Prediction of genotype performance for untested years based on additive main effects and multiplicative interaction and linear mixed models: An illustration using dolichos bean ( Lablab purpureus (L.) Sweet) multiyear data

ABSTRACTGenotype-by-environment interactions (GEIs) are important for not only a precise understanding of genotype–phenotype relationships but also improving the environmental adaptability of crops. Although many formulae have been proposed to model GEI effects, a comprehensive comparison of their efficacy in genome-wide association studies (GWASs) has not been performed. Therefore, the advantages and disadvantages of the formulae are not well recognized. In this study, linear mixed models (LMMs) consisting of various combinations of foreground fixed genetic and background random genetic effect terms were constructed. Next, the power to detect quantitative trait loci (QTLs) with GEI effects was compared across the LMMs by using simulation. The fixed genetic effect terms of the genotype main effects and GEI (GGE) model were preferred over those based on the additive main effects and multiplicative interaction (AMMI) model because the latter showed p-value inflation, whereas the former yielded theoretically expected p-value distribution. With regard to the background random genetic effects, inclusion of genotype-by-trial interaction effects has been recommended to achieve high power and robustness when phenotype data are obtained from multiple environments and multiple trials. The recommended form of LMM was applied to GWASs performed using real agronomic trait data of tomato F1 varieties (Solanum lycopersicum L.) that were obtained from two different cropping seasons. The GWASs detected QTLs with not only persistent effects across the cropping seasons, but also cropping season-specific effects. Thus, the application of GWAS strategy to phenotypic data from multiple environments and trials allowed the detection of more QTLs with GEI effects.

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Additive main effects and multiplicative interaction analysis for storage root yield of cassava genotypes evaluated in Uganda

African Crop Science Journal ◽

10.4314/acsj.v9i4.27581 ◽

2001 ◽

Vol 9 (4) ◽

Cited By ~ 6

Author(s):

P. H. Ntawuruhunga ◽

P. Rubaihayo ◽

J. B.A. Whyte ◽

A. G.O. Dixon ◽

D. S.O. Osiru

Keyword(s):

Interaction Analysis ◽

Storage Root ◽

Root Yield ◽

Multiplicative Interaction ◽

Main Effects

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Effects of NP Fertilizer Placement Depth by Year Interaction on the Number of Maize (Zea mays L.) Plants after Emergence Using the Additive Main Effects and Multiplicative Interaction Model

Agronomy ◽

10.3390/agronomy11081543 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1543

Author(s):

Piotr Szulc ◽

Jan Bocianowski ◽

Kamila Nowosad ◽

Henryk Bujak ◽

Waldemar Zielewicz ◽

...

Keyword(s):

Zea Mays ◽

Zea Mays L ◽

Interaction Model ◽

Fertilizer Application ◽

Fertilizer Placement ◽

Multiplicative Interaction ◽

Maize Cultivation ◽

Main Effects ◽

Placement Depth ◽

Np Fertilizer

Field experiments were carried out at the Department of Agronomy of the Poznań University of Life Sciences to determine the effect of the depth of NP fertilization placement in maize cultivation on the number of plants after emergence. The adopted assumptions were verified based on a six-year field experiment involving four depths of NP fertilizer application (A1—0 cm (broadcast), A2—5 cm (in rows), A3—10 cm (in rows), A4—15 cm (in rows)). The objective of this study was to assess NP fertilizer placement depth, in conjunction with the year, on the number of maize (Zea mays L.) plants after emergence using the additive main effects and multiplicative interaction model. The number of plants after emergence decreased with the depth of NP fertilization in the soil profile, confirming the high dependence of maize on phosphorus and nitrogen availability, as well as greater subsoil loosening during placement. The number of plants after emergence for the experimental NP fertilizer placement depths varied from 7.237 to 8.201 plant m−2 during six years, with an average of 7.687 plant m−2. The 61.51% of variation in the total number of plants after emergence was explained by years differences, 23.21% by differences between NP fertilizer placement depths and 4.68% by NP fertilizer placement depths by years interaction. NP fertilizer placement depth 10 cm (A3) was the most stable (ASV = 1.361) in terms of the number of plants after emergence among the studied NP fertilizer placement depths. Assuming that the maize kernels are placed in the soil at a depth of approx. 5 cm, the fertilizer during starter fertilization should be placed 5 cm to the side and below the kernel. Deeper NP fertilizer application in maize cultivation is not recommended. The condition for the use of agriculture progress, represented by localized fertilization, is the simultaneous recognition of the aspects of yielding physiology of new maize varieties and the assessment of their reaction to deeper seed placement during sowing.

