Regularized selection indices for breeding value prediction using hyper-spectral image data.

ABSTRACT: High-throughput phenotyping (HTP) technologies can produce data on thousands of phenotypes per unit being monitored. These data can be used to breed for economically and environmentally relevant traits (e.g., drought tolerance); however, incorporating high-dimensional phenotypes in genetic analyses and in breeding schemes poses important statistical and computational challenges. To address this problem, we developed regularized selection indices; the methodology integrates techniques commonly used in high-dimensional phenotypic regressions (including penalization and rank-reduction approaches) into the selection index (SI) framework. Using extensive data from CIMMYT's (International Maize and Wheat Improvement Center) wheat breeding program we show that regularized SIs derived from hyper-s

SUBMITTER: Lopez-Cruz M 

PROVIDER: S-EPMC7235263 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature