Paleogenomics is crushing it right now

/ zcofran / 2 Comments

It’s only Valentine’s Day, and already early 2014/late 2013 have provided several fascinating, high profile studies of ancient DNA (all been published in Nature). Forecasting this deluge, last year closed with the announcement of sequenced mtDNA from a ≥400,000 year old human fossil from Sima de los Huesos, Spain (Meyer et al., 2013). This is the oldest DNA obtained for any human fossil, and among the oldest of any animal.Meyer title copy 2

Shortly thereafter, Prüfer and pals (2014) published the complete genome of a Neandertal from the infamous Denisova cave. This study revealed extensive inbreeding in Siberian Neandertals; the fossil individual’s high level of homozygosity is consistent with their parents being half-siblings.  Furthermore, comparison of the genome of this inbred Neandertal with modern humans’ allowed researchers to identify many mutations that have become fixed (shared by all people) by natural selection since the divergence of our and Neandertals’ ancestors. Uncovering these human-specific variants can help us understand the genetic bases for many of humans’ remarkable traits.Prufer title

In January, Olalde y coautores published a genomic analysis of a 7,000 hunter-gatherer from Spain. This ancient genome contained ancestral variants for genes relating to skin pigmentation (SLC45A2, SLC45A5, MC1R, TYR, and KILTG), meaning this Mesolithic European most probably had dark skin. This individual also had a derived variant of the HERC-OCA2 locus, associated with blue eye color in present day people. This suggests that the relatively novel phenotype of non-brown eyes may have increased in frequency more quickly than light skin color in ancient Europe. This guy also had many derived loci associated with immune function, indicating that the rise of agriculture is not solely responsible for the evolution of immune function in present day Europeans.

Olalde title

Around the same time, Sankararamen and team published an analysis of the distribution of Neandertal genes in living people. Whereas previous studies had already shown that Neandertals contributed ≤4% on average to the genomes of living people, this study examined where in modern people’s genomes this Neandertal ancestry tends to be located. One of the most interesting findings is that Neandertal genes are not uniformly or randomly distributed across the modern human genome. Rather, some regions appear to be especially devoid of Neandertal ancestry, implying natural selection acted strongly against Neandertal genes. These Neander-nude areas are preferentially found on the the X chromosome and in genes expressed in the testes, a finding consistent with reduced fertility in hybrid males. Although the genetic contribution of Neandertals to modern humans means that the two belonged to the same species, Sankararaman et al’s findings suggest the two groups were on their way to becoming different species.sanakararaman

Finally, this past week Rasmussen and rascals have published an analysis of a 12,000 year old human from the Anzick site in Montana, associated with the Clovis stone tool culture. I don’t know much about this time period save for what I learned in a class on North American archaeology taught by Dr. John Speth, back when I was a young, bright-eyed graduate student. One thing I recall from this class, when we were going over Clovis, was that this tool industry was found all over the United States at the beginning of the Holocene, but I was always disappointed by the dearth of bones complementing the copious lithics. Turns out, the DNA analyzed by Rasmussen et al. comes from the only known burial from this time period. This lone burial provides compelling genetic evidence that indigenous Americans have descended largely from a single ancestral population that separated into the North and South American populations prior to the Clovis period. This ancestral population was definitely not from Europe, as a minority of researchers have argued. Check out the SEAC Underground blog for more on the archaeology and ethics of the Anzick analyses.rasmussen

So, paleogenomics is really crushing it right now. There have been many of recent advances in sampling and sequencing poorly-preserved ancient DNA, and as we’re seeing now, lots of ancient bones (and teeth) are bringing awesome new, genetic insights into recent human evolution. If this is how well we’re doing so early in 2014, you can bet that the rest of the year promises many more exciting discoveries.

Friday excitement: Panoramic data inspection

/ zcofran / Leave a comment

I teach Tuesdays and Thursdays this year, leaving Fridays welcomely wide open for non-teaching related productivity. Today’s task is arguably the most exhilarating aspect of doing Science – inspecting raw data to make sure there are no major errors or problems in the dataset, so I can then analyze it and change the world. The excitement is truly hard to contain.

Delectable dog food is the dataset; I’m the dog.

