“酉芯一号”在地方鸡遗传多样性和结构分析中的应用效力研究

doi:10.16288/j.yczz.24-068

遗传 ›› 2024, Vol. 46 ›› Issue (8): 640-648.doi: 10.16288/j.yczz.24-068

“酉芯一号”在地方鸡遗传多样性和结构分析中的应用效力研究

王梦燏¹^,²(), 周成浩²(), 薛倩²^,³, 殷建玫², 蒋一秀², 张会永², 李国辉², 韩威¹^,²()

1.中国农业科学院家禽研究所，扬州 225125
2.江苏省家禽科学研究所，扬州 225125
3.江苏省家禽科学研究所科技创新有限公司，扬州 225125

收稿日期:2024-03-14 修回日期:2024-06-12 出版日期:2024-08-20 发布日期:2024-06-24
通讯作者: 韩威，博士，研究员，研究方向：家禽遗传资源保护、评价与利用。E-mail: hanwei830@163.com
作者简介:王梦燏，硕士研究生，专业方向：动物遗传育种与繁殖原理与技术。E-mail: 2664935435@qq.com;
周成浩，博士，助理研究员，研究方向：家禽遗传资源保护、评价与利用。E-mail: 604242123@qq.com
第一联系人：
王梦燏和周成浩并列第一作者。
基金资助:
国家重点研发计划项目(2021YFD1200302);江苏省种业振兴揭榜挂帅项目(JBGS[2021]029);江苏省自然科学基金面上项目(BK20221285)

Application effectiveness of “Youxin-1” in genetic diversity and structure analysis of local chickens

Mengyu Wang¹^,²(), Chenghao Zhou²(), Qian Xue²^,³, Jianmei Yin², Yixiu Jiang², Huiyong Zhang², Guohui Li², Wei Han¹^,²()

1. Poultry Institute, Chinese Academy of Agricultural Sciences, Yangzhou 225125, China
2. Poultry Institute of Jiangsu Province, Yangzhou 225125, China
3. Science and Technology Innovation Co., Ltd., Poultry Institute of JiangsuProvince, Yangzhou 225125, China

Received:2024-03-14 Revised:2024-06-12 Published:2024-08-20 Online:2024-06-24
Supported by:
National Key Research and Development Program of China(2021YFD1200302);Seed Industry Revitalization Project of Jiangsu Province(JBGS[2021]029);Natural Science Foundation Project of Jiangsu Province(BK20221285)

摘要/Abstract

摘要：

我国地方鸡品种资源丰富，且在长期的选择进化过程中形成了各异的种质特性。科学评估群体遗传多样性，明确品种间遗传结构，对地方鸡品种资源的保护与创新利用具有重要价值。为了评估23K SNP芯片“酉芯一号”在地方鸡遗传多样性和遗传结构分析中的应用效力，本文利用RADseq鉴定21个地方鸡种基因组遗传变异，并研发23K芯片“酉芯一号”。基于两组SNP数据集计算各品种的遗传统计量，开展相关性、拟合及系统发育分析，以评估芯片的应用效力。结果表明，基于两组SNP数据集计算的观察杂合度(Ho)、多态信息含量(PIC)、近交系数(F_ROH)和遗传分化系数(Fst)，在21个地方鸡种中趋势基本一致。琅琊鸡(LA)、瓢鸡(PJ)、文昌鸡(WC)的遗传多样性较为丰富；边鸡(BJ)、狼山鸡(LS)、固始鸡(GS)、东乡绿壳蛋鸡(DX)和北京油鸡(BY)的遗传多样性相对匮乏，两组Ho、PIC、F_ROH和平均Fst的相关系数分别为0.794、0.901、0.926和0.984，均达到极显著水平(P<0.01)，且拟合度较高(P<0.001)，R²分别为0.644、0.827、0.916和0.927。针对两组SNP数据集，采用邻接法(NJ)和极大似然法(ML)构建的进化树较为合理，将21个地方鸡种总体上分为6大类，与品种的形成历史和地理分布相吻合；23K芯片对5个新增品种亦实现有效聚类，没有出现个体偏离。利用不同密度的SNP估算遗传统计量会存在一定差异，需要进行数据和分析方法的标准化，23K芯片在地方鸡遗传多样性和结构分析中具有较好的效力。

关键词: RADseq, SNP芯片, 遗传多样性, 遗传结构

Abstract:

