Browsing by Author "Morota, Gota"

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    Allelic variation in rice Fertilization Independent Endosperm 1 contributes to grain width under high night temperature stress
    Dhatt, Balpreet K.; Paul, Puneet; Sandhu, Jaspreet; Hussain, Waseem; Irvin, Larissa; Zhu, Feiyu; Adviento-Borbe, Maria Arlene; Lorence, Argelia; Staswick, Paul; Yu, Hongfeng; Morota, Gota; Walia, Harkamal (2020-10)
    A higher minimum (night-time) temperature is considered a greater limiting factor for reduced rice yield than a similar increase in maximum (daytime) temperature. While the physiological impact of high night temperature (HNT) has been studied, the genetic and molecular basis of HNT stress response remains unexplored. We examined the phenotypic variation for mature grain size (length and width) in a diverse set of rice accessions under HNT stress. Genome-wide association analysis identified several HNT-specific loci regulating grain size as well as loci that are common for optimal and HNT stress conditions. A novel locus contributing to grain width under HNT conditions colocalized withFie1, a component of the FIS-PRC2 complex. Our results suggest that the allelic difference controlling grain width under HNT is a result of differential transcript-level response ofFie1in grains developing under HNT stress. We present evidence to support the role ofFie1in grain size regulation by testing overexpression (OE) and knockout mutants under heat stress. The OE mutants were either unaltered or had a positive impact on mature grain size under HNT, while the knockouts exhibited significant grain size reduction under these conditions.
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    ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science
    Morota, Gota; Cheng, Hao; Cook, Dianne; Tanaka, Emi (2021-02)
    Statistical graphics, and data visualization, play an essential but under-utilized, role for data analysis in animal science, and also to visually illustrate the concepts, ideas, or outputs of research and in curricula. The recent rise in web technologies and ubiquitous availability of web browsers enables easier sharing of interactive and dynamic graphics. Interactivity and dynamic feedback enhance humancomputer interaction and data exploration. Web applications such as decision support systems coupled with multimedia tools synergize with interactive and dynamic graphics. However, the importance of graphics for effectively communicating data, understanding data uncertainty, and the state of the field of interactive and dynamic graphics is underappreciated in animal science. To address this gap, we describe the current state of graphical methodology and technology that might be more broadly adopted. This includes an explanation of a conceptual framework for effective graphics construction. The ideas and technology are illustrated using publicly available animal datasets. We foresee that many new types of big and complex data being generated in precision livestock farming create exciting opportunities for applying interactive and dynamic graphics to improve data analysis and make data-supported decisions.
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    An assessment of genomic connectedness measures in Nellore cattle
    Amorim, Sabrina T.; Yu, Haipeng; Momen, Mehdi; de Albuquerque, Lucia Galvao; Cravo Pereira, Angelica S.; Baldi, Fernando; Morota, Gota (2020-11)
    An important criterion to consider in genetic evaluations is the extent of genetic connectedness across management units (MU), especially if they differ in their genetic mean. Reliable comparisons of genetic values across MU depend on the degree of connectedness: the higher the connectedness, the more reliable the comparison. Traditionally, genetic connectedness was calculated through pedigree-based methods; however, in the era of genomic selection, this can be better estimated utilizing new approaches based on genomics. Most procedures consider only additive genetic effects, which may not accurately reflect the underlying gene action of the evaluated trait, and little is known about the impact of non-additive gene action on connectedness measures. The objective of this study was to investigate the extent of genomic connectedness measures, for the first time, in Brazilian field data by applying additive and non-additive relationship matrices using a fatty acid profile data set from seven farms located in the three regions of Brazil, which are part of the three breeding programs. Myristic acid (C14:0) was used due to its importance for human health and reported presence of non-additive gene action. The pedigree included 427,740 animals and 925 of them were genotyped using the Bovine high-density genotyping chip. Six relationship matrices were constructed, parametrically and non-parametrically capturing additive and non-additive genetic effects from both pedigree and genomic data. We assessed genome-based connectedness across MU using the prediction error variance of difference (PEVD) and the coefficient of determination (CD). PEVD values ranged from 0.540 to 1.707, and CD from 0.146 to 0.456. Genomic information consistently enhanced the measures of connectedness compared to the numerator relationship matrix by at least 63%. Combining additive and non-additive genomic kernel relationship matrices or a non-parametric relationship matrix increased the capture of connectedness. Overall, the Gaussian kernel yielded the largest measure of connectedness. Our findings showed that connectedness metrics can be extended to incorporate genomic information and non-additive genetic variation using field data. We propose that different genomic relationship matrices can be designed to capture additive and non-additive genetic effects, increase the measures of connectedness, and to more accurately estimate the true state of connectedness in herds.
