How Molecular Ecologists Work: David Toews on the joy of making figures, reading in the field, and the magic track pad

Welcome to the next installment of How Molecular Ecologists Work!

This entry is from Dr. David Toews, Banting Postdoctoral Researcher at Cornell University. David’s uses genomic data in combination with phenotypic, geographic, and behavioral data to ask questions about the evolution of birds.

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A Comparative View of Comparative Phylogeography

A recent issue of PNAS includes papers from a Sackler Colloquium on comparative phylogeography. As stated by the organizers, a major purpose of that gathering “was to bring together leading scientists to address the current state of phylogeography as the discipline enters its fourth decade” (Avise et al. 2016).

Four decades?!?

Time flies when the collective goal is identifying evolutionary processes that generated the spatial genetic patterns of (ideally) every species on Earth. Even comparative phylogeography, which synthesizes across single-species phylogeographies to understand how evolution and environment interact at the community scale, is a ripe old age of 30.

Comparative phylogeography occupies an intermediate position between landscape-level investigations and evolutionary biogeographic studies at higher taxonomic levels.

Comparative phylogeography occupies an intermediate position between landscape-level investigations and evolutionary biogeographic studies at higher taxonomic levels. (Figure 7 from Riddle (2016)).

As a contribution to that PNAS issue, Brett Riddle writes a timely review of the field of comparative phylogeography (“CP”). His review is focused specifically on continental systems, and it uses a database of 455 studies to assess trends and biases in the field to date. I talk about a few highlights below (hopefully inspiring you to read the paper in full).

Of course, dataset size and scope exhibit major trends in CP over the past 15 years. Not unexpectedly, Riddle finds that the size and genomic scope of phylogeographic datasets has increased across this period. However, it is also clear that few CP studies are using nuclear data to the exclusion of organelle genomes. This is because:

…organelle DNA, particularly mitochondrial DNA in animals, still likely delivers a very strong and heuristically valuable first approximation of geographic genetic architecture. (Riddle 2016)

Riddle (rightly) points out that organellar DNA is heuristically valuable as well; it can be used to generate hypotheses of past or present secondary contact and introgression. Those hypotheses can then be robustly tested with larger, potentially genomic-scale datasets.

Another trend is in the geographic focus of CP studies. We should celebrate the notable increases in the proportion of studies focused on the Southern Hemisphere. Riddle also recovers a near-global distribution of CP ‘hotspots’ – regions where studies are proliferating and might shed a particularly bright light on community-level evolution.

Yet, there are conspicuous directions in which CP could profitably expand – but hasn’t. For example, incorporation of biological or ecological relationships into CP studies is rare. That is an important direction to pursue because phylogeographic patterns of codistributed taxa are more richly compared and contrasted in light of basic natural history data. Another gap is the focus on trait evolution. If biological or ecological factors are indeed suspected to be driving evolution below the species level, then quantifying traits relevant to these factors may help to explain phylogeographic patterns. Finally, a few regions continue to be underrepresented in CP in general (Middle East, India, Tibetan Plateau).

Each of Riddle’s proposed ways forward for CP is sound; an overarching theme is that there is much to gain by interfacing with other disciplines. Yes, genomic-scale data will continue to help resolve fine-scale phylogeographic patterns and reveal cryptic instances of past contact. But equally auspicious for CP will be advances outside the field of molecular evolution: improved distribution modeling, more precise and higher-resolution trait data, new fossil discoveries, and continued refinement of existing geological and climatic hypotheses.

Cited:

Avise, J. C., Bowen, B. W., and Ayala, F. J.. 2016. In the light of evolution X: Comparative phylogeography. Proceedings of the National Academy of Sciences 113: 7957-7961.

Riddle, B. R. 2016. Comparative phylogeography clarifies the complexity and problems of continental distribution that drove AR Wallace to favor islands. Proceedings of the National Academy of Sciences 113: 7970-7977.

Posted in Coevolution, community, comparative phylogeography, phylogeography, population genetics | 1 Comment

On (mis)interpreting STRUCTURE/ADMIXTURE results

STRUCTURE, ADMIXTURE and other similar software are among the most cited programs in modern population genomics. They are algorithms that estimate allele frequencies and admixture proportions under the premise that sampled genotypes are derived from one of “K” ancestral populations, and have been widely used to (1) detect and estimate population structure, (2) quantify ancestral admixture, and (3) build the basis for complex evolutionary hypotheses about population evolution. However, interpreting the results of these methods has often been contentious (see Gilbert et al. 2012, Lawson et al. 2012), mostly around interpretation of “K” ancestral populations. Additionally, alternate evolutionary scenarios can also produce similar observable patterns using STRUCTURE/ADMIXTURE. For e.g. three alternate scenarios – one of recent admixture, one of admixture with unsampled/unobservable “ghost” populations, and a third with a recent bottleneck are described in Falush et al. 2016 (also see the interesting Twitter conversations here and here).

