Demographic inference from genomic data in nonmodel insect populations

Blog author Martin Sikora is a postdoc in the lab of Carlos Bustamante.

Blog author Martin Sikora is a postdoc in the lab of Carlos Bustamante.

Reconstructing the demographic history of species and populations is one of the major goals of evolutionary genetics. Inferring the timing and magnitude of past events in the history of a population is not only of interest in its own right, but also in order to form realistic null models for the expected patterns of neutral genetic variation in present-day natural populations. A variety of methods exist that allow the inference of these parameters from genomic data, which, in the absence of detailed historical records in most situations, is often the only feasible way to obtain them. As a consequence, it is generally not possible to empirically validate the parameters inferred from genomic data in a direct comparison with a known “truth” from a natural population. Furthermore, until recently, the application of these methods was limited to model organisms with well-developed genomic resources (e.g., humans and fruitflies), excluding a large number of non-model organisms with potentially considerable evolutionary and ecological interest.

Chasing butterflies?

In an elegant study recently published in the journal Molecular Ecology, Rajiv McCoy, a graduate student with Dmitri Petrov and Carol Boggs, and colleagues tackle both of these problems in natural populations of Euphydryas gillettii, a species of butterfly native to the northern Rocky Mountains. About 30 years ago, a small founder population of this species from Wyoming was intentionally introduced to a new habitat at the Rocky Mountain Biological Laboratory field site in Colorado, and population sizes were recorded every year since the introduction. The beauty of this system is that it allows the authors to perform a direct comparison of the known demography (i.e. a recent split from the parental population and bottleneck ~30 generations ago, with census data in the newly introduced population) with estimates inferred from genomic data.

Gillete’s Checkerspot (Euphydryas gillettii). Photo taken by Carol Boggs, co-advisor of Rajiv and one of the senior authors of the study.

Gillete’s Checkerspot (Euphydryas gillettii). Photo taken by Carol Boggs, co-advisor of Rajiv and one of the senior authors of the study.

A genomic dataset from a non-model organism

The researchers sampled eight larvae each from both the parental as well as the derived population for this study. In the world of model organisms, the next steps for constructing the dataset would be straightforward: Extract genomic DNA, sequence to the desired depth, map to the reference genome and finally call SNPs. In the case of E. gillettii however, no reference genome is available, so the authors had to use a different strategy. They decided to use RNA-sequencing in order to first build a reference transcriptome, which was then used as a reference sequence to map against and discover single nucleotide variants. An additional advantage of this approach is that the data generated can potentially also be utilized for other types of research questions, such as analyses of gene expression differences between the populations. On the downside, SNP calling from a transcriptome without a reference genome is challenging and can lead to false positives, for example due to reads from lowly expressed paralogs erroneously mapping to the highly expressed copy present in the assembled transcriptome. The authors therefore went to great lengths to stringently filter these false positive variants from their dataset.

Demographic inference using δαδι

For the demographic inference, McCoy and colleagues used δαδι (diffusion approximation for demographic inference), a method developed by Ryan Gutenkunst while he was a postdoc in the group of CEHG faculty member Carlos Bustamante. This method uses a diffusion approximation to calculate the expected allele frequency spectrum under a demographic model of interest. The observed allele frequency spectrum is then fit to the expected spectrum by optimization of the demographic parameters to maximize the likelihood of the data. δαδι has been widely used to infer the demographic history of a number of species, from humans to domesticated rice, and is particularly suited to large-scale genomic datasets due to its flexibility and computational efficiency.

Excerpt of Figure 2 from McCoy et al., illustrating the demographic models tested using δαδι.

Excerpt of Figure 2 from McCoy et al., illustrating the demographic models tested using δαδι.

Models vs History

The authors then fit a demographic model reflecting the known population history of E. gillettii, as illustrated in Figure 2 of their article (Model A). Encouragingly, they found that the model provided a very good fit to the data, with an the estimate of the split time between 40 and 47 generations ago, which is very close to the known time of establishment of the Colorado population 33 generations ago. Furthermore, they also tested how robust these results were to using a misspecified demographic model, by incorporating migration between the Colorado and Wyoming populations in their model (which in reality are isolated from each other). However, both alternative models with migration (Models B1 and B2) did not significantly improve the fit, again nicely consistent with the known population history.