No, it’s not the funnest, but it’s an important part of doing Science. To make your life easier, you should inspect data daily as you collect them. This way, you can identify mistakes and make notes about outliers early on, so that you are not stupefied and stalemated by what you see when you sit down to begin analysis.

You (corgi) are getting ready to analyze and you find an anomalous observation (door stop) you didn’t notice when you were collecting data.

Today I’m looking at measurements I took from ape mandibles housed in an English museum last summer; I inspected data before I left the UK for KZ, so today should be a breeze. But no matter how meticulous you are in the field/museum, you still need to inspect your data before analyzing them, just to be safe. If you’re as disorganized as I am, there will be lots of programs each with lots of windows. Here’s a tip: plug into multiple monitors (or at least one big ass monitor), so you can easily espy all open windows and programs in prodigious panorama.

Using two monitors helps when checking data for errors and patterns

Using two monitors helps when checking data for errors and patterns. On my left screen I’m using R to visualize and examine the raw data open in Excel on the right screen. If something seems off on the left screen, I can quickly consult the original spreadsheet on the right.

Barely visible in the above screenshot, these are chimpanzee (red) and gorilla (black) mandible measurements plotted against a measure of body size, preliminarily described in this post from last August. I’m looking at whether any mandibular measurements track body size across the subadult growth period, in hopes that bodily growth can be studied in fossil species samples dominated by kid jaws. As you can (barely) see, some jaw measurements correlate with body size better than others, and sometimes the apes follow similar patterns but other times they don’t.

The data look good, so now I can go on to examine relationships between mandible and body size in more detail. Stay tuned for results!

Osteology everywhere: A sign I might have a problem

/ zcofran / 2 Comments

Over the holiday break I was working at a cafe, and was shocked to find the upholstery besprinkled with bones. Looking at this seatback, can you tell what kinds of bones, and from whom, adorn this food establishment?

2013-12-29 16.47.49

Of course there’s no one right answer, but what I saw were the undeveloped shafts of infant limbs. Infants?! Mildly morbid, mayhap, but one of the distinguishing features of juvenile limb bones compared with adults is that babies’ epiphyses (joint ends) are not fused to the shafts. Observe:

From left to right, human perinatal humerus, femur and tibia (from Scheuer and Black, 2000).

From left to right, human perinatal humerus, femur and tibia (from Scheuer and Black, 2000).

Each of the newborn bones pictured above is comprised of a shaft (diaphysis) that flares proximally and distally into a ‘metaphysis.’ In adults, the epiphyses are completely fused to the metaphyses, but in juveniles the epiphyses are separated from metaphyses by a growth plate made of cartilage. Different epiphyses tend to fuse at characteristic ages, and when fusion occurs bone growth ceases.

Functionally, this cartilage growth plate allows the bones to increase in length, as multiplying cartilage cells are replaced by bone cells. Because the epiphyses of different limbs fuse at different times, this means that limb proportions change subtly over the course of growth. Practically, this means that if an archaeologist (or forensic scientist or paleontologist) finds a limb shaft with unfused ends, he or she can estimate the age at which the individual may have died:

Same bones in same order as in previous figure (also from Scheuer and Black, 2000)

Standards for epiphyseal fusion. Same bones in same order as in previous figure (also from Scheuer and Black, 2000). “A” refers to the age (years) when the epiphysis firsts appears, and “F” to when it fuses to the shaft.

So if we assume the bones in the second figure are from the same person, we see a humerus, femur and tibia with completely unfused epiphyses. If we refer to our aging standards (third figure), we can see that the first epiphysis to fuse is the proximal humerus, between 2-6 years, and the next epiphyses to fuse are the distal humerus and femur head/proximal tibia between 12-14 years. So we could conclude that this poor kid was certainly younger than 12, years, if not even younger than 2 years. Again, having more of the skeleton (especially jaws with developing teeth) would help us make a more precise estimation.

Baby bones all over the place?! Shame on you, Panera.

GET THIS BOOK: Scheuer L and Black S. 2000. Juvenile Developmental Osteology. Academic Press.

A new year of bioanthro student blogging in Kazakhstan

/ zcofran / Leave a comment

A new year is upon us, our hair is a bit grayer and our telomeres a touch trimmer. Twenty effing fourteen.