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    Comparison of Single-Breed and Multi-Breed Training Populations for Infrared Predictions of Novel Phenotypes in Holstein Cows
    Mota, Lucio Flavio Macedo; Pegolo, Sara; Baba, Toshimi; Morota, Gota; Peñagaricano, Francisco; Bittante, Giovanni; Cecchinato, Alessio (MDPI, 2021-07-02)
    In general, Fourier-transform infrared (FTIR) predictions are developed using a single-breed population split into a training and a validation set. However, using populations formed of different breeds is an attractive way to design cross-validation scenarios aimed at increasing prediction for difficult-to-measure traits in the dairy industry. This study aimed to evaluate the potential of FTIR prediction using training set combining specialized and dual-purpose dairy breeds to predict different phenotypes divergent in terms of biological meaning, variability, and heritability, such as body condition score (BCS), serum β-hydroxybutyrate (BHB), and kappa casein (k-CN) in the major cattle breed, i.e., Holstein-Friesian. Data were obtained from specialized dairy breeds: Holstein (468 cows) and Brown Swiss (657 cows), and dual-purpose breeds: Simmental (157 cows), Alpine Grey (75 cows), and Rendena (104 cows), giving a total of 1461 cows from 41 multi-breed dairy herds. The FTIR prediction model was developed using a gradient boosting machine (GBM), and predictive ability for the target phenotype in Holstein cows was assessed using different cross-validation (CV) strategies: a within-breed scenario using 10-fold cross-validation, for which the Holstein population was randomly split into 10 folds, one for validation and the remaining nine for training (10-fold_HO); an across-breed scenario (BS_HO) where the Brown Swiss cows were used as the training set and the Holstein cows as the validation set; a specialized multi-breed scenario (BS+HO_10-fold), where the entire Brown Swiss and Holstein populations were combined then split into 10 folds, and a multi-breed scenario (Multi-breed), where the training set comprised specialized (Holstein and Brown Swiss) and dual-purpose (Simmental, Alpine Grey, and Rendena) dairy cows, combined with nine folds of the Holstein cows. Lastly a Multi-breed CV2 scenario was implemented, assuming the same number of records as the reference scenario and using the same proportions as the multi-breed. Within-Holstein, FTIR predictions had a predictive ability of 0.63 for BCS, 0.81 for BHB, and 0.80 for k-CN. Using a specific breed (Brown Swiss) as the training set for prediction in the Holstein population reduced the prediction accuracy by 10% for BCS, 7% for BHB, and 11% for k-CN. Notably, the combination of Holstein and Brown Swiss cows in the training set increased the predictive ability of the model by 6%, which was 0.66 for BCS, 0.85 for BHB, and 0.87 for k-CN. Using multiple specialized and dual-purpose animals in the training set outperforms the 10-fold_HO (standard) approach, with an increase in predictive ability of 8% for BCS, 7% for BHB, and 10% for k-CN. When the Multi-breed CV2 was implemented, no improvement was observed. Our findings suggest that FTIR prediction of different phenotypes in the Holstein breed can be improved by including different specialized and dual-purpose breeds in the training population. Our study also shows that predictive ability is enhanced when the size of the training population and the phenotypic variability are increased.
