precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation
Based on breeding and custom varieties adapted to specific environmental conditions, we must improve our ability to assess the dynamic changes of crops in field conditions.
To this end, we have developed a precision phenotype platform that combines various sensors for non-invasive, high-
Size phenotype of small grain grains.
The platform has high prediction accuracy and genetic ability for rye biomass.
The genetic variation of biomass accumulation was analyzed with 647 double-fold lines from four families.
Using the genome
A wide association mapping method for two major quantitative trait sites (QTL)
The biomass was identified and it was found that the genetic structure of biomass accumulation has the characteristic of dynamic time pattern.
Our findings highlight the potential of accurate phenotype analysis in assessing dynamic genetics of complex traits, especially those that are not suitable for conventional phenotype analysis.
In this study, we used Rye as a model species for small grains.
Two experiments were carried out: one for calibration of a precise phenotype platform and the other for anatomy of the genetic structure of biomass accumulation.
Calibration experiments are based on 25 different Rye lines grown in one location (Stuttgart-
Within two years of plant density (
280 plants per m for the best, reduced to 140)
Two N-fertilizer schemes for each planting density (
Standard practices and a decrease of 50%)
, There are two repetitions in each treatment group.
These plants perform phenotype analysis through a precision phenotype platform, followed by 49 at approximately BBCH stage (awns visible), 69 (late flowering), and 81 (
Dough development early)
Determine the reference fresh weight.
A sample of each plot is dried to determine the dry substance content and dry biomass yield.
The second experiment was based on a map Group of 647 double-fold Rye lines from four designated AxB families (131), AxC (120), DxE (200), and DxF (196)
Alheit and others have already described it.
As populations DH6, DH7, EAW74 and eaw78.
Lines from the map population grow in a partially copied design, including a general inspection, with 960 plots per location and 280 plant densities per m.
Increased map population in two locations (
Germany: StuttgartHohenheim, 48. Latitude 77, 9. 18° longitude; Bohlingen, 47. Latitude 72, 8. 9° longitude)in two years (2011 and 2012).
All plants were evaluated by the phenotype platform between nine o\'clock A. M. and six o\'clock P. M. in approximately BBCH stages 49, 69 and 81 to predict biomass based on the calibration model established in the calibration experiment.
Growth parameters (μ, λ, integral)
Determined with R pack grofit.
The growth rate is represented by the maximum slope μ, λ is the length of the lag phase, and the integral corresponds to the area under the curve ().
The plants were genetically typed with 1710 DArT markers and analyzed using a consistent map location.
Chain imbalance (LD)
Measured [ref ]
The calculation was carried out with the Plabsoft software package. Genome-
Extensive Association mapping was performed using a hybrid model method containing kinship information.
Bonferroni-for main effect traits and superior traits,
For multiple tests of