It’s been a bit quiet here at Lawnchair, as I’ve been enjoying the holidays, but also writing a few things up for print. If I weren’t so old and wise, I’d make a New Year’s resolution to add to the blog more frequently. But I have a nascent career to attend to! So in the mean time, with the new year and semester, I’m adding two new courses to the Nazarbayev University bioanthro student blog that can hopefully keep you entertained & edumacated.

The wintry curtain rises for 2014 in Astana.

The wintry curtain rises for 2014 in Astana.

The first batch of student-written posts for the class “Bones, stones and genomes: Human Evolution” will go up on Monday. There will be a slight lull for a few weeks until this class, as well as “Monkey business: Primate behavior and ecology,” start posting in February. In addition to what’s already been posted by last year’s classes, the human evolution class will be adding posts focused on specific bones and fossils, while the primatology class will be adding article reviews/summaries.

So stay tuned to nazarbioanthro.blogspot.com in the coming months! (I should also have more fun new things to say here at Lawnchair, too)

This human DNA is old as hell

/ zcofran / 6 Comments

If hell were around 400,000 years old. The people who salvaged ancient DNA from fossil Neandertals and “Denisovans” now present mitchondrial DNA (mtDNA) from a human-ish fossils from the Spanish site of Sima de los Huesos (SH; this translates as “pit of bones,” by the way, which is pretty badass). DNA-bearing Neandertal sites and Denisova cave date anywhere from around 30-100 kya, while Sima de los Huesos has been dated by various methods to 300-600 thousand years ago. So the newly announced mtDNA is the oldest human DNA ever recovered…

YET!

Now, we know what Neandertals look like, since they are perhaps the best known group of fossil humans. We don’t really know what Denisovans look like, as their unique DNA came from fossils that are anatomically ambiguous (a large molar and the end of a tiny fragment of the bone at the end of your pinky finger) – they could look like anyone. Even you! The SH fossils predate Neandertals by a few hundred thousand years, but their skulls look pretty similar; quite possibly the SH populations were ancestors of Neandertals, and you’d expect the DNA to be similar in the two groups.

So researchers were surprised to find this SH mtDNA to be more similar to Denisovan than to human or Neandertal mtDNAs. But this actually shouldn’t be that surprising, since we saw the same twist when Denisovan mt and nuclear DNA was sequenced – mtDNA first made it look like humans and Neandertals were more closely related, and the ancestors of Denisovans separated from the human+Neandertal lineage in the deep past. However, mtDNA essentially acts as a single genetic locus – a gene tree isn’t necessarily a species tree – and the more informative nuclear DNA later showed Neandertals and Denisovans to be more closely related to one another than either was to living humans (yet each of these ancient populations contributed some genes to some living people today). Denisovans held on to a very ancient mtDNA lineage, and apparently so did the people represented at Sima de los Huesos. And let’s not forget, we don’t know what Denisovans looked like – maybe they looked just like the older SH fossils.

Hopefully we’ll be able to get human nuclear DNA from Sima de los Huesos. When we do, I predict we’ll see the same kind of twist as with the Denisova DNA, with SH being more similar to Neandertals. But if I’m wrong, maybe we’ll be a step closer to knowing what the bones of the the mysterious “Denisovans” looked like…

Here’s that paper: Meyer et al. in press. A mitochondrial genome sequence of a hominin from Sima de los Huesos. Nature. doi:10.1038/nature12788

Why the long face?

/ zcofran / 1 Comment

As was predicted long ago, and is becoming increasingly apparent, many anatomical differences between individuals are due not so much to the DNA coding for specific proteins (“genes”), but rather to the DNA that helps regulate when, where and how much these genes are expressed. A recent paper by Catia Attanasio and colleagues have identified thousands of these latter regions that appear to influence the development of facial shape, using a mélange of modern molecular, microscopic & morphometric methods. This is an exciting step toward understanding the genetic bases of facial variation within, and probably between, species.

