Experimental test of a genetic constraint hypothesis

We have recently posted a (heavily revised) manuscript to arXiv detailing how we used the fruit fly Drosophila melanogaster (you can read here about why these little flies are so wonderful) to test a particular hypothesis about a genetic constraint, and more generally how our knowledge of development may inform us about the structure of the genetic variance-covariance matrix, G. Also we developed a really cool set of statistical models that evaluated our explicit hypotheses (more on that right at the end of the post)!

As a quick reminder (or introduction), G summarizes both how much genetic variation particular traits have, as well as how much traits co-vary genetically. This covariation can be due to "pleiotropy" which is a fancy word for when a gene (or a mutation in that gene) influences more than one trait. ie. a mutation might influence both your eye and hair colour). These traits can also covary together when two or more alleles (each influencing different traits) are physically close to each other (linked) and recombination has not had enough time to break these combinations apart. I highly recommend Jeff Conner's recent review in Evolution for a nice review of these (and other concepts related to some issues I discuss below).

Evolutionary biology, in particular evolutionary quantitative genetics thinks a lot about the G-matrix, and how it interacts with natural selection (or drift) to generate evolutionary change. This is summarized by the now famous equation linking change in trait means(Δz̄) as a function of both genetic variation (and covariation) and the strength of natural selection (usually measured as a so-called selection gradient, β). This is the multivariate (more than one trait) version of the breeders equation (made most famous by all of the seminal work by R. Lande).

Δz̄=Gβ

Why do we care so much about this little equation? It encapsulates many pretty heady ideas.  First and foremost that you can not have evolutionary change without genetic variation. That's right, natural selection by itself is not enough. You can have very strong selection for traits (such as running speed) to survive better with a predator around, but if there is no heritable variation for running speed, no (evolutionary) change will happen in the proceeding generations (and good luck with that tiger coming your way). However, once you have to consider multiple traits (running speed, endurance and hearing), we have to think about whether there is available genetic variations for combinations of traits, and whether these are "oriented" in a similar direction to natural selection. If not, it may be that evolutionary change with be slowed considerably (even if each traits seems to have lots of heritable variation). Of course if the genetic variation for all of these traits is pointing in the same direction as selection, then evolution may proceed very quickly indeed! The ideas get more interesting and complex from there, but they are not the for this discussion (the paper above by Jeff Conner, and this great review by Katrina McGuigan are definitely worth reading for more on this).

In any case, much thought has been given to how this G matrix can change both by natural selection and by other factors such as new mutation. Depending on how G changes, future evolutionary potential might change, which is pretty cool if you think about it! How might G change then? These are important ideas, because while we can estimate what G looks like, and how it might change (in particular due to natural selection), it is much harder to know what it will look like far in the future, making our ability to predict long term evolutionary change more difficult.
So what might help us predict G? One idea is that our knowledge of developmental biology will help us understand the effects of mutations, and thus G. If so, developmental biology could be a particularly powerful way of predicting the potential for evolutionary change, or lack there of (a so called developmental constraint).

To test this idea, I decided to use a homeotic mutation. Homeosis is the term used for when one structure (like an arm) is transformed (during development) to another (related) structure like a leg.  In fruitflies homeotic mutations are the stuff of legend (and nobel prizes), in particular for the wonderful cases of the poor critters growing with legs (instead of antenna) out of their heads, or four winged flies. You can see wonderful examples of mutations causing such homeotic changes in flies and other critters here.

In our case we used a much weaker and subtler homeotic mutation Ubx¹, which causes slight, largely quantitative changes. For example with this mutation, the third set of legs on the fly would be expected to resemble (in terms of lengths of the different parts of the leg) the second set of legs (flies like all insects have 3 sets of legs as adults). We wanted to know whether when we changed the third legs to look like second legs, would the G for the transformed third leg look that of a normal third leg or a normal second leg? Thus we were trying to predict changes in G based on what we know (a priori) of development and genetics in the fruitfly.

