marmap

A couple years ago, Benoit Simon-Bouhet ended up sharing an office with Eric Pante, then a post-doc fellow in his former lab. The two quickly realized they were in a lab in which few people had the expertise or taste for coding. Thus, on a daily basis, they were both approached by colleagues and students to take a look at their data analysis and graphics. Meanwhile, they had their own not-so-easy tasks of creating publication-quality maps for themselves as well as their colleagues. They both had bits and pieces of R scripts scattered around their hard drives to (i) import bathymetric data previously downloaded locally from public databases, (ii) reshape these data in a form suitable for plotting in R and (iii) plot the bathymetry together with other data such as sampling sites or other locations of interest. The process was tedious, convoluted (especially for the manually download online bathymetric data) and required a good knowledge of the R scripting langage. In order to ease this process, the two embarked on creating marmap (short for marine maps) …

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Dōmo arigatō

Along with my collaborators, Erik Sotka, Courtney Murren, Allan Strand and our battery of students, we have embarked on an intense summer field season. Erik and I are leading the effort of sampling populations of the introduced red seaweed Gracilaria vermiculophylla. It is native to the northwest Pacific, but has been introduced to every continental margin in the Northern Hemisphere in the last few decades.

To date, studies on marine invasions focus principally on demographic and ecological processes, and the importance of evolutionary processes has been rarely tested. Moreover, there are surprisingly few studies that compare native and non-native populations in their biology or ecology. Our current project integrates population genetics, field surveys and common-garden laboratory experiments to address the role of rapid evolutionary adaptation in invasion success.

The weed that launched a massive collaborative project tracings its evolution during invasion

The weed that launched a massive collaborative project tracings its evolution during invasion

For my part, I knew my summer would be filled with a month long sojourn in Japan, with short trips every 10 – 15 days around North America bookended by a month long trip to sample European coastlines with a return to my old haunts in northwestern France.

Alas, Erik’s first leg in Japan (see some photos here) resulted in a mountain (or maybe seamount, see my next post on the R package marmap!) of live algae for culturing and phenotyping! Life became decidedly hurried!

Our students: Paige Bippus (CofC undergrad, Class of '16, middle right), Lauren Lees (CofC undergrad, Class of '17, middle left), Sarah Shainker (CofC undergrad, Class of '16, bottom middle) and Ben Flanagan (CofC GPMB grad student, Class of '17)

Our students: Paige Bippus (CofC undergrad, Class of ’16, middle right), Lauren Lees (CofC undergrad, Class of ’17, middle left), Sarah Shainker (CofC undergrad, Class of ’16, bottom middle) and Ben Flanagan (CofC GPMB grad student, Class of ’17)

No pre-emptive posts were penned … just shepherding our fantastic students into the ins and outs of red algal culturing, while keeping up morale with the endless playlists of Songza (80’s Prom being a particular favorite due to the aptly timed “Turning Japanese” while processing Japanese populations of G. vermiculophylla).

I had spoken with Jeremy about posting field work stories as well as highlighting the research of interest to TME readers from the marine labs we’d be visiting throughout the summer. Yet, once again, time did not allow for live posts while in country … so over the next few weeks, I’ll be posting some field anecdotes as well as a description of the different places we had the opportunity and good fortune to visit.

We are celebrating our Independence Day in the US this weekend, so I’ll leave all of you with a few pictures for the long holiday weekend! As well as a massive thank you to all of our hosts in Japan who made this a hugely successful as well as once as a once in a lifetime trip!

Kelp drying in Muroran, Hokkaido, Japan

Kelp drying in Muroran, Hokkaido, Japan

The tale of an urchin and an anemone

The tale of an urchin and an anemone

Erik Sotka, Rob Hadfield (my partner in crime and in the field in Japan!) and me at one field site in Akkeshi, Hokkaido, Japan

Erik Sotka, Rob Hadfield (my partner in crime in life and in the field in Japan!) and me at one field site in Akkeshi, Hokkaido, Japan

Benten-jinja Shrine in Akkeshi-ko

Benten-jinja Shrine in Akkeshi-ko

Marimo from Akan-ko

Marimo from Akan-ko

Fushimi Inari-taisha in Kyoto

Fushimi Inari-taisha in Kyoto

Kimono

Kimono

Dinner with one of our hosts, Dr. Masahiro Nakaoka in Kimitsu

Dinner with one of our hosts, Dr. Masahiro Nakaoka, in Kimitsu

Dōmo arigatō!

