| Title |
The effect of the H−1 scaling factors τ and ω on the structure of H in the single-step procedure
|
|---|---|
| Published in |
Genetics Selection Evolution, April 2018
|
| DOI | 10.1186/s12711-018-0386-x |
| Pubmed ID | |
| Authors |
Johannes W. R. Martini, Matias F. Schrauf, Carolina A. Garcia-Baccino, Eduardo C. G. Pimentel, Sebastian Munilla, Andres Rogberg-Muñoz, Rodolfo J. C. Cantet, Christian Reimer, Ning Gao, Valentin Wimmer, Henner Simianer |
| Abstract |
The single-step covariance matrix H combines the pedigree-based relationship matrix [Formula: see text] with the more accurate information on realized relatedness of genotyped individuals represented by the genomic relationship matrix [Formula: see text]. In particular, to improve convergence behavior of iterative approaches and to reduce inflation, two weights [Formula: see text] and [Formula: see text] have been introduced in the definition of [Formula: see text], which blend the inverse of a part of [Formula: see text] with the inverse of [Formula: see text]. Since the definition of this blending is based on the equation describing [Formula: see text], its impact on the structure of [Formula: see text] is not obvious. In a joint discussion, we considered the question of the shape of [Formula: see text] for non-trivial [Formula: see text] and [Formula: see text]. Here, we present the general matrix [Formula: see text] as a function of these parameters and discuss its structure and properties. Moreover, we screen for optimal values of [Formula: see text] and [Formula: see text] with respect to predictive ability, inflation and iterations up to convergence on a well investigated, publicly available wheat data set. Our results may help the reader to develop a better understanding for the effects of changes of [Formula: see text] and [Formula: see text] on the covariance model. In particular, we give theoretical arguments that as a general tendency, inflation will be reduced by increasing [Formula: see text] or by decreasing [Formula: see text]. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 49 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 29% |
| Researcher | 12 | 24% |
| Student > Doctoral Student | 5 | 10% |
| Student > Master | 4 | 8% |
| Student > Postgraduate | 3 | 6% |
| Other | 4 | 8% |
| Unknown | 7 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 31 | 63% |
| Biochemistry, Genetics and Molecular Biology | 3 | 6% |
| Business, Management and Accounting | 1 | 2% |
| Veterinary Science and Veterinary Medicine | 1 | 2% |
| Sports and Recreations | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 11 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 October 2018.
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#22,767,715
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#772
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Outputs of similar age from Genetics Selection Evolution
#22
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