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Genomics in animal breeding from the perspectives of matrices and molecules
The document discusses the integration of molecular genetics with traditional agricultural methods to enhance the economic value of domesticated species. It highlights advancements in genomic selection and the development of genetic tools that improve the accuracy and efficiency of breeding programs. The future of animal breeding is expected to see significant changes with advancements in gene function discovery and the incorporation of DNA-based tests into breeding practices.
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Genomics in animal breeding from the perspectives of matrices and molecules
- 1. ‘Artificial selection onthe phenotypes of domesticated species has been practiced consciously or unconsciously for millennia, with dramatic results. Recently, advanced in molecular genetic engineering have promised to revolutionize agricultural practices. There are, however, several reasons why molecular genetics can never replace traditional methods of agricultural improvement, but instead they should be integrated to obtain the maximum improvement in the economic value of domesticated populations.’ Lande & Thompson (1990)
- 2. Martin Johnsson The RoslinInstitute, University of Edinburgh Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences Genomics in animal breeding from the perspectives of matrices and molecules @mrtnj
- 3. The statistical andsequence perspectives How they are used Future genomic breeding
- 4. Animal breeding beforegenomics (Zuidhof et al. 2014 Poultry Science)
- 5. ‘Genomics' ‘Genome’ from GermanGenom, Hans Winkler 1920 The DNA of an organism Journal title Genomics, Thomas Roderick 1986 (Yadav 2007 Journal of Biomolecular Techniques)
- 6. Genomes as matricesor molecules Position 1 Position 2 Position 3 Position 4 Individual 1 0 2 0 1 Individual 2 0 2 0 1 Individual 3 1 1 0 2 Individual 4 0 2 1 2 GTTGTCTTGGCTATTTTGCTGGACCATCCCTGTAAGGCTGCT CTGCCCTGCTCCTCTCTGCCTCCAGCAGTGCTATTAAGGGAA GGAGGGAGCTGTGAATTCCTGCAGTCTCTGGGAGCGGAGCA AAGCGAAGCCGAACTCCCGTTTCCATGTCGCTGCGGGACTC CCGGTAAATATCCGTCCGTCCGCTGCGGGTGATGCGAAGGA CGCTCGCGCGAGGCGCCGTGCCGCTCCGTAACACATAGCAC CCACTGGCGGGCGGGCACGCGCGCACCGAGAGGAGGACGA AAAGGAAGCGCCGACTTTCCGACCGCCGCTTTCCGAACGGA GAAGCCTTCCCCGGCAGAGCCCTTCCTTCTGCCTCCCGCCC CGCCGCCGTAGGCATGTTCCCGAGGCGCTCCGCACCGCGG GGAGCTCCCGTTTTCCGCCCGGCGGCCGCAGAGGAGCAGC AGCAGCGCGGTTCGGAGTAACTCCAAGTGATGCGGGGACCA CAGCCCGAGAGCAGCACGCGCTCCCGCAGACGCCAGCCCC GCCGCACTCAGGTCGACCATGGTTGCCGCCACCCGCTCCCT CCTGGCGCTGCTGCTCTGCCGGGTGCTGCTGGGCGGCGCG GCCGGCCTCATGCCGGAGGTGGGACGGCGGCGCTTCAGCG AACCGGGCCGCGCCGCCTCGGCCGCGCAGCGCCCCGAGGA CCTCCTGGGCGAGTTCGAGCTGCGCCTG vs
- 7. The gene concept Aa A AA Aa A AA Aa vs instrumental nominal (Griffith & Stotz 2006 Theoretical Medicine and Bioethics)
- 8. Statistical perspective: Genomicdata is a large set of markers; we are agnostic about their function. Sequence perspective: Genomic data is a source of causative variants; we need to find them and exploit them.
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- 10. Genomic selection (Pig byAlice Noir from the Noun Project) Reference population Measurements SNP chip genotypes Genomic prediction model Selection candidates SNP chip genotypes Predicted breeding values
- 11. Effect of genomicselection Cattle: Increased accuracy allows shortening generation time (Hayes et al 2009) Pigs: Increased accuracy by half (Knol, Nielsen & Knap 2016) Chickens: Increased accuracy (Wolc et al. 2016)
- 12. From marker assistedto genomic selection
- 13. Landmark genomic selectionpapers Lande & Thompson (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits Haley & Visscher (1998) Strategies to utilize marker-quantitative trait loci associations Meuwissen, Hayes & Goddard (2001) Prediction of total genetic value using genome-wide dense marker maps
- 14. Further statistical genomicstools Designing mating plans to manage genetic variation Population genetic classification, assignment, admixture Genotype imputation
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- 17. Livestock genomes Chicken (InternationalChicken Genome Sequencing Consortium 2004) Cattle (Elsik et al. 2009) Pig (Groenen et al. 2012)
- 18. Annotation Reference genome (assembly) Gene models (cDNAand alignment) Regulatory elements (functional genomics) Anything with genomic coordinates Variant annotation (rules and models)
- 19.
