scholarly journals ThetaMater: Bayesian estimation of population size parameter θ from genomic data

2017 ◽  
Vol 34 (6) ◽  
pp. 1072-1073 ◽  
Author(s):  
Richard H Adams ◽  
Drew R Schield ◽  
Daren C Card ◽  
Andrew Corbin ◽  
Todd A Castoe
Keyword(s):  
Bayesian Estimation ◽  
Population Size ◽  
Genomic Data ◽  
Size Parameter

scholarly journals Genetic Diversity in the Italian Holstein Dairy Cattle Based on Pedigree and SNP Data Prior and After Genomic Selection

2022 ◽  
Vol 8 ◽  
Author(s):  
Michela Ablondi ◽  
Alberto Sabbioni ◽  
Giorgia Stocco ◽  
Claudio Cipolat-Gotet ◽  
Christos Dadousis ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Dairy Cattle ◽  
Population Size ◽  
Genomic Selection ◽  
Effective Population Size ◽  
Genomic Data ◽  
Future Market ◽  
Nucleotide Polymorphisms ◽  
Effective Population ◽  
Snp Data

Genetic diversity has become an urgent matter not only in small local breeds but also in more specialized ones. While the use of genomic data in livestock breeding programs increased genetic gain, there is increasing evidence that this benefit may be counterbalanced by the potential loss of genetic variability. Thus, in this study, we aimed to investigate the genetic diversity in the Italian Holstein dairy cattle using pedigree and genomic data from cows born between 2002 and 2020. We estimated variation in inbreeding, effective population size, and generation interval and compared those aspects prior to and after the introduction of genomic selection in the breed. The dataset contained 84,443 single-nucleotide polymorphisms (SNPs), and 74,485 cows were analyzed. Pedigree depth based on complete generation equivalent was equal to 10.67. A run of homozygosity (ROH) analysis was adopted to estimate SNP-based inbreeding (FROH). The average pedigree inbreeding was 0.07, while the average FROH was more than double, being equal to 0.17. The pattern of the effective population size based on pedigree and SNP data was similar although different in scale, with a constant decrease within the last five generations. The overall inbreeding rate (ΔF) per year was equal to +0.27% and +0.44% for Fped and FROH throughout the studied period, which corresponded to about +1.35% and +2.2% per generation, respectively. A significant increase in the ΔF was found since the introduction of genomic selection in the breed. This study in the Italian Holstein dairy cattle showed the importance of controlling the loss of genetic diversity to ensure the long-term sustainability of this breed, as well as to guarantee future market demands.

scholarly journals Genetic Variability in Polish Lowland Sheepdogs Assessed by Pedigree and Genomic Data

Animals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1520
Author(s):  
Paula Wiebke Michels ◽  
Ottmar Distl
Keyword(s):  
Genetic Variability ◽  
Population Size ◽  
Effective Population Size ◽  
Ductus Arteriosus ◽  
Genomic Data ◽  
Reference Population ◽  
Pedigree Data ◽  
Effective Population ◽  
Inbreeding Coefficients ◽  
Candidate Loci

Genetic variability of Polish Lowland Sheepdog (PON) population was evaluated using both pedigree and genomic data. The analyzed pedigree encompassed 8628 PONs, including 153 individuals genotyped on the Illumina CanineHD BeadChip. Runs of homozygosity (ROH) were defined for homozygous stretches extending over 60 to 4300 kb. The inbreeding coefficients FPed based on pedigree data and FROH50 based on ROHs were at 0.18 and 0.31. The correlation between both was 0.41 but 0.52 when excluding animals with less than seven complete generations. The realized effective population size (Ne¯) was 22.2 with an increasing trend over years. Five PONs explained 79% of the genetic diversity of the reference population. The effective population size derived from linkage disequilibrium measured by r² was 36. PANTHER analysis of genes in ROHs shared by ≥50% of the PONs revealed four highly over- or underrepresented biological processes. One among those is the 7.35 fold enriched “forelimb morphogenesis”. Candidate loci for hip dysplasia and patent ductus arteriosus were discovered in frequently shared ROHs. In conclusion, the inbreeding measures of the PONs were high and the genetic variability small compared to various dog breeds. Regarding Ne¯, PON population was minimally endangered according to the European Association for Animal Production.