China’s local chicken breeds are rich in resources, and have formed different germplasm characteristics in the process of long-term selection and evolution. Scientific assessment of population genetic diversity and identification of inter-breed genetic structure are of great value to the protection and innovative utilization of local chicken breed resource. In order to evaluate the application effectiveness of 23K SNP chip “Youxin-1” in the analysis of genetic diversity and genetic structure of local chickens, we used RADseq to identify genomic genetic variation of 21 local chicken breeds and developed 23K chip “Youxin-1”. The genetic statistics of each variety were calculated based on two sets of SNP data, and correlation, fitting and phylogenetic analysis were carried out to evaluate the application effectiveness of the chip. The results showed that the observed heterozygosity (Ho), polymorphism information content (PIC), inbred coefficient (F_ROH) and genetic differentiation coefficient (Fst) calculated based on the two SNP data sets were basically consistent in the 21 local chicken breeds. The genetic diversity of Langya chicken (LA), Piao chicken (PJ) and Wenchang chicken (WC) was relatively rich. The genetic diversity of Bian chickens (BJ), Langshan chickens (LS), Gushi chickens (GS), Dongxiang blue-eggshell chickens (DX) and Beijing fatty chickens (BY) was relatively poor, and the correlation coefficients of Ho, PIC, F_ROH and average Fst in the two groups were 0.794, 0.901, 0.926 and 0.984, respectively, all reaching extremely significant levels (P<0.01) with a high degree of fit (P<0.001) and R²were 0.644, 0.827, 0.916 and 0.927. For the two sets of SNP data, the evolutionary tree constructed by neighbor-joining (NJ) method and maximum likelihood (ML) method was reasonable, and the 21 local chicken breeds were generally divided into six categories, which was consistent with the formation history and geographical distribution of the varieties. The 23K chip also realized reasonable clustering of the five new varieties without individual deviation. There are some differences in the estimation of genetic statistics using SNP with different densities, and data standardization is needed. 23K chip has good efficacy in the analysis of genetic diversity and structure of local chickens.

Key words: RADseq, SNP chip, genetic diversity, genetic structure

王梦燏, 周成浩, 薛倩, 殷建玫, 蒋一秀, 张会永, 李国辉, 韩威. “酉芯一号”在地方鸡遗传多样性和结构分析中的应用效力研究[J]. 遗传, 2024, 46(8): 640-648.

Mengyu Wang, Chenghao Zhou, Qian Xue, Jianmei Yin, Yixiu Jiang, Huiyong Zhang, Guohui Li, Wei Han. Application effectiveness of “Youxin-1” in genetic diversity and structure analysis of local chickens[J]. Hereditas(Beijing), 2024, 46(8): 640-648.

