A general method for the imputation of genomic data in crop species
Lead Research Organisation:
University of Edinburgh
Department Name: The Roslin Institute
Abstract
The project will develop and test a toolkit to impute dense genomic information in crop breeding populations. Dense genomic information allows geneticists to unravel the genetics of traits using genome wide association studies, and breeders to speed up genetic improvement using genomic selection and genomics assisted breeding. These methods are most powerful when the density of genomic information is very high and the numbers of individuals genotyped are very large but the cost of collecting genotype information to build such datasets is prohibitive. A flexible and effective imputation toolkit will make it possible to build such datasets cheaply using imputed data.
In a genetics and genomics context, imputation is the prediction of an unknown genotype in one individual from the known genotypes of other individuals (to give a trivial example, if individuals 'X' and 'Y' are known to have genotypes AA and CC respectively, then their offspring 'Z' is imputed to be AC). The value of imputation is that when combined with high-density genotype information from a few individuals, high-density information can be imputed for many individuals that have been genotyped at low-density, which vastly reduces the costs of datasets of dense genomic information.
The project has three parts:-
1. We will develop heuristic imputation algorithms that exploit the information in crop pedigrees, that correct pedigree errors and that generate approximate physical maps of the genome. Existing heuristic imputation algorithms, which were designed for livestock, do not work on crops because crop pedigrees are more complex than livestock pedigrees and crop data are of many different types, whereas livestock data is fairly homogeneous in type.
2. We will develop probabilistic algorithms that integrate with the heuristic algorithms to produce a hybrid imputation algorithm for crops that combines the speed of heuristic algorithms with the flexibility and robustness of probabilistic algorithms. Existing probabilistic algorithms are too slow and require too much memory to work well with crop data.
3. We will package the software apply it to a number of specific case datasets and breeding programs in KWS, which is one of the worlds four leading crop-breeding companies.
In a genetics and genomics context, imputation is the prediction of an unknown genotype in one individual from the known genotypes of other individuals (to give a trivial example, if individuals 'X' and 'Y' are known to have genotypes AA and CC respectively, then their offspring 'Z' is imputed to be AC). The value of imputation is that when combined with high-density genotype information from a few individuals, high-density information can be imputed for many individuals that have been genotyped at low-density, which vastly reduces the costs of datasets of dense genomic information.
The project has three parts:-
1. We will develop heuristic imputation algorithms that exploit the information in crop pedigrees, that correct pedigree errors and that generate approximate physical maps of the genome. Existing heuristic imputation algorithms, which were designed for livestock, do not work on crops because crop pedigrees are more complex than livestock pedigrees and crop data are of many different types, whereas livestock data is fairly homogeneous in type.
2. We will develop probabilistic algorithms that integrate with the heuristic algorithms to produce a hybrid imputation algorithm for crops that combines the speed of heuristic algorithms with the flexibility and robustness of probabilistic algorithms. Existing probabilistic algorithms are too slow and require too much memory to work well with crop data.
3. We will package the software apply it to a number of specific case datasets and breeding programs in KWS, which is one of the worlds four leading crop-breeding companies.
Technical Summary
The project will develop and test a toolkit to impute dense genomic information in diploid crop breeding populations. Dense genomic information allows geneticists to unravel the genetics of traits using genome wide association studies, and breeders to speed up genetic improvement using genomic selection and genomics assisted breeding.
The project will develop a hybrid imputation algorithm that combines heuristics to exploit the information in crop pedigrees with improvements to an existing HMM-based imputation algorithm, PlantImpute.
1. We will develop heuristic imputation algorithms that exploit the information in crop pedigrees, that correct pedigree errors and that generate approximate physical maps of the genome.
2. We will develop HMM algorithms that integrate with the heuristics to produce a hybrid imputation algorithm for crops that combines the speed of heuristic algorithms with the flexibility and robustness of HMM models.
3. We will package the software apply it to a number of specific case study datasets and breeding programs in KWS.
The developed algorithms will be implemented in a single stand-alone software package for a range of OS environments that can be run either locally or remotely.
The method will be tested on a range of species and scenarios. It will represent the first generic imputation method developed specifically for crops.
We will evaluate the algorithm on a range of real data sets from the KWS breeding programs (wheat, maize, sugarbeet) with a range of designs and genotyping technologies.
The project will develop a hybrid imputation algorithm that combines heuristics to exploit the information in crop pedigrees with improvements to an existing HMM-based imputation algorithm, PlantImpute.