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Societal constructs, and Genetic diversity

While we grapple with numerous discoveries of variation in genomic diversity in humans, interest has subsequently risen in understanding their causes/results. Two recent papers describe experiments to determine (a) the effects of marital rules (who gets to marry whom) on genomic diversity (Guillot et al. 2015), and (b) the correlations between effectively random-mating, and inbreeding human populations and various health-related quantitative traits (Joshi et al. 2015).

Sums of Runs of Homozygosity (SROH) shown as a function of cohorts of human populations. Figure courtesy: Fig. 1 from Joshi et al. (2015) http://www.nature.com/nature/journal/vaop/ncurrent/fig_tab/nature14618_F1.html

Sums of Runs of Homozygosity (SROH) shown as a function of cohorts of human populations. Figure courtesy: Fig. 1 from Joshi et al. (2015) http://www.nature.com/nature/journal/vaop/ncurrent/fig_tab/nature14618_F1.html

Relaxed Observance of Traditional Marriage Rules Allows Social Connectivity without Loss of Genetic Diversity, Guillot et al. Molecular Biology and Evolution, 2015.

Marital rules – societal constructs on who marries whom are predominant in several human populations. Biologically, one would hypothesize that these rules also influence genetic diversity of the population, and thus the fitness of offspring. Guillot et al. (2015) attempt via simulations, and analyses of SNP diversity in an Indonesian population to quantify relaxed, or strict adherence to these rules, particularly the MBD rule (or Mother’s Brother’s Daughter) wherein men are required to marry their mother’s brother’s daughter. Key findings of the study include (a) strict MBD marital rules lead to a reduction in genomic diversity under simulations, (b) non-adherence of strict MBD rules in the Rindi community in Eastern Indonesia, an island population in which marital rules have been extensively studied.

Certainly, reduced genetic diversity under a strict interpretation of the APA marriage rules suggests that there was little biological incentive for communities to enforce marriage rules strongly, at least for long periods of time.

Directional dominance on stature and cognition in diverse human populations, Joshi PK et al. Nature, 2015.

While the detrimental effects of inbreeding (and marital rules like in Guillot et al. above) have been extensively studied in Mendelian traits in humans, most fitness traits are complex, and polygenic. Joshi et al. (2015) as part of the ROH (Runs of Homozygosity) consortium investigate 16 quantitative traits that have fitness consequences in humans and their correlations with homozygosity. Analyses of SNP arrays for ROH in more than 300,000 individuals revealed (a) differences in ROH lengths, and demography (with African populations containing the least homozygosity, and isolated populations, including Amish, and Hutterites containing the most homozygosity), (b) an average reduction of 1.2 cm in height, and 137 ml in forced expiratory volumes in offspring of first cousins, (c) 0.3 standard deviations reduction in general cognitive ability, and 10 months’ reduction in educational attainment in offspring of first cousins, and (d) no significant effect in 12 other fitness related traits (particularly to do with cardio-metabolism).

We have demonstrated the existence of directional dominance on four complex traits (stature, lung function, cognitive ability and educational attainment), while showing any effect on another 12 health-related traits is at least almost an order of magnitude smaller, non-linear or non-existent.

References:

Joshi, Peter K., et al. “Directional dominance on stature and cognition in diverse human populations.” Nature (2015) DOI:10.1038/nature14618

Guillot, Elsa G., et al. “Relaxed observance of traditional marriage rules allows social connectivity without loss of genetic diversity.” Molecular biology and evolution (2015). DOI: 10.1093/molbev/msv102

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Understanding amphibian disease inside out

1024px-AD2009Sep15_Rana_temporaria_01

A Common Frog (Rana temporaria) – Photo by Bernie Kohl

In the spring of 2010, I was doing amphibian surveys among a few wetlands in Eastern Kentucky that were known for their excellent diversity. As I sauntered up to a familiar study site, I was greeted with an amphibian massacre. Hundreds of dead tadpoles floated on the surface of the wetland, creating a raft of amphibian biomass unlike anything I’d ever seen.

