摘要
The main objective of this study was to evaluate the potential of a sesame （Sesamum indicum L.） panel for association analysis, and investigate the genetic basis of oil content （OC）, protein content, oleic acid concentration, and linoleic acid concentration using association mapping. A panel of 216 sesame accessions was phenotyped in a multi-environment trial and fingerprinted with 608 polymorphic loci produced by 79 primers, including simple sequence repeats （SSRs）, sequence-related amplified polymorphisms （SRAPs）, and amplified fragment length polymorphisms （AFLPs）. Population structure analysis revealed two subgroups in the population. The Q model performed better for its ability to re-identify associations for the four traits at highly significant P-values compared to the other three mixed models. And a total of 35 and 25 associations for the four traits in 2010 and 2011 were identified, respectively, with the Q model after Bonferroni correction. Among those associations, only one for OC was re-identified in two environments, and several markers associated simultaneously with multiple traits were discovered. These results suggest the power and stability of the Q model for association analysis of nutritional traits in this sesame panel for its slight population stratification and familial relationship, which could aid in dissecting complex traits, and could help to develop strategies for improving nutritional quality.
The main objective of this study was to evaluate the potential of a sesame （Sesamum indicum L.） panel for association analysis, and investigate the genetic basis of oil content （OC）, protein content, oleic acid concentration, and linoleic acid concentration using association mapping. A panel of 216 sesame accessions was phenotyped in a multi-environment trial and fingerprinted with 608 polymorphic loci produced by 79 primers, including simple sequence repeats （SSRs）, sequence-related amplified polymorphisms （SRAPs）, and amplified fragment length polymorphisms （AFLPs）. Population structure analysis revealed two subgroups in the population. The Q model performed better for its ability to re-identify associations for the four traits at highly significant P-values compared to the other three mixed models. And a total of 35 and 25 associations for the four traits in 2010 and 2011 were identified, respectively, with the Q model after Bonferroni correction. Among those associations, only one for OC was re-identified in two environments, and several markers associated simultaneously with multiple traits were discovered. These results suggest the power and stability of the Q model for association analysis of nutritional traits in this sesame panel for its slight population stratification and familial relationship, which could aid in dissecting complex traits, and could help to develop strategies for improving nutritional quality.
基金
Financial support for this work was provided by the National Basic Research Program of China(2011 CB1 09304-2)
the China Agriculture Research System of Sesame(CARS-15-1- 07)