1. We will develop heuristic imputation algorithms that exploit the information in crop pedigrees, that correct pedigree errors and that generate approximate physical maps of the genome.
2. We will develop HMM algorithms that integrate with the heuristics to produce a hybrid imputation algorithm for crops that combines the speed of heuristic algorithms with the flexibility and robustness of HMM models.
3. We will package the software apply it to a number of specific case study datasets and breeding programs in KWS.
The developed algorithms will be implemented in a single stand-alone software package for a range of OS environments that can be run either locally or remotely.
The method will be tested on a range of species and scenarios. It will represent the first generic imputation method developed specifically for crops.
We will evaluate the algorithm on a range of real data sets from the KWS breeding programs (wheat, maize, sugarbeet) with a range of designs and genotyping technologies.
Planned Impact
Despite the dramatic reduction in the costs of high density SNP genotyping platforms and in the cost per nucleotide of NGS based sequencing and re-sequencing application in crop plants, imputation has a major role to play in the development of cost-effective genotyping and sequencing strategies and in error correction.
This project will develop a practical tool enabling genotype imputation in a wide variety of crops and a wide variety of scenarios opening up the potential for generating significant volumes of genomic information at low cost. It will develop fundamental scientific knowledge primarily in bioinformatics applied to genomics. The outcomes will be beneficial for:
(i) The academic community. Scientifically, the project constitutes novel imputation and map building methods that will be suited to a wide variety of scenarios in crops. This will enable the generation of large volumes of genomic information at low cost and will have the flexibility to handle different types of genomic information. This will enable larger and hence more powerful experiments than currently feasible, and greater ability to combine data obtained with old technologies with those with new technologies. The direct application of the method will benefit researchers in plant genetics. Methodological developments will benefit human, animal, and evolutionary geneticists concerned with imputation. Major research efforts in crops are continuing to develop effective genotyping and genome reduction technologies (e.g. SNP or GBS platforms). The imputation algorithm developed in this proposal will complement and add value to these efforts.
(ii) Breeding companies and organisations, and levy boards. As indicated by the attached letters of support the crop breeding industry, both in the UK and internationally, will benefit directly. In particular the developed methods will be key to the cost-effective implementation of Genomic Selection based breeding strategies in UK breeding programmes and help sustain the long-term viability of the UK breeding industry.
(iii) Commercial sequence and genotype providers. Companies providing SNP, GBS, or sequence data will be able to use imputation to add value to the data that they generate.
(iv) Society. All members of society who work to improve or depend upon the competitiveness and sustainability of agriculture will benefit from the downstream practical applications outlined above. The application of the algorithm by breeding organisations will lead to faster and more sustainable genetic progress, leading to healthier food, and food production that is more resource efficient and affordable. Increased efficiencies in agriculture have direct societal benefits in greater food security with less environmental impact.
(v) UK science base. The proposed algorithm will provide a platform for increased R&D capabilities in the area of imputation and plant breeding and genetics in the UK, maintaining its scientific reputation and associated institutions, with increased capability for sustainable agricultural production. By underpinning the cost effectiveness of marker based crop genetic experimental studies it will help ensure that UK funding agencies obtain maximum value from their research investment and that the studies they support will have optimised power.
(vi) Training. The proposed research will be embedded within training courses that the PI is regularly invited to give, and the post-doc working on the project will have the opportunity to be trained at a world-class institute in a cutting edge area of research.
(vii) Policy. Genomic data is expensive, but the research and practical benefits are potentially large. Therefore much investment will be made in genomic data in the crops sector in the coming years. To maximise efficiency of investment a co-ordinated national and perhaps international effort may be needed. The method to be developed in this proposal could enhance and underpin such an effort.
This project will develop a practical tool enabling genotype imputation in a wide variety of crops and a wide variety of scenarios opening up the potential for generating significant volumes of genomic information at low cost. It will develop fundamental scientific knowledge primarily in bioinformatics applied to genomics. The outcomes will be beneficial for:
(i) The academic community. Scientifically, the project constitutes novel imputation and map building methods that will be suited to a wide variety of scenarios in crops. This will enable the generation of large volumes of genomic information at low cost and will have the flexibility to handle different types of genomic information. This will enable larger and hence more powerful experiments than currently feasible, and greater ability to combine data obtained with old technologies with those with new technologies. The direct application of the method will benefit researchers in plant genetics. Methodological developments will benefit human, animal, and evolutionary geneticists concerned with imputation. Major research efforts in crops are continuing to develop effective genotyping and genome reduction technologies (e.g. SNP or GBS platforms). The imputation algorithm developed in this proposal will complement and add value to these efforts.
