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研究生: 呂欣蓓
Lu, Hsin-Pei
論文名稱: 兩種臺灣特有薑屬植物的族群遺傳結構及區域性適應
Contrasting pattern of population genetic structure and local adaptation of two Taiwan endemic ginger species
指導教授: 廖培鈞
Liao, Pei-Chun
學位類別: 碩士
Master
系所名稱: 生命科學系
Department of Life Science
論文出版年: 2020
畢業學年度: 108
語文別: 英文
論文頁數: 82
DOI URL: http://doi.org/10.6345/NTNU202000825
論文種類: 學術論文
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  • 研究物種在異質性的地景環境中的地理空間分布及遺傳結構之間的關係是重要的,這有助於了解物種如何適應不同天擇壓力以及牽涉到族群分化、區域性適應、遷徙及拓殖能力,以及族群間基因交流的過程。本研究以廣布及侷限兩種不同分布形式的台灣特有薑屬植物─三奈(Zingiber kawagoii)及雙龍薑 (Z. shuanglongensis)作為研究題材,探討其在高異質性地景中的氣候、地形以及族群遺傳結構之間的關係。推測不同的氣候或地形條件是影響兩物種之地理分布及遺傳結構的可能原因。在此研究中,我利用生態棲位模擬探討三奈及雙龍薑在過去氣候動盪的環境下是如何隨時間在棲位範圍上改變;並透過ddRADseq獲得基因組尺度的單核苷酸多型性(SNPs),利用離群值檢測篩選出潛在適應性SNPs及中性的SNPs去探討地理距離、環境差異、適應程度等是否影響中性的SNPs及適應性SNPs在族群之間的分化。另外,我也藉由遺傳-環境關聯性分析去分析兩物種各自族群間的區域性適應是受到哪些因子影響,並更進一步進行功能性基因註解。結果顯示即使是親緣關係接近且分布於類似環境的物種,其遺傳結構在高異質性的地景下會受氣候差異及地形的影響,導致族群分化或是區域性適應的產生。三奈及雙龍薑在不同地質年代的棲位模擬中擁有不同的分布形式以及漸進式的擴張及限縮。三奈的分布範圍在末次冰盛期時有往低海拔退縮的趨勢,而後在緯度上有逐漸北移,而雙龍薑的分布範圍則是在海拔上有逐漸往高海拔限縮的趨勢。在兩物種族群中發生族群分化的原因中,三奈為受到氣候上的限制影響,而雙龍薑則受到地形上的限制影響。另外,就兩物種族群間發生區域性適應的原因也不相同。從基因註解的結果可以得知三奈族群間在區域性適應與個體發育過程有關,而雙龍薑族群間則是與植物免疫過程有關。所有結果皆指向高異質性地景會影響物種在空間上的遺傳結構分布。本研究顯示空間異質性及區域性適應導致兩個特有薑屬植物族群遺傳結構呈現不一致的模式,並指出兩個近期分化的物種可能是藉由不同的策略來面對不同的環境壓力。

    Landscape genetics is a combination of landscape ecology and population genetics to help the understanding of the microevolutionary processes such as gene flow and local adaptation. Episodes of the spatial genetic structure of species that have different distribution patterns among populations in a heterogeneous landscape are critical events in the evolution of natural species. This would involve population divergence, local adaptation, dispersal ability, colonization, and gene flow among populations. In this study, I focus on two Taiwan endemic ginger species, Zingiber kawagoii and Z. shuanglongensis, which have different distribution patterns across heterogeneous landscapes, and aim to investigate the relationship between climatic/topographic variation and population genetic structure. To assess the species distribution through time, ecological niche modeling (ENM) was performed in four geological periods. Moreover, I acquired genome-wide SNP data from double-digest-Restriction-site Associated DNA Sequencing (ddRADseq) and analyzed the population differentiation and detect the candidate loci for local adaptation of these two species with Maximum Likelihood Population Effects Mixed Model (MLPE) and Latent Factor Mixed Model (LFMM). Furthermore, the functional annotation had shown that the putative adaptive loci of Z. kawagoii and Z. shuanglongensis were related to development process and plant immunity, respectively. The results had shown that even with close phylogenetic relationship and grow in similar habitats, the population genetic structure of species would be affected by climatic difference and topography and lead to population divergence and local adaptation in a heterogeneous landscape. This study gives a chance to a better understanding of how selection drives the spatial genetic distribution of plants with different distribution patterns in a heterogeneous landscape.

