Journal articles: 'Population Genetic Inference'

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Jiang, Rong, Simon Tavaré, and Paul Marjoram. "Population Genetic Inference From Resequencing Data." Genetics 181, no. 1 (November 3, 2008): 187–97. http://dx.doi.org/10.1534/genetics.107.080630.

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Sheehan, Sara, and Yun S. Song. "Deep Learning for Population Genetic Inference." PLOS Computational Biology 12, no. 3 (March 28, 2016): e1004845. http://dx.doi.org/10.1371/journal.pcbi.1004845.

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Pool, J. E., I. Hellmann, J. D. Jensen, and R. Nielsen. "Population genetic inference from genomic sequence variation." Genome Research 20, no. 3 (January 12, 2010): 291–300. http://dx.doi.org/10.1101/gr.079509.108.

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Lynch, Michael, Darius Bost, Sade Wilson, Takahiro Maruki, and Scott Harrison. "Population-Genetic Inference from Pooled-Sequencing Data." Genome Biology and Evolution 6, no. 5 (April 30, 2014): 1210–18. http://dx.doi.org/10.1093/gbe/evu085.

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Battey, C. J., Peter L. Ralph, and Andrew D. Kern. "Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data." Genetics 215, no. 1 (March 24, 2020): 193–214. http://dx.doi.org/10.1534/genetics.120.303143.

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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.

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Tvedebrink, Torben. "Review of the Forensic Applicability of Biostatistical Methods for Inferring Ancestry from Autosomal Genetic Markers." Genes 13, no. 1 (January 14, 2022): 141. http://dx.doi.org/10.3390/genes13010141.

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The inference of ancestry has become a part of the services many forensic genetic laboratories provide. Interest in ancestry may be to provide investigative leads or identify the region of origin in cases of unidentified missing persons. There exist many biostatistical methods developed for the study of population structure in the area of population genetics. However, the challenges and questions are slightly different in the context of forensic genetics, where the origin of a specific sample is of interest compared to the understanding of population histories and genealogies. In this paper, the methodologies for modelling population admixture and inferring ancestral populations are reviewed with a focus on their strengths and weaknesses in relation to ancestry inference in the forensic context.

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Pritchard, Jonathan K., Matthew Stephens, and Peter Donnelly. "Inference of Population Structure Using Multilocus Genotype Data." Genetics 155, no. 2 (June 1, 2000): 945–59. http://dx.doi.org/10.1093/genetics/155.2.945.

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Abstract We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.

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Crisci, Jessica L., Yu-Ping Poh, Angela Bean, Alfred Simkin, and Jeffrey D. Jensen. "Recent Progress in Polymorphism-Based Population Genetic Inference." Journal of Heredity 103, no. 2 (January 12, 2012): 287–96. http://dx.doi.org/10.1093/jhered/esr128.

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Loog, Liisa. "Sometimes hidden but always there: the assumptions underlying genetic inference of demographic histories." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1816 (November 30, 2020): 20190719. http://dx.doi.org/10.1098/rstb.2019.0719.

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Demographic processes directly affect patterns of genetic variation within contemporary populations as well as future generations, allowing for demographic inference from patterns of both present-day and past genetic variation. Advances in laboratory procedures, sequencing and genotyping technologies in the past decades have resulted in massive increases in high-quality genome-wide genetic data from present-day populations and allowed retrieval of genetic data from archaeological material, also known as ancient DNA. This has resulted in an explosion of work exploring past changes in population size, structure, continuity and movement. However, as genetic processes are highly stochastic, patterns of genetic variation only indirectly reflect demographic histories. As a result, past demographic processes need to be reconstructed using an inferential approach. This usually involves comparing observed patterns of variation with model expectations from theoretical population genetics. A large number of approaches have been developed based on different population genetic models that each come with assumptions about the data and underlying demography. In this article I review some of the key models and assumptions underlying the most commonly used approaches for past demographic inference and their consequences for our ability to link the inferred demographic processes to the archaeological and climate records. This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.

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Wu, Yufeng. "Inference of population admixture network from local gene genealogies: a coalescent-based maximum likelihood approach." Bioinformatics 36, Supplement_1 (July 1, 2020): i326—i334. http://dx.doi.org/10.1093/bioinformatics/btaa465.

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Abstract Motivation Population admixture is an important subject in population genetics. Inferring population demographic history with admixture under the so-called admixture network model from population genetic data is an established problem in genetics. Existing admixture network inference approaches work with single genetic polymorphisms. While these methods are usually very fast, they do not fully utilize the information [e.g. linkage disequilibrium (LD)] contained in population genetic data. Results In this article, we develop a new admixture network inference method called GTmix. Different from existing methods, GTmix works with local gene genealogies that can be inferred from population haplotypes. Local gene genealogies represent the evolutionary history of sampled haplotypes and contain the LD information. GTmix performs coalescent-based maximum likelihood inference of admixture networks with inferred local genealogies based on the well-known multispecies coalescent (MSC) model. GTmix utilizes various techniques to speed up the likelihood computation on the MSC model and the optimal network search. Our simulations show that GTmix can infer more accurate admixture networks with much smaller data than existing methods, even when these existing methods are given much larger data. GTmix is reasonably efficient and can analyze population genetic datasets of current interests. Availability and implementation The program GTmix is available for download at: https://github.com/yufengwudcs/GTmix. Supplementary information Supplementary data are available at Bioinformatics online.

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Alvarado‐Serrano, Diego F., and Michael J. Hickerson. "Spatially explicit summary statistics for historical population genetic inference." Methods in Ecology and Evolution 7, no. 4 (November 3, 2015): 418–27. http://dx.doi.org/10.1111/2041-210x.12489.

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Bamshad, Michael J., Stephen Wooding, W. Scott Watkins, Christopher T. Ostler, Mark A. Batzer, and Lynn B. Jorde. "Human Population Genetic Structure and Inference of Group Membership." American Journal of Human Genetics 72, no. 3 (March 2003): 578–89. http://dx.doi.org/10.1086/368061.

