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Shang J, Xu A, Bi M, Zhang Y, Li F, Liu JX. A review: simulation tools for genome-wide interaction studies. Brief Funct Genomics 2024:elae034. [PMID: 39173096 DOI: 10.1093/bfgp/elae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 07/25/2024] [Accepted: 08/10/2024] [Indexed: 08/24/2024] Open
Abstract
Genome-wide association study (GWAS) is essential for investigating the genetic basis of complex diseases; nevertheless, it usually ignores the interaction of multiple single nucleotide polymorphisms (SNPs). Genome-wide interaction studies provide crucial means for exploring complex genetic interactions that GWAS may miss. Although many interaction methods have been proposed, challenges still persist, including the lack of epistasis models and the inconsistency of benchmark datasets. SNP data simulation is a pivotal intermediary between interaction methods and real applications. Therefore, it is important to obtain epistasis models and benchmark datasets by simulation tools, which is helpful for further improving interaction methods. At present, many simulation tools have been widely employed in the field of population genetics. According to their basic principles, these existing tools can be divided into four categories: coalescent simulation, forward-time simulation, resampling simulation, and other simulation frameworks. In this paper, their basic principles and representative simulation tools are compared and analyzed in detail. Additionally, this paper provides a discussion and summary of the advantages and disadvantages of these frameworks and tools, offering technical insights for the design of new methods, and serving as valuable reference tools for researchers to comprehensively understand GWAS and genome-wide interaction studies.
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Affiliation(s)
- Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Anqi Xu
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Mingyuan Bi
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
| | - Feng Li
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Jin-Xing Liu
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266114, China
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2
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The statistical power of genome-wide association studies for threshold traits with different frequencies of causal variants. Genetica 2021; 150:51-57. [PMID: 34705138 DOI: 10.1007/s10709-021-00140-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 10/20/2021] [Indexed: 10/20/2022]
Abstract
This study aimed to investigate the effects of incidence rate, heritability, and polygenic variance on the statistical power of genome-wide association studies (GWAS) for threshold traits. Different incidence rates of threshold trait (1, 3, 5, 10, 25, 40, 50, 60, 75 and 90%), heritability (10 and 25%), and polygenic variance ratio (0 and 25%) were simulated separately for common (MAF ≥ 0.05), low-frequency (0.05 > MAF ≥ 0.01), and rare (MAF < 0.01) variants. Association studies were performed by logistic and linear mixed models. The highest statistical powers were observed in common and low-frequency variants with an incidence of 25-50% and 10-40%, respectively, but for rare variants, the highest statistical power was observed at low incidence. For all causal variant frequencies, the estimated heritability decline with an increase in incidence rate. We found high statistical power for traits with high heritability. In contrast, those with a high polygenic variance ratio have lower statistical power to detect common causal variants using a linear mixed model. These results demonstrate that the incidence rate of threshold traits, heritability, and polygenic variance may affect the statistical power of GWAS. Therefore, it is recommended that the effect of incidence rate, heritability, and polygenic variance be considered in designing GWAS for threshold traits.
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3
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Khanzadeh H, Ghavi Hossein-Zadeh N, Ghovvati S. Statistical power and heritability in whole-genome association studies for quantitative traits. Meta Gene 2021. [DOI: 10.1016/j.mgene.2021.100869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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4
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Jones BR, Joy JB. Simulating within host human immunodeficiency virus 1 genome evolution in the persistent reservoir. Virus Evol 2020; 6:veaa089. [PMID: 34040795 PMCID: PMC8132731 DOI: 10.1093/ve/veaa089] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The complexities of viral evolution can be difficult to elucidate. Software simulating viral evolution provides powerful tools for exploring hypotheses of viral systems, especially in situations where thorough empirical data are difficult to obtain or parameters of interest are difficult to measure. Human immunodeficiency virus 1 (HIV-1) infection has no durable cure; this is primarily due to the virus’ ability to integrate into the genome of host cells, where it can remain in a transcriptionally latent state. An effective cure strategy must eliminate every copy of HIV-1 in this ‘persistent reservoir’ because proviruses can reactivate, even decades later, to resume an active infection. However, many features of the persistent reservoir remain unclear, including the temporal dynamics of HIV-1 integration frequency and the longevity of the resulting reservoir. Thus, sophisticated analyses are required to measure these features and determine their temporal dynamics. Here, we present software that is an extension of SANTA-SIM to include multiple compartments of viral populations. We used the resulting software to create a model of HIV-1 within host evolution that incorporates the persistent HIV-1 reservoir. This model is composed of two compartments, an active compartment and a latent compartment. With this model, we compared five different date estimation methods (Closest Sequence, Clade, Linear Regression, Least Squares, and Maximum Likelihood) to recover the integration dates of genomes in our model’s HIV-1 reservoir. We found that the Least Squares method performed the best with the highest concordance (0.80) between real and estimated dates and the lowest absolute error (all pairwise t tests: P < 0.01). Our software is a useful tool for validating bioinformatics software and understanding the dynamics of the persistent HIV-1 reservoir.
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Affiliation(s)
- Bradley R Jones
- BC Centre for Excellence in HIV/AIDS, 608-1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - Jeffrey B Joy
- BC Centre for Excellence in HIV/AIDS, 608-1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
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5
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Takeda M, Uemoto Y, Satoh M. Effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci detection and genomic evaluation in a simulated cattle population. Anim Sci J 2020; 91:e13432. [PMID: 32779330 PMCID: PMC7507195 DOI: 10.1111/asj.13432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/18/2020] [Accepted: 07/01/2020] [Indexed: 02/02/2023]
Abstract
The objective of this study was to assess the effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci (QTL) detection and genomic evaluation using a simulated cattle population. Twelve generations (G1–G12) were simulated from the base generation (G0). The recent population had different effective population sizes, heritability, and number of QTL. G0–G4 were used for pedigree information. A total of 300 genotyped bulls from G5–G10 were randomly selected. Their progenies were generated in G6–G11 with different numbers of progeny per bull. Scenarios were considered according to the number of progenies and whether the genotypes were possessed by the bulls or the progenies. A genome‐wide association study and genomic evaluation were performed with a single‐step genomic best linear unbiased prediction method to calculate the power of QTL detection and the genomic estimated breeding value (GEBV). We found that genotyped bulls could be available for QTL detection depending on conditions. Additionally, using a reference population, including genotyped bulls, which had more progeny phenotypes, enabled a more accurate prediction of GEBV. However, it is desirable to have more than 4,500 individuals consisting of both genotypes and phenotypes for practical genomic evaluation.
