Molecular Ecology views: PCR gods, blue jays, and panoramas

Contributor Kim Gilbert’s photos of molecular ecology in action.

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The “PCR gods” keep the old thermal cyclers going. Photo courtesy Kim Gilbert.


Here’s a gallery of all Kim’s photos of lab work, a bird census, and a panoramic view of lodgepole pine.
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Molecular Ecology views: Stickleback trapping

Our first response to the call for photos of molecular ecology in action.

Anne Dalziel places a minnow trap for stickleback collection.

Anne Dalziel places a minnow trap for stickleback collection. Photo courtesy Simone Des Roches.


And what happens if those traps aren’t placed carefully in a tidal zone?
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What we're reading

Bookshelf
As we head into the weekend, here’s a few things we’ve noticed that might be worth your screen-time.
In the journals
Smith, S.A., Beaulieu, J.M., Stamatakis, A. & Donoghue, M.J. 2011. Understanding angiosperm diversification using small and large phylogenetic trees. American Journal of Botany 98: 404–14. doi: 10.3732/ajb.1000481.

… we found that diversification rate shifts are not directly associated with the major named clades examined here, with the sole exception of Fabaceae in the GenBank mega-phylogeny. These agreements are encouraging and may support a generality about angiosperm evolution: major shifts in diversification may not be directly associated with major named clades, but rather with clades that are nested not far within these groups.

Makowsky, R., Pajewski, N.M., Klimentidis, Y.C., Vazquez, A.I., Duarte, C.W., Allison, D.B., et al. 2011. Beyond missing heritability: prediction of complex traits. PLoS Genetics 7: e1002051. doi: 10.1371/journal.pgen.1002051.

Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h2 up to 0.83, R2 up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset)

In the blogosphere
Requiescat: Godfrey Hewitt.
PLOS Genetics cuts some ethical corners to publish a paper using crowdsourced human genome data.
Where should you submit your next paper?

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The end of Primer Notes, the start of Genomic Resources Notes

Molecular Ecology Notes published its first issue back in March 2001 – an issue containing a brief editorial, four technical notes, and 35 primer notes. The latter, brief papers describing new primer pairs useful for studying natural populations, have been the main output for the journal for the last twelve years: we have published almost 2,500 of them, and archived 35,000 primer pairs on the MER primer database.
A few things have happened in the last five years that have undermined the importance of primer notes (aka Permanent Genetic Resources Notes). First, in silico techniques and the wide availability of EST or Next Generation Sequence data mean that optimising 10-20 microsatellites is much easier than it used to be, and this was the primary reason behind our switch to the summary article format for primer notes at the start of 2009. Our ability to generate large volumes of sequence data has progressed very quickly, to the extent that microsats will soon be replaced by SNP and sequence data in many projects, with analyses based on individual assignment (e.g. paternity analysis) being the likely exception.
Given the declining importance of primer notes, and the existence of other good outlets (e.g. Cons. Gen. Resources and APPS), we will stop considering them for publication at the end of March 2013. This is the end of an era for ME Resources, but we feel it’s important to keep moving things forward.
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Q&A: Yannick Wurm wrangles RADseq to learn why some fire ants bow to more than one queen

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Fire ants, Solenopsis invicta. Photo by Flickr user HankPlank.


Yannick Wurm grew up in Redwood City, California, and his initial plan was to design interfaces for Apple. But he went to university at the Institut National des Sciences Appliquées in Lyon—where, after two years of general engineering courses, the new Bioinformatics and Modeling department looked like “fun.” Looking for opportunities in research, he found an opening with a “red fire ant genomics” project on Google … and eight years later, he’s still studying exactly that.
Yannick got in touch to let us know that a cool new result in that project had just been published in Nature, and he was kind enough to answer some questions about the discovery.—Jeremy
Wang, J., Wurm, Y., Nipitwattanaphon, M., Riba-grognuz, O., Huang, Y., Shoemaker, D., et al. 2013. A Y-like social chromosome causes alternative colony organiziation in fire ants. Nature, doi: 10.1038/nature11832.
Okay, so let’s start off with the key natural history: some fire ant colonies have more than one queen? How does that work? What features of worker behavior or other phenotypes are associated with supporting more than one queen?