图/表 6

表1

表2

表3

图1

图2

图3

参考文献 32

[1]	Vekić M, Kalamujić Stroil B, Trivunović S, Pojskić N, Djukić Stojčić M. Genetic diversity of Banat Naked Neck, indigenous chicken breed from Serbia, inferred from mitochondrial DNA D-loop sequence and microsatellite markers. Anim Biotechnol, 2023, 34(7): 2197-2206.
[2]	Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet, 2016, 17(2): 81-92. doi: 10.1038/nrg.2015.28 pmid: 26729255
[3]	Zhang KF, Zhou YD, Song WH, Jiang LH, Yan XJ. Genome-wide RADseq reveals genetic differentiation of wild and cultured populations of large yellow croaker. Genes (Basel), 2023, 14(7): 1508.
[4]	Clugston JAR, Kenicer GJ, Milne R, Overcast I, Wilson TC, Nagalingum NS. RADseq as a valuable tool for plants with large genomes-A case study in cycads. Mol Ecol Resour, 2019, 19(6): 1610-1622. doi: 10.1111/1755-0998.13085 pmid: 31484214
[5]	Zhu YF, Yin JM, Zhang JF, Li GH, Shen HY, Xue Q, Zhang HY, DOU XH, Su YJ, Han W. Genomic selection signal analysis of Langshan chicken based on RAD-seq sequencing. China Anim Husb Vet Med, 2020, 47(9): 2706-2714.
	朱云芬, 殷建玫, 张吉发, 李国辉, 沈海玉, 薛倩, 张会永, 窦新红, 苏一军, 韩威. 基于RAD-seq测序的狼山鸡基因组选择信号分析. 中国畜牧兽医, 2020, 47(9): 2706-2714. doi: 10.16431/j.cnki.1671-7236.2020.09.002
[6]	Yin JM, Zhu YF, Li GH, Zhang HY, Xue Q, Zhou CH, Jiang YX, Su YJ, Han W. Genetic evolution of Langya chicken based on simplified genome sequencing based on RAD-Seq. China Poult, 2023, 45(5): 19-23.
	殷建玫, 朱云芬, 李国辉, 张会永, 薛倩, 周成浩, 蒋一秀, 苏一军, 韩威. 基于RAD-Seq简化基因组测序分析琅琊鸡的遗传进化. 中国家禽, 2023, 45(5): 19-23.
[7]	Ma LX, Cao GW, Zhu HF, Deng ZZ, Cai ZY, Zhou CH, Han W, Gu YL, Zhang J. Analysis of genetic variation based on RAD-seq breeding population of Jingyuan chickens. Acta Vet Zootech Sin, 2022, 53(7): 2104-2117.
	马丽霞, 曹国伟, 朱红芳, 邓占钊, 蔡正云, 周成浩, 韩威, 顾亚玲, 张娟. 基于RAD-seq静原鸡保种群体的遗传变异分析. 畜牧兽医学报, 2022, 53(7): 2104-2117. doi: 10.11843/j.issn.0366-6964.2022.07.008
[8]	Han W, Zhu YF, Yin JM, Li GH, Xue Q, Zhang HY, Shen HY, Su YJ, Dou XH, Wang KH, Zou JM. Genetic evolution of 19 local chicken breeds based on simplified genome sequencing by RAD-seq. Acta Vet Zootech Sin, 2020, 51(4): 670-678.
	韩威, 朱云芬, 殷建玫, 李国辉, 薛倩, 张会永, 沈海玉, 苏一军, 窦新红, 王克华, 邹剑敏. 基于RAD-seq简化基因组测序的19个地方鸡种遗传进化研究. 畜牧兽医学报, 2020, 51(4): 670-678. doi: 10.11843/j.issn.0366-6964.2020.04.003
[9]	Wang F, Guo YW, Liu ZY, Wang Q, Jiang Y, Zhao GP. New insights into the novel sequences of the chicken pan-genome by liquid chip. J Anim Sci, 2022, 100(12): skac336.
[10]	Groenen MAM, Megens HJ, Zare Y, Warren WC, Hillier LW, Crooijmans RPMA, Vereijken A, Okimoto R, Muir WM, Cheng HH. The development and characterization of a 60K SNP chip for chicken. BMC Genomics, 2011, 12(1): 274. doi: 10.1186/1471-2164-12-274 pmid: 21627800
[11]	Kranis A, Gheyas AA, Boschiero C, Turner F, Yu L, Smith S, Talbot R, Pirani A, Brew F, Kaiser P, Hocking PM, Fife M, Salmon N, Fulton J, Strom TM, Haberer G, Weigend S, Preisinger R, Gholami M, Qanbari S, Simianer H, Watson KA, Woolliams JA, Burt DW. Development of a high density 600K SNP genotyping array for chicken. BMC Genomics, 2013, 14: 59. doi: 10.1186/1471-2164-14-59 pmid: 23356797
[12]	Liu RR, Xing SY, Wang J, Zheng MQ, Cui HX, Crooijmans RPMA, Li QH, Zhao GP, Wen J. A new chicken 55K SNP genotyping array. BMC Genomics, 2019, 20(1): 410. doi: 10.1186/s12864-019-5736-8 pmid: 31117951
[13]	Liu Z, Sun CJ, Yan YY, Li GQ, Li XC, Wu GQ, Yang N. Design and evaluation of a custom 50K Infinium SNP array for egg-type chickens. Poult Sci, 2021, 100(5): 101044.
[14]	Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet, 2007, 81(3): 559-575. doi: 10.1086/519795 pmid: 17701901
[15]	Serrote CML, Reiniger LRS, Silva KB, Rabaiolli SMDS, Stefanel CM. Determining the polymorphism information content of a molecular marker. Gene, 2020, 726: 144175.
