候选基因集法及IGF1-FoxO通路与猪生长发育的关联研究
|
摘要
猪的生长发育影响增重速度、饲料转化效率以及胴体组成等诸多性状,进而影响经济效益。然而至目前为止,对于猪的生长发育调控机理仍不是十分清楚,以致严重制约了猪种的遗传改良。近些年来,候选基因分析、全基因组扫描以及全基因组关联分析等均被用于揭示生长发育等复杂性状的遗传机制,但是这些方法可以找出影响数量性状的DNA片段(QTL)或SNP位点,却不能反映从基因到性状的生物学过程。为此,本项目首先提出了候选基因集法,进而将这种方法运用到了猪生长发育遗传机制的研究中。
一、候选基因集法的建立:候选基因法具有一定的盲目性并易受环境因素影响,QTL定位需要特定的群体资源并且区间跨度太大,全基因组关联分析则面临费用高昂等问题。有鉴于此,本文提出了候选基因集法,包含基因集的定义、QTL上的映射、SNP点的推断、SNP检测分型、表型分型、关联分析以及功能验证等规范化的步骤与流程。该方法具有较强的目的性、针对性、操作简单、费用合理等优点。
二、骨骼肌再生时间序列芯片的转录因子分析:骨骼肌在受到创伤后具有很强的再生能力。这一过程在诸多转录因子的精细调控下完成。这些调控进程往往通过调控网络发挥其功能。我们利用已有的时间序列芯片及启动子区域结合位点信息研究了骨骼肌再生过程的转录调控网络。通过对共表达基因启动子区域结合位点信息的分析,我们获得了一系列转录因子与靶基因的关系。时间延迟分析通过相关系数及调控时间延迟量进一步探究了真正的转录因子与靶基因间的调控关系。通过分析我们获得了13个在骨骼肌再生过程中发挥重要作用的转录因子,其中的6个转录因子已经有文献报道过。这些转录因子中的FoxO3富集于胰岛素信号通路(IGF1-FoxO),该信号通路曾被报道与生长发育密切相关。因此,鼠的骨骼肌再生芯片分析提供的IGF1-FoxO可以作为影响猪生长发育的候选通路。
三、IGF1-FoxO通路进化分析:IGF1-FoxO信号转导通路在内分泌调控进程中发挥着重要作用,该信号通路在脊椎动物物种中保持一定的一致性。虽然该信号通路成员组成在模式生物物种中已经被广泛研究,但是通路的进化模式还有待于进行深入的研究。我们首先对通路成员组成即在不同的物种中信号通路成员的组成是否存在差异开展了研究,进而对在这一复杂的系统中各个成员组成在进化历程中是否具有相似的选择约束等问题做了探索。我们的结果表明大多数的通路成员存在于所有的待研究的物种中,他们遭受较强的选择约束。通路成员中的2个基因受到正选择的影响。我们发现整个通路的选择强度呈梯度变化,即上游基因相对于下游基因具有相对较强的选择约束。我们的研究也发现,通路成员的选择压力影响因素较为复杂,除受密码子偏性、蛋白长度影响外,还有其它的待研究因素。
四、猪IGF1-FoxO通路成员的确定及通路成员SNP信息的推断:通过对模式生物进行进化研究,我们获得了通路成员的保守组成。我们利用人、鼠模式生物的27个保守基因分别到猪的基因组数据库进行blast搜索,用来获取猪的通路成员。因为猪的基因组注释信息的不完整性,所以我们同时也采用搜索通路成员的侧翼序列,用来尽可能获取猪的通路成员。最终我们获得保守通路27个成员中的22个基因。我们将这些基因定义为要研究的基因集。这些通路成员基因序列与NCBI Trace Repository和GenBank中的鸟枪序列进行blast搜索及序列比对操作,进而推断通路成员的SNP位点信息。
五、IGF1-FoxO通路SNP的检测分型及性状的关联分析:在对猪IGF1-FoxO通路SNP推断分析的基础上,我们运用SNaPshot与PCR-RFLP技术对猪部分通路基因多态位点进行检测分型,同时运用奥斯本自动喂料记录系统对111头杜长大猪做了生长育肥试验,进而对多态位点与生长发育相关性状进行关联分析,以期阐明通路中的哪些基因、哪些SNP位点决定了性状的不同表型,这些基因和多态位点是如何发挥作用的,即其生物学功能及作用模式如何,从而在通路水平上揭示猪的生长发育机制。研究结果表明,FoxO3可能是控制生长发育的一个主效基因或者与控制生长发育的主效基因紧密连锁。
我们所开展的这些研究,为揭示复杂性状的遗传机制尤其是进行猪生长发育性状的遗传机制分析及标记辅助选择,进而对猪进行遗传改良提供了坚实的基础。
The growth and development of porcine will influence the growth rate, feed efficiency, body composition and carcass traits, and so on, which last influence economic efficiency. However, studying the mechanism of controlling growth and development confronts significant challenges at present and it restricts genetic improvement of pigs. Candidate genes analyzing, whole genome scanning, and whole genome association analysis were widely used to study the mechanism of controlling growth and development and obtained some candidate QTLs and SNPs, but it could not reflect biological process from genes to traits. Thus, this project first put forward a Candidate Gene Set Approach and used it to study the the mechanism of controlling growth and development of pig.
1. The establishment of Candidate Gene Set Approach
There is some blindness and arbitrariness for candidate gene method and this method is vulnerable to the influence by environment. Moreover, QTL used to location by linkage analysis spans tens of map units, or hundreds of genes, and this method need special population. Further, it costs much by GWAS method. In this case, we present the Candidate Gene Set Approach, and the standardize process includes the gene set selection, QTL mapping, candiadted SNP prediction, genotyping and associated analysis. This method has the virtue of motivated, easy for performance, reasonable for costs, etc.
2. Uncovering the transcriptional circuitry in skeletal muscle regeneration
Skeletal muscle has a remarkable ability to regenerate after repeated and complete destruction of the tissue. The healing phases for an injured muscle, including degeneration, inflammation, regeneration and remodeling, undergo an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network confronts significant challenges and requires the integration of multiple experimental data types. In this work, we focus on getting insights into the transcriptional circuitry during skeletal muscle regeneration using time-course expression data and motif scanning information. A set of→TtFarget associations was derived from predicted TF binding sites in promoter regions of co-expressed genes. Time-lagged correlation analysis was utilized to evaluate the TF→target associations. Our analysis identified 13 TFs that potentially play a central role throughout the regeneration process. Six of which have previously been described to be important for muscle regeneration and differentiation. The transcriptional factor FoxO3 was enrichment into the insulin pathway, and this pathway was involved into growth and development reported by previous literature. Therefore, mouse skeletal muscle regeneration analysis offered the clues that IGF1-FoxO was a candidate pathway that influence pigs’growth and development.
3. The molecular evolutionary patterns of the insulin/FOXO signaling pathway
The insulin/insulin growth factor-1(IGF1)/FOXO (IIF) signal transduction pathway plays a core role in endocrine system and maintains remarkable consistency across all vertebrate species. Although the components of this pathway have been best-characterized, the evolutionary pattern remains poorly understood. Here, we performed a comprehensive analysis to study whether the differences of signaling transduction elements exist among different animal species and to determine whether the genes are subject to equivalent evolutionary forces and how natural selection shapes the evolution of proteins in an interacting system. Our results demonstrate that most IIF pathway components are present throughout all animal phyla investigated here, and they are under strong selective constraint but two of which show evidence for positive selection. Remarkably, we detected a gradient in the strength of purifying selection along the pathway, increasing from the upstream to the downstream genes. We also found that the dN/dS may be influenced by quite complicated factors including codon bias, protein length and other factors.
4. The identification of the porcine components of IGF1-FoxO pathway and SNP inference of pathway component members
We got conserved pathway members though evolutionary analysis of model animals. We make use of 27 conserved members from model animals of human and mouse to blast pig genome to get pathway components. Because of incomplete annotation informations of pig’s genome, flanking sequences of pathway componences were also used to blast pig’s genome to get whole pathway members. At last, we got 22 genes from 27 pathway members. These members were then blast to shotgun sequences from NCBI Trace Repository and GenBank to mine SNP informatics.
5. Experimental validation of candidate SNPs from IGF1-FoxO pathway and the association between polymorphism and traits of pig The polymorphism (SNP) of IGF1-FoxO pathway genes mining by previous research
were chosen for experimental validation by NaPshot sequencing and PCR-RFLP. We then performed association analysis between polymorphisms and growth and development traits to illustrate how different phenotypes were influenced by different genes and SNPs and how different genes and SNPs excerting their roles. Namely, knowing the biological function and regualatory mode, we could recovery the growth and development mechanism of pig from pathway perspective. Our result demonstrated that FoxO3 may be one or linkage with one of the major genes that influence the growth and development of pig. The above results help to lay a foundation for the further research of molecular genetic mechanisms of pig growth and development performance and provide evidence for pig marker assistant selection and marker-assisted introgression.