Attanasio and colleagues identified “enhancers,” bits of DNA that enhance or increase the transcription of certain genes, relating to the embryonic development of the face. One interesting thing about these enhancers is that they aren’t usually found within the genes they enhance, but may be as far away as a few hundred thousand nucleotides. This is part of why these regulatory elements can be so hard to ascertain. What’s more, in the researchers’ own words, enhancers “often control the expression of their target genes in a modular fashion, where different enhancers activate the expression of the same gene in different cell types, anatomical regions, or at different developmental time points.” So in addition to the difficulty in finding enhancers, their varied ‘behavior’ makes it difficult to figure out exactly what each one does.

I won’t get into the methods they used to do this, but basically they were able to visualize when and where many of these enhancers were active in the developing face of mouse embryos. They also showed that tinkering with these enhancers had characteristic effects on bony facial shape in adults. The results are amazing:

Figure 5 from the paper. Blue/red indicate presence of a given enhancer. The white/blue images are actual mouse embryos, from younger (left) to older (right). Each green/red image is a 3D reconstruction of the blue/white embryo above, based on optical projection tomography.

Figure 5 from the paper. Blue/red indicate presence of a given enhancer. The white/blue images are actual mouse embryos, from younger (left) to older (right). Each green/red image is a 3D reconstruction of the blue/white embryo above, based on optical projection tomography.

Science has also made a very informative and visually stunning video to accompany the paper. Check it out. NOW.

So. Facial shape is the result of massively complex interactions between not just numerous genes, but also the coordination of thousands enhancers and other types of non-coding DNA regulating gene expression. Many other studies have tried to uncover the genetic bases of complex phenotypes (usually diseases) via genome wide association studies (GWAS), scanning genomes for shared genetic variants between individuals with similar phenotypes (I discussed this approach briefly Friday). In contrast to GWAS, what I really like about this study by Attanasio and colleagues is that they not only identify specific stretches of DNA as enhancers, but they also mapped their activity in developing embryos. Thus they could actually see how genetic variants contribute to phenotypes.

This is an important step toward understanding exactly how various genetic diseases affecting the face manifest. In addition, this and other studies uncovering the complex molecular interactions influencing facial shape could form the bases for computational models of development, to predict the genetic and developmental origins of facial evolution.

The paper: Attanasio C et al. 2013. Fine tuning of craniofacial morphology by distant-acting enhancers. Science 342: 1241006.

Is it worth seeking a genetic basis for math genius?

/ zcofran / 1 Comment

The topic this week in my Human Variation and Race class is intelligence. We’ve read about and discussed what intelligence is, how it is quantified, and the extent to which ‘intelligence,’ however defined, is biologically and/or environmentally determined. Intelligence (test score) has been shown to be heritable, meaning that a proportion of the variation in IQ test scores in a population can be explained by genetic variation. But that is not the same as saying that it is genetically determined. Similarly, complex traits such as intelligence, behaviors, and diseases almost never have a simple genetic basis – a common theme over at the Mermaid’s Tale, one that seems too rarely heeded. So you can imagine my surprise and delight at finding this news piece just published in Nature: “Root of maths genius sought: Entrepreneure’s ‘Project Einstein’ taps 400 top academics for their DNA.” Of course “roots” meant “genes.”

Apparently, bioinformatics entrepreneur and multimillionaire Jon Rothberg has set out to identify the genetic bases of peak mathletics, by analyzing the genomes of hundreds of mathematicians and physicists. Good luck, buddy! My initial reaction was to be appalled that an educated biologist these days could be such a flagrant biological determinist. What’s more, when approached about participating in the study, mathematician Curtis McMullen asked about the ethics of the project and its outcomes: “The uniform answer to my questions was that ‘we are not responsible for how the information is used after the study is completed.'” Ew. The project as briefly described reeked of some eugenics programme.

My prediction is that if this study takes off, Rothberg & buddies will be horribly disappointed. Assuming they are able to identify any genetic variants, these will probably only explain a small amount of variation in “maths genius.” Which itself is problematic, since there is probably not a single manifestation of math genius, and even if there were a single way to be a math genius, there may be several genetic pathways relating to the phenotype (not an uncommon finding of many genome-wide association studies). But hey, it seems to be Rothberg’s own money going into the study, so why not.