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    Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling
    Yu, Haipeng; Morota, Gota; Celestino, Elfren F., Jr.; Dahlen, Carl R.; Wagner, Sarah A.; Riley, David G.; Hulsman Hanna, Lauren L. (2020-06-12)
    The animal's reaction to human handling (i.e., temperament) is critical for work safety, productivity, and welfare. Subjective phenotyping methods have been traditionally used in beef cattle production. Even so, subjective scales rely on the evaluator's knowledge and interpretation of temperament, which may require substantial experience. Selection based on such subjective scores may not precisely change temperament preferences in cattle. The objectives of this study were to investigate the underlying genetic interrelationships among temperament measurements using genetic factor analytic modeling and validate a movement-based objective method (four-platform standing scale, FPSS) as a measure of temperament. Relationships among subjective methods of docility score (DS), temperament score (TS), 12 qualitative behavior assessment (QBA) attributes and objective FPSS including the standard deviation of total weight on FPSS over time (SSD) and coefficient of variation of SSD (CVSSD) were investigated using 1,528 calves at weaning age. An exploratory factor analysis (EFA) identified two latent variables account for TS and 12 QBA attributes, termeddifficultandeasyfrom their characteristics. Inclusion of DS in EFA was not a good fit because it was evaluated under restraint and other measures were not. A Bayesian confirmatory factor analysis inferred thedifficultandeasyscores discovered in EFA. This was followed by fitting a pedigree-based Bayesian multi-trait model to characterize the genetic interrelationships amongdifficult, easy, DS, SSD, and CVSSD. Estimates of heritability ranged from 0.18 to 0.4 with the posterior standard deviation averaging 0.06. The factors ofdifficultandeasyexhibited a large negative genetic correlation of -0.92. Moderate genetic correlation was found between DS anddifficult(0.36),easy(-0.31), SSD (0.42), and CVSSD (0.34) as well as FPSS withdifficult(CVSSD: 0.35; SSD: 0.42) andeasy(CVSSD: -0.35; SSD: -0.4). Correlation coefficients indicate selection could be performed with either and have similar outcomes. We contend that genetic factor analytic modeling provided a new approach to unravel the complexity of animal behaviors and FPSS-like measures could increase the efficiency of genetic selection by providing automatic, objective, and consistent phenotyping measures that could be an alternative of DS, which has been widely used in beef production.
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    Designing and modeling high-throughput phenotyping data in quantitative genetics
    Yu, Haipeng (Virginia Tech, 2020-04-09)
    Quantitative genetics aims to bridge the genome to phenome gap. The advent of high-throughput genotyping technologies has accelerated the progress of genome to phenome mapping, but a challenge remains in phenotyping. Various high-throughput phenotyping (HTP) platforms have been developed recently to obtain economically important phenotypes in an automated fashion with less human labor and reduced costs. However, the effective way of designing HTP has not been investigated thoroughly. In addition, high-dimensional HTP data bring up a big challenge for statistical analysis by increasing computational demands. A new strategy for modeling high-dimensional HTP data and elucidating the interrelationships among these phenotypes are needed. Previous studies used pedigree-based connectetdness statistics to study the design of phenotyping. The availability of genetic markers provides a new opportunity to evaluate connectedness based on genomic data, which can serve as a means to design HTP. This dissertation first discusses the utility of connectedness spanning in three studies. In the first study, I introduced genomic connectedness and compared it with traditional pedigree-based connectedness. The relationship between genomic connectedness and prediction accuracy based on cross-validation was investigated in the second study. The third study introduced a user-friendly connectedness R package, which provides a suite of functions to evaluate the extent of connectedness. In the last study, I proposed a new statistical approach to model high-dimensional HTP data by leveraging the combination of confirmatory factor analysis and Bayesian network. Collectively, the results from the first three studies suggested the potential usefulness of applying genomic connectedness to design HTP. The statistical approach I introduced in the last study provides a new avenue to model high-dimensional HTP data holistically to further help us understand the interrelationships among phenotypes derived from HTP.
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    Digital Phenotyping and Genomic Prediction Using Machine and Deep Learning in Animals and Plants
    Bi, Ye (Virginia Tech, 2024-10-03)
    This dissertation investigates the utility of deep learning and machine learning approaches for livestock management and quantitative genetic modeling of rice grain size under climate change. Monitoring the live body weight of animals is crucial to support farm management decisions due to its direct relationship with animal growth, nutritional status, and health. However, conventional manual weighing methods are time consuming and can cause potential stress to animals. While there is a growing trend towards the use of three-dimensional cameras coupled with computer vision techniques to predict animal body weight, their validation with deep learning models as well as large-scale data collected in commercial environments is still limited. Therefore, the first two research chapters show how deep learning-based computer vision systems can enable accurate live body weight prediction for dairy cattle and pigs. These studies also address the challenges of managing large, complex phenotypic data and highlight the potential of deep learning models to automate data processing and improve prediction accuracy in an industry-scale commercial setting. The dissertation then shifts the focus to crop resilience, particularly in rice, where the asymmetric increase in average nighttime temperatures relative to the increase in average daytime temperatures due to climate change is reducing grain yield and quality in rice. Through the use of deep learning and machine learning models, the last two chapters explore how metabolic data can be used in quantitative genetic modeling in rice under environmental stress conditions such as high night temperatures. These studies showed that the integration of metabolites and genomics provided an improvement in the prediction of rice grain size-related traits, and certain metabolites were identified as potential candidates for improving multi-trait genomic prediction. Further research showed that metabolic accumulation was low to moderately heritable, and genomic prediction accuracies were consistent with expected genomic heritability estimates. Genomic correlations between control and high night temperature conditions indicated genotype-by-environment interactions in metabolic accumulation and the effectiveness of genomic prediction models for metabolic accumulation varied across metabolites. Joint analysis of multiple metabolites improved the accuracy of genomic prediction by exploiting correlations between metabolite accumulation. Overall, this dissertation highlights the potential of integrating digital technologies and multi-omic data to advance data analytics in agriculture, with applications in livestock management and quantitative genetic modeling of rice.
