Analysis Of Population Genetic Data Research Articles

Characterization of Microsatellite Markers in Crossbred Ducks

Background: Chicken represents a valuable genetic resource and protein source, it remains a potential threat to human health as they serve as a reservoir for diseases and food borne pathogens. Diversification of the poultry production is one of the viable options to enhance the food production with less susceptibility to the threats from the emerging diseases and changing climatic conditions. Ducks are promising species suitable for the diversification and will be complimentary with an adaption potential from small to commercial farming. Majority of ducks in the country are desi type with a meagre proportion of improved duck strains. genetic characterization by developing duck specific microsatellite markers and designing suitable improvement program are required to be undertaken at the earliest. The microsatellite markers are extensively used for assessing genetic structure, diversity, and relationships. Information about genetic diversity of indigenous duck breeds is important to design effective improvement and conservation strategies. Therefore, the current studies aim at genetic characterization and evaluation of growth, production and reproduction traits of the crosses of local duck population besides undertaking a suitable duck improvement program. Methods: Spectrophotometric evaluation of DNA extraction, Electrophoretic evaluation of DNA extraction , microsatellite primers- Presently, a panel of 10 informative duck microsatellite markers were identified from database as reported by Alyethodi and Kumar (2010) and Huang et al., (2006) and used for the studies, Polymerase chain reaction (PCR) , Documentation of PCR products by Agarose Gel Electrophoresis, Metaphor agarose gel electrophoresis(MAGE)of microsatellites alleles, Determination of molecular size of microsatellite alleles and genotypes, Statistical analysis of population genetics data, Data on genotype of all experimental birds at ten microsatellites were compiled and analyzed using POPGENE® 3.1 software (Yeh et al., 1999) for their population genetics parameters. Results: The study was conducted at College of Veterinary Science, Assam Agricultural University, Khanapara, Guwahati, Assam, India. In this study, the genomic DNA was isolated and characterized microsatellite loci in crossbred duck (Pati x White Pekin). Ten microsatellites were used to detect polymorphisms in 50 cross bred ducks. A total of 28 nos of alleles were observed and all loci were polymorphic. The number of alleles ranged from 2 to 5 with an average of 2.6 ±0.08 per microsatellite locus. The observed and expected heterozygosity of these polymorphic makers ranged from 0.00 to 0.42 with an average number of 0.047 and 0.41 to 0.82 with an average number of 0.57, respectively. The Polymorphic markers consist of observed heterozygosities of loci were less than 0.50. The polymorphism information content (PIC) of 10 loci ranged from 0.32 to 0.78 with an average of 0.477. Microsatellite markers will be useful tool for constructing the genetic linkage map of the duck as well as comparative mapping with the chicken.

Guidelines for genetic data analysis

The IWC Scientific Committee recently adopted guidelines for quality control of DNA data. Once data have been collected, the next step is to analyse the data and make inferences that are useful for addressing practical problems in conservation and management of cetaceans. This is a complex exercise, as numerous analyses are possible and users have a wide range of choices of software programs for implementing the analyses. This paper reviews the underlying issues, illustrates application of different types of genetic data analysis to two complex management problems (involving common minke whales and humpback whales), and concludes with a number of recommendations for best practices in the analysis of population genetic data. An extensive Appendix provides a detailed review and critique of most types of analyses that are used with population genetic data for cetaceans.

Uniting genetic and geographic databases to understand the relationship between latitude and population demography

It was not known that phylogenetic analysis of population genetic data would show geographic structure when Avise et al.(1979) introduced the unnamed field of phylogeography. Since then, the field has grown from using single gene fragments to whole genomes resulting in more than 22,000 publications. This seemingly simple relationship between geography and genetic variation has provided the foundation for studying speciation, species delimitation, hybrid zone dynamics, adaptation, conservation genetics, community assembly, and historical demography to name a few (see Burbrink & Ruane,2021; Edwards et al.,2016). As with many burgeoning fields, there is often little consideration of how to make data sets accessible for future researchers addressing more comprehensive questions under a common framework. For example, it is important in biogeography to examine how shared environments or barriers affect population structure across communities of species, or test if range size or latitude are correlated with genetic diversity across taxa (Myers et al.,2019; Smith et al.,2017). However, addressing these types of questions using existing data requires researchers to assemble large databases manually. Genetic and geographic databases used to store this information like NCBI Genbank (National Center for Biotechnology Information) and GBIF (Global Biodiversity Information Facility) are disconnected, and often of limited general use for conducting multitaxon studies. In this issue of Molecular Ecology Resources, Pelletier et al.(2022) have automated the process of connecting geography to DNA sequences via the phylogatR (phylogeographic data aggregation and repurposing) database.

