to web tools to prioritize candidate genes. Brief Bioinform. 12: Yang, J., S. H. Lee, M. E. Goddard, and P. M. Visscher GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88: Yates, A., W. Akanni, M. R. Amode, D. Barrell, K. Billis, D. Carvalho-Silva, C. Cummins, P. Clapham, S. Fitzgerald, L. Gil, C. G. Girón, L. Gordon, T. Hourlier, S. E. Hunt, S. H. Janacek, N. Johnson, T. Juettemann, S. Keenan, I. Lavidas, F. J. Martin, T. Maurel, W. McLaren, D. N. Murphy, R. Nag, M. Nuhn, A. Parker, M. Patricio, M. Pignatelli, M. Rahtz, H. S. Riat, D. Sheppard, K. Taylor, A. Thormann, A. Vullo, S. P. Wilder, A. Zadissa, E. Birney, J. Harrow, M. Muffato, E. Perry, M. Ruffier, G. Spudich, S. J. Trevanion, F. Cunningham, B. L. Aken, D. R. Zerbino, and P. Flicek Ensembl Nucleic Acids Res. 44:D

15 中國畜牧學會會誌 45(4):285~299, 雞體組成性狀全基因組關聯性分析 (1)(2)(3) 練慶儀 Michèle Tixier-Boichard (1) (2) 譚發瑞 (2)(5)(6) 陳志峰 (4) 吳詩雯 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 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 < ), 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 < ) 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, 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, cfchen@dragon.nchu.edu.tw