As I’ve been harping on and on for the past few years that the patterns of contemporary genetic variation are probably only weakly tied to past patterns of genetic variation (though Henry Harpending warned me about this as far back as 2004). A major reason that scholars operated under this presupposition is the axiom that most of the variation we see around us crystallized during the Last Glacial Maximum (~20 thousand years before the present).
This may be true in some cases, but I doubt it is true in most cases. I was pointed to a classic case of this problem just today. A reader alerted me to a short paper from this spring which attempts to ascertain the point of origin of the dominant mtDNA haplogroup among the Onge tribe of the Andaman Islanders, M31a1. This is an interesting issue because some researchers proposed, plausibly in the past, that these indigenous people in the Andaman Islands represent the descendants of the first wave “Out of Africa,” who took the rapid “beachcomber” path. Understanding the key to their genetics may then unlock the key to the “Out of Africa” event. Or so we thought. It looks like the human evolutionary past was a lot more complicated than we’d presumed.
The paper is in the Journal of Genetics and Genomics. Mitochondrial DNA evidence supports northeast Indian origin of the aboriginal Andamanese in the Late Paleolithic:
In view of the geographically closest location to Andaman archipelago, Myanmar was suggested to be the origin place of aboriginal Andamanese. However, for lacking any genetic information from this region, which has prevented to resolve the dispute on whether the aboriginal Andamanese were originated from mainland India or Myanmar. To solve this question and better understand the origin of the aboriginal Andamanese, we screened for haplogroups M31 (from which Andaman-specific lineage M31a1 branched off) and M32 among 846 mitochondrial DNAs (mtDNAs) sampled across Myanmar. As a result, two Myanmar individuals belonging to haplogroup M31 were identified, and completely sequencing the entire mtDNA genomes of both samples testified that the two M31 individuals observed in Myanmar were probably attributed to the recent gene flow from northeast India populations. Since no root lineages of haplogroup M31 or M32 were observed in Myanmar, it is unlikely that Myanmar may serve as the source place of the aboriginal Andamanese. To get further insight into the origin of this unique population, the detailed phylogenetic and phylogeographic analyses were performed by including additional 7 new entire mtDNA genomes and 113 M31 mtDNAs pinpointed from South Asian populations, and the results suggested that Andaman-specific M31a1 could in fact trace its origin to northeast India. Time estimation results further indicated that the Andaman archipelago was likely settled by modern humans from northeast India via the land-bridge which connected the Andaman archipelago and Myanmar around the Last Glacial Maximum (LGM), a scenario in well agreement with the evidence from linguistic and palaeoclimate studies.
Zack Ajmal now has over 50 participants in the Harappa Ancestry Project. This does not include the Pakistani populations in the HGDP, the HapMap Gujaratis, the Indians from the SVGP. Nevertheless, all these samples still barely cover vast heart of South Asia, the Indo-Gangetic plain. Here is the provenance of the submitted samples Zack has so far:
Again, note the underrepresentation of two of India’s most populous states, Uttar Pradesh, ~200 million, and Bihar, ~100 million. Nevertheless, there are already some interesting yields from the project. Below I’ve reedited Zack’s static images (though go to his website for something more dynamic) with the labels of individuals. I’ve highlighted myself and my parents with the red pointers.
Last week I announced the Harappa Ancestry Project. It now has its own dedicate website, http://www.harappadna.org. Additionally, it has its own Facebook page. For Zack to get his own URL he needs about 10 more “likes,” so please like it! (if you are so disposed) Finally, from what I’ve heard the first wave of the 23andMe holiday sale results are coming online this week. Actually, one of the relatives who I purchased the kit for is in processing currently, so I know that we should have a bunch of new people in the system very, very, soon.
Speaking of people, last I heard Zack had gotten about a dozen responses. That’s enough to start an initial round of runs, but obviously he needs more people. More importantly, the goal here is to get better population coverage. One of the things we know intuitively and also from the most current research is the existence of a lot of within-region population variation in South Asia which is structured by community. In other words, a sample of 30 people, where you have 3 from 10 different communities exhibiting geographical and caste diversity is going to be far more useful right now than 300 Jatts from Indian Haryana. Getting 300 Jatts for Haryana would be interesting in that it would give you a window into intra-communal variance, but there’s diminishing returns on the inferences you could make about South Asians as a whole.
If you know someone who has done the 23andMe testing and has preponderant ancestry from South Asia, Iran, Burma, or Tibet, please forward the the URL for the Harappa Ancestry Project. If you are a 23andMe member, and involved in the forums, it might be useful to post a comment thread on this project, as the people you share genes with would see it.
I have put up a few posts warning readers to be careful of confusing PCA plots with real genetic variation. PCA plots are just ways to capture variation in large data sets and extract out the independent dimensions. Its great at detecting population substructure because the largest components of variation often track between population differences, which consist of sets of correlated allele frequencies. Remeber that PCA plots usually are constructed from the two largest dimensions of variation, so they will be drawn from just these correlated allele frequency differences between populations which emerge from historical separation and evolutionary events. Observe that African Americans are distributed along an axis between Europeans and West Africans. Since we know that these are the two parental populations this makes total sense; the between population differences (e.g., SLC24A5 and Duffy) are the raw material from which independent dimensions can pop out. But on a finer scale one has to be cautious because the distribution of elements on the plot as a function of principal components is sensitive to the variation you input to generate the dimensions in the first place.
I can give you a concrete example: me. I showed you my 23andMe ancestry painting yesterday. I didn’t show you my position on the HGDP data set because I’ve shared genes with others and I don’t want to take the step of displaying other peoples’ genetic data, even if at a remove. But, I have reedited some “demo” screenshots and placed where I am on the plot to illustrate what I’m talking about above. The first shot is my position on the two-dimensional plot of first and second principal components of genetic variation from the HGDP data set.