From February 19-24, NextGen cassava partners and collaborators will be in Dar es Salaam, Tanzania for the 6th Annual Meeting of NextGen Cassava. This will serve both to share results from the fifth year of the project and to set goals and targets for the next five years.
Day 2 sessions continued with presentations from team leaders on roadmaps for the next phase of the project.
May 13, 2016, Ithaca NY: Olumide Alabi, NextGen Cassava PhD student with the International Institute for Tropical Agriculture (IITA) in Ibadan, Nigeria, recently visited Jean-Luc Jannink’s laboratory group at Cornell University for training on prediction modeling. Olumide reports on his visit here:
Date: 8th March to 6th April, 2016
Location: Dr. Jean-Luc Jannink’s Research group, Plant breeding and genetics department, Bradfield Hall, Cornell University, Ithaca, NY
Practical skill acquisition in genomic prediction modeling forms the basis of my brief visit to Cornell. I got handy explanation on prediction modeling processes as they apply to past and present genomic selection cycles as being implemented in IITA-NextGen Cassava Breeding Project.
Three major objective activities included:
Marnin Wolfe, postdoctoral associate at Cornell, was able to guide me from the known in Genomic predictions in general to the unknown with practical step-by-step activities using the IITA-NextGen cassava dataset. I received concrete training on the use of single step model and information on the limitation to it, as it could be computationally intensive with large datasets. Also, I was trained on two-step model, formation of the kinship matrix using the “A.mat” function, model.matrix, kin.blup phenotype dataset curation for prediction modeling, G-BLUP model, RR-BLUP model, the inclusion of multiple random effects in prediction modeling using the EMMREML model and general theories and coding syntaxes associated with these above-mentioned models. One of the newest concepts to me in all was when I was guided through the IITA-Cycle 3 prediction, de-regressed BLUPs, especially with the theory and concept of reliability estimation, PEV, and how these influence the accuracy of our predictions. Marnin did well in guiding me through these concepts both theoretically and practically, coupled with exercises, reading assignments, brainstorming sessions. To wrap it up, I was guided through the entire IITA-GS Cycle 3 prediction model; the code was provided to me by Marnin with detailed explanations.
Estimating the “Expected Gain” in GS application in cassava is not a straight-forward thing, as the selection of the parents is based on selection index built from the GEBVs of traits and individuals. In the gain estimation using the conventional breeder’s equation, there is a little adjustment in GS concept, which is basically the selection accuracy factor in the model. To obtain this, we had to correlate the S.I_GEBVs (Predicted) of lines and the S.I_BLUPs (Observed). In my brainstorming with Marnin, we came up with the concept highlighted below:
rA = corr(S.I_GEBVs, S.I_BLUPs)
Where S.I_GEBVs = wtGEBVT1 + wtGEBVT2 + wtGEBVT2…+ wtGEBVTN
wt = the economic weight used for trait T in the selection index model
S.I_BLUPs = wtBLUPT1 + wtBLUPT2 + wtBLUPT2…+ wtBLUPTN
Hence, the rA could be appropriately fitted in the breeder’s equation for the expected gain estimation.
Dunia (Research Associate) guided me through GWA-studies with the use of datasets on plant type and the associated SNP data. For better handling of the categorical nature of the Plant Type trait (compact_1, open_2, umbrella_3 and cylinderica_4), Marnin suggested the classification of the trait as binomial scores (E.g. Compact: 0_absent, 1_present), hence coding the scores as a trait per time. It was to enable us to fit a GLIMMIX model with the flexibility of a link function for variance components.
Skills acquired
I can practically implement Genomic prediction with more confidence on availability of appropriate dataset. I got a detailed understanding of the past IITA GS Cycle selection and a first-hand understanding of the present Cycle 3 predictions (Thanks to Marnin). I got a better clue on several aspects in statistical modeling to be included in my thesis report, especially the expected gain estimation concept and some genomic prediction steps.
