基因型填充提高湿地松SNP密度与遗传分析准确性

doi:10.16288/j.yczz.25-335

遗传 ›› 2026, Vol. 48 ›› Issue (5): 506-521.doi: 10.16288/j.yczz.25-335

基因型填充提高湿地松SNP密度与遗传分析准确性

蒋雨萱¹(), 边黎明¹(), 周师亮¹, 陈志强¹()

¹ 南京林业大学林草学院，林木遗传育种全国重点实验室，南方现代林业协同创新中心，南京 210037
² 加拿大不列颠哥伦比亚大学林学院，温哥华 V6T 1Z4
³ 瑞典农业科学大学林学院，于默奥植物科学中心，于默奥 90183
⁴ 美国俄克拉荷马州立大学，生物安全与微生物取证研究所，生物化学与分子生物学系，斯蒂尔沃特 74075

收稿日期:2025-12-19 修回日期:2026-01-14 出版日期:2026-03-11 发布日期:2026-03-11
通讯作者: 边黎明，博士，教授，研究方向：林木遗传改良。E-mail: Lmbian@njfu.edu.cn；
陈志强，博士，教授，研究方向：林木多组学育种。E-mail: zhiqiang_chen@njfu.edu.cn
作者简介:蒋雨萱，硕士研究生，专业方向：林木遗传育种。E-mail: 806939913@qq.com
基金资助:
中华人民共和国科技部科技创新2030农业生物育种国家科技重大专项(2023ZD0405805)

Genotype imputation improves SNP density and genetic analysis accuracy in slash pine

Yuxuan Jiang¹(), Liming Bian¹(), Shiliang Zhou¹, Charles Chen⁴, Yousry A. El-Kassaby², Zhiqiang Chen¹(), Harry X. Wu¹^,³

¹ State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, College of Forestry and Grassland, Nanjing Forestry University, Nanjing 210037, China
² Faculty of Forestry, University of British Columbia, Vancouver, V6T 1Z4, Canada
³ College of Forestry, Umeå Plant Science Centre, Swedish University of Agricultural Sciences, Umeå 90183, Sweden
⁴ Institute for Biosecurity & Microbial Forensics, Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater OK 74075, USA

Received:2025-12-19 Revised:2026-01-14 Published:2026-03-11 Online:2026-03-11
Supported by:
Scientific and Technological Innovation 2030 “Major Project in Agricultural Biotechnology Breeding” of Ministry of Science and Technology of the People’s Republic of China(2023ZD0405805)

摘要/Abstract

摘要：

湿地松(Pinus elliottii)基因组庞大且重复序列占比高，现有低密度单核苷酸多态性(single nucleotide polymorphism，SNP)芯片在标记覆盖与连锁信息刻画方面存在局限。为提高群体遗传分析的标记密度及基因组亲缘关系矩阵(genomic relationship matrix，GRM)估计精度，本研究以50株母本约10×全基因组重测序数据构建参考面板，并对715株半同胞子代的51K芯片分型数据进行全基因组填充。芯片原有位点采用遮盖实验评估填充准确性，参考面板扩增且芯片未覆盖的新增位点采用子代重测序外部验证评估一致性并筛选，遮盖验证一致性稳定在约95.5%，参考面板扩增位点经阈值筛选后保留高可信位点，构建了覆盖715个体的高密度基因型矩阵，包含120,650,180个SNP。局部连锁不平衡(linkage disequilibrium，LD)热图对比显示，增密后LD信号更连续且块结构更清晰；以第4号染色体上10.22~10.33 Mb区段为例，高LD位点对比例由14.5%增至27.6%。基于增密数据构建的基因组关系矩阵与芯片矩阵在非对角线元素上保持高度一致，Pearson相关系数约为0.984；距离分层分析进一步显示，距芯片标记500 kb以内的填充位点构建的矩阵一致性更高，而更远距离窗口的一致性逐步下降，提示远距离填充位点带来的改进相对有限。综上，本研究建立的参考面板驱动填充、验证与整合流程可为湿地松及相关针叶树的全基因组关联分析与基因组选择研究提供高密度基因型数据基础。

关键词: 湿地松, SNP芯片, 基因型填充, 参考面板, 高密度基因型矩阵, 基因组亲缘关系矩阵

Abstract:

