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数量遗传课题组 Quantitative Genetics Group |
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中国农业科学院 作物科学研究所 |
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The Maize RIL population
1. Introduction
The two maize inbred parental lines (i.e. Ac7643S5 and Ac7729/TZSRWS5) crossed to develop the 224 recombinant inbred lines (RIL) are tropical S5 lines derived from Tuxpe?o germplasm. Ac7643S5 derived from Population 43 ( La Posta ) is the drought tolerant line and Ac7729/TZSRWS5 from Population 29 (Tuxpe?o Caribe) is the drought susceptible line. The RIL population was developed to identify and characterize QTL involved in the expression of drought tolerance. Using 132 RFLP probes, a linkage map of a total length of 2250 cM with an average density of 17 cM was constructed. All RIL and the two parents have been tested under a broad set of environments under different water regimes to characterize QTL for yield components and salient secondary traits related to drought tolerance. It is quite important for the two parental lines to have the same flowering time to understand drought tolerance mechanisms in a segregating population. Indeed, male flowering is a very polygenic trait with large transgressive segregation and marked difference in flowering time can generate confounding effects between drought tolerance and drought escape. In all trials time to male flowering (MFLW) was measured on an individual plant basis (with 10 plants per plot) and represents the number of days between sowing and anther extrusion. The MFLW data collected in Tlaltizapan , Mexico during the 1996 dry plant cycle under intermediate water stress conditions were used in this study.
2. Number of markers on each chromosome
Chromosome NumMarkers in each chromosome
Ch1 18
Ch2 14
Ch3 17
Ch4 13
Ch5 12
Ch6 13
Ch7 9
Ch8 15
Ch9 12
Ch10 9
3. Linkage map represented by marker interval length (cM) (MaizeRILLinkageMap)
4. Marker type (0, Ac7643S5; 2, Ac7729/TZSRWS5; -1, missing) (MaizeRILMarkerType)
5. Male flowering time (d)
103.96 100.77 97.64 108.78 103.35 101.04 102.34 102.88 105.63 103.55 101.83 94.28 98.12 102.95 97.58 102.72 99.07 107.76 104.70 99.44 104.22 99.31 101.64 104.25 100.65 103.52 107.87 102.14 100.07 94.36 101.45 108.86 104.03 102.76 101.56 97.25 101.50 105.97 106.85 100.00 105.33 98.71 102.09 103.04 103.66 103.28 93.72 100.29 102.56 104.43 98.99 100.60 102.46 106.91 101.81 104.73 100.76 102.13 105.46 105.05 103.13 106.00 105.17 103.98 103.90 100.13 104.50 103.51 99.54 102.49 96.45 105.12 102.54 98.91 103.48 100.11 103.74 102.32 100.68 98.90 102.63 104.23 107.24 99.73 97.04 99.66 102.69 108.72 100.08 97.07 101.66 103.17 102.30 104.35 98.48 105.76 99.92 104.25 106.37 101.29 107.53 98.07 103.14 99.34 105.29 101.47 104.14 104.15 106.00 96.85 104.03 96.77 93.88 101.70 104.76 103.78 99.87 102.76 105.89 102.15 104.40 102.79 107.03 104.04 102.99 111.55 103.97 102.28 104.33 100.54 106.64 107.06 104.84 112.83 107.17 106.67 101.29 104.72 105.09 95.15 104.50 102.94 104.35 103.85 110.90 104.95 105.46 103.00 110.41 98.61 105.59 101.29 106.02 102.57 100.22 109.55 103.26 103.75 106.28 112.91 100.58 100.10 108.38 102.80 103.86 102.72 104.18 103.75 109.71 102.68 102.47 97.88 100.43 102.63 102.08 105.14 97.72 101.78 107.40 104.24 97.21 98.62 103.06 99.15 100.10 106.91 102.32 106.76 100.64 103.06 98.24 100.08 110.87 103.96 99.36 103.61 103.75 103.88 105.98 101.59 102.42 100.32 107.46 107.45 108.23 101.03 99.77 107.45 105.47 97.22 100.75 102.88 105.58 106.51 99.62 106.46 111.99 104.89 99.76 103.42 107.40 100.39 102.61 102.97
6. Input file for Windows QTL IciMapping (MaizeRIL.qtl)
7.One-dimensional scanning for additive QTL (step = 1 cM )
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8. Additive QTL identified by ICIM (PIN=0.01, PUT=0.02)
9. Two-dimensional scanning for digenic interactions (step = 2 cM )
10. Digenic interactions identified by ICIM (step = 2 cM )
11. Input file for simulation based on QTL information identified by ICIM (MFLWbyICIM.qtl)
11.1 100 RIL populations for Windows QTL Cartographer (MFLWbyICIM-CIM)
11.2 100 RIL populations for Empiricam Bayesian Model (MFLWbyICIM-BAYES)
11.3 100 RIL populations for Windows QTL IciMapping (MFLWbyICIM-ICIM)
12. Input file for simulation based on QTL information identified by the empirical Bayesian model (MFLWbyBAYES.qtl)
12.1 100 RIL populations for Windows QTL Cartographer (MFLWbyBAYES-CIM)
12.2 100 RIL populations for empirical Bayesian Model (MFLWbyBAYES-BAYES)
12.3 100 RIL populations for Windows QTL IciMapping (MFLWbyBAYES-ICIM)