- 20. SNP chips Cattle 50Kchip (Matukumalli et al. 2009) Pig 60K chip (Ramos et al. 2009) Chicken 60K chip (Groenen et al. 2011)
- 21. Genome-wide association (geneticmapping) (Meredith et al. 2012 BMC Genetics)
- 22. Why haven’t wefound the causative variants yet? Polygenicity The fine mapping problem
- 23. ‘We can nowsee it as progressing in four phases: (i) making a broad sweep map (~20 cM) with both highly informative (microsatellite) and evolutionary conserved (gene) markers; (ii) using the informative markers to identify regions of chromosomes containing quantitative trait loci (QTL) controlling commercially important traits–this requires complex pedigrees or crosses between phenotypically and genetically divergent strains; (iii) progressing from the informative markers into the QTL and identifying trait genes(s) themselves either by complex pedigrees or back-crossing experiments, and/or using the conserved markers to identify candidate genes from their position in the gene-rich species; (iv) functional analysis of the trait genes to link the genome through physiology to the trait–the ‘phenotype gap’.’ Bulfield (2000)
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- 25. More genomic selection Moreintricate mating plan design? More biological structure in models? Statistical futures
- 26. Sequence futures Tait-Burkard etal. (2019) Genome Biology Wallace, Rodgers-Melnick & Buckler (2018) Ann Rev Genetics
- 27. 1. Single-edit traits 2.Removal of deleterious alleles 3. Quantitative traits Genome editing
- 28. Burkard et al.(2017) PLOS Pathogens Example: CD163 and PRRSV Homozygous edited pigs
- 29. Breeding goal trait Fitnesstrait Discover 75% of deleterious variants Removal of alleles by genome editing (RAGE)
- 30. Johnsson et al(2019) Genetics Selection Evolution
- 31. Jenko et al. 2015 Jenkoet al (2015) Genet Select Evol Promotion of alleles by genome editing (PAGE) Genomic selection + PAGE Geneticgain Generations Genomic selection only
- 32. The myostatin misapprehension Wanget al. (2015) Scientific Reports
- 33. Rodríguez-Leal et al(2017) Cell Creating new variation
- 34. There are (atleast) two ways to think of genomics in animal breeding The statistical perspective is doing the heavy lifting, currently The bright sequence future—are we there yet?
- 35. ‘It is rarelypossible to identify the pertinent genes in a Mendelian way or to map the chromosomal position of any of them. Fortunately this inability to identify and describe the genes individually is almost no handicap to the breeder of economic plants or animals. What he would actually do if he knew the details about all the genes which affect a quantitative character in that population differs little from what he will do if he merely knows how heritable it is and whether much of the hereditary variance comes from dominance or overdominance, and from epistatic interactions between the genes.’ Lush (1949)
- 36. ‘I believe animalbreeding in the post-genomic era will be dramatically different to what it is today. There will be a massive research effort to discover the function of genes including the effect of DNA polymorphisms on phenotype. Breeding programmes will utilize a large number of DNA-based tests for specific genes combined with new reproductive techniques and transgenes to increase the rate of genetic improvement and to produce for, or allocate animals to, the product line to which they are best suited. However, this stage will not be reached for some years by which time many of the early investors will have given up, disappointed with the early benefits. ‘ Goddard (2003)
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Editor's Notes
- #3 Thanks for inviting me. Gregor has talked about quantitative genetics – I will talk about genomics thinking of genomics from a statistical perspective, or a molecular perspective, which I think is a fruitful way to understand some of the things that are going on in animal breeding and modern quantitative genetics. Twitter handle.