Acknowledgement
My appreciation goes to Dr. Jean-Luc Jannink for the time and audience given to me while I was in Ithaca; the meeting for updates in his office and facilitation of my visit; amidst other.
Many thanks to Marnin for devoting much time in coaching me. In fact, he was my tutor all through the period I was in Ithaca. Dunia did a great job as well as my NextGen graduate student colleagues, Ugo, Uche, and Alfred. Alex of BTI is appreciated for his kind gestures all through my time in Ithaca. I would not but mention the logistics from Dan’s end, Karen and the team in IP-CALS office.
I want to thank my supervisors in IITA, Drs. Peter Kulakow and Ismail Rabbi, for granting the home-support needed to visit Cornell this period. Thanks to Dr. Chiedozie Egesi and Dr. Hale Tufan. My final appreciation goes to the Cornell-NextGen Cassava project for the full support. My regards to all.
Olumide during his training at Cornell and with Marnin Wolfe, bottom left
Dr. Chiedozie Egesi, NextGen Cassava project manager
During the recent annual AAAS (American Association for the Advancement of Science) meeting in Washington, D.C., The Economist interviewed Dr. Chiedozie Egesi, NextGen Cassava project manager. Chiedozie spoke about the potential of the NextGen Cassava project to improve the cassava crop and address challenges such as disease, low yield, and vitamin deficiency.
See the full story, “Cassava-nova,” in the online edition of The Economist.
Thanks to Myriam Vitovec for highlighting the work of NEXTGEN Cassava’s Gender-Responsive Cassava Breeding Initiative in a recent post for the CGIAR Research Program on Roots, Tubers and Bananas (RTB):
There have been many cases in which improved crop varieties released by national agricultural research and extension systems (NARES) were poorly received by farmers because they lacked the flavor or another trait that farmers or consumers wanted. To ensure high adoption rates for the varieties they develop, breeding programs usually survey farmers about the traits they prefer, but all too often, those researchers rely disproportionately on the opinions of men. However, specialization of household roles means that women and men have different knowledge about and preferences for varietal traits. Women are usually responsible for food preparation and small scale processing, but their knowledge is rarely used for the varietal development process.
As RTB works to unlock the genetic potential of roots, tubers and bananas for improving food security, nutrition and incomes, it is also supporting field research to document gender-disaggregated trait preferences. The aim is to ensure that the improved RTB varieties developed in the coming years will have as widespread and gender-equitable an impact as possible.
“Next-generation breeding is helping breeders to speed up the process of developing new RTB varieties, but if we overlook the traits that farmers want, if we don’t have the right targets, then next-generation breeding could simply get us to the wrong place faster,” observed RTB Program Director Graham Thiele.
An example of this problem was discovered by CIP gender researcher Netsayi Moris Mudege in a project promoting the cultivation and consumption of nutritious orange-fleshed sweetpotato varieties in Malawi. Farmer consultations had resulted in the release of a variety that produces large roots, which men prefer because they fetch a good market price. However, most women prefer another variety that wasn’t released, because sweetpotato leaves are an important part of the local diet and the lobe-shaped leaves of that variety are better for cooking.
Cornell PhD student Paula Iragaba (fifth from the left) and her colleague, Winifred Candiru, (first from the left) together with adult women cassava farmers after a focus group discussion in the Arua district.
To avoid such oversights, RTB supported various initiatives in 2014 to get the trait preferences of both men and women into breeding pipelines. For example, Mudege and CIP potato breeder Asrat Amele produced an FAQ sheet on integrating gender into the participatory varietal selection of potato in Ethiopia and organized a training workshop in Addis Ababa for 20 representatives of CIP’s main partners there.
RTB and NEXTGEN Cassava have co-funded the collection of gender-disaggregated trait preference data for cassava in Nigeria, using a methodology developed by NEXTGEN Cassava Project Manager Hale Tufan and IITA Gender Focal Point Holger Kirscht. Tufan and Kirscht coordinated research in 2014 by interdisciplinary teams from IITA and NRCRI in eight farming communities in southeast and southwest Nigeria. The teams interviewed 10 women and 10 men of diverse ages and marital status in each village and conducted sex-disaggregated focus groups with 20-30 participants in most of them.