[16]	Biscarini F, Mastrangelo S, Catillo G, Senczuk G, Ciampolini R. Insights into genetic diversity, runs of homozygosity and heterozygosity-rich regions in maremmana semi-feral cattle using pedigree and genomic data. Animals (Basel), 2020, 10(12): 2285.
[17]	Vahedi SM, Ardestani SS. FSTest: an efficient tool for cross-population fixation index estimation on variant call format files. J Genet, 2024, 103: 04.
[18]	Price MN, Dehal PS, Arkin AP. FastTree 2--approximately maximum-likelihood trees for large alignments. PLoS One, 2010, 5(3): e9490.
[19]	Tamura K, Stecher G, Kumar S. MEGA11: molecular evolutionary genetics analysis version 11. Mol Biol Evol, 2021, 38(7): 3022-3027. doi: 10.1093/molbev/msab120 pmid: 33892491
[20]	Tian SS, Li W, Zhong ZQ, Wang FF, Xiao Q. Genome-wide re-sequencing data reveals the genetic diversity and population structure of Wenchang chicken in China. Anim Genet, 2023, 54(3): 328-337.
[21]	Tang XJ, Jia XX, Fan YF, Gao YS, Ge QL, Gu R, Lu JX, Liu YY, Wang J. Genetic diversity and phylogeny of Langya chickens based on mtDNA D-loop region. J Yangzhou Univ(Agric Life Sci Ed), 2017, 38(2): 47-51.
	唐修君, 贾晓旭, 樊艳凤, 高玉时, 葛庆联, 顾荣, 陆俊贤, 刘茵茵, 王珏. 基于mtDNA D-loop区全序列的琅琊鸡遗传多样性和系统进化研究. 扬州大学学报(农业与生命科学版), 2017, 38(2): 47-51.
[22]	Tang XJ, Jia XX, Fan YF, Ge QL, Tang MJ, Chen DW, Zhang XY, Lu JX, Gao YS. Genetic polymorphism and origin of 4 chicken breeds based on Cytb gene complete sequence. Chin J Agric Biotechnol, 2020, 28(6): 83-91.
	唐修君, 贾晓旭, 樊艳凤, 葛庆联, 唐梦君, 陈大伟, 张小燕, 陆俊贤, 高玉时. 基于Cytb基因全序列4个鸡品种遗传多态性和起源. 农业生物技术学报, 2020, 28(6): 83-91.
[23]	Makanjuola BO, Maltecca C, Miglior F, Marras G, Abdalla EA, Schenkel FS, Baes CF. Identification of unique ROH regions with unfavorable effects on production and fertility traits in Canadian Holsteins. Genet Sel Evol, 2021, 53(1): 68. doi: 10.1186/s12711-021-00660-z pmid: 34461820
[24]	Gorssen W, Meyermans R, Janssens S, Buys N. A publicly available repository of ROH islands reveals signatures of selection in different livestock and pet species. Genet Sel Evol, 2021, 53(1): 2. doi: 10.1186/s12711-020-00599-7 pmid: 33397285
[25]	Grilz-Seger G, Druml T, Neuditschko M, Mesarič M, Cotman M, Brem G. Analysis of ROH patterns in the Noriker horse breed reveals signatures of selection for coat color and body size. Anim Genet, 2019, 50(4): 334-346. doi: 10.1111/age.12797 pmid: 31199540
[26]	Solmundson K, Bowman J, Manseau M, Taylor RS, Keobouasone S, Wilson PJ. Genomic population structure and inbreeding history of Lake Superior caribou. Ecol Evol, 2023, 13(7): e10278.
[27]	Fatma R, Chauhan W, Afzal M. The coefficients of inbreeding revealed by ROH study among inbred individuals belonging to each type of the first cousin marriage: a preliminary report from North India. Genes Genomics, 2023, 45(6): 813-825.
[28]	Di Gregorio P, Perna A, Di Trana A, Rando A. Identification of ROH islands conserved through generations in pigs belonging to the Nero Lucano breed. Genes (Basel), 2023, 14(7): 1503.
[29]	Chen KW, Li HF, Wang JY, Tang QP, Shen JC, Zhang SJ. Genetic variation of 27 local chicken breeds (lines) in East China. Acta Vet Zootech Sin, 2006, 37(1): 7-11.
	陈宽维, 李慧芳, 王金玉, 汤青萍, 沈见成, 章双杰. 华东27个地方鸡品种(品系)的遗传变异. 畜牧兽医学报, 2006, 37(1): 7-11.
[30]	Li HF, Han W, Zhu YF, Shu JT, Zhang XY, Chen KW. Analysis of genetic structure and relationship among nine indigenous Chinese chicken populations by the structure program. J Genet, 2009, 88(2): 197-203. doi: 10.1007/s12041-009-0028-8 pmid: 19700858
[31]	Zhuang Z, Zhao L, Zong WC, Guo QX, Li XF, Bi YL, Wang ZX, Jiang Y, Chen GH, Li BC, Chang GB, Bai H. Genetic diversity and breed identification of Chinese and Vietnamese local chicken breeds based on microsatellite analysis. J Anim Sci, 2023, 101: skad182.
[32]	Wang DQ, Chen GH, Wu XS, Zhang XY, Wang KH. Performance measurement and cluster analysis of main local chicken breeds in China. J Zhejiang Agric Sci, 2011, (4): 930-932.
	王德前, 陈国宏, 吴信生, 张学余, 王克华. 我国主要地方鸡品种性能测定与聚类分析. 浙江农业科学, 2011, (4): 930-932.