I was stunned. What happened? Continue reading

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Gene expression analysis- are we doing it wrong?

In the last few weeks, three new preprints have come out suggesting that like Jack Butler dropping his kids off at school in the movie Mr. Mom, when it comes to differential gene expression analyses, we’re doing it wrong. Continue reading

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The Kennewick, and the Oase I

Last week was glorious for ancient DNA enthusiasts – here are some quick blurbs on findings from genomic analyses of the Kennewick man, and the Oase I individual.

Facial reconstructions of the Oase I individual (L), and the Kennewick man (R). Image courtesies: The Smithsonian Magazine  (http://thumbs.media.smithsonianmag.com//filer/51/9f/519fea8a-a215-48fe-ba09-fae09a0bb3e3/kennewick-hero.jpg__800x600_q85_crop.jpg), Dons Maps (http://donsmaps.com/romaniancaveskull.html)

Facial reconstructions of the Oase I individual (L), and the Kennewick man (R). Image courtesies: The Smithsonian Magazine (http://thumbs.media.smithsonianmag.com//filer/51/9f/519fea8a-a215-48fe-ba09-fae09a0bb3e3/kennewick-hero.jpg__800x600_q85_crop.jpg), Dons Maps (http://donsmaps.com/romaniancaveskull.html)

The ancestry and affiliations of Kennewick Man, Rasmussen et al. (2015) Nature DOI: 10.1038/nature14625

There has been much ado over the ancestry of the Kennewick Man – carbon-dating studies have dated his remains to ~8,500 ybp, morphological studies maintain distinction from Native American (Pacific Northwestern) ancestry, and teams of scientists and tribes battle on over the impending fate of the remains. Rasmussen et al. (2015) in an interesting turn of events, analyze genomic ancestry of the Kennewick man, and find clear evidence of Native American ancestry using both a PCA, and f3-statistics. Important findings of this study include: (a) rejection of Ainu/Polynesian ancestry of the Kennewick man, as suggested by morphological studies, (b) similarity in admixture proportions to Native American, particularly among claimant tribes, and (c) direct or derivative ancestry of current Native Americans from the Kennewick man. While this answers some questions about the ancestry of the Kennewick man, it also brings forth unaddressed details of ancestral admixture, and migration in Holocene Americas.

Identifying which modern Native American groups are most closely related to Kennewick Man is not possible at this time, since our comparative DNA database of modern people is limited, particularly for Native American groups in the United States.

An early modern human from Romania with a recent Neanderthal ancestor, Fu et al. (2015) Nature DOI: 10.1038/nature14558

Meanwhile, in a much more distant past, the Kennewick man’s ancestors were still diversifying out of Africa, admixing with Neanderthals around 37,000-86,000 ybp, with little knowledge of the process of admixture, or understanding of Neanderthal extinction. Fu et al. (2015) analyze ancient genomic DNA from the Oase I individual, one of the oldest modern human remains yet discovered to report 6.0% -9.4%  Neanderthal ancestry. In comparison with the Ust’-Ishim, Kostenki, and modern Chinese and European individuals, the Oase I individual contains 2-4 fold higher Neanderthal alleles. Analysis of IBD segment lengths (i.e. identical segments, unbroken by recombination) also indicates that the Neanderthal admixture occurred within 4-6 generations ancestral to the Oase I individual.

However, the absence of a clear relationship of the Oase 1 individual to later modern humans in Europe suggests that he may have been a member of an initial early modern human population that interbred with Neanderthals but did not contribute much to later European populations

References:

Rasmussen, Morten, et al. “The ancestry and affiliations of Kennewick Man.”Nature (2015). DOI: 10.1038/nature14625

Fu, Qiaomei, et al. “An early modern human from Romania with a recent Neanderthal ancestor.” Nature (2015). DOI: 10.1038/nature14558

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IBE/IBD Contour plots in R

Rob’s post from yesterday motivated me to find an alternate way of visualizing correlations between matrices of geographical or ecological data, and genetic data. I have seen plenty of Mantel, or partial Mantel tests of correlation, as well as plots of “IBD”, or Isolation By Distance (which plots some genetic distance versus some ecological/geographical distance), but how fun would it be if you could visualize these correlations between sampling locales in different variables (here genetic differentiation, genetic distances, geographical distances, ecological distances, etc) on the same plot? So that’s exactly what I set out to do.