“We’re trying to bring diverse voices, including those of women and youth, into the breeding process. Because we want to tailor breeding programs for the diversity of users rather than opting for one-size-fits-all solutions,” said Tufan.
Tufan explained that traits mentioned by the farmers range from agronomic advantages such as good yield to things like ‘drawing’ when cooked, which is important for making the traditional cassava dish gari. The goal is to get those most difficult quality traits into selection indices, to translate them into standardized, measureable breeding variables, and to link them to genetic markers for genomic selection. Cassava breeders Peter Kulakow (IITA) and Chiedozie Egesi (NRCRI) have helped to tailor the data collection tools in order to ensure that they yield data that will be useful for breeding.
Paula holding two cassava roots during a visit to cassava farmer (in the white T-shirt) in the Apac district, one of her study sites.
RTB and NEXTGEN Cassava are also co-funding Cornell PhD student Paula Iragaba, who returned to her native Uganda in 2015 to conduct gender-differentiated field research on cassava trait preferences.
Iragaba is working closely with Kirscht, CIRAD postharvest expert Dominique Dufour, and breeders at the National Crops Resources Research Institute (NaCRRI) to help them incorporate the preferred cassava traits that she documents into their cassava improvement program.
“This is really exciting because there is an opportunity for Paula to provide information and set up a model on how to capture and integrate gendered trait preferences into breeding programs,” said Tufan.
Paula and a farmer picking cassava leaf samples in one of the farmer’s cassava gardens to be used for studying genetic diversity of cassava varieties.
Iragaba had an opportunity to explain her research to Bill Gates in October 2014, when Gates visited Cornell’s campus to learn about the work of NEXTGEN Cassava, which the Bill & Melinda Gates Foundation funds. Iragaba was one of several graduate students who gave short presentations about their research and answered questions from Gates.
“I talked about how women play a vital role in cassava production and processing in Uganda, and how their role needs to be considered by breeding programs in order to improve the adoption rates of new varieties,” Iragaba said. “I’m sure that if gender issues are taken into consideration by our breeding programs, we are going to have tremendous improvements in adoption rates.”
Find this and other interesting articles in the RTB Annual Report 2014
Ismail Kayondo, Dunia Pino del Carpio, and Olumide Alabi at Aarhus University
NEXTGEN Cassava PhD students Olumide Alabi (IITA) and Ismail Kayondo (NaCRRI) and postdoctoral fellow Dunia Pino del Carpio (Cornell) recently attended a week-long course at Aarhus University, Denmark, titled “Statistical Models for Genomic Predictions in Animals and Plants.” Below is Olumide Alabi’s report on the course:
Day 1: Background on genomic selection; classical MAS; basic prediction using GWAS; environmental effects
The course started in the morning with a theoretical background and introduction to the topics stated above. A hands-on exercise on the optimization of breeding plans using phenotypic selection and genomic selection was simulated with varying population sizes and marker densities. In the afternoon, a real dataset was provided to work with, from which we individually ran a simple GWAS model using R and later fitted prediction models from the GWAS result after we had identified significant SNP effects. The last exercise of the day was fitting the GWAS model with environmental effects and comparing prediction models using different cross validation schemes.
Lessons: Making conclusions from computing predictions from larger sets of SNPs by different thresholds for p-values in the modeling for predictions using the GWAS results obtained
Day 2: Whole-genome SNP regression model; introduction to single-trait and multi-trait GBLUP; cross‐validation systems
With preliminary theoretical discussions on each of the topics listed above, much time was devoted to hands-on exercises on them. The first exercise of the day was the Random Regression with R-BGLR. It was noted that BGLR does not accept missing data, hence, a replacement with the mean genotype (2p: allele frequency). A DMU R package developed by the Danish group was installed, and we used this to run a multi-trait WGRR and GBLUP. Finally, an exercise cross-validation of varying k-fold schemes was carried out.