一、候选基因集法的建立:候选基因法具有一定的盲目性并易受环境因素影响,QTL定位需要特定的群体资源并且区间跨度太大,全基因组关联分析则面临费用高昂等问题。有鉴于此,本文提出了候选基因集法,包含基因集的定义、QTL上的映射、SNP点的推断、SNP检测分型、表型分型、关联分析以及功能验证等规范化的步骤与流程。该方法具有较强的目的性、针对性、操作简单、费用合理等优点。
二、骨骼肌再生时间序列芯片的转录因子分析:骨骼肌在受到创伤后具有很强的再生能力。这一过程在诸多转录因子的精细调控下完成。这些调控进程往往通过调控网络发挥其功能。我们利用已有的时间序列芯片及启动子区域结合位点信息研究了骨骼肌再生过程的转录调控网络。通过对共表达基因启动子区域结合位点信息的分析,我们获得了一系列转录因子与靶基因的关系。时间延迟分析通过相关系数及调控时间延迟量进一步探究了真正的转录因子与靶基因间的调控关系。通过分析我们获得了13个在骨骼肌再生过程中发挥重要作用的转录因子,其中的6个转录因子已经有文献报道过。这些转录因子中的FoxO3富集于胰岛素信号通路(IGF1-FoxO),该信号通路曾被报道与生长发育密切相关。因此,鼠的骨骼肌再生芯片分析提供的IGF1-FoxO可以作为影响猪生长发育的候选通路。
三、IGF1-FoxO通路进化分析:IGF1-FoxO信号转导通路在内分泌调控进程中发挥着重要作用,该信号通路在脊椎动物物种中保持一定的一致性。虽然该信号通路成员组成在模式生物物种中已经被广泛研究,但是通路的进化模式还有待于进行深入的研究。我们首先对通路成员组成即在不同的物种中信号通路成员的组成是否存在差异开展了研究,进而对在这一复杂的系统中各个成员组成在进化历程中是否具有相似的选择约束等问题做了探索。我们的结果表明大多数的通路成员存在于所有的待研究的物种中,他们遭受较强的选择约束。通路成员中的2个基因受到正选择的影响。我们发现整个通路的选择强度呈梯度变化,即上游基因相对于下游基因具有相对较强的选择约束。我们的研究也发现,通路成员的选择压力影响因素较为复杂,除受密码子偏性、蛋白长度影响外,还有其它的待研究因素。
四、猪IGF1-FoxO通路成员的确定及通路成员SNP信息的推断:通过对模式生物进行进化研究,我们获得了通路成员的保守组成。我们利用人、鼠模式生物的27个保守基因分别到猪的基因组数据库进行blast搜索,用来获取猪的通路成员。因为猪的基因组注释信息的不完整性,所以我们同时也采用搜索通路成员的侧翼序列,用来尽可能获取猪的通路成员。最终我们获得保守通路27个成员中的22个基因。我们将这些基因定义为要研究的基因集。这些通路成员基因序列与NCBI Trace Repository和GenBank中的鸟枪序列进行blast搜索及序列比对操作,进而推断通路成员的SNP位点信息。
五、IGF1-FoxO通路SNP的检测分型及性状的关联分析:在对猪IGF1-FoxO通路SNP推断分析的基础上,我们运用SNaPshot与PCR-RFLP技术对猪部分通路基因多态位点进行检测分型,同时运用奥斯本自动喂料记录系统对111头杜长大猪做了生长育肥试验,进而对多态位点与生长发育相关性状进行关联分析,以期阐明通路中的哪些基因、哪些SNP位点决定了性状的不同表型,这些基因和多态位点是如何发挥作用的,即其生物学功能及作用模式如何,从而在通路水平上揭示猪的生长发育机制。研究结果表明,FoxO3可能是控制生长发育的一个主效基因或者与控制生长发育的主效基因紧密连锁。
我们所开展的这些研究,为揭示复杂性状的遗传机制尤其是进行猪生长发育性状的遗传机制分析及标记辅助选择,进而对猪进行遗传改良提供了坚实的基础。
The growth and development of porcine will influence the growth rate, feed efficiency, body composition and carcass traits, and so on, which last influence economic efficiency. However, studying the mechanism of controlling growth and development confronts significant challenges at present and it restricts genetic improvement of pigs. Candidate genes analyzing, whole genome scanning, and whole genome association analysis were widely used to study the mechanism of controlling growth and development and obtained some candidate QTLs and SNPs, but it could not reflect biological process from genes to traits. Thus, this project first put forward a Candidate Gene Set Approach and used it to study the the mechanism of controlling growth and development of pig.
1. The establishment of Candidate Gene Set Approach
There is some blindness and arbitrariness for candidate gene method and this method is vulnerable to the influence by environment. Moreover, QTL used to location by linkage analysis spans tens of map units, or hundreds of genes, and this method need special population. Further, it costs much by GWAS method. In this case, we present the Candidate Gene Set Approach, and the standardize process includes the gene set selection, QTL mapping, candiadted SNP prediction, genotyping and associated analysis. This method has the virtue of motivated, easy for performance, reasonable for costs, etc.
2. Uncovering the transcriptional circuitry in skeletal muscle regeneration
Skeletal muscle has a remarkable ability to regenerate after repeated and complete destruction of the tissue. The healing phases for an injured muscle, including degeneration, inflammation, regeneration and remodeling, undergo an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network confronts significant challenges and requires the integration of multiple experimental data types. In this work, we focus on getting insights into the transcriptional circuitry during skeletal muscle regeneration using time-course expression data and motif scanning information. A set of→TtFarget associations was derived from predicted TF binding sites in promoter regions of co-expressed genes. Time-lagged correlation analysis was utilized to evaluate the TF→target associations. Our analysis identified 13 TFs that potentially play a central role throughout the regeneration process. Six of which have previously been described to be important for muscle regeneration and differentiation. The transcriptional factor FoxO3 was enrichment into the insulin pathway, and this pathway was involved into growth and development reported by previous literature. Therefore, mouse skeletal muscle regeneration analysis offered the clues that IGF1-FoxO was a candidate pathway that influence pigs’growth and development.
3. The molecular evolutionary patterns of the insulin/FOXO signaling pathway
The insulin/insulin growth factor-1(IGF1)/FOXO (IIF) signal transduction pathway plays a core role in endocrine system and maintains remarkable consistency across all vertebrate species. Although the components of this pathway have been best-characterized, the evolutionary pattern remains poorly understood. Here, we performed a comprehensive analysis to study whether the differences of signaling transduction elements exist among different animal species and to determine whether the genes are subject to equivalent evolutionary forces and how natural selection shapes the evolution of proteins in an interacting system. Our results demonstrate that most IIF pathway components are present throughout all animal phyla investigated here, and they are under strong selective constraint but two of which show evidence for positive selection. Remarkably, we detected a gradient in the strength of purifying selection along the pathway, increasing from the upstream to the downstream genes. We also found that the dN/dS may be influenced by quite complicated factors including codon bias, protein length and other factors.
4. The identification of the porcine components of IGF1-FoxO pathway and SNP inference of pathway component members
We got conserved pathway members though evolutionary analysis of model animals. We make use of 27 conserved members from model animals of human and mouse to blast pig genome to get pathway components. Because of incomplete annotation informations of pig’s genome, flanking sequences of pathway componences were also used to blast pig’s genome to get whole pathway members. At last, we got 22 genes from 27 pathway members. These members were then blast to shotgun sequences from NCBI Trace Repository and GenBank to mine SNP informatics.
5. Experimental validation of candidate SNPs from IGF1-FoxO pathway and the association between polymorphism and traits of pig The polymorphism (SNP) of IGF1-FoxO pathway genes mining by previous research
were chosen for experimental validation by NaPshot sequencing and PCR-RFLP. We then performed association analysis between polymorphisms and growth and development traits to illustrate how different phenotypes were influenced by different genes and SNPs and how different genes and SNPs excerting their roles. Namely, knowing the biological function and regualatory mode, we could recovery the growth and development mechanism of pig from pathway perspective. Our result demonstrated that FoxO3 may be one or linkage with one of the major genes that influence the growth and development of pig. The above results help to lay a foundation for the further research of molecular genetic mechanisms of pig growth and development performance and provide evidence for pig marker assistant selection and marker-assisted introgression.
引文
1. Fujii, J., Otsu, K., Zorzato, F., et al., Identification of a mutation in porcine ryanodine receptor associated with malignant hyperthermia. Science, 1991. 253(5018). 448-451.
2. Rothschild, M., Jacobson, C., Vaske, D., et al., The estrogen receptor locus is associated with a major gene influencing litter size in pigs. Proceedings of the National Academy of Sciences of the United States of America, 1996. 93(1). 201-205.