(ii) Breeding companies and organisations, and levy boards. As indicated by the attached letters of support the crop breeding industry, both in the UK and internationally, will benefit directly. In particular the developed methods will be key to the cost-effective implementation of Genomic Selection based breeding strategies in UK breeding programmes and help sustain the long-term viability of the UK breeding industry.
(iii) Commercial sequence and genotype providers. Companies providing SNP, GBS, or sequence data will be able to use imputation to add value to the data that they generate.
(iv) Society. All members of society who work to improve or depend upon the competitiveness and sustainability of agriculture will benefit from the downstream practical applications outlined above. The application of the algorithm by breeding organisations will lead to faster and more sustainable genetic progress, leading to healthier food, and food production that is more resource efficient and affordable. Increased efficiencies in agriculture have direct societal benefits in greater food security with less environmental impact.
(v) UK science base. The proposed algorithm will provide a platform for increased R&D capabilities in the area of imputation and plant breeding and genetics in the UK, maintaining its scientific reputation and associated institutions, with increased capability for sustainable agricultural production. By underpinning the cost effectiveness of marker based crop genetic experimental studies it will help ensure that UK funding agencies obtain maximum value from their research investment and that the studies they support will have optimised power.
(vi) Training. The proposed research will be embedded within training courses that the PI is regularly invited to give, and the post-doc working on the project will have the opportunity to be trained at a world-class institute in a cutting edge area of research.
(vii) Policy. Genomic data is expensive, but the research and practical benefits are potentially large. Therefore much investment will be made in genomic data in the crops sector in the coming years. To maximise efficiency of investment a co-ordinated national and perhaps international effort may be needed. The method to be developed in this proposal could enhance and underpin such an effort.
People |
ORCID iD |
John Hickey (Principal Investigator) |
Publications

Antolín R
(2017)
A hybrid method for the imputation of genomic data in livestock populations.
in Genetics, selection, evolution : GSE

Bancic J
(2024)
Plant breeding simulations with AlphaSimR
in Crop Science

Bancic J
(2023)
Plant breeding simulations with AlphaSimR

Baumdicker F
(2022)
Efficient ancestry and mutation simulation with msprime 1.0.
in Genetics

Dawson IK
(2019)
The role of genetics in mainstreaming the production of new and orphan crops to diversify food systems and support human nutrition.
in The New phytologist

De Jong G
(2023)
Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs
in Crop Science

Gonen S
(2018)
A heuristic method for fast and accurate phasing and imputation of single-nucleotide polymorphism data in bi-parental plant populations.
in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik


Gonen S
(2021)
Phasing and imputation of single nucleotide polymorphism data of missing parents of biparental plant populations.
in Crop science
Description | Specific algorithms have been developed for imputation in crop populations that are commonly obtained by crossings between two or more parental lines. These have been implemented in the software AlphaPlantImpute, that has been made publicly available. Next, a HMM method has been developed for imputation in crop diversity panels, from which the members are not necessarily directly family related. This method was successfully tested in three real crop data sets (Maize, Wheat and Sugar beet). |
Exploitation Route | The software resulting from this project is expected to be broadly used by breeding companies to save genotyping resources, most notably our strategic partner KWS. A final software version that implements all imputation methods developed in this project is scheduled to be delivered at the end of this project (Sep 2020). |
Sectors | Agriculture Food and Drink Environment |
URL | https://alphagenes.roslin.ed.ac.uk/wp/software/alphaplantimpute/ |
Description | The software resulting from this project enables cost-effective genotyping strategies in plant breeding and is of great economic value for breeding companies. It is expected to be broadly used by companies to save genotyping resources, most notably our strategic partner KWS. |
First Year Of Impact | 2018 |
Sector | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software) |
Impact Types | Economic |
Description | Enabling and optimising utilisation of potato gene-bank resources |
Amount | £1,000 (GBP) |
Funding ID | WT iTPA PIII-012 (209710/Z/17/Z) |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 01/2020 |
End | 01/2020 |