First, you’ve got to remember that there are more than 20,000 species of ant, and their lifestyles are very diverse. For example in some species each colony contains only a few dozen individuals living in a dead twig or an acorn, but Oecophylla weaver ants make large networks of nests up in the trees by sticking leaves together, and Eciton army ant colonies of hundreds of thousands of workers lack permanent home base and instead are regularly on the move.
But there can also be variation within species. In particular this has been extensively studied in the red Solenopsis invicta fire ant: some colonies have up to hundreds of wingless queens, but other colonies contain strictly one single wingless queen. And this is stable: any additional queen you try to add to a single-queen colony is executed by the workers.
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Posted in bioinformatics, interview, next generation sequencing | Tagged , , , | 1 Comment

What we're reading

Office Bookshelf

As we head into the weekend, here’s a few things we’ve noticed that might be worth your screen-time.
In the journals
Epps, C.W., Wasser, S.K., Keim, J.L., Mutayoba, B.M. & Brashares, J.S. 2013. Quantifying past and present connectivity illuminates a rapidly changing landscape for the African elephant. Molecular Ecology. doi: 10.1111/mec.12198

The connectivity of elephant populations in Tanza- nian protected areas reflects a landscape in transition: elephants are still moving surprising distances outside protected areas (Fig. 2), even over steep terrain and near human settlements, but areas of dense human set- tlement and poaching threats have likely greatly reduced or eliminated many such movements (Figs 3 and 4).

Sunday, J.M. & Hart, M.W. 2013. Sea star populations diverge by positive selection at a sperm-egg compatibility locus. Ecology and Evolution. doi: 10.1002/ece3.487

We find a different pattern of selection in the bindin locus in two geographically separated populations of a single species. Our results specifically indicate that there are sites under positive selection (dN > dS) in one population that are under purifying or neutral selection in the other population (dN ≤ dS), and the sites found to be under positive selection differed between the two populations.

In the blogosphere
A non-exhaustive list of hurdles for crowdfunding science.
The U.S. National Science Foundation Division of Environmental Biology (NSF-DEB) has launched a blog.

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Fear and loathing in academia – Getting the right kind of mentorship

This week I’ve invited a friend and colleague, Hayley Lanier, to contribute a guest post to the Molecular Ecologist.  Hayley is a Postdoctoral Fellow at the University of Michigan, where she works with Lacey Knowles.  She has contributed an excellent post on getting the most out of the mentor/mentee relationship….
My research is focused on species tree estimation, phylogeography, and population structure, but I’ve opted not to talk about any of that today.  Instead, I’d like to focus on mentorship, and how having a good support system can make the uncertain waters of academia easier to navigate.
Noted science blogger and marine scientist Kevin Zelnio hung up his quill this week and moved on to greener pastures (a microbrewery and hostel in Sweden). He chronicles his journey from enthusiastic graduate student to discouraged PhD seeker and how his scientific blogging and freelance writing have developed through the years over at Deep Sea News. His enthusiastic writings and witty posts at DSN and The Other 95% will be missed. Although we have never personally met, Kevin strikes me as a clearly intelligent individual with an amazing capacity for communicating science, and yet his journey through graduate school left him feeling embittered and discouraged. While Kevin identifies many factors that contributed to his disillusionment with academia, one of the things that really stuck out to me was the role that his mentors have played in his frustration (and successes) in science.
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Show us your molecular ecology!