So what did we find? The most important points are summarized in figure 2 and table 3 (if you want to check out the paper that is). The TL'DR version is this: Yes, the legs homeotically transformed like we expected, but G of the mutant legs did not really change very much from that of a normal third leg. In other words, our knowledge of development did not really help us much in understanding changes in G. There are a few reasons why (which we explain in the paper), but I think that it is an interesting punchline, and I will leave it up to you to decide what it means (and if our experiment, analysis and interpretation are reasonable and logically consistent).

I also really want to give a shout out to one of the co-authors (JH) who developed the particular statistical model that we ended up using. He developed a set of explicit models that really helped us test our specific hypotheses directly with the data and experimental design at hand. This is sadly rarely done with statistics, so it is worth reading just for that! I really think (hope?) that this combination of approaches can be very useful for evolutionary genetics. Let me know what you think!

How and what to teach in undergraduate genetics

Here at Michigan State University, we are considering how to "fix" the primary undergraduate Genetics class. Why does it need to be fixed? Many reasons. For instance it has for many years been taught with little "institutional memory" from semester to semester. So what concepts are covered (and how) may depend heavily on when the students have taken it. This class is taught each semester (fall, spring and summer) with enrollments exceeding 300 students, and is required for practically every life sciences undergraduate major across many departments and colleges at the University. Indeed in my college alone (Natural Sciences) ~75% of the 4800 UGs in the college are in biological disciplines. Thus there is an extremely wide diversity of backgrounds, in particular with respect to basic quantitative skills. It is also generally a poorly regarded course from the perspective of students, and is  seemingly considered a "weeder" course where the hopes of many pre-med students are crushed (the course currently does not require calculas or physics as a pre-requisites, which at least when I was an UG, represented the sieve courses).

While we are just at the beginning of this process (and we are just  starting to collect information ) I already have a number of questions that I am trying to make sense of, and I would really appreciate feedback from everyone, especially people who have already been involved with a similar process at other schools

I will probably write about all of these questions (and what I am thinking on each one) in the future, but for now I will just get them down.

So my questions for the moment (let me know if you have any others I should be thinking about.

What sorts of background/ pre-requisites are reasonable for a "fundamentals of genetics"? Just 1st year biology? chemistry? physics? calculus? stats?

There has been a lot of recent discussion on the concepts (and the order that they should be taught), most notably the recent paper by Rosemary Redfield, as well as her blog about teaching genetics. This has also generated a lot of useful discussion (here and here for example).  I have reviewed several proposals for genetics textbooks, so many other organizing principles are also being used. Once I have organized my own thoughts I will write my own thoughts on this.  I am curious what has worked (or has not worked) as well. Thoughts?

Who is the target audience for a genetics course? Unlike introductory level courses (biology, physics, calculus) genetics is often taught as a second or third year course (here at MSU it is a 300 level course). Usually such more fundamental disciplinary courses are being taught from a disciplinary perspective. However, the audience for Genetics seems far broader. In particular many students hoping to be involved in medical sciences.  To whom do we teach? Those fundamentally interested in biology in general, or genetics in particular? Or to the much broader audience who include many who have no desire to be in the class (but have to to fulfill their degree while trying to get into medical school)? Is there a happy medium? Are two different classes (one for each audience) a better idea?

Thoughts?

Some further thoughts on "risky" research and the culture of science

This is just some further thoughts on an old post regarding the New York Times article " Grant system leads cancer researchers to play it safe". In that post I mulled over the idea that the mentoring process for young scientists trains us (as a community) to be hyper-critical and skeptical. Now of course scientists are individuals, and we vary a lot. Indeed there are lots of scientists who tend to be optimists, and take their (and other peoples) data at face value, while others spend their careers taking apart the ideas of others. There is of course room for all of these approaches. We need the creative spark of people to generate new models, and data to test them, and other scientists who test the logic or validity of these ideas and models. This is part of what makes the scientific process work so well. But, how might this affect the potential funding of risky "science"? Given that there are limited resources available to fund science research, if one reviewer of a proposal is highly skeptical of the ideas, while all of the other reviewers like them, will that be enough to have the proposal rejected for funding?