Detecting selection using extended haplotype homozygosity (EHH)-based statistics in unphased or unpolarized data.

Analysis of population genetic data often includes a search for genomic regions with signs of recent positive selection. One of such approaches involves the concept of extended haplotype homozygosity (EHH) and its associated statistics. These statistics typically require phased haplotypes, and some of them necessitate polarized variants. Here, we unify and extend previously proposed modifications to loosen these requirements. We compare the modified versions with the original ones by measuring the false discovery rate in simulated whole-genome scans and by quantifying the overlap of inferred candidate regions in empirical data. We find that phasing information is indispensable for accurate estimation of within-population statistics (for all but very large samples) and of cross-population statistics for small samples. Ancestry information, in contrast, is of lesser importance for both types of statistic. Our publicly available R package rehh incorporates the modified statistics presented here.

Population genetic structure and dispersal of Pinus occidentalis in the Dominican Republic by chloroplastic SSR, with implications for its conservation, management, and reforestation

Pinus occidentalis Swartz is the dominant species of forest ecosystems in the Dominican Republic, located between 200 and 3000 m above sea level, with extensive and overexploited natural populations. However, over the years, various restoration plans have been performed, which could affect the genetic structure of P. occidentalis. Despite being the species with the highest occurrence in the Dominican forests, there is no existing information on the genetic structure and molecular characterization among natural populations with limited information on both phenological and productive characterization. In this study, the genetic structure, diversity, and gene flow of the five P. occidentalis natural populations of the Dominican Republic were determined using microsatellite markers. A total of 145 individuals were genotyped with eight polymorphic chloroplastic microsatellites, producing an average of 41 haplotypes with high genetic diversity across populations (HE = 0.90). Significant population genetic structure was found between populations (FST = 0.123). These results reflect the impact of reforestation programs on natural populations and diluting the natural genetic signature. Analysis of population genetic data are, therefore, crucial for the breeding and conservation programs of P. occidentalis in the country.

Evaluation and comparison of population genetics software in Rabari Tribe of Gujarat population

BackgroundToday, when forensic experts talk about quantifiable hereditary traits, they do not just depend on the assessment and examination of DNA profiles but also relate them to the population structures. The use of high-throughput molecular marker technologies and advanced statistical and software tools have improved the accuracy of human genetic diversity analysis in many populations with limited time and resources. The present study aimed to investigate the genomic diversity in Gujarat’s Rabari population, using 20 autosomal genetic markers.Numerous bio-statistical software programs are available for the interpretation of population data in forensics. These statistics deal with the measurement of uncertainty and also provides a probability of a random match. The present paper aims to provide a practical guide to the analysis of population genetics data. Three statistical software packages named Cervus, Genepop, and Fstat are compared and contrasted. The comparison is performed on the profiles obtained from fifty unrelated blood samples of healthy male individuals. DNA was extracted using the organic extraction method, 20 autosomal STR loci were amplified using PowerPlex 21 kit (Promega, Madison, WI, USA) and detected on 3100 Genetic Analyser (Life Technologies Corporation, Carlsbad, CA, USA).ResultsA total of 170 alleles were observed in the Rabari Tribe of Gujarat population, and allele frequencies ranged from 0.010 to 0.480. The highest allele frequency detected was 0.480 for allele 9 at locus TH01. Based on heterozygosity and the polymorphism information content, FGA may be considered as the most informative markers. Both the combined power of discrimination (CPD) and the combined power of exclusion (CPE) for the 20 analyzed loci were higher than 0.999999. The combined match probability (CPM) for all 20 loci was 2.5 × 10−22.ConclusionsWith respect to the results, the 20 STR loci are highly polymorphic and discriminating in the Gujarat population and could be used for forensic practice and population genetics studies. However, Fstat demonstrated better genetic software for analysis of the demographic structure of a specific or set of populations.

