Map of Mexico with sampled-from states highlighted.

Recent studies of genetic diversity among Europeans (blogged about here and here) show that DNA is a surprisingly good predictor of where a person lives; people from the same country tend to be more similar to one another than to those from other parts of the continent. This latest study, which was published earlier this week in the Proceedings of the National Academy of Sciences, shows a similar pattern in Mexico: Mexican Mestizos (people of mixed European and Native American ancestry) from the same state tend to group together genetically, and the groups themselves fall along a genetic continuum that corresponds roughly to their latitude. You can see for yourself in this plot from the paper (below), which we’ve modified a bit for clarity. This is the same kind of plot used in 23andMe’s Global Similarity: Advanced feature — each point in the plot represents a person. The closer two points appear in the plot, the closer those two individuals are to each other genetically. The 300 Mexican Mestizos fall into a line stretching from a group of Europeans at the upper right to a group of Amerindians¹ at the lower left.

PCA map of Mexican genotypes.

The people from Sonora, the northernmost state, appear closest to the European cluster, and the people from the sourthern states Guerrero, Veracruz, and Yucatan appear closest to the Amerind cluster. This makes you wonder whether this pattern might correlate with the proportion of European ancestry. The researchers wondered that too, so they investigated further by analyzing their dataset with a computer program that estimates the proportion of ancestry a person’s DNA derives from each of several reference populations. When they set the program loose, they found that the six states did vary widely in proportion of European ancestry, from an average of 65% in Sonora (fifth column from the left) to an average of 35% in Guerrero (second from the right):

Admixture proportion estimates for Mexican and HapMap samples.

The authors note that this pattern makes sense, since Amerindian population density declines as you head north. Also, you might note that there’s a green sliver of African ancestry in each of the Mestizo populations, which approaches 5% in the southern states of Veracruz and Guerrero. This also meshes well with the historical record, since it’s through these coastal states, among others, that African slaves entered Mexico during the Spanish colonization.

These analyses tell us much about Mexico’s history — could the same dataset serve to improve Mexico’s future? This paper also examines the prospects for conducting medical genetic studies in Mexico.

The most popular study design nowadays, and the one that the researchers focus on, is the genomewide association study, or GWAS, also called a case-control study. GWASes are the basis for essentially all the studies discussed in the Spittoon’s SNPWatch section, and the majority of the findings underlying 23andMe’s Health and Traits reports, and have recently been the subject of intense debate in the genetics community.

The core idea of a GWAS is to look for genetic markers (these days, usually common single-letter DNA variations known as SNPs) in a population that are at very different frequencies in a group of people with a particular disease, say type 2 diabetes, than in a group of people without that disease. In order to do that, you first have to identify a comprehensive set of the common genetic variations within a population, and then create a DNA array (commonly called a SNP chip) to probe all those variable locations in a large number of people with and without the condition you’re studying.

There’s the rub. In recent years, a project known as HapMap has created catalogs of common variations in European, African and East Asian populations, and chips have been produced based on it². But Mexico’s population is a mixture of two of those (European and African) and another population (Amerindian) that is related, but not identical to, the third. There is no Mexican SNP chip.

That’s why the authors of this paper are suggesting a project to characterize common genetic variation within the Mexican population itself. They estimated that a catalog of common genetic variation using any two of the Mestizo groups they analyzed would capture enough variation to fuel quality GWAS studies, and would require fewer markers to do so than the alternative, which would be to use all the common markers from HapMap itself. This would substantially lower the cost of genotyping, they argue, and the reduced cost of using a platform based on the Mexico-specific catalog would allow researchers to genotype many more people for their GWAS studies for the same number of pesos. Since sample size is often the limiting factor in the ability of the GWAS design to find disease genes, this could improve their ability to find the genetic causes of inherited disease.

Thanks to 23andMe Founding R&D Architect Brian Naughton for his assistance in the preparation of this blog post.

