Breeding for Quantitative Traits in Plants

By Joshua Udall. 2003

714 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/83155b91272fa7eb7e7b65a0905e4a7265ba35e4

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In Ecology, Evolution, Behavior and Systematics

In Genetics

In Plant Science

Citations in the corpus

Combining ability, heterosis and genetic diversity in tropical maize (Zea mays L.) under stress and non-stress conditions (2011, background): “Synthetic varieties are not expected to experience a decrease in heterosis after the F2 generation (Bernardo 2002).”
SOYBEAN QTL FOR YIELD AND YIELD COMPONENTS ASSOCIATED WITH GLYCINE SOJA ALLELES (2008, background): “In plants, more than 900 QTL have been reported from studies that involved relatively large mapping populations (Bernardo 2002).”
Genotype by environment interactions in wheat quality breeding programs in southeast Europe (2010, methodology): “Moreover, the diversity among locations was the principal source of variation of all the analyzed traits, most of which being unforeseeable and thus causing problems even when the multiplicative approach was used (Bernardo 2002).”
Genetic Improvement of U.S. Soybean in Maturity Groups II, III, and IV (2014, methodology): “, 2001; Bernardo, 2010b; Hefner et al., 2009), and DNA and RNA sequencing will increase our understanding of gene function, which can be utilized in the development of future cultivars (Poland and Rife, 2012; Schmutz et al., 2010). In addition to gains in genetic yield potential, genotype × environm”
Implementation of molecular markers for quantitative traits in breeding programs - challenges and opportunities (2004, methodology): “Furthermore, breeding values of lines based on their phenotype and QTL genotype can be obtained from the same analysis used for QTL mapping (Bernardo 2002).”
Drought Tolerance in Maize: Indirect Selection through Secondary Traits versus Genomewide Selection (2013, background): “On the other hand, results from selection with a Smith-Hazel index in plants have been erratic (Elgin et al., 1970) and plant breeders have generally preferred less formal indices (e.g., base index; Williams, 1962) for combining information for different traits (Bernardo, 2010).”
Application of population genetic theory and simulation models to efficiently pyramid multiple genes via marker-assisted selection (2007, background): “Two types of selection involving markers are widely utilized (Bernardo, 2002).”
Improving Lives: 50 Years of Crop Breeding, Genetics, and Cytology (C‐1) (2006, background): “Although the Smith-Hazel index remains the best index in theory (Bernardo, 2002), exact determination of the selection weights requires not only economic weights, but also error-free estimates of phenotypic and genetic covariances.”
Genetic mapping of biomass production in tetraploid alfalfa (2007, methodology): “We opted to use single-marker analysis even though it cannot precisely localize QTLs or determine the number of QTLs in a given region due to confounding of recombination and genotypic value (Bernardo, 2002).”
Survey of Plant Density Tolerance in U.S. Maize Germplasm (2014, background): “Genetic correlations are associated with notoriously large standard errors because variance components that have large standard errors are used to calculate them (Bernardo, 2010); therefore, r values can be outside of theoretical values (Isik, 2009).”
Status and Prospects of Association Mapping in Plants (2008, methodology): “…h e sample size for many linkage analysis studies in plants involves about 250 individuals (F 2 , BC 1 , RIL, etc.) with a homogenous, bi-parental genetic background (Bernardo, 2002). h e genetic variation within an association-mapping panel is usually much greater than of linkage populations.”
Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer (2016, neutral): “Currently, three algorithms for variance component estimation are supported; efficient mixed model association (EMMA) [18], average information (AI) [21,22], and expectation maximization (EM) [23,24].”
From Means to QTL : The Illinois Long-Term Selection Experiment as a Case Study in Quantitative Genetics (2007, result): “As in other studies cited and as expected from theory (Bernardo, 2002) the genetic variance for testcross progenies was considerably lower than for lines per se (Table 2).”
Accuracy of Genomewide Selection for Different Traits with Constant Population Size, Heritability, and Number of Markers (2013, methodology): “…populations using a mixed model in which inbreds had i xed ef ects and other ef ects were random. h e values of h2 and g2 were expressed on an entry-mean basis (Bernardo, 2010, p. 156) and therefore accounted for both within-environment experimental error and genotype × environment interaction.”
Prospects for genomewide selection for quantitative traits in maize (2007, methodology): “As such, pedigree-based BLUP is not useful in withinpopulation selection (Steps 2 and 3; Bernardo, 2002, p. 234–235) unless strong family structures are developed during selfi ng.”
Classification of North Dakota maize inbred lines into heterotic groups based on molecular and testcross data (2006, methodology): “The genotype by environment mean square was also used to calculate the LSD value for every trait (Bernardo, 2002).”