In their new preprint, Falush et al. 2016 describe a goodness of fit assessment of the admixture model results compared to results using matrix factorization of “chromosome painting” palettes (Lawson et al. 2012), which uses haplotype information to estimate ancestry across individuals. A residual plot between the two results can then be computed, which clearly demonstrates differences across evolutionary scenarios. For instance, a scenario of recent admixture doesn’t show any discernible residuals, whereas admixture with a ghost clearly indicates underestimation of admixture proportions in the admixed population.

Figure 1 of Falush et al. 2016 showing three simulated models, estimated admixture proportions, and residual plots of admixture proportions and estimated ancestries. Image courtesy: http://biorxiv.org/content/biorxiv/early/2016/07/28/066431.full.pdf

Figure 1 of Falush et al. 2016 showing three simulated models, estimated admixture proportions, and residual plots of admixture proportions and estimated ancestries. Image courtesy: http://biorxiv.org/content/biorxiv/early/2016/07/28/066431.full.pdf

Falush et al. 2016 also show the efficacy of this new method using an empirical data set from Ari blacksmiths and cultivators. The origins of these ethnic peoples in Ethiopia have been argued, with some studies pointing to recent admixture from neighboring ethnic groups, and others towards recent bottlenecks. Their analyses using the new method however clearly indicate support for the recent bottleneck scenario, which has also been studied to be more plausible using model-based demographic analyses. The authors also point to the importance of adequate sampling of individuals from populations of focal interest while using STRUCTURE/ADMIXTURE using an example of adding Melanesians in analyses of large human population genetic data-sets.

Overall, these results show that in recent history, genetic drift has been at least as important in shaping variation within these populations as admixture. A simple history comprising a differentiation phase followed by a mixture phase is false and inferences based on this model are liable to be misleading. Other, qualitatively different scenarios should also be considered, such as one in which in which the processes of mixture and divergence in ancient history was similar to that in recent history and the differentiation into four major ancestries reflects sustained differences in connectedness between populations.

References:

Falush, D., van Dorp, L. and Lawson, D., 2016. A tutorial on how (not) to over-interpret STRUCTURE/ADMIXTURE bar plots. bioRxiv, p.066431.

Lawson, D.J., Hellenthal, G., Myers, S. and Falush, D., 2012. Inference of population structure using dense haplotype data. PLoS Genet, 8(1), p.e1002453.

Pritchard, J.K., Stephens, M. and Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics, 155(2), pp.945-959.

Alexander, D.H., Novembre, J. and Lange, K., 2009. Fast model-based estimation of ancestry in unrelated individuals. Genome research, 19(9), pp.1655-1664.

Gilbert, K.J., Andrew, R.L., Bock, D.G., Franklin, M.T., Kane, N.C., Moore, J.S., Moyers, B.T., Renaut, S., Rennison, D.J., Veen, T. and Vines, T.H., 2012. Recommendations for utilizing and reporting population genetic analyses: the reproducibility of genetic clustering using the program structure. Molecular Ecology, 21(20), pp.4925-4930.

Posted in bioinformatics, genomics, howto, methods, population genetics, software, STRUCTURE | Tagged , , , , | Leave a comment

A tale of mammoths and a disappearing lake

A Herd of Mammoths. Source: WikimediaCommons/Kira Sokolovskaia

A Herd of Mammoths. Source: WikimediaCommons/Kira Sokolovskaia

A wonderful study revealed a sad story of the St. Paul Island population of woolly mammoths. Using a creative and diverse set of analytical approaches, scientists identified freshwater shortage as the likely cause of their extinction.

A cross-disciplinary collaboration of scientists from The Pennsylvania State University, University of Alaska, and University of California, Santa Cruz, yielded five different types of evidence suggesting that the last mammoth disappeared from St. Paul Island at the same time as the main (or only) freshwater source evaporated.

What goes around, comes around – even extinction

St. Paul Island is a rather small island of 110 km2. It was originally part of the Bering Land Bridge, but became isolated due to rising sea levels at the end of the Ice Age. A population of mammoths was trapped on the island, which strangely saved them from extinction.

When the St. Paul mammoths finally went extinct 5,600 years ago, there was only one other population of mammoths – their distant relatives also struggling for survival about 1,500 kilometers from there, on the much larger but mountainous Wrangel Island.

While we still don’t know what exactly happened to the very last mammoths on Wrangel Island, climate is probably to blame for the St. Paul extinction. The rising sea “consumed” coastal lakes and the increasingly arid conditions between 8,000 and 5,300 years ago led to freshwater shortage. Continue reading

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How Molecular Ecologists Work: Aaron Shafer on the perfect sentence, making phone calls, and German hip-hop

Welcome to the next installment of the How Molecular Ecologists Work series!