3. Botstein, D., White, R.L., Skolnick, M., et al., Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American journal of human genetics, 1980. 32(3). 314-331.
4. Roberts, A., McMillan, L., Wang, W., et al., Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows. Bioinformatics, 2007. 23(13). i401-7.
5. Hirschhorn, J.N. and Daly, M.J., Genome-wide association studies for common diseases and complex traits. Nat Rev Genet, 2005. 6(2). 95-108.
6. Kingsmore, S.F., Lindquist, I.E., Mudge, J., et al., Genome-wide association studies: progress in identifying genetic biomarkers in common, complex diseases. Biomark Insights, 2007. 2. 283-92.
7. Kanehisa, M. and Goto, S., KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 2000. 28(1). 27-30.
8.陈静,杜红丽,张细权,猪基因组研究进展.广东农业科学2007. 5. 81-83.
9. Archibald, A.L., Haley, C.S., Brown, J.F., et al., The PiGMaP consortium linkage map of the pig (Sus scrofa). Mamm Genome, 1995. 6(3). 157-75.
10. Rohrer, G.A., Alexander, L.J., Hu, Z., et al., A comprehensive map of the porcine genome. Genome Res, 1996. 6(5). 371-91.
11. Womack, J.E., Advances in livestock genomics: opening the barn door. Genome Res, 2005. 15(12). 1699-705.
12. Wang, Y., Price, S.E., and Jiang, H., Cloning and characterization of the bovine class 1 and class 2 insulin-like growth factor-I mRNAs. Domest Anim Endocrinol, 2003. 25(4). 315-28.
13. Furstenberger, G. and Senn, H.J., Insulin-like growth factors and cancer. Lancet Oncol, 2002. 3(5). 298-302.
14. Salmon, W.D. and Daughaday, W.H., A hormonally controlled serum factor which stimulates sulfate incorporation by cartilage in vitro. The Journal of laboratory and clinical medicine, 1990. 116(3). 408-419.
15.徐金先,二花脸猪生长激素基因表达的发育性变化及其与大白猪的比较研究.南京农业大学硕士论文, 2004.
16. Jones, J.I. and Clemmons, D.R., Insulin-like growth factors and their binding proteins: biological actions. Endocrine Reviews, 1995. 16(1). 3-34.
17. Bunter, K., Hermesch, B., and Luxford, K., IGF-I concentration measured in juvenile pigs provides information for breeding programs: A mini review. . 7th World Cong Genet Appl Livest Prod,2002:03-09, 2002.
18. Suzuki, K., Nakagawa, M., Katoh, K., et al., Genetic correlation between serum insulin-like growth factor-1 concentration and performance and meat quality traits in Duroc pigs. Journal of animal science, 2004. 82(4). 994-999.
19. Taniguchi, C.M., Emanuelli, B., and Kahn, C.R., Critical nodes in signalling pathways: insights into insulin action. Nature Reviews Molecular Cell Biology, 2006. 7(2). 85-96.
20. Waters, S.B. and Pessin, J.E., Insulin receptor substrate 1 and 2 (IRS1 and IRS2): what a tangled web we weave. Trends in Cell Biology, 1996. 6(1). 1-4.
21. Lavan, B.E., Fantin, V.R., Chang, E.T., et al., A novel 160-kDa phosphotyrosine protein in insulin-treated embryonic kidney cells is a new member of the insulin receptor substrate family. Journal of Biological Chemistry, 1997. 272(34). 21403-21407.
22. Edinger, A.L. and Thompson, C.B., Akt maintains cell size and survival by increasing mTOR-dependent nutrient uptake. Mol Biol Cell, 2002. 13(7). 2276-88.
23. Condorelli, G., Drusco, A., Stassi, G., et al., Akt induces enhanced myocardial contractility and cell size in vivo in transgenic mice. Proc Natl Acad Sci U S A, 2002. 99(19). 12333-8.
24. Fingar, D.C., Salama, S., Tsou, C., et al., Mammalian cell size is controlled by mTOR and its downstream targets S6K1 and 4EBP1/eIF4E. Genes & development, 2002. 16(12). 1472.
25. Martín, J., Hunt, S.L., Dubus, P., et al., Genetic rescue of Cdk4 null mice restores pancreaticβ-cell proliferation but not homeostatic cell number. Oncogene, 2003. 22(34). 5261-5269.
26. Nakae, J., Kitamura, T., Kitamura, Y., et al., The forkhead transcription factor Foxo1 regulates adipocyte differentiation. Dev Cell, 2003. 4(1). 119-29.
27. Hu, P., Geles, K.G., Paik, J.H., et al., Codependent activators direct myoblast-specific MyoD transcription. Dev Cell, 2008. 15(4). 534-46.
28. Pawlikowska, L., Hu, D., Huntsman, S., et al., Association of common genetic variation in the insulin/IGF1 signaling pathway with human longevity. Aging Cell, 2009. 8(4). 460-72.
29. Salminen, A. and Kaarniranta, K., Insulin/IGF-1 paradox of aging: regulation via AKT/IKK/NF-kappaB signaling. Cell Signal, 2009. 22(4). 573-7.
30. Burgering, B.M.T. and Medema, R.H., Decisions on life and death: FOXO Forkhead transcription factors are in command when PKB/Akt is off duty. Journal of leukocyte biology, 2003. 73(6). 689.
31. McPherron, A.C., Lawler, A.M., and Lee, S.J., Regulation of skeletal muscle mass in mice by a new TGF-beta superfamily member. Nature, 1997. 387(6628). 83-90.
32. Szabo, G., Dallmann, G., Muller, G., et al., A deletion in the myostatin gene causes the compact (Cmpt) hypermuscular mutation in mice. Mamm Genome, 1998. 9(8). 671-2.
33. Lee, S.J. and McPherron, A.C., Regulation of myostatin activity and muscle growth. Proc Natl Acad Sci U S A, 2001. 98(16). 9306-11.
34. Zhu, X., Hadhazy, M., Wehling, M., et al., Dominant negative myostatin produces hypertrophy without hyperplasia in muscle. FEBS letters, 2000. 474(1). 71-75.
35. Yang, J., Ratovitski, T., Brady, J.P., et al., Expression of myostatin pro domain results in muscular transgenic mice. Mol Reprod Dev, 2001. 60(3). 351-61.
36. Hill, J.J., Davies, M.V., Pearson, A.A., et al., The myostatin propeptide and the follistatin-related gene are inhibitory binding proteins of myostatin in normal serum. J Biol Chem, 2002. 277(43). 40735-41.
37. Zimmers, T.A., Davies, M.V., Koniaris, L.G., et al., Induction of cachexia in mice by systemically administered myostatin. Science, 2002. 296(5572). 1486-8.
38. Thomas, M., Langley, B., Berry, C., et al., Myostatin, a negative regulator of muscle growth, functions by inhibiting myoblast proliferation. J Biol Chem, 2000. 275(51). 40235-43.
39. Dominique, J.E. and Gérard, C., Myostatin regulation of muscle development: molecular basis, natural mutations, physiopathological aspects. Experimental cell research, 2006. 312(13). 2401-2414.
40. Dezan, C., Meierhans, D., Künne, A.G.E., et al., Acquisition of Myogenic Specificity through Replacement of One Amino Acid of MASH-1 and Introduction of an Additional -Helical Turn. Biological chemistry, 1999. 380. 705-710.
41. Te, P.A.S., Genetic regulation of meat production by embryonic muscle formation―a review. Journal of animal breeding and genetics, 1994. 111(5-6). 404-412.
42. Te Pas, M.F.W. and Soumillion, A., The use of physiologic and functional genomicinformation of the regulation of the determination of skeletal muscle mass in livestock breeding strategies to enhance meat production. Current Genomics, 2001. 2. 285-304.
43. Pownall, M.E. and Emerson, C.P., Sequential activation of three myogenic regulatory genes during somite morphogenesis in quail embryos* 1. Developmental biology, 1992. 151(1). 67-79.
44. Cossu, G., Tajbakhsh, S., and Buckingham, M., How is myogenesis initiated in the embryo? Trends in Genetics, 1996. 12(6). 218-223.
45.李冲,猪MyoD、MSTN基因RNA干扰及其功能相互关系的研究.东北农业大学硕士论文, 2007.
46. Wilson, E.M. and Rotwein, P., Control of MyoD function during initiation of muscle differentiation by an autocrine signaling pathway activated by insulin-like growth factor-II. Journal of Biological Chemistry, 2006. 281(40). 29962.
47. Polesskaya, A., Duquet, A., Naguibneva, I., et al., CREB-binding protein/p300 activates MyoD by acetylation. Journal of Biological Chemistry, 2000. 275(44). 34359.
