QTL mapping for seven quality traits was conducted by using 254 recombinant inbred lines （RIL） derived from a japonica-japonica rice cross of Xiushui 79/C Bao. The seven traits investigated were grain length （GL）,...QTL mapping for seven quality traits was conducted by using 254 recombinant inbred lines （RIL） derived from a japonica-japonica rice cross of Xiushui 79/C Bao. The seven traits investigated were grain length （GL）, grain length to width ratio （LWR）, chalk grain rate （CGR）, chalkiness degree （CD）, gelatinization temperature （GT）, amylose content （AC） and gel consistency （GC） of head rice. Three mapping methods employed were composite interval mapping in QTLMapper 2.0 software based on mixed linear model （MCIM）, inclusive composite interval mapping in QTL IciMapping 3.0 software based on stepwise regression linear model （ICIM） and multiple interval mapping with regression forward selection in Windows QTL Cartographer 2.5 based on multiple regression analysis （MIMR）. Results showed that five QTLs with additive effect （A-QTLs） were detected by all the three methods simultaneously, two by two methods simultaneously, and 23 by only one method. Five A-QTLs were detected by MCIM, nine by ICIM and 28 by MIMR. The contribution rates of single A-QTL ranged from 0.89% to 38.07%. All the QTLs with epistatic effect （E-QTLs） detected by MIMR were not detected by the other two methods. Fourteen pairs of E-QTLs were detected by both MCIM and ICIM, and 142 pairs of E-QTLs were detected by only one method. Twenty-five pairs of E-QTLs were detected by MCIM, 141 pairs by ICIM and four pairs by MIMR. The contribution rates of single pair of E-QTL were from 2.60% to 23.78%. In the Xiu-Bao RIL population, epistatic effect played a major role in the variation of GL and CD, and additive effect was the dominant in the variation of LWR, while both epistatic effect and additive effect had equal importance in the variation of CGR, AC, GT and GC. QTLs detected by two or more methods simultaneously were highly reliable, and could be applied to improve the quality traits in japonica hybrid rice.展开更多
As a problem in data science the inverse Ising(or Potts)problem is to infer the parameters of a Gibbs-Boltzmann distributions of an Ising(or Potts)model from samples drawn from that distribution.The algorithmic and co...As a problem in data science the inverse Ising(or Potts)problem is to infer the parameters of a Gibbs-Boltzmann distributions of an Ising(or Potts)model from samples drawn from that distribution.The algorithmic and computational interest stems from the fact that this inference task cannot be carried out efficiently by the maximum likelihood criterion,since the normalizing constant of the distribution(the partition function)cannot be calculated exactly and efficiently.The practical interest on the other hand flows from several outstanding applications,of which the most well known has been predicting spatial contacts in protein structures from tables of homologous protein sequences.Most applications to date have been to data that has been produced by a dynamical process which,as far as it is known,cannot be expected to satisfy detailed balance.There is therefore no a priori reason to expect the distribution to be of the Gibbs-Boltzmann type,and no a priori reason to expect that inverse Ising(or Potts)techniques should yield useful information.In this review we discuss two types of problems where progress nevertheless can be made.We find that depending on model parameters there are phases where,in fact,the distribution is close to Gibbs-Boltzmann distribution,a non-equilibrium nature of the under-lying dynamics notwithstanding.We also discuss the relation between inferred Ising model parameters and parameters of the underlying dynamics.展开更多
基金supported by the National High Technology Research and Development Program of China (Grant No. 2010AA101301)the Program of Introducing International Advanced Agricultural Science and Technology in China (Grant No. 2006-G8[4]-31-1)the Program of Science-Technology Basis and Conditional Platform in China (Grant No. 505005)
文摘QTL mapping for seven quality traits was conducted by using 254 recombinant inbred lines （RIL） derived from a japonica-japonica rice cross of Xiushui 79/C Bao. The seven traits investigated were grain length （GL）, grain length to width ratio （LWR）, chalk grain rate （CGR）, chalkiness degree （CD）, gelatinization temperature （GT）, amylose content （AC） and gel consistency （GC） of head rice. Three mapping methods employed were composite interval mapping in QTLMapper 2.0 software based on mixed linear model （MCIM）, inclusive composite interval mapping in QTL IciMapping 3.0 software based on stepwise regression linear model （ICIM） and multiple interval mapping with regression forward selection in Windows QTL Cartographer 2.5 based on multiple regression analysis （MIMR）. Results showed that five QTLs with additive effect （A-QTLs） were detected by all the three methods simultaneously, two by two methods simultaneously, and 23 by only one method. Five A-QTLs were detected by MCIM, nine by ICIM and 28 by MIMR. The contribution rates of single A-QTL ranged from 0.89% to 38.07%. All the QTLs with epistatic effect （E-QTLs） detected by MIMR were not detected by the other two methods. Fourteen pairs of E-QTLs were detected by both MCIM and ICIM, and 142 pairs of E-QTLs were detected by only one method. Twenty-five pairs of E-QTLs were detected by MCIM, 141 pairs by ICIM and four pairs by MIMR. The contribution rates of single pair of E-QTL were from 2.60% to 23.78%. In the Xiu-Bao RIL population, epistatic effect played a major role in the variation of GL and CD, and additive effect was the dominant in the variation of LWR, while both epistatic effect and additive effect had equal importance in the variation of CGR, AC, GT and GC. QTLs detected by two or more methods simultaneously were highly reliable, and could be applied to improve the quality traits in japonica hybrid rice.
基金the National Natural Science Foundation of China(Grant No.11705097)the Natural Science Foundation of Jiangsu Province of China(Grant No.BK20170895)+1 种基金the Jiangsu Government Scholarship for Overseas Studies of 2018 and Scientific Research Foundation of Nanjing University of Posts and Telecommunications,China(Grant No.NY217013)the Foundation for Polish Science through TEAM-NET Project(Grant No.POIR.04.04.00-00-17C1/18-00).
文摘As a problem in data science the inverse Ising(or Potts)problem is to infer the parameters of a Gibbs-Boltzmann distributions of an Ising(or Potts)model from samples drawn from that distribution.The algorithmic and computational interest stems from the fact that this inference task cannot be carried out efficiently by the maximum likelihood criterion,since the normalizing constant of the distribution(the partition function)cannot be calculated exactly and efficiently.The practical interest on the other hand flows from several outstanding applications,of which the most well known has been predicting spatial contacts in protein structures from tables of homologous protein sequences.Most applications to date have been to data that has been produced by a dynamical process which,as far as it is known,cannot be expected to satisfy detailed balance.There is therefore no a priori reason to expect the distribution to be of the Gibbs-Boltzmann type,and no a priori reason to expect that inverse Ising(or Potts)techniques should yield useful information.In this review we discuss two types of problems where progress nevertheless can be made.We find that depending on model parameters there are phases where,in fact,the distribution is close to Gibbs-Boltzmann distribution,a non-equilibrium nature of the under-lying dynamics notwithstanding.We also discuss the relation between inferred Ising model parameters and parameters of the underlying dynamics.