Contour plot of correlated geographical (black) and ecological distances (heat map), from Manthey and Moyle (2015).

Contour plot of correlated geographical (black) and ecological distances (heat map), from Manthey and Moyle (2015).

As an example, I downloaded the data from Table 2 of Manthey and Moyle (2015), which shows geographical distances versus ‘ecological’ distances. As a second example, I also downloaded data from Table 2 of Sethuraman et al. (2014), which shows population pairwise differentiation (Fst), versus geographical distances. I constructed symmetric matrices from these, and used the modified “filled.contour2” function (modified version of filled.contour) for these plots. So, assuming that you have symmetric matrices saved as text files (you can download these examples from this link),

#Function courtesy http://tinyurl.com/oqxt8dq
matrix.axes <- function(data) {
x <- (1:dim(data)[1] - 1) / (dim(data)[1] - 1);
axis(side=1, at=x, labels=rownames(data), las=2);
x <- (1:dim(data)[2] - 1) / (dim(data)[2] - 1);
axis(side=2, at=x, labels=colnames(data), las=2);
}
source(“http://tinyurl.com/psdtkgr”)
n1<-as.matrix(read.table(“nuthatches-1”,row.names=1,header=TRUE))
n2<-as.matrix(read.table(“nuthatches-2”,row.names=1,header=TRUE))
par(mar=c(6,6,4,2)+0.5)
filled.contour2(n1,plot.axes=matrix.axes(n1))
contour(n2,col="black",add=T)

Contour plot of geographical distance (black) versus genetic differentiation (heat map) from the data of Sethuraman et al. (2013).

Contour plot of geographical distance (black) versus genetic differentiation (heat map) from the data of Sethuraman et al. (2013).

And voila! I modified the code above with different color palettes for the data of Sethuraman et al. (2013). I find these plots a lot more informative than say Figure 3 of Manthey and Moyle (2015), or Figure 2 of Sethuraman et al. (2013). What are your thoughts? Do let me know! Ideally, I’d like to have this contour plot over a geographical map – but I’ll save that for a future post. Good luck!

Posted in bioinformatics, howto, population genetics, R, software | Tagged , , , | 5 Comments

Adapting to the new wave of isolation by environment

Image by Steve Ryan

Isolation by environment, not distance, explains the genetic relationship between an avian taxon among Madrean Sky Islands, according to a new study appearing in Molecular Ecology by Manthey and Moyle.

The authors throw the kitchen sink of new analyses at a combination of geographic, ecological, and genomic data and provide an interesting example of isolation by environment in a widespread species within a strongly heterogeneous landscape.

Figure 1 from Manthey and Moyle (2015) displaying the sampling areas of the Madrean Sky Islands

Figure 1 from Manthey and Moyle (2015) displaying the sampling areas of the Madrean Sky Islands

This new paper is significant in a couple ways. First, this is one of the first investigations of isolation by environment that utilizes hundreds of SNP loci, opening the door for the detection of adaptive loci that may relate to environmental distances. Second, the geographic scope of this taxon is unique since you may not expect a highly mobile bird to show such variation across a relatively small geographic scale.

If you are out there looking for a blueprint for the future of IBE studies, Manthey and Moyle have kindly provided a preview of the type of papers you are going to be reading quite often over the next few years.

Manthey, J. D., & Moyle, R. G. (2015). Isolation by environment in White‐breasted Nuthatches (Sitta carolinensis) of the Madrean Archipelago sky islands: a landscape genomics approach. Molecular ecology. DOI: 10.1111/mec.13258

Posted in adaptation, Molecular Ecology, the journal, phylogeography | Tagged , , | 1 Comment

The evolution of phylogeography in the next gen era: 20 years in review

Phylogeographers have long known about the limitations of single locus studies (ie, the effects of selective sweeps, stochasticity in lineage sorting among loci) and that adding loci improves the accuracy of demographic parameter estimates. As we continue to shift towards collecting multi-locus datasets thanks to high throughput sequencing, some interesting questions have come up. For example, what is the best ratio of genetic loci to individuals sampled? What is the role of mitochondrial (mtDNA) and chloroplast (cpDNA) loci in the next gen era? And most broadly, how has the field of phylogeography itself evolved in the last 20 years since the advent of high throughput sequencing?