48. Shi, L., Zhao, G., Qiu, D., et al., NF90 regulates cell cycle exit and terminal myogenic differentiation by direct binding to the 3′-untranslated region of MyoD and p21WAF1/CIP1 mRNAs. Journal of Biological Chemistry, 2005. 280(19). 18981.
49. Wu, H.Y., Hamamori, Y., Xu, J., et al., Nuclear hormone receptor coregulator GRIP1 suppresses, whereas SRC1A and p/CIP coactivate, by domain-specific binding of MyoD. J Biol Chem, 2005. 280(5). 3129-37.
50. Micheli, L., Leonardi, L., Conti, F., et al., PC4 coactivates MyoD by relieving the histone deacetylase 4-mediated inhibition of myocyte enhancer factor 2C. Molecular and cellular biology, 2005. 25(6). 2242.
51. Molkentin, J.D., Black, B.L., Martin, J.F., et al., Cooperative activation of muscle gene expression by MEF2 and myogenic bHLH proteins. Cell, 1995. 83(7). 1125-1136.
52. Liu, N., Williams, A.H., Kim, Y., et al., An intragenic MEF2-dependent enhancer directs muscle-specific expression of microRNAs 1 and 133. Proceedings of the National Academy of Sciences, 2007. 104(52). 20844-20849.
53. Naidu, P.S., Ludolph, D.C., To, R.Q., et al., Myogenin and MEF2 function synergistically to activate the MRF4 promoter during myogenesis. Molecular and cellular biology, 1995. 15(5). 2707-2718.
54. Mansouri, A., Stoykova, A., Torres, M., et al., Dysgenesis of cephalic neural crest derivatives in Pax7-/- mutant mice. Development, 1996. 122(3). 831-838.
55. Tajbakhsh, S., Rocancourt, D., Cossu, G., et al., Redefining the genetic hierarchiescontrolling skeletal myogenesis: Pax-3 and Myf-5 act upstream of MyoD. Cell, 1997. 89(1). 127-138.
56. Goulding, M., Lumsden, A., and Paquette, A.J., Regulation of Pax-3 expression in the dermomyotome and its role in muscle development. Development, 1994. 120(4). 957-971.
57. Buckingham, M., Bajard, L., Chang, T., et al., The formation of skeletal muscle: from somite to limb. Journal of anatomy, 2003. 202(1). 59-68.
58. Brunsch, C., Sternstein, I., Reinecke, P., et al., Analysis of associations of PIT1 genotypes with growth, meat quality and carcass composition traits in pigs. Journal of Applied Genetics, 2002. 43(1). 85-92.
59.王启贵,通过候选基因法对影响肉质性状基因的研究.东北农业大学硕士学位论文, 2001.
60. Ashburner, M., Ball, C.A., Blake, J.A., et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000. 25(1). 25-9.
61. Sonnhammer, E.L., Eddy, S.R., and Durbin, R., Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins, 1997. 28(3). 405-20.
62. Bateman, A., Birney, E., Durbin, R., et al., The Pfam protein families database. Nucleic Acids Res, 2000. 28(1). 263-6.
63. Schuler, G.D., Epstein, J.A., Ohkawa, H., et al., Entrez: molecular biology database and retrieval system. Methods Enzymol, 1996. 266. 141-62.
64. Williams, R.D., Hing, S.N., Greer, B.T., et al., Prognostic classification of relapsing favorable histology Wilms tumor using cDNA microarray expression profiling and support vector machines. Genes, Chromosomes and Cancer, 2004. 41(1). 65-79.
65. Sorlie, T., Perou, C.M., Tibshirani, R., et al., Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. PNAS, 2001. 98(19). 10869-74.
66. Makretsov, N.A., Huntsman, D.G., Nielsen, T.O., et al., Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma. Clin Cancer Res, 2004. 10(18 Pt 1). 6143-51.
67. Liu, L., Gong, G., Liu, Y., et al., Multi-species comparative mapping in silico using the COMPASS strategy. Bioinformatics, 2004. 20(2). 148-154.
68. Fitch, W.M., Estimating the total number of nucleotide substitutions since the common ancestor of a pair of homologous genes: comparison of several methods and three beta hemoglobin messenger RNA's. Journal of Molecular Evolution, 1980. 16(3). 153-209.
69. Jensen, L.J., Saric, J., and Bork, P., Literature mining for the biologist: from informationretrieval to biological discovery. Nat Rev Genet, 2006. 7(2). 119-29.
70. Bader, G.D., Donaldson, I., Wolting, C., et al., BIND--The Biomolecular Interaction Network Database. Nucleic Acids Res, 2001. 29(1). 242-5.
71. Stark, C., Breitkreutz, B.J., Reguly, T., et al., BioGRID: a general repository for interaction datasets. Nucleic Acids Res, 2006. 34(Database issue). D535-9.
72. Keseler, I.M., Bonavides-Martinez, C., Collado-Vides, J., et al., EcoCyc: a comprehensive view of Escherichia coli biology. Nucleic Acids Res, 2009. 37(Database issue). D464-70.
73. Keshava Prasad, T.S., Goel, R., Kandasamy, K., et al., Human Protein Reference Database--2009 update. Nucleic Acids Res, 2009. 37(Database issue). D767-72.
74. Cooper, D.N., Smith, B.A., Cooke, H.J., et al., An estimate of unique DNA sequence heterozygosity in the human genome. Hum Genet, 1985. 69(3). 201-5.
75.赵琼一,李信,周德贵等,后基因组时代下作物的SNP分型方法.分子植物育种, 2010. 8(1). 125-133.
76. Gabriel, S.B., Schaffner, S.F., Nguyen, H., et al., The structure of haplotype blocks in the human genome. Science, 2002. 296(5576). 2225-9.
77. Yanling, H., Sinnwell, J., Qishan, W., et al., Regression-based approach for testing the association between multi-region haplotype configuration and complex trait. BMC Genetics. 10.
78.陆璇,数理统计基础清华大学出版社, 1998.
79. Goldman, N. and Yang, Z., A codon-based model of nucleotide substitution for protein-coding DNA sequences. Mol Biol Evol, 1994. 11(5). 725-36.
80. Zhao, X.S., Gallardo, T.D., Lin, L., et al., Transcriptional mapping and genomic analysis of the cardiac atria and ventricles. Physiol Genomics, 2002. 12(1). 53-60.
81. 'R: A Language and Environment for Statistical Computing', R Development Core Team, (2009)
82. R: A language and environment for statistical computing. R Foundation for Statistical Computing 2009 [cited; Available from: http://www.R-project.org/.
83. Gentleman, R.C., Carey, V.J., Bates, D.M., et al., Bioconductor: open software development for computational biology and bioinformatics. Genome Biol, 2004. 5(10). R80.
84. Irizarry, R.A., Hobbs, B., Collin, F., et al., Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 2003. 4(2). 249-64.
85. Tusher, V.G., Tibshirani, R., and Chu, G., Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A, 2001. 98(9). 5116-21.
86. Fulton, D.L., Sundararajan, S., Badis, G., et al., TFCat: the curated catalog of mouse and human transcription factors. Genome Biol, 2009. 10(3). R29.
87. Karolchik, D., Kuhn, R.M., Baertsch, R., et al., The UCSC Genome Browser Database: 2008 update. Nucleic Acids Res, 2008. 36(Database issue). D773-9.
88. Sandelin, A., Alkema, W., Engstrom, P., et al., JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res, 2004. 32(Database issue). D91-4.
89. Frith, M.C., Fu, Y., Yu, L., et al., Detection of functional DNA motifs via statistical over-representation. Nucleic Acids Res, 2004. 32(4). 1372-81.
90. Ramsey, S.A., Klemm, S.L., Zak, D.E., et al., Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics. PLoS Comput Biol, 2008. 4(3). e1000021.
91. Stanton, L.W., Garrard, L.J., Damm, D., et al., Altered patterns of gene expression in response to myocardial infarction. Circ Res, 2000. 86(9). 939-45.
92. Barrio, M., Burrage, K., Leier, A., et al., Oscillatory regulation of Hes1: Discrete stochastic delay modelling and simulation. PLoS Comput Biol, 2006. 2(9). e117.
93. LeMaire, M.F. and Thummel, C.S., Splicing precedes polyadenylation during Drosophila E74A transcription. Mol Cell Biol, 1990. 10(11). 6059-63.
94. Kanehisa, M., Araki, M., Goto, S., et al., KEGG for linking genomes to life and the environment. Nucleic Acids Res, 2008. 36(Database issue). D480-4.
95. Yu, H., Luscombe, N.M., Qian, J., et al., Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet, 2003. 19(8). 422-7.
96. D'Haeseleer, P., Liang, S., and Somogyi, R., Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics, 2000. 16(8). 707-26.
97. Tavazoie, S., Hughes, J.D., Campbell, M.J., et al., Systematic determination of genetic network architecture. Nat Genet, 1999. 22(3). 281-5.