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Affiliation(s)
- Masayuki Takeda
- National Livestock Breeding Center, Nishigo, Japan.,Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Yoshinobu Uemoto
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Masahiro Satoh
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
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6
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Bacigalupe R, Tormo-Mas MÁ, Penadés JR, Fitzgerald JR. A multihost bacterial pathogen overcomes continuous population bottlenecks to adapt to new host species. SCIENCE ADVANCES 2019; 5:eaax0063. [PMID: 31807698 PMCID: PMC6881152 DOI: 10.1126/sciadv.aax0063] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 10/01/2019] [Indexed: 06/10/2023]
Abstract
While many bacterial pathogens are restricted to single host species, some have the capacity to undergo host switches, leading to the emergence of new clones that are a threat to human and animal health. However, the bacterial traits that underpin a multihost ecology are not well understood. Following transmission to a new host, bacterial populations are influenced by powerful forces such as genetic drift that reduce the fixation rate of beneficial mutations, limiting the capacity for host adaptation. Here, we implement a novel experimental model of bacterial host switching to investigate the ability of the multihost pathogen Staphylococcus aureus to adapt to new species under continuous population bottlenecks. We demonstrate that beneficial mutations accumulated during infection can overcome genetic drift and sweep through the population, leading to host adaptation. Our findings highlight the remarkable capacity of some bacteria to adapt to distinct host niches in the face of powerful antagonistic population forces.
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Affiliation(s)
- Rodrigo Bacigalupe
- The Roslin Institute, University of Edinburgh, Easter Bush Campus, Edinburgh EH25 9RG, UK
| | - María Ángeles Tormo-Mas
- Centro de Investigación y Tecnología Animal, Instituto Valenciano de Investigaciones Agrarias, Segorbe 12400, Spain
- Severe Infection Group of Instituto de Investigación Sanitaria La Fe, 106 Avenida Fernando Abril Martorell, Valencia 46026, Spain
| | - José R. Penadés
- Departamento de Ciencias Biomédicas, Universidad CEU Cardenal Herrera, Moncada 46113, Spain
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Sir Graeme Davies Building, 120 University Place, Glasgow G12 8TA, UK
| | - J. Ross Fitzgerald
- The Roslin Institute, University of Edinburgh, Easter Bush Campus, Edinburgh EH25 9RG, UK
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Meyer HV, Birney E. PhenotypeSimulator: A comprehensive framework for simulating multi-trait, multi-locus genotype to phenotype relationships. Bioinformatics 2019; 34:2951-2956. [PMID: 29617944 PMCID: PMC6129313 DOI: 10.1093/bioinformatics/bty197] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 03/28/2018] [Indexed: 11/13/2022] Open
Abstract
Motivation Simulation is a critical part of method development and assessment. With the increasing sophistication of multi-trait and multi-locus genetic analysis techniques, it is important that the community has flexible simulation tools to challenge and explore the properties of these methods. Results We have developed PhenotypeSimulator, a comprehensive phenotype simulation scheme that can model multiple traits with multiple underlying genetic loci as well as complex covariate and observational noise structure. This package has been designed to work with many common genetic tools both for input and output. We describe the underlying components of this simulation tool and illustrate its use on an example dataset. Availability and implementation PhenotypeSimulator is available as a well documented R/CRAN package and the code is available on github: https://github.com/HannahVMeyer/PhenotypeSimulator. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hannah Verena Meyer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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8
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Jariani A, Warth C, Deforche K, Libin P, Drummond AJ, Rambaut A, Matsen Iv FA, Theys K. SANTA-SIM: simulating viral sequence evolution dynamics under selection and recombination. Virus Evol 2019; 5:vez003. [PMID: 30863552 PMCID: PMC6407609 DOI: 10.1093/ve/vez003] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Simulations are widely used to provide expectations and predictive distributions under known conditions against which to compare empirical data. Such simulations are also invaluable for testing and comparing the behaviour and power of inference methods. We describe SANTA-SIM, a software package to simulate the evolution of a population of gene sequences forwards through time. It models the underlying biological processes as discrete components: replication, recombination, point mutations, insertion-deletions, and selection under various fitness models and population size dynamics. The software is designed to be intuitive to work with for a wide range of users and executable in a cross-platform manner.
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Affiliation(s)
- Abbas Jariani
- Laboratory for Genetics and Genomics, Center of Microbial and Plant Genetics, KU Leuven, Leuven, Belgium.,Laboratory for Systems Biology, VIB, Leuven, Belgium
| | - Christopher Warth
- Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Pieter Libin
- Laboratory Clinical and Evolutionary Virology, Rega Institute for Medical Research - KU Leuven, Leuven, Belgium.,Artifical Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium
| | - Alexei J Drummond
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.,Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule Zurich, Basel, Switzerland
| | - Andrew Rambaut
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Frederick A Matsen Iv
- Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Kristof Theys
- Laboratory Clinical and Evolutionary Virology, Rega Institute for Medical Research - KU Leuven, Leuven, Belgium
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9
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Al Kawam A, Alshawaqfeh M, Cai JJ, Serpedin E, Datta A. Simulating variance heterogeneity in quantitative genome wide association studies. BMC Bioinformatics 2018; 19:72. [PMID: 29589560 PMCID: PMC5872534 DOI: 10.1186/s12859-018-2061-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value. RESULTS A handful of vGWAS analysis methods have been recently introduced in the literature. However, very little work has been done for evaluating these methods. To enable the development of better vGWAS analysis methods, this work presents the first quantitative vGWAS simulation procedure. To that end, we describe the mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles. Our simulation model accounts for both haploid and diploid genotypes under different modes of dominance. Our model is also able to simulate any number of genetic loci causing mean and variance heterogeneity. CONCLUSIONS We demonstrate the utility of our simulation procedure through generating a variety of genetic loci types to evaluate common GWAS and vGWAS analysis methods. The results of this evaluation highlight the challenges current tools face in detecting GWAS and vGWAS loci.
