Journal club of one: ”A unifying concept of animal breeding programmes”

The developers of the MoBPS breeding programme simulators have published three papers about it over the last years: one about the MoBPS R package (Pook et al. 2020), one about their web server MoBPSweb (Pook et al. 2021), and one that discusses the logic of the specification for breeding programmes they use in the web interface (Simianer et al. 2021). The idea is that this specification can be used to describe breeding programmes in a precise and interoperable manner. The latter — about breeding programme specification — reads as if the authors had a jolly good time thinking about what breeding and breeding programmes are; at least, I had such feelings while reading it. It is accompanied by an editorial by Simianer (2021) talking about what he thinks are the important research directions for animal breeding research. Using simulation to aid breeding programme design is one of them.

Defining and specifying breeding programmes

After defining a breeding programme in a narrow genetic sense as a process achieving genetic change (more about that later), Simianer et al. (2021) go on to define a specification of such a breeding programme — or more precisely, of a model of a breeding programme. They use the word ”definition” for both things, but they really talk about two different things: defining what ”a breeding programme” is and specifying a particular model of a particular breeding programme.

Here, I think it is helpful to think of Guest & Martin’s (2020) distinction, borrowed from psychology, between a specification of a model and an implementation of a model. A specification is a description of a model based in natural language or math. Breeding programme modelling often takes the shape of software packages, where the software implementation is the model and a specification is quite vague. Simianer et al. (2021) can be seen as a step towards a way of writing specifications, on a higher level in Guest & Martin’s hierarchy, of what such a breeding programme model should achieve.

They argue that such a specification (”formal description”) needs to be comprehensive, unambiguous and reproducible. They claim that this can be achieved with two parts: the breeding environment and the breeding structure.

The breeding environment includes:

• founder populations,
• quantitative genetic parameters for traits,
• genetic architectures for traits,
• economic values for traits in breeding goal,
• description of genomic information,
• description of breeding value estimation methods.

Thus, the ”formal” specification depends on a lot of information that is either unknowable in practice (genetic architecture), estimated with error (genetic parameters), hard to describe other than qualitatively (founder population) and dependent on particular software implementations and procedures (breeding value estimation). This illustrates the need to distinguish the map from the territory — to the extent that the specification is exact, it describes a model of a breeding programme, not a real breeding programme.

The other part of their specification is the graph-based model of breeding structure. I think this is their key new idea. The breeding structure, in their specification, consists of nodes that represent groups of elementary objects (I would say ”populations”) and edges that represent transformations that create new populations (such as selection or mating) or are a shorthand for repeating groups of edges and nodes.

The elementary objects could be individuals, but they also give the example of gametes and genes (I presume they mean in the sense of alleles) as possible elementary objects. One could also imagine groups of genetically identical individuals (”genotypes” in a plant breeding sense). Nodes contain a given number of individuals, and can also have a duration.

Edges are directed, and correspond to processes such as ageing, selection, reproduction, splitting or merging populations. They will carry attributes related to the transformation. Edges can have a time associated with them that it takes for the transformation to happen (e.g. for animals to gestate or grow to a particular age). Here is an example from Pook et al. (2021) of a breeding structure graph for dairy cattle:

If we ignore the red edges for now, we can follow the flow of reproduction (yellow edges) and selection (green edges): The part on the left is what is going on in the breeding company: cows (BC-Cows) reproduce with selected bulls (BC-SelectedBulls), and their offspring become the next generation of breeding company cows and bulls (BC-NextCows and BC-NextBulls). On the right is the operation of a farm, where semen from the breeding company is used to inseminate cows and heifers (heifer, cow-L1, cow-L2, cow-L3) to produce calfs (calf-h, calf-L1, calf-L2, calf-L3). Each cycle each of these groups, through a selection operation, give rise to the next group (heifers becoming cows, first lactation cows becoming second lactation cows etc).

Breeding loops vs breeding graphs vs breeding forms

Except for the edges that specify breeding operations, there is also a special meta-edge type, the repeat edge, that is used to simplify breeding graphs with repeated operations.

