Robust Inference of Population Size Histories from Genomic Sequencing Data

Abstract

Unraveling the complex demographic histories of natural populations is a central problem in population genetics. Understanding past demographic events is of general anthropological interest and is also an important step in establishing accurate null models when identifying adaptive or disease-associated genetic variation. An important class of tools for inferring past population size changes are Coalescent Hidden Markov Models (CHMMs). These models make efficient use of the linkage information in population genomic datasets by using the local genealogies relating sampled individuals as latent states that evolve across the chromosome in an HMM framework. Extending these models to large sample sizes is challenging, since the number of possible latent states increases rapidly. Here, we present our method CHIMP (CHMM History-Inference ML Procedure), a novel CHMM method for inferring the size history of a population. It can be applied to large samples (hundreds of haplotypes) and only requires unphased genomes as input. The two implementations of CHIMP that we present here use either the height of the genealogical tree (TMRCA) or the total branch length, respectively, as the latent variable at each position in the genome. The requisite transition and emission probabilities are obtained by numerically solving certain systems of differential equations derived from the ancestral process with recombination. The parameters of the population size history are subsequently inferred using an Expectation-Maximization algorithm. In addition, we implement a composite likelihood scheme to allow the method to scale to large sample sizes. We demonstrate the efficiency and accuracy of our method in a variety of benchmark tests using simulated data and present comparisons to other state-of-the-art methods. Specifically, our implementation using TMRCA as the latent variable shows comparable performance and provides accurate estimates of effective population sizes in intermediate and ancient times. Our method is agnostic to the phasing of the data, which makes it a promising alternative in scenarios where high quality data is not available, and has potential applications for pseudo-haploid data.

Improved inference of population histories by integrating genomic and epigenomic data

“…Our simulations demonstrate that if the SNP mutation rate is known, the mutation rate of other markers can be recovered (under the condition that the marker follow all hypotheses described in Figure 1). Moreover, our method accounts for the finite site problem that arises at reversible (hyper-mutable) markers and/or where effective population size is high [70, 72]. Overall, the simulator and SMC methods presented here therefore pave the way for a rigorous statistical framework to test if a common ARG can explain the observed diversity patterns under the model hypotheses laid out in Figure 1.…”

“…Our simulations demonstrate that if the SNP mutation rate is known, the mutation rate of other markers can be recovered. Moreover, our method accounts for the finite site problem that arises at reversible (hyper-mutable) markers and/or effective population size is high [62,64].…”

Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent

“…With the rise in popularity of SMC approaches for demographic inferences [58], most current methods leverage information from whole genome sequences by simultaneously reconstructing a portion of the ARG to infer past demographic history [58,82,93,94],…”

“…With the rise in popularity of SMC approaches for demographic inferences [60], most current methods leverage information from whole genome sequences by simultaneously reconstructing a portion of the ARG to infer past demographic history [60, 84, 95, 96], migration rates [52, 97], variation in recombination and mutation along the genome [5, 4], as well as ecological life history traits such as selfing or seed banking [87, 93]. However, other previous studies proposed to uncouple both steps, namely by first reconstructing the ARG and by then inferring parameters from its distribution [88, 35, 75].…”

“…Similarly, when the simulated data contains strong selection, a small α value is recovered at the locus under selection and the past variation of population size is accurately recovered, albeit with a small underestimation of population size in recent times for SMβC (Figure 8). With the rise of SMC approaches [53], most current methods leverage information from 435 whole genome sequences by simultaneously reconstructing a portion of the ARG in order 436 to infer past demographic history [53,77,88,89], migration rates [46,90], variation in 437 recombination and mutation along the genome [5,4], as well as ecological life history traits 438 such as selfing or seed banking [80,86]. However in previous studies authors discussed 439 uncoupling both steps, namely reconstructing the ARG and then inferring parameters 440 from its distribution [81,31,68].…”