scholarly journals High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production

2021 ◽  
Vol 12 ◽  
Author(s):  
Fabiana Freitas Moreira ◽  
Hinayah Rojas de Oliveira ◽  
Miguel Angel Lopez ◽  
Bilal Jamal Abughali ◽  
Guilherme Gomes ◽  
...  
Keyword(s):  
High Throughput ◽  
Regression Models ◽  
Genetic Architecture ◽  
Crop Improvement ◽  
Random Regression ◽  
Genome Wide Association Studies ◽  
Breeding Values ◽  
High Throughput Phenotyping ◽  
General Response ◽  
Over Time

Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.

scholarly journals Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping

2019 ◽  
Author(s):  
Toshimi Baba ◽  
Mehdi Momen ◽  
Malachy T. Campbell ◽  
Harkamal Walia ◽  
Gota Morota
Keyword(s):  
High Throughput ◽  
Genomic Prediction ◽  
Regression Models ◽  
Joint Modeling ◽  
Random Regression ◽  
Shoot Biomass ◽  
Time Points ◽  
Large Populations ◽  
High Throughput Phenotyping ◽  
Daily Water

AbstractRandom regression models (RRM) are used extensively for genomic inference and prediction of time-valued traits in animal breeding, but only recently have been used in plant systems. High-throughput phenotyping (HTP) platforms provide a powerful means to collect high-dimensional phenotypes throughout the growing season for large populations. However, to date, selection of an appropriate statistical genomic framework to integrate multiple temporal traits for genomic prediction in plants remains unexplored. Here, we demonstrate the utility of a multi-trait RRM (MT-RRM) for genomic prediction of daily water usage (WU) in rice (Oryza sativa) through joint modeling with shoot biomass (projected shoot area, PSA). Three hundred and fifty-seven accessions were phenotyped daily for WU and PSA over 20 days using a greenhouse-based HTP platform. MT-RRMs that modeled additive genetic and permanent environmental effects for both traits using quadratic Legendre polynomials were used to assess genomic correlations between traits and genomic prediction for WU. Predictive abilities of the MT-RRMs were assessed using two cross-validation (CV) scenarios. The first scenario was designed to predict genetic values for WU at all time points for a set of accessions with unobserved WU. The second scenario was designed to forecast future genetic values for WU for a panel of known accessions with records for WU at earlier time periods. In each scenario we evaluated two MT-RRMs in which PSA records were absent or available for time points in the testing population. Moderate to strong genomic correlations between WU and PSA were observed across the days of imaging (0.29-0.87). In both CV scenarios, MT-RRMs showed better predictive abilities compared to single-trait RRM, and prediction accuracies were greatly improved when PSA records were available for the testing population. In summary, these frameworks provide an effective approach to predict temporal physiological traits that are difficult or expensive to quantify in large populations.

GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system based on germination paper to quantify crop phenotypic diversity and plasticity of root traits under varying nutrient supply

2017 ◽  
Vol 44 (1) ◽  
pp. 76 ◽  
Author(s):  
Tania Gioia ◽  
Anna Galinski ◽  
Henning Lenz ◽  
Carmen Müller ◽  
Jonas Lentz ◽  
...  
Keyword(s):  
High Throughput ◽  
Phenotypic Diversity ◽  
Crop Improvement ◽  
Root Traits ◽  
Root Systems ◽  
Non Invasive ◽  
Large Sets ◽  
High Throughput Phenotyping ◽  
Improvement Programs ◽  
Root Phenotyping

New techniques and approaches have been developed for root phenotyping recently; however, rapid and repeatable non-invasive root phenotyping remains challenging. Here, we present GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system (4 plants min–1) based on flat germination paper. GrowScreen-PaGe allows the acquisition of time series of the developing root systems of 500 plants, thereby enabling to quantify short-term variations in root system. The choice of germination paper was found to be crucial and paper ☓ root interaction should be considered when comparing data from different studies on germination paper. The system is suitable for phenotyping dicot and monocot plant species. The potential of the system for high-throughput phenotyping was shown by investigating phenotypic diversity of root traits in a collection of 180 rapeseed accessions and of 52 barley genotypes grown under control and nutrient-starved conditions. Most traits showed a large variation linked to both genotype and treatment. In general, root length traits contributed more than shape and branching related traits in separating the genotypes. Overall, results showed that GrowScreen-PaGe will be a powerful resource to investigate root systems and root plasticity of large sets of plants and to explore the molecular and genetic root traits of various species including for crop improvement programs.