Day 3: Making, scaling and interpreting Genomic Relationships matrices; single step GBLUP and scaling G and A
I initially found it difficult to comprehend some aspects of the day 3 topics and exercises; however, the given publication (VanRaden, 2008 and Legarra et al, 2015) and additional explanation by the instructor and interaction with colleagues in the class helped somehow. Time was dedicated to the single step approach for genomic evaluation, compatibility of G and A matrix and the single-step in Rdmu using the pedigree file.
Day 4: Bayesian shrinkage models; Bayesian mixture/variable selection models
There was theoretical explanation on posterior distribution and prior distribution information of parameters used for the modeling. Exercises on Mixture model approach were practiced, comparison of different model approach for GWAS and genomic prediction was part of the exercises for the day (LASSO, Bayes A, Bayes B.) using the BGLR and the Rdmu packages. My personal motivation is to read more on Bayesian statistics.
Day 5: Relationships in data; genomic feature models; usual SNP QC
One of the fascinating lessons of the day for me was the Genetic Feature model using the GBLUP models and the Bayesian approach. You can either use a GBLUP model, building G-matrices for SNPs from one chromosome versus the other chromosomes, or a Bayesian model that directly models 19 different variances for the SNPs in each chromosome.
General comments
Olumide Alabi
Acknowledgement
I acknowledge the NEXTGEN program management for the capacity-building investment by giving us the opportunity of attending courses that are of relevance to our present research efforts and preparing us for future research endeavours.
Ugandan and Nigerian scientists attend a training at CIAT in Cali, Colombia
In the framework of the RTB-ENDURE project and in collaboration with the NEXTGEN Cassava Project, Ugandan and Nigerian colleagues from the Ugandan National Agricultural Research Organization, IITA, and IIRR are now in Colombia to attend a training organized by CIAT for strengthening the capacities to assess the postharvest physiological deterioration of cassava and the feasibility of adopting technologies for extending the shelf-life of the roots in Uganda.
Thanks to Anja Timm and Craig Cramer for a great post about the new greenhouses on Cornell’s campus that house the NEXTGEN Cassava Project’s cassava plants!
Cassava plants in the new greenhouse.
Photo courtesy of Anja Timm.
From Anja Timm (ait4@cornell.edu), Cornell University Agricultural Experiment Station (CUAES):
“Cornell researchers now have a new, state-of-the-art greenhouse facility available to house tall crops important to New York State growers, such as corn, trellised peas, alfalfa and biofuel grasses.
Part of the Guterman Greenhouse Range east of the School of Veterinary Medicine, the 8,000-square-foot facility is also home to research projects with international impact, such as the cassava breeding project.
Precision environmental controls, 16-foot double-pane glass side walls, and shade- and insulation-curtains in all eight compartments create a highly energy-efficient research environment…”
Read Anja’s full post.
Photo by Dan Chung for the Guardian
A recent article in The Guardian by Jean-Luc Jannink discusses NEXTGEN Cassava’s objectives and its progress as the project enters its third year. Jean-Luc outlines the importance of cassava production in the developing world, the challenges that cassava farmers face, and the scientific techniques NEXTGEN Cassava is employing to unlock cassava’s true potential in Africa.
Jean-Luc Jannink is a Research Plant Geneticist with the United States Department of Agriculture, Agricultural Research Service and an Adjunct Associate Professor in the Department of Plant Breeding and Genetics at Cornell University. He leads NextGen Cassava’s Genomic Selection objective.
Read the full article.
Great work from our partners at the National Root Crops Research Institute! As an Assistant Director and head of the cassava breeding team at the National Root Crops Research Institute, Umudike, Chiedozie Egesi has led efforts at developing and releasing to farmers improved varieties of cassava including pro-vitamin A cassava. His research activities involve the use of cross-cutting biotechnology tools in the genetic improvement of cassava including transgenic technologies. Read full article. Continue reading
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