Efficient toolkit implementing best practices for principal component analysis of population genetic data

ABSTRACTMotivationPrincipal component analysis (PCA) of genetic data is routinely used to infer ancestry and control for population structure in various genetic analyses. However, conducting PCA analyses can be complicated and has several potential pitfalls. These pitfalls include (i) capturing linkage disequilibrium (LD) structure instead of population structure, (ii) projected PCs that suffer from shrinkage bias, (iii) detecting sample outliers and (iv) uneven population sizes. In this work, we explore these potential issues when using PCA, and present efficient solutions to these. Following applications to the UK Biobank and the 1000 Genomes project datasets, we make recommendations for best practices and provide efficient and user-friendly implementations of the proposed solutions in R packages bigsnpr and bigutilsr.ResultsFor example, we find that PC19–PC40 in the UK Biobank capture complex LD structure rather than population structure. Using our automatic algorithm for removing long-range LD regions, we recover 16 PCs that capture population structure only. Therefore, we recommend using only 16–18 PCs from the UK Biobank to account for population structure confounding. We also show how to use PCA to restrict analyses to individuals of homogeneous ancestry. Finally, when projecting individual genotypes onto the PCA computed from the 1000 Genomes project data, we find a shrinkage bias that becomes large for PC5 and beyond. We then demonstrate how to obtain unbiased projections efficiently using bigsnpr. Overall, we believe this work would be of interest for anyone using PCA in their analyses of genetic data, as well as for other omics data.Availability and implementationR packages bigsnpr and bigutilsr can be installed from either CRAN or GitHub (see https://github.com/privefl/bigsnpr). A tutorial on the steps to perform PCA on 1000G data is available at https://privefl.github.io/bigsnpr/articles/bedpca.html. All code used for this paper is available at https://github.com/privefl/paper4-bedpca/tree/master/code.Supplementary information Supplementary data are available at Bioinformatics online.

Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data.

Real geography is continuous, but standard models in population genetics are based on discrete, well-mixed populations. As a result, many methods of analyzing genetic data assume that samples are a random draw from a well-mixed population, but are applied to clustered samples from populations that are structured clinally over space. Here, we use simulations of populations living in continuous geography to study the impacts of dispersal and sampling strategy on population genetic summary statistics, demographic inference, and genome-wide association studies (GWAS). We find that most common summary statistics have distributions that differ substantially from those seen in well-mixed populations, especially when Wright’s neighborhood size is < 100 and sampling is spatially clustered. “Stepping-stone” models reproduce some of these effects, but discretizing the landscape introduces artifacts that in some cases are exacerbated at higher resolutions. The combination of low dispersal and clustered sampling causes demographic inference from the site frequency spectrum to infer more turbulent demographic histories, but averaged results across multiple simulations revealed surprisingly little systematic bias. We also show that the combination of spatially autocorrelated environments and limited dispersal causes GWAS to identify spurious signals of genetic association with purely environmentally determined phenotypes, and that this bias is only partially corrected by regressing out principal components of ancestry. Last, we discuss the relevance of our simulation results for inference from genetic variation in real organisms.

Genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids.

genodive version 3.0 is a user‐friendly program for the analysis of population genetic data. This version presents a major update from the previous version and now offers a wide spectrum of different types of analyses. genodive has an intuitive graphical user interface that allows direct manipulation of the data through transformation, imputation of missing data, and exclusion and inclusion of individuals, population and/or loci. Furthermore, genodive seamlessly supports 15 different file formats for importing or exporting data from or to other programs. One major feature of genodive is that it supports both diploid and polyploid data, up to octaploidy (2n = 8x) for some analyses, but up to hexadecaploidy (2n = 16x) for other analyses. The different types of analyses offered by genodive include multiple statistics for estimating population differentiation (φ ST, F ST, FʹST, G ST, GʹST, GʹʹST, D est, R ST, ρ), analysis of molecular variance‐based K‐means clustering, Hardy–Weinberg equilibrium, hybrid index, population assignment, clone assignment, Mantel test, Spatial Autocorrelation, 23 ways of calculating genetic distances, and both principal components and principal coordinates analyses. A unique feature of genodive is that it can also open data sets with nongenetic variables, for example environmental data or geographical coordinates that can be included in the analysis. In addition, genodive makes it possible to run several external programs (lfmm, structure, instruct and vegan) directly from its own user interface, avoiding the need for data reformatting and use of the command line. genodive is available for computers running Mac OS X 10.7 or higher and can be downloaded freely from: http://www.patrickmeirmans.com/software.

