Several of the items I’ve run across while surfing this week involve genomics (not surprising, given my job).

• First, from the National Human Genome Research Institute GenomicsCareers: Find Your Future, a website detailing the myriad career choices in the emerging field of genomics. With interactive videos and career profiles, a nice resource for the budding scientist. This one is on my list to share with my oldest, who has expressed an interest in forensics and neuroscience.
• Next, HuGE Navigator, “An integrated, searchable knowledge base of genetic associations and human genome epidemiology.”  BiteSizeBio has a nice review of the high points here and here. I realize these posts are from over a year ago, but I was trapped in dissertation purgatory at the time.
• Finally, Genomicus, a genome browser that lets users compare genomes across species.

Here’s the Genomicus output from the FOXP2 gene,with the human gene at the bottom of the figure, and a handy demo video.

As reported in the New York Times, the cover article of Nature this week describes the sequencing of a Paleo-Eskimo genome from Greenland.  This is the first ancient sequence from the New World, and is important for a number of reasons:

• The sequence analysis was conducted from a sample of human hair that was recovered from permafrost, and the DNA was recovered from the hair shaft, not the root. This opens a whole new avenue of paleogenomic research.
• SNP analysis shows that the hair belonged to an individual who carried a number of traits frequently found in modern Asian populations, including shovel-shaped incisors and dry ear wax.
• 85-87% of the genome was sequenced – phenomenal coverage given the age of the sample.

Chukchi women (seated front right), circa 1906

• Population genetic analysis (principle component analysis of genetic distances) revealed the closest relationship between the Greenland genome and the Chukchi, a population in northern Siberia, suggesting a recent migration (in the last ~5,000 years) from Siberia across the Arctic to Greenland. This would be in addition to the three migrations (Amerind, Na-Dene, and Eskimo-Aleut) traditionally hypothesized for the peopling of the New World.

Rasmussen et al. (2010) Figure 3b. PCA of populations - Saqqaq genome indicated by star.

I was disappointed that more North American samples weren’t included in the analysis.  With the exception of populations from West and East Greenland, Na-Dene in western Canada, and Aleuts, the only other Native American groups are from Central and South America. But overall, this study demonstrates how ancient DNA analysis can help answer historical questions.

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Disclaimer: One of the co-authors (Michael Crawford) was my graduate mentor. Which, for me, makes this study even more awesome.

Image credit: Wikimedia Commons

Rasmussen, M., Li, Y., Lindgreen, S., Pedersen, J., Albrechtsen, A., Moltke, I., Metspalu, M., Metspalu, E., Kivisild, T., Gupta, R., Bertalan, M., Nielsen, K., Gilbert, M., Wang, Y., Raghavan, M., Campos, P., Kamp, H., Wilson, A., Gledhill, A., Tridico, S., Bunce, M., Lorenzen, E., Binladen, J., Guo, X., Zhao, J., Zhang, X., Zhang, H., Li, Z., Chen, M., Orlando, L., Kristiansen, K., Bak, M., Tommerup, N., Bendixen, C., Pierre, T., Grønnow, B., Meldgaard, M., Andreasen, C., Fedorova, S., Osipova, L., Higham, T., Ramsey, C., Hansen, T., Nielsen, F., Crawford, M., Brunak, S., Sicheritz-Pontén, T., Villems, R., Nielsen, R., Krogh, A., Wang, J., & Willerslev, E. (2010). Ancient human genome sequence of an extinct Palaeo-Eskimo Nature, 463 (7282), 757-762 DOI: 10.1038/nature08835

A new study in PLoS Biology suggests one of the most common Western European Y halplogroups, R1b1b2, might have originated in Turkey and radiated into Europe with the spread of agriculture during the Neolithic.  This is significant because this haplogroup is the most frequent in Western Europe, and has been posited as a signal from Paleolithic populations who were less impacted by the Neolithic Revolution.

The researchers compared STR variance for this haplotype in several European populations and three Turkish groups, and found a significant correlation (R² = 0.358; p = 0.004) between that variance and the longitude of the population (i.e., how far east the population was located).

Balaresque et al. Figure 1C. Distribution of haplogroup R1b1b STR variance

From the plot, the greatest variance (indicated by the most intense color) within haplogroup R1b1b2 is found in Turkey. They also calculated the time to most recent common ancestor (TMRCA) using STR variance, and found that the oldest lineages, dated between 7,000-7,989 years, are also in Turkey.  The youngest lineage is in Cornwall, dating from 5,460 years ago. The researchers inferred that R1b1b2 originated in Anatolia and spread rapidly into Europe with the spread of agriculture.

