Biology subjects

Campbell, M. T.

Publications and source records attributed to Campbell, M. T..

3 recordsLinked to original sources

bioRxiv2018

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

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. O_LSTCore Ideas:C_LSTO_LIRandom regression models are an appealing framework for GWAS of longitudinal traits C_LIO_LIThis approach provides improvements over a conventional single time point analyses for GWAS C_LIO_LIWe identify QTL with transient and persistent effects on shoot growth in rice C_LI

genetics↗

bioRxiv2018

Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes

Drawing biological inferences from large data generated to dissect the genetic basis of complex traits remains a challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can accelerate the genetic improvement of plants and animals. A Bayesian network depicts a probabilistic directed acyclic graph representing conditional dependencies among variables. This study aims to characterize various phenotypes in rice (Oryza sativa) via confirmatory factor analysis and Bayesian network. Confirmatory factor analysis under the Bayesian treatment hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology (e.g., ion content), yield, and morphological salt response. This was followed by studying the genetics of each latent variable. Bayesian network structures involving the genomic component of six latent variables were established by fitting four different algorithms. Negative genomic correlations were obtained between salt response and yield, salt response and grain morphology, salt response and physiology, and morphology and yield, whereas a positive correlation was obtained between yield and grain morphology. There were four common directed edges across the different Bayesian networks. Physiological components influenced the flowering time and grain morphology, and morphology and 4 grain morphology influenced yield. This work suggests that the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the high-dimensional interrelated complex traits systems.

genetics↗

bioRxiv2018

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping

The accessibility of high-throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, have provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study is to demonstrate the utility of random regression (RR) models using Legendre polynomials for genomic prediction of shoot growth trajectories in rice (Oryza sativa). An estimate of shoot biomass, projected shoot area (PSA), was recored over a period of 20 days for a panel of 357 diverse rice accessions using an image-based greenhouse phenotyping platform. A RR that included a fixed second-order Legendre polynomial, a random second-order Legendre polynomial for the additive genetic effect, a first-order Legendre polynomial for the environmental effect, and heterogeneous residual variances was used to model PSA trajectories. The utility of the RR model over a single time point (TP) approach, where PSA is fit at each time point independently, is shown through four prediction scenarios. In the first scenario, the RR and TP approaches were used to predict PSA for a set of lines lacking phenotypic data. The RR approach showed a 11.6% increase in prediction accuracy over the TP approach. Much of this improvement could be attributed to the greater additive genetic variance captured by the RR approach. The remaining scenarios focused forecasting future phenotypes using a subset of early time points for known lines with phenotypic data, as well new lines lacking phenotypic data. In all cases, PSA could be predicted with high accuracy (r: 0.79 to 0.89 and 0.55 to 0.58 for known and unknown lines, respectively). This study provides the first application of RR models for genomic prediction of a longitudinal trait in rice, and demonstrates that RR models can be effectively used to improve the accuracy of genomic prediction for complex traits compared to a TP approach.

genomics↗