I am certain that if I "polled" many of my fellow scientists, they would all point to at least one proposal they submitted that failed to be funded based on one review, while all of the other reviewers loved it. It is not so different from what going onto Rottentomatoes. There are some movies where many reviewers love it, while others hate it. Indeed I have never seen a movie reviewed where there is complete agreement. Not surprisingly, the same is true for the scientific review process (although I would hope for different reasons).

However, this has all made me think about the differences in the way countries provide public funds for scientific research. In particular, in the U.S., the funding system tends to have both strong "boom-bust" cycles, naturally tied to the economy as a whole, but also strongly tied to fads in scientific research (sometimes called "sexy science"). Now, we are only human, and while nerdly as it may be, scientists can be enamoured by new and very interesting findings. Naturally this leads to many other scientists to want to join into this new area, and when grant proposals are reviewed on this research, the reviewers may themselves be entranced by the ideas, and pin their own hopes for future research successes on these new ideas or methods or approaches.

Indeed in my own field of Genetics, I have watched such transformations occur numerous times in my relatively short experience working in the field. This has happened both due to changes in technology as well as statistical methodology ( more on this in a future posting). In each instance, the same basic pattern emerged. First there was almost unanimous excitement and hope that these new approaches would solve all sorts of persistant problems in the field (for instance finding the set of genes that contribute to disease X). Shortly after, there were a few dissenting voices (largely ignored) that pointed out some of the shortcomings of the approach or method. Then in the next 2-3 years, as more and more people used these approaches or methods (or tested these new ideas), more and more issues were uncovered. And just then, when hope was beginning to fade, a new idea/method/technology was discovered, and so the cycle continued....

So how does all of this affect the funding for "risky research". Honestly I do not know. But I think it is worth considering. Any thoughts?

I finally joined the ranks of those who tweet
https://twitter.com/IanDworkin

A new manuscript on experimental tests for genetic constraints from the lab

Just a quick note, we have posted an updated manuscript (submitted to Evolution) to arXiv. I will post more about it soon (and hope to have it linked to Haldane's Sieve). In any case, while it has taken a really long time to get the analysis and interpretation quite right, I think it is a nice example of merging developmental genetic and evolutionary quantitative genetic insights!

Where to publish.... Is there still a role for society journals?

Many folks who follow scientific blogs, or science in the media are probably aware of the recent uptick in the discussion of the role of publishing companies in the dissemination of scientific papers. While the Open Access movement (access to the scientific papers are free to all, at least those with access to the internet) has been steadily gaining steam over the past decade or so, there has also been an effort to make the publications associated with any publicly (via Government sources) financed scientific work available to the public. The  logic to this is essentially that since funds from public coffers made the work possible, the summary of the results (as presented in a scientific paper) should be available at the very least to those who paid for it (if not to everyone on the planet, keeping with best scientific traditions). Publishers are still able to recoup their costs, and some profits via page costs, and library subscriptions during an initial phase of "paid access only". In my own estimation, this is pretty sound logic. Of course, I am a Canadian....

The reason that this effort is necessary is that most scientific journals are published via commercial publishers, and that access to many of the papers in these journals is behind a paywall. If you are at an institution that pays for it, you are granted access, otherwise you need to shell out $$. This does not seem to make much sense given that public funds were already used for the work itself. Several years ago the NIH created a policy where published papers associated with research performed with NIH funds need to be made publicly available six months after publication. The reason you may have heard about some or all of this during the past six months was because of the "research works act", which attempted, but failed to get rid of this policy (and other ones that might come to fruition in the future).  The retraction of the proposed legislation was in no small part due to a very irate scientific community and general public (including a well publicized boycott)

 I will in the future add some links to the great discussions available about this. However, I think a few places (here, here and here) sum it up well. Indeed, in addition to damaging the reputation of one publisher, Elsevier in particular, it has acted to really to generate a great deal of energy and discussion within the scientific community about the role of publishing companies in disseminating scientific papers, and more generally in how to open up the scientific process more generally. My friend has a nice review of the open science model, and I also recommend looking at sites like ResearchGate for an interesting experiment in combining science and social networking. There are also some interesting points to discuss about how the movements for open access to publishing, data and reproducible research seem to have not really connected well, despite some obvious shared goals (of access to the raw scientific data, the analysis used with the data, and the published summary of the findings associates with the data). However, that will need to wait for another post.