98. Buckingham, M., Making muscle in mammals. Trends Genet, 1992. 8(4). 144-8.
99. Birney, E., Stamatoyannopoulos, J.A., Dutta, A., et al., Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature, 2007. 447(7146). 799-816.
100. Beggs, A.H., Byers, T.J., Knoll, J.H., et al., Cloning and characterization of two human skeletal muscle alpha-actinin genes located on chromosomes 1 and 11. J Biol Chem, 1992. 267(13). 9281-8.
101. Weins, A., Schlondorff, J.S., Nakamura, F., et al., Disease-associated mutantalpha-actinin-4 reveals a mechanism for regulating its F-actin-binding affinity. Proc Natl Acad Sci U S A, 2007. 104(41). 16080-5.
102. Wang, M., Zhang, X., Zhao, H., et al., FoxO gene family evolution in vertebrates. BMC Evol Biol, 2009. 9. 222.
103. Riley, R.M., Jin, W., and Gibson, G., Contrasting selection pressures on components of the Ras-mediated signal transduction pathway in Drosophila. Mol Ecol, 2003. 12(5). 1315-23.
104. Edgar, R.C., MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res, 2004. 32(5). 1792-7.
105. Suyama, M., Torrents, D., and Bork, P., PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res, 2006. 34(Web Server issue). W609-12.
106. Ronquist, F. and Huelsenbeck, J.P., MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics, 2003. 19(12). 1572-4.
107. Posada, D. and Crandall, K.A., MODELTEST: testing the model of DNA substitution. Bioinformatics, 1998. 14(9). 817-8.
108. Rozas, J. and Rozas, R., DnaSP version 3: an integrated program for molecular population genetics and molecular evolution analysis. Bioinformatics, 1999. 15(2). 174-5.
109. Wong, W.S., Yang, Z., Goldman, N., et al., Accuracy and power of statistical methods for detecting adaptive evolution in protein coding sequences and for identifying positively selected sites. Genetics, 2004. 168(2). 1041-51.
110. Yang, Z., Nielsen, R., Goldman, N., et al., Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics, 2000. 155(1). 431-49.
111. Gronke, S., Clarke, D.F., Broughton, S., et al., Molecular evolution and functional characterization of Drosophila insulin-like peptides. PLoS Genet, 2010. 6(2). e1000857.
112. Kelley, K.M., Experimental diabetes mellitus in a teleost fish. I. Effect of complete isletectomy and subsequent hormonal treatment on metabolism in the goby, Gillichthys mirabilis. Endocrinology, 1993. 132(6). 2689-95.
113. Nagasawa, H., Kataoka, H., Isogai, A., et al., Amino-terminal amino Acid sequence of the silkworm prothoracicotropic hormone: homology with insulin. Science, 1984. 226(4680). 1344-5.
114. Wolkow, C.A., Munoz, M.J., Riddle, D.L., et al., Insulin receptor substrate and p55 orthologous adaptor proteins function in the Caenorhabditis elegans daf-2/insulin-like signaling pathway. J Biol Chem, 2002. 277(51). 49591-7.
115. Duret, L., Guex, N., Peitsch, M.C., et al., New insulin-like proteins with atypical disulfidebond pattern characterized in Caenorhabditis elegans by comparative sequence analysis and homology modeling. Genome Res, 1998. 8(4). 348-53.
116. Krieger, M.J., Jahan, N., Riehle, M.A., et al., Molecular characterization of insulin-like peptide genes and their expression in the African malaria mosquito, Anopheles gambiae. Insect Mol Biol, 2004. 13(3). 305-15.
117. Li, C., The ever-expanding neuropeptide gene families in the nematode Caenorhabditis elegans. Parasitology, 2005. 131 Suppl. S109-27.
118. Olinski, R.P., Lundin, L.G., and Hallb k, F., Conserved synteny between the Ciona genome and human paralogons identifies large duplication events in the molecular evolution of the insulin-relaxin gene family. Molecular biology and evolution, 2006. 23(1). 10.
119. Taylor, J.S., Braasch, I., Frickey, T., et al., Genome duplication, a trait shared by 22000 species of ray-finned fish. Genome Res, 2003. 13(3). 382-90.
120. Taylor, J.S., Van de Peer, Y., Braasch, I., et al., Comparative genomics provides evidence for an ancient genome duplication event in fish. Philos Trans R Soc Lond B Biol Sci, 2001. 356(1414). 1661-79.
121. Holland, P.W., Garcia-Fernandez, J., Williams, N.A., et al., Gene duplications and the origins of vertebrate development. Dev Suppl, 1994. 125-33.
122. Friedman, R. and Hughes, A.L., Pattern and timing of gene duplication in animal genomes. Genome Res, 2001. 11(11). 1842-7.
123. Hughes, A.L., Phylogenies of developmentally important proteins do not support the hypothesis of two rounds of genome duplication early in vertebrate history. Journal of Molecular Evolution, 1999. 48(5). 565-576.
124. Hughes, A.L., Phylogenetic tests of the hypothesis of block duplication of homologous genes on human chromosomes 6, 9, and 1. Mol Biol Evol, 1998. 15(7). 854-70.
125. Martin, A., Is tetralogy true? Lack of support for the“one-to-four rule”. Molecular biology and evolution, 2001. 18(1). 89.
126. Rausher, M.D., Miller, R.E., and Tiffin, P., Patterns of evolutionary rate variation among genes of the anthocyanin biosynthetic pathway. Mol. Biol. Evol., 1999. 16(2). 266-274.
127. Rausher, M.D., Lu, Y., and Meyer, K., Variation in constraint versus positive selection as an explanation for evolutionary rate variation among anthocyanin genes. Journal of Molecular Evolution, 2008. 67(2). 137-144.
128. Alvarez-Ponce, D., Aguade, M., and Rozas, J., Network-level molecular evolutionary analysis of the insulin/TOR signal transduction pathway across 12 Drosophila genomes. Genome Res, 2009. 19(2). 234-42.
129. Vitkup, D., Kharchenko, P., and Wagner, A., Influence of metabolic network structure and function on enzyme evolution. Genome Biol, 2006. 7(5). R39.
130. Olsen, K.M., Womack, A., Garrett, A.R., et al., Contrasting evolutionary forces in the Arabidopsis thaliana floral developmental pathway. Genetics, 2002. 160(4). 1641-50.
131. Cork, J.M. and Purugganan, M.D., The evolution of molecular genetic pathways and networks. Bioessays, 2004. 26(5). 479-84.
132. Fraser, H.B., Hirsh, A.E., Steinmetz, L.M., et al., Evolutionary rate in the protein interaction network. Science, 2002. 296(5568). 750-2.
133. Wang, D.G., Fan, J.B., Siao, C.J., et al., Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science, 1998. 280(5366). 1077-82.
134. Jungerius, B.J., Gu, J., Crooijmans, R.P., et al., Estimation of the extent of linkage disequilibrium in seven regions of the porcine genome. Anim Biotechnol, 2005. 16(1). 41-54.
135. Humphray, S.J., Scott, C.E., Clark, R., et al., A high utility integrated map of the pig genome. Genome Biol, 2007. 8(7). R139.
136. Wernersson, R., Schierup, M.H., Jorgensen, F.G., et al., Pigs in sequence space: a 0.66X coverage pig genome survey based on shotgun sequencing. BMC Genomics, 2005. 6(1). 70.
137. Zhang, Z., Schwartz, S., Wagner, L., et al., A greedy algorithm for aligning DNA sequences. J Comput Biol, 2000. 7(1-2). 203-14.
138. Gu, Z., Hillier, L., and Kwok, P.Y., Single nucleotide polymorphism hunting in cyberspace. Hum Mutat, 1998. 12. 221-225.
139. Taillon-Miller, P., Gu, Z., Li, Q., et al., Overlapping genomic sequences: a treasure trove of single-nucleotide polymorphisms. Genome Res, 1998. 8. 748-754.
140. Picoult-Newberg, L., Ideker, T.E., Pohl, M.G., et al., Mining SNPs from EST databases. Genome Res, 1999. 9. 167-174.
141. Kwok, P.Y., Deng, Q., Zakeri, H., et al., Increasing the information content of STS-based genome maps: identifying polymorphisms in mapped STSs. Genomics, 1996. 31. 123-126.
142. Garg, K., Green, P., and Nickerson, D.A., Identification of candidate coding region single nucleotide polymorphisms in 165 human genes using assembled expressed sequence tags. Genome Res, 1999. 9. 1087-1092.
143. Marth, G.T., Korf, I., Yandell, M.D., et al., A general approach to singlenucleotide polymorphism discovery. Nat Genet 1999. 23. 452-456.