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    Divergent phenotypic response of rice accessions to transient heat stress during early seed development
    Paul, Puneet; Dhatt, Balpreet K.; Sandhu, Jaspreet; Hussain, Waseem; Irvin, Larissa; Morota, Gota; Staswick, Paul; Walia, Harkamal (2020-01-12)
    Increasing global surface temperatures is posing a major food security challenge. Part of the solution to address this problem is to improve crop heat resilience, especially during grain development, along with agronomic decisions such as shift in planting time and increasing crop diversification. Rice is a major food crop consumed by more than 3 billion people. For rice, thermal sensitivity of reproductive development and grain filling is well-documented, while knowledge concerning the impact of heat stress (HS) on early seed development is limited. Here, we aim to study the phenotypic variation in a set of diverse rice accessions for elucidating the HS response during early seed development. To explore the variation in HS sensitivity, we investigated aus (1), indica (2), temperate japonica (2), and tropical japonica (4) accessions for their HS (39/35 degrees C) response during early seed development that accounts for transition of endosperm from syncytial to cellularization, which broadly corresponds to 24 and 96 hr after fertilization (HAF), respectively, in rice. The two indica and one of the tropical japonica accessions exhibited severe heat sensitivity with increased seed abortion; three tropical japonicas and an aus accession showed moderate heat tolerance, while temperate japonicas exhibited strong heat tolerance. The accessions exhibiting extreme heat sensitivity maintain seed size at the expense of number of fully developed mature seeds, while the accessions showing relative resilience to the transient HS maintained number of fully developed seeds but compromised on seed size, especially seed length. Further, histochemical analysis revealed that all the tested accessions have delayed endosperm cellularization upon exposure to the transient HS by 96 HAF; however, the rate of cellularization was different among the accessions. These findings were further corroborated by upregulation of cellularization-associated marker genes in the developing seeds from the heat-stressed samples.
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    Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice
    Bi, Ye; Yassue, Rafael Massahiro; Paul, Puneet; Dhatt, Balpreet Kaur; Sandhu, Jaspreet; Do, Phuc Thi; Walia, Harkamal; Obata, Toshihiro; Morota, Gota (Oxford University Press, 2023-05)
    The asymmetric increase in average nighttime temperatures relative to increase in average daytime temperatures due to climate change is decreasing grain yield and quality in rice. Therefore, a better genome-level understanding of the impact of higher night temperature stress on the weight of individual grains is essential for future development of more resilient rice. We investigated the utility of metabolites obtained from grains to classify high night temperature (HNT) conditions of genotypes, and metabolites and single-nucleotide polymorphisms (SNPs) to predict grain length, width, and perimeter phenotypes using a rice diversity panel. We found that the metabolic profiles of rice genotypes alone could be used to classify control and HNT conditions with high accuracy using random forest or extreme gradient boosting. Best linear unbiased prediction and BayesC showed greater metabolic prediction performance than machine learning models for grain-size phenotypes. Metabolic prediction was most effective for grain width, resulting in the highest prediction performance. Genomic prediction performed better than metabolic prediction. Integrating metabolites and genomics simultaneously in a prediction model slightly improved prediction performance. We did not observe a difference in prediction between the control and HNT conditions. Several metabolites were identified as auxiliary phenotypes that could be used to enhance the multi-trait genomic prediction of grain-size phenotypes. Our results showed that, in addition to SNPs, metabolites collected from grains offer rich information to perform predictive analyses, including classification modeling of HNT responses and regression modeling of grain-size-related phenotypes in rice.