Description | GenoForage: Genomic breeding of forages |
Amount | £294,000 (GBP) |
Organisation | Lantmännen |
Sector | Academic/University |
Country | Sweden |
Start | 03/2022 |
End | 03/2024 |
Description | Lunbanga, Nelson - Train@ED Fellowship |
Amount | £72,690 (GBP) |
Funding ID | 801215 |
Organisation | European Commission H2020 |
Sector | Public |
Country | Belgium |
Start | 04/2020 |
End | 04/2023 |
Description | Next generation Sitka spruce breeding informed by predictive and comparative genomics |
Amount | £175,351 (GBP) |
Funding ID | BB/P018653/1 |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2017 |
End | 09/2022 |
Description | Oliveira, Thiago - Train@ED Fellowship |
Amount | £72,690 (GBP) |
Funding ID | 801215 |
Organisation | European Commission H2020 |
Sector | Public |
Country | Belgium |
Start | 04/2020 |
End | 04/2023 |
Description | Oliveira, Thiago - Train@ED Fellowship |
Amount | £72,690 (GBP) |
Funding ID | 80215 |
Organisation | Marie Sklodowska-Curie Actions |
Sector | Charity/Non Profit |
Country | Global |
Start | 05/2020 |
End | 05/2023 |
Description | On-line course on in-silico modelling of breeding programmes (DataLab) |
Amount | £105,000 (GBP) |
Organisation | The Datalab |
Sector | Charity/Non Profit |
Start | 01/2020 |
End | 03/2021 |
Description | Optimising selection and maintenance of diversity in plant breeding |
Amount | £18,045 (GBP) |
Funding ID | BBSRC IAA PIII-036 (main award R45393) |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 06/2019 |
End | 11/2019 |
Description | Temporal and genomic analysis of non-additive genetic variance |
Amount | £20,676 (GBP) |
Funding ID | BBSRC IAA PIII086 |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 07/2021 |
End | 11/2021 |
Description | Flexible optimal contribution selection with KWS |
Organisation | KWS Group |
Country | Germany |
Sector | Private |
PI Contribution | Developing flexible extensions to the optimal contribution selection method and software implementation |
Collaborator Contribution | Supplying data and species knowledge |
Impact | Still active |
Start Year | 2019 |
Description | NextGen sequencing in crop breeding |
Organisation | KWS Group |
Country | Germany |
Sector | Private |
PI Contribution | Development of imputation algorithm |
Collaborator Contribution | Making available test data |
Impact | Imputation algorithm |
Start Year | 2017 |
Title | AlphaAssign - a parentage assignment algorithm that works with SNP array and sequencing data |
Description | AlphaAssign is a parentage assignment algorithm that works with SNP array and GBS data - https://onlinelibrary.wiley.com/doi/full/10.1111/jbg.12370 |
Type Of Technology | Software |
Year Produced | 2018 |
Open Source License? | Yes |
Impact | AlphaAssign has been used by researchers and practitioners in breeding and genetics |
URL | https://github.com/AlphaGenes/AlphaAssign |
Title | AlphaFamImpute |
Description | AlphaFamImpute is a genotype calling, phasing, and imputation software package for large full-sib families in diploid plants and animals which supports individuals genotyped with SNP array or GBS data. |
Type Of Technology | Software |
Year Produced | 2019 |
Impact | The software package is currently used by our industrial partners in crop breeding |
URL | https://alphagenes.roslin.ed.ac.uk/wp/software-2/alphafamimpute/ |
Title | AlphaImpute |
Description | Imputation can cost-effectively generate high-density genotypes of many individuals. Typical genotyping strategies involve genotyping a small number of individuals with expensive high-density marker panels, and a large number of individuals with cheaper low-density panels. Imputation is the used to infer the un-typed high-density markers in the individuals genotyped at low-density. AlphaImpute is a flexible tool that imputes genotypes and alleles accurately and quickly for datasets with large pedigrees and large numbers of genotyped markers. It combines basic rules of Mendelian inheritance, probabilistic inferences of genotypes, phasing of long stretches of haplotypes, and imputation of genotypes from a haplotype library. AlphaImpute consists of a single program however it calls both AlphaPhase1.1 and GeneProbForAlphaImpute. All information on the model of analysis, input files and their layout, is specified in a single parameter file. |
Type Of Technology | Software |
Year Produced | 2016 |
Impact | The AlphaImpute package is freely available in AlphSuite and includes supporting manual, and access to technical support with the aim of benefiting the academic research community in animal breeding. The program has been downloaded over 200 times in recent years, attracting users from a number of different academic institutions internationally. AlphaImpute has supported collaboration with a number of industrial partner. One such example is the Innovate UK funded project in collaboration with PIC. This project has accelerated the rate of genetic gain by 35% in pigs, enabled by AlphaImpute. Major emphasis has been put on making AlphaImpute more computationally effective and accessible to small animal breeding operation and/or academic institutions, we have succeeded in improved the computing time by 75%. |