2. Rothschild, M., Jacobson, C., Vaske, D., et al., The estrogen receptor locus is associated with a major gene influencing litter size in pigs. Proceedings of the National Academy of Sciences of the United States of America, 1996. 93(1). 201-205.
3. Botstein, D., White, R.L., Skolnick, M., et al., Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American journal of human genetics, 1980. 32(3). 314-331.
4. Roberts, A., McMillan, L., Wang, W., et al., Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows. Bioinformatics, 2007. 23(13). i401-7.
5. Hirschhorn, J.N. and Daly, M.J., Genome-wide association studies for common diseases and complex traits. Nat Rev Genet, 2005. 6(2). 95-108.
6. Kingsmore, S.F., Lindquist, I.E., Mudge, J., et al., Genome-wide association studies: progress in identifying genetic biomarkers in common, complex diseases. Biomark Insights, 2007. 2. 283-92.
7. Kanehisa, M. and Goto, S., KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 2000. 28(1). 27-30.
8.陈静,杜红丽,张细权,猪基因组研究进展.广东农业科学2007. 5. 81-83.
9. Archibald, A.L., Haley, C.S., Brown, J.F., et al., The PiGMaP consortium linkage map of the pig (Sus scrofa). Mamm Genome, 1995. 6(3). 157-75.
10. Rohrer, G.A., Alexander, L.J., Hu, Z., et al., A comprehensive map of the porcine genome. Genome Res, 1996. 6(5). 371-91.
11. Womack, J.E., Advances in livestock genomics: opening the barn door. Genome Res, 2005. 15(12). 1699-705.
12. Wang, Y., Price, S.E., and Jiang, H., Cloning and characterization of the bovine class 1 and class 2 insulin-like growth factor-I mRNAs. Domest Anim Endocrinol, 2003. 25(4). 315-28.
13. Furstenberger, G. and Senn, H.J., Insulin-like growth factors and cancer. Lancet Oncol, 2002. 3(5). 298-302.
14. Salmon, W.D. and Daughaday, W.H., A hormonally controlled serum factor which stimulates sulfate incorporation by cartilage in vitro. The Journal of laboratory and clinical medicine, 1990. 116(3). 408-419.
15.徐金先,二花脸猪生长激素基因表达的发育性变化及其与大白猪的比较研究.南京农业大学硕士论文, 2004.
16. Jones, J.I. and Clemmons, D.R., Insulin-like growth factors and their binding proteins: biological actions. Endocrine Reviews, 1995. 16(1). 3-34.
17. Bunter, K., Hermesch, B., and Luxford, K., IGF-I concentration measured in juvenile pigs provides information for breeding programs: A mini review. . 7th World Cong Genet Appl Livest Prod,2002:03-09, 2002.
18. Suzuki, K., Nakagawa, M., Katoh, K., et al., Genetic correlation between serum insulin-like growth factor-1 concentration and performance and meat quality traits in Duroc pigs. Journal of animal science, 2004. 82(4). 994-999.
19. Taniguchi, C.M., Emanuelli, B., and Kahn, C.R., Critical nodes in signalling pathways: insights into insulin action. Nature Reviews Molecular Cell Biology, 2006. 7(2). 85-96.
20. Waters, S.B. and Pessin, J.E., Insulin receptor substrate 1 and 2 (IRS1 and IRS2): what a tangled web we weave. Trends in Cell Biology, 1996. 6(1). 1-4.
21. Lavan, B.E., Fantin, V.R., Chang, E.T., et al., A novel 160-kDa phosphotyrosine protein in insulin-treated embryonic kidney cells is a new member of the insulin receptor substrate family. Journal of Biological Chemistry, 1997. 272(34). 21403-21407.
22. Edinger, A.L. and Thompson, C.B., Akt maintains cell size and survival by increasing mTOR-dependent nutrient uptake. Mol Biol Cell, 2002. 13(7). 2276-88.
23. Condorelli, G., Drusco, A., Stassi, G., et al., Akt induces enhanced myocardial contractility and cell size in vivo in transgenic mice. Proc Natl Acad Sci U S A, 2002. 99(19). 12333-8.
24. Fingar, D.C., Salama, S., Tsou, C., et al., Mammalian cell size is controlled by mTOR and its downstream targets S6K1 and 4EBP1/eIF4E. Genes & development, 2002. 16(12). 1472.
25. Martín, J., Hunt, S.L., Dubus, P., et al., Genetic rescue of Cdk4 null mice restores pancreaticβ-cell proliferation but not homeostatic cell number. Oncogene, 2003. 22(34). 5261-5269.
26. Nakae, J., Kitamura, T., Kitamura, Y., et al., The forkhead transcription factor Foxo1 regulates adipocyte differentiation. Dev Cell, 2003. 4(1). 119-29.
27. Hu, P., Geles, K.G., Paik, J.H., et al., Codependent activators direct myoblast-specific MyoD transcription. Dev Cell, 2008. 15(4). 534-46.
28. Pawlikowska, L., Hu, D., Huntsman, S., et al., Association of common genetic variation in the insulin/IGF1 signaling pathway with human longevity. Aging Cell, 2009. 8(4). 460-72.
29. Salminen, A. and Kaarniranta, K., Insulin/IGF-1 paradox of aging: regulation via AKT/IKK/NF-kappaB signaling. Cell Signal, 2009. 22(4). 573-7.
30. Burgering, B.M.T. and Medema, R.H., Decisions on life and death: FOXO Forkhead transcription factors are in command when PKB/Akt is off duty. Journal of leukocyte biology, 2003. 73(6). 689.
31. McPherron, A.C., Lawler, A.M., and Lee, S.J., Regulation of skeletal muscle mass in mice by a new TGF-beta superfamily member. Nature, 1997. 387(6628). 83-90.
32. Szabo, G., Dallmann, G., Muller, G., et al., A deletion in the myostatin gene causes the compact (Cmpt) hypermuscular mutation in mice. Mamm Genome, 1998. 9(8). 671-2.
33. Lee, S.J. and McPherron, A.C., Regulation of myostatin activity and muscle growth. Proc Natl Acad Sci U S A, 2001. 98(16). 9306-11.
34. Zhu, X., Hadhazy, M., Wehling, M., et al., Dominant negative myostatin produces hypertrophy without hyperplasia in muscle. FEBS letters, 2000. 474(1). 71-75.
35. Yang, J., Ratovitski, T., Brady, J.P., et al., Expression of myostatin pro domain results in muscular transgenic mice. Mol Reprod Dev, 2001. 60(3). 351-61.
36. Hill, J.J., Davies, M.V., Pearson, A.A., et al., The myostatin propeptide and the follistatin-related gene are inhibitory binding proteins of myostatin in normal serum. J Biol Chem, 2002. 277(43). 40735-41.
37. Zimmers, T.A., Davies, M.V., Koniaris, L.G., et al., Induction of cachexia in mice by systemically administered myostatin. Science, 2002. 296(5572). 1486-8.
38. Thomas, M., Langley, B., Berry, C., et al., Myostatin, a negative regulator of muscle growth, functions by inhibiting myoblast proliferation. J Biol Chem, 2000. 275(51). 40235-43.
39. Dominique, J.E. and Gérard, C., Myostatin regulation of muscle development: molecular basis, natural mutations, physiopathological aspects. Experimental cell research, 2006. 312(13). 2401-2414.
40. Dezan, C., Meierhans, D., Künne, A.G.E., et al., Acquisition of Myogenic Specificity through Replacement of One Amino Acid of MASH-1 and Introduction of an Additional -Helical Turn. Biological chemistry, 1999. 380. 705-710.
41. Te, P.A.S., Genetic regulation of meat production by embryonic muscle formation―a review. Journal of animal breeding and genetics, 1994. 111(5-6). 404-412.
42. Te Pas, M.F.W. and Soumillion, A., The use of physiologic and functional genomicinformation of the regulation of the determination of skeletal muscle mass in livestock breeding strategies to enhance meat production. Current Genomics, 2001. 2. 285-304.
43. Pownall, M.E. and Emerson, C.P., Sequential activation of three myogenic regulatory genes during somite morphogenesis in quail embryos* 1. Developmental biology, 1992. 151(1). 67-79.
44. Cossu, G., Tajbakhsh, S., and Buckingham, M., How is myogenesis initiated in the embryo? Trends in Genetics, 1996. 12(6). 218-223.
45.李冲,猪MyoD、MSTN基因RNA干扰及其功能相互关系的研究.东北农业大学硕士论文, 2007.
46. Wilson, E.M. and Rotwein, P., Control of MyoD function during initiation of muscle differentiation by an autocrine signaling pathway activated by insulin-like growth factor-II. Journal of Biological Chemistry, 2006. 281(40). 29962.
47. Polesskaya, A., Duquet, A., Naguibneva, I., et al., CREB-binding protein/p300 activates MyoD by acetylation. Journal of Biological Chemistry, 2000. 275(44). 34359.