Three butterflies is enough?

Finally, the researchers also tested the robustness of the results to variations in the number of samples or SNPs used in the analysis, from datasets simulated under the best-fit model A. They found that δαδι performed remarkably well even with sample sizes as low as three individuals per population. While this is in principle good news for researchers limited by low number of available samples, one has to be aware of the fact that this results will be to a certain extent specific to this particular type of system, where one population undergoes a very strong bottleneck resulting in large effects on the allele frequency spectrum. A good strategy suggested by McCoy and colleagues is then to use these types of simulations in the planning stages of an experiment, in order to inform researchers of the number of samples and markers necessary to confidently estimate the demographic parameters of interest.

Conclusions and future directions

For me, this study is a great example of how next-generation sequencing and sophisticated statistical modeling can open up a new world of possibilities to researchers interested in the ecology and evolution of natural populations. McCoy and colleagues constructed their genomic dataset essentially from scratch, without the “luxuries” of a reference genome or database of known polymorphisms. Moving forward, Rajiv has been busy collecting more samples over the past year. He and his colleagues plan to sequence over a thousand of them for the next phase of the project, as well as assemble a reference genome for E. gillettii, and important next step in the development of genomic tools for this fascinating ecological system.

The author of the paper Rajiv McCoy, sampling larvae of Euphydryas gillettii

The author of the paper Rajiv McCoy, sampling larvae of Euphydryas gillettii

McCoy, R. C., Garud, N. R., Kelley, J. L., Boggs, C. L. and Petrov, D. A. (2013), Genomic inference accurately predicts the timing and severity of a recent bottleneck in a nonmodel insect population. Molecular Ecology. doi: 10.1111/mec.12591

Genomic analyses of ancestry of Caribbean populations

Blog author Rajiv McCoy is a graduate student in the lab of Dmitri Petrov.

Blog author Rajiv McCoy is a graduate student in the lab of Dmitri Petrov.

In the Author Summary of their paper, “Reconstructing the Population Genetic History of the Caribbean”, Andrés Moreno-Estrada and colleagues point out that Latinos are often falsely depicted as a homogeneous ethnic or cultural group.  In reality, however, Latinos, including inhabitants of the Caribbean basin, represent a diverse mixture of previously separate human populations, such as indigenous groups, European colonists, and West Africans brought over during the Atlantic slave trade.  This mixing process, which geneticists call “admixture”, left a distinct footprint on genetic variation within and between Caribbean populations.  By surveying genotypes of 330 Caribbean individuals and comparing to a database of variation from more than 3000 individuals from European, African, and Native American populations, Moreno et al., explore the genomic outcomes of this complex admixture process and reveal intriguing demographic patterns that could not be obtained from the historical record alone. The paper, featured in the latest edition of PLOS Genetics, represents a collaborative project with co-senior authorship by Stanford CEHG professor Carlos Bustamante and Professor Eden Martin from the University of Miami Miller School of Medicine.

Reconstructing the demographic history of admixed populations

Because parental DNA is only moderately shuffled before being incorporated into gametes (the process of meiotic recombination), admixture results in discrete genomic segments that can be traced to a particular ancestral population.  In early generations after the onset of admixture, these segments are large.  However, after many generations, segments will be quite small.  By investigating the distribution of sizes of these ancestry “tracts”, Moreno and colleagues inferred the timing of various waves of migration and admixture.  For Caribbean Island populations, they infer that European gene flow first occurred ~16-17 generations ago, which matches very closely to the historical record of ~500 years, assuming ~30 years per generation.  In contrast, for neighboring mainland populations from Colombia and Honduras, they find that European gene flow occurred in waves, starting more recently (~14 generations ago).

