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818 results on '"Eeuwijk, Fred A."'

1. Improving Genomic Prediction using High-dimensional Secondary Phenotypes: the Genetic Latent Factor Approach

Author

Melsen, Killian A. C., Kunst, Jonathan F., Crossa, José, Krause, Margaret R., van Eeuwijk, Fred A., Kruijer, Willem, and Peeters, Carel F. W.

Subjects
Statistics - Methodology, Quantitative Biology - Quantitative Methods
Abstract

Decreasing costs and new technologies have led to an increase in the amount of data available to plant breeding programs. High-throughput phenotyping (HTP) platforms routinely generate high-dimensional datasets of secondary features that may be used to improve genomic prediction accuracy. However, integration of this data comes with challenges such as multicollinearity, parameter estimation in $p > n$ settings, and the computational complexity of many standard approaches. Several methods have emerged to analyze such data, but interpretation of model parameters often remains challenging. We propose genetic factor best linear unbiased prediction (gfBLUP), a seven-step prediction pipeline that reduces the dimensionality of the original secondary HTP data using generative factor analysis. In short, gfBLUP uses redundancy filtered and regularized genetic and residual correlation matrices to fit a maximum likelihood factor model and estimate genetic latent factor scores. These latent factors are subsequently used in multi-trait genomic prediction. Our approach performs on par or better than alternatives in extensive simulations and a real-world application, while producing easily interpretable and biologically relevant parameters. We discuss several possible extensions and highlight gfBLUP as the basis for a flexible and modular multi-trait genomic prediction framework.

Published
2024

11. Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions

Author

Welcker, Claude, Spencer, Nadir Abusamra, Turc, Olivier, Granato, Italo, Chapuis, Romain, Madur, Delphine, Beauchene, Katia, Gouesnard, Brigitte, Draye, Xavier, Palaffre, Carine, Lorgeou, Josiane, Melkior, Stephane, Guillaume, Colin, Presterl, Thomas, Murigneux, Alain, Wisser, Randall J., Millet, Emilie J., van Eeuwijk, Fred, Charcosset, Alain, and Tardieu, François

Published
2022
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13. Modeling QTL-by-environment interactions for multi-parent populations.

Author

Wenhao Li, Boer, Martin P., Joosen, Ronny V. L., Chaozhi Zheng, Percival-Alwyn, Lawrence, Cockram, James, and Van Eeuwijk, Fred A.

Subjects
GENETIC variation, MATRIX functions, HOME environment, WHEAT, CORN
Abstract

Multi-parent populations (MPPs) are attractive for genetic and breeding studies because they combine genetic diversity with an easy-to-control population structure. Most methods for mapping QTLs in MPPs focus on the detection of QTLs in single environments. Little attention has been given to mapping QTLs in multienvironment trials (METs) and to detecting and modeling QTL-byenvironment interactions (QEIs). We present mixed model approaches for the detection and modeling of consistent versus environment-dependent QTLs, i.e., QTL-by-environment interaction (QEI). QTL effects are assumed to be normally distributed with variances expressing consistency or dependence on environments and families. The entries of the corresponding design matrices are functions of identity-by-descent (IBD) probabilities between parents and offspring and follow from the parental origin of offspring DNA. A polygenic effect is added to the models to account for background genetic variation. We illustrate the wide applicability of our method by analyzing several public MPP datasets with observations from METs. The examples include diallel, nested association mapping (NAM), and multi-parent advanced inter-cross (MAGIC) populations. The results of our approach compare favorably with those of previous studies that used tailored methods. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Genes and gene clusters related to genotype and drought-induced variation in saccharification potential, lignin content and wood anatomical traits in Populus nigra

Author

Wildhagen, Henning, Paul, Shanty, Allwright, Mike, Smith, Hazel K, Malinowska, Marta, Schnabel, Sabine K, Paulo, M João, Cattonaro, Federica, Vendramin, Vera, Scalabrin, Simone, Janz, Dennis, Douthe, Cyril, Brendel, Oliver, Buré, Cyril, Cohen, David, Hummel, Irène, Le Thiec, Didier, van Eeuwijk, Fred, Keurentjes, Joost JB, Flexas, Jaume, Morgante, Michele, Robson, Paul, Bogeat-Triboulot, Marie-Béatrice, Taylor, Gail, and Polle, Andrea