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Affiliation(s)
- Ahmad Al Kawam
- Electrical & Computer Engineering Dept., Texas A&M University, College Station, TX, USA. .,Veterinary Integrative Biosciences Dept., Texas A&M University, College Station, TX, USA. .,TEES AgriLife Center for Bioinformatics and Genomic Systems Engineering (CBGSE), Texas A&M University, College Station, TX, USA.
| | - Mustafa Alshawaqfeh
- Electrical & Computer Engineering Dept., German Jordanian University, Amman, Jordan
| | - James J Cai
- Veterinary Integrative Biosciences Dept., Texas A&M University, College Station, TX, USA
| | - Erchin Serpedin
- Electrical & Computer Engineering Dept., Texas A&M University, College Station, TX, USA
| | - Aniruddha Datta
- Electrical & Computer Engineering Dept., Texas A&M University, College Station, TX, USA.,TEES AgriLife Center for Bioinformatics and Genomic Systems Engineering (CBGSE), Texas A&M University, College Station, TX, USA
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10
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An Eigenvalue test for spatial principal component analysis. BMC Bioinformatics 2017; 18:562. [PMID: 29246102 PMCID: PMC5732370 DOI: 10.1186/s12859-017-1988-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Accepted: 12/05/2017] [Indexed: 11/12/2022] Open
Abstract
Background The spatial Principal Component Analysis (sPCA, Jombart (Heredity 101:92-103, 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated tests used for assessing the existence of spatial patterns (global and local test; (Heredity 101:92-103, 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-parametric test for the significance of specific patterns recovered by sPCA. Results We compared the performance of this new test to the original global and local tests using datasets simulated under classical population genetic models. Results show that our test outperforms the original global and local tests, exhibiting improved statistical power while retaining similar, and reliable type I errors. Moreover, by allowing to test various sets of axes, it can be used to guide the selection of retained sPCA components. Conclusions As such, our test represents a valuable complement to the original analysis, and should prove useful for the investigation of spatial genetic patterns. Electronic supplementary material The online version of this article (10.1186/s12859-017-1988-y) contains supplementary material, which is available to authorized users.
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11
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Abstract
Bacteria can exchange and acquire new genetic material from other organisms directly and via the environment. This process, known as bacterial recombination, has a strong impact on the evolution of bacteria, for example, leading to the spread of antibiotic resistance across clades and species, and to the avoidance of clonal interference. Recombination hinders phylogenetic and transmission inference because it creates patterns of substitutions (homoplasies) inconsistent with the hypothesis of a single evolutionary tree. Bacterial recombination is typically modeled as statistically akin to gene conversion in eukaryotes, i.e., using the coalescent with gene conversion (CGC). However, this model can be very computationally demanding as it needs to account for the correlations of evolutionary histories of even distant loci. So, with the increasing popularity of whole genome sequencing, the need has emerged for a faster approach to model and simulate bacterial genome evolution. We present a new model that approximates the coalescent with gene conversion: the bacterial sequential Markov coalescent (BSMC). Our approach is based on a similar idea to the sequential Markov coalescent (SMC)-an approximation of the coalescent with crossover recombination. However, bacterial recombination poses hurdles to a sequential Markov approximation, as it leads to strong correlations and linkage disequilibrium across very distant sites in the genome. Our BSMC overcomes these difficulties, and shows a considerable reduction in computational demand compared to the exact CGC, and very similar patterns in simulated data. We implemented our BSMC model within new simulation software FastSimBac. In addition to the decreased computational demand compared to previous bacterial genome evolution simulators, FastSimBac provides more general options for evolutionary scenarios, allowing population structure with migration, speciation, population size changes, and recombination hotspots. FastSimBac is available from https://bitbucket.org/nicofmay/fastsimbac, and is distributed as open source under the terms of the GNU General Public License. Lastly, we use the BSMC within an Approximate Bayesian Computation (ABC) inference scheme, and suggest that parameters simulated under the exact CGC can correctly be recovered, further showcasing the accuracy of the BSMC. With this ABC we infer recombination rate, mutation rate, and recombination tract length of Bacillus cereus from a whole genome alignment.
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Affiliation(s)
- Nicola De Maio
- Institute for Emerging Infections, Oxford Martin School, University of Oxford, Oxford, OX1 3PA, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, OX1 3PA, United Kingdom
| | - Daniel J Wilson
- Institute for Emerging Infections, Oxford Martin School, University of Oxford, Oxford, OX1 3PA, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, OX1 3PA, United Kingdom
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX1 3PA, United Kingdom
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12
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Carvajal-Rodríguez A. HacDivSel: Two new methods (haplotype-based and outlier-based) for the detection of divergent selection in pairs of populations. PLoS One 2017; 12:e0175944. [PMID: 28423003 PMCID: PMC5397020 DOI: 10.1371/journal.pone.0175944] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Accepted: 04/03/2017] [Indexed: 01/10/2023] Open
Abstract
The detection of genomic regions involved in local adaptation is an important topic in current population genetics. There are several detection strategies available depending on the kind of genetic and demographic information at hand. A common drawback is the high risk of false positives. In this study we introduce two complementary methods for the detection of divergent selection from populations connected by migration. Both methods have been developed with the aim of being robust to false positives. The first method combines haplotype information with inter-population differentiation (FST). Evidence of divergent selection is concluded only when both the haplotype pattern and the FST value support it. The second method is developed for independently segregating markers i.e. there is no haplotype information. In this case, the power to detect selection is attained by developing a new outlier test based on detecting a bimodal distribution. The test computes the FST outliers and then assumes that those of interest would have a different mode. We demonstrate the utility of the two methods through simulations and the analysis of real data. The simulation results showed power ranging from 60-95% in several of the scenarios whilst the false positive rate was controlled below the nominal level. The analysis of real samples consisted of phased data from the HapMap project and unphased data from intertidal marine snail ecotypes. The results illustrate that the proposed methods could be useful for detecting locally adapted polymorphisms. The software HacDivSel implements the methods explained in this manuscript.