URL | http://www.alphagenes.roslin.ed.ac.uk/alphasuite-softwares/ |
Title | AlphaImpute2 - Fast and accurate pedigree and population based imputation |
Description | AlphaImpute2 is a phasing and imputation algorithm for massive livestock populations. The method uses a approximate version of multi-locus iterative peeling for pedigree based imputation, and a novel imputation algorithm that uses the Positional Burrows Wheeler Transform for population imputation. AlphaImpute2 has been successfully used to perform imputation in populations of hundreds of thousands of individuals. |
Type Of Technology | Software |
Year Produced | 2020 |
Open Source License? | Yes |
Impact | AlphaImpute2 enabled large-scale phasing and imputation in world-leading animal breeding programmes that operate with hundreds of thousands of genotyped individuals. |
URL | https://github.com/AlphaGenes/AlphaImpute2 |
Title | AlphaPeel |
Description | AlphaPeel is a software package for calling, phasing, and imputing genotype and sequence data in pedigree populations. This program implements single locus peeling, multi locus peeling, and hybrid peeling |
Type Of Technology | Software |
Year Produced | 2018 |
Open Source License? | Yes |
Impact | AlphaPeel enabled genotype calling, phasing, and imputation of the largest whole-genome sequencing data in a pedigreed population to date. |
URL | https://github.com/AlphaGenes/AlphaPeel |
Title | AlphaPhase - phasing genotype data |
Description | AlphaPhase is a software package for phasing genotype data. The program implements methods to determine phase using an extended Long Range Phasing and Haplotype Library Imputation. AlphaPhase consists of a single program. All information on the model of analysis, input files and their layout, is specified in a parameter file. |
Type Of Technology | Software |
Year Produced | 2019 |
Impact | AlphaPhase has been used to phase SNP array genotype data in large pig and poultry breeding programmes. |
URL | https://github.com/AlphaGenes/AlphaPhase |
Title | AlphaPlantImpute |
Description | AlphaPlantImpute is a software package designed for phasing and imputing genotype data in plant breeding populations. |
Type Of Technology | Software |
Year Produced | 2019 |
Impact | AlphaPlanImpute is used by the groups collaborating partners in the crop breeding industry (maize, sugar beet, wheat). |
URL | https://alphagenes.roslin.ed.ac.uk/wp/software-2/alphaplantimpute/ |
Title | AlphaPlantImpute |
Description | AlphaPlantImpute is a software package designed for phasing and imputing genotype data in plant breeding populations. AlphaPlantImpute can be implemented within and across bi-parental populations to phase and impute focal individuals genotyped at low-density to high-density. |
Type Of Technology | Software |
Year Produced | 2018 |
Impact | This package was found to be extremely useful by our project partner global breeder KWS Saat SE. |
URL | https://alphagenes.roslin.ed.ac.uk/wp/software/alphaplantimpute/ |
Title | AlphaPlantImpute2 |
Description | This software package conducts imputation in genotype datasets of diversity panels in crop breeding. The imputation principle is based on a hidden Markov model. |
Type Of Technology | Software |
Year Produced | 2019 |
Impact | The software package is currently tested by one of our industrial partners in the crop breeding sector (Wheat, Maize, Sugar beet). |
Title | AlphaSimR: An R-package for Breeding Program Simulations |
Description | AlphaSimR is an R package for stochastic simulations of plant and animal breeding programs. AlphaSimR is a highly flexible software package able to simulate a wide range of plant and animal breeding programs for diploid and autopolyploid species. AlphaSimR is ideal for testing the overall strategy and detailed design of breeding programs. AlphaSimR utilizes a scripting approach to building simulations that is particularly well suited for modeling highly complex breeding programs, such as commercial breeding programs. The primary benefit of this scripting approach is that it frees users from preset breeding program designs and allows them to model nearly any breeding program design. |
Type Of Technology | Software |
Year Produced | 2017 |
Open Source License? | Yes |
Impact | AlphaSimR has been used widely by researchers and practitioners in breeding and genetics, most notably it drives and supports development of genomic and quantitative genetic methods and tools at Roslin, optimisation of world-leading breeding programmes, such as Genus, PIC, Bayer CropScience, KWS, Limagrain, BASF, Beta Bugs, and CGIAR Excellence in Breeding platform. |
URL | https://github.com/AlphaGenes/AlphaSimR |
Title | AlphaSuite of software for data science, genetics, and breeding |