2010.04.20 - Camassia

Anyone who’s hung around the blogosphere long enough is familiar with the “View From Your Window” feature at Andrew Sullivan’s blog, in which readers send in photos of, well, exactly what it says on the tin. The result is a stream of posts showcasing the worldwide community gathered around Sullivan’s site, and it’s really quite lovely.
Well, you know what other blog has a worldwide audience that must surely include many a talented amateur photographer? The Molecular Ecologist! Okay, yes, we’re not exactly on the same level as the Daily Dish, but we’d love to see the diversity of molecular ecology through the eyes of the working scientists who read this blog.
So here’s the proposition: We’d like you, our readers, to send in your photos and images of molecular ecology in action—from the lab, from the field, and from the greenhouse. Photos of scientists at work or their favorite study organisms are what we’re mostly interested in, but we’d also love to see your very best data visualizations—the more colorful, creative, and informative, the better. We’ll post submissions to the blog and in a gallery on our Facebook page—and we’ll incorporate the very best ones into our rotating list of header images.
To submit, post a Creative Commons-licensed copy of the photo to Flickr with the tag MolecularEcologistView; or e-mail a high-resolution image to Jeremy. (This may seem obvious, but: By submitting a photo or image, you’re understood to be giving The Molecular Ecologist permission to post it to the blog and use it as a header image, and to make any modifications necessary to do so.)
We can’t wait to see what you’ll send in!

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What we're reading

Bookshelf
As we head into the weekend, here’s a few things we’ve noticed that might be worth your screen-time.
In the journals
Rodelo-Urrego, M., Pagán, I., González-Jara, P., Betancourt, M., Moreno-Letelier, a, Ayllón, M. a, et al. 2013. Landscape heterogeneity shapes host-parasite interactions and results in apparent plant-virus codivergence. Molecular Ecology, doi: 10.1111/mec.12232.

Also, environ- mental heterogeneity similarly shaped the spatial genetic structures of host and viruses. This resulted in the congruence between host and virus phylogenies, which does not seem to be due to host-virus co-evolution.

Dart, S. & Eckert, C.G. 2013. Experimental and genetic analyses reveal that inbreeding depression declines with increased self-fertilization among populations of a coastal dune plant. Journal of Evolutionary Biology, doi: 10.1111/jeb.12075.

Based on lifetime measures of dry mass and flower production, ID [inbreeding depression] was stronger in nine [outcrossing] populations [mean ∂ = 1-(fitness of selfed seed/fitness of outcrossed seed) = 0.39] than 16 [selfing] populations (mean ∂ = 0.03). However, predispersal ID during seed maturation was not stronger for LF populations, and ID was not more pronounced under simulated drought, a pervasive stress in sand dune habitat.

In the blogosphere
Our own Tim Vines discusses how to decide what to put in a public archive on the Dryad blog.
An adjunct lecturer explains why her course syllabus instructs her students not to call her “professor.”
A new study concludes that there’s no racial bias in NIH funding, but the sample size is, um, kind of underwhelming.

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Where's the heritability? Right where you'd expect—if you look close enough

Where's the heritability?

Where’s the heritability?


Biologists have at our disposal two major ways to assess how much genetics contributes to variation in the most interesting traits, or phenotypes, of our favorite study organisms—that is, the heritability of those phenotypes.
There’s what you might call the “top-down” approach, under a classic quantitative genetics framework, in which we can measure a whole bunch of individuals with known pedigree relationships, then use some form of linear regression to ask how well that known pedigree predicts the phenotype of each individual—the extent to which individuals with closer relationships have more similar phenotypes is the extent to which genes contribute to that phenotype.
Alternatively, we can use genetic marker data coupled with planned crosses between selected indivdiuals in a quantitative trait locus (QTL) study, or, if controlled crosses aren’t feasible, a genome-wide association (GWA) analysis. These are more “bottom-up” approaches, because they identify discrete pieces of the genetic code that individually explain some fraction of the total phenotype variation within the sample. Add up all the effects of all those individual pieces, and maybe account for some interactions between them, and you should find that you explain just as much of the phenotypic variation as you would with a top-down approach.
Except, it turns out, that doesn’t often happen.
When researchers have gone looking for specific genetic loci underlying traits for which we already have well-established, robust top-down estimates of heritability, they find that they loci they can detect using either QTL or GWA methods don’t account for some—and sometimes a lot—of the known heritability. The classic example is human height. Top-down (heh) methods give height a narrow-sense heritability—that is, the portion of heritable variation not due to interactions between loci or different variants at the same locus—of more than 80%. Yet studies looking for specific loci responsible for height have explained much less than that—sometimes as low as 5%.
Phenotypic variation, in other words, is left unexplained. This has made a lot of people very puzzled, and been widely regarded as a bad thing.
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Posted in quantitative genetics | Tagged , , , | 10 Comments