以番茄重組自交系進行數量性狀基因座定位及RNA定序資料分析探討耐熱性的候選基因

Abstract

為因應全球氣候變遷的挑戰，番茄耐熱育種愈發重要。然而目前對於番茄耐熱機制的了解仍然有限，導致育種效率難以提升。花粉生育力的下降是番茄遭遇高溫逆境一個主要的問題。本研究利用一個由不耐熱親本CA4與耐熱親本CLN1621L雜交的重組自交系進行花粉耐熱性相關的數量性狀基因座(QTL)定位，並搭配轉錄體定序資料的分析來推論耐熱的候選基因。試驗中，外表型調查測量了高溫逆境（日夜溫30/25°C）與常溫（日夜溫20/15°C）下，番茄的花粉活性與花粉數量兩個性狀。同時，收取1~3 mm的小花苞進行RNA定序。序列資料分析一共開發了16,788個單核甘酸分子標誌，並建構出總長1071.5 cM，且分子標誌平均距離為2.5 cM的高密度遺傳圖譜。一共定位到六個耐熱性QTLs，兩個為花粉活性與四個為花粉數量，並根據LOD計算出區間的範圍。經過後續轉錄體定序的分析，將區間內候選基因限定於兩類，包括偵測到轉錄體序列變異的基因以及認定為cis調控表現量的基因。在本研究中也特別提出兩個候選基因，分別位於第三號染色體花粉活性的QTL與第十一號染色體花粉數量的QTL，建議為優先進行功能性印證的基因。另外，本研究透過實際試驗資料的分析，展示出以數量性狀基因座定位搭配轉錄體分析以探勘候選基因的潛力。

Global warming has been leading to the need of heat-tolerant tomato varieties. However, breeding for tomato heat tolerance is still inefficient due to limited knowledge on physiological and molecular mechanisms behind heat tolerance. Decline in pollen fertility is one of the major problem when tomatoes encounter heat stress. In this study, our goal is to identify the candidate genes of thermal tolerance for pollen traits in tomato. QTL mapping and RNA sequencing (RNA-Seq) analysis were conducted using a 78 RIL population of a cross between the heat-sensitive variety CA4 and the heat-tolerant variety CLN1621L. Pollen viability (PV) and pollen number (PN) of the RILs were evaluated under both moderate heat stress condition (30/25°C day/night) and control condition (20/15°C day/night). Meanwhile, 1-3 mm flower buds of each line were sampled for RNA-Seq library construction. A total of 16,788 SNP markers were detected from the sequence data, and were used to construct a high-density genetic map with total length of 1071.5 cM and an average spacing of 2.5 cM. Six heat tolerant QTLs were identified, two for PV and four for PN. Within the intervals of these QTLs, candidate genes of two categories were listed: genes with detectable sequence variants in their coding sequences; and differential expressed genes which were recognized as the cis-eQTL. The specific candidate genes for PV on chromosome 3 (PV03) and for PN on chromosome 11, were suggested. This work also demonstrates the power of RNA-Seq-based QTL analysis in inferring candidate genes on a moderate-size RIL population.