48. Shi, L., Zhao, G., Qiu, D., et al., NF90 regulates cell cycle exit and terminal myogenic differentiation by direct binding to the 3′-untranslated region of MyoD and p21WAF1/CIP1 mRNAs. Journal of Biological Chemistry, 2005. 280(19). 18981.
49. Wu, H.Y., Hamamori, Y., Xu, J., et al., Nuclear hormone receptor coregulator GRIP1 suppresses, whereas SRC1A and p/CIP coactivate, by domain-specific binding of MyoD. J Biol Chem, 2005. 280(5). 3129-37.
50. Micheli, L., Leonardi, L., Conti, F., et al., PC4 coactivates MyoD by relieving the histone deacetylase 4-mediated inhibition of myocyte enhancer factor 2C. Molecular and cellular biology, 2005. 25(6). 2242.
51. Molkentin, J.D., Black, B.L., Martin, J.F., et al., Cooperative activation of muscle gene expression by MEF2 and myogenic bHLH proteins. Cell, 1995. 83(7). 1125-1136.
52. Liu, N., Williams, A.H., Kim, Y., et al., An intragenic MEF2-dependent enhancer directs muscle-specific expression of microRNAs 1 and 133. Proceedings of the National Academy of Sciences, 2007. 104(52). 20844-20849.
53. Naidu, P.S., Ludolph, D.C., To, R.Q., et al., Myogenin and MEF2 function synergistically to activate the MRF4 promoter during myogenesis. Molecular and cellular biology, 1995. 15(5). 2707-2718.
54. Mansouri, A., Stoykova, A., Torres, M., et al., Dysgenesis of cephalic neural crest derivatives in Pax7-/- mutant mice. Development, 1996. 122(3). 831-838.
55. Tajbakhsh, S., Rocancourt, D., Cossu, G., et al., Redefining the genetic hierarchiescontrolling skeletal myogenesis: Pax-3 and Myf-5 act upstream of MyoD. Cell, 1997. 89(1). 127-138.
56. Goulding, M., Lumsden, A., and Paquette, A.J., Regulation of Pax-3 expression in the dermomyotome and its role in muscle development. Development, 1994. 120(4). 957-971.
57. Buckingham, M., Bajard, L., Chang, T., et al., The formation of skeletal muscle: from somite to limb. Journal of anatomy, 2003. 202(1). 59-68.
58. Brunsch, C., Sternstein, I., Reinecke, P., et al., Analysis of associations of PIT1 genotypes with growth, meat quality and carcass composition traits in pigs. Journal of Applied Genetics, 2002. 43(1). 85-92.
59.王启贵,通过候选基因法对影响肉质性状基因的研究.东北农业大学硕士学位论文, 2001.
60. Ashburner, M., Ball, C.A., Blake, J.A., et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000. 25(1). 25-9.
61. Sonnhammer, E.L., Eddy, S.R., and Durbin, R., Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins, 1997. 28(3). 405-20.
62. Bateman, A., Birney, E., Durbin, R., et al., The Pfam protein families database. Nucleic Acids Res, 2000. 28(1). 263-6.
63. Schuler, G.D., Epstein, J.A., Ohkawa, H., et al., Entrez: molecular biology database and retrieval system. Methods Enzymol, 1996. 266. 141-62.
64. Williams, R.D., Hing, S.N., Greer, B.T., et al., Prognostic classification of relapsing favorable histology Wilms tumor using cDNA microarray expression profiling and support vector machines. Genes, Chromosomes and Cancer, 2004. 41(1). 65-79.
65. Sorlie, T., Perou, C.M., Tibshirani, R., et al., Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. PNAS, 2001. 98(19). 10869-74.
66. Makretsov, N.A., Huntsman, D.G., Nielsen, T.O., et al., Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma. Clin Cancer Res, 2004. 10(18 Pt 1). 6143-51.
67. Liu, L., Gong, G., Liu, Y., et al., Multi-species comparative mapping in silico using the COMPASS strategy. Bioinformatics, 2004. 20(2). 148-154.
68. Fitch, W.M., Estimating the total number of nucleotide substitutions since the common ancestor of a pair of homologous genes: comparison of several methods and three beta hemoglobin messenger RNA's. Journal of Molecular Evolution, 1980. 16(3). 153-209.
69. Jensen, L.J., Saric, J., and Bork, P., Literature mining for the biologist: from informationretrieval to biological discovery. Nat Rev Genet, 2006. 7(2). 119-29.
70. Bader, G.D., Donaldson, I., Wolting, C., et al., BIND--The Biomolecular Interaction Network Database. Nucleic Acids Res, 2001. 29(1). 242-5.
71. Stark, C., Breitkreutz, B.J., Reguly, T., et al., BioGRID: a general repository for interaction datasets. Nucleic Acids Res, 2006. 34(Database issue). D535-9.
72. Keseler, I.M., Bonavides-Martinez, C., Collado-Vides, J., et al., EcoCyc: a comprehensive view of Escherichia coli biology. Nucleic Acids Res, 2009. 37(Database issue). D464-70.
73. Keshava Prasad, T.S., Goel, R., Kandasamy, K., et al., Human Protein Reference Database--2009 update. Nucleic Acids Res, 2009. 37(Database issue). D767-72.
74. Cooper, D.N., Smith, B.A., Cooke, H.J., et al., An estimate of unique DNA sequence heterozygosity in the human genome. Hum Genet, 1985. 69(3). 201-5.
75.赵琼一,李信,周德贵等,后基因组时代下作物的SNP分型方法.分子植物育种, 2010. 8(1). 125-133.
76. Gabriel, S.B., Schaffner, S.F., Nguyen, H., et al., The structure of haplotype blocks in the human genome. Science, 2002. 296(5576). 2225-9.
77. Yanling, H., Sinnwell, J., Qishan, W., et al., Regression-based approach for testing the association between multi-region haplotype configuration and complex trait. BMC Genetics. 10.
78.陆璇,数理统计基础清华大学出版社, 1998.
79. Goldman, N. and Yang, Z., A codon-based model of nucleotide substitution for protein-coding DNA sequences. Mol Biol Evol, 1994. 11(5). 725-36.
80. Zhao, X.S., Gallardo, T.D., Lin, L., et al., Transcriptional mapping and genomic analysis of the cardiac atria and ventricles. Physiol Genomics, 2002. 12(1). 53-60.
81. 'R: A Language and Environment for Statistical Computing', R Development Core Team, (2009)
82. R: A language and environment for statistical computing. R Foundation for Statistical Computing 2009 [cited; Available from: http://www.R-project.org/.
83. Gentleman, R.C., Carey, V.J., Bates, D.M., et al., Bioconductor: open software development for computational biology and bioinformatics. Genome Biol, 2004. 5(10). R80.
84. Irizarry, R.A., Hobbs, B., Collin, F., et al., Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 2003. 4(2). 249-64.
85. Tusher, V.G., Tibshirani, R., and Chu, G., Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A, 2001. 98(9). 5116-21.
86. Fulton, D.L., Sundararajan, S., Badis, G., et al., TFCat: the curated catalog of mouse and human transcription factors. Genome Biol, 2009. 10(3). R29.
87. Karolchik, D., Kuhn, R.M., Baertsch, R., et al., The UCSC Genome Browser Database: 2008 update. Nucleic Acids Res, 2008. 36(Database issue). D773-9.
88. Sandelin, A., Alkema, W., Engstrom, P., et al., JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res, 2004. 32(Database issue). D91-4.
89. Frith, M.C., Fu, Y., Yu, L., et al., Detection of functional DNA motifs via statistical over-representation. Nucleic Acids Res, 2004. 32(4). 1372-81.
90. Ramsey, S.A., Klemm, S.L., Zak, D.E., et al., Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics. PLoS Comput Biol, 2008. 4(3). e1000021.
91. Stanton, L.W., Garrard, L.J., Damm, D., et al., Altered patterns of gene expression in response to myocardial infarction. Circ Res, 2000. 86(9). 939-45.
92. Barrio, M., Burrage, K., Leier, A., et al., Oscillatory regulation of Hes1: Discrete stochastic delay modelling and simulation. PLoS Comput Biol, 2006. 2(9). e117.
93. LeMaire, M.F. and Thummel, C.S., Splicing precedes polyadenylation during Drosophila E74A transcription. Mol Cell Biol, 1990. 10(11). 6059-63.
94. Kanehisa, M., Araki, M., Goto, S., et al., KEGG for linking genomes to life and the environment. Nucleic Acids Res, 2008. 36(Database issue). D480-4.
95. Yu, H., Luscombe, N.M., Qian, J., et al., Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet, 2003. 19(8). 422-7.
96. D'Haeseleer, P., Liang, S., and Somogyi, R., Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics, 2000. 16(8). 707-26.