The Dundee Resource for Sequence Analysis and Structure Prediction.

The Dundee Resource for Sequence Analysis and Structure Prediction (DRSASP; http://www.compbio.dundee.ac.uk/drsasp.html) is a collection of web services provided by the Barton Group at the University of Dundee. DRSASP's flagship services are the JPred4 webserver for secondary structure and solvent accessibility prediction and the JABAWS 2.2 webserver for multiple sequence alignment, disorder prediction, amino acid conservation calculations, and specificity‐determining site prediction. DRSASP resources are available through conventional web interfaces and APIs but are also integrated into the Jalview sequence analysis workbench, which enables the composition of multitool interactive workflows. Other existing Barton Group tools are being brought under the banner of DRSASP, including NoD (Nucleolar localization sequence detector) and 14‐3‐3‐Pred. New resources are being developed that enable the analysis of population genetic data in evolutionary and 3D structural contexts. Existing resources are actively developed to exploit new technologies and maintain parity with evolving web standards. DRSASP provides substantial computational resources for public use, and since 2016 DRSASP services have completed over 1.5 million jobs.

From typical sequences to typical genotypes

We demonstrate an application of a core notion of information theory, typical sequences and their related properties, to analysis of population genetic data. Based on the asymptotic equipartition property (AEP) for nonstationary discrete-time sources producing independent symbols, we introduce the concepts of typical genotypes and population entropy and cross entropy rate. We analyze three perspectives on typical genotypes: a set perspective on the interplay of typical sets of genotypes from two populations, a geometric perspective on their structure in high dimensional space, and a statistical learning perspective on the prospects of constructing typical-set based classifiers. In particular, we show that such classifiers have a surprising resilience to noise originating from small population samples, and highlight the potential for further links between inference and communication.

Weird genotypes? Don't discard them, transmissible cancer could be an explanation.

Genetic chimerism is rarely considered in the analysis of population genetics data, because assumed to be an exceptionally rare, mostly benign, developmental accident. An unappreciated source of chimerism is transmissible cancer, when malignant cells have become independent parasites and can infect other individuals. Parasitic cancers were thought to be rare exceptions, only reported in dogs (Murgia et al., Cell, 2006, 126, 477; Rebbeck et al., Evolution, 2009, 63, 2340), Tasmanian devils (Pearse and Swift, Nature, 2006, 439, 549; Pye et al., Proceedings of the National Academy of Sciences, 2016, 113, 374), and soft‐shell clams (Metzger et al., Cell, 2015, 161, 255). However, the recent simultaneous report of four new contagious leukemias in marine mollusks (Metzger et al., Nature, 2016, 534, 705) might change the rules. By doubling up the number of naturally occurring transmissible cancers, this discovery suggests they may essentially be missed because not sufficiently searched for, especially outside mammals. We encourage population geneticists to keep in mind infectious cancer when interpreting weird genotypes in their molecular data. It would then contribute in the investigation of how widespread contagious cancer could really be in the wild. We provide an example with our own data in Mytilus mussels, a commercially important shellfish. We identified genetic chimerism in a few mussels that suggests the possible occurrence at low prevalence in European M. edulis populations of a M. trossulus contagious cancer related to the one described by Metzger et al. (Nature, 2016, 534, 705) in populations of British Columbia.

Towards an integrated ecosystem of R packages for the analysis of population genetic data.

Towards an integrated ecosystem of R packages for the analysis of population genetic data.

