Friday, January 25, 2013

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(Δ) 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).


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!

Thursday, January 24, 2013

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?


Wednesday, January 23, 2013

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

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!