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Kierepka, E. M., E. K. Latch, and B. J. Swanson. "Influence of sampling scheme on the inference of sex-biased gene flow in the American badger (Taxidea taxus)." Canadian Journal of Zoology 90, no. 10 (October 2012): 1231–42. http://dx.doi.org/10.1139/z2012-094.

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Population genetics has fueled a substantial growth in studies of dispersal, a life-history trait that has important applications in ecology and evolution. Mammals typically exhibit male-biased gene flow, so this pattern often serves as a null hypothesis in empirical studies. Estimation of dispersal using population genetics is not without biases, so we utilized a combination of population genetic methods and simulations to evaluate gene flow within the American badger ( Taxidea taxus (Schreber, 1777)), a highly elusive and poorly understood mustelid. A total of 132 badgers captured between 2001 and 2002 were genotyped at nine microsatellite loci to investigate fine-scale genetic structure consistent with philopatry in females and dispersal in males. Resultant genetic patterns were largely consistent with a panmictic population with little evidence for sex-biased dispersal, and simulations confirmed that our sampling scheme did not substantially impact our statistics. An overall deficiency of heterozygotes was observed across the Lower Peninsula, which indicates either a Wahlund effect, mixing of separate populations, or inbreeding. Our study emphasizes the importance in deciphering between actual behavioral mechanisms and sampling effects when interpreting genetic data to understand other factors that influence dispersal like population density and territoriality.

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Kennett, Debbie, Adrian Timpson, David Balding, and Mark Thomas. "The Rise and Fall of BritainsDNA: A Tale of Misleading Claims, Media Manipulation and Threats to Academic Freedom." Genealogy 2, no. 4 (November 2, 2018): 47. http://dx.doi.org/10.3390/genealogy2040047.

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Direct-to-consumer genetic ancestry testing is a new and growing industry that has gained widespread media coverage and public interest. Its scientific base is in the fields of population and evolutionary genetics and it has benefitted considerably from recent advances in rapid and cost-effective DNA typing technologies. There is a considerable body of scientific literature on the use of genetic data to make inferences about human population history, although publications on inferring the ancestry of specific individuals are rarer. Population geneticists have questioned the scientific validity of some population history inference approaches, particularly those of a more interpretative nature. These controversies have spilled over into commercial genetic ancestry testing, with some companies making sensational claims about their products. One such company—BritainsDNA—made a number of dubious claims both directly to its customers and in the media. Here we outline our scientific concerns, document the exchanges between us, BritainsDNA and the BBC, and discuss the issues raised about media promotion of commercial enterprises, academic freedom of expression, science and pseudoscience and the genetic ancestry testing industry. We provide a detailed account of this case as a resource for historians and sociologists of science, and to shape public understanding, media reporting and scientific scrutiny of the commercial use of population and evolutionary genetics.

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Peñuela, Mauricio, Fernando Rondón, Ranulfo González, and Heiber Cárdenas. "Transcontinental genetic inference of urban pigeon populations using phenotypic markers." Avian Biology Research 12, no. 4 (August 4, 2019): 152–62. http://dx.doi.org/10.1177/1758155919866550.

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Domestic pigeons have high polymorphism in plumage morphs and colours. The genes that affect colour and coat patterns can be used to estimate genetic profiles that allow us to deduce the structures of populations, establish whether they are in a population equilibrium and learn the genetic similarity among them. This article tested these population components and the existing relationships among cities in northern South America, Western Europe and Singapore (Southeast Asia) through the inventory of phenotypic frequencies and the estimation of allele frequencies for the Pattern, Grizzle, Background colour, Spread, Crest, Recessive white and Feathered feet loci. The Hardy–Weinberg equilibrium was evaluated based on the Pattern and Grizzle loci. The results showed a higher genetic diversity in populations from northern South America with respect to the one from Western Europe, although the differentiation among cities was low ( GST = 0.0759). Several populations were not in the Hardy–Weinberg equilibrium for the evaluated loci, and a significant correlation between genetic and geographic distances was not found. The relatively small home range of the pigeons and the dispersion carried out by humans are discussed as possible explanations for the current genetic profiles.

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Deng, Hong-Wen. "Population Admixture May Appear to Mask, Change or Reverse Genetic Effects of Genes Underlying Complex Traits." Genetics 159, no. 3 (November 1, 2001): 1319–23. http://dx.doi.org/10.1093/genetics/159.3.1319.

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Abstract Association studies using random population samples are increasingly being applied in the identification and inference of genetic effects of genes underlying complex traits. It is well recognized that population admixture may yield false-positive identification of genetic effects for complex traits. However, it is less well appreciated that population admixture can appear to mask, change, or reverse true genetic effects for genes underlying complex traits. By employing a simple population genetics model, we explore the effects and the conditions of population admixture in masking, changing, or even reversing true genetic effects of genes underlying complex traits.

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Wilson, Gregory A., and Bruce Rannala. "Bayesian Inference of Recent Migration Rates Using Multilocus Genotypes." Genetics 163, no. 3 (March 1, 2003): 1177–91. http://dx.doi.org/10.1093/genetics/163.3.1177.

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Abstract A new Bayesian method that uses individual multilocus genotypes to estimate rates of recent immigration (over the last several generations) among populations is presented. The method also estimates the posterior probability distributions of individual immigrant ancestries, population allele frequencies, population inbreeding coefficients, and other parameters of potential interest. The method is implemented in a computer program that relies on Markov chain Monte Carlo techniques to carry out the estimation of posterior probabilities. The program can be used with allozyme, microsatellite, RFLP, SNP, and other kinds of genotype data. We relax several assumptions of early methods for detecting recent immigrants, using genotype data; most significantly, we allow genotype frequencies to deviate from Hardy-Weinberg equilibrium proportions within populations. The program is demonstrated by applying it to two recently published microsatellite data sets for populations of the plant species Centaurea corymbosa and the gray wolf species Canis lupus. A computer simulation study suggests that the program can provide highly accurate estimates of migration rates and individual migrant ancestries, given sufficient genetic differentiation among populations and sufficient numbers of marker loci.