A useful edge class to describe breeding programmes that are composed of several breeding cycles is ”repeat.” It can be used to copy resulting nodes from one breeding cycle into the nodes of origin of the next cycle, assuming that exactly the same breeding activities are to be repeated in each cycle. The “repeat” edge has the attribute “number of repeats” which allows to determine the desired number of cycles.

In the MoBPSweb paper (Pook et al. 2020), they describe how it is implemented in MoBPS: Given a breeding specification in MoBPSweb JSON format, the simulator will generate a directed graph by copying the nodes on the breeding cycle as many times as is specified by the repeat number. In this way, repeat edges are eliminated to make the breeding graph acyclic.

The conversion of the breeding scheme itself is done by first detecting if the breeding scheme has any “Repeat” edges (Simianer et al. 2020), which are used to indicate that a given part of the breeding programme is carried out multiple times (breeding cycles). If that is the case, it will subsequently check which nodes can be generated without the use of any repeat. Next, all repeats that can be executed based on the already available nodes are executed by generating copies of all nodes between the node of origin and the target node of the repeat (including the node of origin and excluding the target node). Nodes generated via repeat are serial-numbered via “_1,” “_2” etc. to indicate the repeat number. This procedure is repeated until all repeat edges are resolved, leading to a breeding programme without any repeats remaining.

There are at least three ways to specify the breeding structures for breeding programme simulations: these breeding graphs, breeding loops (or more generally, specifying breeding in a programming language) and breeding forms (when you get a pre-defined breeding structure and are allowed fill in the numbers).

If I’m going to compare the graph specification to what I’m more familiar with, this is how you would create a breeding structure in AlphaSimR:

```library(AlphaSimR)

## Breeding environment

founderpop <- runMacs(nInd = 100,
nChr = 20)
simparam <- SimParam\$new(founderpop)
simparam\$setSexes("yes_sys")
simparam\$setVarE(h2 = 0.3)

## Breeding structure

n_time_steps <- 10

populations <- vector(mode = "list",
length = n_time_steps + 1)

populations[[1]] <- newPop(founderpop,
simParam = simparam)

for (gen_ix in 2:(n_time_steps + 1)) {

## Breeding cycle happens here

}
```

In the AlphaSimR script, the action typically happens within the loop. You apply different functions on population objects to make your selection, move individuals between parts of the breeding programme, create offspring etc. That is, in the MoBPS breeding structure, populations are nodes and actions are edges. In AlphaSimR, populations are objects and operations are functions. In order to not have to copy paste your breeding code, you use the control flow structures of R to make a loop (or some functional equivalent). In MoBPS graph structure, in order to not have to create every node and edge manually, you use the Repeat edge.

Breeding graphs with many repeat edges with different times attached to them have the potential to be complicated, and the same is true of breeding loops. I would have to use both of them more to have an opinion about what is more or less intuitive.

Now that we’ve described what they do in the paper, let’s look at some complications.

Formal specifications only apply to idealised breeding programmes

The authors claim that their concept provides a formal breeding programme specification (in their words, ”formal description”) that can be fully understood and implemented by breeders. It seems like the specification fails to live up to this ambition, and it appears doubtful whether any type of specification can. This is because they do not distinguish between specifying a model of a breeding programme and specifying a real implementation of a breeding programme.

First, as mentioned above, the ”breeding environment” as described by them, contains information that can never be specified for any real population, such as the genetic architecture of complex traits.

Second, their breeding structure is described in terms of fixed numbers, which will never be precise due to mortality, conception rates, logistics and practical concerns. They note such fluctuations in population size as a limitation in the Discussion. To some extent, random mortality, reproductive success etc an be modelled by putting random distributions on various parameters. (I am not sure how easy this is to do in the MoBPS framework; it is certainly possible.) However, this adds uncertainty about what these hyperparameter should be and whether they are realistic.

Such complications would just be nit-picking if the authors had not suggested that their specification can be used to communicate breeding programmes between breeders and between breeders and authorities, such as when a breeding programme is seeking approval. They acknowledge that the authorities, for example in the EU, want more detailed information that are beyond the scope of their specification.

And the concept is not very formal in the first place

Despite the claimed formality, every class of object in the breeding structure is left open, with many possible actions and many possible attributes that are never fully defined.