Subjects
Genetics, Droughts, Gene Expression, Genes, Plant, Genotype, Hydrolysis, Lignin, Multigene Family, Polysaccharides, Populus, Wood, biofuels, cell wall, drought, genotypic variation, glucose release, lignin, saccharification, wood traits, Ecology, Plant Biology, Forestry Sciences, Plant Biology & Botany
Abstract

Wood is a renewable resource that can be employed for the production of second generation biofuels by enzymatic saccharification and subsequent fermentation. Knowledge on how the saccharification potential is affected by genotype-related variation of wood traits and drought is scarce. Here, we used three Populus nigra L. genotypes from habitats differing in water availability to (i) investigate the relationships between wood anatomy, lignin content and saccharification and (ii) identify genes and co-expressed gene clusters related to genotype and drought-induced variation in wood traits and saccharification potential. The three poplar genotypes differed in wood anatomy, lignin content and saccharification potential. Drought resulted in reduced cambial activity, decreased vessel and fiber lumina, and increased the saccharification potential. The saccharification potential was unrelated to lignin content as well as to most wood anatomical traits. RNA sequencing of the developing xylem revealed that 1.5% of the analyzed genes were differentially expressed in response to drought, while 67% differed among the genotypes. Weighted gene correlation network analysis identified modules of co-expressed genes correlated with saccharification potential. These modules were enriched in gene ontology terms related to cell wall polysaccharide biosynthesis and modification and vesicle transport, but not to lignin biosynthesis. Among the most strongly saccharification-correlated genes, those with regulatory functions, especially kinases, were prominent. We further identified transcription factors whose transcript abundances differed among genotypes, and which were co-regulated with genes for biosynthesis and modifications of hemicelluloses and pectin. Overall, our study suggests that the regulation of pectin and hemicellulose metabolism is a promising target for improving wood quality of second generation bioenergy crops. The causal relationship of the identified genes and pathways with saccharification potential needs to be validated in further experiments.

Published
2018

17. Spatial Models for Field Trials

Author

Rodríguez-Álvarez, María Xosé, Boer, Martin P., van Eeuwijk, Fred A., and Eilers, Paul H. C.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

An important aim of the analysis of agricultural field trials is to obtain good predictions for genotypic performance, by correcting for spatial effects. In practice these corrections turn out to be complicated, since there can be different types of spatial effects; those due to management interventions applied to the field plots and those due to various kinds of erratic spatial trends. This paper presents models for field trials in which the random spatial component consists of tensor product Penalized splines (P-splines). A special ANOVA-type reformulation leads to five smooth additive spatial components, which form the basis of a mixed model with five unknown variance components. On top of this spatial field, effects of genotypes, blocks, replicates, and/or other sources of spatial variation are described by a mixed model in a standard way. We show the relation between several definitions of heritability and the effective dimension or the effective degrees of freedom associated to the genetic component. The approach is illustrated with large-scale field trial experiments. An R-package is provided.

Published
2016

20. Whole genome resequencing and phenotyping of MAGIC population for high resolution mapping of drought tolerance in chickpea

Author

Thudi, Mahendar, Samineni, Srinivasan, Li, Wenhao, Boer, Martin P., Roorkiwal, Manish, Yang, Zuoquan, Ladejobi, Funmi, Zheng, Chaozhi, Chitikineni, Annapurna, Nayak, Sourav, He, Zhang, Valluri, Vinod, Bajaj, Prasad, Khan, Aamir W., Gaur, Pooran M., van Eeuwijk, Fred, Mott, Richard, Xin, Liu, Varshney, Rajeev K., Thudi, Mahendar, Samineni, Srinivasan, Li, Wenhao, Boer, Martin P., Roorkiwal, Manish, Yang, Zuoquan, Ladejobi, Funmi, Zheng, Chaozhi, Chitikineni, Annapurna, Nayak, Sourav, He, Zhang, Valluri, Vinod, Bajaj, Prasad, Khan, Aamir W., Gaur, Pooran M., van Eeuwijk, Fred, Mott, Richard, Xin, Liu, and Varshney, Rajeev K.