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Peterman, William, Emily R. Brocato, Raymond D. Semlitsch, and Lori S. Eggert. "Reducing bias in population and landscape genetic inferences: the effects of sampling related individuals and multiple life stages." PeerJ 4 (March 14, 2016): e1813. http://dx.doi.org/10.7717/peerj.1813.

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In population or landscape genetics studies, an unbiased sampling scheme is essential for generating accurate results, but logistics may lead to deviations from the sample design. Such deviations may come in the form of sampling multiple life stages. Presently, it is largely unknown what effect sampling different life stages can have on population or landscape genetic inference, or how mixing life stages can affect the parameters being measured. Additionally, the removal of siblings from a data set is considered best-practice, but direct comparisons of inferences made with and without siblings are limited. In this study, we sampled embryos, larvae, and adultAmbystoma maculatumfrom five ponds in Missouri, and analyzed them at 15 microsatellite loci. We calculated allelic richness, heterozygosity and effective population sizes for each life stage at each pond and tested for genetic differentiation (FSTandDC) and isolation-by-distance (IBD) among ponds. We tested for differences in each of these measures between life stages, and in a pooled population of all life stages. All calculations were done with and without sibling pairs to assess the effect of sibling removal. We also assessed the effect of reducing the number of microsatellites used to make inference. No statistically significant differences were found among ponds or life stages for any of the population genetic measures, but patterns of IBD differed among life stages. There was significant IBD when using adult samples, but tests using embryos, larvae, or a combination of the three life stages were not significant. We found that increasing the ratio of larval or embryo samples in the analysis of genetic distance weakened the IBD relationship, and when usingDC, the IBD was no longer significant when larvae and embryos exceeded 60% of the population sample. Further, power to detect an IBD relationship was reduced when fewer microsatellites were used in the analysis.

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Mary-Huard, Tristan, and David Balding. "Fast and accurate joint inference of coancestry parameters for populations and/or individuals." PLOS Genetics 19, no. 1 (January 19, 2023): e1010054. http://dx.doi.org/10.1371/journal.pgen.1010054.

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We introduce a fast, new algorithm for inferring from allele count data the FST parameters describing genetic distances among a set of populations and/or unrelated diploid individuals, and a tree with branch lengths corresponding to FST values. The tree can reflect historical processes of splitting and divergence, but seeks to represent the actual genetic variance as accurately as possible with a tree structure. We generalise two major approaches to defining FST, via correlations and mismatch probabilities of sampled allele pairs, which measure shared and non-shared components of genetic variance. A diploid individual can be treated as a population of two gametes, which allows inference of coancestry coefficients for individuals as well as for populations, or a combination of the two. A simulation study illustrates that our fast method-of-moments estimation of FST values, simultaneously for multiple populations/individuals, gains statistical efficiency over pairwise approaches when the population structure is close to tree-like. We apply our approach to genome-wide genotypes from the 26 worldwide human populations of the 1000 Genomes Project. We first analyse at the population level, then a subset of individuals and in a final analysis we pool individuals from the more homogeneous populations. This flexible analysis approach gives advantages over traditional approaches to population structure/coancestry, including visual and quantitative assessments of long-standing questions about the relative magnitudes of within- and between-population genetic differences.

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Jenkins, Paul A. "Stopping-Time Resampling and Population Genetic Inference under Coalescent Models." Statistical Applications in Genetics and Molecular Biology 11, no. 1 (January 6, 2012): 1–20. http://dx.doi.org/10.2202/1544-6115.1770.

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Dearlove, Bethany, and Daniel J. Wilson. "Coalescent inference for infectious disease: meta-analysis of hepatitis C." Philosophical Transactions of the Royal Society B: Biological Sciences 368, no. 1614 (March 19, 2013): 20120314. http://dx.doi.org/10.1098/rstb.2012.0314.

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Genetic analysis of pathogen genomes is a powerful approach to investigating the population dynamics and epidemic history of infectious diseases. However, the theoretical underpinnings of the most widely used, coalescent methods have been questioned, casting doubt on their interpretation. The aim of this study is to develop robust population genetic inference for compartmental models in epidemiology. Using a general approach based on the theory of metapopulations, we derive coalescent models under susceptible–infectious (SI), susceptible–infectious–susceptible (SIS) and susceptible–infectious–recovered (SIR) dynamics. We show that exponential and logistic growth models are equivalent to SI and SIS models, respectively, when co-infection is negligible. Implementing SI, SIS and SIR models in BEAST, we conduct a meta-analysis of hepatitis C epidemics, and show that we can directly estimate the basic reproductive number ( R 0 ) and prevalence under SIR dynamics. We find that differences in genetic diversity between epidemics can be explained by differences in underlying epidemiology (age of the epidemic and local population density) and viral subtype. Model comparison reveals SIR dynamics in three globally restricted epidemics, but most are better fit by the simpler SI dynamics. In summary, metapopulation models provide a general and practical framework for integrating epidemiology and population genetics for the purposes of joint inference.

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SAZONOVA, NADEZHDA, and E. JAMES HARNER. "HAPLOTYPE INFERENCE AND BLOCK PARTITIONING IN MIXED POPULATION SAMPLES." Journal of Bioinformatics and Computational Biology 06, no. 06 (December 2008): 1177–92. http://dx.doi.org/10.1142/s0219720008003898.