It is somewhat ambiguous what is to be the ”formal” specification — it cannot be the description in the paper as it is not very formal or complete; it shouldn’t be the implementation in MoBPS and MoBPSweb, as the concept is claimed to be universal; maybe it is the JSON specification of the breeding structure and background as described in the MoBPSweb paper (Pook et al. 2020). The latter seems the best candidate for a well-defined formal way to specify breeding programme models, but then again, the JSON format appears not to have a published specification, and appears to contain implementation-specific details relating to MoBPS.

This also matters to the suggested use of the specification to communicate real breeding programme designs. What, precisely, is it that will be communicated? Are breed societies and authorities expected to communicate with breeding graphs, JSON files, or with verbal descriptions using their terms (e.g. ”breeding environment”, ”breeding structure”, their node names and parameters for breeding activities)?

There is almost never a need for a definition

As I mentioned before, the paper starts by asking what a breeding programme is. They refer to different descriptions of breeding programme design from textbooks, and a legal definition from EU regulation 2016/1012; article 2, paragraph 26, which goes:

‘breeding programme’ means a set of systematic actions, including recording, selection, breeding and exchange of breeding animals and their germinal products, designed and implemented to preserve or enhance desired phenotypic and/or genotypic characteristics in the target breeding population.

There seems to be some agreement that a breeding programme, in addition to being the management of reproduction of a domestic animal population, also is systematic and goal-directed activity. Despite these descriptions of breeding programmes and their shared similarity, the authors argue that there is no clear formal definition of what a breeding programme is, and that this would be useful to delineate and specify breeding programmes.

They define a breeding programme as an organised process that aims to change the genetic composition in a desired direction, from one group of individuals to a group of individuals at a later time. The breeding programme comprises those individuals and activities that contribute to this process. For example, crossbred individuals in a multiplier part of a terminal crossbreeding programme would be included to the extent that they contribute information to the breeding of nucleus animals.

We define a breeding programme as a structured, man-driven process in time that starts with a group of individuals X at time $t_1$ and leads to a group of individuals Y at time $t_2 > t_1$. The objective of a breeding programme is to transform the genetic characteristics of group X to group Y in a desired direction, and a breeding programme is characterized by the fact that the implemented actions aim at achieving this transformation.

They actually do not elaborate on what it means that a genetic change has direction, but since they want the definition to apply both to farm animal and conservation breeding programmes, the genetic goals could be formulated both in terms of changes in genetic values for traits and in terms of genetic relationships.

Under many circumstances, this is a reasonable proxy also for an economic target: The breeding structures and interventions considered in theoretical breeding programme designs can often be evaluated in terms of their effect on the response to selection, and if the response improves, so will the economic benefit. However, this definition seems a little unnecessary and narrow. If you wanted to, say, add a terminal crossbreeding step to the simulation and evaluate the performance in terms of the total profitability of the crossbreeding programme (that is, something that is outside of the breeding programme in the sense of the above definition), nothing is stopping you, and the idea is not in principle outside of the scope of animal breeding.

Finally, an interesting remark about efficiency

When discussing the possibility of using their concept to communicate breeding programmes to authorities when seeking approval the authors argue that authorities should not take efficiency of the breeding programme into account when they evaluate breeding programmes for approval. They state this point forcefully without explaining their reasoning:

It should be noted, though, that an evaluation of the efficiency of breeding programme is not, and should never be, a precondition for the approval of a breeding programme by the authorities.

This raises the question: why not? There might be both environmental, economical and animal ethical reasons to consider not approving breeding programmes that can be shown to make inefficient use of resources. Maybe such evaluation would be impractical — breeding programme analysis and simulation might have to be put on a firmer scientific grounding and be made more reproducible and transparent before we trust it to make such decisions — but efficiency does seem like an appropriate thing to want in a breeding scheme, also from the perspective of society and the authorities.

I am not advocating any particular new regulation for breeding programmes here, but I wonder where the ”should never” came from. This reads like a comment added to appease a reviewer — the passage is missing from the preprint version.

