Paper: ”Feralisation targets different genomic loci to domestication in the chicken”

It is out: Feralisation targets different genomic loci to domestication in the chicken. This is the second of our papers on the Kauai feral and admixed chicken population, and came out a few days ago.

The Kauai chicken population is kind of famous: you can find them for instance on Flickr, or on YouTube. We’ve previously looked at their plumage, listened to the roosters’ crowings, and sequenced mitochondrial DNA to investigate their origins. Based on this, we concur with the common view that the chickens of Kauai probably are a mixture of feral birds of domestic origin and wild Junglefowl. The Kauai chickens look and sound like a mix of wild and domestic, and we found mitochondrial DNA of two haplogroups, one of which (called D) is typical in ancient chicken DNA from Pacific islands (Gering et al 2015).

In this paper, we looked at the rest of the genome of the same chickens — you didn’t think we sequenced the whole thing just to look at the mitochondrion plus a subset of markers, did you? We turn to population genomics, and a family of methods called selective sweep mapping, to search for regions of their genome that show signs of being affected by natural selection. This lets us: 1) draw pretty rainbow plots such as  this one …


(Figure 1a from the paper in question, Johnsson & al 2016. cc:by The chromosomes have been laid out on the horizontal axis with different colours, and split into windows of 40 kb. Each dot represents the heterozygosity of that windows. For all the details, see the paper.)

… 2) highlight a regions of the genome that may have been selected during feralisation on Kauai (these are the icicles in the graph, highligthed by arrows); 3) conclude that the regions that look like they’ve been selected in feralisation overlap very little with the ones that look like they’ve been selected in chicken domestication. Hence the title.

That was the main result, but of course we also look at what genes are highlighted. Mostly we have no idea how they may contribute to feralisation, but a couple of regions overlap with those that we’ve previously found in genetic mapping of comb size and egg laying in our wild-by-domestic intercross. We also compare the potentially selected regions to domestic chicken sequences.

Last year, Ewen Callaway visited Dominic Wright, Eben Gering and Rie Henriksen on the last fieldtrip to Kauai. The article, When chickens go wild, was published in Nature News in January, and it explains a lot of the ideas nicely. This paper was submitted by then, so the samples they gathered on that trip do not feature in it. But, spoiler alert: there is more to come. (I don’t know what role I personally will play, but that is less important.)

As you may have guessed if you looked at the author list, this was a collaboration between quite a lot of people in Linköping, Michigan, London, and Victoria. Thanks to all involved! This was great fun, and for those of you who like this sort of thing, I hope the paper will be an interesting read.


M. Johnsson, E. Gering, P. Willis, S. Lopez, L. Van Dorp, G. Hellenthal, R. Henriksen, U. Friberg & D. Wright. (2016) Feralisation targets different genomic loci to domestication in the chicken. Nature Communications. doi:10.1038/ncomms12950

R in genomics @ SciLifeLab, Solna

Dear diary,

I went to the Stockholm R useR group meetup on R in genomics at the Stockholm node of SciLifeLab. It was nice. If I had worked a bit closer I would attend meetups all the time. I even got to be pretentious with my notebook while waiting for the train.


The speakers were:

Jakub Orzechowski Westholm on R and genomics in general. He demonstrated genome browser-style tracks with Gviz, some GenomicRanges, and a couple of common plots of gene expression data. I have been on the fence about what package I should use for drawing genes and variants along the genome. I should play with Gviz.

Daniel Klevebring on clinical sequencing and how he uses R (not that much) in sequencing pipelines aimed at targeting the right therapy to patients based on the mutations in their cancer cells. He mentioned some getopt snippets for getting R to play nicely on the command line, which is something I should definitely try more!

Finally, Arvind Singh Mer on predictive modelling for clinical genomics (like the abovementioned ClinSeq data). He showed the caret package for machine learning, with an elastic net regression.

I don’t know the rest of the audience, so maybe the choice to gear talks towards the non-bio* person was spot on, but that made things a bit less interesting for me. For instance, in Jakub’s talk about gene expression, I would’ve preferred more about the messy stuff: how to make that nice gene-by-sample matrix in the first place, and if R can be of any help in that process; also, in the other end, what models one would use after that first pass of visualisation. But this isn’t a criticism of the presenters — time and complexity constraints apply. (If I was asked to present how I use R any demos would be toy analyses of clean datasets. That is the way these things go.)

We also heard repeated praise for and recommendations of the hadleyverse and data.table. I’m not a data.tabler myself, but I probably should be. And I completely agree about the value of dplyr — there’s this one analysis where a couple of lines with dplyr changed it from ”argh, do I have to rewrite this in C?” to being workable. I think we also saw all the three plotting systems: base graphics, ggplot2 and lattice in action.

From my halftime seminar

A couple of weeks ago I presented my halftime seminar at IFM Biology, Linköping university. The halftime at our department isn’t a particularly dramatic event, but it means that after you’ve been going for two and a half years (since a typical Swedish PhD programme is four years plus 20% teaching to a total of five years), you get to talk about what you’ve been up to and discuss it with an invited opponent. I talked about combining genetic mapping and gene expression to search for quantitative trait genes for chicken domestication traits, and the work done so far particularly with relative comb mass. To give my esteemed readers an overview of what my project is about, here come a few of my slides about the mapping work — it is described in detail in Johnsson & al (2012). Yes, it does feel very good to write that — shout-outs to all the coauthors! This is part what I said on the seminar, part digression more suited for the blog format. Enjoy!

Slide04(Photo: Dominic Wright)

The common theme of my PhD project is genetic mapping and genetical genomics in an experimental intercross of wild and domestic chickens. The photo shows some of them as chicks. Since plumage colour is one of the things that segregate in this cross, their feathers actually make a very nice illustration of what is going on. We’re interested in traits that differ between wild and domestic chickens, so we use a cross based on a Red Jungefowl male and three domestic White Leghorn females. Their offspring have been mated with each other for several generations, giving rise to what is called an advanced intercross line. Genetic variants that cause differences between White Leghorn and Red Jungefowl chickens will segregate among the birds of the cross, and are mixed by recombination at meiosis. Some of the birds have the Red Junglefowl variant and some have the White Leghorn variant at a given part of their genome. By measuring traits that vary in the cross, and genotyping the birds for a map of genetic markers, we can find chromosomal chunks that are associated with particular traits, i.e. regions of the genome where we’re reasonably confident harbour a variant affecting the trait. These chromosomal chunks tend to be rather large, though, and contain several genes. My job is to use gene expression measurements from the cross to help zero in on the right genes.

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