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Heat, Hills and the High Season: A Model-Based Comparative Analysis of Spatio-Temporal Factors Affecting Shared Bicycle Use in Three Southern European Islands

Sustainability ◽

10.3390/su13063274 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3274

Author(s):

Suzanne Maas ◽

Paraskevas Nikolaou ◽

Maria Attard ◽

Loukas Dimitriou

Keyword(s):

Correlation Analysis ◽

Mixed Models ◽

Linear Mixed Models ◽

Positive Association ◽

Sustainable Mobility ◽

Weather Factors ◽

Gran Canaria ◽

Bivariate Correlation ◽

Temporal Factors

Bicycle sharing systems (BSSs) have been implemented in cities worldwide in an attempt to promote cycling. Despite exhibiting characteristics considered to be barriers to cycling, such as hot summers, hilliness and car-oriented infrastructure, Southern European island cities and tourist destinations Limassol (Cyprus), Las Palmas de Gran Canaria (Canary Islands, Spain) and the Valletta conurbation (Malta) are all experiencing the implementation of BSSs and policies to promote cycling. In this study, a year of trip data and secondary datasets are used to analyze dock-based BSS usage in the three case-study cities. How land use, socio-economic, network and temporal factors influence BSS use at station locations, both as an origin and as a destination, was examined using bivariate correlation analysis and through the development of linear mixed models for each case study. Bivariate correlations showed significant positive associations with the number of cafes and restaurants, vicinity to the beach or promenade and the percentage of foreign population at the BSS station locations in all cities. A positive relation with cycling infrastructure was evident in Limassol and Las Palmas de Gran Canaria, but not in Malta, as no cycling infrastructure is present in the island’s conurbation, where the BSS is primarily operational. Elevation had a negative association with BSS use in all three cities. In Limassol and Malta, where seasonality in weather patterns is strongest, a negative effect of rainfall and a positive effect of higher temperature were observed. Although there was a positive association between BSS use and the number of visiting tourists in Limassol and Malta, this is predominantly explained through the multi-collinearity with weather factors rather than by intensive use of the BSS by tourists. The linear mixed models showed more fine-grained results and explained differences in BSS use at stations, including differences for station use as an origin and as a destination. The insights from the correlation analysis and linear mixed models can be used to inform policies promoting cycling and BSS use and support sustainable mobility policies in the case-study cities and cities with similar characteristics.

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Confidence, prediction, and tolerance in linear mixed models

Statistics in Medicine ◽

10.1002/sim.8386 ◽

2019 ◽

Vol 38 (30) ◽

pp. 5603-5622 ◽

Cited By ~ 3

Author(s):

Bernard G. Francq ◽

Dan Lin ◽

Walter Hoyer

Keyword(s):

Mixed Models ◽

Linear Mixed Models

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Power for balanced linear mixed models with complex missing data processes

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2021.1909732 ◽

2021 ◽

pp. 1-19

Author(s):

Kevin P. Josey ◽

Brandy M. Ringham ◽

Anna E. Barón ◽

Margaret Schenkman ◽

Katherine A. Sauder ◽

...

Keyword(s):

Missing Data ◽

Mixed Models ◽

Linear Mixed Models

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l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time

Statistical Methods in Medical Research ◽

10.1177/09622802211017583 ◽

2021 ◽

pp. 096228022110175

Author(s):

Jan P Burgard ◽

Joscha Krause ◽

Ralf Münnich ◽

Domingo Morales

Keyword(s):

Parameter Estimation ◽

Medical Treatment ◽

Mixed Models ◽

Generalized Linear Mixed Models ◽

Linear Mixed Models ◽

Obesity Prevalence ◽

Model Parameter ◽

Model Parameter Estimation ◽

Public Health Reporting ◽

Over Time

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

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