Literature

Pook, T., Schlather, M., & Simianer, H. (2020a). MoBPS-modular breeding program simulator. G3: Genes, Genomes, Genetics, 10(6), 1915-1918. https://academic.oup.com/g3journal/article/10/6/1915/6026363

Pook, T., Büttgen, L., Ganesan, A., Ha, N. T., & Simianer, H. (2021). MoBPSweb: A web-based framework to simulate and compare breeding programs. G3, 11(2), jkab023. https://academic.oup.com/g3journal/article/11/2/jkab023/6128572

Simianer, H., Büttgen, L., Ganesan, A., Ha, N. T., & Pook, T. (2021). A unifying concept of animal breeding programmes. Journal of Animal Breeding and Genetics, 138 (2), 137-150. https://onlinelibrary.wiley.com/doi/full/10.1111/jbg.12534

Simianer, H. (2021), Harvest Moon: Some personal thoughts on past and future directions in animal breeding research. J Anim Breed Genet, 138: 135-136. https://doi.org/10.1111/jbg.12538

Guest, O., & Martin, A. E. (2020). How computational modeling can force theory building in psychological science. Perspectives on Psychological Science, 1745691620970585. https://journals.sagepub.com/doi/full/10.1177/1745691620970585

Temple Grandin at Roslin: optimisation and overselection

A couple of months ago (16 May to be precise), I listened to a talk by Temple Grandin at the Roslin Institute.

Grandin is a captivating speaker, and as an animal scientist (of some kind), I’m happy to have heard her talk at least once. The lecture contained a mix of:

• practical experiences from a career of troubleshooting livestock management and systems,
• how thinking differently (visually) helps in working with animal behaviour,
• terrific personal anecdotes, among other things about starting up her business as a livestock management consultant from a student room,
• a recurring theme, throughout the talk, of unintended side-effects in animal breeding, framed as a risk of ”overselecting” for any one trait, uncertainty about ”what is optimal”, and the importance of measuring and soberly evaluating many different things about animals and systems.

This latter point interests me, because it concerns genetics and animal breeding. Judging by the question in the Q&A, it also especially interested rest of the audience, mostly composed of vet students.

Grandin repeatedly cautioned against ”overselecting”. She argued that if you take one trait, any trait, and apply strong directional selection, bad side-effects will emerge. As a loosely worded biological principle, and taken to extremes, this seems likely to be true. If we assume that traits are polygenic, that means both that variants are likely to be pleiotropic (because there are many causal variants and a limited number of genes; this one argument for the omnigenic model) and that variants are likely to be linked to other variants that affect other traits. So changing one trait a lot is likely to affect other traits. And if we assume that the animal was in a pretty well-functioning state before selection, we should expect that if some trait that we’re not consciously selecting on changes far enough from that state, that is likely to cause problems.

We can also safely assume that there are always more traits that we care about than we can actually measure, either because they haven’t become a problem yet, or because we don’t have a good way to measure them. Taken together, this sound like a case for being cautious, measuring a lot of things about animal performance and welfare, and continuously re-evaluating what one is doing. Grandin emphasised the importance of measurement, drumming in that: ”you will manage what you measure”, ”this happens gradually”, and therefore, there is a risk that ”the bad becomes the new normal” if one does not keep tabs on the situation by recording hard quantitative data.

Doesn’t this sound a lot like the conventional view of mainstream animal breeding? I guess it depends: breeding is a big field, covering a lot of experiences and views, from individual farmers’ decisions, through private and public breeding organisations, to the relative Castalia of academic research. However, the impression from my view of the field, is Grandin and mainstream animal breeders are in agreement about the importance of:

1. recording lots of traits about all aspects of the performance and functioning of the animal,
2. optimising them with good performance on the farm as the goal,
3. constantly re-evaluating practice and updating the breeding goals and management to keep everything on track.

To me, what Grandin presented as if it was a radical message (and maybe it was, some time ago, or maybe it still is, in some places) sounded much like singing the praises of economic selection indices. I had expected something more controversial. Then again, that depends on what assumptions are built into words like ”good performance”, ”on track”, ”functioning of the animal” etc. For example, she talked a bit about the strand of animal welfare research that aims to quantify positive emotions in animals; one could take the radical stance that we should measure positive emotions and include that in the breeding goal.