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13
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Detection of genomic loci associated with environmental variables using generalized linear mixed models. Genomics 2014; 105:69-75. [PMID: 25499197 DOI: 10.1016/j.ygeno.2014.12.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2014] [Revised: 11/27/2014] [Accepted: 12/05/2014] [Indexed: 11/21/2022]
Abstract
We tested the use of Generalized Linear Mixed Models to detect associations between genetic loci and environmental variables, taking into account the population structure of sampled individuals. We used a simulation approach to generate datasets under demographically and selectively explicit models. These datasets were used to analyze and optimize GLMM capacity to detect the association between markers and selective coefficients as environmental data in terms of false and true positive rates. Different sampling strategies were tested, maximizing the number of populations sampled, sites sampled per population, or individuals sampled per site, and the effect of different selective intensities on the efficiency of the method was determined. Finally, we apply these models to an Arabidopsis thaliana SNP dataset from different accessions, looking for loci associated with spring minimal temperature. We identified 25 regions that exhibit unusual correlations with the climatic variable and contain genes with functions related to temperature stress.
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14
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A C++ template library for efficient forward-time population genetic simulation of large populations. Genetics 2014; 198:157-66. [PMID: 24950894 DOI: 10.1534/genetics.114.165019] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
fwdpp is a C++ library of routines intended to facilitate the development of forward-time simulations under arbitrary mutation and fitness models. The library design provides a combination of speed, low memory overhead, and modeling flexibility not currently available from other forward simulation tools. The library is particularly useful when the simulation of large populations is required, as programs implemented using the library are much more efficient than other available forward simulation programs.
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15
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Guillot EG, Cox MP. SMARTPOP: inferring the impact of social dynamics on genetic diversity through high speed simulations. BMC Bioinformatics 2014; 15:175. [PMID: 24913447 PMCID: PMC4064502 DOI: 10.1186/1471-2105-15-175] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Accepted: 06/03/2014] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Social behavior has long been known to influence patterns of genetic diversity, but the effect of social processes on population genetics remains poorly quantified - partly due to limited community-level genetic sampling (which is increasingly being remedied), and partly to a lack of fast simulation software to jointly model genetic evolution and complex social behavior, such as marriage rules. RESULTS To fill this gap, we have developed SMARTPOP - a fast, forward-in-time genetic simulator - to facilitate large-scale statistical inference on interactions between social factors, such as mating systems, and population genetic diversity. By simultaneously modeling genetic inheritance and dynamic social processes at the level of the individual, SMARTPOP can simulate a wide range of genetic systems (autosomal, X-linked, Y chromosomal and mitochondrial DNA) under a range of mating systems and demographic models. Specifically designed to enable resource-intensive statistical inference tasks, such as Approximate Bayesian Computation, SMARTPOP has been coded in C++ and is heavily optimized for speed and reduced memory usage. CONCLUSION SMARTPOP rapidly simulates population genetic data under a wide range of demographic scenarios and social behaviors, thus allowing quantitative analyses to address complex socio-ecological questions.
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Affiliation(s)
- Elsa G Guillot
- Statistics and Bioinformatics Group, Institute of Fundamental Sciences, Massey University Palmerston North, New Zealand
| | - Murray P Cox
- Statistics and Bioinformatics Group, Institute of Fundamental Sciences, Massey University Palmerston North, New Zealand
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16
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Bielejec F, Lemey P, Carvalho LM, Baele G, Rambaut A, Suchard MA. πBUSS: a parallel BEAST/BEAGLE utility for sequence simulation under complex evolutionary scenarios. BMC Bioinformatics 2014; 15:133. [PMID: 24885610 PMCID: PMC4020384 DOI: 10.1186/1471-2105-15-133] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Accepted: 04/24/2014] [Indexed: 01/12/2023] Open
Abstract
Background Simulated nucleotide or amino acid sequences are frequently used to assess the performance of phylogenetic reconstruction methods. BEAST, a Bayesian statistical framework that focuses on reconstructing time-calibrated molecular evolutionary processes, supports a wide array of evolutionary models, but lacked matching machinery for simulation of character evolution along phylogenies. Results We present a flexible Monte Carlo simulation tool, called πBUSS, that employs the BEAGLE high performance library for phylogenetic computations to rapidly generate large sequence alignments under complex evolutionary models. πBUSS sports a user-friendly graphical user interface (GUI) that allows combining a rich array of models across an arbitrary number of partitions. A command-line interface mirrors the options available through the GUI and facilitates scripting in large-scale simulation studies. πBUSS may serve as an easy-to-use, standard sequence simulation tool, but the available models and data types are particularly useful to assess the performance of complex BEAST inferences. The connection with BEAST is further strengthened through the use of a common extensible markup language (XML), allowing to specify also more advanced evolutionary models. To support simulation under the latter, as well as to support simulation and analysis in a single run, we also add the πBUSS core simulation routine to the list of BEAST XML parsers. Conclusions πBUSS offers a unique combination of flexibility and ease-of-use for sequence simulation under realistic evolutionary scenarios. Through different interfaces, πBUSS supports simulation studies ranging from modest endeavors for illustrative purposes to complex and large-scale assessments of evolutionary inference procedures. Applications are not restricted to the BEAST framework, or even time-measured evolutionary histories, and πBUSS can be connected to various other programs using standard input and output format.
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Affiliation(s)
- Filip Bielejec
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
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17
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Qiu S, McSweeny A, Choulet S, Saha-Mandal A, Fedorova L, Fedorov A. Genome evolution by matrix algorithms: cellular automata approach to population genetics. Genome Biol Evol 2014; 6:988-99. [PMID: 24723728 PMCID: PMC4007542 DOI: 10.1093/gbe/evu075] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Mammalian genomes are replete with millions of polymorphic sites, among which those genetic variants that are colocated on the same chromosome and exist close to one another form blocks of closely linked mutations known as haplotypes. The linkage within haplotypes is constantly disrupted due to meiotic recombination events. Whole ensembles of such numerous haplotypes are subjected to evolutionary pressure, where mutations influence each other and should be considered as a whole entity—a gigantic matrix, unique for each individual genome. This idea was implemented into a computational approach, named Genome Evolution by Matrix Algorithms (GEMA) to model genomic changes taking into account all mutations in a population. GEMA has been tested for modeling of entire human chromosomes. The program can precisely mimic real biological processes that have influence on genome evolution such as: 1) Authentic arrangements of genes and functional genomic elements, 2) frequencies of various types of mutations in different nucleotide contexts, and 3) nonrandom distribution of meiotic recombination events along chromosomes. Computer modeling with GEMA has demonstrated that the number of meiotic recombination events per gamete is among the most crucial factors influencing population fitness. In humans, these recombinations create a gamete genome consisting on an average of 48 pieces of corresponding parental chromosomes. Such highly mosaic gamete structure allows preserving fitness of population under the intense influx of novel mutations (40 per individual) even when the number of mutations with deleterious effects is up to ten times more abundant than those with beneficial effects.