Slash pine (Pinus elliottii) possesses an exceptionally large, repeat-rich genome, and existing low-density SNP arrays provide limited marker coverage and resolution of linkage patterns. To increase marker density for population-based genetic analyses and improve the accuracy of genomic relationship matrix (GRM) estimation, we constructed a reference panel from about 10× whole-genome resequencing of 50 maternal parents and performed genome-wide imputation for 51K SNP-array genotypes of 715 half-sib progeny. Imputation accuracy at array loci was quantified using chromosome-local masking experiments, whereas reference-panel-expanded loci not represented on the array were evaluated for concordance and filtered via external validation using progeny resequencing data. Masking-based concordance remained stable at 95.5%, and after threshold-based filtering of expanded loci, we generated a high-density genotype matrix for all 715 individuals comprising 120,650,180 SNPs. Comparative local linkage disequilibrium (LD) heatmaps indicated more continuous LD signals and clearer block structures after densification; for the Chr4 10.22-10.33 Mb interval, the proportion of high-LD SNP pairs increased from 14.5% to 27.6%. The GRM derived from the densified dataset was highly consistent with the array-based GRM in off-diagonal elements (Pearson’s r≈0.984). Distance-stratified analyses further showed higher concordance for GRMs constructed from imputed loci within 500 kb of array markers, with concordance decreasing progressively in more distant windows, suggesting limited incremental benefit from long-range imputed loci. Collectively, the reference panel-driven imputation, validation, and integration framework established here provides a high-density genotypic resource for genome-wide association studies and genomic selection in slash pine and other conifer species.

Key words: Pinus elliottii, SNP array, genotype imputation, reference panel, high-density genotype matrix, genomic relationship matrix

蒋雨萱, 边黎明, 周师亮, 陈志强. 基因型填充提高湿地松SNP密度与遗传分析准确性[J]. 遗传, 2026, 48(5): 506-521.

Yuxuan Jiang, Liming Bian, Shiliang Zhou, Charles Chen, Yousry A. El-Kassaby, Zhiqiang Chen, Harry X. Wu. Genotype imputation improves SNP density and genetic analysis accuracy in slash pine[J]. Hereditas(Beijing), 2026, 48(5): 506-521.

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筛选步骤	筛选条件	筛选后SNP的数量
原始数据	—	1,945,526,716
步骤1	过滤掉多等位位点及位于Ctg的标记，最终仅保留双等位SNP	1,680,088,854
步骤2	将样本层面测序深度(DP)≤4的基因型统一置为缺失，随后按位点缺失率筛选，保留缺失率<0.40的SNP	676,768,116

染色体	仅保留双等位位点的SNP数量
Chr1	55,828
Chr2	70,182
Chr3	56,901
Chr4	52,183
Chr5	52,745
Chr6	56,679
Chr7	56,554
Chr8	53,633
Chr9	49,066
Chr10	47,400
Chr11	49,690
Chr12	37,987
合计	638,848

染色体	因填充面板而扩增的SNP数量
Chr1	63,211,769
Chr2	62,970,612
Chr3	56,919,379
Chr4	59,027,611
Chr5	56,699,604
Chr6	61,581,730
Chr7	58,097,792
Chr8	56,115,830
Chr9	59,082,719
Chr10	51,899,457
Chr11	51,832,653
Chr12	38,926,127
合计	676,365,283

染色体	芯片与填充参考面板共有的SNP
Chr1	31,394
Chr2	40,086
Chr3	31,177
Chr4	29,385
Chr5	29,013
Chr6	31,824
Chr7	31,464
Chr8	30,029
Chr9	28,536
Chr10	26,980
Chr11	27,569
Chr12	20,690
合计	358,147

染色体	芯片仅有的SNP数量
Chr1	24,434
Chr2	30,096
Chr3	25,724
Chr4	22,798
Chr5	23,732
Chr6	24,855
Chr7	25,090
Chr8	23,604
Chr9	20,530
Chr10	20,420
Chr11	22,121
Chr12	17,297
合计	280,701

基因型填充提高湿地松SNP密度与遗传分析准确性

Genotype imputation improves SNP density and genetic analysis accuracy in slash pine

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参考文献 41

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Metrics

染色体	扩增位点与重测序共有SNP 数量
Chr1	40,653,291
Chr2	40,578,014
Chr3	37,457,443
Chr4	39,199,138
Chr5	37,655,767
Chr6	40,779,077
Chr7	38,431,645
Chr8	36,618,202
Chr9	38,801,777
Chr10	33,749,449
Chr11	33,904,415
Chr12	25,607,723
合计	443,435,941

染色体		3类 SNP 数量汇总
Chr1	10,882,327
Chr2	10,722,277
Chr3	10,314,349
Chr4	11,071,999
Chr5	10,212,229
Chr6	11,016,514
Chr7	10,452,761
Chr8	9,974,964
Chr9	10,603,347
Chr10	9,382,111
Chr11	9,282,621
Chr12	6,734,909
合计	120,650,408

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[2]	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.
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