”Overselection” as a term also carries connotations that I don’t agree with, because I don’t think that the framing as biological overload is helpful. To talk about overload and ”overselection” makes one think of selection as a force that strains the animal in itself, and the alternative as ”backing off” (an expression term Grandin repeatedly used in the talk). But if the breeding goal is off the mark, in the sense that it doesn’t get towards what’s actually optimal for the animal on the farm, breeding less efficiently is not getting you to a better outcome; it only gets towards the same, suboptimal, outcome more slowly. The problem isn’t efficiency in itself, but misspecification, and uncertainty about what the goal should be.

Grandin expands on this idea in the introductory chapter to ”Are we pushing animals to their biological limits? Welfare and ethical applications” (Grandin & Whiting 2018, eds). (I don’t know much about the pig case used as illustration, but I can think of a couple of other examples that illustrate the same point.) It ends with this great metaphor about genomic power tools, that I will borrow for later:

We must be careful not to repeat the mistakes that were made with conventional breeding where bad traits were linked with desirable traits. One of the best ways to prevent this is for both animal and plant breeders to do what I did in the 1980s and 1990s: I observed many different pigs from many places and the behaviour problems became obvious. This enabled me to compare animals from different lines in the same environment. Today, both animal and plant breeders have ‘genomic power tools’ for changing an organism’s genetics. Power tools are good things, but they must be used carefully because changes gan be made more quickly. A circular saw can chop your hand off much more easily than a hand saw. It has to be used with more care.

‘Approaches to genetics for livestock research’ at IASH, University of Edinburgh

A couple of weeks ago, I was at a symposium on the history of genetics in animal breeding at the Institute of Advanced Studies in the Humanities, organized by Cheryl Lancaster. There were talks by two geneticists and two historians, and ample time for discussion.

First geneticists:

Gregor Gorjanc presented the very essence of quantitative genetics: the pedigree-based model. He illustrated this with graphs (in the sense of edges and vertices) and by predicting his own breeding value for height from trait values, and from his personal genomics results.

Then, yours truly gave this talk: ‘Genomics in animal breeding from the perspectives of matrices and molecules’. Here are the slides (only slightly mangled by Slideshare). This is the talk I was preparing for when I collected the quotes I posted a couple of weeks ago.

I talked about how there are two perspectives on genomics: you can think of genomes either as large matrices of ancestry indicators (statistical perspective) or as long strings of bases (sequence perspective). Both are useful, and give animal breeders and breeding researchers different tools (genomic selection, reference genomes). I also talked about potential future breeding strategies that use causative variants, and how they’re not about stopping breeding and designing the perfect animal in a lab, but about supplementing genomic selection in different ways.

Then, historians:

Cheryl Lancaster told the story of how ABGRO, the Animal Breeding and Genetics Research Organisation in Edinburgh, lost its G. The organisation was split up in the 1950s, separating fundamental genetics research and animal breeding. She said that she had expected this split to be do to scientific, methodological or conceptual differences, but instead found when going through the archives, that it all was due to personal conflicts. She also got into how the ABGRO researchers justified their work, framing it as ”fundamental research”, and aspired to do long term research projects.

Jim Lowe talked about the pig genome sequencing and mapping efforts, how it was different from the human genome project in organisation, and how it used comparisons to the human genome a lot. Here he’s showing a photo of Alan Archibald using the gEVAL genome browser to quality-check the pig genome. He also argued that the infrastructural outcomes of a project like the human genome project, such as making it possible for pig genome scientists to use the human genome for comparisons, are more important and less predictable than usually assumed.

The discussion included comments by some of the people who were there (Chris Haley, Bill Hill), discussion about the breed concept, and what scientists can learn from history.

What is a breed? Is it a genetical thing, defined by grouping individuals based on their relatedness, a historical thing, based on what people think a certain kind of animal is supposed to look like, or a marketing tool, naming animals that come from a certain system? It is probably a bit of everything. (I talked with Jim Lowe during lunch; he had noticed how I referred to Griffith & Stotz for gene concepts, but omitted the ”post-genomic” gene concept they actually favour. This is because I didn’t find it useful for understanding how animal breeding researchers think. It is striking how comfortable biologists are with using fuzzy concepts that can’t be defined in a way that cover all corner cases, because biology doesn’t work that way. If the nominal gene concept is broken by trans-splicing, practicing genomicists will probably think of that more as a practical issue with designing gene databases than a something that invalidates talking about genes in principle.)

