Suggestions(1)
Exact(60)
The purpose of the phenotyping experiments was to scan the population for multiple traits and combinations of traits captured at a relatively early generation (S4).
The purpose is to fine map QTLs for multiple traits and to directly and indirectly use the highly recombined lines in breeding programs.
Also, in some cases favorable alleles for multiple traits were detected in the same chromosome regions, thus CSSLs with these chromosome segments are potentially useful breeding materials for simultaneously improving multiple traits.
A set of CSSLs can be grown in different environments by different researchers, and evaluated for multiple traits to determine whether particular genes, QTLs or chromosomal segment(s) from the donor are responsible for trait variation in the recurrent parent.
These studies validate the use, in linkage analysis, of large cohorts of unselected twins phenotyped for multiple traits, and they highlight the importance of conducting genome scans in replicate populations as a prelude to positional cloning and gene discovery.
Many QTLs for multiple traits were identified, such as traits associated with agronomic characteristics (Huang et al. 2010; Zhao et al. 2011; Yang et al. 2014), and with responses to abiotic stresses (Famoso et al. 2011; Pan et al. 2015; Lv et al. 2016), and to biotic stresses (Jia et al. 2012; Wang et al. 2014; Kang et al. 2016; Wang et al. 2015).
At the S4 stage of SSD a subset (200 lines) of this population was genotyped using a genotyping-by-sequencing (GBS) approach and was phenotyped for multiple traits, including: blast and bacterial blight resistance, salinity and submergence tolerance, and grain quality.
Considering the huge genetic diversity available for multiple traits within rice germplasm, GWAS can be a feasible approach to simultaneously map loci for many traits and the improved mapping resolution helps in precisely identifying the genes/SNPs associated with the traits.
An outline of the model, using notation adapted from Burdon ([1989]), is presented in the equations below: A selection index (I) can be constructed to account for multiple traits as follows: I = b 1 X 1 + b 2 X 2.......... + b n X n (7 where b1X1 is the product of the weight b given to the breeding value X for trait 1, and so forth.
The analysis of genetic correlation coefficients showed overlap of genetic determinants for multiple traits.
Bonferroni correction was not done for multiple traits due to lack of any significant association.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com