A Common Frog (Rana temporaria) – Photo by Bernie Kohl
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
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)
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
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).
#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).
Image by Steve Ryan
Figure 1 from Manthey and Moyle (2015) displaying the sampling areas of the Madrean Sky Islands
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:
The final dataset analyzed by Garrick et al. contained 508 single-species datasets drawn from 370 papers.
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)
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
…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
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.
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).
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
I had a lot of ideas for future posts, but “landscape genetics” keeps pulling me back.
Beyond the new methodology, reviews, and empirical findings, I suppose someone has to pump the brakes and get more existential. Rodney Dyer does just that in the upcoming Molecular Ecology opinion “Is there such a thing as landscape genetics?“, first seen on bioRxiv and Haldane’s Sieve.
Continue reading
It was a good fortnight for large mammals! Two recent studies attempt to date the emergence of modern canids, and offer insights into the gut microbiomes of giant pandas.
In retrospect, Po could quite possibly kick butt, and chomp on the Wolf Boss just as well…(Image courtesy: Kung Fu Panda 2)
Admixture history (model) fitted to the data. Branch lengths are proportional to drift. Image courtesy: Figure 2 of Skoglund et al. (2015)
…we find that the ancestry of present-day dog breeds descends from more than a single domestication event, since high-latitude dog breeds such as the Siberian Husky and Greenland Sledge Dogs can trace part of their ancestry to the now-extinct Taimyr wolf lineage. This introgression could have provided early dogs in high latitudes with phenotypic variation beneficial for adaptation to a new challenging environment.
The Bamboo-Eating Giant Panda Harbors a Carnivore-Like Gut Microbiota, with Excessive Seasonal Variations. Xue et al. (2015) mBio
In another interesting find, Xue et al. (2015) report that the 16s rRNA gene-based profile of fecal microbiota of the strictly bamboo-eating giant panda (Ailuropoda melanoleuca) indicates a surprisingly ‘carnivorous’ microbiome (Enterobacteriacae, and Streptococcus), as against surmised microbiota that are seen in other herbivores (Clostridiales, Fibrobacterales, etc).
PCoA of gut microbiota structure of giant pandas and other herbivores, omnivores, and carnivores. Image courtesy: Figure 4 of Xue et al. (2015)
Unlike other mammalian species that have evolved gut microbiota (and also digestive system anatomies) optimized for their specific diets, the aberrant coevolution of the giant panda, its dietary preferences, and its gut microbiota remains enigmatic.
References
Skoglund, Pontus, et al. “Ancient Wolf Genome Reveals an Early Divergence of Domestic Dog Ancestors and Admixture into High-Latitude Breeds.” Current Biology (2015). DOI: http://dx.doi.org/10.1016/j.cub.2015.04.019
Xue, Zhengsheng, et al. “The Bamboo-Eating Giant Panda Harbors a Carnivore-Like Gut Microbiota, with Excessive Seasonal Variations.” mBio 6.3 (2015): e00022-15. DOI: http://dx.doi.org/10.1128/mBio.00022-15
Patterson N, Moorjani P, Luo Y, Mallick S, Rohland N, Zhan Y, Genschoreck T, Webster T, Reich D. 2012. Ancient admixture in human history. Genetics 192:1065–1093. DOI: http://dx.doi.org/10.1534/genetics.112.145037
Chicheley Hall, satellite of The Royal Society.
“Can’t wait for the one helpful comment I’ll get during this poster session”