Garrick et al. (2015) tackled these questions by exploring how phylogeography datasets have changed in the last 20 years. The authors collected empirical papers published in Molecular Ecology from 1992 to 2013 that had the search term term “phylogeograp*” in the title, abstract, keywords, or main text, sampling at 3 year intervals. The search resulted in over 1,200 hits. From these papers, the authors recored the following metrics:

  • total number of independent loci sampled (complete mtDNA or cpDNA genomes were treated as a single haploid locus)
  • total number of alleles sampled (identical alleles contributed to the count- this gave an idea of the number of individuals sampled per study)
  • total length in base pairs of DNA sequences collected
  • total number of SNPs identified
  • number of species surveyed (ie were data collected from a single species or was it a multi-species comparative study?)

The final dataset analyzed by Garrick et al. contained 508 single-species datasets drawn from 370 papers.

Fig. 2 Linear regression of a weighted metric (number of loci 9 total number of alleles sampled, log-transformed) as a function of time, partitioned by major taxonomic group. (a) vertebrates (N = 272 data sets). (b) invertebrates (N = 153). (c) plants (N = 52). (d) fungi, protists, algae and bacteria combined (i.e. ‘other,’ N = 16)

Figure and caption from Garrick et al 2015. Linear regression of a weighted metric (number of loci x total number of alleles sampled, log-transformed) as a function of time, partitioned by major taxonomic group. (a) vertebrates (N = 272 data sets). (b) invertebrates (N = 153). (c) plants (N = 52). (d) fungi, protists, algae and bacteria combined (i.e. ‘other,’ N = 16)

An increase in the size of phylogeographic datasets was found across most major taxonomic groups (see figure above) in terms of the number of loci and the number of alleles sampled, suggesting researchers are putting more effort into collecting genomic and geographic samples.

The use of mtDNA and cpDNA loci has declined in the last two decades, but few datasets contained autosomal loci only. As pointed out by Garrick et al., organellar markers are still useful for questions about sex-biased dispersal, directional introgression, and molecular rate estimation and therefore, “are unlikely to become obsolete, but rather will continue to represent an important part of the phylogeography toolbox.”

Using exploratory forecast modeling, Garrick et al. predicted that the number of SNPs per data set is likely to reach ~20,000 by the end of 2016 (95% CI 16,590 – 23,133) which represents more than a doubling over the preceding 3 year period (see figure below).

Forward-time projection of the total number of single nucleotide polymorphisms (SNPs) per published phylogeo- graphic data set, through to the end of the year 2016. Forecasts were generated using autoregressive integrated moving aver- age (median values in black, 95% confidence intervals in pale grey), conditioned on survey data spanning 1992–2013, sam- pled at 3-year intervals. For each year, only the five highest values for the total number of SNPs are shown

Figure and caption from Garrick et al. 2015 Forward-time projection of the total number of single nucleotide polymorphisms (SNPs) per published phylogeo- graphic data set, through to the end of the year 2016. Forecasts were generated using autoregressive integrated moving aver- age (median values in black, 95% confidence intervals in pale grey), conditioned on survey data spanning 1992–2013, sam- pled at 3-year intervals. For each year, only the five highest values for the total number of SNPs are shown

An interesting conclusion from the survey is the author’s claim about the field of landscape genetics, a topic that my fellow TME contributor Rob Denton wrote about last week (Landscape genetics gets existential). According to Garrick et al.:

…in the era of next-generation sequencing, the perceived distinction between landscape genetics and phylogeography (e.g. Wang 2010) increasingly represents a false dichotomy, as the resulting large DNA sequence data sets should be informative over a broad temporal spectrum. Indeed, the timescales on which inferences can be made are likely to depend more on geographic sampling of individuals than on choices relating to genetic data (Robinson et al. 2014a).

