Seed protein content and its relationships with agronomic traits in pigeonpea is controlled by both main and epistatic effects QTLs

被引:19
作者
Obala, Jimmy [1 ,2 ]
Saxena, Rachit K. [1 ]
Singh, Vikas K. [1 ]
Kale, Sandip M. [1 ]
Garg, Vanika [1 ]
Kumar, C. V. Sameer [3 ]
Saxena, K. B. [4 ]
Tongoona, Pangirayi [2 ]
Sibiya, Julia [2 ]
Varshney, Rajeev K. [1 ]
机构
[1] Int Crops Res Inst Semi Arid Trop, Hyderabad 502324, India
[2] Univ KwaZulu Natal, African Ctr Crop Improvement, ZA-3209 Pietermaritzburg, South Africa
[3] Prof Jayashankar Telangana State Agr Univ, Hyderabad 500030, Telangana, India
[4] Int Crops Res Inst Semi Arid Trop, 17 NMC Housing, Abu Dhabi, U Arab Emirates
关键词
GROWTH HABIT; MULTIPLE POPULATIONS; GENETIC-ANALYSIS; CANDIDATE GENES; FLOWERING-TIME; GRAIN-YIELD; IDENTIFICATION; LOCI; REVEALS; DISEASE;
D O I
10.1038/s41598-019-56903-z
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The genetic architecture of seed protein content (SPC) and its relationships to agronomic traits in pigeonpea is poorly understood. Accordingly, five F-2 populations segregating for SPC and four agronomic traits (seed weight (SW), seed yield (SY), growth habit (GH) and days to first flowering (DFF)) were phenotyped and genotyped using genotyping-by-sequencing approach. Five high-density population-specific genetic maps were constructed with an average inter-marker distance of 1.6 to 3.5 cM, and subsequently, integrated into a consensus map with average marker spacing of 1.6 cM. Based on analysis of phenotyping data and genotyping data, 192 main effect QTLs (M-QTLs) with phenotypic variation explained (PVE) of 0.7 to 91.3% were detected for the five traits across the five populations. Major effect (PVE = 10%) M-QTLs included 14 M-QTLs for SPC, 16 M-QTLs for SW, 17 M-QTLs for SY, 19 M-QTLs for GH and 24 M-QTLs for DFF. Also, 573 epistatic QTLs (E-QTLs) were detected with PVE ranging from 6.3 to 99.4% across traits and populations. Colocalization of M-QTLs and E-QTLs explained the genetic basis of the significant (P < 0.05) correlations of SPC with SW, SY, DFF and GH. The nature of genetic architecture of SPC and its relationship with agronomic traits suggest that genomics-assisted breeding targeting genome-wide variations would be effective for the simultaneous improvement of SPC and other important traits.
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页数:17
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