Shasidhar Yaduru, Vishwakarma Manish K, Pandey Manish K, Janila Pasupuleti, Variath Murali T, Manohar Surendra S, Nigam Shyam N, Guo Baozhu, Varshney Rajeev K
International Crops Research Institute for the Semi-Arid TropicsHyderabad, India.
Department of Genetics, Osmania UniversityHyderabad, India.
Front Plant Sci. 2017 May 22;8:794. doi: 10.3389/fpls.2017.00794. eCollection 2017.
Enhancing seed oil content with desirable fatty acid composition is one of the most important objectives of groundnut breeding programs globally. Genomics-assisted breeding facilitates combining multiple traits faster, however, requires linked markers. In this context, we have developed two different F mapping populations, one for oil content (OC-population, ICGV 07368 × ICGV 06420) and another for fatty acid composition (FA-population, ICGV 06420 × SunOleic 95R). These two populations were phenotyped for respective traits and genotyped using Diversity Array Technology (DArT) and DArTseq genotyping platforms. Two genetic maps were developed with 854 (OC-population) and 1,435 (FA-population) marker loci with total map distance of 3,526 and 1,869 cM, respectively. Quantitative trait locus (QTL) analysis using genotyping and phenotyping data identified eight QTLs for oil content including two major QTLs, and , with 22.11 and 10.37% phenotypic variance explained (PVE), respectively. For seven different fatty acids, a total of 21 QTLs with 7.6-78.6% PVE were identified and 20 of these QTLs were of major effect. Two mutant alleles, and , also had 18.44 and 10.78% PVE for palmitic acid, in addition to oleic (33.8 and 17.4% PVE) and linoleic (41.0 and 19.5% PVE) acids. Furthermore, four QTL clusters harboring more than three QTLs for fatty acids were identified on the three LGs. The QTLs identified in this study could be further dissected for candidate gene discovery and development of diagnostic markers for breeding improved groundnut varieties with high oil content and desirable oil quality.
提高具有理想脂肪酸组成的种子油含量是全球花生育种计划的最重要目标之一。基因组辅助育种有助于更快地整合多个性状，然而，这需要连锁标记。在此背景下，我们开发了两个不同的F作图群体，一个用于油含量（OC群体，ICGV 07368×ICGV 06420），另一个用于脂肪酸组成（FA群体，ICGV 06420×SunOleic 95R）。对这两个群体的各自性状进行了表型分析，并使用多样性阵列技术（DArT）和DArTseq基因分型平台进行了基因分型。构建了两个遗传图谱，分别包含854个（OC群体）和1435个（FA群体）标记位点，总图距分别为3526和1869 cM。利用基因分型和表型数据进行的数量性状位点（QTL）分析确定了8个油含量QTL，包括两个主要QTL，分别解释了22.11%和10.37%的表型变异（PVE）。对于七种不同的脂肪酸，共鉴定出21个QTL，PVE为7.6-78.6%，其中20个QTL具有主要效应。两个突变等位基因，除油酸（33.8%和17.4% PVE）和亚油酸（41.0%和19.5% PVE）外，对棕榈酸也有18.44%和10.78%的PVE。此外，在三个连锁群上鉴定出四个包含三个以上脂肪酸QTL的QTL簇。本研究中鉴定出的QTL可进一步剖析，以发现候选基因并开发诊断标记，用于培育具有高油含量和理想油品质的改良花生品种。
