An instantaneous coalescent method insensitive to population structure

Zeqi Yao; Kehui Liu; Shanjun Deng; Xionglei He

doi:10.1016/j.jgg.2021.03.005

Volume 48 Issue 3

Mar. 2021

Turn off MathJax

Article Contents

Article Navigation > Journal of Genetics and Genomics > 2021 > 48(3): 219-224

PDF( 1067 KB)

An instantaneous coalescent method insensitive to population structure

doi: 10.1016/j.jgg.2021.03.005

State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China

More Information

Corresponding author: E-mail address: hexiongl@mail.sysu.edu.cn (Xionglei He)
Received Date: 2020-12-07
Accepted Date: 2021-03-05
Rev Recd Date: 2021-02-23

Available Online: 2021-04-09

Publish Date: 2021-03-20

Abstract

Abstract

Conventional coalescent inferences of population history make the critical assumption that the population under examination is panmictic. However, most populations are structured. This complicates the prevailing coalescent analyses and sometimes leads to inaccurate estimates. To develop a coalescent method unhampered by population structure, we perform two analyses. First, we demonstrate that the coalescent probability of two randomly sampled alleles from the immediate preceding generation (one generation back) is independent of population structure. Second, motivated by this finding, we propose a new coalescent method: i-coalescent analysis. The i-coalescent analysis computes the instantaneous coalescent rate by using a phylogenetic tree of sampled alleles. Using simulated data, we broadly demonstrate the capability of i-coalescent analysis to accurately reconstruct population size dynamics of highly structured populations, although we find this method often requires larger sample sizes for structured populations than for panmictic populations. Overall, our results indicate i-coalescent analysis to be a useful tool, especially for the inference of population histories with intractable structure such as the developmental history of cell populations in the organs of complex organisms.
- Coalescent,
- Population structure,
- Demographic history,
- Population size,
- Phylogenetic tree

These authors contributed equally to this work.

FullText(HTML)

References(17)

References

[1]	Bhaskar, A., Clark, A.G., Song, Y.S., 2014. Distortion of genealogical properties when the sample is very large. Proc. Natl. Acad. Sci. U. S. A. 111, 2385-2390.
[2]	Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L.T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al., 2018. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203-209.
[3]	Chikhi, L., Sousa, V.C., Luisi, P., Goossens, B., Beaumont, M.A., 2010. The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics 186, 983-995.
[4]	Drummond, A.J., Rambaut, A., Shapiro, B., Pybus, O.G., 2005. Bayesian coalescent inference of past population dynamics from molecular sequences. Mol. Biol. Evol. 22, 1185-1192.
[5]	Grusea, S., Rodriguez, W., Pinchon, D., Chikhi, L., Boitard, S., Mazet, O., 2019. Coalescence times for three genes provide sufficient information to distinguish population structure from population size changes. J. Math. Biol. 78, 189-224.
[6]	Hu, Z., Fu, Y.X., Greenberg, A.J., Wu, C.I., Zhai, W., 2013. Age-dependent transition from cell-level to population-level control in murine intestinal homeostasis revealed by coalescence analysis. PLoS Genet. 9, e1003326.
[7]	Kingman, J.F.C., 1982. The coalescent. Stoch. Process. their Appl. 13, 235-248.
[8]	Langley, C.H., Fitch, W.M., 1974. An examination of the constancy of the rate of molecular evolution. J. Mol. Evol. 3:161-177.
[9]	Leblois, R., Estoup, A., Streiff, R., 2006. Genetics of recent habitat contraction and reduction in population size: Does isolation by distance matter? Mol. Ecol. 15, 3601-3615.
[10]	Lee-Six, H., OEbro, N.F., Shepherd, M.S., Grossmann, S., Dawson, K., Belmonte, M., Osborne, R.J., Huntly, B.J.P., Martincorena, I., Anderson, E., et al., 2018. Population dynamics of normal human blood inferred from somatic mutations. Nature 561, 473-478.
[11]	Li, H., Durbin, R., 2011. Inference of human population history from individual whole-genome sequences. Nature 475, 493-496.
[12]	Liu, X., Fu, Y.X., 2015. Exploring population size changes using SNP frequency spectra. Nat. Genet. 47, 555-559.
[13]	Mazet, O., Rodriguez, W., Grusea, S., Boitard, S., Chikhi, L., 2016. On the importance of being structured: Instantaneous coalescence rates and human evolution-lessons for ancestral population size inference? Heredity (Edinb). 116, 362-371.
[14]	Peter, B.M., Wegmann, D., Excoffier, L., 2010. Distinguishing between population bottleneck and population subdivision by a Bayesian model choice procedure. Mol. Ecol. 19, 4648-4660.
[15]	Sanderson, M.J., 2003. r8s: Inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics 19, 301-302.
[16]	Strimmer, K., Pybus, O.G., 2001a. Exploring the demographic history of DNA sequences using the generalized skyline plot. Mol. Biol. Evol. 18, 2298-2305.
[17]	Wakeley, J., 1999. Nonequilibrium migration in human history. Genetics 153, 1863-1871.