Demographic inference for spatially heterogeneous populations using long shared haplotypes

被引:0
|
作者
Forien, Raphael [1 ]
Ringbauer, Harald [2 ]
Coop, Graham [3 ]
机构
[1] Ctr INRAE PACA, INRAE BioSP, 228 route aerodrome, F-84914 Avignon 9, France
[2] Max Planck Inst Evolutionary Anthropol, Dept Archaeogenet, Deutsch Pl 6, D-04103 Leipzig, Germany
[3] Univ Calif Davis, Ctr Populat Biol, Dept Evolut & Ecol, 2320 Storer Hall, Davis, CA 95616 USA
基金
美国国家卫生研究院;
关键词
Population genetics; Spatial A-Fleming-Viot process; Spatial coalescent; Segments of shared haplotypes; Skew Brownian motion; Isolation by distance; STEPPING STONE MODEL; DETECTING IDENTITY; STATISTICS; MIGRATION; DISTANCE; GENETICS; SEGMENTS;
D O I
10.1016/j.tpb.2024.03.002
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
We introduce a modified spatial Lambda-Fleming-Viot process to model the ancestry of individuals in a population occupying a continuous spatial habitat divided into two areas by a sharp discontinuity of the dispersal rate and effective population density. We derive an analytical formula for the expected number of shared haplotype segments between two individuals depending on their sampling locations. This formula involves the transition density of a skew diffusion which appears as a scaling limit of the ancestral lineages of individuals in this model. We then show that this formula can be used to infer the dispersal parameters and the effective population density of both regions, using a composite likelihood approach, and we demonstrate the efficiency of this method on a range of simulated data sets.
引用
收藏
页码:108 / 124
页数:17
相关论文
共 50 条
  • [21] Causal Genetic Inference Using Haplotypes as Instrumental Variables
    Wang, Fan
    Meyer, Nuala J.
    Walley, Keith R.
    Russell, James A.
    Feng, Rui
    GENETIC EPIDEMIOLOGY, 2016, 40 (01) : 35 - 44
  • [22] Ancestry inference using reference labeled clusters of haplotypes
    Wang, Yong
    Song, Shiya
    Schraiber, Joshua G.
    Sedghifar, Alisa
    Byrnes, Jake K.
    Turissini, David A.
    Hong, Eurie L.
    Ball, Catherine A.
    Noto, Keith
    BMC BIOINFORMATICS, 2021, 22 (01)
  • [23] Identity Testing Using Haplotypes in Three Populations
    Debeljak, M.
    Morrison, M. C.
    Mocci, E.
    Klein, A. P.
    Eshleman, J. R.
    JOURNAL OF MOLECULAR DIAGNOSTICS, 2016, 18 (06): : 1040 - 1040
  • [24] Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi
    Tang, Jinjun
    Gao, Fan
    Han, Chunyang
    Cen, Xuekai
    Li, Zhitao
    JOURNAL OF TRANSPORT GEOGRAPHY, 2021, 95
  • [25] Optimal Network Architectures for Spatially Structured Populations with Heterogeneous Diffusion
    Ruiz-Herrera, Alfonso
    Torres, Pedro J.
    AMERICAN NATURALIST, 2020, 196 (01): : 29 - 44
  • [26] On the interplay of harvesting and various diffusion strategies for spatially heterogeneous populations
    Braverman, Elena
    Ilmer, Ilia
    JOURNAL OF THEORETICAL BIOLOGY, 2019, 466 : 106 - 118
  • [27] Demographic and genetic collapses in spatially structured populations: insights from a long-term survey in wild fish metapopulations
    Mathieu-Begne, Eglantine
    Loot, Geraldine
    Chevalier, Mathieu
    Paz-Vinas, Ivan
    Blanchet, Simon
    OIKOS, 2019, 128 (02) : 196 - 207
  • [28] Demographic variance in heterogeneous populations: matrix models and sensitivity analysis
    Caswell, Hal
    Vindenes, Yngvild
    OIKOS, 2018, 127 (05) : 648 - 663
  • [29] Efficient Sensory Encoding and Bayesian Inference with Heterogeneous Neural Populations
    Ganguli, Deep
    Simoncelli, Eero P.
    NEURAL COMPUTATION, 2014, 26 (10) : 2103 - 2134
  • [30] Yin Yang Haplotypes Revisited - Long, Disparate Haplotypes Observed in European Populations in Regions of Increased Homozygosity
    Curtis, David
    Vine, Anna E.
    HUMAN HEREDITY, 2010, 69 (03) : 184 - 192