| Chapter title |
Applications of Genomic Selection in Breeding Wheat for Rust Resistance
|
|---|---|
| Chapter number | 15 |
| Book title |
Wheat Rust Diseases
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-7249-4_15 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7248-7, 978-1-4939-7249-4
|
| Authors |
Leonardo Ornella, Juan Manuel González-Camacho, Susanne Dreisigacker, Jose Crossa |
| Abstract |
There are a lot of methods developed to predict untested phenotypes in schemes commonly used in genomic selection (GS) breeding. The use of GS for predicting disease resistance has its own particularities: (a) most populations shows additivity in quantitative adult plant resistance (APR); (b) resistance needs effective combinations of major and minor genes; and (c) phenotype is commonly expressed in ordinal categorical traits, whereas most parametric applications assume that the response variable is continuous and normally distributed. Machine learning methods (MLM) can take advantage of examples (data) that capture characteristics of interest from an unknown underlying probability distribution (i.e., data-driven). We introduce some state-of-the-art MLM capable to predict rust resistance in wheat. We also present two parametric R packages for the reader to be able to compare. |
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|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 30% |
| Student > Ph. D. Student | 4 | 20% |
| Other | 2 | 10% |
| Student > Doctoral Student | 1 | 5% |
| Student > Bachelor | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 4 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 45% |
| Biochemistry, Genetics and Molecular Biology | 3 | 15% |
| Medicine and Dentistry | 2 | 10% |
| Engineering | 1 | 5% |
| Unknown | 5 | 25% |