97. Tavazoie, S., Hughes, J.D., Campbell, M.J., et al., Systematic determination of genetic network architecture. Nat Genet, 1999. 22(3). 281-5.
98. Buckingham, M., Making muscle in mammals. Trends Genet, 1992. 8(4). 144-8.
99. Birney, E., Stamatoyannopoulos, J.A., Dutta, A., et al., Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature, 2007. 447(7146). 799-816.
100. Beggs, A.H., Byers, T.J., Knoll, J.H., et al., Cloning and characterization of two human skeletal muscle alpha-actinin genes located on chromosomes 1 and 11. J Biol Chem, 1992. 267(13). 9281-8.
101. Weins, A., Schlondorff, J.S., Nakamura, F., et al., Disease-associated mutantalpha-actinin-4 reveals a mechanism for regulating its F-actin-binding affinity. Proc Natl Acad Sci U S A, 2007. 104(41). 16080-5.
102. Wang, M., Zhang, X., Zhao, H., et al., FoxO gene family evolution in vertebrates. BMC Evol Biol, 2009. 9. 222.
103. Riley, R.M., Jin, W., and Gibson, G., Contrasting selection pressures on components of the Ras-mediated signal transduction pathway in Drosophila. Mol Ecol, 2003. 12(5). 1315-23.
104. Edgar, R.C., MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res, 2004. 32(5). 1792-7.
105. Suyama, M., Torrents, D., and Bork, P., PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res, 2006. 34(Web Server issue). W609-12.
106. Ronquist, F. and Huelsenbeck, J.P., MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics, 2003. 19(12). 1572-4.
107. Posada, D. and Crandall, K.A., MODELTEST: testing the model of DNA substitution. Bioinformatics, 1998. 14(9). 817-8.
108. Rozas, J. and Rozas, R., DnaSP version 3: an integrated program for molecular population genetics and molecular evolution analysis. Bioinformatics, 1999. 15(2). 174-5.
109. Wong, W.S., Yang, Z., Goldman, N., et al., Accuracy and power of statistical methods for detecting adaptive evolution in protein coding sequences and for identifying positively selected sites. Genetics, 2004. 168(2). 1041-51.
110. Yang, Z., Nielsen, R., Goldman, N., et al., Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics, 2000. 155(1). 431-49.
111. Gronke, S., Clarke, D.F., Broughton, S., et al., Molecular evolution and functional characterization of Drosophila insulin-like peptides. PLoS Genet, 2010. 6(2). e1000857.
112. Kelley, K.M., Experimental diabetes mellitus in a teleost fish. I. Effect of complete isletectomy and subsequent hormonal treatment on metabolism in the goby, Gillichthys mirabilis. Endocrinology, 1993. 132(6). 2689-95.
113. Nagasawa, H., Kataoka, H., Isogai, A., et al., Amino-terminal amino Acid sequence of the silkworm prothoracicotropic hormone: homology with insulin. Science, 1984. 226(4680). 1344-5.
114. Wolkow, C.A., Munoz, M.J., Riddle, D.L., et al., Insulin receptor substrate and p55 orthologous adaptor proteins function in the Caenorhabditis elegans daf-2/insulin-like signaling pathway. J Biol Chem, 2002. 277(51). 49591-7.
115. Duret, L., Guex, N., Peitsch, M.C., et al., New insulin-like proteins with atypical disulfidebond pattern characterized in Caenorhabditis elegans by comparative sequence analysis and homology modeling. Genome Res, 1998. 8(4). 348-53.
116. Krieger, M.J., Jahan, N., Riehle, M.A., et al., Molecular characterization of insulin-like peptide genes and their expression in the African malaria mosquito, Anopheles gambiae. Insect Mol Biol, 2004. 13(3). 305-15.
117. Li, C., The ever-expanding neuropeptide gene families in the nematode Caenorhabditis elegans. Parasitology, 2005. 131 Suppl. S109-27.
118. Olinski, R.P., Lundin, L.G., and Hallb k, F., Conserved synteny between the Ciona genome and human paralogons identifies large duplication events in the molecular evolution of the insulin-relaxin gene family. Molecular biology and evolution, 2006. 23(1). 10.
119. Taylor, J.S., Braasch, I., Frickey, T., et al., Genome duplication, a trait shared by 22000 species of ray-finned fish. Genome Res, 2003. 13(3). 382-90.
120. Taylor, J.S., Van de Peer, Y., Braasch, I., et al., Comparative genomics provides evidence for an ancient genome duplication event in fish. Philos Trans R Soc Lond B Biol Sci, 2001. 356(1414). 1661-79.
121. Holland, P.W., Garcia-Fernandez, J., Williams, N.A., et al., Gene duplications and the origins of vertebrate development. Dev Suppl, 1994. 125-33.
122. Friedman, R. and Hughes, A.L., Pattern and timing of gene duplication in animal genomes. Genome Res, 2001. 11(11). 1842-7.
123. Hughes, A.L., Phylogenies of developmentally important proteins do not support the hypothesis of two rounds of genome duplication early in vertebrate history. Journal of Molecular Evolution, 1999. 48(5). 565-576.
124. Hughes, A.L., Phylogenetic tests of the hypothesis of block duplication of homologous genes on human chromosomes 6, 9, and 1. Mol Biol Evol, 1998. 15(7). 854-70.
125. Martin, A., Is tetralogy true? Lack of support for the“one-to-four rule”. Molecular biology and evolution, 2001. 18(1). 89.
126. Rausher, M.D., Miller, R.E., and Tiffin, P., Patterns of evolutionary rate variation among genes of the anthocyanin biosynthetic pathway. Mol. Biol. Evol., 1999. 16(2). 266-274.
127. Rausher, M.D., Lu, Y., and Meyer, K., Variation in constraint versus positive selection as an explanation for evolutionary rate variation among anthocyanin genes. Journal of Molecular Evolution, 2008. 67(2). 137-144.
128. Alvarez-Ponce, D., Aguade, M., and Rozas, J., Network-level molecular evolutionary analysis of the insulin/TOR signal transduction pathway across 12 Drosophila genomes. Genome Res, 2009. 19(2). 234-42.
129. Vitkup, D., Kharchenko, P., and Wagner, A., Influence of metabolic network structure and function on enzyme evolution. Genome Biol, 2006. 7(5). R39.
130. Olsen, K.M., Womack, A., Garrett, A.R., et al., Contrasting evolutionary forces in the Arabidopsis thaliana floral developmental pathway. Genetics, 2002. 160(4). 1641-50.
131. Cork, J.M. and Purugganan, M.D., The evolution of molecular genetic pathways and networks. Bioessays, 2004. 26(5). 479-84.
132. Fraser, H.B., Hirsh, A.E., Steinmetz, L.M., et al., Evolutionary rate in the protein interaction network. Science, 2002. 296(5568). 750-2.
133. Wang, D.G., Fan, J.B., Siao, C.J., et al., Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science, 1998. 280(5366). 1077-82.
134. Jungerius, B.J., Gu, J., Crooijmans, R.P., et al., Estimation of the extent of linkage disequilibrium in seven regions of the porcine genome. Anim Biotechnol, 2005. 16(1). 41-54.
135. Humphray, S.J., Scott, C.E., Clark, R., et al., A high utility integrated map of the pig genome. Genome Biol, 2007. 8(7). R139.
136. Wernersson, R., Schierup, M.H., Jorgensen, F.G., et al., Pigs in sequence space: a 0.66X coverage pig genome survey based on shotgun sequencing. BMC Genomics, 2005. 6(1). 70.
137. Zhang, Z., Schwartz, S., Wagner, L., et al., A greedy algorithm for aligning DNA sequences. J Comput Biol, 2000. 7(1-2). 203-14.
138. Gu, Z., Hillier, L., and Kwok, P.Y., Single nucleotide polymorphism hunting in cyberspace. Hum Mutat, 1998. 12. 221-225.
139. Taillon-Miller, P., Gu, Z., Li, Q., et al., Overlapping genomic sequences: a treasure trove of single-nucleotide polymorphisms. Genome Res, 1998. 8. 748-754.
140. Picoult-Newberg, L., Ideker, T.E., Pohl, M.G., et al., Mining SNPs from EST databases. Genome Res, 1999. 9. 167-174.
141. Kwok, P.Y., Deng, Q., Zakeri, H., et al., Increasing the information content of STS-based genome maps: identifying polymorphisms in mapped STSs. Genomics, 1996. 31. 123-126.
142. Garg, K., Green, P., and Nickerson, D.A., Identification of candidate coding region single nucleotide polymorphisms in 165 human genes using assembled expressed sequence tags. Genome Res, 1999. 9. 1087-1092.
143. Marth, G.T., Korf, I., Yandell, M.D., et al., A general approach to singlenucleotide polymorphism discovery. Nat Genet 1999. 23. 452-456.