- #4 We are going to do three things in this talk: Look at the statistical and sequence perspectives on genomics in animal breeding (the matrices and the molecules) Then, we’re going to look at the tools of the trade, and what these two perspectives allow animal breeders and animal breeding researchers to do Finally, we’re going to go into future genomic breeding. In short, I would argue, that the statistical perspective is what powers contemporary animal breeding, and the sequence perspective is a matter of research, maybe will become more important in the future
- #5 I did my PhD on chickens, which is great because it’s a livestock animal people often have a relationship to ‘My grandma had chickens …’ People also say, with resignation in their voice, ‘those huge chickens, that’s all genetic manipulation, isn’t it?’ This is from a study where they raised commercial Aviagen Ross broilers (2005) with lines that have been unselected since 50s and 70s This is genetic improvement: a lot more of the products that humans care about from animals, and per unit of input. Better productivity, fertility, welfare, that slowly accumulates This is before genomics – the first genotyping SNP chip for the chicken was released in 2005 .. Aviagen writes on their website that they started with genomics in 2012
- #6 Brief detour: the word ‘genomics’ from genome The genome is all the DNA in an organism Apparently they met in a bar to discuss founding the journal Genomics ... I feel like this is the kind of story biologists love to tell
- #7 There are two perspectives on genomics in animal breeding. On the one hand, we can view the genomes of a population as a large matrix of ancestry indicators. Row individual, column variable position (we only store the ones that differ between individuals). If the variable positions come in two variants, arbitrarily coded as 1/0, and individuals are diploid, there are three states 0,1,2 (genotypes). Possibly we should show two rows for each individual, if we knew which variants occur together on one chromosome (genotypes are then said to be phased). On the other hand, we can think of DNA as a long string of ACTG. Often we talk of “the genome” as the overall structure of one species or population, but really, each individual has its own, and nowadays, we have the technology to interrogate the whole genome of individuals. It’s just expensive.
- #8 I think that these two perspectives on genomics align with two different conceptions of genes, described by Griffith and Stotz (others have made similar observations) instrumental, which dates back to classical genetics, before we knew that genes were made of DNA. Genes are indirectly observed in crosses, they can follow the laws of Mendelian inheritance, and thanks to quantitative genetics theory (Fisher et al.) we know that quantitative traits can also be explained by individual genes that segregate like this, only many of them. Then there’s the nominal gene … Where a gene is a piece of DNA that has a name and some (known or putative) function. This is the concept that underpins genome browsers like Ensembl. FUNDAMENTALLY DIFFERENT -- NOT REDUCIBLE
- #9 In summary, the two perspectives are … MARKERS – they tell us the genotype in a certain part of the genome, but we don’t care so much about if they’re in genes, if they have any effect on their own, or whatever CAUSATIVE VARIANST – variants that make a difference to a trait we care about (either qualitative but most often quantitative)
- #10 So, what can you do with a large matrix of anonymous ancestry markers? It turns out, a lot.
- #11 Most importantly, I think, you can do genomic selection, which is the way to make selection decisions in breeding, over the last ten years or so. (introduced in chickens around 2012, diary 2009) USE MARKERS THROUGHOUT THE WHOLE GENOME TO ESTIMATE BREEDING VALUES You take your reference population, where you measure things, genotype markers (usually with a SNP chip, that contains the order of a few 10 000s of markers), and build a statistical model that allows you to predict the breeding values of animals from genotypes. Then you go to your selection candidates, genotype them, and then you can predict their breeding values, as soon as you can get a DNA sample. This can make selection faster or more accurate, depending on the species
- #13 Marker assisted selection … the idea that you would find variants that matter, and then put them into your selection program. At some point during the 1990s to early 2000s, a shift happened, went from marker assisted selection to genomic selection. Went from arguing that we should find the variants, to arguing that we should ignore function, and just cover the genome with markers. You will notice that this is a shift from the sequence perspective to statistical perspective. "we're going to find the variants that give the most growth, genotype and increase them in our breeding program" to "we're going to genotype every variant in every chromosome in every pig"
- #14 MHG -- simulation study of the full case for genomic selection, showing that it could work -- mind you, BEFORE SNP chips were available other papers -- developing MAS towards GS LT -- the idea of covering the genome in markers, a genomic score to select on HV -- replacing relationship matrix with genomic relationship, getting estimates for the entire genome in proportion to its variance explained -- my favourite candidate for the paper that has the GS idea articulated
- #18 Chicken: polymorphic maps (this is what will lead to SNP chips), and ”framework for discovering functional polymorphisms underlying quantitative traits, thus fully exploiting the genetic potential of the chicken” Cattle: “The cattle genome and associated resources will facilitate the identification of novel functions and regulatory systems of general importance in mammals and may provide an enabling tool for genetic improvement within the beef and dairy industries.” Pig: pushes the evolutionary and biomedical angle, but “The genome sequence also provides a valuable resource enabling effective uses of pigs both in agricultural production and in biomedical research.”