Demographic history and asynchronous spawning shape genetic differentiation among populations of the hard coral Acropora tenuis in Western Australia

Genetic subdivision within populations can ultimately lead to the evolution of new species, and in populations of broadcast-spawners a potential facilitator of genetic subdivision is asynchronous reproduction. However, the factors that shape genetic variation in marine systems are complex and ambiguous, and ecological genetic structure may be influenced by the overriding signature of past demographic events. Here, the relative roles of the timing of reproduction and historical geography on the partitioning of genetic variation were examined in seven populations of the broadcast-spawning coral Acropora tenuis over 12° of latitude. The analysis of multiple loci (mitochondrial control region, two nuclear introns and six microsatellites) revealed significant genetic division between the most northern reef and all other reefs, suggesting that WA reefs were re-colonized from two different sources after the Pleistocene glaciation. Accompanying this pattern was significant genetic differentiation associated with different breeding seasons (spring and autumn), which was greatest in PaxC, in which there were two divergent lineages (ΦST=0.98). This is the second study to find divergent clades of PaxC associated with spring and autumn spawners, strengthening the suggestion of some selective connection to timing of reproduction in corals. This study reiterates the need to incorporate reproductive timing into population genetic studies of corals because it facilitates genetic differentiation; however, careful analysis of population genetic data is required to separate ecological and evolutionary processes.

On-shelf larval retention limits population connectivity in a coastal broadcast spawner

Broadcast-spawning marine organisms with long pelagic larval duration are often expected to be genetically homogeneous throughout their ranges. When genetic structure is found in such taxa, it may be in the form of chaotic genetic patchiness: i.e. patterns that might seem independent of any un- derlying environmental variation. The joint analysis of population genetic data and marine environmental data can elucidate factors driving such spatial genetic diversity patterns. Using meso-scale sampling (at a scale of 10s to 100s of km), microsatellite data and advection connectivity simulations, we studied the effect of temperate southern Australian ocean circu - lation on the genetic structure of the snail Nerita atramentosa. This species has a long pelagic larval duration and is represented as a single metapopula- tion throughout its ~3000 km range, but even so, we found that its dispersal potential is lower than ex- pected. Connectivity simulations indicate that this is a result of the larvae that remain on the continental shelf (where currents are erratic and often shoreward) re- turning to the coast in much larger numbers than lar- vae that become entrained in the region's shelf-edge boundary currents. Our study contributes to the growing evidence that departures from the expecta- tions of panmixia along continuous and environ - mentally homogeneous coastlines are not limited to low-dispersal species, and it identifies on-shelf larval retention as an important factor limiting dispersal.

Evolutionary Systematics and Paraphyly: Introduction1

Abstract Phylogenetic systematics, especially involving molecular data, has had a remarkable impact on systematic biology. Numerous tree-building computer programs exist for the reconstruction of phylogenies, and many packages are available for analysis of population genetic data for estimating genetic divergence within and among populations. These advances have come about through the joining of statistical algorithms, computer programs, and DNA base-pair sequence and fragment data. Deeper genomic data are on the horizon for use with similar questions, and the next several years will witness many spectacular genetic advances. While great progress is being made on analytical approaches with molecular data in systematics, the use of the results of these analyses in biological classification has solidified into a dogmatic view, which has impeded further progress. Emphasis still remains on using only synapomorphies, even single characters, for delimitation of groups, on insisting that sister groups should hav...

Statistical decision from k test series with particular focus on population genetics tools: A DIY notice

In population genetics data analysis, researchers are often faced to the problem of decision making from a series of tests of the same null hypothesis. This is the case when one wants to test differentiation between pathogens found on different host species sampled from different locations (as many tests as number of locations). Many procedures are available to date but not all apply to all situations. Finding which tests are significant or if the whole series is significant, when tests are independent or not do not require the same procedures. In this note I describe several procedures, among the simplest and easiest to undertake, that should allow decision making in most (if not all) situations population geneticists (or biologists) should meet, in particular in host–parasite systems.