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    Forecasting dynamic body weight of nonrestrained pigs from images using an RGB-D sensor camera
    Yu, Haipeng; Lee, Kiho; Morota, Gota (Oxford University Press, 2021-01-01)
    Average daily gain is an indicator of the growth rate, feed efficiency, and current health status of livestock species including pigs. Continuous monitoring of daily gain in pigs aids producers to optimize their growth performance while ensuring animal welfare and sustainability, such as reducing stress reactions and feed waste. Computer vision has been used to predict live body weight from video images without direct handling of the pig. In most studies, videos were taken while pigs were immobilized at a weighing station or feeding area to facilitate data collection. An alternative approach is to capture videos while pigs are allowed to move freely within their own housing environment, which can be easily applied to the production system as no special imaging station needs to be established. The objective of this study was to establish a computer vision system by collecting RGB-D videos to capture top-view red, green, and blue (RGB) and depth images of nonrestrained, growing pigs to predict their body weight over time. Over a period of 38 d, eight growers were video recorded for approximately 3 min/d, at the rate of six frames per second, and manually weighed using an electronic scale. An image-processing pipeline in Python using OpenCV was developed to process the images. Specifically, each pig within the RGB frame was segmented by a thresholding algorithm, and the contour of the pig was identified to extract its length and width. The height of a pig was estimated from the depth images captured by the infrared depth sensor. Quality control included removing pigs that were touching the fence and sitting, as well as those showing extremely distorted shape or motion blur owing to their frequent movement. Fitting all of the morphological image descriptors simultaneously in linear mixed models yielded prediction coefficients of determination of 0.72-0.98, 0.65-0.95, 0.51-0.94, and 0.49-0.93 for 1-, 2-, 3-, and 4-d ahead forecasting, respectively, of body weight in time series cross-validation. Based on the results, we conclude that our RGB-D sensor-based imaging system coupled with the Python image-processing pipeline could potentially provide an effective approach to predict the live body weight of nonrestrained pigs from images.
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    GCA: an R package for genetic connectedness analysis using pedigree and genomic data
    Yu, Haipeng; Morota, Gota (2021-02-15)
    Background Genetic connectedness is a critical component of genetic evaluation as it assesses the comparability of predicted genetic values across units. Genetic connectedness also plays an essential role in quantifying the linkage between reference and validation sets in whole-genome prediction. Despite its importance, there is no user-friendly software tool available to calculate connectedness statistics. Results We developed the GCA R package to perform genetic connectedness analysis for pedigree and genomic data. The software implements a large collection of various connectedness statistics as a function of prediction error variance or variance of unit effect estimates. The GCA R package is available at GitHub and the source code is provided as open source. Conclusions The GCA R package allows users to easily assess the connectedness of their data. It is also useful to determine the potential risk of comparing predicted genetic values of individuals across units or measure the connectedness level between training and testing sets in genomic prediction.
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    Genetic Heterogeneity of Residual Variance for Production and Functional Traits in American Angus Cattle
    Amorim, Sabrina Thaise (Virginia Tech, 2024-08-14)
    Beef cattle are continuously selected for different traits and the success in improving these traits has been remarkable. However, for certain traits, it is essential not only to improve the average performance, but also to control the variation around the mean. There is evidence that residual variance may be under genetic control, which opens the possibility of selecting for uniformity. In this sense, the objectives of the present dissertation were: 1) to investigate the extent of genetic heterogeneity of residual variance at the pedigree level in birth weight (BW), weaning weight (WW), yearling weight (YW), foot angle (FA), and claw set (CS) in American Angus cattle; 2) to compare the results of different genetic heterogeneity models; 3) to evaluate the effectiveness of Box-Cox transformation in continuous traits; and 4) to address limitations and explore alternative solutions for implementing genetic parameters for residual variance in genetic evaluations. The first study investigated the genetic heterogeneity of residual variances for BW, WW, and YW. Three models were compared: a homoscedastic residual variance model (M1), a double hierarchical generalized linear model (DHGLM, M2), and a genetically structured environmental variance model (MCMC, M3). The results showed significant genetic heterogeneity of residual variances in growth traits, suggesting the possibility of selection for uniformity. The genetic coefficient of variation for residual variance ranged from 0.90 to 0.92 in M2 and 0.31 to 0.38 in M3 for BW, 0.64 in M2 and 0.01 to 0.29 in M3 for WW, and 0.67 to 0.63 in M2 and 0.25 to 0.31 in M3 for YW. Low heritability estimates for residual variance were found, particularly in M2 (0.08 for BW, 0.06 for WW, and 0.09 for YW). The study identified both negative and positive genetic correlations between mean and residual variance, depending on the trait and data transformation. Negative correlations suggest the potential to increase trait means while decreasing residual variance. However, positive correlations indicate that the genetic response to selection for uniformity may be limited unless a selection index is used. Data