Abstract

Terminal drought is one of the major constraints to crop production in chickpea (Cicer arietinum L.). In order to map drought tolerance related traits at high resolution, we sequenced multi-parent advanced generation intercross (MAGIC) population using whole genome resequencing approach and phenotyped it under drought stress environments for two consecutive years (2013–14 and 2014–15). A total of 52.02 billion clean reads containing 4.67 TB clean data were generated on the 1136 MAGIC lines and eight parental lines. Alignment of clean data on to the reference genome enabled identification of a total, 932,172 of SNPs, 35,973 insertions, and 35,726 deletions among the parental lines. A high-density genetic map was constructed using 57,180 SNPs spanning a map distance of 1606.69 cM. Using compressed mixed linear model, genome-wide association study (GWAS) enabled us to identify 737 markers significantly associated with days to 50% flowering, days to maturity, plant height, 100 seed weight, biomass, and harvest index. In addition to the GWAS approach, an identity-by-descent (IBD)-based mixed model approach was used to map quantitative trait loci (QTLs). The IBD-based mixed model approach detected major QTLs that were comparable to those from the GWAS analysis as well as some exclusive QTLs with smaller effects. The candidate genes like FRIGIDA and CaTIFY4b can be used for enhancing drought tolerance in chickpea. The genomic resources, genetic map, marker-trait associations, and QTLs identified in the study are valuable resources for the chickpea community for developing climate resilient chickpeas.

Published
2024

21. High-throughput field phenotyping reveals that selection in breeding has affected the phenology and temperature response of wheat in the stem elongation phase

Author

Roth, Lukas, Kronenberg, Lukas, Aasen, Helge, Walter, Achim, Hartung, Jens, van Eeuwijk, Fred, Piepho, Hans-Peter, Hund, Andreas, Roth, Lukas, Kronenberg, Lukas, Aasen, Helge, Walter, Achim, Hartung, Jens, van Eeuwijk, Fred, Piepho, Hans-Peter, and Hund, Andreas

Abstract

Crop growth and phenology are driven by seasonal changes in environmental variables, with temperature as one important factor. However, knowledge about genotype-specific temperature response and its influence on phenology is limited. Such information is fundamental to improve crop models and adapt selection strategies. We measured the increase in height of 352 European winter wheat varieties in 4 years to quantify phenology, and fitted an asymptotic temperature response model. The model used hourly fluctuations in temperature to parameterize the base temperature (Tmin), the temperature optimum (rmax), and the steepness (lrc) of growth responses. Our results show that higher Tmin and lrc relate to an earlier start and end of stem elongation. A higher rmax relates to an increased final height. Both final height and rmax decreased for varieties originating from the continental east of Europe towards the maritime west. A genome-wide association study (GWAS) indicated a quantitative inheritance and a large degree of independence among loci. Nevertheless, genomic prediction accuracies (GBLUPs) for Tmin and lrc were low (r≤0.32) compared with other traits (r≥0.59). As well as known, major genes related to vernalization, photoperiod, or dwarfing, the GWAS indicated additional, as yet unknown loci that dominate the temperature response.

Published
2024

22. Multienvironment genomic prediction in tetraploid potato

Author

Wilson, Stefan, Zheng, Chaozhi, Maliepaard, Chris, Mulder, Han, Visser, Richard G.F., van Eeuwijk, Fred, Wilson, Stefan, Zheng, Chaozhi, Maliepaard, Chris, Mulder, Han, Visser, Richard G.F., and van Eeuwijk, Fred