scholarly journals Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees

2019 ◽  
Author(s):  
Julia A. Palacios ◽  
Amandine Véber ◽  
Lorenzo Cappello ◽  
Zhangyuan Wang ◽  
John Wakeley ◽  
...  
Keyword(s):  
State Space ◽  
Bayesian Estimation ◽  
Population Size ◽  
Simulated Data ◽  
Effective Population ◽  
Large State Space ◽  
Population Sizes ◽  
Efficient Alternative ◽  
Inference Methods ◽  
Kingman’S Coalescent

AbstractThe large state space of gene genealogies is a major hurdle for inference methods based on Kingman’s coalescent. Here, we present a new Bayesian approach for inferring past population sizes which relies on a lower resolution coalescent process we refer to as “Tajima’s coalescent”. Tajima’s coalescent has a drastically smaller state space, and hence it is a computationally more efficient model, than the standard Kingman coalescent. We provide a new algorithm for efficient and exact likelihood calculations for data without recombination, which exploits a directed acyclic graph and a correspondingly tailored Markov Chain Monte Carlo method. We compare the performance of our Bayesian Estimation of population size changes by Sampling Tajima’s Trees (BESTT) with a popular implementation of coalescent-based inference in BEAST using simulated data and human data. We empirically demonstrate that BESTT can accurately infer effective population sizes, and it further provides an efficient alternative to the Kingman’s coalescent. The algorithms described here are implemented in the R package phylodyn, which is available for download at https://github.com/JuliaPalacios/phylodyn.

Matrix heterogeneity affects population size of the harvest mice: Bayesian estimation of matrix resistance and model validation

Oikos ◽  
2010 ◽  
Vol 120 (2) ◽  
pp. 271-279 ◽  
Author(s):  
Misako Kuroe ◽  
Noriyuki Yamaguchi ◽  
Taku Kadoya ◽  
Tadashi Miyashita
Keyword(s):  
Bayesian Estimation ◽  
Population Size ◽  
Model Validation ◽  
Matrix Resistance ◽  
Matrix Heterogeneity

A cautionary note on Bayesian estimation of population size by removal sampling with diffuse priors

2018 ◽  
Vol 60 (3) ◽  
pp. 450-462 ◽  
Author(s):  
Séverine Bord ◽  
Christèle Bioche ◽  
Pierre Druilhet
Keyword(s):  
Bayesian Estimation ◽  
Population Size ◽  
Cautionary Note ◽  
Removal Sampling

scholarly journals Demographic inference using genetic data from a single individual: separating population size variation from population structure

2014 ◽  
Author(s):  
Olivier Mazet ◽  
Willy Rodríguez ◽  
Lounès Chikhi
Keyword(s):  
Population Size ◽  
Genomic Data ◽  
Genetic Data ◽  
Structured Populations ◽  
Parameter Estimates ◽  
Single Individual ◽  
Model Choice ◽  
Size Change ◽  
History Of ◽  
Population Size Change

The rapid development of sequencing technologies represents new opportunities for population genetics research. It is expected that genomic data will increase our ability to reconstruct the history of populations. While this increase in genetic information will likely help biologists and anthropologists to reconstruct the demographic history of populations, it also represents new challenges. Recent work has shown that structured populations generate signals of population size change. As a consequence it is often difficult to determine whether demographic events such as expansions or contractions (bottlenecks) inferred from genetic data are real or due to the fact that populations are structured in nature. Given that few inferential methods allow us to account for that structure, and that genomic data will necessarily increase the precision of parameter estimates, it is important to develop new approaches. In the present study we analyse two demographic models. The first is a model of instantaneous population size change whereas the second is the classical symmetric island model. We (i) re-derive the distribution of coalescence times under the two models for a sample of size two, (ii) use a maximum likelihood approach to estimate the parameters of these models (iii) validate this estimation procedure under a wide array of parameter combinations, (iv) implement and validate a model choice procedure by using a Kolmogorov-Smirnov test. Altogether we show that it is possible to estimate parameters under several models and perform efficient model choice using genetic data from a single diploid individual.