transformation reduced skewness but did not eliminate genetic heterogeneity of residual variances. The Bayesian approach provided higher estimates of additive genetic variance for residual variance compared to DHGLM. Overall, the findings indicate the potential to reduce variability through selection and lay the groundwork for incorporating uniformity of growth traits into breeding goals. The second study focused on the genetic heterogeneity of residual variance for two foot conformation traits, FA and CS. Using 45,667 phenotypic records collected between 2009 and 2021, three models were compared: a traditional homoscedastic residual variance model (M1), a DHGLM (M2), and a genetically structured environmental variance model (M3). Results showed that heritability estimates for FA and CS means were within expected ranges, although lower in M2. Despite low heritability estimates for residual variance (0.07 for FA and 0.05 for CS in M2), significant genetic coefficients of variation were found, suggesting that selection on trait mean would also influence residual variance. Positive genetic correlations between mean and residual variance in M2 and M3 indicate that selection for uniformity is feasible, but may require additional strategies such as selection indices. The study highlights the potential of FA and CS as indicators for breeding programs aimed at improving production uniformity in beef cattle. Our findings suggest that selection for uniformity in growth and foot score traits in beef cattle may be limited by low heritability of residual variance and moderate to high positive genetic correlations between mean and residual variance. This was observed for most of the traits studied. To overcome these challenges, further research is needed, particularly to explore genomic information to improve the prediction accuracy of estimated breeding values (EBV) for residual variance. Although studies of uniformity using genomic data are limited, they have shown improved EBV accuracy for residual variance. Additionally, alternative methods for measuring uniformity, such as different uniformity or resilience indicators, should be considered, especially with advances in digital phenotyping. Precision livestock farming technologies that allow for extensive data collection on various production traits should be integrated into the development of new uniformity indicators. This dissertation provides valuable insights into the genetic heterogeneity of residual variance in American Angus cattle and highlights the complexity of selecting for uniformity while improving mean traits. Continued research with larger data sets, genomic information, and further methodological refinement will be critical to advance these findings to improve uniformity and productivity in beef cattle breeding.
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    Genome-wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth-related traits in maize under plant growth-promoting bacteria inoculation
    Yassue, Rafael Massahiro; Galli, Giovanni; Chen, Chun-Peng James; Fritsche-Neto, Roberto; Morota, Gota (Wiley, 2023-04)
    Plant growth-promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth-related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome-wide association analyses to examine maize growth-related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386-1021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome-wide association analysis as potential markers for growth-related traits under PGPB inoculation. Eight SNPs were detected, which were commonly associated with manually measured and hyperspectral phenotypes. Different genomic regions were found for plant growth and hyperspectral phenotypes between with and without PGPB inoculation. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multiphenotype genome-wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral-based phenotyping for studying maize growth-related traits in response to PGPB inoculation.
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    Genome-wide association analysis of sucrose and alanine contents in edamame beans
    Wang, Zhibo; Yu, Dajun; Morota, Gota; Dhakal, Kshitiz; Singer, William; Lord, Nilanka; Huang, Haibo; Chen, Pengyin; Mozzoni, Leandro; Li, Song; Zhang, Bo (Frontiers, 2023-02-03)
    The sucrose and Alanine (Ala) content in edamame beans significantly impacts the sweetness flavor of edamame-derived products as an important attribute to consumers' acceptance. Unlike grain-type soybeans, edamame beans are harvested as fresh beans at the R6 to R7 growth stages when beans are filled 80-90% of the pod capacity. The genetic basis of sucrose and Ala contents in fresh edamame beans may differ from those in dry seeds. To date, there is no report on the genetic basis of sucrose and Ala contents in the edamame beans. In this study, a genome-wide association study was conducted to identify single nucleotide polymorphisms (SNPs) related to sucrose and Ala levels in edamame beans using an association mapping panel of 189 edamame accessions genotyped with a SoySNP50K BeadChip. A total of 43 and 25 SNPs was associated with sucrose content and Ala content in the edamame beans, respectively. Four genes (Glyma.10g270800, Glyma.08g137500, Glyma.10g268500, and Glyma.18g193600) with known effects on the process of sucrose biosynthesis and 37 novel sucrose-related genes were characterized. Three genes (Gm17g070500, Glyma.14g201100 and Glyma.18g269600) with likely relevant effects in regulating Ala content and 22 novel Ala-related genes were identified. In addition, by summarizing the phenotypic data of edamame beans from three locations in two years, three PI accessions (PI 532469, PI 243551, and PI 407748) were selected as the high sucrose and high Ala parental lines for the perspective breeding of sweet edamame varieties. Thus, the beneficial alleles, candidate genes, and selected PI accessions identified in this study will be fundamental to develop edamame varieties with improved consumers' acceptance, and eventually promote edamame production as a specialty crop in the United States.