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Affiliation(s)
- Shuhao Qiu
- Program in Bioinformatics and Proteomics/Genomics, University of Toledo
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García-Pereira MJ, Carvajal-Rodríguez A, Whelan S, Caballero A, Quesada H. Impact of deep coalescence and recombination on the estimation of phylogenetic relationships among species using AFLP markers. Mol Phylogenet Evol 2014; 76:102-9. [PMID: 24631855 DOI: 10.1016/j.ympev.2014.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2013] [Revised: 02/26/2014] [Accepted: 03/04/2014] [Indexed: 10/25/2022]
Abstract
Deep coalescence and the nongenealogical pattern of descent caused by recombination have emerged as a common problem for phylogenetic inference at the species level. Here we use computer simulations to assess whether AFLP-based phylogenies are robust to the uncertainties introduced by these factors. Our results indicate that phylogenetic signal can prevail even in the face of extensive deep coalescence allowing recovering the correct species tree topology. The impact of recombination on tree accuracy was related to total tree depth and species effective population size. The correct tree topology could be recovered upon many simulation settings due to a trade-off between the conflicting signals resulting from intra-locus recombination and the benefits of the joint consideration of unlinked loci that better matched overall the true species tree. Errors in tree topology were not only determined by deep coalescence, but also by the timing of divergence and the tree-building errors arising from an insufficient number of characters. DNA sequences generally outperformed AFLPs upon any simulated scenario, but this difference in performance was nearly negligible when a sufficient number of AFLP characters were sampled. Our simulations suggest that the impact of deep coalescence and intra-locus recombination on the reliability of AFLP trees could be minimal for effective population sizes equal to or lower than 10,000 (typical of many vertebrates and tree plants) given tree depths above 0.02 substitutions per site.
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Affiliation(s)
- María Jesús García-Pereira
- Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, 36310 Vigo, Spain.
| | - Antonio Carvajal-Rodríguez
- Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, 36310 Vigo, Spain.
| | - Simon Whelan
- Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Uppsala 75236-SE, Sweden.
| | - Armando Caballero
- Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, 36310 Vigo, Spain.
| | - Humberto Quesada
- Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, 36310 Vigo, Spain.
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19
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Arenas M, Posada D. Simulation of genome-wide evolution under heterogeneous substitution models and complex multispecies coalescent histories. Mol Biol Evol 2014; 31:1295-301. [PMID: 24557445 PMCID: PMC3995339 DOI: 10.1093/molbev/msu078] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Genomic evolution can be highly heterogeneous. Here, we introduce a new framework to simulate genome-wide sequence evolution under a variety of substitution models that may change along the genome and the phylogeny, following complex multispecies coalescent histories that can include recombination, demographics, longitudinal sampling, population subdivision/species history, and migration. A key aspect of our simulation strategy is that the heterogeneity of the whole evolutionary process can be parameterized according to statistical prior distributions specified by the user. We used this framework to carry out a study of the impact of variable codon frequencies across genomic regions on the estimation of the genome-wide nonsynonymous/synonymous ratio. We found that both variable codon frequencies across genes and rate variation among sites and regions can lead to severe underestimation of the global dN/dS values. The program SGWE—Simulation of Genome-Wide Evolution—is freely available from http://code.google.com/p/sgwe-project/, including extensive documentation and detailed examples.
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Affiliation(s)
- Miguel Arenas
- Centre for Molecular Biology "Severo Ochoa," Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
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20
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Kessner D, Novembre J. forqs: forward-in-time simulation of recombination, quantitative traits and selection. ACTA ACUST UNITED AC 2013; 30:576-7. [PMID: 24336146 PMCID: PMC3928523 DOI: 10.1093/bioinformatics/btt712] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Summary: forqs is a forward-in-time simulation of recombination, quantitative traits and selection. It was designed to investigate haplotype patterns resulting from scenarios where substantial evolutionary change has taken place in a small number of generations due to recombination and/or selection on polygenic quantitative traits. Availability and implementation: forqs is implemented as a command-line C++ program. Source code and binary executables for Linux, OSX and Windows are freely available under a permissive BSD license: https://bitbucket.org/dkessner/forqs. Contact:jnovembre@uchicago.edu Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Darren Kessner
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA 90095 and Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
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21
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Ruths T, Nakhleh L. Boosting forward-time population genetic simulators through genotype compression. BMC Bioinformatics 2013; 14:192. [PMID: 23763838 PMCID: PMC3700844 DOI: 10.1186/1471-2105-14-192] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2013] [Accepted: 05/24/2013] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Forward-time population genetic simulations play a central role in deriving and testing evolutionary hypotheses. Such simulations may be data-intensive, depending on the settings to the various parameters controlling them. In particular, for certain settings, the data footprint may quickly exceed the memory of a single compute node. RESULTS We develop a novel and general method for addressing the memory issue inherent in forward-time simulations by compressing and decompressing, in real-time, active and ancestral genotypes, while carefully accounting for the time overhead. We propose a general graph data structure for compressing the genotype space explored during a simulation run, along with efficient algorithms for constructing and updating compressed genotypes which support both mutation and recombination. We tested the performance of our method in very large-scale simulations. Results show that our method not only scales well, but that it also overcomes memory issues that would cripple existing tools. CONCLUSIONS As evolutionary analyses are being increasingly performed on genomes, pathways, and networks, particularly in the era of systems biology, scaling population genetic simulators to handle large-scale simulations is crucial. We believe our method offers a significant step in that direction. Further, the techniques we provide are generic and can be integrated with existing population genetic simulators to boost their performance in terms of memory usage.
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Affiliation(s)
- Troy Ruths
- Department of Computer Science, Rice University, Houston, USA
| | - Luay Nakhleh
- Department of Computer Science, Rice University, Houston, USA
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22
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Abstract
SLiM is an efficient forward population genetic simulation designed for studying the effects of linkage and selection on a chromosome-wide scale. The program can incorporate complex scenarios of demography and population substructure, various models for selection and dominance of new mutations, arbitrary gene structure, and user-defined recombination maps.
