Biology subjects

Rodriguez-Alvarez, M. X.

Publications and source records attributed to Rodriguez-Alvarez, M. X..

3 recordsLinked to original sources

bioRxiv2023

A one-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

This work is motivated by the need to accurately estimate genetic effects over time when analysing data from high-throughput phenotyping (HTP) experiments. The HTP data we deal with here are characterised by phenotypic traits measured multiple times in the presence of spatial and temporal noise and a hierarchical organisation at three levels (populations, genotypes within populations, and plants within genotypes). We propose a feasible one-stage spatio-temporal P-spline-based hierarchical approach to model the evolution of the genetic signal over time on a given phenotype while accounting for spatio-temporal noise. We provide the user with appealing tools that take advantage of the sparse model matrices structure to reduce computational complexity. We illustrate the performance of our method using spatio-temporal simulated data and data from the PhenoArch greenhouse platform at INRAE Montpellier. In the plant breeding context, we show that information extracted for genomic selection purposes from our fitted genotypic curves is similar to those obtained using a comparable two-stage P-spline-based approach.

genetics↗

bioRxiv2021

Phenomics data processing: A plot-level model for repeated measurements to extract the timing of key stages and quantities at defined time points

Decision-making in breeding increasingly depends on the ability to capture and predict crop responses to changing environmental factors. Advances in crop modeling as well as high-throughput field phenotyping (HTFP) hold promise to provide such insights. Processing HTFP data is an interdisciplinary task that requires broad knowledge on experimental design, measurement techniques, feature extraction, dynamic trait modeling, and prediction of genotypic values using statistical models. To get an overview of sources of variation in HTFP, we develop a general plot-level model for repeated measurements. Based on this model, we propose a seamless step-wise procedure that allows for carry on of estimated means and variances from stage to stage. The process builds on the extraction of three intermediate trait categories; (1) timing of key stages, (2) quantities at defined time points or periods, and (3) dose-response curves. In a first stage, these intermediate traits are extracted from low-level traits time series (e.g., canopy height) using P-splines and the quarter of maximum elongation rate method (QMER), as well as final height percentiles. In a second and third stage, extracted traits are further processed using a stage-wise linear mixed model analysis. Using a wheat canopy growth simulation to generate canopy height time series, we demonstrate the suitability of the stage-wise process for traits of the first two above-mentioned categories. Results indicate that, for the first stage, the P-spline/QMER method was more robust than the percentile method. In the subsequent two-stage linear mixed model processing, weighting the second and third stage with error variance estimates from the previous stages improved the root mean squared error. We conclude that processing phenomics data in stages represents a feasible approach if estimated means and variances are carried forward from one processing stage to the next. P-splines in combination with the QMER method are suitable tools to extract timing of key stages and quantities at defined time points from HTFP data. HighlightsO_LIGeneral plot-level model for repeated high-throughput field phenotyping measurements C_LIO_LIExtraction of three main intermediate trait categories for dynamic modeling C_LIO_LISeamless processing approach that integrates temporal and spatial modeling C_LIO_LIPhenomics data processing cheatsheet C_LI

plant biology↗

bioRxiv2019

Reconstruction of networks with direct and indirect genetic effects

Genetic variance of a phenotypic trait can originate from direct genetic effects, or from indirect effects, i.e., through genetic effects on other traits, affecting the trait of interest. This distinction is often of great importance, for example when trying to improve crop yield and simultaneously controlling plant height. As suggested by Sewall Wright, assessing contributions of direct and indirect effects requires knowledge of (1) the presence or absence of direct genetic effects on each trait, and (2) the functional relationships between the traits. Because experimental validation of such relationships is often unfeasible, it is increasingly common to reconstruct them using causal inference methods. However, most of the current methods require all genetic variance to be explained by a small number of QTLs with fixed effects. Only few authors considered the missing heritability case, where contributions of many undetectable QTLs are modelled with random effects. Usually, these are treated as nuisance terms, that need to be eliminated by taking residuals from a multi-trait mixed model (MTM). But fitting such MTM is challenging, and it is impossible to infer the presence of direct genetic effects. Here we propose an alternative strategy, where genetic effects are formally included in the graph. This has important advantages: (1) genetic effects can be directly incorporated in causal inference, implemented via our PCgen algorithm, which can analyze many more traits and (2) we can test the existence of direct genetic effects and improve the orientation of edges between traits. Finally, we show that reconstruction is much more accurate if individual plant or plot data are used, instead of genotypic means. We have implemented the PCgen-algorithm in the R-package pcgen.

genetics↗