    致謝 I 摘要 II Abstract III Introduction 1 Material and Methods 12 Plant Materials Collection 12 Environmental and topological variables 12 Ecological niche modeling 14 DNA extraction and ddRAD sequencing 15 De novo Assemblage and Data Filtering 16 Categorizing neutral and adaptive loci between populations within a species 17 Genetic diversity and population structure 18 Estimating potential geneflow hotspot 19 Resistance surface generation 20 Testing for IBD, IBE, IBRclim, and IBRalt models 20 Genetic-environment association analysis 22 Functional annotation 22 Result 24 Distribution pattern of the two species under the current and paleo-climates 24 SNPs filtering process 26 Outlier detection 26 Genetic diversity and population structure 27 Estimating Effective Migration Surfaces (EEMS) 29 Resistance surface generation 30 Selecting effective factors of population divergence 31 Functional annotation 32 Discussion 36 Potential colonization routes of Z kawagoii and Z shuanglongensis 36 Population genetic structure along an environmentally heterogeneous landscape 37 The effective factor of population differentiation 39 Local adaptation and functional annotation 40 Conclusion 44 Reference 45 Appendix 54 Table 54 Table 1 Sampling sites and coordination records of Z kawagoii 54 Table 2 Sampling sites and coordination records of Z shuanglongensis 55 Table 3 Variables used for current and historical ENMs 56 Table 4 Raw reads of 12 ddRADseq libraries 57 Table 5 Genetic diversity of Z kawagoii and Z shuanglongensis 58 Table 6 Summary results of the MLPE for testing the best model in explaining the population genetic differentiation 60 Table 7 Summary of the sequences of Zkawagoii containing outlier SNPs matches to GenBank gene sequences using BLASTN 61 Table 8 Summary of the sequences of Zshuanglongensis containing outlier SNPs matches to GenBank gene sequences using BLASTN 62 Figure 63 Figure 1 The distribution map of Z kawagoii and Z shuanglongensis 63 Figure 2 Sampling site of Z kawagoii and Z shuanglongensis in Taiwan 64 Figure 3 ENM of Z kawagoii and Z shuanglongensis in four different geological periods 65 Figure 4 The major contributing variables of ENMs model construction 66 Figure 5 Outlier detection analysis obtained by Bayescan 67 Figure 6 Manhattan plots of the results obtained with pcadapt 68 Figure 7 Venn diagram of outlier detection obtained by pcadapt, BayeScan, and LFMM 69 Figure 8 Pairwise FST of neutral and adaptive dataset of Z kawagoii 70 Figure 9 Pairwise FST of neutral and adaptive dataset of Z shuanglongensis 71 Figure 10 PCA of 37 Z kawagoii populations 72 Figure 11 PCA of 7 Z shuanglongensis populations 73 Figure 12 Bayesian clustering analysis of Z kawagoii by STRUCTURE 74 Figure 13 Bayesian clustering analysis of Z shuanglongensis by STRUCTURE 75 Figure 14 EEMS of Z kawagoii in Taiwan 76 Figure 15 EEMS of Z shuanglongensis in Taiwan 77 Figure 16 Climatic resistance layers of Z kawagoii and Z shuanglongensis 78 Figure 17 Topographic resistance layers of Z kawagoii and Z shuanglongensis 79 Figure 18 PCA map of neutral and adaptive dataset Z kawagoii 80 Figure 19 PCA map of neutral and adaptive dataset Zshuanglongensi s81 Figure 20 The major contribution variable of current ENM model of Z kawagoii 82

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