AlphaGenes decided to support farmers through the COVID-19 crisis. We donated to The Farming Community Network Organisation as they are helping farm businesses to adapt and stay resilient through this challenging time.
We can all do something to support those who are currently struggling.
COVID-19 has forced the Alphagenes group to rethink our working environment. We are fortunate to continue our work from home. Daily meetings with our colleagues and collaborators are essential to keep in touch.
We recently published a preprint for how to use high-throughput phenotypes to infer an individuals genotype and enable genomic selection. Using genomic selection is key for increasing genetic gain in breeding programs by more accurately evaluating which individuals to select. However genotyping is expensive. To help make genomic selection more cost effective, AlphaGenes has been researching different ways to decrease genotyping costs over the last 7 years.
One way to decrease genotyping costs is to use genomic imputation. Under this approach, most of the individuals in the population are genotyped using a low-cost and low-density genotyping array, and only a small number of individuals are genotyped using a higher-cost high-density array. We can then use statistical regularities between the low and high-density individuals to impute (or fill in) the ungenotyped markers. The key question is how to balance genotyping costs (by using lower-density marker panels) while maintaining selection accuracy.
This work lies at one end of the spectrum where the goal is to obtain moderate accuracy imputation at very low-costs. For the past few years, we have consistently found that in structured populations(e.g., bi-parental crosses or full-sib families), moderate imputation accuracies can be obtained using 1-5 markers per chromosome. Can we push marker densities even lower? Do we even need genotypes? What if we just had high-throughput phenotypes?
High-throughput phenotypes encapsulate a range of sensor technologies which can be used to non-invasively evaluate phenotypes on large numbers of individuals. Examples include things such as flying a drone with an infrared camera over a field, or sending an animal through a suite a sensors. The advantage of high-throughput phenotypes is that they can be collected at very low costs.
This paper demonstrates (in simulation) that it may be possible to use high-throughput phenotypes as a stand-in for genetic markers to infer genotypes. In order to get moderate imputation accuracies (~0.5), there need to be ~100 quantitative phenotypes measured (traits such as yield, or spectrometry data) each with a heritability of 0.5.
Here’s some intuition for how it works. Imagine you have a bi-parental cross or full-sib family. We assume the parents are genotyped (and phased if they are outbred). Our goal is to impute an offspring based on the high-throughput phenotypes. To do this, (1) we simulate a large number of putative offspring, (2) calculate estimated genetic values for each offspring for each of the high-throughput phenotypes, then (3) impute the offspring by finding simulated genotypes that produce estimated genetic values close to the observed phenotypes. The large possible number of offspring genotypes, means that we cannot simulate all of them by direct simulation so we actually use a sampling approach that does a guided search to find likely haplotypes.
The paper presents just a proof of concept of this idea. We have not tested it on real data, and the number of phenotypes and the heritability of the phenotypes is likely outside of what we could easily (and cheaply) collect. But this idea has a lot of potential: maybe in the future a breeder could fly a drone over a field, or send a pig through a sensor and get very low cost (albeit low-accuracy) genotypes on hundreds or thousands of individuals.
We have a new preprint posted, showing that recombination rate in the pig is lowly heritable and associated with alleles at RNF212.We developed a new method to estimate recombinations in 150,000 pigs, and used that to estimate heritability and perform genome-wide association studies.
In this paper, we estimated recombination rate variation within the genome and between individuals in the pig ufor 150,000 pigs across nine genotyped pedigrees. We used this to estimate the heritability of recombination and perform a genome-wide association study of recombination in the pig.ResultsOur results confirmed known features of the pig recombination landscape, including differences in chromosome length, and marked sex differences. The recombination landscape was repeatable between lines, but at the same time, the lines also showed differences in average genome-wide recombination rate. The heritability of genome-wide recombination was low but non-zero (on average 0.07 for females and 0.05 for males). We found three genomic regions associated with recombination rate, one of them harbouring the RNF212 gene, previously associated with recombination rate in several other species.
Our results from the pig agree with the picture of recombination rate variation in vertebrates, with low but nonzero heritability, and a major locus that is homologous to one detected in several other species. This work also highlights the utility of using large-scale livestock data to understand biological processes.
Chris Gaynor is a creator of the AlphaSimR software package for stochastic simulations of breeding programs. He trained breeders and quantitative geneticists in the use of the breeding scheme optimisation method based on project management (continuous improvement) and simulation-based tools (AlphaSimR) at the Breeding Scheme Optimisation Training in Netherlands organised by Excellence in Breeding Platform and CGIAR.
Christian Werner, helped with tutorials at the Training.