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Multi-population haplotype inference and block partitioning is a difficult task when dealing with mixed genotype samples. A number of studies have shown that the haplotype block structures, as well as the collections of common haplotypes and their frequencies, vary significantly among world populations. These differences are more extreme when the geographical locations for the populations are more distant. Some of the previous studies performed haplotype inference in multi-population samples with known population assignment. Others developed algorithms for clustering of the mixed haplotype or genotype samples with different block structures or genetic marker profiles. We present a new algorithm that performs haplotype inference and block partitioning in a mixed sample of genotypes from two populations when the population assignments are not known. Given a mixed genotype sample, the proposed algorithm (HAPLOCLUST) extracts two clusters of genotypes with different block structures in addition to performing haplotype inference on each of these clusters. When tested on a set of unrelated individuals, our algorithm provides correct assignments comparable to those of two state-of-the-art algorithms for population stratification. The contribution of HAPLOCLUST consists of performing haplotype/block-based population stratification and simultaneously finding the haplotype resolution and block partitioning for the extracted clusters.

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Sackman, Andrew M., Rebecca B. Harris, and Jeffrey D. Jensen. "Inferring Demography and Selection in Organisms Characterized by Skewed Offspring Distributions." Genetics 211, no. 3 (January 16, 2019): 1019–28. http://dx.doi.org/10.1534/genetics.118.301684.

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The recent increase in time-series population genomic data from experimental, natural, and ancient populations has been accompanied by a promising growth in methodologies for inferring demographic and selective parameters from such data. However, these methods have largely presumed that the populations of interest are well-described by the Kingman coalescent. In reality, many groups of organisms, including viruses, marine organisms, and some plants, protists, and fungi, typified by high variance in progeny number, may be best characterized by multiple-merger coalescent models. Estimation of population genetic parameters under Wright-Fisher assumptions for these organisms may thus be prone to serious mis-inference. We propose a novel method for the joint inference of demography and selection under the Ψ-coalescent model, termed Multiple-Merger Coalescent Approximate Bayesian Computation, or MMC-ABC. We first demonstrate mis-inference under the Kingman, and then exhibit the superior performance of MMC-ABC under conditions of skewed offspring distributions. In order to highlight the utility of this approach, we reanalyzed previously published drug-selection lines of influenza A virus. We jointly inferred the extent of progeny-skew inherent to viral replication and identified putative drug-resistance mutations.

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Xie, Tong, Chunmei Shen, Xiaoye Jin, Qiong Lan, Yating Fang, and Bofeng Zhu. "Genetic Structural Differentiation Analyses of Intercontinental Populations and Ancestry Inference of the Chinese Hui Group Based on a Novel Developed Autosomal AIM-InDel Genotyping System." BioMed Research International 2020 (August 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/2124370.

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In the present study, we investigated the genetic polymorphisms of 39 ancestry informative marker-insertion/deletion (AIM-InDel) loci in the Chinese Hui group using a previously self-developed panel, further clarified the genetic relationships between the Hui group and other reference populations, and assessed the ancestry inference efficiency of the AIM-InDel panel based on the worldwide population data from 1000 Genomes Phase 3. The results of the locus-specific informativeness (In) and pairwise fixation index (Fst) values, multidimensional scaling analysis, and success ratio of estimation with cross-validation showed that the novel panel could well reveal the genetic structural differentiations of the East Asian, European, African, and South Asian populations. Besides, the biogeographical ancestry origin inference both at the individual and population levels was conducted on the Chinese Hui group by principal component analysis and STRUCTURE analysis, and the results revealed that the Hui group had the East Asian origin, and the East Asian component ratio of Hui group was approximately 88.87%. Furthermore, the population genetic analyses among the Hui group and reference populations were performed based on the insertion allele frequency heat map, population pairwise Fst values and phylogenetic tree, and the results indicated that the Hui group was genetically closer to East Asian populations, especially two Chinese Han populations (CHS and CHB populations).

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Greenbaum, Gili, Alan R. Templeton, and Shirli Bar-David. "Inference and Analysis of Population Structure Using Genetic Data and Network Theory." Genetics 202, no. 4 (February 17, 2016): 1299–312. http://dx.doi.org/10.1534/genetics.115.182626.

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Schubert, Ryan, Angela Andaleon, and Heather E. Wheeler. "Comparing local ancestry inference models in populations of two- and three-way admixture." PeerJ 8 (October 2, 2020): e10090. http://dx.doi.org/10.7717/peerj.10090.

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Local ancestry estimation infers the regional ancestral origin of chromosomal segments in admixed populations using reference populations and a variety of statistical models. Integrating local ancestry into complex trait genetics has the potential to increase detection of genetic associations and improve genetic prediction models in understudied admixed populations, including African Americans and Hispanics. Five methods for local ancestry estimation that have been used in human complex trait genetics are LAMP-LD (2012), RFMix (2013), ELAI (2014), Loter (2018), and MOSAIC (2019). As users rather than developers, we sought to perform direct comparisons of accuracy, runtime, memory usage, and usability of these software tools to determine which is best for incorporation into association study pipelines. We find that in the majority of cases RFMix has the highest median accuracy with the ranking of the remaining software dependent on the ancestral architecture of the population tested. Additionally, we estimate the O(n) of both memory and runtime for each software and find that for both time and memory most software increase linearly with respect to sample size. The only exception is RFMix, which increases quadratically with respect to runtime and linearly with respect to memory. Effective local ancestry estimation tools are necessary to increase diversity and prevent population disparities in human genetics studies. RFMix performs the best across methods, however, depending on application, other methods perform just as well with the benefit of shorter runtimes. Scripts used to format data, run software, and estimate accuracy can be found at https://github.com/WheelerLab/LAI_benchmarking.

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Deleporte, Pierre. "Behavioral innovation and phylogeography." Behavioral and Brain Sciences 30, no. 4 (August 2007): 408–9. http://dx.doi.org/10.1017/s0140525x07002397.

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AbstractIndirect identification of innovations in wild populations involves inferring past, unobserved behavioral events. Such historical inference can make simple use of present distribution patterns of differently behaving individuals, but population genetic studies are a potential source of complementary relevant information. Methodological lessons can be taken from phylogeography, that is, molecular approaches to the history of population spatial distribution patterns and gene flows. Opportunities for such studies in primates should increase with the developing population genetic studies used for management and conservation purposes.

