2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits

      Preprint
      , , ,
      bioRxiv

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Understanding the genetic basis of dynamic plant phenotypes has largely been limited due to lack of space and labor resources needed to record dynamic traits, often destructively, for a large number of genotypes. However, the recent advent of image-based phenotyping platforms has provided the plant science community with an effective means to non-destructively evaluate morphological, developmental, and physiological processes at regular, frequent intervals for a large number of plants throughout development. The statistical frameworks typically used for genetic analyses (e.g. genome-wide association mapping, linkage mapping, and genomic prediction) in plant breeding and genetics are not particularly amenable for repeated measurements. Random regression (RR) models are routinely used in animal breeding for the genetic analysis of longitudinal traits, and provide a robust framework for modeling traits trajectories and performing genetic analysis simultaneously. We recently used a RR approach for genomic prediction of shoot growth trajectories in rice using 33,674 SNPs. In this study, we have extended this approach for genetic inference by leveraging genomic breeding values derived from RR models for rice shoot growth during early vegetative development. This approach provides improvements over a conventional single time point analyses for discovering loci associated with shoot growth trajectories. The RR approach uncovers persistent, as well as time-specific, transient quantitative trait loci. This methodology can be widely applied to understand the genetic architecture of other complex polygenic traits with repeated measurements.

          Related collections

          Author and article information

          Journal
          bioRxiv
          October 05 2018
          Article
          10.1101/435685
          8e393bae-c2cb-4105-9681-4af8508f6124
          © 2018
          History

          Genetics
          Genetics

          Comments

          Comment on this article