RT Journal Article
SR Electronic
T1 Genetic image-processing using regularized selection indices
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 625251
DO 10.1101/625251
A1 Marco Lopez-Cruz
A1 Eric Olson
A1 Gabriel Rovere
A1 Jose Crossa
A1 Susanne Dreisigacker
A1 Sushismita Mondal
A1 Ravi Singh
A1 Gustavo de los Campos
YR 2019
UL http://biorxiv.org/content/early/2019/05/02/625251.abstract
AB 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 image-based regularized SIs offer consistently higher accuracy for grain yield than those achieved by canonical SIs and by vegetation indices commonly used to predict agronomic traits. Regularized SIs offer an effective approach to leverage HTP data that is routinely generated in agriculture; the methodology can also be used to conduct genetic studies using high-dimensional phenotypes that are often collected in humans and model organisms including body images and whole-genome gene expression profiles.Author summary A more intensive use of High-throughput phenotyping (HTP) in breeding programs can increase selection gains and can enable breeding for traits that are otherwise difficult to measure and to breed for (e.g., drought resistance in plants). Most of the phenotypes generated by HTP platforms are high-dimensional, making the use of these data for breeding decisions challenging. We propose to address this problem by using regularized selection indices (SIs). The methodology combines ideas from quantitative genetics with methods used in high-dimensional regressions. Using wheat data from CIMMYT’s wheat breeding program we show that regularized SIs deliver more accurate selection decisions than that of canonical SIs.