- #19 The genome sequence itself is not much use unless we understand what’s in it
- #21 After genome sequencing -- variant discovery "Sequencing leads to more sequencing" Here are publications for important SNP chips for livestock. Also lower density, and later high density chips … but these are the industry standard chips. Genomic selection has hit the animal genomics community! They explicitly motivate this with genomic selection. The cattle and pig papers cite Meuwissen, Hayes, Goddard (2001).
- #22 GWAS … localize causative variants in the genome ... Before that Quantitative trait locus mapping Look for variants associated with traits – chromosomal chunks associated with traits Manhattan ... Dundee
- #23 Causative variant identification has turned out to be a very hard problem
- #24 It is funny to look back at early reviews of genomics in animal breeding .. All these technologies … expressed sequence tags, line-cross linkage QTL mapping, candidate genes, radiation hybrid maps, even microarrays … all sound so quaint now. They did not lead to massive causative variant identification 20 years ago Our impressive list of technologies now: open chromatin sequencing, single cell rna sequencing, genomewide CRISPR screens, … will they lead to massive causative variant identification? We hope so
- #25 And that brings us to the genomic breeding of the future. What will we be able to do with genomics?
- #26 GS in more species, industries, maybe smallholder farmers bypassing the need for pedigrees, larger datasets GS and optimal contributions selection with dominance, maybe in crossbreeding systems GS that somehow accounts for functional information about variants
- #27 Looking in the literature, a sort of consensus about what will happen -- similar to the pre-GS consensus, except it now acknowledges GS as an intermediary stage Funny that they’ve chosen different version numbers New stage where we can engineer genotypes, put the causative variants that we want in
- #28 This is the list people usually give for what can be done with genome editing and roughly the order it will happen. Single high value edits – PRRS resistance in pigs, polling in cattle Repair broken variants – there are lots of them in domestic animals Promote beneficial alleles for quantitative traits
- #29 The PRRS virus exploits the CD163 protein to get from a vesicle into the cytosol when it enters the cell. If you knock it out, you lock the virus out – like changing locks after you get your keys stolen. This figure shows infected macrophages and virus does in cells from homozygous knockout pigs Several research groups have done that – I think this one seems the most promising (I wasn’t involved in the project, so I feel free to say that, but bear in mind they are from the Roslin too) – it just removes part of the gene, the domain that the virus uses, which seems to leave the rest of the function of the gene intact
- #30 As sequence data accumulates, we get the opportunity to identify variants directly from sequence data. There is a series of bioinformatic methods to do this. Guessing causative variants from sequence data alone is hard, but getting at deleterious variants could be possible (as opposed to the more subtle, often regulatory, variants that we believe cause beneficial variation). So we’ve come up with a strategy to do this
- #32 So, a few years ago, my colleagues (especially John Hickey and Janez Jenko) were interested in whether genome editing can actually improve quantitative traits. The short answer is: yes, if you can find the causative variants, you can! So, we assume that we can discover causal variants and rank them in order of effect, and edit the 20 largest effect variants in all the sires over 20 generations. That doubles genetic gain. That is assuming a lot, but I’d like to highlight one important thing that it doesn’t assume: we don’t assume extremely large effect quanitative trait loci (we work with a polygenic trait with 10000 variants, and normal distribution for effects)
- #33 Myostatin – double muscling in Belgian blue, Piedmonese Knock it in other animals, double muscled pigs, some indications that they may have problems – most importantly, no-one is really excited about putting this into breeding, why? This is great as research into gene function, and into developing editing BUT FUNDAMENTALLY THE WRONG MENTAL MODEL OF ANIMAL BREEDING – we don’t know enough about animals to design genomes – what we can do is discover some causative variants and adding them to our breeding practice in a good way
- #34 Here is an example from tomato – they have two genes in a developmental pathway where one inhibits the other, and they now that less of one gene will lead to more of the good stuff This is the kind of hypothetical relationship we will get from doing expression QTL mapping and screening in cell lines. Here they take a regulatory region of one of the genes, and scramble it by cutting it and letting the cell repair, and measure the outcomes. They get this synthetic allelic series that modulates gene expression, and then they can pick the ones that give the most of what they want (many locules per fruit). A humble form of pathway engineering.
- #35 I’m going to end on two quotes of how nothing is new under the sun. We’re going to start with Jay Lush, one of the originators of quantitative genetics for animal breeding, who pronounces the proud agnosticism about gene function that has served animal breeding well Then, some tempered optimism by Mike Goddard, who still in 2003, two years after Meuwissen Hayes Goddard, believed that functional genes are important – and still works towards discovering them, in parallel with developing new methods for genomic selection