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Flagel, Lex, Yaniv Brandvain, and Daniel R. Schrider. "The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference." Molecular Biology and Evolution 36, no. 2 (December 4, 2018): 220–38. http://dx.doi.org/10.1093/molbev/msy224.

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Buzbas, Erkan O., and Noah A. Rosenberg. "AABC: Approximate approximate Bayesian computation for inference in population-genetic models." Theoretical Population Biology 99 (February 2015): 31–42. http://dx.doi.org/10.1016/j.tpb.2014.09.002.

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Beerli, P. "Comparison of Bayesian and maximum-likelihood inference of population genetic parameters." Bioinformatics 22, no. 3 (November 29, 2005): 341–45. http://dx.doi.org/10.1093/bioinformatics/bti803.

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Bank, Claudia, Gregory B. Ewing, Anna Ferrer-Admettla, Matthieu Foll, and Jeffrey D. Jensen. "Thinking too positive? Revisiting current methods of population genetic selection inference." Trends in Genetics 30, no. 12 (December 2014): 540–46. http://dx.doi.org/10.1016/j.tig.2014.09.010.

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Wilson, Ian J., and David J. Balding. "Genealogical Inference From Microsatellite Data." Genetics 150, no. 1 (September 1, 1998): 499–510. http://dx.doi.org/10.1093/genetics/150.1.499.

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Abstract Ease and accuracy of typing, together with high levels of polymorphism and widespread distribution in the genome, make microsatellite (or short tandem repeat) loci an attractive potential source of information about both population histories and evolutionary processes. However, microsatellite data are difficult to interpret, in particular because of the frequency of back-mutations. Stochastic models for the underlying genetic processes can be specified, but in the past they have been too complicated for direct analysis. Recent developments in stochastic simulation methodology now allow direct inference about both historical events, such as genealogical coalescence times, and evolutionary parameters, such as mutation rates. A feature of the Markov chain Monte Carlo (MCMC) algorithm that we propose here is that the likelihood computations are simplified by treating the (unknown) ancestral allelic states as auxiliary parameters. We illustrate the algorithm by analyzing microsatellite samples simulated under the model. Our results suggest that a single microsatellite usually does not provide enough information for useful inferences, but that several completely linked microsatellites can be informative about some aspects of genealogical history and evolutionary processes. We also reanalyze data from a previously published human Y chromosome microsatellite study, finding evidence for an effective population size for human Y chromosomes in the low thousands and a recent time since their most recent common ancestor: the 95% interval runs from ~15,000 to 130,000 years, with most likely values around 30,000 years.

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Oyamakin, S. Oluwafemi, Angela U. Chukwu, Wale-Orojo Oluwaseun A, and Ogunjobi E. O. "Allele Based Inference on Evolution and Extinction; A Genetic Drift Approach." Journal of Cancer Genetics and Biomarkers 1, no. 4 (August 29, 2019): 1–15. http://dx.doi.org/10.14302/issn.2572-3030.jcgb-19-2597.

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In other to present a series of stochastic models from population dynamics capable of describing rudimentary aspects of genetic evolution, we studied two-allele Wright–Fisher and the Moran models for evolution of the relative frequencies of two alleles at a diploid locus under random genetic drift in a population of fixed size “simplest form, selection, and random mutation”. Principal results were presented in qualitative terms, illustrated by Monte Carlo simulations from R and http://www.radford.edu/~rsheehy/Gen_flash/popgen. Moran and the Wright-Fisher Models exhibited the same fixation probabilities, only that the Moran model runs twice as fast as the Wright-Fisher Model. A clue that can help us to understand this result is provided by the variance in reproductive success in the two models. Genetic changes due to drift were neither directional nor predictable in any deterministic way. Nonetheless, genetic drift led to evolutionary change in the absence of mutation (P=0.5), natural selection or gene flow. In general, alleles drift to fixation is significantly faster in smaller populations. Probability of fixation of an allele A was approximately equivalent to the initial frequency of that allele. With the inclusion of selection in our model, probability of fixation of a favoured allele due to natural selection increased with increase in fitness advantage and population size. The time taken to reach fixation is much slower, in case of no selective advantage, than a fixation under mutation with selective advantage.

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Navascués, Miguel, Raphaël Leblois, and Concetta Burgarella. "Demographic inference through approximate-Bayesian-computation skyline plots." PeerJ 5 (July 18, 2017): e3530. http://dx.doi.org/10.7717/peerj.3530.

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The skyline plot is a graphical representation of historical effective population sizes as a function of time. Past population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the shape of the demographic trajectory. Because of this flexibility in shape, skyline plots can, in principle, provide realistic descriptions of the complex demographic scenarios that occur in natural populations. Currently, demographic estimates needed for skyline plots are estimated using coalescent samplers or a composite likelihood approach. Here, we provide a way to estimate historical effective population sizes using an Approximate Bayesian Computation (ABC) framework. We assess its performance using simulated and actual microsatellite datasets. Our method correctly retrieves the signal of contracting, constant and expanding populations, although the graphical shape of the plot is not always an accurate representation of the true demographic trajectory, particularly for recent changes in size and contracting populations. Because of the flexibility of ABC, similar approaches can be extended to other types of data, to multiple populations, or to other parameters that can change through time, such as the migration rate.

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Volpato, Leonardo, João Romero do Amaral Santos de Carvalho Rocha, Rodrigo Silva Alves, Willian Hytalo Ludke, Aluízio Borém, and Felipe Lopes Silva. "Inference of population effect and progeny selection via a multi-trait index in soybean breeding." Acta Scientiarum. Agronomy 43 (August 14, 2020): e44623. http://dx.doi.org/10.4025/actasciagron.v43i1.44623.

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The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.