scholarly journals Reliability of breeding values between random regression and 305-day lactation models

2016 ◽  
Vol 51 (11) ◽  
pp. 1848-1856
Author(s):  
Alessandro Haiduck Padilha ◽  
◽  
Jaime Araujo Cobuci ◽  
Darlene dos Santos Daltro ◽  
José Braccini Neto
Keyword(s):  
Legendre Polynomials ◽  
Regression Models ◽  
Protein Yield ◽  
Random Regression ◽  
Holstein Cattle ◽  
Average Increase ◽  
Data Set ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Genetic Evaluations

Abstract The objective of this work was to verify the gain in reliability of estimated breeding values (EBVs), when random regression models are applied instead of conventional 305-day lactation models, using fat and protein yield records of Brazilian Holstein cattle for future genetic evaluations. Data set contained 262,426 test-day fat and protein yield records, and 30,228 fat and protein lactation records at 305 days from first lactation. Single trait random regression models using Legendre polynomials and single trait lactation models were applied. Heritability for 305-day yield from lactation models was 0.24 (fat) and 0.17 (protein), and from random regression models was 0.20 (fat) and 0.21 (protein). Spearman correlations of EBVs, between lactation models and random regression models, for 305-day yield, ranged from 0.86 to 0.97 and 0.86 to 0.98 (bulls), and from 0.80 to 0.89 and 0.81 to 0.86 (cows), for fat and protein, respectively. Average increase in reliability of EBVs for 305-day yield of bulls ranged from 2 to 16% (fat) and from 4 to 26% (protein), and average reliability of cows ranged from 24 to 38% (fat and protein), which is higher than in the lactation models. Random regression models using Legendre polynomials will improve genetic evaluations of Brazilian Holstein cattle due to the reliability increase of EBVs, in comparison with 305-day lactation models.

scholarly journals Development of genetic models for estimation of racing performances in German thoroughbreds

2004 ◽  
Vol 47 (6) ◽  
pp. 505-516
Author(s):  
A.-E. Bugislaus ◽  
R. Roehe ◽  
H. Uphaus ◽  
E. Kalm
Keyword(s):  
Animal Model ◽  
Linear Regression ◽  
Statistical Models ◽  
Regression Models ◽  
Genetic Variance ◽  
Fixed Effect ◽  
Random Regression ◽  
Square Root ◽  
Breeding Values ◽  
High Performing

Abstract. The objective of this study was to develop new statistical models for genetic estimation of racing performances in German thoroughbreds. Analysed performance traits were "square root of rank at finish", "square root of distance to first placed horse in a race" and "log of earnings". These traits were found to be influenced by the carried weight, which was determined by the horse's earlier performance. Therefore, new traits were developed based on random regression models, which were independent from the carried weights. Heritabilities were first estimated for these created traits "new rank at finish" (h2 = 0.101) and "new distance to first placed horse in a race" (h2 = 0.142) by using two univariate animal models. When considering a linear regression of carried weights as fixed effect in the statistical model, heritabilities for "square root of rank at finish" (h2 = 0.086) and "square root of distance to first placed horse in a race" (h2 = 0.124) decreased. Breeding values of “new rank at finish” and "new distance to first placed horse in a race" were compared with breeding values of "square root of rank at finish" and "square root of distance to first placed horse in a race", in which carried weight was considered as fixed regression in the model. These two different models were compared by two criteria. Breeding values were overestimated for low performing thoroughbreds and underestimated for high performing horses when considering a linear regression of carried weights as fixed effect in the model. Statistical models considering new created traits ("new rank at finish" and "new distance to first placed horse in a race") which were independent of carried weights, showed better suitability for genetic estimation. Due to high genetic correlation with other traits and showing highest genetic variance a univariate animal model for the trait “new distance to first placed horse in a race” was recommended for genetic estimation.