But then, if my prediction were to hold, this wouldn’t necessarily be a failure – it would point to an important role of society and learning environment in shaping individuals’ mathematic capability. And then maybe big money could begin to be diverted to more productive programs investigating and improving how people learn, rather than to large scale projects seeking simple answers when there isn’t necessarily any reason to expect them in the first place.

Osteology Everywhere: Head for the hills

/ zcofran / Leave a comment

Last week I was exploring central England with the brilliant Jess Beck, an archaeology PhD student at the University of Michigan. Both of us avid (nay, rabid) connoisseurs of everything skeletal, we espied the likes of a specific human bone in the scenic landscape of the the Cotswolds. Check out JB’s blog, Bone Broke, for her take on this geographical/geological/skeletal formation (as well as for lots of killer osteology and bioarchaeology tips and tricks). Do it now! NOW!

After you’ve checked out her site, behold this sight – what bone is lurking in the landscape?

osteourrywhere Cotswolds

As with Rorschach inkblots, probably lots of bones could be seen in this image. But what Jess & I saw was a hamate, the greener hue hewn into the hills, whose sizable hamulus runs from the bottom right to join the rest of the carpal around the center of the image.

The hamulus of the hamate is an attachment point for the flexor retinaculum, the band of fascia stretching across your wrist to hold your extrinsic digital flexor muscles (or rather, their tendons) in place; you could think of it as the bridge covering the carpal tunnel. Now, comparing the grassy hamulus with an actual human one, you’ll spot two important differences: first, the grassy one isn’t blunt like the humans’, but ends in a long point. Oops! Just pretend it’s rounded off. Second, the grassy hamulus is huge relative to the overall size of the bone (or valley) compared with the human form. The size of the hamulus partially reflects the size of the carpal tunnel: chimpanzees, with powerful wrists and forearms, have long hamuli.

A huge nerd, I didn’t just see any hamate in this Cotswold vale. I also immediately thought of KNM-WT 22944, an Australopithecus afarensis hamate from the 3.5 million year old site of South Turkwel in Kenya (Ward et al., 1997):

WT 22944-Ward &al 1997

From Ward et al., 1999. Sorry it’s not in the same orientation as the above image. Hamulus is the projection pointing to the bottom left corner of the “medial” image.

An absolutely and relatively massive hamulus in WT 22944 suggests whoever this bone belonged to had some powerful gripping capabilities, while a geologically younger A. afarensis hamate from Hadar (AL 333-50) had a smaller, more human-like hamulus. Maybe (some) A. afarensis were still using their arms a lot for tree-climbing, in spite of being more than capable bipeds (I’ve talked about this before here)….

One final thought: People do like the way she says, “hamate.”

Ward et al., 1999. South Turkwel: a new pliocene hominid site in Kenya. Journal of Human Evolution 36: 69-95. link

White et al., 2012. Human Osteology 3rd Edition. link

The small, big new Dmanisi skull

/ zcofran / 6 Comments

The 5th skull early Homo skull from the site of Dmanisi was announced last week. The skull was discovered nearly 10 years ago, but is finally (and very comprehensively) published in Science (Lordkipanidze et al. 2013). The ‘new’ D4500 cranium goes with the massive D2600 mandible, making this the earliest and most complete skull of Homo that I know of. It’s really a remarkable specimen, for a number of reasons beyond its age and completeness. I’ve been busy traveling, teaching and writing lately, so I haven’t yet gotten to pore over the details as much as I’d like. So I hope to sporadically post thoughts on this badass new skull as they come to me. In the mean time, several of what I’d consider the top biology/anthropology blogs*** have discussed the skull, so do check those out if you haven’t already.

The first thing I noted about D4500 is its small brain size, estimated at a mere 546 cubic centimeters. For perspective, D4500 is the green point in the following plot showing brain size in early human evolution:

Endocranial volume for various fossil hominin fossils. 1: Australopithecus afarensis, africanus & boisei; 2: Dmanisi specimens; 3: "habilines" 4: early African Homo erectus; 5: Indonesian and Chinese Homo erectus

Endocranial volume for various fossil hominin fossils. 1: Australopithecus afarensis, africanus & boisei; 2: Dmanisi specimens; 3: “habilines” 4: early African Homo erectus; 5: Indonesian and Chinese Homo erectus. D4500 is green with envy.

