At The Molecular Ecologist, guest contributor K.E. Lotterhos discusses an important consideration in designing studies that “scan” the genome for regions experiencing natural selection—to be truly informative, they must “triangulate” using independent data:
Let’s say a number of individuals were collected from heterogeneous environments on the landscape. Some SNPs were significant both in an FST outlier analysis and a [genetic-environement association]. Would we consider these SNPs to have two independent sources of evidence?
NO, because the two tests were performed on the same sets of individuals.
What counts as “independent” in this context? I think that’s still something of an open question—but go read the whole thing and se what you think!◼
The postdoc-to-faculty metamorphosis: mysterious, magical, sometimes kind of gross. Photo by chekabuje .
Over at The Molecular Ecologist this week, K. E. Lotterhos has been writing about making the jump from a postdoctoral research position to being an actual, honest-to-gods faculty member. It’s in two parts, one about finding the faculty job and the other about getting started once you land it.
After I took the job, everyone told me how relaxed I must be to have a job lined up. Relaxed? There has been a substantial amount of busy work (ramping up the conference schedule, fielding emails and scheduling skype conversations with potential graduate students, dealing with lab renovations…). Plus, I’m still trying to work on my postdoc research and get it published, so more people will know who I am and so my grants will be more competitive. Everything I do now has a sense of urgency.
Congratulations! You have a job. Now get to work! But seriously, this all covers the career stage I’m hoping to enter myself, any year now. It’s definitely worthwhile reading, and bookmarking, the whole thing.◼
Over at The Molecular Ecologist, guest contributor Jacob Tennessan suggests that for those of us who study the genetics of natural populations, the ultimate “model organism” may be … us.
Thus, the field of human population genetics has always been a step or two ahead of the molecular ecology of wildlife. Common techniques like mitochondrial- or microsatellite-based phylogeography analyses were pioneered with data from humans. Research into human molecular ecology has yielded countless fascinating stories that provide a baseline for what to expect when examining other taxa. Some are well-known textbook examples, like the sickle-cell hemoglobin balanced polymorphism that conveys resistance to malaria, or the human global diaspora reflected in sequence diversity that traces back to “mitochondrial Eve” and “Y-chromosome Adam.”
Does that make Homo sapiens a “model organism” in the same sense as fruitflies and Caenorhabditis elegans, or more of a proving ground for new molecular methods? Go read the whole thing, and tell us what you think in the comments.◼
This week at The Molecular Ecologist, we’re kicking off a new interview series, “People Behind the Science,” by John Stanton-Geddes. The inaugural interview is with Loren Rieseberg, the Chief Editor of Molecular Ecology and an expert in the evolutionary consequences of hybridization between species.
When I arrived at Washington State University (WSU) in the fall of 1984 to begin my PhD, my advisor, Doug Soltis, handed me a copy of Verne Grant’s Plant Speciation and told me to find a problem. I was especially intrigued by Grant’s discussion of the potential role of hybridization in adaptation and speciation.
The interview ranges from Rieseberg’s philosophy for Molecular Ecology to which one paper (out of over 300 he’s authored!) that he wishes more people would read. So go read the whole thing.◼
Over at The Molecular Ecologist, I have a new post up discussing an interesting new modeling paper. It suggests that, for some viruses, variation in the rate of evolutionary change may be driven not by selection imposed by their hosts, but by the dynamics of the viral population within, and spreading among, host individuals.
Viruses based on RNA, as opposed to DNA, generally have very high mutation rates—in part because the process of replicating RNA is more error-prone than DNA replication. But there’s also tremendous variation in the substitution rate between different RNA viruses, even between populations of closely related viruses.
To find out how simple population dynamics could shape this wide variation in substitution rates, go read the whole thing.◼
Coding is better when done together. Photo by hackNY.
Over at the Molecular Ecologist, Kim Gilbert announces a new initiative, the Molecular Ecologist code snippet repository. It’ll be a place to put bits of useful code that wouldn’t warrant their own publication as a package or program, but would still be helpful to other biologists:
Do you have a script you regularly run to convert between data formats? A quick and easy way to run a certain analysis? Making a common figure for a given type of data? If you’re willing to share your code, we’ll put it online for public access with credit to your name.
To find out how to submit your snippets, go read the whole thing.◼
Over at the Molecular Ecologist, guest contributor Arianne Albert walks through how to make heatmap figures in R.
Heatmaps are incredibly useful for the visual display of microarray data or data from high-trhoughput sequencing studies such as microbiome analysis. Basically, they are false colour images where cells in the matrix with high relative values are coloured differently from those with low relative values. Heatmaps can range from very simple blocks of colour with lists along 2 sides, or they can include information about hierarchical clustering, and/or values of other covariates of interest. Fortunately, R provides lots of options for constructing and annotating heatmaps.
I’ve personally used heatmap graphics for visualizing population structure in a sample, or linkage disequilibrium along a stretch of genetic sequence, but I haven’t done anything very complex. Arianne’s examples use a data set that’s freely available on Dryad, and she includes a lot of step-by-step detail to build up complex figures—if you’re going to be visualizing some microarrary results or metagenomics data any time soon, you should read the whole thing, and probably bookmark it.◼
This week at the Molecular Ecologist, I’m discussing a new study from the blog’s parent publication, Molecular Ecology, which traces the origins of gene variants in a wild population of Soay sheep … back to domestic sheep.
The Soay sheep haven’t been completely isolated from other breeds. In recent centuries, they shared the Saint Kilda islands with humans, who kept domesticated sheep—providing several hundred years of opportunity for what geneticists call “an admixture event,” and everyone else calls “sex,” between the Soay breed and those domesticated sheep.
To learn how the study’s authors pinpointed the origin of the domestic genes variants, and how those variants have fared in the wild sheep, go read the whole thing.◼
This week at The Molecular Ecologist, Mark Christie shares some tips for how to take care of that massive genetic dataset that’s just come off the high-throughput sequencer:
Congratulations! You have recently received a file path to retrieve your hard-earned next-generation sequencing data. You quickly transfer the files to the computing cluster you work on or perhaps, if you only have a few lanes of data, to your own computer. But before you begin messing around with your data, you quickly realize that you should come up with a plan to back up and store unadulterated versions of your files.
For a nice set of recommendations with some step-by-step instructions, go read the whole thing.◼
Over at The Molecular Ecologist, I discuss a new study that uses phylogenetic estimates for 17 families of vertebrates to estimate how rapidly those animals have evolved in response to past climate change, and compares those estimates to how fast they’ll need to evolve to keep up with projected climate change. Spoiler alert: past rates of adaptation to climate aren’t anywhere near fast enough.
To keep up with projected climate change, Quintero and Wiens estimated that the species in their dataset would have to undergo adaptive change at from 10,000 to 100,000 times faster than the rates estimated in their evolutionary past.
Well, but maybe. To learn whether the data are telling us what the study’s authors say they’re telling us, go read the whole thing.◼