Abstract

Multienvironment genomic prediction was applied to tetraploid potato using 147 potato varieties, tested for 2 years, in 3 locations representative of 3 distinct regions in Europe. Different prediction scenarios were investigated to help breeders predict genotypic performance in the regions from one year to the next, for genotypes that were tested this year (scenario 1), as well as new genotypes (scenario 3). In scenario 2, we predicted new genotypes for any one of the 6 trials, using all the information that is available. The choice of prediction model required assessment of the variance–covariance matrix in a mixed model that takes into account heterogeneity of genetic variances and correlations. This was done for each analyzed trait (tuber weight, tuber length, and dry matter) where examples of both limited and higher degrees of heterogeneity was observed. This explains why dry matter did not need complex multienvironment modeling to combine environments and increase prediction ability, while prediction in tuber weight, improved only when models were flexible enough to capture the heterogeneous variances and covariances between environments. We also found that the prediction abilities in a target trial condition decreased, if trials with a low genetic correlation to the target were included when training the model. Genomic prediction in tetraploid potato can work once there is clarity about the prediction scenario, a suitable training set is created, and a multienvironment prediction model is chosen based on the patterns of G×E indicated by the genetic variances and covariances.

Published
2024

23. Marker-based estimation of heritability in immortal populations

Author

Kruijer, Willem, Boer, Martin, Malosetti, Marcos, Flood, Padraic J., Engel, Bas, Kooke, Rik, Keurentjes, Joost, and van Eeuwijk, Fred

Subjects
Statistics - Applications, Quantitative Biology - Populations and Evolution, 62P10
Abstract

Heritability is a central parameter in quantitative genetics, both from an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within and between genotype variability. This approach estimates broad-sense heritability, and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here, is to use mixed models at individual plant or plot level. Using statistical arguments, simulations and real data we investigate the feasibility of both approaches, and how these affect genomic prediction with G-BLUP and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For GWAS on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi., Comment: 6 figures. Contains all the supplementary information (p. 30-54)

Published
2014
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25. Genotype by Environment Interaction and Adaptation

Author

Bustos-Korts, Daniela, Romagosa, Ignacio, Borràs-Gelonch, Gisela, Casas, Ana Maria, Slafer, Gustavo A., van Eeuwijk, Fred, Savin, Roxana, Section editor, Slafer, Gustavo, Section editor, Meyers, Robert A., Editor-in-Chief, Savin, Roxana, editor, and Slafer, Gustavo A., editor

Published
2019
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32. Reciprocal cybrids reveal how organellar genomes affect plant phenotypes

Author

Flood, Pádraic J., Theeuwen, Tom P. J. M., Schneeberger, Korbinian, Keizer, Paul, Kruijer, Willem, Severing, Edouard, Kouklas, Evangelos, Hageman, Jos A., Wijfjes, Raúl, Calvo-Baltanas, Vanesa, Becker, Frank F. M., Schnabel, Sabine K., Willems, Leo A. J., Ligterink, Wilco, van Arkel, Jeroen, Mumm, Roland, Gualberto, José M., Savage, Linda, Kramer, David M., Keurentjes, Joost J. B., van Eeuwijk, Fred, Koornneef, Maarten, Harbinson, Jeremy, Aarts, Mark G. M., and Wijnker, Erik

Published
2020
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33. Local adaptation of Arabidopsis thaliana in a small geographic region with mild environmental clines

Author

Wijfjes, Raul Y., primary, Boesten, Rene, additional, Becker, Frank F.M., additional, Theeuwen, Tom P.J.M., additional, Snoek, Basten L., additional, Mastoraki, Maria, additional, Verheijen, Jelle J., additional, Guvencli, Nuri, additional, Denkers, Lissy-Anne M., additional, Koornneef, Maarten, additional, van Eeuwijk, Fred A., additional, Smit, Sandra, additional, de Ridder, Dick, additional, and Aarts, Mark G.M., additional

Published
2023
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38. 4th Conference of Cereal Biotechnology and Breeding jointly organized by EUCARPIA Cereal Section November 6–9, 2017 • Budapest, Hungary : BOOK OF ABSTRACTS