I got to see (but not study) the cranium a few years ago when I was helping with the Dmanisi Paleoanthropology field school, and I remember noting just how “robust” the specimen was – big mastoid processes, prominent and thick brow ridge, huge attachments for the neck muscles. In humans, and presumably our fossil forebears, these features are more developed in males than females, and so presumably D4500 was a male (consistent with the huge, associated D2600 mandible). In many primates, and 4 to ~1 mya hominins so far as we can tell, males are larger than females. So it is surprising that a robust probable male cranium is in fact not only the smallest in the Dmanisi sample, but also at the low end of early African Homo (i.e. habilis or rudolfensis), comparable to the largest australopiths. Of course, the only other faces known from Dmanisi are either not fully grown (D2700 and D2282) or old and decrepit (D3444), so perhaps the larger-brained specimens would have been at least as robust as D4500. An untestable hypothesis!

The new skull really highlights the overlap, or continuous variation between later australopiths and early Homo known also from eastern Africa. In association with the postcranial remains known from Dmanisi, the authors the paper posit that early Homo may have been distinguished from Australopithecus not so much in brain size as in body size. We could probably add body shape (limb proportions) and tool use to that list of distinguishing features, and to be sure there are Oldowan tools and small but human-like body size and shape indicated by postcrania at Dmanisi. But then, evidence for body proportions and for/against tool use in Australopithecus, especially later in the record, is somewhat equivocal…

More thoughts to follow.

*** https://blogs.wellesley.edu/vanarsdale/2013/10/17/uncategorized/the-new-wonderful-dmanisi-skull/; http://johnhawks.net/weblog/fossils/lower/dmanisi/d4500-lordkipanidze-2013.html; http://ecodevoevo.blogspot.com/2013/10/how-many-human-species-are-there-is-it.html

Reference: David Lordkipanidze, Marcia S. Ponce de León, Ann Margvelashvili, Yoel Rak, G. Philip Rightmire, Abesalom Vekua, and Christoph P. E. Zollikofer. 2013. A Complete Skull from Dmanisi, Georgia, and the Evolutionary Biology of Early Homo. Science: 342 (6156), 326-331.

More FREE badass bioanthro science resources!

/ zcofran / Leave a comment

Hark! There’s been quite a long silence here, as I’ve been busy preparing manuscripts related to this post and this post. Also teaching; my new Intro to Biological Anthropology students are writing posts over at nazarbioanthro.blogspot.com – check them out!

Anyway, some more FREE DATA have come to my attention that I figured people may find useful (I’ve posted links to other great resources here and here).

First, my buddy and advisor Milford Wolpoff has helped compile an open online dental dataset. This consists of length and breadth measurements for teeth from humans, fossil humans and non-human apes. And promises of more to come! You can read about the data, and online data-sharing more generally, in this paper at the Paleoanthropology Society website.

Secondably, Herman Pontzer has put together a website, Australopithecus, with lots of great information about human evolution for teachers and students, as well as a datamine of links and metrics and pictures of fossil hominins and apes. Pretty boss.

Third, announced in the American Journal of Physical Anthropology just yesterday is a database of cranial non-metric data, pioneered by Nancy Ossenberg. This is a very comprehensive dataset, with info about up to 84 non-metric traits on over 8,000 individual crania from all over the world. Ossenberg also links to the WW Howells craniometric dataset (thousands of cranial measurements of individuals all over dodge); I’m not sure if/how much Ossenberg’s and Howells’ datsets overlap, but the covariance of size, shape and non-metric traits could be a very interesting investigation (if it hasn’t been done already; sorry for my ignorance!).

Finally, if you’re looking to analyze these or any other tantalizing data, you’ll want to download and learn to use R. This free statistical computing program will let you analyze pretty much anything with either traditional statistics, or you can be a badass and make up your own custom tests. I’ve been blabbing incessantly about how awesome this program is since at least 2009, but here’s the link just in case. R takes some time to figure out how to use, but its help files are all online, and you can probably find out how to do anything else your dreams can concoct on the Internets.

Now you are ready to take on the world. Go forth!