Life history traits and patterns of diversification in oceanic archipelagos: a meta-analysis

Ecological conditions, such as high habitat diversity and the absence of competitors, have been proposed as key determinants of the patterns of speciation observed in oceanic island floras. However, the relationship between plant traits and lineage diversification has received less attention. Here, we review 120 published phylogenetic and population genetic studies of three well-studied oceanic archipelagos (Canary Islands, Galapagos and Hawai'i) to investigate potential associations between life history characters (growth form and fruit type) and patterns of diversification. The available data suggest that the phenotypic syndrome 'herbaceous-dry fruited' was predominant among ancestors of species-rich lineages, although the Hawaiian flora also shows a substantial proportion of 'woody-fleshy fruited' ancestors. Growth form, unlike fruit type, is shown to be a labile character strongly selected for woodiness, particularly in radiating lineages. Dry fruits, although representative of diverse dispersal modes and efficacies, are generally associated with a low incidence of inter-island colonization, and the analysis of population genetic data confirms strong genetic differentiation among islands for dry fruited species of radiating lineages. In contrast, fleshy fruited species of monotypic lineages typically show widespread distributions coupled with extensive gene flow among islands, which probably impedes speciation. Our analyses suggest that fruit types associated with limited evidence of dispersal promote recurrent speciation within lineages, although particular character states related to speciation appear to be context dependent. This study reinforces the idea that plant traits associated with island colonization and population persistence are, in addition to ecological conditions, important factors in understanding the patterns of diversification on islands. © 2013 The Linnean Society of London.

A Fast Estimate for the Population Recombination Rate Based on Regression

Recombination is a fundamental evolutionary force. Therefore the population recombination rate ρ plays an important role in the analysis of population genetic data; however, it is notoriously difficult to estimate. This difficulty applies both to the accuracy of commonly used estimates and to the computational efforts required to obtain them. Some particularly popular methods are based on approximations to the likelihood. They require considerably less computational efforts than the full-likelihood method with not much less accuracy. Nevertheless, the computation of these approximate estimates can still be very time consuming, in particular when the sample size is large. Although auxiliary quantities for composite likelihood estimates can be computed in advance and stored in tables, these tables need to be recomputed if either the sample size or the mutation rate θ changes. Here we introduce a new method based on regression combined with boosting as a model selection technique. For large samples, it requires much less computational effort than other approximate methods, while providing similar levels of accuracy. Notably, for a sample of hundreds or thousands of individuals, the estimate of ρ using regression can be obtained on a single personal computer within a couple of minutes while other methods may need a couple of days or months (or even years). When the sample size is smaller (n ≤ 50), our new method remains computational efficient but produces biased estimates. We expect the new estimates to be helpful when analyzing large samples and/or many loci with possibly different mutation rates.

Association of HLA-DRB1*15 and HLA-DQB1* 06 with SLE in Saudis

BACKGROUND AND OBJECTIVESSystemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by humoral autoimmunity. The etiology of SLE is thought to be multifactorial including environmental, hormonal, and genetic factors. The human leukocyte antigen (HLA) has extensively been associated with the susceptibility to SLE; however, the association is heterogeneous among different ethnic groups. The aim of this study was to determine the association of HLA-A, HLA-B, HLA-DRB1, and HLA-DQB1 with SLE susceptibility in the Saudi population.DESIGN AND SETTINGSA total of 86 consecutive SLE patients attending the rheumatology clinic at King Abdulaziz Medical City, Riyadh, were recruited for this study.METHODSHLA types were determined by the polymerase chain reaction sequence-specific oligonucleotide (PCR-SSP) method in 86 patients and 356 control subjects.RESULTSThe following HLA alleles were found to be positively associated with SLE: HLA-A*29 (OR=2.70; 95% CI=1.03–7.08; P=.0035), HLA-B*51 (OR=1.81; 95% CI=1.17–2.79; P=.0066), HLA-DRB1*15 (OR=1.45; 95% CI=0.98–2.29; P=.063), and HLA-DQB1*06 (OR=1.67; 95% CI=1.19–2.36; P=.0032), whereas HLA-DRB1*16 was negatively associated with the disease (OR=0.18; 95% CI=0.02–1.3; P=.055). HLA-DRB1*15 haplotypes were significantly associated with SLE (OR=2.01, 95% CI=1.20–3.68, P=.008); this was mainly due to the HLA-DRB1* 15-DQB1*06 association.CONCLUSIONSOur data suggest an association between MHC class I and class II (HLA-A*29, HLA-B*51, HLA-DRB1*15, and HLA-DQB1*06) and susceptibility to SLE in the Saudi population. HLA-DRB1*15-DQB1*06 haplotype showed the highest risk factor for the disease that is similar to what was seen in the African American patients, suggesting shared susceptibility genetic factors among these ethnic groups.