Balaresque et al. Figure 1B. Frequency distribution of Haplogroup R1b1b2. More intense color indicate higher frequency.

A couple of things strike me about this study. 1) Haplogroup R1b1b2 reaches it’s highest frequencies in Western Europe, up to 85% of Y-chromosomes in Ireland belong to this haplogroup (Figure 1B). And there are two populations, one in Germany (GE1) and one on the northwest coast of France (FR2), with TMRCA dates in the range of the Turkish dates (7,282 and 7,384 years, respectively). 2) The Turkish data come from Cinnioglu et al. (2004), and consist of samples collected in 90 cities from blood banks, paternity clinics, and university students classified into geographical areas by self-reported “paternal residential heritage” (128).   There is the possibility of introducing error into the sample from this self-reported residence. It’s also possible that the high variance present in the Turkish R1b1b2 lineages reflects more recent immigration.  In addition, TMRCA applies to the molecule, not the populations in which it is found, so while a particular lineage may be 7,000 years old it does not mean that the population has been in that particular location for that length of time. And the authors note, in the supplemental information, “…there is a tendency for TMRCA to be underestimated when single-haplogroup data are considered.”

It’s an interesting hypothesis, though, and I’m curious to see what analyses with additional populations will show.

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Balaresque P, Bowden GR, Adams SM, Leung HY, King TE, Rosser ZH, Goodwin J, Moisan JP, Richard C, Millward A, Demaine AG, Barbujani G, Previderè C, Wilson IJ, Tyler-Smith C, & Jobling MA (2010). A predominantly neolithic origin for European paternal lineages. PLoS biology, 8 (1) PMID: 20087410

Cinnioğlu C, King R, Kivisild T, Kalfoğlu E, Atasoy S, Cavalleri GL, Lillie AS, Roseman CC, Lin AA, Prince K, Oefner PJ, Shen P, Semino O, Cavalli-Sforza LL, & Underhill PA (2004). Excavating Y-chromosome haplotype strata in Anatolia. Human genetics, 114 (2), 127-48 PMID: 14586639

As a grad student in anthropological genetics, one of the more tedious tasks I had was aligning mtDNA sequences manually, noting the mutations (differences from the revised Cambridge Reference Sequence, which belongs to haplogroup H), and determining the haplogroup (or lineage).  The difficulty was compounded by a lack of comprehensive definitions.  I had a stack of references listing diagnostic mutations, but not for every haplogroup, not even for the subset of haplogroups found in Europe, which was the focus of my research. Part of the problem was that when I started whole genome sequencing wasn’t available. Whenever a new haplogroup was discovered, the authors would name it, and in some cases the same name was given to different sequences because there was no standardized nomenclature. What I needed was a phylogenetic tree, showing the relationships between lineages, and all diagnostic mutations for each haplogroup.

That’s what you can find at PhyloTree.org.  The “updated comprehensive phylogenetic tree of global human mitochondrial DNA variation” lists all mitochondrial haplogroups, with diagnostic mutations from both the coding and control regions, based on full sequences deposited in GenBank. The figure below shows a portion of the tree for Haplogroup H1a:

Haplogroup H1a. Coding region mutations are in black, control region mutations in blue.

Coding region mutations are in black, control region mutations in blue.  Established haplogroup names are in in black at the base of the branches, and proposed haplogroup labels are shown in red.  GenBank accession numbers are provided at the tips of the branches. Assigning haplogroups to your samples is as easy as using the find function in your Internet brower. PhyloTree is continuously updated as new sequences are published; they’re currently on Build 7.0 as of November 2009.

PhyloTree is a valuable and much need resource for the anthropological genetics community, especially poor grad students.
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van Oven, M., & Kayser, M. (2009). Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation Human Mutation, 30 (2) DOI: 10.1002/humu.20921

Svante Pääbo’s group at the Max Plank Institute have a paper coming out in the February issue of Cell Biology. In it, they describe sequencing a complete early human mitochondrial genome from the Markina Gora specimen from the Kostenki 14 site in Russia. The remains date to around 30,000 years ago, not the oldest human sequence, but interesting nonetheless because the authors have identified new ways to determine if ancient DNA sequences are genuine vs. contamination.  This is especially important for more anatomically modern human fossils, who may have similar sequences to extant populations.

For Neandertal mtDNA, identifying contamination is relatively simple, because their mtDNA sequences fall outside the range of variation found in modern humans. Not so for more recent fossils.  So how can researchers identify true archaic sequences?

fragment length, deamination-induced sequence errors at ends of molecules, and purine-associated fragmentation represent features by which endogenous and contaminating populations of DNA molecules can be distinguished in at least some late Pleistocene specimens (1).