Aaron_goat_hair_resizeFor this entry, we have Dr. Aaron Shafer, who is currently transitioning from a postdoc position at Uppsala University to an assistant professor position at Trent University. Aaron uses molecular tools to understand the history and future of wild mammal populations (mountain goats, deer, even walrus!). His approach has produced multiple projects with both conservation applications and insights into the evolution of wildlife populations.

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Understanding diverse microbial communities: An interview with A. Murat Eren (Meren)

A. Murat Eren

It’s clear that microbes play a crucial role in practically every aspect of ecosystems globally. From the deepest, most remote and unexplored regions of the ocean, to the human oral cavity, there are diverse microbial assemblages driving Earth’s biogeochemical cycles.

Back in March, I was fortunate enough to catch a presentation Dr. A. Murat Eren (Meren) gave at Susan Holmes’ lab at Stanford. Meren is an assistant professor at the University of Chicago, and was just a few weeks ago was named an MBL fellow. His lab focuses on a range of interesting topics that generally fall under the broader umbrella of microbial metagenomics.

I was intrigued by his research, which has included projects concerning the microbiota associated with bacterial vaginosis, oligotyping to differentiate microbial taxa, and even an assessment of the contamination in the recently published tardigrade genome. The talk was great, and I was particularly struck by one point he emphasized: without the appropriate approach to analyzing and visualizing data, you can’t fully understand what your genomes (or metagenomes) are telling you.

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The Genomics of Rapid Adaptation

Phenology (the timing of life cycle events such as growth, breeding, or migration) is among the most sensitive organismal traits to climate and environmental change. In recent years, phenological shifts have been documented in numerous taxa, in traits such as flowering time, migration, hibernation and/or emergence. These shifts can be rapid, and they are presumably a key part of how species adapt to changing conditions. But to what extent are phenologies evolving at the molecular level?

Hoffmann and Sgro tried to address that question in a 2011 Nature review:

In an attempt to demonstrate evolutionary responses to climate change, genetic differences in space have been compared over time in a few cases… These have provided the strongest evidence for evolutionary responses in traits related to the timing of activity or reproduction.

A new study by Franks et al. in Molecular Ecology can now be added to that catalog. These authors focus on the invasive annual, field mustard (Brassica rapa), which in southern California was subjected to a natural, multi-year drought. Previous work (Franks et al. 2007 PNAS) described B. rapa’s response to that drought; specifically, there were shifts in flowering time of 1.9 and 8.6 days for populations from two contrasting habitats.

Field mustard (Brassica rapa). (Source: Wikipedia)

In their most recent paper, Franks et al. (2016) performed whole-genome shotgun sequencing of samples collected pre- and post-drought from the two habitats. After mapping reads to the B. rapa draft genome, the authors calculated gene-wise fixation indices between the temporal samples. They identified 855 genes as statistically significant outliers (all comparisons had FST > 0.1). Many of the most strongly differentiated genes they identified are indeed involved in drought response or other key physiological processes.

[Our] approach allowed us to observe genetic changes in the populations directly, uncovering clear evidence of the evolution of allele frequencies, which likely include the alleles responsible for the evolutionary change in phenotypes… It thus appears that genetic changes occurred in these populations over a very short period of time, supporting the idea that evolutionary changes in natural populations can occur rapidly enough to be observed.

Perhaps the most intriguing finding of Franks et al. is that, while both populations evolved in response to drought, the majority of genes under selection (>98%) is unique to each habitat. This suggests that selection is acting in complex ways, across different gene pools and in different settings, to drive phenological adaptation. It is possible that such adaptive flexibility may indeed bode well for species’ ability to cope with shorter-term climatic changes. However, the work of Franks et al. and others reminds us that sufficient standing genetic variation on which selection can work is a crucial prerequisite.

Cited:

Franks, S.J., Kane, N.C., O’Hara, N.B., Tittes, S., Rest, J.S., 2016. Rapid genome-wide evolution in Brassica rapa populations following drought revealed by sequencing of ancestral and descendant gene pools. Mol. Ecol. 25: 3622–3631. doi:10.1111/mec.13615

Franks, S.J., Sim, S., Weis, A.E., 2007. Rapid evolution of flowering time by an annual plant in response to a climate fluctuation. Proc. Natl. Acad. Sci. 104: 1278–1282. doi:10.1073/pnas.0608379104

Hoffmann, A.A., Sgrò, C.M., 2011. Climate change and evolutionary adaptation. Nature 470:479–485. doi:10.1038/nature09670

Posted in adaptation, genomics, Molecular Ecology, the journal, plants, population genetics, selection | Leave a comment