Description | AlphaSuite of software for data science, genetics, and breeding available from https://github.com/AlphaGenes The major tools include: * AlphaSimR for simulation of breeding programmes https://github.com/AlphaGenes/AlphaSimR * AlphaBayes for estimation of SNP effects on phenotype https://github.com/AlphaGenes/AlphaBayes * AlphaAssign for finding progeny-parent (pedigree) relationships https://github.com/AlphaGenes/AlphaAssign * AlphaPhase for phasing and imputation of SNP array genotype data https://github.com/AlphaGenes/AlphaPhase * AlphaImpute for phasing and imputation of SNP array genotype data https://github.com/AlphaGenes/AlphaImpute * AlphaImpute2 for phasing and imputation of SNP array genotype data (version 2) https://github.com/AlphaGenes/AlphaImpute2 * AlphaPeel for genotype calling, phasing, and imputation in pedigreed populations https://github.com/AlphaGenes/AlphaPeel * AlphaFamImpute for genotype calling, phasing, and imputation in families https://github.com/AlphaGenes/AlphaFamImpute * AlphaPlantImpute for phasing and imputation in plant populations (version 2) https://github.com/AlphaGenes/AlphaPlantImpute * AlphaPlantImpute2 for phasing and imputation in plant populations (version 2) https://github.com/AlphaGenes/AlphaPlantImpute2 * AlphaMate for balancing selection and management of genetic diversity in breeding programmes https://github.com/AlphaGenes/AlphaMate * AlphaPart for analysing trend in genetic means and variances https://github.com/AlphaGenes/AlphaPart |
Type Of Technology | Software |
Year Produced | 2018 |
Open Source License? | Yes |
Impact | AlphaSuite is used by leading public and private animal and plant breeding programmes that supply genetics worldwide in the Global North and Global South. |
URL | https://github.com/AlphaGenes |
Description | AlphaGenes Twitter channel |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | The AlphaGenes updates the scientific community and a broader audience about news around our research group, scientific output and engagement activities |
Year(s) Of Engagement Activity | 2012,2013,2014,2015,2016,2017,2018,2019,2020 |
URL | https://twitter.com/Alpha_Genes |
Description | AlphaGenes website |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | The AlphaGenes website informs the scientific community about the groups research activities, outputs, courses and available software tools. |
Year(s) Of Engagement Activity | 2017,2018,2019,2020 |
URL | https://alphagenes.roslin.ed.ac.uk |
Description | Big Data in Agriculture, Part of the DuPont Pioneer Plant Sciences Symposia Series, at Roslin Institute, 14-15 May 2018 |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Symposium held at the Roslin institute, organised by members of my group, sponsored by third parties from the breeding industry |
Year(s) Of Engagement Activity | 2018 |
Description | Course on The Next Generation Breeding (Iowa State University) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | We organised a course on The Next Generation Breeding at The Iowa State University in Ames in May 2018 to present and teach about our research, principles of the developed methods and application of our software with real data. The course was very well received with plenty of discussions involving both academia, research and industry participants. It also initiated a series of offline research and application discussions. |
Year(s) Of Engagement Activity | 2018 |
Description | Course on The Next Generation Breeding (KWS group) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | We organised an internal course on The Next Generation Breeding at The KWS group in Germany in Einbeck in March 2019 to present and teach about our research, principles of the developed methods and application of our software with real data. The course was very well received with plenty of discussions involving both academia, research and industry participants. It also initiated a series of offline research and application discussions. |
Year(s) Of Engagement Activity | 2019 |
Description | Course on The Next Generation Breeding (University of Zagreb) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | We organised a course on The Next Generation Breeding at The University Of Zagreb (Croatia) in July 2018 to present and teach about our research, principles of the developed methods and application of our software with real data. The course was very well received with plenty of discussions involving both academia, research and industry participants. It also initiated a series of offline research and application discussions. |
Year(s) Of Engagement Activity | 2018 |
