Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations

Lauterbur, M. Elise; Cavassim, Maria Izabel A.; Gladstein, Ariella L.; Gower, Graham; Pope, Nathaniel S.; Tsambos, Georgia; Adrion, Jeff; Belsare, Saurabh; Biddanda, Arjun; Caudill, Victoria; Cury, Jean; Echevarria, Ignacio; Haller, Benjamin C.; Hasan, Ahmed R.; Huang, X. T.; Iasi, Leonardo Nicola Martin; Noskova, Ekaterina; Obšteter, Jana; Pavinato, Vitor Antonio Corrêa; Pearson, Alice; Peede, David; Perez, Manolo F.; Rodrigues, Murillo F.; Smith, Chris C. R.; Spence, Jeffrey P.; Teterina, Anastasia; Tittes, Silas; Unneberg, Per; Vazquez, Juan Manuel; Waples, Ryan K.; Wohns, Anthony Wilder; Wong, Yan; Baumdicker, Franz; Cartwright, R.A.; Gutenkunst, Ryan N.; Kelleher, Jerome; Kern, Andrew D.; Ragsdale, Aaron P.; Ralph, Peter L.; Schrider, Daniel R.; Gronau, Ilan

doi:10.7554/elife.84874.3

Cited by 8 publications

(7 citation statements)

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“…We also generated a test set incorporating background selection into this scenario of introgression. To accomplish this, we used stdpopsim version 0.2.0 [82, 83] to generate SLiM scripts simulating negative and background selection using a genetic map [84] and distribution of fitness effects [85] for mutations in exonic regions, and exon annotations (the FlyBase BDGP6.32.51 exons set in stdpopsim, taken from FlyBase [86]) all obtained from D. melanogaster . We then programmatically modified the SLiM scripts to include bidirectional introgression under the same scenario examined above, which each script generating one test replicate of a 1 Mb region with recombination and annotation data taken from chr3L, before running on the central 100 kb of each test example.…”

Section: Methodsmentioning

confidence: 99%

“…We also investigated the impact of background selection (BGS), the impact of linked negative selection on neutral diversity [97], on IntroUNET's accuracy. To do this, we used stdpopsim [82,83] incorporate background selection (modeled after randomly chosen regions of the D. melanogaster genome) into our simulations of the same introgression scenario described above (see Methods for details). We generated a test set of 1000 regions each 1 Mb in length and ran IntroUNET on the central 100 kb of each region.…”

Section: Introunet Performs Well Under Scenarios Of Model Misspecific...mentioning

confidence: 99%

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IntroUNET: identifying introgressed alleles via semantic segmentation

Ray

Flagel

Schrider³

2023

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A growing body of evidence suggests that gene flow between closely related species is a widespread phenomenon. Alleles that introgress from one species into a close relative are typically neutral or deleterious, but sometimes confer a significant fitness advantage. Given the potential relevance to speciation and adaptation, numerous methods have therefore been devised to identify regions of the genome that have experienced introgression. Recently, supervised machine learning approaches have been shown to be highly effective for detecting introgression. One especially promising approach is to treat population genetic inference as an image classification problem, and feed an image representation of a population genetic alignment as input to a deep neural network that distinguishes among evolutionary models (i.e. introgression or no introgression). However, if we wish to investigate the full extent and fitness effects of introgression, merely identifying genomic regions in a population genetic alignment that harbor introgressed loci is insufficient---ideally we would be able to infer precisely which individuals have introgressed material and at which positions in the genome. Here we adapt a deep learning algorithm for semantic segmentation, the task of correctly identifying the type of object to which each individual pixel in an image belongs, to the task of identifying introgressed alleles. Our trained neural network is thus able to infer, for each individual in a two-population alignment, which of those individual's alleles were introgressed from the other population. We use simulated data to show that this approach is highly accurate, and that it can be readily extended to identify alleles that are introgressed from an unsampled "ghost" population, performing comparably to a supervised learning method tailored specifically to that task. Finally, we apply this method to data from Drosophila, showing that it is able to accurately recover introgressed haplotypes from real data. This analysis reveals that introgressed alleles are typically confined to lower frequencies within genic regions, suggestive of purifying selection, but are found at much higher frequencies in a region previously shown to be affected by adaptive introgression. Our method's success in recovering introgressed haplotypes in challenging real-world scenarios underscores the utility of deep learning approaches for making richer evolutionary inferences from genomic data.