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Fortier, Alyssa Lyn, Jaehee Kim, and Noah A. Rosenberg. "Human-Genetic Ancestry Inference and False Positives in Forensic Familial Searching." G3: Genes|Genomes|Genetics 10, no. 8 (June 25, 2020): 2893–902. http://dx.doi.org/10.1534/g3.120.401473.

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In forensic familial search methods, a query DNA profile is tested against a database to determine if the query profile represents a close relative of a database entrant. One challenge for familial search is that the calculations may require specification of allele frequencies for the unknown population from which the query profile has originated. The choice of allele frequencies affects the rate at which non-relatives are erroneously classified as relatives, and allele-frequency misspecification can substantially inflate false positive rates compared to use of allele frequencies drawn from the same population as the query profile. Here, we use ancestry inference on the query profile to circumvent the high false positive rates that result from highly misspecified allele frequencies. In particular, we perform ancestry inference on the query profile and make use of allele frequencies based on its inferred genetic ancestry. In a test for sibling matches on profiles that represent unrelated individuals, we demonstrate that false positive rates for familial search with use of ancestry inference to specify the allele frequencies are similar to those seen when allele frequencies align with the population of origin of a profile. Because ancestry inference is possible to perform on query profiles, the extreme allele-frequency misspecifications that produce the highest false positive rates can be avoided. We discuss the implications of the results in the context of concerns about the forensic use of familial searching.

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Wang, Liang-Jong, Yen-Wei Chou, and Jen-Pan Huang. "Testing the Effect of Sampling Effort on Inferring Phylogeographic History in Psolodesmus mandarinus (Calopterygidae, Odonata)." Diversity 14, no. 10 (September 28, 2022): 809. http://dx.doi.org/10.3390/d14100809.

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Phylogeographic studies have revealed spatial genetic structure and inferred geographical processes that may have generated genetic diversity and divergence. These study results have implications not only on the processes that generate intraspecific and interspecific diversity but also on the essential integrals for defining evolutionary entities (e.g., species). However, the resulting phylogeographic inferences might be impacted by the sampling design, i.e., the number of individuals per population and the number of geographic populations studied. The effect of sampling bias on phylogeographic inferences remains poorly explored. With a comprehensive sampling design (including 186 samples from 56 localities), we studied the phylogeographic history of a Taiwanese endemic damselfly, Psolodesmus mandarinus, with a specific focus on testing the impact of the sampling design on phylogeographic inference. We found a significant difference in the genetic structure of eastern and western populations separated by the Central Mountain Range (CMR) of Taiwan. However, isolation by the CMR did not lead to reciprocally monophyletic geographic populations. We further showed that, when only a subset of individuals was randomly included in the study, monophyletic geographic populations were obtained. Furthermore, historical demographic expansion could become undetectable when only a subset of samples was used in the analyses. Our results demonstrate the impact of sampling design on phylogeographic inferences. Future studies need to be cautious when inferring the effect of isolation by a physical barrier.

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Rosa, J. O., G. C. Venturini, T. C. S. Chud, B. C. Pires, M. E. Buzanskas, N. B. Stafuzza, G. R. Furquim, et al. "Bayesian inference of genetic parameters for reproductive and performance traits in White Leghorn hens." Czech Journal of Animal Science 63, No. 6 (May 25, 2018): 230–36. http://dx.doi.org/10.17221/116/2017-cjas.

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This study estimated the genetic parameters for reproductive and performance traits and determined which ones can be used as selection criteria for egg production in laying hens using the Bayesian inference. The data of 1894 animals from three generations of White Leghorn laying hens were analyzed for fertility (FERT), hatchability (HATC), and birth rate measurements at 60 weeks of age (BIRTH), body weight at 16 and 60 weeks of age (BW16 and BW60), age at sexual maturity (ASM), egg height/width ratio, weight, and density at 28, 36, and 40 weeks of age (RHW28, RHW36, RHW40, WEGG28, WEGG36, WEGG40, DENS28, DENS36, and DENS40, respectively) traits. The genetic parameters were estimated by the Bayesian inference method of multi-trait animal model. The model included the additive and residual genetic random effects and the fixed effects of generation. The a posteriori mean distributions of the heritability estimates for reproductive traits ranged from 0.14 ± 0.003 (HATC) to 0.22 ± 0.005 (FERT) and performance from 0.07 ± 0.001 (RHW28) to 0.42 ± 0.001 (WEGG40). The a posteriori mean distributions of the genetic correlation between reproductive traits ranged from 0.18 ± 0.026 (FERT and HACT) to 0.79 ± 0.007 (FERT and BIRTH) and those related to performance ranged from –0.49 ± 0.001 (WEGG36 and DENS36) to 0.75 ± 0.003 (DENS28 and DENS36). Reproductive and performance traits showed enough additive genetic variability to respond to selection, except for RHW28. This trait alone would have little impact on the genetic gain because environmental factors would have a higher impact compared to those from the additive genetic factors. Based on the results of this study, the selection applied on the BIRTH trait can be indicated to improve FERT and HATC of eggs. Furthermore, the use of the WEGG40 could improve egg quality in this population.

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Shringarpure, Suyash, and Eric P. Xing. "mStruct: Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations." Genetics 182, no. 2 (April 10, 2009): 575–93. http://dx.doi.org/10.1534/genetics.108.100222.

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KITAKADO, TOSHIHIDE. "Statistical studies on advanced inference and genetic modelling in fishery population analyses." NIPPON SUISAN GAKKAISHI 83, no. 3 (2017): 341–44. http://dx.doi.org/10.2331/suisan.wa2414.

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Shafer, Aaron B. A., Claire R. Peart, Sergio Tusso, Inbar Maayan, Alan Brelsford, Christopher W. Wheat, and Jochen B. W. Wolf. "Bioinformatic processing of RAD‐seq data dramatically impacts downstream population genetic inference." Methods in Ecology and Evolution 8, no. 8 (November 28, 2016): 907–17. http://dx.doi.org/10.1111/2041-210x.12700.

