Flexible modelling of spatial variation in agricultural field trials with the R package INLA

The objective of this paper was to fit different established spatial models for analysing agricultural field trials using the open-source R package INLA. Spatial variation is common in field trials, and accounting for it increases the accuracy of estimated genetic effects. However, this is still hindered by the lack of available software implementations. We compare some established spatial models and show possibilities for flexible modelling with respect to field trial design and joint modelling over multiple years and locations. We use a Bayesian framework and for statistical inference the integrated nested Laplace approximations (INLA) implemented in the R package INLA. The spatial models we use are the well-known independent row and column effects, separable first-order autoregressive ( AR1⊗AR1 ) models and a Gaussian random field (Matérn) model that is approximated via the stochastic partial differential equation approach. The Matérn model can accommodate flexible field trial designs and yields interpretable parameters. We test the models in a simulation study imitating a wheat breeding programme with different levels of spatial variation, with and without genome-wide markers and with combining data over two locations, modelling spatial and genetic effects jointly. The results show comparable predictive performance for both the AR1⊗AR1 and the Matérn models. We also present an example of fitting the models to a real wheat breeding data and simulated tree breeding data with the Nelder wheel design to show the flexibility of the Matérn model and the R package INLA

O modelo autologístico espaço-temporal para dados de doenças em plantas.

Pôste

Modelo autologístico espaço-temporal com aplicação à análise de padrões espaciais da leprose-dos-citros Spatial temporal autologistic model with an application to the analysis of spatial patterns of citrus leprosis

O objetivo deste trabalho foi propor estratégias de modelagem aplicadas aos dados de incidência de leprose-dos-citros, por meio do uso de um modelo autologístico espaço-temporal. A adequação do modelo autologístico foi avaliada quanto à: análise de dados provenientes de avaliações feitas em diferentes momentos; detecção de padrões espaciais da doença, pela avaliação de diferentes estruturas de vizinhança; consideração do efeito defasado no tempo de covariáveis de vizinhança; e ao efeito do ácaro transmissor na probabilidade de nova infecção. O modelo autologístico espaço-temporal adotado estendeu o modelo logístico usual, em que a estrutura de vizinhança é descrita por meio da construção de covariáveis, a partir da resposta observada em plantas vizinhas à planta avaliada, na mesma avaliação, ou em avaliações anteriores. Os dados de incidência de leprose nas plantas de citros foram coletados em pontos referenciados no espaço, durante aproximadamente dois anos. Os modelos detectam o efeito da presença do vetor e os padrões espaciais na ocorrência de novas infecções, tanto para covariáveis de vizinhança da mesma avaliação, quanto para covariáveis de vizinhança da avaliação anterior. Além disso, os modelos considerados permitem quantificar as variações na probabilidade de ocorrência da doença de acordo com o estado da doença e com a incidência do ácaro transmissor.<br>The goal of this study was to propose modeling strategies applied to the analysis of citrus leprosis incidence, through the use of a spatial temporal autologistic model. We evaluated the adequacy of autologistic model to consider data collected at different times; to detect spatial-temporal patterns through different neighboring structures; to consider the effect of covariates from previous times; and assessing the effect of the presence of the disease vector in the probability of new infections occurrence. The spatial temporal autologistic model adopted has extended the usual logistic model, in which the neighboring structures is described by means of covariates built from the status of plants nearby, at the same or at previous times. Data regarding the presence of the leprosis on plants were collected at field points referenced in space, over a period of approximately two years. Models detect the presence of spatial patterns on new infections for the studied neighboring structures, at the same or previous time. Additionally, probability estimates of a plant become infected can be obtained from the fitted models, given the occurrence of the disease and vector

Progresso espaço-temporal da leprose dos citros.

A leprose dos citros tem sido considerada uma das mais importantes doenças da citricultura brasileira, pois reduz a produção e o período de vida das plantas afetadas (Rodrigues et aI., 2003), ocorre em quase todos os estados do País que produzem citros e é considerada endêmica no Estado de São Paulo. Esta doença é causada pelo Citrus feprosis vírus (CiLV) transmitido exclusivamente, nas condições de campo, pelo ácaro Brevipalpus phoenícis Geijskes (Acari: Tenuipalpidae), conhecido vulgarmente como ácaro plano ou ácaro da leprose

Autologistic model with an application to the citrus "sudden death" disease Modelo autologístico com aplicação para a doença "morte súbita" dos citrus