So, fragments sequenced from ancient samples are typically shorter than modern contaminants.  In many cases, the fragments are shorter than what can be amplified using PCR, meaning high-throughput direct sequencing methods are required to analyze these ancient samples.  In addition, the cytosine bases at the 5′ ends of ancient DNA fragments are susceptible to deamination (removal of an -NH₃ group), causing those bases to be misread as thymine. The 3′ ends of ancient sequences have a commensurate increase in G-A errors. Finally, fragmentation of ancient sequences occurs more frequently at purine bases (guanine and adenine).

With these criteria in mind, the researchers determined that the Markina Gora sequence belongs to mitochondrial haplogroup U2, a haplogroup still present in Europe today.

Figure 3D from Krause et al. (2010) - with the EMH sequence highlighted in red.

The authors determine that it is unlikely that this sequence is the result of modern contamination, because the nucleotide difference between the Markina Gora specimen and the ancestral U sequence is much shorter than than seen between the root and modern sequences, which have accumulated many more mutations over time.  Their results also support the hypothesis of pre-agricultural genetic continuity in Europe, so that genetic lineages which were present on the continent prior to the Neolithic transition can still be found in modern European populations.

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Krause J, Briggs AW, Kircher M, Maricic T, Zwyns N, Derevianko A, & Pääbo S (2009). A Complete mtDNA Genome of an Early Modern Human from Kostenki, Russia. Current biology : CB PMID: 20045327

What’s caught my attention this week:

Claude Levi-Strauss passes away at 100. One of the giants, instrumental in establishing the field of modern cultural anthropology.

NOVA: Becoming Human. The first episode in this three-part series aired on Tuesday, but you can watch it online. Nice update of recent fossil discoveries, and how they fit on the hominin evolutionary tree.

Illustration by John Gerrard Keulemans (1842-1912)

Whence the Falklands Wolf? – DNA analysis of museum specimens of an extinct canid species, one collected by Darwin on his journey aboard The Beagle, establishes their closest living relatives as the maned wolves of South America, suggesting the ancestors of the Falkland wolves rafted to the islands from the mainland.

Maned Wolf

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Image Credits: Wikipedia – Falkland Islands Wolf, Wikipedia – Maned Wolf

Link courtesy of the hubby. Way better than the overhead transparencies I used to teach with.

Click here to view the embedded video.

In celebration of the successful defense of my dissertation yesterday, here’s the BioRad GTCA video.

Feel free to sing along.

I ran into an issue while doing some analysis for my dissertation.  I’ve been working on comparing genetic distances between populations using a variety of molecular markers (mtDNA sequences, Y-STRs, and autosomal STRs).  I wanted to generate several neighbor-joining trees to display the results, but I also wanted a way to test the statistical significance of the tree, or how accurate a representation of the underlying genetic distance data the tree actually was.

One way to do this is with bootstrapping, where thousands of random data sets are generated from the original data (by dropping data and recalculating the tree).  In the end you have a tree with internal branch values, showing how many times each node turned up in the analysis. It’s a standard technique, and is the method I used with my autosomal STR data. But the software I used to handle sequence data in particular (MEGA, Phylip), starts with the raw sequences and generates bootstrapped trees from that data.  The trees created show each sequence on its own branch, rather than each population.  With over 8,000 sequences in my data set, this type of analysis really wasn’t useful.

But last week I found TreeFit, a little Windows program that generates an overall R² value by comparing the genetic distance matrix with the distances calculated based on the neighbor-joining algorithm.  Basically, it appears comparable to the STRESS value used for multidimensional scaling (MDS), measuring how well the representation of the data (the NJ tree) matches the variation present in the original distance matrix.  A perfect fit would generate an R² value of 1.0, while anything above 0.90 is considered a good fit (or an accurate representation of the underlying data).  Values less than 0.90 suggest that another graphical display method (MDS) might be a better choice, as not all data fit the hierarchical model on which the NJ algorithm is based.

Using TreeFit, I got some reassurance that my NJ trees were accurate, and the statistical significance I needed to convince my committee that my data is not “merely descriptive.”

Technical specs:

• OS: Windows (runs fine on my XP virtural machine)
• Requires MS .Net framework
  edfa
  • if this is not installed on your system (as it wasn’t on mine), it can be downloaded from the Windows update site
• Input file: any lower left genetic distance matrix, meaning that this program works with ANY type of genetic data.
• Output: observed and fitted genetic distances, these can be plotted for a nice visual, plus overall R²
• Reference: Kalinowski, ST (2009) How well do evolutionary trees describe genetic relationships between populations? Heredity (28 Jan 2009) doi: 10.1038/hdy.2008.136. (PDF available from the author’s publication page).