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RAMOS-ONSINS, SEBASTIÁN E., SYLVAIN MOUSSET, THOMAS MITCHELL-OLDS, and WOLFGANG STEPHAN. "Population genetic inference using a fixed number of segregating sites: a reassessment." Genetical Research 89, no. 4 (August 2007): 231–44. http://dx.doi.org/10.1017/s0016672307008877.

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SummaryCoalescent theory is commonly used to perform population genetic inference at the nucleotide level. Here, we examine the procedure that fixes the number of segregating sites (henceforth the FS procedure). In this approach a fixed number of segregating sites (S) are placed on a coalescent tree (independently of the total and internode lengths of the tree). Thus, although widely used, the FS procedure does not strictly follow the assumptions of coalescent theory and must be considered an approximation of (i) the standard procedure that uses a fixed population mutation parameter θ, and (ii) procedures that condition on the number of segregating sites. We study the differences in the false positive rate for nine statistics by comparing the FS procedure with the procedures (i) and (ii), using several evolutionary models with single-locus and multilocus data. Our results indicate that for single-locus data the FS procedure is accurate for the equilibrium neutral model, but problems arise under the alternative models studied; furthermore, for multilocus data, the FS procedure becomes inaccurate even for the standard neutral model. Therefore, we recommend a procedure that fixes the θ value (or alternatively, procedures that condition on S and take into account the uncertainty of θ) for analysing evolutionary models with multilocus data. With single-locus data, the FS procedure should not be employed for models other than the standard neutral model.

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Ahrens, Collin W., and Carol A. Auer. "Genetic Relationship between Cultivated and Feral Creeping Bentgrass (Agrostis stolonifera) in a Cultural Landscape." Weed Science 60, no. 4 (December 2012): 583–88. http://dx.doi.org/10.1614/ws-d-12-00041.1.

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Gene flow is an important consideration in the adoption of crops with novel traits or transgenes when sexually compatible relatives occur in the landscape. Unfortunately, gene flow and its long-term environmental impacts are very difficult to predict without releasing and studying the novel genotype. This project uses a retrospective population genetics approach to characterize the relationship between cultivated creeping bentgrass (CB) on a golf course and the same species in five feral populations nearby. CB plants were collected from an 8-yr-old golf course, five weedy populations up to 1,020 m from the golf course, and four modern CB cultivars. Using microsatellite markers and Bayesian inference, two major genetic clusters were distinguished: (1) CB cultivars and individuals from the golf course (cultivar genotype), and (2) the majority of individuals (62%) from the five feral populations (feral genotype). Two feral CB individuals (3.3% of all feral plants) were partially assigned to the cultivar genotype. Principal coordinates analysis agreed with this assignment, suggesting that an intraspecific hybridization event may have occurred. Plants in four feral populations showed a high degree of genetic similarity, but one feral population (Reservoir) was heterogeneous indicating that genetically complex CB populations can develop in cultural landscapes. While recognizing the limitations inherent in a single study of CB population genetics, these results add to the relevant knowledge for predictive ecological risk assessment.

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Nichols, Courtney, Jerry Herman, Oscar E. Gaggiotti, Keith M. Dobney, Kim Parsons, and A. Rus Hoelzel. "Genetic isolation of a now extinct population of bottlenose dolphins ( Tursiops truncatus )." Proceedings of the Royal Society B: Biological Sciences 274, no. 1618 (April 24, 2007): 1611–16. http://dx.doi.org/10.1098/rspb.2007.0176.

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A number of dolphin species, though highly mobile, show genetic structure among parapatric and sometimes sympatric populations. However, little is known about the temporal patterns of population structure for these species. Here, we apply Bayesian inference and data from ancient DNA to assess the structure and dynamics of bottlenose dolphin ( Tursiops truncatus ) populations in the coastal waters of the UK. We show that regional population structure in UK waters is consistent with earlier studies suggesting local habitat dependence for this species in the Mediterranean Sea and North Atlantic. One genetically differentiated UK population went extinct at least 100 years ago and has not been replaced. The data indicate that this was a local extinction, and not a case of historical range shift or contraction. One possible interpretation is a declining metapopulation and conservation need for this species in the UK.

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Chang, Dan, and Beth Shapiro. "Using ancient DNA and coalescent-based methods to infer extinction." Biology Letters 12, no. 2 (February 2016): 20150822. http://dx.doi.org/10.1098/rsbl.2015.0822.

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DNA sequences extracted from preserved remains can add considerable resolution to inference of past population dynamics. For example, coalescent-based methods have been used to correlate declines in some arctic megafauna populations with habitat fragmentation during the last ice age. These methods, however, often fail to detect population declines preceding extinction, most likely owing to a combination of sparse sampling, uninformative genetic markers, and models that cannot account for the increasingly structured nature of populations as habitats decline. As ancient DNA research expands to include full-genome analyses, these data will provide greater resolution of the genomic consequences of environmental change and the genetic signatures of extinction.

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Mazet, Olivier, Willy Rodríguez, and Lounès Chikhi. "Demographic inference using genetic data from a single individual: Separating population size variation from population structure." Theoretical Population Biology 104 (September 2015): 46–58. http://dx.doi.org/10.1016/j.tpb.2015.06.003.

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Upadhya, Gautam, and Matthias Steinrücken. "Robust inference of population size histories from genomic sequencing data." PLOS Computational Biology 18, no. 9 (September 16, 2022): e1010419. http://dx.doi.org/10.1371/journal.pcbi.1010419.

