Abstract
The recent increase in time-series population genomic data from experimental, natural, and ancient populations has been accompanied by a promising growth in methodologies for inferring demographic and selective parameters from such data. However, these methods have largely presumed that the populations of interest are well-described by the Kingman coalescent. In reality, many groups of organisms, including viruses, marine organisms, and some plants, protists, and fungi, typified by high variance in progeny number, may be best characterized by multiple-merger coalescent models. Estimation of population genetic parameters under Wright-Fisher assumptions for these organisms may thus be prone to serious mis-inference. We propose a novel method for the joint inference of demography and selection under the ψ-coalescent model, termed Multiple-Merger Coalescent Approximate Bayesian Computation, or MMC-ABC. We first demonstrate mis-inference under the Kingman, and then exhibit the superior performance of MMC-ABC under conditions of skewed offspring distributions. In order to highlight the utility of this approach, we reanalyzed previously published drug-selection lines of influenza A virus. We jointly inferred the extent of progeny-skew inherent to viral replication and identified putative drug-resistance mutations.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1019-1028 |
| Number of pages | 10 |
| Journal | Genetics |
| Volume | 211 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 1 2019 |
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Keywords
- Coalescent theory
- Population genetics
- Selection
- Sweepstakes reproduction
- Time-sampled inference
ASJC Scopus subject areas
- Genetics
Cite this
Inferring demography and selection in organisms characterized by skewed offspring distributions. / Sackman, Andrew M.; Harris, Rebecca B.; Jensen, Jeffrey.
In: Genetics, Vol. 211, No. 3, 01.03.2019, p. 1019-1028.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Inferring demography and selection in organisms characterized by skewed offspring distributions
AU - Sackman, Andrew M.
AU - Harris, Rebecca B.
AU - Jensen, Jeffrey
PY - 2019/3/1
Y1 - 2019/3/1
N2 - The recent increase in time-series population genomic data from experimental, natural, and ancient populations has been accompanied by a promising growth in methodologies for inferring demographic and selective parameters from such data. However, these methods have largely presumed that the populations of interest are well-described by the Kingman coalescent. In reality, many groups of organisms, including viruses, marine organisms, and some plants, protists, and fungi, typified by high variance in progeny number, may be best characterized by multiple-merger coalescent models. Estimation of population genetic parameters under Wright-Fisher assumptions for these organisms may thus be prone to serious mis-inference. We propose a novel method for the joint inference of demography and selection under the ψ-coalescent model, termed Multiple-Merger Coalescent Approximate Bayesian Computation, or MMC-ABC. We first demonstrate mis-inference under the Kingman, and then exhibit the superior performance of MMC-ABC under conditions of skewed offspring distributions. In order to highlight the utility of this approach, we reanalyzed previously published drug-selection lines of influenza A virus. We jointly inferred the extent of progeny-skew inherent to viral replication and identified putative drug-resistance mutations.
AB - The recent increase in time-series population genomic data from experimental, natural, and ancient populations has been accompanied by a promising growth in methodologies for inferring demographic and selective parameters from such data. However, these methods have largely presumed that the populations of interest are well-described by the Kingman coalescent. In reality, many groups of organisms, including viruses, marine organisms, and some plants, protists, and fungi, typified by high variance in progeny number, may be best characterized by multiple-merger coalescent models. Estimation of population genetic parameters under Wright-Fisher assumptions for these organisms may thus be prone to serious mis-inference. We propose a novel method for the joint inference of demography and selection under the ψ-coalescent model, termed Multiple-Merger Coalescent Approximate Bayesian Computation, or MMC-ABC. We first demonstrate mis-inference under the Kingman, and then exhibit the superior performance of MMC-ABC under conditions of skewed offspring distributions. In order to highlight the utility of this approach, we reanalyzed previously published drug-selection lines of influenza A virus. We jointly inferred the extent of progeny-skew inherent to viral replication and identified putative drug-resistance mutations.
KW - Coalescent theory
KW - Population genetics
KW - Selection
KW - Sweepstakes reproduction
KW - Time-sampled inference
UR - http://www.scopus.com/inward/record.url?scp=85062637352&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062637352&partnerID=8YFLogxK
U2 - 10.1534/genetics.118.301684
DO - 10.1534/genetics.118.301684
M3 - Article
VL - 211
SP - 1019
EP - 1028
JO - Genetics
JF - Genetics
SN - 0016-6731
IS - 3
ER -