编辑推荐

Metrics

www.chinagene.cn
备案号：京ICP备09063187号-4
总访问:,今日访问:,当前在线:

品种	观察杂合度(Ho)	多态信息含量(PIC)	近交系数 (F_ROH)	平均遗传分化系数 (Fst)
白耳黄鸡(BE)	0.2049	0.2712	0.1562	0.1632
边鸡(BJ)	0.1833	0.2717	0.1697	0.1443
北京油鸡(BY)	0.1887	0.2586	0.2328	0.1781
茶花鸡(CH)	0.2104	0.2844	0.1547	0.1592
大骨鸡(DG)	0.2121	0.2953	0.1373	0.1362
东乡绿壳蛋鸡(DX)	0.1876	0.262	0.1806	0.1614
固始鸡(GS)	0.1901	0.2558	0.2277	0.1715
天津猴鸡(HOU)	0.2214	0.2822	0.2077	0.1785
惠阳胡须鸡(HX)	0.2179	0.3016	0.0972	0.1235
金湖乌凤鸡(JH)	0.2039	0.284	0.169	0.1423
琅琊鸡(LA)	0.2535	0.3182	0.0536	0.1028
狼山鸡(LS)	0.205	0.2691	0.1989	0.1795
鹿苑鸡(LY)	0.2447	0.3202	0.048	0.1026
瓢鸡(PJ)	0.2359	0.3149	0.0181	0.1079
文昌鸡(WC)	0.224	0.2981	0.0732	0.1277
安义瓦灰鸡(WH)	0.2172	0.2879	0.1369	0.1408
汶上芦花鸡(WS)	0.2163	0.2853	0.1703	0.1515
大围山微型鸡(WX)	0.2154	0.2886	0.1264	0.1455
仙居鸡(XJ)	0.2047	0.2816	0.1445	0.1503
萧山鸡(XS)	0.2146	0.2842	0.1657	0.1544
藏鸡(ZZ)	0.2127	0.2979	0.145	0.1396

品种	观察杂合度(Ho)	多态信息含量(PIC)	近交系数 (F_ROH)	平均遗传分化系数 (Fst)
白耳黄鸡(BE)	0.1955	0.2714	0.1543	0.1902
边鸡(BJ)	0.1856	0.2813	0.1787	0.1598
北京油鸡(BY)	0.1735	0.2482	0.1911	0.2001
茶花鸡(CH)	0.2066	0.2867	0.1444	0.1927
大骨鸡(DG)	0.2205	0.2963	0.1364	0.1557
东乡绿壳蛋鸡(DX)	0.1411	0.2413	0.2147	0.1811
固始鸡(GS)	0.1824	0.2577	0.1899	0.1862
天津猴鸡(HOU)	0.1966	0.2644	0.2105	0.2010
惠阳胡须鸡(HX)	0.2096	0.3085	0.1201	0.1438
金湖乌凤鸡(JH)	0.2008	0.3016	0.1724	0.1718
琅琊鸡(LA)	0.2472	0.3156	0.0736	0.1273
狼山鸡(LS)	0.1867	0.2556	0.1609	0.2039
鹿苑鸡(LY)	0.213	0.3102	0.0568	0.1229
瓢鸡(PJ)	0.2242	0.3139	0.0780	0.1282
文昌鸡(WC)	0.2493	0.3174	0.0940	0.1453
安义瓦灰鸡(WH)	0.2135	0.2888	0.1331	0.1615
汶上芦花鸡(WS)	0.2165	0.2862	0.1657	0.1729
大围山微型鸡(WX)	0.2145	0.2912	0.1177	0.1693
仙居鸡(XJ)	0.1834	0.2755	0.1350	0.1735
萧山鸡(XS)	0.2096	0.283	0.1470	0.1779
藏鸡(ZZ)	0.1983	0.2961	0.1413	0.1659