Description | Data-Driven Breeding and Genetics course (2 weeks) on-line |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | The principles of animal and plant breeding are increasingly coalescing due to advances in technology and increasing demands and opportunities for agriculture. This two-week graduate level course of integrated lectures and practicals is designed to equip students, academics, and practitioners with theoretical and applied knowledge, skills and tools to design, optimise, and deploy Data Driven Breeding and Genetics techniques for Animals and Plants. It was jointly delivered by scientists and teachers from the University of Edinburgh and colleagues from the Swedish University of Agricultural Sciences and the CGIAR's Excellence in Breeding Platform, with guest lectures from various academic and industry collaborators. Due to the pandemic the course took place in virtual format from the 20th Sep and 1st Oct 2021. The course lectures were pre-recorded to enable asynchronous worldwide delivery. Course participants engaged with the lectures and practicals at their own pace. They engaged with course instructors and other participants via Slack and daily Zoom sessions (one in the UK morning and one in the UK afternoon time). Day 1 - Introduction to breeding Welcome and Introduction (Gregor Gorjanc) Introduction to breeding programme modelling (Gregor Gorjanc) AlphaSimR MOOC - Introduction (Gregor Gorjanc) AlphaSimR MOOC - Relationship between DNA & traits (Gregor Gorjanc) R crash course on using ggplot and tidyverse (Thiago Paula Oliveira) The role of livestock in global food security (Geoff Simm) Day 2 - Breeding programme design AlphaSimR MOOC - DNA lottery (Gregor Gorjanc) AlphaSimR MOOC - Response to selection (Gregor Gorjanc) AlphaSimR MOOC - Modelling complex breeding programmes (Gregor Gorjanc) How does a major multinational animal breeding programme operate in the 21st century (Andreas Kranis) How does a major multinational plant breeding programme operate in the 21st century (Brian Gardunia) Day 3 - Genomic data in breeding Genomic data, SNP array genotyping and sequencing, and Strategies to generate genomic data in breeding programmes (Gregor Gorjanc) Phasing genomic data with heuristic and probabilistic methods (Gregor Gorjanc) Imputation of genomic data (Gregor Gorjanc) AlphaPeel practical - probabilistic genotype calling, phasing, and imputation of genomic data in pedigreed populations (Jana Obsteter) AlphaImpute2 practical - fast phasing and imputation (Jana Obsteter) AlphaFamImpute practical - genotype calling, phasing, and imputation algorithm for large full-sib families (Jana Obsteter) AlphaAssign practical - parentage assignment (Jana Obsteter) Breeding in aquaculture (Ross Houston) Tea breeding and a genomic selection outlook (Nelson Lubanga) Day 4 - Modelling phenotype data to estimate environmental effects Introduction to experimental design of field trials (Daniel Tolhurst) Introduction to linear mixed models for plant breeding (Daniel Tolhurst) Analysis of phenotype data, including data collected from i) single field trials (with spatial) and ii) field trials across multiple (Daniel Tolhurst) ASReml practicals (Daniel Tolhurst & Thiago Paula Oliveira) Overview of forest tree breeding (Jaroslav Klapste) Genomic selection provides new opportunities for intercrop breeding (Jon Bancic) Day 5 - Population and Quantitative genetics for breeding Introduction to population and quantitative genetics for breeding (Martin Johnsson) Change in frequencies with drift (Martin Johnsson) Change in frequencies with mutation, migration and selection (Martin Johnsson) Additive effects (Martin Johnsson) Non-additive effects (Martin Johnsson) Inbreeding depression and heterosis (Martin Johnsson) Practicals (Martin Johnsson) Genetic evaluation in a multinational plant breeding programs AND/OR CGIAR Excellence in Breeding platform (Eduardo Covarrubias-Pazaran) Roadmap for black soldier fly breeding (Leticia de Castro Lara) Day 6 - Quantitative genetics for breeding II Variance, covariance, correlation and heritability (Eduardo Covarrubias-Pazaran) Correlated response to selection (Eduardo Covarrubias-Pazaran) Recurrent selection strategies (Eduardo Covarrubias-Pazaran) Practicals (Eduardo Covarrubias-Pazaran) National breeding programme for the Norwegian Red dairy cattle (Janez Jenko) Breeding a man's best friend (Joanna Ilska) Day 7 - Modelling phenotype data to estimate genetic effects Genetic evaluations with focus on pedigree-based BLUP (Ivan Pocrnic) Introduction to genome-wide association studies (Ivan Pocrnic) Genomic evaluations (Ivan Pocrnic) Practicals (Ivan Pocrnic) A multipart breeding strategy for introgression of exotic germplasm in elite breeding programs using genomic selection (Irene Breider) Population genetics tools with perspective in dog research (Mateja Janes) Day 8 - Sustainable breeding Breeders' dillema Optimal contribution selection Optimal cross selection AlphaMate practical - optimising selection, management of diversity, and mate allocation in breeding programs A walk-through of three examples AlphaPart - quantifying the drivers of genetic change (Jana Obsteter & Thiago Paula Oliveira) Recursive models in animal breeding (Maria Martinez Castillero) Economic objectives in animal and plant breeding (Cheryl Quinton) Day 9 - Exploiting modern technologies in breeding programmes The role of reproductive technologies to boost animal breeding (Gabriela Mafra Fortuna & Gerson Oliveira) Breeding for disease resistance in animals (Andrea Doeschel-Wilson) Editing livestock genomes (Simon Lillico) Evaluating the use of gene drives to limit the spread of invasive populations (Nicky Faber) The potential of genome editing and gene drives for improving complex traits (Gregor Gorjanc) Day 10 - Open-ended work on topics of participants' interest |