What would researchers like to learn from history? Probably how to succeed with large research endeavors and how to get funding for them. Can one learn that from history? Maybe not, but there might be lessons about thinking of research as ”basic”, ”fundamental”, ”applied” etc, and about what the long term effects of research might be.

Excerpts about genomics in animal breeding

Here are some good quotes I’ve come across while working on something.

Artificial selection on the phenotypes of domesticated species has been practiced consciously or unconsciously for millennia, with dramatic results. Recently, advances 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 economic value of domesticated populations.

Lande R & Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics.

Smith and Smith suggested that the way to proceed is to map QTL to low resolution using standard mapping methods and then to increase the resolution of the map in these regions in order to locate more closely linked markers. In fact, future developments should make this approach unnecessary and make possible high resolution maps of the whole genome, even, perhaps, to the level of the DNA sequence. In addition to easing the application of selection on loci with appreciable individual effects, we argue further that the level of genomic information available will have an impact on infinitesimal models. Relationship information derived from marker information will replace the standard relationship matrix; thus, the average relationship coefficients that this represents will be replaced by actual relationships. Ultimately, we can envisage that current models combining few selected QTL with selection on polygenic or infinitesimal effects will be replaced with a unified model in which different regions of the genome are given weights appropriate to the variance they explain.

Haley C & Visscher P. (1998) Strategies to utilize marker–quantitative trait loci associations. Journal of Dairy Science.

Instead, since the late 1990s, DNA marker genotypes were included into the conventional BLUP analyses following Fernando and Grossman (1989): add the marker genotype (0, 1, or 2, for an animal) as a fixed effect to the statistical model for a trait, obtain the BLUP solutions for the additive polygenic effect as before, and also obtain the properly adjusted BLUE solution for the marker’s allele substitution effect; multiply this BLUE by 0, 1, or 2 (specic for the animal) and add the result to the animal’s BLUP to obtain its marker-enhanced EBV. A logical next step was to treat the marker genotypes as semi-random effects, making use of several different shrinkage strategies all based on the marker heritability (e.g., Tsuruta et al., 2001); by 2007, breeding value estimation packages such as PEST (Neumaier and Groeneveld, 1998) supported this strategy as part of their internal calculations. At that time, a typical genetic evaluation run for a production trait would involve up to 30 markers.

Knol EF, Nielsen B, Knap PW. (2016) Genomic selection in commercial pig breeding. Animal Frontiers.

Although it has not caught the media and public imagination as much as transgenics and cloning, genomics will, I believe, have just as great a long-term impact. Because of the availability of information from genetically well-researched species (humans and mice), genomics in farm animals has been established in an atypical way. We can now see 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 anc 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 G. (2000) Biotechnology: advances and impact. Journal of the Science of Food and Agriculture.

I believe animal breeding in the post-genomic era will be dramatically different to what it is today. There will be 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 M. (2003). Animal breeding in the (post-) genomic era. Animal Science.

Genetics is a quantitative subject. It deals with ratios, with measurements, and with the geometrical relationships of chromosomes. Unlike most sciences that are based largely on mathematical techniques, it makes use of its own system of units. Physics, chemistry, astronomy, and physiology all deal with atoms, molecules, electrons, centimeters, seconds, grams–their measuring systems are all reducible to these common units. Genetics has none of these as a recognizable component in its fundamental units, yet it is a mathematically formulated subject that is logically complete and self-contained.

Sturtevant AH & Beadle GW. (1939) An introduction to genetics. W.B. Saunders company, Philadelphia & London.