scholarly journals The IICR and the non-stationary structured coalescent: demographic inference with arbitrary changes in population structure

2018 ◽  
Author(s):  
Willy Rodríguez ◽  
Olivier Mazet ◽  
Simona Grusea ◽  
Simon Boitard ◽  
Lounès Chikhi
Keyword(s):  
Population Structure ◽  
Population Size ◽  
Environmental Changes ◽  
Genomic Data ◽  
Size Change ◽  
Structured Model ◽  
Coalescence Rate ◽  
Wide Range ◽  
Structured Coalescent ◽  
Demographic Inference

AbstractIn the last years, a wide range of methods allowing to reconstruct past population size changes from genome-wide data have been developed. At the same time, there has been an increasing recognition that population structure can generate genetic data similar to those produced under models of population size change. Recently, Mazet et al. (2016) showed that, for any model of population structure, it is always possible to find a panmictic model with a particular function of population size changes, having exactly the same distribution of T2 (the coalescence time for a sample of size two) to that of the structured model. They called this function IICR (Inverse Instantaneous Coalescence Rate) and showed that it does not necessarily correspond to population size changes under non panmictic models. Besides, most of the methods used to analyse data under models of population structure tend to arbitrarily fix that structure and to minimise or neglect population size changes. Here we extend the seminal work of Herbots (1994) on the structured coalescent and propose a new framework, the Non-Stationary Structured Coalescent (NSSC) that incorporates demographic events (changes in gene flow and/or deme sizes) to models of nearly any complexity. We show how to compute the IICR under a wide family of stationary and non-stationary models. As an example we address the question of human and Neanderthal evolution and discuss how the NSSC framework allows to interpret genomic data under this new perspective.Author summaryGenomic data are becoming available for a rapidly increasing number of species, and contain information about their recent evolutionary history. If we wish to understand how they expanded, contracted or admixed as a consequence of recent and ancient environmental changes, we need to develop general inferential methods. Currently, demographic inference is either done assuming that a species is a single panmictic population or using arbitrary structured models. We use the concept of IICR (Inverse of the Instantaneous Coalescence Rate) together with Markov chains theory to develop a general inferential framework which we call the Non-Stationary Structured Coalescent and apply it to explain human and Neanderthal genomic data in a single structured model.

scholarly journals The Site Frequency Spectrum under Finite and Time-Varying Mutation Rates

2018 ◽  
Author(s):  
Andrew Melfi ◽  
Divakar Viswanath
Keyword(s):  
Population Size ◽  
Frequency Spectrum ◽  
Genomic Data ◽  
Mutation Rates ◽  
Single Mutation ◽  
Time Varying ◽  
Mutation Probability ◽  
Human Genomes ◽  
Haploid Population ◽  
Site Frequency Spectrum

AbstractThe diversity in genomes is due to the accumulation of mutations and the site frequency spectrum (SFS) is a popular statistic for summarizing genomic data. The current coalescent algorithm for calculating the SFS for a given demography assumes the μ → 0 limit, where μ is the mutation probability (or rate) per base pair per generation. The algorithm is applicable when μN, N being the haploid population size, is negligible. We derive a coalescent based algorithm for calculating the SFS that allows the mutation rate μ(t) as well as the population size N(t) to vary arbitrarily as a function of time. That algorithm shows that the probability of two mutations in the genealogy becomes noticeable already for μ = 10-8 for samples of n = 105 haploid human genomes and increases rapidly with μ. Our algorithm calculates the SFS under the assumption of a single mutation in the genealogy, and the part of the SFS due to a single mutation depends only mildly on the finiteness of μ. However, the dependence of the SFS on variation in μ can be substantial for even n = 100 samples. In addition, increasing and decreasing mutation rates alter the SFS in different ways and to different extents.

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