Statistical methods for the quantitative genetic analysis of high-throughput phenotyping data

Abstract

The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Section 1, Section 2 discusses field-based HTP, including the use of unmanned aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal GS. The chapter concludes with a discussion of some standing issues.

Plant phenotyping relevance

HTPデータの統計解析と縦断的・多形質モデルを中心に扱う方法論的章であり、植物表現型データの解析ワークフローが主要内容である。

Code and data availability

The supplied blocks are from a review chapter on statistical methods for high-throughput phenotyping in quantitative genetics. The text only describes and cites prior studies (e.g., Rutkoski et al., Spindel et al., Sun et al.) and presents no paper-specific phenotype datasets, images, code, models, or supplements with,

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