Methods in population genomics

There are many methods in population genomics. To help newcomers, we published a review on the main methods in the field, available here

This simple graph should explain it all…

A few more resources.

I try to regularly update the tables so they reflect the most recent methods. You can also check the blog posts, where I pin reviews or articles that summarize recent methods.

I also design lectures and workshops in population genomics. I recently contributed with Thibault Leroy to an online Physalia course.

In collaboration with Julie Orjuela at IRD, we designed a three-days workshop on population genomics. It is inspired by the one designed for Physalia, but also includes new material, particularly for demographic inference. Data for workshops #1 and #2 can be found at this address

Workshop #1: Population structure

Workshop #2: Demographic inference, introduction to ABC and likelihood approaches

Workshop #3: Selection and association scans

I am also planning another set of workshops on spatial genomics. I recently started developing pipelines using slendr, which is a R package interacting with tskit and SLiM, and can be very useful if your research has a spatial component and you want to test the robustness of your analyses. I will post it here once done.

I am also working on developing pipelines using machine/deep learning (see also Table 4). You can check these two resources, which aim at helping researchers build their own pipelines:

dnadna popgen-npe

Note that the latter has been designed with the ability to produce posterior distributions and confidence intervals.

Table 1: Summary of methods for population structure inference.

Inferring population structure from genetic data can be challenging, particularly when distinguishing true barriers to gene flow from other patterns. Methods that incorporate spatial or environmental data (landscape genomics) are often better suited to identify physical barriers or continuous clines. Additionally, some approaches, such as tfa, can explicitly account for temporal structure when samples are collected across generations. Caution is advised to avoid overinterpreting patterns as evidence of isolation.

Table 2: Summary of methods for genome scans of selection.

Detecting selection in genomic data is not merely a technical challenge but also a conceptual one. The risk lies in overinterpreting results to fit a compelling narrative (a pitfall famously illustrated by Gould and Lewontin’s spandrels of San Marco critique). To tackle such biases, it is a good idea to use simulations to assess the power and limitations of detection methods (see also Table 4). For practical examples of genome scan workflows, refer to this workshop material.

This table also includes methods for estimating recombination landscapes, as recombination rates can influence the signatures of linked selection.

Table 3: Summary of methods for demographic history reconstruction

Demographic inference methods can be broadly categorized into two groups: those requiring user-defined models (e.g., fastsimcoal2) and those providing predefined models (e.g., MSMC2). While predefined models offer a convenient starting point for exploring signals in genetic data, they should not be relied upon exclusively. User-defined models, though more labor-intensive, encourage critical thinking about alternative demographic scenarios that could produce similar genetic patterns.

It is important to recognize that different demographic histories can generate nearly identical genetic signatures. Whenever possible, validate inferred models by checking whether simulations under the best-fit parameters can reproduce independent summary statistics (e.g., if the method is based on the site frequency spectrum, verify that linkage disequilibrium patterns are also recovered).

Table 4: Summary of simulators and simulation-based methods for selection and demographic inference.

The field is increasingly moving toward integrated models of demography and selection, though this remains computationally demanding. The interplay between these evolutionary forces shapes genomic diversity in complex ways, often making parameters difficult to disentangle. The methods listed below provide tools for simulating datasets, training algorithms, and exploring the range of possible evolutionary trajectories for genes or genomes under combined demographic and selective pressures.

Note however that more recent does not necessarily mean “better”. For example, ABC is a bit less of a black box than more recent machine/deep learning algorithms, and may be a valuable complement to understand the link between a summary statistic and the evolutionary processes that may impact it. I would not recommend using these advanced techniques in isolation. Start with descriptive statistics, and take the time to cross-validate the outputs of the more sophisticated algorithms to identify possible issues. See also Table 5 below.

Table 5: A possible list of methods to begin with

The landscape of population genetic methods is vast, with each tool designed for specific applications. However, a few core methods are widely applicable and serve as a foundation for most analyses. Below is a curated list of these essential tools to help you begin your workflow.

Table 6: A primer to museomics/ancient DNA

Disclaimer: While I am not a specialist in these methods, I have begun incorporating them into my research. Museum and herbarium samples offer invaluable resources for reconstructing the past history of species and are often available in large quantities. However, analyzing such data requires dedicated pipelines to address challenges like low sequencing depth and post-mortem damage. The table below provides an overview of the key considerations and tools for working with these data types.

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