Another interesting finding of Garrick et al.’s analyses is that the number of individuals sampled has increased along with the increase in the number of loci being collected. Is this because we are obsessed with the idea that more data are always better? Or because the savings we accumulate as the cost of sequencing goes down are being spent adding more individuals to the experimental design? I wrote a few weeks ago that adding replicates trumps increasing sequencing depth in testing for differential gene expression but what is the optimum ratio of loci to individuals sampled now that phylogeographic studies are on pace to collect 20,000 SNPs per dataset? It feels a bit like blasphemy to write this but perhaps we can afford to scale back the number of individuals we sample per population and instead devote our time and money to collecting from additional geographic locations or to other projects entirely. Now that Garrick et al. have summarized how far the field has come in the last 20 years, I am excited to see where phylogeography goes next.

Reference

Garrick, R. C., Bonatelli, I. A., Hyseni, C., Morales, A., Pelletier, T. A., Perez, M. F., … & Carstens, B. C. (2015). The evolution of phylogeographic data sets. Molecular Ecology, 24 (6), 1164-1171. DOI: 10.1111/mec.13108

 

Posted in evolution, genomics, Molecular Ecology, the journal, next generation sequencing, phylogeography, Uncategorized | 2 Comments

Genomic history of Eurasia

The route of modern humans out of Africa has been contentious, with archaeological and genetic finds pointing towards a route through Egypt, versus one through Ethiopia. Pagani et al. (2015) analyze the genomic admixture of individuals sampled from both Egypt and Ethiopia in the context of the 1000 Genome Project dataset to get at this question, hypothesizing that individuals from either location would be closer “related” to Eurasians. Key findings of this study include (a) 80% of non-African ancestry in Egyptians, dated to ~750 ybp, coinciding with the Islamic expansion, (b) varying levels (up to 50%) non-African ancestry in Ethiopians, with admixture dating back to 2,500-3,000 ybp, (c) more predominant Egyptian haplotypes in CHB (Han Chinese) and Toscani Italian (TSI) samples, pointing to a northern route (via Egypt), contributing to greater levels of ancestry outside of Africa.

These findings point to the northern route as the preferential direction taken out of Africa. In doing this, they resolve the puzzles of archaeological similarities and Neandertal admixture, which are readily accommodated by a northern-exit model, but not by a southern exit, and fit well with the recent discovery of human remains dating to around 55,000 years ago in Israel (close to the northern route)
Neolithic sculptures from the Yamnaya culture.

Neolithic sculptures from the Yamnaya culture.

Continuing the story from above, as modern humans migrated out of Africa via Egypt into Eurasia, much less is known about the migratory epochs of the Bronze Age (3000-1000 BC). Alltentoft et al. (2015) sequence whole genomes of 101 Eurasian archaeological samples, and analyze population genomic history using admixture statistics.

Findings of Allentoft et al. (2015) depicting routes of migration of the Yamnaya culture into Europe and Asia. Image courtesy: Figure 1 of Alltentoft et al. (2015).

Findings of Allentoft et al. (2015) depicting routes of migration of the Yamnaya culture into Europe and Asia. Image courtesy: Figure 1 of Alltentoft et al. (2015).

Findings from this study include (a) Caucasian admixture early on into hunter-gatherers and Neolithic farmers in north-central Europe, coinciding with the expansion of the Yamnaya into Europe (b) a tandem Yamnaya expansion eastward into Asia, (c) fixation of light skin pigmentation SNP’s in Europeans during the Bronze Age, and (d) low frequencies of lactose tolerance alleles despite high tolerance in present-day Europeans.

 

We show that the Bronze Age was a highly dynamic period involving large-scale population migrations and replacements, responsible for shaping major parts of present-day demographic structure in both Europe and Asia.

References:

Allentoft ME. et al. (2015) Population genomics of Bronze Age Eurasia. Nature, June 2015. DOI: http://dx.doi.org/10.1038/nature14507

Pagani L. et al. (2015) Tracing the Route of Modern Humans out of Africa by Using 225 Human Genome Sequences from Ethiopians and Egyptians. AJHG Volume 96, Issue 6, p986-991. DOI: http://dx.doi.org/10.1016/j.ajhg.2015.04.019

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