We begin by asking why genes on nonhomologous chromosomes assort independently. The simple cytological story rehearsed above answers the questions. That story generates further questions. For example, we might ask why nonhomologous chromosomes are distributed independently at meiosis. To answer this question we could describe the formation of the spindle and the migration of chromosomes to the poles of the spindle just before meiotic division. Once again, the narrative would generate yet further questions. Why do the chromosomes ”condense” at prophase? How is the spindle formed? Perhaps in answering these questions we would begin to introduce the chemical details of the process. Yet simply plugging a molecular account into the narratives offered at the previous stages would decrease the explanatory power of those narratives.

Kitcher, P. (1984) 1953 and all that. A tale of two sciences. Philosophical Review.

And, of course, this great quote by Jay Lush.

‘Hard cash paid down, over and over again’

The whole subject of inheritance is wonderful. When a new character arises, whatever its nature may be, it generally tends to be inherited, at least in a temporary and sometimes in a most persistent manner. What can be more wonderful than that some trifling peculiarity, not primordially attached to the species, should be transmitted through the male or female sexual cells, which are so minute as not to be visible to the naked eye, and afterwards through the incessant changes of a long course of development, undergone either in the womb or in the egg, and ultimately appear in the offspring when mature, or even when quite old, as in the case of certain diseases? Or again, what can be more wonderful than the well-ascertained fact that the minute ovule of a good milking cow will produce a male, from whom a cell, in union with an ovule, will produce a female, and she, when mature, will have large mammary glands, yielding an abundant supply of milk, and even milk of a particular quality?

Today is Charles Darwin’s birthday. I’m not such a serious Darwin reader, but it’s fun how it seems like you can open a Darwin book at almost any chapter and find something interesting or amusing. This is from The Variation of Animals And Plants Under Domestication, chapter twelve, ‘Inheritance’. Here we find Darwin overflowing with enthusiasm when trying to convince a sceptic about the importance of inheritance. In true Darwin style he launches into a long list of examples:

Some writers, who have not attended to natural history, have attempted to show that the force of inheritance has been much exaggerated. The breeders of animals would smile at such simplicity; and if they condescended to make any answer, might ask what would be the chance of winning a prize if two inferior animals were paired together? They might ask whether the half-wild Arabs were led by theoretical notions to keep pedigrees of their horses? Why have pedigrees been scrupulously kept and published of the Shorthorn cattle, and more recently of the Hereford breed? Is it an illusion that these recently improved animals safely transmit their excellent qualities even when crossed with other breeds? have the Shorthorns, without good reason, been purchased at immense prices and exported to almost every quarter of the globe, a thousand guineas having been given for a bull? With greyhounds pedigrees have likewise been kept, and the names of such dogs, as Snowball, Major, &c., are as well known to coursers as those of Eclipse and Herod on the turf. Even with the Gamecock, pedigrees of famous strains were formerly kept, and extended back for a century. With pigs, the Yorkshire and Cumberland breeders ”preserve and print pedigrees;” and to show how such highly-bred animals are valued, I may mention that Mr. Brown, who won all the first prizes for small breeds at Birmingham in 1850, sold a young sow and boar of his breed to Lord Ducie for 43 guineas; the sow alone was afterwards sold to the Rev. F. Thursby for 65 guineas; who writes, ”She paid me very well, having sold her produce for 300l., and having now four breeding sows from her.” Hard cash paid down, over and over again, is an excellent test of inherited superiority. In fact, the whole art of breeding, from which such great results have been attained during the present century, depends on the inheritance of each small detail of structure. But inheritance is not certain; for if it were, the breeder’s art would be reduced to a certainty, and there would be little scope left for that wonderful skill and perseverance shown by the men who have left an enduring monument of their success in the present state of our domesticated animals.

For the rest of the chapter, he will go on to talk about humans, again with long lists of examples, and then mixing in domestic animals and plants again. A lot of these examples of heredity surely hold up, and others seem like anecdotes. Here and even more in the following chapters–with subtitles including ‘reversion to atavism’, ‘prepotency’ and ‘on the good effects of crossing, and the evil effects of close interbreeding’–Darwin is trying hard to make sense of heredity. Why are certain features heritable? Why do they sometimes go away in the offspring but reappear in later generations? Why are offspring sometimes more like one parent than the other? In chapter 27, he will present his ‘provisional hypthesis of pangenesis’.

Literature

Darwin. 1875. The variation of animals and plants under domestication.