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23
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Li Q, Schwender H, Louis TA, Fallin MD, Ruczinski I. Efficient simulation of epistatic interactions in case-parent trios. Hum Hered 2013; 75:12-22. [PMID: 23548797 DOI: 10.1159/000348789] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2012] [Accepted: 02/11/2013] [Indexed: 12/26/2022] Open
Abstract
Statistical approaches to evaluate interactions between single nucleotide polymorphisms (SNPs) and SNP-environment interactions are of great importance in genetic association studies, as susceptibility to complex disease might be related to the interaction of multiple SNPs and/or environmental factors. With these methods under active development, algorithms to simulate genomic data sets are needed to ensure proper type I error control of newly proposed methods and to compare power with existing methods. In this paper we propose an efficient method for a haplotype-based simulation of case-parent trios when the disease risk is thought to depend on possibly higher-order epistatic interactions or gene-environment interactions with binary exposures.
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Affiliation(s)
- Qing Li
- Statistical Genetics Section, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD, USA
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24
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Arenas M. Computer programs and methodologies for the simulation of DNA sequence data with recombination. Front Genet 2013; 4:9. [PMID: 23378848 PMCID: PMC3561691 DOI: 10.3389/fgene.2013.00009] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2012] [Accepted: 01/17/2013] [Indexed: 11/13/2022] Open
Abstract
Computer simulations are useful in evolutionary biology for hypothesis testing, to verify analytical methods, to analyze interactions among evolutionary processes, and to estimate evolutionary parameters. In particular, the simulation of DNA sequences with recombination may help in understanding the role of recombination in diverse evolutionary questions, such as the genome structure. Consequently, plenty of computer simulators have been developed to simulate DNA sequence data with recombination. However, the choice of an appropriate tool, among all currently available simulators, is critical if recombination simulations are to be biologically meaningful. This review provides a practical survival guide to commonly used computer programs and methodologies for the simulation of coding and non-coding DNA sequences with recombination. It may help in the correct design of computer simulation experiments of recombination. In addition, the study includes a review of simulation studies investigating the impact of ignoring recombination when performing various evolutionary analyses, such as phylogenetic tree and ancestral sequence reconstructions. Alternative analytical methodologies accounting for recombination are also reviewed.
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Affiliation(s)
- Miguel Arenas
- Centre for Molecular Biology "Severo Ochoa," Consejo Superior de Investigaciones Científicas Madrid, Spain
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25
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Kitchen JL, Allaby RG. Systems Modeling at Multiple Levels of Regulation: Linking Systems and Genetic Networks to Spatially Explicit Plant Populations. PLANTS (BASEL, SWITZERLAND) 2013; 2:16-49. [PMID: 27137364 PMCID: PMC4844292 DOI: 10.3390/plants2010016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2012] [Revised: 12/21/2012] [Accepted: 01/16/2013] [Indexed: 11/16/2022]
Abstract
Selection and adaptation of individuals to their underlying environments are highly dynamical processes, encompassing interactions between the individual and its seasonally changing environment, synergistic or antagonistic interactions between individuals and interactions amongst the regulatory genes within the individual. Plants are useful organisms to study within systems modeling because their sedentary nature simplifies interactions between individuals and the environment, and many important plant processes such as germination or flowering are dependent on annual cycles which can be disrupted by climate behavior. Sedentism makes plants relevant candidates for spatially explicit modeling that is tied in with dynamical environments. We propose that in order to fully understand the complexities behind plant adaptation, a system that couples aspects from systems biology with population and landscape genetics is required. A suitable system could be represented by spatially explicit individual-based models where the virtual individuals are located within time-variable heterogeneous environments and contain mutable regulatory gene networks. These networks could directly interact with the environment, and should provide a useful approach to studying plant adaptation.
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Affiliation(s)
- James L Kitchen
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Robin G Allaby
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK.
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26
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Affiliation(s)
- Miguel Arenas
- Computational and Molecular Population Genetics Lab-CMPG, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland.
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27
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Yuan X, Miller DJ, Zhang J, Herrington D, Wang Y. An overview of population genetic data simulation. J Comput Biol 2011; 19:42-54. [PMID: 22149682 DOI: 10.1089/cmb.2010.0188] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Simulation studies in population genetics play an important role in helping to better understand the impact of various evolutionary and demographic scenarios on sequence variation and sequence patterns, and they also permit investigators to better assess and design analytical methods in the study of disease-associated genetic factors. To facilitate these studies, it is imperative to develop simulators with the capability to accurately generate complex genomic data under various genetic models. Currently, a number of efficient simulation software packages for large-scale genomic data are available, and new simulation programs with more sophisticated capabilities and features continue to emerge. In this article, we review the three basic simulation frameworks--coalescent, forward, and resampling--and some of the existing simulators that fall under these frameworks, comparing them with respect to their evolutionary and demographic scenarios, their computational complexity, and their specific applications. Additionally, we address some limitations in current simulation algorithms and discuss future challenges in the development of more powerful simulation tools.
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Affiliation(s)
- Xiguo Yuan
- School of Computer Science and Technology, Xidian University, Xi'an, P.R. China
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28
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Peng B, Liu X. Simulating sequences of the human genome with rare variants. Hum Hered 2011; 70:287-91. [PMID: 21212684 DOI: 10.1159/000323316] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2010] [Accepted: 12/02/2010] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE Simulated samples have been widely used in the development of efficient statistical methods identifying genetic variants that predispose to human genetic diseases. Although it is well known that natural selection has a strong influence on the number and diversity of rare genetic variations in human populations, existing simulation methods are limited in their ability to simulate multi-locus selection models with realistic distributions of the random fitness effects of newly arising mutants. METHODS We developed a computer program to simulate large populations of gene sequences using a forward-time simulation approach. This program is capable of simulating several multi-locus fitness schemes with arbitrary diploid single-locus selection models with random or locus-specific fitness effects. Arbitrary quantitative trait or disease models can be applied to the simulated populations from which individual- or family-based samples can be drawn and analyzed. RESULTS Using realistic demographic and natural selection models estimated from empirical sequence data, datasets simulated using our method differ significantly in the number and diversity of rare variants from datasets simulated using existing methods that ignore natural selection. Our program thus provides a useful tool to simulate datasets with realistic distributions of rare genetic variants for the study of genetic diseases caused by such variants.