Author

Consortium INEW, PPP Barley Consortium, Börner, Andreas, Nagel, Manuela, Tarawneh, Rasha, Arif, Mian Abdur Rehman, Agacka-Mołdoch, Monika, Börner, Mariann, Lohwasser, Ulrike, Rieweach, David, Wieb, Janine, Altmann, Thomas, Góralska, Magdalena, Lenarczyk-Puri, Natalia, Podgórska, Katarzyna, Berdzik, Marcin, Stojałowski, Stefan, Myśków, Beata, Kiss, Tibor, Veisz, Ottó, Karsai, Ildikó, Martín, Antonio, Rasmussen, Søren K., Legzdina, Linda, Fan, Weiyao, Tikhenko, Natalia, Rutten, Twan, Senula, Angelika, Rubtsova, Myroslava, Keller, E. R. Joachim, Vitányi, Beáta, Nagy, Katalin, Fekete, Anna Katalin, Dudás, Brigitta, Lantos, Csaba, Pauk, János, Jenes, Barnabás, Yu, Bianyun, Zhang, Wentao, Gao, Peng, Sidebottom, Christine, Lokuruge, Prabhath, Polley, Brittany, Clarke, Carling, Sharpe, Andrew, Knox, Ron, Cuthbert, Richard, Ruan, Yuefeng, Pozniak, Curtis J., Fobert, Pierre, Dziurka, Kinga, Noga, Angelika, Marcińska, Izabela, Czyczyło-Mysza, Ilona, Warchoł, Marzena, Kapłoniak, Kamila, Warzecha, Tomasz, Sutkowska, Agnieszka, Werwińska, Krystyna, Nita, Zygmunt, Skrzypek, Edyta, Fradgley, Nick, Bentley, Alison, Gardner, Keith, Howell, Phil, Mackay, Ian, Scott, Mike, Mott, Richard, Cockram, James, Galani, Saddia, Sahar, Shagufta, Musharraf, Syed Ghulam, Azhar, Abid, Ferjaoui, Sahbi, Bouslimi, Chadha, Udupa, Sripada M., Hamza, Sonia, Mariana, Ittu, Ciuca, Matilda, Marinciu, Cristina, Cristina, Daniel, Turcu, Alina, Mandea, Vasile, Ittu, Gheorghe, Jabłoński, Bartosz, Onyśk, Agnieszka, Wyszyńska, Renata, Boczkowska, Maja, Przyborowski, Mateusz, Gasparis, Sebastian, Orczyk, Wacław, Nadolska-Orczyk, Anna, Bińkowski, Jan, Kubiś, Jakub, Pogorzelska, Katarzyna, Pellny, Till K, Bolibok-Brągoszewska, Hanna, Idziak-Helmcke, Dominika, Rojek, Magdalena, Hosiawa-Barańska, Marta, Tsai, Hsin-Yuan, Cericola, Fabio, Edriss, Vahid, Andersen, Jeppe, Orabi, Jihad, Jensen, Jens, Jahoor, Ahmed, Janss, Luc, Jensen, Just, Lawrenson, Tom, Hayta, Sadiye, Smedley, Mark, Hinchliffe, Alison, Harwood, Wendy, Gimenez, Guillermo G., Barakate, Abdellah, Russell, Joanne, Stephens, Jennifer, Lampugnani, Edwin R., Doblin, Monika S., Fincher, Geoffrey B., Burton, Rachel A., Waugh, Robbie, Tucker, Matthew, Houston, Kelly, Jouanin, Aurelie, Leigh, Fiona, Wallington, Emma, Schaart, Jan, Smulders, Marinus J.M., Kała, Maciej, Kim, Sung-Yong, Brueggeman, Robert, Åhman, Inger, Zhu, Li-Hua, Bouchet, Sophie, Lemarié, Stéphane, Fugeray-Scarbel, Aline, Auzanneau, Jérôme, Charmet, Gilles, Ali, Ahmad, Mastrangelo, Anna Maria, De Vita, Pasquale, Laido, Giovanni, Marone, Daniale, Arshad, Muhammad, Naqvi, S.M. Sawlan, Sher, Hassan, Rasheed, Awais, Kazi, Abdul