Breeders and Quantitative Geneticists play an important role in the seed sector as pipeline engineers (not products), but for a long time breeders have been commissioned with the responsibility of executing many other aspects. As plant breeding programs are starting to grow and become a team of specialists, the role of the breeders and quantitative geneticists as pipeline designers becomes clearer. In any process, the design of manufacturing pipelines is a complicated task that requires knowledge of the materials used (germplasm), process optimization (simulation and other mathematical approaches) and project management tools. This training aims to provide knowledge on what are these tools and how to use them in the context of plant breeding to enable continuous improvement/optimisation of the breeding schemes.
Excellence in Breeding is committed with bringing all CGIAR breeding programs as close as possible to state of the art programs for them to be competitive, increase impact and fulfil the mission of the CGIAR. This training has as main objective to train breeders and quantitative geneticists in the use of the breeding scheme optimisation method based on project management (continuous improvement) and simulation – based tools (AlphaSimR).
The Royal Dick Christmas Bag Appeal is a yearly donation scheme to aid the homeless of Edinburgh. Staff, students and friends of the Dick Vet and the Roslin Institute provide bags of essential items to be distributed, with the help of local homeless charities and soup kitchens, throughout the streets of Edinburgh close to Christmas.
AlphaGenes is proud to contribute to the Royal Dick Christmas Bag Appeal this year, and hope to bring some Christmas spirit to ones in need.
Pruthviraj is a graduate of veterinary science with a research interest in animal genetics & breeding. During his masters, he worked on expression profiling & genetic polymorphism of porcine beta defensin-1 gene in which they could identify a novel SNP. In his PhD research, he is working on the phenotypic data of Vrindavani cattle to obtain genetic parameters, breeding value and genetic trend for various performance traits. At Alphagenes group, Pruthviraj is interested to learn methods to incorporate genomic information in the estimation of breeding value either with the help of some real data set or through simulation studies.
Peter Bradbury is a USDA-ARS computational biologist working in the lab of Edward Buckler in Ithaca, NY and affiliated with Cornell University. He is one of the developers of the TASSEL software package and the PHG (Practical Haplotype Graph). His current research projects involve genotype imputation and phasing using DNA sequence data. One of the primary objectives of that research is the development of a low cost genotyping platform that can be used by plant breeders and that will be implemented by the PHG software. Peter is visiting the AlphaGenes lab to learn about the methods we have developed to help improve and extend the methods used by the PHG software and to explore the potential for future collaboration in other areas.
Alpha Genes hosted a visit from four students of the 1992 MSc in Animal Breeding graduate class to talk. Dr Andrew Cromie, Dr Jens van Bebber, Prof Jesus Baro and Dr Mike Bradfield talked to us about their interesting and impressive careers since finishing their MSc.
They shared some lifelong lessons:
“Always be there for each other” – Andrew,
“Just because its written down, don’t take it as fact” – Jens,
“Learning from the best” – Mike,
“Don’t be afraid modify your models/tools in line with circumstance” – Jesus.
In the start June, Jon went to Kenya to discuss his work with our collaborators at ICRISAT (Nairobi), to collect a phenotypic and genotypic dataset for his future PhD work on orphan crop Finger millet, and to gain practical experience in the field as a plant breeder. He spent 6 weeks in Busia, a small town in Kenya bordering with Uganda, working at Kenyan Agricultural and Research Organisation (KALRO) station, Alupe, learning about ICRISAT’s breeding activities in finger millet, sorghum, and other local crops. For the majority of time, he spent working in the field trial collecting. His trial was a part of ICRISAT’s East African breeding program for finger millet, so healso visited ICRISAT’s site in Uganda to meet with the collaborators there including Johnie Ebiyau who developed the famous Epuripur sorghum and is one of Africa’s most distinct plant breeders. On a daily basis, he also had an opportunity to visit and interact with a number of famers closely working with ICRISAT and to see ICRISAT’s efforts and difficulties on the ground.
Jon lived with a revenant’s family which was an excellent opportunity for him to learn about the culture and religion.
We are grateful to SRUC and our colleagues in ICRISAT (namely, dr. Henry Ojulong and dr. Damaris Odeny) to have made this unique opportunity possible for Jon. The experience helped him gain a wider understanding in plant breeding and the work carried out by CGIAR centres in low- and middle-income countries. He also gained a good understanding of production constraints Eastern African countries face. We are sure Jon learned a lot on a personal level as well which will definitely benefit him in the future.
Jon finished his Kenya Visit by summiting Mount Kenya (4985m), the highest mountain in Kenya and second-highest mountain in Africa.