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Unraveling the complex demographic histories of natural populations is a central problem in population genetics. Understanding past demographic events is of general anthropological interest, but is also an important step in establishing accurate null models when identifying adaptive or disease-associated genetic variation. An important class of tools for inferring past population size changes from genomic sequence data are Coalescent Hidden Markov Models (CHMMs). These models make efficient use of the linkage information in population genomic datasets by using the local genealogies relating sampled individuals as latent states that evolve along the chromosome in an HMM framework. Extending these models to large sample sizes is challenging, since the number of possible latent states increases rapidly. Here, we present our method CHIMP (CHMM History-Inference Maximum-Likelihood Procedure), a novel CHMM method for inferring the size history of a population. It can be applied to large samples (hundreds of haplotypes) and only requires unphased genomes as input. The two implementations of CHIMP that we present here use either the height of the genealogical tree (TMRCA) or the total branch length, respectively, as the latent variable at each position in the genome. The requisite transition and emission probabilities are obtained by numerically solving certain systems of differential equations derived from the ancestral process with recombination. The parameters of the population size history are subsequently inferred using an Expectation-Maximization algorithm. In addition, we implement a composite likelihood scheme to allow the method to scale to large sample sizes. We demonstrate the efficiency and accuracy of our method in a variety of benchmark tests using simulated data and present comparisons to other state-of-the-art methods. Specifically, our implementation using TMRCA as the latent variable shows comparable performance and provides accurate estimates of effective population sizes in intermediate and ancient times. Our method is agnostic to the phasing of the data, which makes it a promising alternative in scenarios where high quality data is not available, and has potential applications for pseudo-haploid data.

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Mercer, Juliane Rezende, Milena de Luna Alves Lima, Antonio Rioyei Higa, Chirlei Glienke, and Marina Isabel Mateus de Almeida. "Genetic Structure of a Loblolly Pine Breeding Population at Brazil." ISRN Forestry 2013 (June 4, 2013): 1–7. http://dx.doi.org/10.1155/2013/747591.

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The genetic structure of a Brazilian loblolly pine (Pinus taeda L.) breeding population, represented by 120 open-pollinated families, was determined using Bayesian inference and genotypes of 15 microsatellite (simple sequence repeat (SSR)) loci in 1,130 seedling progeny. The 120 maternal parents had been phenotypically selected about 15 years ago for wood volume in five different forestry plantations (FPs) in the south of Brazil. Additional selection for wood volume, based on a previous progeny test, was applied to the first best (i) and second best (ii) tree per block within each family. We adopted a procedure of “learning samples” to find the most likely number of inferred genetic clusters (K) or ancestral populations. The first hypothesis that was rejected was that the most probable value of K=5 was coincident with the five FPs, since the FPs were, a priori, assumed to be from 5 different backgrounds or origins. It was used the familiar structure of the population to infer the genotypes of maternal ancestors. It was concluded that the maternal generation is the most likely to have been planted by the mixture of three different seed sources or origins, that there are five genetic groups (K=5) in the population of progeny, and that they have been formed from the occurrence of assortative mating and also from a strong pressure in the selection within families. The trees with the best genetic value (i) maintained a higher genetic variability when compared to the trees of second best performance (ii), with higher values of heterozygosity and of numbers of maternal alleles that were kept the same. The migration model that best explains the results is the contact zone model. The population differentiation (FST) was 2-3 times higher in offspring than in relation to the maternal generation. The relevancy of the results and the way they were explored may be of value both for studies of population genetics, as for plant breeding programs, since they help monitoring the population's genetic variability during generations of selection.

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Pybus, Oliver G., Andrew Rambaut, and Paul H. Harvey. "An Integrated Framework for the Inference of Viral Population History From Reconstructed Genealogies." Genetics 155, no. 3 (July 1, 2000): 1429–37. http://dx.doi.org/10.1093/genetics/155.3.1429.

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Abstract We describe a unified set of methods for the inference of demographic history using genealogies reconstructed from gene sequence data. We introduce the skyline plot, a graphical, nonparametric estimate of demographic history. We discuss both maximum-likelihood parameter estimation and demographic hypothesis testing. Simulations are carried out to investigate the statistical properties of maximum-likelihood estimates of demographic parameters. The simulations reveal that (i) the performance of exponential growth model estimates is determined by a simple function of the true parameter values and (ii) under some conditions, estimates from reconstructed trees perform as well as estimates from perfect trees. We apply our methods to HIV-1 sequence data and find strong evidence that subtypes A and B have different demographic histories. We also provide the first (albeit tentative) genetic evidence for a recent decrease in the growth rate of subtype B.

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Guo, Fangfang, Ignazio Carbone, and David A. Rasmussen. "Recombination-aware phylogeographic inference using the structured coalescent with ancestral recombination." PLOS Computational Biology 18, no. 8 (August 19, 2022): e1010422. http://dx.doi.org/10.1371/journal.pcbi.1010422.

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Movement of individuals between populations or demes is often restricted, especially between geographically isolated populations. The structured coalescent provides an elegant theoretical framework for describing how movement between populations shapes the genealogical history of sampled individuals and thereby structures genetic variation within and between populations. However, in the presence of recombination an individual may inherit different regions of their genome from different parents, resulting in a mosaic of genealogical histories across the genome, which can be represented by an Ancestral Recombination Graph (ARG). In this case, different genomic regions may have different ancestral histories and so different histories of movement between populations. Recombination therefore poses an additional challenge to phylogeographic methods that aim to reconstruct the movement of individuals from genealogies, although also a potential benefit in that different loci may contain additional information about movement. Here, we introduce the Structured Coalescent with Ancestral Recombination (SCAR) model, which builds on recent approximations to the structured coalescent by incorporating recombination into the ancestry of sampled individuals. The SCAR model allows us to infer how the migration history of sampled individuals varies across the genome from ARGs, and improves estimation of key population genetic parameters such as population sizes, recombination rates and migration rates. Using the SCAR model, we explore the potential and limitations of phylogeographic inference using full ARGs. We then apply the SCAR to lineages of the recombining fungus Aspergillus flavus sampled across the United States to explore patterns of recombination and migration across the genome.

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Journal articles on the topic 'Population Genetic Inference'

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