Theoretical and Applied Genetics

, Volume 116, Issue 4, pp 577–587 | Cite as

QTL analysis of early-season cold tolerance in sorghum

  • Joseph Knoll
  • Nilupa Gunaratna
  • Gebisa Ejeta
Original Paper


Cool temperatures during the early-growing season are a major limitation to growing sorghum [Sorghum bicolor (L.) Moench] in temperate areas. Several landraces from China have been found to exhibit higher emergence and greater seedling vigor under cool conditions than most breeding lines currently available, but tend to lack desirable agronomic characteristics. The introgression of desirable genes from Chinese landraces into elite lines could be expedited by marker-assisted selection. Using a population of 153 RI lines, developed from a cross between Chinese landrace ‘Shan Qui Red,’ (SQR, cold-tolerant) and SRN39 (cold-sensitive), QTL associated with early-season performance under both cold and optimal conditions were identified by single marker analysis, simple interval mapping (SIM), and composite interval mapping (CIM). Germination was observed under controlled conditions, and other traits were measured in field plantings. Two QTL for germination were identified: one on linkage group SBI-03a, derived from SRN39, was significant under cold and optimal temperatures. The other, on group SBI-07b, showed greater significance under cold temperatures and was contributed by SQR. A region of group SBI-01a, derived from SQR, showed strong associations with seedling emergence and seedling vigor scores under early and late field plantings. A QTL for both early and late emergence was identified by CIM on SBI-02 which favored the SRN39 allele. SIM identified a QTL for early vigor on SBI-04 favoring the SQR genotype. Further studies are needed to validate the effects of these QTL, but they represent the first step in development of a marker-assisted breeding effort to improve early-season performance in sorghum.


Quantitative Trait Locus Linkage Group Sorghum Simple Sequence Repeat Marker Recombinant Inbred Line 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We are grateful for the assistance provided by several colleagues. Dr. Cécile Grenier collated all of the RFLP, RAPD, and SSR data from previous studies into a common, usable dataset. Terry Lemming assisted in planting of the field tests. Prof. R. W. Doerge provided instruction on using the mapping and QTL analysis software. Funding for this project was provided by International Sorghum and Millets (INTSORMIL)-USAID Grant # DAN254-G-00-002.


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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Department of AgronomyPurdue UniversityWest LafayetteUSA
  2. 2.Department of StatisticsPurdue UniversityWest LafayetteUSA

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