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    Genome-wide association analysis uncovers the genetic architecture of tradeoff between flowering date and yield components in sesame
    Sabag, Idan; Morota, Gota; Peleg, Zvi (2021-11-22)
    Background Unrevealing the genetic makeup of crop morpho-agronomic traits is essential for improving yield quality and sustainability. Sesame (Sesamum indicum L.) is one of the oldest oil-crops in the world. Despite its economic and agricultural importance, it is an ‘orphan crop-plant’ that has undergone limited modern selection, and, as a consequence preserved wide genetic diversity. Here we established a new sesame panel (SCHUJI) that contains 184 genotypes representing wide phenotypic variation and is geographically distributed. We harnessed the natural variation of this panel to perform genome-wide association studies for morpho-agronomic traits under the Mediterranean climate conditions. Results Field-based phenotyping of the SCHUJI panel across two seasons exposed wide phenotypic variation for all traits. Using 20,294 single-nucleotide polymorphism markers, we detected 50 genomic signals associated with these traits. Major genomic region on LG2 was associated with flowering date and yield-related traits, exemplified the key role of the flowering date on productivity. Conclusions Our results shed light on the genetic architecture of flowering date and its interaction with yield components in sesame and may serve as a basis for future sesame breeding programs in the Mediterranean basin.
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    Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes
    Yu, Haipeng; Campbell, Malachy T.; Zhang, Qi; Walia, Harkamal; Morota, Gota (Genetics Society of America, 2019-06-01)
    With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multi-trait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (i.e., Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show 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 interrelated complex traits systems.
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    Heat Stress Effects on Physiological and Milk Yield Traits of Lactating Holstein Friesian Crossbreds Reared in Tanga Region, Tanzania
    Habimana, Vincent; Nguluma, Athumani Shabani; Nziku, Zabron Cuthibert; Ekine - Dzivenu, Chinyere Charlotte; Morota, Gota; Mrode, Raphael; Chenyambuga, Sebastian Wilson (MDPI, 2024-06-28)
    Global warming caused by climate change is a challenge for dairy farming, especially in sub-Saharan countries. Under high temperatures and relative humidity, lactating dairy cows suffer from heat stress. The objective of this study was to investigate the effects and relationship of heat stress (HS) measured by the temperature–humidity index (THI) regarding the physiological parameters and milk yield and composition of lactating Holstein Friesian crossbred dairy cows reared in the humid coastal region of Tanzania. A total of 29 lactating Holstein Friesian x Zebu crossbred dairy cows with 50% (HF50) and 75% (HF75) Holstein Friesian gene levels in the second and third months of lactation were used. The breed composition of Holstein Friesians was determined based on the animal recording system used at the Tanzania Livestock Research Institute (TALIRI), Tanga. The data collected included the daily temperature, relative humidity, daily milk yield, and physiological parameters (core body temperature, rectal temperature, respiratory rate, and panting score). THI was calculated using the equation of the National Research Council. The THI values were categorized into three classes, i.e., low THI (76–78), moderate THI (79–81), and high THI (82–84). The effects of THI on the physiological parameters and milk yield and composition were assessed. The effects of the genotype, the parity, the lactation month, and the interaction of these parameters with THI on the milk yield, milk composition, and physiological parameters were also investigated. The results show that THI and its interaction with genotypes, parity, and the lactation month had a highly significant effect on all parameters. THI influenced (p ˂ 0.05) the average daily milk yield and milk fat %, protein %, lactose %, and solids–not–fat %. As the THI increased from moderate to high levels, the average daily milk yield declined from 3.49 ± 0.04 to 3.43 ± 0.05 L/day, while the fat % increased from 2.66 ± 0.05% to 3.04 ± 0.06% and the protein decreased from 3.15 ± 0.02% to 3.13 ± 0.03%. No decline in lactose % was observed, while the solid–not–fat % declined from 8.56 ± 0.08% to 8.55 ± 0.10% as the THI values increased from moderate to high. Also, the THI influenced physiological parameters (p ˂ 0.05). The core body temperature (CBT), rectal temperature (RT), respiratory rate (RR) and panting score (PS) increased from 35.60 ± 0.01 to 36.00 ± 0.01 °C, 38.03 ± 0.02 to 38.30 ± 0.02 °C, 62.53 ± 0.29 to 72.35 ± 0.28 breaths/min, and 1.35 ± 0.01 to 1.47 ± 0.09, respectively, as the THI increased from low to high. The THI showed a weak positive correlation with the average daily milk yield and fat percentage, whereas the protein, lactose, and solids–not–fat percentages showed negative relationships with THI (p ≤ 0.05). CBT, RT, RR, and PS showed positive relationships (p ≤ 0.05) with THI. These negative relationships indicate that there is an antagonistic correlation between sensitivity to HS and the level of production. It is concluded that the THI, the genotype, the parity, and the lactation month, along with their interactions with THI, significantly influenced the milk yield, milk composition, and physiological parameters of lactating Holstein Friesian dairy crosses at THI thresholds ranging from 77 to 84.