PSXV-1 Genetic evaluation of longevity of cows culled due to fertility-related problems using random regression models and censored data

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 261-261
Author(s):  
Hinayah R Oliveira ◽  
Stephen P Miller ◽  
Luiz F Brito ◽  
Flavio S Schenkel
Keyword(s):  
Censored Data ◽  
Regression Models ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Breeding Values ◽  
Structural Problems ◽  
Time Period ◽  
And Performance ◽  
Genetic Evaluations ◽  
The Impact

Abstract A recent study showed that longevity based on different culling reasons should be considered as different traits in genetic evaluations. However, it is still necessary to create a pipeline that avoid including/excluding animals culled for different reasons in every genetic evaluation run. This study aimed to: 1) perform a genetic evaluation of the longevity of cows culled due to fertility-related problems including records of animals culled for other reasons (i.e., age, structural problems, disease, and performance) as censored records; and, 2) identify the impact of censored data in the genetic parameters and breeding values estimated. Two longevity indicators were evaluated: traditional (TL; time from first calving to culling) and functional (FL; time period in which the cow was alive and also calving after its first calving) longevity. Both TL and FL were evaluated from 2 to 15 years-old, and codified as binary traits for each age (0 = culled and 1 = alive/calved). Both trait definitions were analyzed using a Bayesian random regression linear model. Animals culled for reasons other than fertility were either excluded from the data (standard) or had their records censored after the culling date reported in the dataset (censored). After the quality control, 154,419 and 450,124 animals had uncensored and censored records, respectively. Heritabilities estimated for TL over the ages ranged from 0.02 to 0.13 for standard, and from 0.01 to 0.12 for censored datasets. Heritabilities estimated for FL ranged from 0.01 to 0.14 (standard), and from 0.01 to 0.13 (censored). Average (SD) correlation of breeding values predicted over all ages, using the standard and censored datasets, was 0.77 (0.16) for TL, and 0.83 (0.11) for FL. Our findings suggest that including censored data in the analyses might impact the genomic evaluations and further work is need to determine the optimal predictive approach.

Predicting breeding values for milk yield of Guzerá (Bos indicus) cows using random regression models

2014 ◽  
Vol 167 ◽  
pp. 41-50 ◽  
Author(s):  
D.J.A. Santos ◽  
M.G.C.D. Peixoto ◽  
R.R. Aspilcueta Borquis ◽  
J.C.C. Panetto ◽  
L. El Faro ◽  
...  
Keyword(s):  
Milk Yield ◽  
Regression Models ◽  
Bos Indicus ◽  
Random Regression ◽  
Breeding Values

Analysis of censored survival data using random regression models

2001 ◽  
Vol 72 (1) ◽  
pp. 1-10 ◽  
Author(s):  
R. F. Veerkamp ◽  
S. Brotherstone ◽  
B. Engel ◽  
T. H. E. Meuwissen
Keyword(s):  
Milk Yield ◽  
Survival Data ◽  
Variance Components ◽  
Regression Models ◽  
Proportional Hazards ◽  
Random Regression ◽  
Breeding Values ◽  
Censored Survival Data ◽  
Proportional Hazards Models ◽  
Randomly Censored Data

AbstractCensoring of records is a problem in the prediction of breeding values for longevity, because breeding values are required before actual lifespan is known. In this study we investigated the use of random regression models to analyse survival data, because this method combines some of the advantages of a multitrait approach and the more sophisticated proportional hazards models. A model was derived for the binary representation of survival data and links with proportional hazards models and generalized linear models are shown. Variance components and breeding values were predicted using a linear approximation, including time-dependent fixed effects and random regression coefficients. Production records in lactations 1 to 5 were available on 24741 cows in the UK, all having had the opportunity to survive five lactations. The random regression model contained a linear regression on milk yield within herd (no. = 1417) by lactation number (no. = 4), Holstein percentage and year-month of calving effect (no. = 72). The additive animal genetic effects were modelled using orthogonal polynomials of order 1 to 4 with random coefficients and the error terms were fitted for each lactation separately, either correlated or not. Variance components from the full (i.e. uncensored) data set, were used to predict breeding values for survival in each lactation from both uncensored and randomly censored data. In the uncensored data, estimates of heritabilities for culling probability in each lactation ranged from 0·02 to 0·04. Breeding values for lifespan (calculated from the survival breeding values) had a range of 2·4 to 3·6 lactations and a standard deviation of 0·25. Correlations between predicted breeding values for 129 bulls, each with more than 30 daughters, from the various data sets ranged from 0·81 to 0·99 and were insensitive to the model used. It is concluded that random regression analysis models used for test-day records analysis of milk yield, might also be of use in the analysis of censored survival data.

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