The citrus sudden death (CSD) disease affects dramatically citrus trees causing a progressive plant decline and death. The disease has been identified in the late 90's in the main citrus production area of Brazil and since then there are efforts to understand the etiology as well as the mechanisms its spreading. One relevant aspect of such studies is to investigate spatial patterns of the occurrence within a field. Methods for determining whether the spatial pattern is aggregated or not has been frequently used. However it is possible to further explore and describe the data by means of adopting an explicit model to discriminate and quantify effects by attaching parameters to covariates which represent aspects of interest to be investigated. One alternative involves autologistic models, which extend a usual logistic model in order to accommodate spatial effects. In order to implement such model it is necessary to take into account the reuse of data to built spatial covariates, which requires extensions in methodology and algorithms to assess the variance of the estimates. This work presents an application of the autologistic model to data collected at 11 time points from citrus fields affected by CSD. It is shown how the autologistic model is suitable to investigate diseases of this type, as well as a description of the model and the computational aspects necessary for model fitting.<br>A morte súbita dos citros (MSC) é uma doença com efeitos dramáticos em árvores de citros causando declínio progressivo e morte. Ela foi identificada no final da década de 90 em uma das principais áreas de produção no Brasil e desde então esforços são empregados para entender a sua etiologia e os seus mecanismos de dispersão. Um aspecto relevante para estudos é a investigação do padrão espacial da incidência dentro de um campo. Métodos para determinar se o padrão espacial é agregado ou não têm sido freqüentemente utilizados. Entretanto é possível explorar e descrever os dados adotando um modelo explícito, com o qual é possível discriminar e quantificar os efeitos com parâmetros para covariáveis que representam aspectos de interesse investigados. Uma das alternativas é adoção de modelos autologísticos, que estendem o modelo de regressão logística para acomodar efeitos espaciais. Para implementar esse modelo é necessário que se reutilize os dados para extrair covariáveis espaciais, o que requer extensões na metodologia e algoritmos para avaliar a variância das estimativas. Este trabalho apresenta uma aplicação do modelo autologístico a dados coletados em 11 pontos no tempo em um campo de citros afetado pela MSC. É mostrado como o modelo autologístico é apropriado para investigar doenças desse tipo, bem como é feita uma descrição do modelo e dos aspectos computacionais necessários para a estimação dos parâmetros

Human NANOS1 Represses Apoptosis by Downregulating Pro-Apoptotic Genes in the Male Germ Cell Line

While two mouse NANOS paralogues, NANOS2 and NANOS3, are crucial for maintenance of germ cells by suppression of apoptosis, the mouse NANOS1 paralogue does not seem to regulate these processes. Previously, we described a human NANOS1 p.[(Pro34Thr);(Ser83del)] mutation associated with the absence of germ cells in seminiferous tubules of infertile patients, which might suggest an anti-apoptotic role of human NANOS1. In this study, we aimed to determine a potential influence of human NANOS1 on the maintenance of TCam-2 model germ cells by investigating proliferation, cell cycle, and apoptosis. Constructs encoding wild-type or mutated human NANOS1 were used for transfection of TCam-2 cells, in order to investigate the effect of NANOS1 on cell proliferation, which was studied using a colorimetric assay, as well as apoptosis and the cell cycle, which were measured by flow cytometry. RNA-Seq (RNA sequencing) analysis followed by RT-qPCR (reverse transcription and quantitative polymerase chain reaction) was conducted for identifying pro-apoptotic genes repressed by NANOS1. Here, we show that overexpression of NANOS1 downregulates apoptosis in TCam-2 cells. Moreover, we found that NANOS1 represses a set of pro-apoptotic genes at the mRNA level. We also found that the infertility-associated p.[(Pro34Thr);(Ser83del)] mutation causes NANOS1 to functionally switch from being anti-apoptotic to pro-apoptotic in the human male germ cell line. Thus, this report is the first to show an anti-apoptotic role of NANOS1 exerted by negative regulation of mRNAs of pro-apoptotic genes

Spatial patterns of the Citrus leprosis virus and its associated mite vector in systems without intervention.

Leprosis is caused by the Citrus leprosis virus cytoplasmic type and is vectored by the mite Brevipalpus yothersi. Miticide applications, which cost $54 million annually, are based on inspection for the presence of mites. The aim of the present study was to characterize the spatial patterns of B. yothersi-infested trees and trees with leprosis symptoms for further improvement in sampling and disease control. The presence of mites and the occurrence of leprosis were assessed over two years in 1160 Valencia trees and 720 Natal trees in a commercial sweet orange grove in Sao Paulo State, Brazil. To assess the natural growth and dispersal of mites and leprosis, mite populations were not controlled during the experimental period. Maps of mite-infested trees and trees with leprosis symptoms were analysed at three different levels of spatial hierarchy using complementary methods, i.e. among adjacent trees within and across rows, within quadrats, and the strength and orientation of aggregation among quadrats. The study showed that the spatial patterns of virus-infected and mite-infested trees were different, with a strong aggregation pattern of trees with leprosis symptoms that increased over time. Conversely, the spatial pattern of B. yothersi showed randomness or weak aggregation at all three spatial hierarchical levels. Disease incidence increased steadily in plots of both cultivars, unlike in miteinfested trees where incidence fluctuated over time. These results have important implications for the development of better management strategies for leprosis. Sampling methods and action thresholds for mite control should consider primary disease inoculum in addition to the incidence of mites. Keywords: Brevipalpus yothersi, Citrus leprosis virus, Citrus sinensis, epidemiology, flat mite, spatial analysisPublicação impressa em: Plant Pathology, v. 68, p. 85-93, 2019