Author: kent

I’ve added a link to notes on the evolution of multivariate phenotypes in the lecture detail page for Thursday’s lecture. We won’t cover Arnold’s extension of the approach in lecture, but you may want to take a look at it. You might find it useful in some of your own work.

I’ve posted notes on using the phenotypic resemblance among relatives to estimate quantitative genetic parameters and on understanding the evolution of quantitative traits. I’m going to post a link to additional notes on multivariate selection gradients in a couple of hours. I want to spend a little time introducing the approach during Thursday’s lecture. That means that I’ll have to cut some of the mathematical details short during lecture and focus on the results and general principles. I know you’re all disappointed in that, but I’m sure you’ll be able to adjust.

We start our survey of quantitative genetics on Tuesday. It’s a field with a long history, and we’ll only scratch the surface, but when we finish out the course, you should understand the basics well enough that you can figure out anything else you need to know on your own. (Of course, I’m alway available for advice if you get involved in something that you can’t figure out. I’m not an expert, but I’ve been around long enough, that I can probably help you figure it out.)

I think you’ll find that this block of material is the most challenging and difficult that we’ve encountered all semester. There is substantially more algebra involved because there’s no other way to understand some of the fundamental concepts (like additive genetic variance). I’ll do my best to keep reminding us why we’re suffering through all of the math, but don’t hesitate to ask for a lifeline if – when – you feel as if you’re drowning.

As you’ll recall, I gave a very crude overview of RAD-seq and GBS on Thursday. I promised that I’d post a link to a recent paper that provides a good review of these and other techniques. You’ll now find the link on the lecture detail page for last Thursday’s lecture and on the consolidated readings page. You’ll also discover that the paper isn’t so recent. It was published in 2011. One of the hazards of getting old is that 6-year-old papers now seem recent. Heck, papers that are 20 years old sometimes seem recent. In any case, it’s still a good overview, and if you use Scopus or Google Scholar to find out who cited it, you’ll find more recent reviews if you’re interested in what’s happening now.

We won’t discuss this very recent paper in class, but in case you think that I’m making things more complicated than they need to be, take a look at this abstract (and then read the whole paper, if you’re really interested):

Population structure can be described by genotypic correlation coefficients between groups of individuals, the most basic of which are the pair-wise relatedness coefficients between any two individuals. There are nine pair-wise relatedness coefficients in the most general model, and we show that these can be reduced to seven coefficients for biallelic loci. Although all nine coefficients can be estimated from pedigrees, six coefficients have been beyond empirical reach. We provide a numerical optimization procedure that estimates all seven reduced coefficients from population-genomic data. Simulations show that the procedure is nearly unbiased, even at 3x coverage, and errors in five of the seven coefficients are statistically uncorrelated. The remaining two coefficients have a negative correlation of errors, but their sum provides an unbiased assessment of the overall correlation of heterozygosity between two individuals. Application of these new methods to four populations of the freshwater crustacean Daphnia pulex reveal the occurrence of half-siblings in our samples, as well as a number of identical individuals that are likely obligately asexual clone mates. Statistically significant negative estimates of these pair-wise relatedness coefficients, including inbreeding coefficents that were typically negative, underscore the difficulties that arise when interpreting genotypic correlations as estimations of the probability that alleles are identical by descent.

Ackerman, M.S., P. Johri, K. Spitze, S. Xu, T.G. Doak, K. Young and M. Lynch.  2017.  Estimating Seven Coefficients of Pairwise Relatedness Using Population Genomic Data. Genetics (online early) doi: 10.1534/genetics.116.190660

This morning I was reviewing the notes I posted yesterday, and I found a couple of small typos. The version that’s posted now fixes the typos, so you have a good excuse for not having downloaded them yet.

I’ve posted the notes for Tuesday’s lecture on Approximate Bayesian Computation and for Thursday’s lecture on population genomics. We won’t have time to do more than scratch the surface of either topic, but we’ll dive into a few examples in enough detail that you should be able to understand papers or seminars where people present this kind of work, and you may be inspired to use some of the techniques in your own research. I’d particularly encourage you to think about ways in which you could use Approximate Bayesian Computation to gain insight into ecological or evolutionary processes that interest you. I’m no expert, but if there were enough interest in the topic, I’d be happy to organize a 1-credit seminar next year in which we dove in and learned more about it together.

I returned from my trip to Dublin¹ Friday evening. Over the past day and a half, I’ve revised notes for this week’s lectures and put together Project #5. We’ll start on Tuesday by answering any questions left over from Nora’s lectures last week on detecting selection on molecular sequences and on evolution in multigene families. You’ll want to be sure that you understand Tajima’s D, and it will be obvious why I say that when you see Project #5.

Once we’ve finished dealing with questions, we’ll move onto discussing how to use molecular data, and especially nucleotide sequence data, to provide insights into the history and demography of populations. The first approach we’ll examine extends Wright’s F-statistics to nucleotide sequence data. If we have time, I’ll introduce some very sophisticated approaches to phylogeography, and we’ll discuss them in detail on Thursday.

I’ve posted notes for next week’s lectures about detecting selection on nucleotide sequences (which will introduce you to Tajima’s D) and on evolution in multigene families (which will introduce you to orthology, paralogy, and concerted evolution). Remember that Nora will be presenting these lectures, since I’ll be in Dublin at the annual meetings of the Deans and Directors of Graduate Studies (DDoGS) for universities in Universitas 21 (http://universitas21.com/event/details/269/re-cluster-workshop-and-meetings). Nora knows population genetics very well, and it’s likely she’ll be able to answer any question you have. If for some reason she can’t, keep track of it. We’ll spend time dealing with any of those questions on the Tuesday when I return.

In catching up on my reading today, I noticed that Sònia Casillas and Antonio Barbadilla have a review article in the March 2017 issue of Genetics on the history of molecular population genetics. I haven’t had a chance to read it carefully, but I did look it over quickly, and it appears to give a very nice overview of developments from the era of protein electrophoresis to the present day and population genomics. I’m pasting the abstract below, but I encourage you to follow the link and read the whole thing.

Casillas, S., and A. Barbabadilla.  2017.  Molecular population genetics. Genetics 205:1003-1035.  doi: 10.1534/genetics.116.196493

Molecular population genetics aims to explain genetic variation and molecular evolution from population genetics principles. The field was born 50 years ago with the first measures of genetic variation in allozyme loci, continued with the nucleotide sequencing era, and is currently in the era of population genomics. During this period, molecular population genetics has been revolutionized by progress in data acquisition and theoretical developments. The conceptual elegance of the neutral theory of molecular evolution or the footprint carved by natural selection on the patterns of genetic variation are two examples of the vast number of inspiring findings of population genetics research. Since the inception of the field, Drosophila has been the prominent model species: molecular variation in populations was first described in Drosophila and most of the population genetics hypotheses were tested in Drosophila species. In this review, we describe the main concepts, methods, and landmarks of molecular population genetics, using the Drosophila model as a reference. We describe the different genetic data sets made available by advances in molecular technologies, and the theoretical developments fostered by these data. Finally, we review the results and new insights provided by the population genomics approach, and conclude by enumerating challenges and new lines of inquiry posed by increasingly large population scale sequence data.