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Section: Methodsmentioning

confidence: 99%

Section: Introunet Performs Well Under Scenarios Of Model Misspecific...mentioning

confidence: 99%

IntroUNET: identifying introgressed alleles via semantic segmentation

Ray

Flagel

Schrider³

2023

Preprint

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“…We simulated the trait with h 2 = 1 to obtain true marginal effect sizes. We simulated chromosome 22 using the information from stdpopsim catalog 35,36 . The first five generations were simulated using the Wright-Fisher process and more upstream generations followed the coalescent 29 .…”

Section: Methodsmentioning

confidence: 99%

Single locus theory of admixture is insufficient for the study of complex traits in admixed populations

Lee,

Hou

et al. 2023

Preprint

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Admixed populations offer valuable insights into the genetic architecture of complex traits. Investigation of such a topic demands a delicate theory that can handle the complex demographic history of admixed populations. With additional assumptions, we show that Pritchard-Stephens-Donnelly (PSD) can predict empirical GWAS findings. We name this the extended PSD (ePSD) model and evaluate the predictions using real data. When applied to real data of admixed genomes, the prediction of the ePSD model is very successful in explaining single-locus behaviors while falling short in predicting two-locus phenomena involving linkage disequilibrium. Our results show that a mosaic of independent single-continental segments is an insufficient approximation of contemporary admixed populations. A more advanced theory that better models linkage disequilibrium of admixed populations will be crucial to better understanding the genetic architecture of complex traits.

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“…We set this baseline rate to 500 c , where c is the average crossover rate per base pair estimated for a species. We used the following c values which we obtained from version 0.2.0 (Adrion et al ., 2020; Lauterbur et al ., 2023a): 1.30981 × 10 − 8 for humans (the mean value on chromosome 12 using data from Consortium (2007)), 1.7966 e × 10 − 8 for D. melanogaster (the mean on chr3L using data from Comeron et al . (2012)), and 8.06452 × 10 − 10 for A. thaliana (the genome-wide average rate from Huber et al .…”

Section: Methodsmentioning

confidence: 99%

Allelic gene conversion softens selective sweeps

Schrider

2023

Preprint

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The prominence of positive selection, in which beneficial mutations are favored by natural selection and rapidly increase in frequency, is a subject of intense debate. Positive selection can result in selective sweeps, in which the haplotype(s) bearing the adaptive allele “sweep” through the population, thereby removing much of the genetic diversity from the region surrounding the target of selection. Two models of selective sweeps have been proposed: classical sweeps, or “hard sweeps”, in which a single copy of the adaptive allele sweeps to fixation, and “soft sweeps”, in which multiple distinct copies of the adaptive allele leave descendants after the sweep. Soft sweeps can be the outcome of recurrent mutation to the adaptive allele, or the presence of standing genetic variation consisting of multiple copies of the adaptive allele prior to the onset of selection. Importantly, soft sweeps will be common when populations can rapidly adapt to novel selective pressures, either because of a high mutation rate or because adaptive alleles are already present. The prevalence of soft sweeps is especially controversial, and it has been noted that selection on standing variation or recurrent mutations may not always produce soft sweeps. Here, we show that the inverse is true: selection on single-originde novomutations may often result in an outcome that is indistinguishable from a soft sweep. This is made possible by allelic gene conversion, which “softens” hard sweeps by copying the adaptive allele onto multiple genetic backgrounds, a process we refer to as a “pseudo-soft” sweep. We carried out a simulation study examining the impact of gene conversion on sweeps from a singlede novovariant in models of human,Drosophila, andArabidopsispopulations. The fraction of simulations in which gene conversion had produced multiple haplotypes with the adaptive allele upon fixation was appreciable. Indeed, under realistic demographic histories and gene conversion rates, even if selection always acts on a single-origin mutation, sweeps involving multiple haplotypes are more likely than hard sweeps in large populations, especially when selection is not extremely strong. Thus, even when the mutation rate is low or there is no standing variation, hard sweeps are expected to be the exception rather than the rule in large populations. These results also imply that the presence of signatures of soft sweeps does not necessarily mean that adaptation has been especially rapid or is not mutation limited.

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Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations

Cited by 8 publications

References 88 publications

IntroUNET: identifying introgressed alleles via semantic segmentation

IntroUNET: identifying introgressed alleles via semantic segmentation

Single locus theory of admixture is insufficient for the study of complex traits in admixed populations

Allelic gene conversion softens selective sweeps

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