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    Improved pig behavior analysis by optimizing window sizes for individual behaviors on acceleration and angular velocity data
    Alghamdi, Saleh; Zhao, Zhuqing; Ha, Dong S.; Morota, Gota; Ha, Sook S. (Oxford University Press, 2022-11-01)
    This paper presents the application of machine learning algorithms to identify pigs' behaviors from data collected using the wireless sensor nodes mounted on pigs. The sensor node attached to a pig's back senses the acceleration and angular velocity in three axes, and the sensed data are transmitted to a host computer wirelessly. Two video cameras, one attached to the ceiling of the pigpen and the other one to a fence, provided ground truth for data annotations. The data were collected from pigs for 131 h over 2 mo. As the typical behavior period depends on the behavior type, we segmented the acceleration data with different window sizes (WS) and step sizes (SS), and tested how the classification performance of different activities varied with different WS and SS. After exploring the possible combinations, we selected the optimum WS and SS. To compare performance, we used five machine learning algorithms, specifically support vector machine, k-nearest neighbors, decision trees, naive Bayes, and random forest (RF). Among the five algorithms, RF achieved the highest F1 score for four major behaviors consisting of 92.36% in total. The F1 scores of the algorithm were 0.98 for "eating,"0.99 for "lying,"0.93 for "walking,"and 0.91 for "standing"behaviors. The optimal WS was 7 s for "eating"and "lying,"and 3 s for "walking"and "standing."The proposed work demonstrates that, based on the length of behavior, the adaptive window and step sizes increase the classification performance.
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    Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Models
    Momen, Mehdi; Mehrgardi, Ahmad Ayatollahi; Roudbar, Mahmoud Amiri; Kranis, Andreas; Pinto, Renan Mercuri; Valente, Bruno D.; Morota, Gota; Rosa, Gullherme J. M.; Gianola, Daniel (Frontiers, 2018-10-09)
    Network based statistical models accounting for putative causal relationships among multiple phenotypes can be used to infer single-nucleotide polymorphism (SNP) effect which transmitting through a given causal path in genome-wide association studies (GWAS). In GWAS with multiple phenotypes, reconstructing underlying causal structures among traits and SNPs using a single statistical framework is essential for understanding the entirety of genotype-phenotype maps. A structural equation model (SEM) can be used for such purposes. We applied SEM to GWAS (SEM-GWAS) in chickens, taking into account putative causal relationships among breast meat (BM), body weight (Btu), hen-house production (HHP), and SNPs. We assessed the performance of SEM-GWAS by comparing the model results with those obtained from traditional multi-trait association analyses (MTM-GWAS). Three different putative causal path diagrams were inferred from highest posterior density (HPD) intervals of 0.75, 0.85, and 0.95 using the inductive causation algorithm. A positive path coefficient was estimated for BM -> BW, and negative values were obtained for BM -> HHP and BW -> HHP in all implemented scenarios. Further, the application of SEM-GWAS enabled the decomposition of SNP effects into direct, indirect, and total effects, identifying whether a SNP effect is acting directly or indirectly on a given trait. In contrast, MTM-GWAS only captured overall genetic effects on traits, which is equivalent to combining the direct and indirect SNP effects from SEM-GWAS. Although MTM-GWAS and SEM-GWAS use the similar probabilistic models, we provide evidence that SEM-GWAS captures complex relationships in terms of causal meaning and mediation and delivers a more comprehensive understanding of SNP effects compared to MTM-GWAS. Our results showed that SEM-GWAS provides important insight regarding the mechanism by which identified SNPs control traits by partitioning them into direct, indirect, and total SNP effects.