Notes

1. Amerindian, sometimes just Amerind, is short for “American Indian”, and it denotes a descendant of the indigenous peoples of the Americas; anthropologist-types use the word to avoid confusion with the Indians that live in South Asia.
2. Because full-genome sequencing is still a few years from being affordable, researchers cannot look at every single one of the 3 billion nucleotides in the genome to find the one (or combinations of more than one) that are directly linked to a particular condition. For now, they must make do with the half-million to million SNPs that the current crop of SNP-genotyping chips allow.At first blush, this sounds like a fool’s errand. How can you possibly say anything about 3 billion DNA nucleotides with a collection of just a million markers? What if the marker you have on your SNP chip is near to but not actually the disease-causing SNP? Won’t this be like ships passing in the night? In just the last decade or so, geneticists have learned that the human genome has a very peculiar property: the 22 numbered chromosomes, or autosomes, and the X, tend to do the bulk of their recombining in a very small fraction of the spans of those chromosomes. These highly-recombining locations are termed hotspots; it’s kind of like chromosomes are trains, with hotspots as the links between boxcars (although unlike boxcars, which are all the same length, there’s considerable variation in the distance between hotspots). This state of affairs is good news for GWASes, because it means that these boxcars — big chunks (mean length ~200,000 DNA base pairs) of chromosome that tend to be passed from parent to child as a unit — aid the task of marker selection greatly. If you sample a bunch of people, as these researchers have done in Mexico, then you can build up a catalog of the specific chromosome chunks that occur. The idea is that when a disease-causing allele occurs, it can’t help but sit on one of these chunks, so the problem of building a GWAS chip is transformed into choosing markers that reliably distinguish between the chunks. It’s like being able to assign one inspector to each boxcar, rather than each crate inside it. All you need to do, then, is to look at SNP that’s diagnostic for the chromosome chunk that your disease-causing SNP is sitting on, instead of having to find the causal SNP itself. The technical word for these chunks or boxcars is haplotype³, and such catalogs when built are called haplotype maps; it’s actually what was meant by “medical genetic reference database” in the title of this post. It’s for the same purpose that the International HapMap Project, which has built reference haplotype maps for African, Asian, and European populations, was conceived, and it’s the origin of the name HapMap.
3. Haplotype is short for haploid genotype. Haploid means that you’re concerned with one chromosome, so haplotype means a contiguous segment from a single chromosome. Diploid means two (paired) chromosomes; humans are a diploid organism, because our chromosomes come in pairs. The fun doesn’t stop there. Some organisms are tolerant of higher ploidy, so there are tetraploid, hexaploid, and even octoploid species. For example wheat is hexaploid, so it has six copies of each chromosome.

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Art Nouveau architecture at the Codorníu winery outside of Barcelona.

So much new research was discussed in Barcelona it’s hard to decide which were the most notable presentations. But here are a few of the ones I found most interesting:
Were humans shaped more by history or local environment?
A major debate in the human evolutionary genetics talks and posters considered the origin of the genetic differences seen in humanity today: Were they shaped more by populations splitting apart and coming together, or evolutionary adaptation to local environments? Interestingly, people from the lab of our SAB member Jonathan Pritchard presented arguments on both sides. Both talks presented strong evidence using similar data sets. Perhaps one phenomenon has more impact locally and the other more regionally. Certainly the debate continues.

James Cai and coauthors from Stanford (including our very own R&D scientist Mike Macpherson) and The Hebrew University of Jerusalem showed that the history of the human genome cannot be explained simply by neutral variants – variants that do not cause a functional change. All across the genome there is evidence of “selective sweeps” where an advantageous version of a gene quickly increased in frequency in a population or species. For example, the gene FOXP2 has undergone a selective sweep in all humans within the past several hundred thousand years and may have contributed to our ability to use advanced language. More recent selective sweeps in the Duffy and Lactase genes (both have variants that 23andMe customers or demo account holders can read more about in My Gene Journal (now called Health and Traits)) happened after human populations diverged and thus didn’t sweep across the entire globe but are confined to specific regions: primarily western Africa for the Duffy-0 variant and Europe, the Near East, eastern Africa, and southern Asia for Lactose Tolerance.