Year(s) Of Engagement Activity | 2021 |
Description | Excellence in Breeding: Breeding Scheme Optimization Tools Training |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Breeders and Quantitative Geneticists play an important role in the seed sector as designers of manufacturing pipelines. In this workshop, the students learned to work with our breeding simulation platform AlphaSimR. The students reported back they had become aware of the power of the simulation of breeding programs, as well as the possibilities to make breeding programs more effective by using less conventional breeding schemes. |
Year(s) Of Engagement Activity | 2020 |
URL | https://excellenceinbreeding.org/module2 |
Description | HighlanderLab Twitter channel |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | The HighlanderLab updates the scientific community and a broader audience about news around our research group, scientific output and engagement activities - on management and improvement of populations using data science, genetics, and breeding. |
Year(s) Of Engagement Activity | 2019,2020,2021,2022 |
URL | https://twitter.com/HighlanderLab |
Description | HighlanderLab website |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | The HighlanderLab updates the scientific community and a broader audience about news around our research group, scientific output and engagement activities - on management and improvement of populations using data science, genetics, and breeding. |
Year(s) Of Engagement Activity | 2021,2022 |
URL | http://www.ed.ac.uk/roslin/HighlanderLab |
Description | Massive Online On-demand Course on Modelling breeding programmes using AlphaSimR |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Breeding programmes are key to the genetic improvement of plant varieties and animal breeds used in agriculture. This unique course shows how to model an existing or new breeding programme and the evaluation of alternative breeding scenarios.The course is free and lasts for 5 weeks. https://www.edx.org/course/breeding-programme-modelling-with-alphasimr |
Year(s) Of Engagement Activity | 2022,2023 |
URL | https://www.edx.org/course/breeding-programme-modelling-with-alphasimr |
Description | Modern plant and animal applied genomics driven by genotype and sequence data, Universitat Politècnica de Valencia, |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Visiting teaching activity with advanced course in plant and animal breeding. |
Year(s) Of Engagement Activity | 2018 |
Description | Modern plant and animal applied genomics driven by genotype and sequence data, University of Zagreb, Croatia, 17-19 July 2018 |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Workshop organised and given by me and two other members of my group. |
Year(s) Of Engagement Activity | 2018 |
Description | Newton Fund workshop UK-Mexico on Genetic Improvement of Populations |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Newton Fund workshop UK-Mexico on Genetic Improvement of Populations took place in February 2018 at the Centro Nacional de Recursos Genéticos (Jalisco, Mexico). Participants (undergraduate and postgraduate students, group leaders and professionals) from UK and Mexico exchanged research results and showed applications of genetic improvement in different agricultural populations, including livestock, fish, crops, grasses and trees. I have contributed with a lecture on "Statistical methods for genetic evaluation of populations" and two talks titled "Economics of genotyping for genomic selection" and "Optimising selection, maintenance of genetic diversity and logistic constraints". The local organisers have increased interest in the presented topics. |
Year(s) Of Engagement Activity | 2018 |
Description | Public engagement at the Royal Highland Show |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Public/other audiences |
Results and Impact | All members of the research group engaged the visitors of the RHS, to show the importance of their research towards the enhancement of the agricultural sector in direct or indirect ways. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.royalhighlandshow.org |
Description | Training: Next generation plant breeding programs |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Workshop to teach professionals in the crop breeding sector to use modern bioinformatics tools to process molecular data and simulate breeding programs in order to make these more efficient. The audience was very pleased with their acquired insights and skills, and considered the training extremely useful. |
Year(s) Of Engagement Activity | 2019 |
URL | https://alphagenes.roslin.ed.ac.uk/wp/teaching-2/kwsgermany/ |