Identifying sub-continental ancestry of admixed individuals

Those familiar with human population genetics will recognize principal component analysis (PCA), which transforms a matrix of correlated observed genotypes into a set of uncorrelated variables where the first component explains the most possible variance, the second variable explains the second most variance, and so on.  Individuals’ transformed genotypes can be plotted on the first two principle components, and when performed on a worldwide scale, distinct clusters appear which represent populations of ancestry.  On conventional PCA plots, admixed individuals fall between their different ancestral populations, as they possess sets of genotypes diagnostic of multiple ancestral groups.  As virtually all Caribbean individuals are admixed to some degree, this pattern is apparent for Caribbean populations (see Figure 1B from the paper, reproduced below).


While interesting, this means that the sub-continental ancestry of these admixed individuals is difficult to ascertain.  An individual may want to know which Native American, West African, and European populations contribute to his or her ancestry, and this analysis does not have sufficient resolution to answer these questions.

Moreno and colleagues therefore devised a new version of PCA called ancestry-specific PCA (ASPCA), which extracts genomic segments assigned to Native American, West African, and European ancestry, then analyzes these segments separately, dealing with the large proportions of missing data that result.  In the case of Native American ASPCA, they observe two overlapping clusters.  The first represents mostly Colombians and Hondurans, who cluster most closely with indigenous groups from Western Colombia and Central America and have a greater overall proportion of Native American ancestry.  The second cluster represents mostly Cubans, Dominicans, and Puerto Ricans, who cluster most closely with Eastern Colombian and Amazonian indigenous groups.  This makes sense in light of the fact that Amazonian populations from the Lower Orinoco Valley settled on rivers and streams, which could have facilitated their migration.  Because indigenous ancestry proportions were relatively consistent and closely clustered across different Caribbean Islands, the authors posit that there was a single pulse of expansion of Amazonian natives across the Caribbean prior to European arrival, along with gene flow among the islands.

In the case of European ASPCA, Moreno et al. found that Caribbean samples clustered closest to, but clearly distinct from, present day individuals from the Iberian Peninsula in Southern Europe.  In fact, the differentiation between this “Latino-specific component” and Southern Europe is at least as great as the differentiation between Northern and Southern Europe.  The authors hypothesize that this is due to very small population sizes among European colonists, which would have introduced noise into patterns of genomic variation through the process of random genetic drift.

Finally, the authors demonstrate that Caribbean populations have a higher proportion of African ancestry compared to mainland American populations, a result of admixture during and after the Atlantic slave trade.  Surprisingly, the authors found that all samples tightly clustered with present day Yoruba samples from Nigeria rather than being dispersed throughout West Africa.  However, because other analyses suggested that there might have been two major waves of migration from West Africa, the authors decided to analyze “old” and “young” blocks of African ancestry separately.  This analysis revealed that “older” segments are primarily derived from groups from the Senegambia region of Northwest Africa, while “younger” segments likely trace to groups from the Gulf of Guinea and Equatorial West Africa (including the Yoruba).

Conclusions and perspectives

This groundbreaking study has immediate implications for the field of personalized medicine, especially due to the discovery of a distinct Latino-specific component of European ancestry.  The hypothesis that European colonists underwent a demographic bottleneck (a process termed the “founder effect”) has expected consequences for the frequency of damaging mutations contributing to genetic disease. The observation of extensive genetic differences among Caribbean populations also argues for more such studies characterizing genetic variation on a smaller geographic scale. The newly developed ASPCA method will surely be valuable for other admixed populations.  In addition to medical implications, studies such as this help dispel simplistic notions of race and ethnicity and inform cultural identities based on unique and complex demographic history.

Citation: Moreno-Estrada A, Gravel S, Zakharia F, McCauley JL, Byrnes JK, et al. (2013) Reconstructing the Population Genetic History of the Caribbean. PLoS Genet 9(11): e1003925. doi:10.1371/journal.pgen.1003925

Paper author Andres Moreno-Estrada is a research associate in the lab of Carlos Bustamante.

Paper author Andrés Moreno-Estrada is a research associate in the lab of Carlos Bustamante.