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Affiliation(s)
- Bo Peng
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
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29
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Peng B, Amos CI. Forward-time simulation of realistic samples for genome-wide association studies. BMC Bioinformatics 2010; 11:442. [PMID: 20809983 PMCID: PMC2939614 DOI: 10.1186/1471-2105-11-442] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2010] [Accepted: 09/01/2010] [Indexed: 12/21/2022] Open
Abstract
Background Forward-time simulations have unique advantages in power and flexibility for the simulation of genetic samples of complex human diseases because they can closely mimic the evolution of human populations carrying these diseases. However, a number of methodological and computational constraints have prevented the power of this simulation method from being fully explored in existing forward-time simulation methods. Results Using a general-purpose forward-time population genetics simulation environment, we developed a forward-time simulation method that can be used to simulate realistic samples for genome-wide association studies. We examined the properties of this simulation method by comparing simulated samples with real data and demonstrated its wide applicability using four examples, including a simulation of case-control samples with a disease caused by multiple interacting genetic and environmental factors, a simulation of trio families affected by a disease-predisposing allele that had been subjected to either slow or rapid selective sweep, and a simulation of a structured population resulting from recent population admixture. Conclusions Our algorithm simulates populations that closely resemble the complex structure of the human genome, while allows the introduction of signals of natural selection. Because of its flexibility to generate different types of samples with arbitrary disease or quantitative trait models, this simulation method can simulate realistic samples to evaluate the performance of a wide variety of statistical gene mapping methods for genome-wide association studies.
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Affiliation(s)
- Bo Peng
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
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30
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Abstract
MOTIVATION Most population genetic simulators fall into one of two classes, backward time simulators that quickly generate trees but accommodate only relatively simple selective and demographic regimes, and forward simulators that allow for a broader range of evolutionary scenarios but which cannot produce genealogies. Thus, few tools are available that allow for producing genealogies under arbitrarily complex selective and demographic models. RESULTS TreesimJ is a forward time population genetic simulator that allows for sampling of genealogies, genetic data and many population parameters from populations evolving under complex evolutionary scenarios. The application provides many fitness and demographic models and new models are easy to develop. Data collection is performed by a variety of independently configurable collectors which periodically sample the population and record statistics. Output options include writing traces, histograms and summary statistics from the data collectors in addition to sampled genetic sequences and genealogies. SUMMARY TreesimJ allows researchers to easily sample and analyze gene genealogies and related data from populations evolving under a wide variety of selective and demographic regimes. It is likely to be useful for population genetic researchers seeking to understand the links between evolutionary and demographic forces, genealogical structure and the resulting patterns of genetic variation. AVAILABILITY TreesimJ home : http://staff.washington.edu/brendano/treesimj. Source and developer resources: http://code.google.com/p/treesimj.
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Affiliation(s)
- Brendan O'Fallon
- Department of Genome Sciences, Foege Building S-250, Box 355065 3720 15th Ave NE, Seattle, WA 98195-5065, USA.
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31
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Mizuno H, Atwal G, Wang H, Levine AJ, Vazquez A. Fine-scale detection of population-specific linkage disequilibrium using haplotype entropy in the human genome. BMC Genet 2010; 11:27. [PMID: 20416085 PMCID: PMC2873552 DOI: 10.1186/1471-2156-11-27] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2009] [Accepted: 04/23/2010] [Indexed: 01/14/2023] Open
Abstract
Background The creation of a coherent genomic map of recent selection is one of the greatest challenges towards a better understanding of human evolution and the identification of functional genetic variants. Several methods have been proposed to detect linkage disequilibrium (LD), which is indicative of natural selection, from genome-wide profiles of common genetic variations but are designed for large regions. Results To find population-specific LD within small regions, we have devised an entropy-based method that utilizes differences in haplotype frequency between populations. The method has the advantages of incorporating multilocus association, conciliation with low allele frequencies, and independence from allele polarity, which are ideal for short haplotype analysis. The comparison of HapMap SNPs data from African and Caucasian populations with a median resolution size of ~23 kb gave us novel candidates as well as known selection targets. Enrichment analysis for the yielded genes showed associations with diverse diseases such as cardiovascular, immunological, neurological, and skeletal and muscular diseases. A possible scenario for a selective force is discussed. In addition, we have developed a web interface (ENIGMA, available at http://gibk21.bse.kyutech.ac.jp/ENIGMA/index.html), which allows researchers to query their regions of interest for population-specific LD. Conclusion The haplotype entropy method is powerful for detecting population-specific LD embedded in short regions and should contribute to further studies aiming to decipher the evolutionary histories of modern humans.
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Affiliation(s)
- Hideaki Mizuno
- The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, New Jersey, USA.
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32
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Carvajal-Rodríguez A. Simulation of genes and genomes forward in time. Curr Genomics 2010; 11:58-61. [PMID: 20808525 PMCID: PMC2851118 DOI: 10.2174/138920210790218007] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2009] [Revised: 08/06/2009] [Accepted: 08/11/2009] [Indexed: 11/22/2022] Open
Abstract
The importance of simulation software in current and future evolutionary and genomic studies is just confirmed by the recent publication of several new simulation tools. The forward-in-time simulation strategy has, therefore, re-emerged as a complement of coalescent simulation. Additionally, more efficient coalescent algorithms, the same as new ideas about the combined use of backward and forward strategies have recently appeared. In the present work, a previous review is updated to include some new forward simulation tools. When simulating at the genome-scale the conflict between efficiency (i.e. execution speed and memory usage) and flexibility (i.e. complex modeling capabilities) emerges. This is the pivot around which simulation of evolutionary processes should improve. In addition, some effort should be made to consider the process of developing simulation tools from the point of view of the software engineering theory. Finally, some new ideas and technologies as general purpose graphic processing units are commented.