Mujeeb, Oakey, Helena, Bayer, Micha, Macaulay, Malcolm, Byrt, Caitlin, Gilliham, Matthew, Salt, David E, István, Monostori, Fruzsina, Szira, Alessandro, Tondelli, Tamás, Árendás, Krisztián, Gierczik, Luigi, Cattivelli, Gábor, Galiba, Attila, Vágújfalvi, Bakuma, Alla, Chebotar, Galyna, Filimonov, Vadim, Kyrylyuk, Tetyana, Chebotar, Sabina, Kristensen, Peter Skov, Andersen, Jeppe Reitan, Guzmán, Carlos, Schurr, Ulrich, Pieruschka, Roland, Neumann, Kerstin, Zhao, Yusheng, Dhanagond, Sidram, Reif, Jochen C., Graner, Andreas, Raymaekers, Dries, Taylor, Mike, Åhman, Inger M., Crespo-Herrera, Leonardo A., Kobiljski, Borislav, Mladenović, Gojko, Jestrović, Zorica, Jancsó, Mihály, Ács, Katalin, Bóna, Lajos, Purgel, Szandra, Békés, Ferenc, Schwember, A.R., Matus, I., Sharma, Dew Kumari, Rosenqvist, Eva, Carl-Otto-Ottosen, Torp, Anna Maria, Andersen, Sven Bode, El-Haddoury, Jamal, Henkrar, Fatima, Grana, Zahra, Nyiraguhirwa, Solange, Gyawali, Sanjaya, Verma, Ramesh, Ouabbou, Hassan, Ibriz, Mohammed, Iraqi, Driss, Forgeois, Paul, Grausgruber, Heinrich, Cuendet, Catherine, Dell’Avo, Franca, Koppel, Reine, Weyermann, Verena, Mikó, Péter, Vida, Gyula, Rakszegi, Marianna, Lafferty, Julia, Lorentz, Bruno, Longin, Carl Friedrich Horst, Megyeri, Mária, Kaskarbayev, Zheksenbay, Zhloba, Ludmila, Kiyas, Aldabergen, Kunanbayev, Kairat, Churkina, Galina, Zueva, Nataliya, Cattivelli, Luigi, Guerra, Davide, Tondelli, Alessandro, Bustos-Korts, D., Rizza, Fulvia, Bretani, Gianluca, Ferrandi, Chiara, Lazzari, Barbara, Rossini, Laura, Ozkan, Hakan, Molnar-Lang, Marta, van Eeuwijk, Fred, Dawson, Ian K, Fricano, Agostino, Azad, Md. Abul Kalam, Kamruzzaman, Md., Yasmine, Fahmina, Islam, Mirza Mofazzal, Jankuloski, Ljupcho, Christopher, John (Jack), Anzooman, Monia, Menzies, Neal, Dalal, Ram, Dang, Yash, Novák, Aliz, Boldizsár, Ákos, Gierczik, Krisztián, Mohamed, Ahres, Ádám, Éva, Bognár, László Kozma, Vágújfalvi, Attila, Båga, Monica, Chibbar, Ravindra, Galiba, Gábor, Gottwald, Sven, Bhandari, Dhaka Ram, Wang, Qing, Li, Bin, Römpp, Andreas, Friedt, Wolfgang, Spengler, Bernhard, Jäger, Katalin, Sáfrán, Eszter, Fábián, Attila, Deák, Csilla, Papp, István, Barnabás, Beáta, Mesterhazy, Akos, Varga, Mónika, Bartók, Tibor, Lehoczki-Krjak, Szabolcs, Toth, Beata, Miloud, Hafsi, Pál, Magda, Szalai, Gabriella, Janda, Tibor, Molnár, István, Lovegrove, Alison, Darkó, Éva, Farkas, András, Láng, László, Bedő, Zoltán, Doležel, Jaroslav, Molnár-Láng, Márta, Shewry, Peter, Tiryakioğlu, Murat, Sakallı, Abdulla, Alloudane, Rajae, El