Selective sweeps tend leave evidence in the form of nearby DNA that gets dragged along with the variant as it sweeps across a population. Similarly, new variants that are disadvantageous (or become disadvantageous when, say, moving into a new environment) can leave these similar signals as they are dragged out of the population. However, it is often difficult to separate out effects of population history from these selective forces. By using a novel statistic that controls for population history, Cai and colleagues show that many locations on the human genome have been affected by these selective sweeps. While previous scans for positive selection required these selective sweeps to be incomplete (see here and here, for example), the authors use a metric which can go back even further to look at the timing and strength of selective sweeps which have affected the entire human population, even going back as far as one million years. This work is an extension of previous research on Drosophila.

Interestingly, one of the data sets used for this work was the complete genome of Jim Watson, who co-discovered the structure of DNA.

Population Structure, History, and Migrations
Sarah Tishkoff of U. Penn gave a talk on her incredible data set of sub-Saharan African populations. So much of the world’s genetic diversity is located in this region, yet its inhabitants have been relatively under-sampled so far. Tishkoff’s data, in the context of global variation, makes it apparent just how important it is to understand the history of sub-Saharan populations in order to understand the history of our species. In one example, Tishkoff used a technique known as Principal Components Analysis (PCA) to collapse all their genetic data into three dimensions. Individuals near each other in PCA are more similar. In her plot, a hunter-gatherer population from Tanzania known as the Hadza can be found in their own dimension on the plot, which suggests that the Hadza, while having a small population size, have been isolated for a long, long time and are quite divergent from other populations, even including the 52 in the CEPH-HGDP data.
Tishkoff also showed how difficult it is to extrapolate from one African population to the next, even if they neighbor each other. One example of this is in parts of western Africa where the Fulani have increased malaria resistance compared to other groups such as the Mossi and Rimaibe – even within the same town.
Several talks and posters looked at the new lactase persistence variants discovered last year in sub-Saharan Africa and the Near East. These variants are functionally the same as their much more common counterparts, which allows Europeans and South Asians to drink milk into adulthood without experiencing lactose intolerance (23andMe customers can look up their genotype for this variant in My Gene Journal (now called Health and Traits)). But because they differ genetically, these newly discovered variants illustrate the importance of milk digestion for populations that relied on herding in their past. Multiple research groups showed that the eastern African persistence variants made their way down to the San Bushmen and neighboring populations of southern Africa.
When normal inheritance breaks down

Genomic imprinting in action. Here, the color of the offspring comes from the father, regardless of which genotype he has.

Andrew Clark of Cornell has been looking at versions of genes in mice that change the traits of offspring depending on whether they are inherited from the mother or father. This phenomenon, called Genomic Imprinting, has been detected in many mammals before, including humans, although interestingly it isn’t found in marsupials or the egg-laying monotremes like the Platypus. However, the traits affected by genomic imprinting have not been surveyed using a genome-wide approach.
Clark and colleagues used the Solexa sequencing platform to look for differences in the mouse brain between mice crossed from two different strains. By switching the strains of the mother and father researchers can detect traits that derive exclusively, or “imprint on”, one parent.
It turns out a good number of genes exhibit genomic imprinting Genes imprinted on the father tend to show only the trait of the father. Genes imprinted on the mother tend to let some of the father’s trait come through, albeit at much lower numbers. In addition, the researchers found differences in the organs affected by imprinting: genes imprinted on the mother were more likely to be expressed in the reproductive organs and those imprinted on the father were found more in the brain.
It appears that imprinting has no immediate benefit for offspring and may have originated in mammals completely by accident, a quirk of our histories. But learning about how imprinting evolved will help us understand how they came to be.

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