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Ritchie MD, Bush WS. Genome simulation approaches for synthesizing in silico datasets for human genomics. ADVANCES IN GENETICS 2010; 72:1-24. [PMID: 21029846 DOI: 10.1016/b978-0-12-380862-2.00001-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Simulated data is a necessary first step in the evaluation of new analytic methods because in simulated data the true effects are known. To successfully develop novel statistical and computational methods for genetic analysis, it is vital to simulate datasets consisting of single nucleotide polymorphisms (SNPs) spread throughout the genome at a density similar to that observed by new high-throughput molecular genomics studies. In addition, the simulation of environmental data and effects will be essential to properly formulate risk models for complex disorders. Data simulations are often criticized because they are much less noisy than natural biological data, as it is nearly impossible to simulate the multitude of possible sources of natural and experimental variability. However, simulating data in silico is the most straightforward way to test the true potential of new methods during development. Thus, advances that increase the complexity of data simulations will permit investigators to better assess new analytical methods. In this work, we will briefly describe some of the current approaches for the simulation of human genomics data describing the advantages and disadvantages of the various approaches. We will also include details on software packages available for data simulation. Finally, we will expand upon one particular approach for the creation of complex, human genomic datasets that uses a forward-time population simulation algorithm: genomeSIMLA. Many of the hallmark features of biological datasets can be synthesized in silico; still much research is needed to enhance our capabilities to create datasets that capture the natural complexity of biological datasets.
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Affiliation(s)
- Marylyn D Ritchie
- Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, USA
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34
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Eriksson A, Mahjani B, Mehlig B. Sequential Markov coalescent algorithms for population models with demographic structure. Theor Popul Biol 2009; 76:84-91. [PMID: 19433100 DOI: 10.1016/j.tpb.2009.05.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2009] [Revised: 05/04/2009] [Accepted: 05/04/2009] [Indexed: 10/24/2022]
Abstract
We analyse sequential Markov coalescent algorithms for populations with demographic structure: for a bottleneck model, a population-divergence model, and for a two-island model with migration. The sequential Markov coalescent method is an approximation to the coalescent suggested by McVean and Cardin, and by Marjoram and Wall. Within this algorithm we compute, for two individuals randomly sampled from the population, the correlation between times to the most recent common ancestor and the linkage probability corresponding to two different loci with recombination rate R between them. These quantities characterise the linkage between the two loci in question. We find that the sequential Markov coalescent method approximates the coalescent well in general in models with demographic structure. An exception is the case where individuals are sampled from populations separated by reduced gene flow. In this situation, the correlations may be significantly underestimated. We explain why this is the case.
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Affiliation(s)
- A Eriksson
- Department of Physics, University of Gothenburg, SE-41296 Gothenburg, Sweden
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35
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Parreira B, Trussart M, Sousa V, Hudson R, Chikhi L. SPAms: A user-friendly software to simulate population genetics data under complex demographic models. Mol Ecol Resour 2009; 9:749-53. [PMID: 21564735 DOI: 10.1111/j.1755-0998.2009.02533.x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Patterns of genetic diversity can be used to reconstruct populations demographic history. Thus, it is crucial to understand the statistical properties of data sets generated under different scenarios. SPAms is a graphical application that allows the simulation of data under a set of demographic models of increasing complexity: (i) population size change (ii) admixture and (iii) population structure (n-island or stepping stone model). SPAms generates data under the infinite site and the stepwise mutation models and computes a set of commonly used statistics. It should thus be a useful tool for both research and teaching purposes.
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Affiliation(s)
- B Parreira
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande, nº6, 2780-156 Oeiras, Portugal
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36
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Liu Y, Athanasiadis G, Weale ME. A survey of genetic simulation software for population and epidemiological studies. Hum Genomics 2009; 3:79-86. [PMID: 19129092 PMCID: PMC3525177 DOI: 10.1186/1479-7364-3-1-79] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
A number of programs have been developed for simulating population genetic and genetic epidemiological data conforming to one of three main algorithmic approaches: 'forwards', 'backwards' and 'sideways'. This review aims to make the reader aware of the range of options currently available to them. While no one program emerges as the best choice in all circumstances, we nominate a set of those which currently appear most promising.
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Affiliation(s)
- Youfang Liu
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7566, USA
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37
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Hernandez RD. A flexible forward simulator for populations subject to selection and demography. Bioinformatics 2008; 24:2786-7. [PMID: 18842601 DOI: 10.1093/bioinformatics/btn522] [Citation(s) in RCA: 180] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED This article introduces a new forward population genetic simulation program that can efficiently generate samples from populations with complex demographic histories under various models of natural selection. The program (SFS_CODE) is highly flexible, allowing the user to simulate realistic genomic regions with several loci evolving according to a variety of mutation models (from simple to context-dependent), and allows for insertions and deletions. Each locus can be annotated as either coding or non-coding, sex-linked or autosomal, selected or neutral, and have an arbitrary linkage structure (from completely linked to independent). AVAILABILITY The source code (written in the C programming language) is available at http://sfscode.sourceforge.net, and a web server (http://cbsuapps.tc.cornell.edu/sfscode.aspx) allows the user to perform simulations using the high-performance computing cluster hosted by the Cornell University Computational Biology Service Unit.
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Affiliation(s)
- Ryan D Hernandez
- Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA.
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Carvajal-Rodríguez A. Detecting recombination and diversifying selection in human alpha-papillomavirus. INFECTION GENETICS AND EVOLUTION 2008; 8:689-92. [PMID: 18675939 DOI: 10.1016/j.meegid.2008.07.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2008] [Revised: 07/04/2008] [Accepted: 07/08/2008] [Indexed: 11/26/2022]
Abstract
Intragenic recombination and selection analyses were performed in DNA sequences of human alpha-papillomavirus. Recombination was estimated and the corresponding breakpoints obtained by re-analyzing data grouped by phylogenetic and epidemiological criteria, using different alignment methods. Diversifying or positive selection has been scarcely studied in these viruses probably due to the high divergence rates. We have applied maximum likelihood, empirical Bayesian and maximum parsimony methods to detect the presence of positive selection. Within the HPV 16 type, significant positive selection was detected at the time of the separation of the African 1 and African 2 branches from the other populations. At the inter-type level, positive selection can be traced in some codons of the gene L2 of the high and low risk groups. These results indicate that positive selection could have been important in the evolution of HPV both at inter- and intra-type levels.
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Affiliation(s)
- A Carvajal-Rodríguez
- Dpto. de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, 36310 Vigo, Spain.
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