Mourabit, Nourdin, Barrijal, Said, Bengtsson, Therése, Manninen, Outi, Bulatova, Kulpash, Mazkirat, Shynar, Babissekova, Dilyara, Aytimbetova, Klara, Goncharova, Anastasiya, Lavrinenko, Yuriy, Bazaliy, Galyna, Cope, Jonathan, Norton, Gareth, George, Timothy, Newton, Adrian, Császár, Orsolya, Lajos, Károly, Szalai, Márk, Dziurka, Michał, Dashkevich, Svetlana, Utebayev, Maral, Chilimova, Irina, Babkenov, Adilchan, Akhmetova, Galiya, Chaimae, Senhaji, Khadija, Ahansal, Rabha, Abdelwahd, Allal, Douira, Driss, Iraqi, Gracz, Joanna, Karłowski, Wojciech, Kuczyński, Jakub, Tyczewska, Agata, Haberle, Jan, Svoboda, Pavel, Neumannová, Adéla, Kurešová, Gabriela, Raimanová, Ivana, Holková, Ludmila, Solařová, Eva, Bradáčová, Marta, Smutná, Pavlína, Horčička, P., Pospíšilová, V., Veškrna, O., Bížova, I., Chrpova, J., Sumíková, T., Ittu, Mariana, Marinciu, Cristina Mihaela, Voica, Maria, Banateanu, Cecilia, Mayer, Marianna, Karsai, Idikó, Mirosavljević, Milan, Momčilović, Vojislava, Mikić, Sanja, Trkulja, Dragana, Denčić, Srbislav, Jocković, Bojan, Aćin, Vladimir, Pržulj, Novo, Nakhoda, Babak, Mohammadi-Nejad, Ghasem, Mardi, Mohsen, Amini, Ashkboos, Tabatabaei, Mohammad-Taghi, Parsayev, Evgeniy, Filippova, Nadezhda, Permana, Hadian, Ishii, Kazuo, Murata, Kazumasa, Yamada, Tetsuya, Kanekatsu, Motoki, Prokesova, Lenka, Smutna, Pavlina, Holkova, Ludmila, Rukavitsina, Irina, Suleimenov, Mechlis, Yerpasheva, Dana, Shtefan, Galina, Ivanova, Galina, Ferderer, Ella, Kalapos, Balázs, Ahres, Mohamed, Székely, András, Gulyás, Zsolt, Kovács, Kristóf, Jones, Huw D, Dalmay, Tamás, Soltész, Alexandra, Trávníčková, Martina, Chrpová, Jana, Martinek, Petr, Hnilička, František, Żywicki, Marek, Twardowski, Tomasz, Végh, Balázs, Marček, Tihana, Wiśniewska, Halina, Kwiatek, Michał, Majka, Maciej, Gawłowska, Magdalena, Korbas, Marek, Danielewicz, Jakub, Belter, Jolanta, Bilgrami, Sayedeh Saba, Sichani, Jalal Sabouri, Zandi, Peiman, Zhunusbayeva, Zhazira, Omirbekova, Nargul, Zhussupova, Aizhan, Mursalieva, Valentina, Nam, Svetlana, Żur, Iwona, Surówka, Ewa, Dubas, Ewa, Nowicka, Anna, Krzewska, Monika, Hura, Tomasz, Rapacz, Marcin, Wójcik-Jagła, Magdalena, Ostrowska, Agnieszka, Malaga, Sabina, Janowiak, Franciszek, Pellny, Till, Patil, Archana, Freeman, Jackie, Plummer, Amy, Hess, Linde, Phillips, Andy, Berry, Simon, Mitchell, Rowan, Larkin, Philip, Tanner, Gregory, Colgrave, Michelle, Blundell, Malcolm, Howitt, Crispin, Morita, Naofumi, Van Hung, Pham, Cegiełko, Małgorzata, Kiecana, Irena, Mielniczuk, Elżbieta, Pastucha, Alina, Perkowski, Juliusz, Áy, Zoltán, Czirle, Csaba, Palágyi, András, Mihály, Róbert, Vermeulen, Philippe, Suman, Michele, Pierna, Juan Antonio Fernández, and Baeten, Vincent

