Special Issue "Tools for Population and Evolutionary Genetics"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Population and Evolutionary Genetics and Genomics".

Deadline for manuscript submissions: 31 July 2019

Special Issue Editors

Guest Editor
Dr. David Alvarez-Ponce

University of Nevada, Reno
Website | E-Mail
Interests: Molecular Evolution; Comparative Genomics; Bioinformatics.
Guest Editor
Dr. Julie M. Allen

University of Nevada, Reno
Website | E-Mail
Interests: Co-evolution; Phylogenomics; Bioinformatics
Guest Editor
Dr. Won C. Yim

University of Nevada, Reno
Website | E-Mail
Interests: Genomics; Comparative genomics; Crop genetics; Bioinformatics
Guest Editor
Dr. Marco Fondi

Università degli Studi di Firenze
Website | E-Mail
Interests: Systems Biology; Evolutionary Genomics; Metabolic Modelling

Special Issue Information

Dear Colleagues,

In recent years, the development of next generation sequencing techniques has fueled an explosion in the pace at which genomic data sets are generated, while dramatically decreasing the costs of genome sequencing. Comparison of these datasets can uncover remarkable information about the evolution of organisms. The availability of datasets of ever-increasing size and complexity has resulted in a growing need for computational tools that allow their effective and efficient analysis.

This special issue focuses on tools for population and evolutionary genetics, including, but not limited to, bioinformatics approaches, and computational tools, algorithms and resources. We welcome submissions of reviews, research articles, and short communications. We also encourage the submission of manuscripts describing new tools, in the form of “concept papers”.

Dr. David Alvarez-Ponce
Dr. Julie M. Allen
Dr. Won C. Yim
Dr. Marco Fondi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bioinformatics
  • Comparative Genomics
  • Population genomics
  • Phylogenomics
  • Evolution

Published Papers (3 papers)

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Research

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Open AccessArticle Ancient Ancestry Informative Markers for Identifying Fine-Scale Ancient Population Structure in Eurasians
Genes 2018, 9(12), 625; https://doi.org/10.3390/genes9120625
Received: 6 November 2018 / Revised: 5 December 2018 / Accepted: 10 December 2018 / Published: 12 December 2018
PDF Full-text (1108 KB) | Supplementary Files
Abstract
The rapid accumulation of ancient human genomes from various areas and time periods potentially enables the expansion of studies of biodiversity, biogeography, forensics, population history, and epidemiology into past populations. However, most ancient DNA (aDNA) data were generated through microarrays designed for modern-day
[...] Read more.
The rapid accumulation of ancient human genomes from various areas and time periods potentially enables the expansion of studies of biodiversity, biogeography, forensics, population history, and epidemiology into past populations. However, most ancient DNA (aDNA) data were generated through microarrays designed for modern-day populations, which are known to misrepresent the population structure. Past studies addressed these problems by using ancestry informative markers (AIMs). It is, thereby, unclear whether AIMs derived from contemporary human genomes can capture ancient population structures, and whether AIM-finding methods are applicable to aDNA, provided that the high missingness rates in ancient—and oftentimes haploid—DNA can also distort the population structure. Here, we define ancient AIMs (aAIMs) and develop a framework to evaluate established and novel AIM-finding methods in identifying the most informative markers. We show that aAIMs identified by a novel principal component analysis (PCA)-based method outperform all of the competing methods in classifying ancient individuals into populations and identifying admixed individuals. In some cases, predictions made using the aAIMs were more accurate than those made with a complete marker set. We discuss the features of the ancient Eurasian population structure and strategies to identify aAIMs. This work informs the design of single nucleotide polymorphism (SNP) microarrays and the interpretation of aDNA results, which enables a population-wide testing of primordialist theories. Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
Open AccessArticle Cryptic Diversity Hidden within the Leafminer Genus Liriomyza (Diptera: Agromyzidae)
Genes 2018, 9(11), 554; https://doi.org/10.3390/genes9110554
Received: 17 September 2018 / Revised: 24 October 2018 / Accepted: 12 November 2018 / Published: 15 November 2018
PDF Full-text (2000 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Leafminer insects of the genus Liriomyza are small flies whose larvae feed on the internal tissue of some of the most important crop plants for the human diet. Several of these pest species are highly uniform from the morphological point of view, meaning
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Leafminer insects of the genus Liriomyza are small flies whose larvae feed on the internal tissue of some of the most important crop plants for the human diet. Several of these pest species are highly uniform from the morphological point of view, meaning molecular data represents the only reliable taxonomic tool useful to define cryptic boundaries. In this study, both mitochondrial and nuclear molecular markers have been applied to investigate the population genetics of some Tunisian populations of the polyphagous species Liriomyza cicerina, one of the most important pest of chickpea cultivars in the whole Mediterranean region. Molecular data have been collected on larvae isolated from chickpea, faba bean, and lentil leaves, and used for population genetics, phylogenetics, and species delimitation analyses. Results point toward high differentiation levels between specimens collected on the three different legume crops, which, according to the species delimitation methods, are also sufficient to define incipient species differentiation and cryptic species occurrence, apparently tied up with host choice. Genetic data have also been applied for a phylogenetic comparison among Liriomyza species, further confirming their decisive role in the systematic studies of the genus. Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
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Review