统计量	皮尔逊相关系数	拟合曲线	拟合度R²
杂合度(Ho)	0.794**	y=0.12×15.71^x	0.644***
近交系数(F_ROH)	0.926**	y=0.17-4.69x+50.97x²-132.84x³	0.916***
多态信息含量(PIC)	0.901**	y=0.43-1.79x+4.48x²	0.827***
平均遗传分化系数(Fst)	0.984**	$y={{e}^{-0.88-\frac{0.17}{x}}}$	0.972***

“酉芯一号”在地方鸡遗传多样性和结构分析中的应用效力研究

Application effectiveness of “Youxin-1” in genetic diversity and structure analysis of local chickens

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 32

相关文章 15

编辑推荐

Metrics

[1]	马钧, 樊安平, 王武生, 张金川, 江晓军, 马瑞军, 贾社强, 刘飞, 雷初朝, 黄永震. 全基因组重测序解析秦川牛保种群遗传多样性和遗传结构[J]. 遗传, 2023, 45(7): 602-616.
[2]	寇洁, 李严, 王鹏, 刘红, 刘佳文, 王涓, 王也, 张亮, 沈富军. 大熊猫遗传多样性评估的微卫星分型体系优化[J]. 遗传, 2022, 44(3): 253-266.
[3]	王浩宇, 胡渝涵, 曹悦岩, 朱强, 黄雨果, 李茜, 张霁. 基于全基因组数据的AI-SNPs筛选及大陆次级区域内群体遗传结构差异研究[J]. 遗传, 2021, 43(10): 938-948.
[4]	徐志伟, 魏云林, 季秀玲. 假单胞菌噬菌体基因组学研究进展[J]. 遗传, 2020, 42(8): 752-759.
[5]	李智,何俊,蒋隽,Richard G. Tait Jr.,Stewart Bauck,过伟,吴晓林. 牛SNP芯片分型检出率和分型错误率对基因型填充准确率的影响[J]. 遗传, 2019, 41(7): 644-652.
[6]	郑建敏,罗江陶,万洪深,李式昭,杨漫宇,李俊,杨恩年,蒋云,刘于斌,王相权,蒲宗君. 四川省小麦育成品种系谱分析及发展进程[J]. 遗传, 2019, 41(7): 599-610.
[7]	何俊,钱长嵩,RichardG.TaitJr.,StewartBauck,吴晓林. SNP芯片数据估计动物个体基因组品种构成的方法及应用[J]. 遗传, 2018, 40(4): 305-314.
[8]	赵永欣, 李孟华, 赵要风. 中国绵羊起源、进化和遗传多样性研究进展[J]. 遗传, 2017, 39(11): 958-973.
[9]	弓弦,张超,伊利亚斯·艾萨,时瑛,杨雪唯,努尔斯曼古丽奥斯曼,关亚群,徐书华. 2型糖尿病易感候选基因在世界不同人群中的多样性比较分析[J]. 遗传, 2016, 38(6): 543-559.
[10]	黄益敏夏梦颖黄石. 遗传多样性上限假说所揭示的进化历程[J]. 遗传, 2013, 35(5): 599-606.
[11]	温莹逯晓萍任锐米福贵韩平安薛春雷. 高丹草EST-SSR标记的开发及其遗传多样性[J]. 遗传, 2013, 35(2): 225-232.
[12]	李铎，柴志欣，姬秋梅，张成福，信金伟. 西藏牦牛微卫星DNA的遗传多样性[J]. 遗传, 2013, 35(2): 175-184.
[13]	马志杰，钟金城，韩建林，徐惊涛，刘仲娜，白文林. 牦牛分子遗传多样性研究进展[J]. 遗传, 2013, 35(2): 151-160.
[14]	傅建军，李家乐，沈玉帮，王荣泉，宣云峰，徐晓雁，陈勇. 草鱼野生群体遗传变异的微卫星分析[J]. 遗传, 2013, 35(2): 192-201.
[15]	王晓庆，王传超，邓琼英，李辉. 广西仫佬族Y染色体和mtDNA的遗传结构分析[J]. 遗传, 2013, 35(2): 168-174.