Published
2017

46. Whole genome resequencing and phenotyping of MAGIC population for high resolution mapping of drought tolerance in chickpea

Author

Thudi, Mahendar, primary, Samineni, Srinivasan, additional, Li, Wenhao, additional, Boer, Martin P., additional, Roorkiwal, Manish, additional, Yang, Zuoquan, additional, Ladejobi, Funmi, additional, Zheng, Chaozhi, additional, Chitikineni, Annapurna, additional, Nayak, Sourav, additional, He, Zhang, additional, Valluri, Vinod, additional, Bajaj, Prasad, additional, Khan, Aamir W., additional, Gaur, Pooran M., additional, van Eeuwijk, Fred, additional, Mott, Richard, additional, Xin, Liu, additional, and Varshney, Rajeev K., additional

Published
2023
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47. Optimizing Sparse Testing for Genomic Prediction of Plant Breeding Crops

Author

Montesinos-López, Osval A., primary, Saint Pierre, Carolina, additional, Gezan, Salvador A., additional, Bentley, Alison R., additional, Mosqueda-González, Brandon A., additional, Montesinos-López, Abelardo, additional, van Eeuwijk, Fred, additional, Beyene, Yoseph, additional, Gowda, Manje, additional, Gardner, Keith, additional, Gerard, Guillermo S., additional, Crespo-Herrera, Leonardo, additional, and Crossa, José, additional

Published
2023
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49. Genetic Mapping of Genotype-by-Ploidy Effects in Arabidopsis thaliana

Author

Wijnen, Cris L., Becker, Frank F.M., Okkersen, Andries A., de Snoo, C.B., Boer, Martin P., van Eeuwijk, Fred A., Wijnker, Erik, Keurentjes, Joost J.B., Wijnen, Cris L., Becker, Frank F.M., Okkersen, Andries A., de Snoo, C.B., Boer, Martin P., van Eeuwijk, Fred A., Wijnker, Erik, and Keurentjes, Joost J.B.

Abstract

Plants can express different phenotypic responses following polyploidization, but ploidy-dependent phenotypic variation has so far not been assigned to specific genetic factors. To map such effects, segregating populations at different ploidy levels are required. The availability of an efficient haploid inducer line in Arabidopsis thaliana allows for the rapid development of large populations of segregating haploid offspring. Because Arabidopsis haploids can be self-fertilised to give rise to homozygous doubled haploids, the same genotypes can be phenotyped at both the haploid and diploid ploidy level. Here, we compared the phenotypes of recombinant haploid and diploid offspring derived from a cross between two late flowering accessions to map genotype × ploidy (G × P) interactions. Ploidy-specific quantitative trait loci (QTLs) were detected at both ploidy levels. This implies that mapping power will increase when phenotypic measurements of monoploids are included in QTL analyses. A multi-trait analysis further revealed pleiotropic effects for a number of the ploidy-specific QTLs as well as opposite effects at different ploidy levels for general QTLs. Taken together, we provide evidence of genetic variation between different Arabidopsis accessions being causal for dissimilarities in phenotypic responses to altered ploidy levels, revealing a G × P effect. Additionally, by investigating a population derived from late flowering accessions, we revealed a major vernalisation-specific QTL for variation in flowering time, countering the historical bias of research in early flowering accessions.

Published
2023

50. Pairwise ratio-based differential abundance analysis of infant microbiome 16S sequencing data

Author

Mildau, Kevin, Te Beest, Dennis E., Engel, Bas, Gort, Gerrit, Lambert, Jolanda, Swinkels, Sophie H.N., van Eeuwijk, Fred A., Mildau, Kevin, Te Beest, Dennis E., Engel, Bas, Gort, Gerrit, Lambert, Jolanda, Swinkels, Sophie H.N., and van Eeuwijk, Fred A.

Abstract

Differential abundance analysis of infant 16S microbial sequencing data is complicated by challenging data properties, including high sparsity, extreme dispersion and the relative nature of the information contained within the data. In this study, we propose a pairwise ratio analysis that uses the compositional data analysis principle of subcompositional coherence and merges it with a beta-binomial regression model. The resulting method provides a flexible and easily interpretable approach to infant 16S sequencing data differential abundance analysis that does not require zero imputation. We evaluate the proposed method using infant 16S data from clinical trials and demonstrate that the proposed method has the power to detect differences, and demonstrate how its results can be used to gain insights. We further evaluate the method using data-inspired simulations and compare its power against related methods. Our results indicate that power is high for pairwise differential abundance analysis of taxon pairs that have a large abundance. In contrast, results for sparse taxon pairs show a decrease in power and substantial variability in method performance. While our method shows promising performance on well-measured subcompositions, we advise strong filtering steps in order to avoid excessive numbers of underpowered comparisons in practical applications.

Published
2023

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