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Open AccessReview Selecting among Alternative Scenarios of Human Evolution by Simulated Genetic Gradients
Genes 2018, 9(10), 506; https://doi.org/10.3390/genes9100506
Received: 13 September 2018 / Revised: 11 October 2018 / Accepted: 16 October 2018 / Published: 18 October 2018
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Abstract
Selecting among alternative scenarios of human evolution is nowadays a common methodology to investigate the history of our species. This strategy is usually based on computer simulations of genetic data under different evolutionary scenarios, followed by a fitting of the simulated data with
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Selecting among alternative scenarios of human evolution is nowadays a common methodology to investigate the history of our species. This strategy is usually based on computer simulations of genetic data under different evolutionary scenarios, followed by a fitting of the simulated data with the real data. A recent trend in the investigation of ancestral evolutionary processes of modern humans is the application of genetic gradients as a measure of fitting, since evolutionary processes such as range expansions, range contractions, and population admixture (among others) can lead to different genetic gradients. In addition, this strategy allows the analysis of the genetic causes of the observed genetic gradients. Here, we review recent findings on the selection among alternative scenarios of human evolution based on simulated genetic gradients, including pros and cons. First, we describe common methodologies to simulate genetic gradients and apply them to select among alternative scenarios of human evolution. Next, we review previous studies on the influence of range expansions, population admixture, last glacial period, and migration with long-distance dispersal on genetic gradients for some regions of the world. Finally, we discuss this analytical approach, including technical limitations, required improvements, and advice. Although here we focus on human evolution, this approach could be extended to study other species. Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1)    Tentative Title: R package for data mining and analysis of the GWAS catalog 

Tentative Abstract: An R script will be provided to extract useful information from the GWAS catalog and carry out analyses such as obtaining distributions of SNP frequencies and effects, contributions to heritability, ascertaining the level of pleiotropy of variants, analysis of the genomic position of SNPs and correlation between traits for SNP positions and effects.

Authors: Eugenio López-Cortegano and Armando Caballero*

 

2)    Tentative Title: Bayesian inference for agent-based epidemiological population simulations

Tentative Abstract: Computer simulation is a generally used tool in modeling infectious diseases, offering extensive exibility by avoiding the limitations of analytical tractability. For example, common factors such as heterogenous mixing and population structure can be efficiently taken into account by using agent-based simulations. The trade-off arising from eschewing analytically tractable models is the increased difficulty in parameter estimation. Approximate Bayesian Computation (ABC) provides a principled approach to inferring model parameters from computer simulations, however, due to its computational complexity it has not yet been widely applied to agent-based simulators. Using the latest advances in ABC inference utilizing Bayesian optimization and automated parallelization, we develop a Python software module embedded in ELFI for inferring parameters in models implemented in the generic epidemiological population modeling platform EMOD. Analysing emerging infectious disease outbreaks presents a particularly challenging statistical problem due to the limited amount of observational data and the complex relationships between key epidemiological parameters and latent variables, such as the basic reproduction number, growth rate, incubation times and case fatality rates. To demonstrate the usefulness of our software we present a case study of using EMOD and ELFI to analyze the 2014-2015 Ebola outbreak.

Authors: Kusti Skytén, Jukka Corander*

 

3)    Tentative Title: PanGloss a tool for Pan-genome analysis of microbial Eukaryotes

Tentative Abstract: The concept of the species “pan-genome”, the union of “core” conserved genes and all “accessory” non-conserved genes across all strains of a species, was first proposed in prokaryotes to account for intraspecific variability. Species pan-genomes have been extensively studied in prokaryotes, but evidence of species pan-genomes has also been demonstrated in eukaryotes such as plants and fungi. Using a previously-published prokaryote methodology based on sequence homology and conserved microsynteny we have developed a bespoke pipelines to investigate the the pan-genomes of a number of fungal species. Our initial analyses show that between 80-90% of gene models per strain in each species are core genes that are highly-conserved across all strains of that species, many of which are involved in housekeeping and conserved survival processes. In many of these species the remaining “accessory” gene models are clustered within subterminal regions and may be involved in pathogenesis and antimicrobial resistance.

Authors: Charley McCarthy, David Fitzpatrick*

 

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