However my point for this post is somewhat different. It is easy to generate a caricature of the publishing companies as greedy corporate profiteers who use  free labour (in the form of reviewers and editors of scientific papers), often charge scientists "page charges" (to copyedit and format the manuscript for publication), and then charge again for the finished product (to University libraries, and the public at large). Certainly a number of companies have lived up to this caricature as well. The open access publishing movement in its various forms (examples include journals by PLoS, Frontiers, BMC and the new PeerJ) are publishing many new journals that counter these issues (well the third issue, which is really what gets most people upset). I provide a role for several journals from these publishers (PLoS One and Frontiers in Genetics), and I am in general a strong supporter of them. But....

One nagging concern (other than a Hollywood style event that simultaneously destroys all of the hard-drives in the world, thus making all of this work vanish) is the fate of "Society Journals". Most scientific disciplines are backed by a society of researchers working on (often loosely) related research questions. For my own work, two of the societies I belong to are the Genetics Society of America (GSA), and the Society for the Study of Evolution (SSE). Now many scientists think that there societies primary role is to A) Organize a big annual scientific meeting, and B) to publish the "Journal of Record" for the field, where scientific advances are summarized in publication. Again in my field, those journals would be "Genetics" and "Evolution" respectively (I know very surprising names given the field). However, in addition to the two roles discussed above, many of the societies have other roles like public outreach & education, and lobbying on behalf of the scientists.  Of course, until recently most small organizations did not have the ability to copy-edit, typeset and publish journals by themselves so they partnered to varying degrees with private publishing companies.  Now of course, any computer savvy individual can do all of this.

I have absolutely no idea of how the proceeds/profits are split, and how much the scientific society receives (as compared to the publishing company). What I am wondering (and simply do not have any answers to) is how (assuming scientific publishing moves largely to Open Access), will scientific societies fund themselves? Do the journals (like Genetics and Evolution) have a plan to transition to pure Open Access? Do they have a model to sustain themselves? I am a strong supported of my societies, and I think that a lot of harm would be done if they vanished. At the same time, I am in complete support of the OA movement, and think it is likely the future. I have spoken with a number of people about this casually, most notably Michael Eisen, who has blogged a great deal on the need to move entirely to Open Access. However, so far, I have not heard any real mechanism for this. Any ideas?

Retreating to Communicate

While I know many people expect my job as an evolutionary geneticist is to work alone in a dark tower (but only during a thunder storm), creating new breeds of monstrous fruit-flies (maniacal laugh... maniacal laugh), I actually spend most of my time doing something else entirely, trying to communicate how cool my fruit-flies really are. Well not so much the fruit-flies, but the questions we address using our little critters. Since I am unlikely to be creating a freeze ray to help me take over the city some time soon (Sorry Dr. Horrible, but it is unlikely I will be in your posse anytime soon), I generally use the more traditional means of communication, like presenting my work to my peers. This generally occurs in one of two ways, first presenting a "talk" or seminar at a University or a professional conference, or the far more common (and meat and potatoes of my field), by writing research manuscripts.

Now as many folks are aware of, writing can be (at least for some people, and that includes me) hard work. As a scientist, in addition to helping other people in the lab with their experiments, analyzing their hard earned data, and writing their papers (not to mention teaching, meetings, etc...). It turns out that many others struggle with this as well. Well, a few months ago I received an interesting email for a Writers Retreat held on the campus of the University of Nebraska-Lincoln. So here I am (having just arrived an hour or so ago). In addition to quiet time to write, there will be opportunities for professional coaching, and peer feedback, which I am very excited about.  We have also been asked to read the first thirteen pages of "Explaining Research: How to reach key audiences to advance your work" by Dennis Meredith. This book (or at least the first thirteen pages) reminds us as scientists to do a better job communicating, not only with each other, but with everyone!

In any case I am very excited about this opportunity, and I will let you know how I fare!