scholarly journals The distribution of waiting distances in ancestral recombination graphs and its applications

2020 ◽  
Author(s):  
Yun Deng ◽  
Yun S. Song ◽  
Rasmus Nielsen
Keyword(s):  
Accurate Approximation ◽  
Ancestral Recombination Graph ◽  
Recombination Rates ◽  
Inference Problems ◽  
Topology Changes ◽  
Population Genetic Inference ◽  
Ancestral Recombination Graphs ◽  
Inference Methods ◽  
Genetic Inference ◽  
Genealogical Information

AbstractThe ancestral recombination graph (ARG) contains the full genealogical information of the sample, and many population genetic inference problems can be solved using inferred or sampled ARGs. In particular, the waiting distance between tree changes along the genome can be used to make inference about the distribution and evolution of recombination rates. To this end, we here derive an analytic expression for the distribution of waiting distances between tree changes under the sequentially Markovian coalescent model and obtain an accurate approximation to the distribution of waiting distances for topology changes. We use these results to show that some of the recently proposed methods for inferring sequences of trees along the genome provide strongly biased distributions of waiting distances. In addition, we provide a correction to an undercounting problem facing all available ARG inference methods, thereby facilitating the use of ARG inference methods to estimate temporal changes in the recombination rate.

scholarly journals Inferring human population size and separation history from multiple genome sequences

2014 ◽  
Author(s):  
Stephan Schiffels ◽  
Richard Durbin
Keyword(s):  
Population Genetic ◽  
Human Population ◽  
Evolutionary History ◽  
Population History ◽  
Genome Sequences ◽  
Peopling Of The Americas ◽  
The World ◽  
Population Genetic Inference ◽  
Inference Methods ◽  
Genetic Inference

The availability of complete human genome sequences from populations across the world has given rise to new population genetic inference methods that explicitly model their ancestral relationship under recombination and mutation. So far, application of these methods to evolutionary history more recent than 20-30 thousand years ago and to population separations has been limited. Here we present a new method that overcomes these shortcomings. The Multiple Sequentially Markovian Coalescent (MSMC) analyses the observed pattern of mutations in multiple individuals, focusing on the first coalescence between any two individuals. Results from applying MSMC to genome sequences from nine populations across the world suggest that the genetic separation of non-African ancestors from African Yoruban ancestors started long before 50,000 years ago, and give information about human population history as recently as 2,000 years ago, including the bottleneck in the peopling of the Americas, and separations within Africa, East Asia and Europe.

scholarly journals Gene flow biases population genetic inference of recombination rate

2021 ◽  
Author(s):  
Kieran Samuk ◽  
Mohamed A.F. Noor
Keyword(s):  
Gene Flow ◽  
Recombination Rate ◽  
Population Genetic ◽  
Natural Populations ◽  
Recombination Rates ◽  
False Negatives ◽  
Isolation With Migration ◽  
Simplifying Assumptions ◽  
Population Genetic Inference ◽  
Genetic Inference

Accurate estimates of the rate of recombination are key to understanding a host of evolutionary processes as well as the evolution of recombination rate itself. Model-based population genetic methods that infer recombination rates from patterns of linkage disequilibrium (LD) in the genome have become a popular method to estimate rates of recombination. However, these LD-based methods make a variety of simplifying assumptions about the populations of interest that are often not met in natural populations. One such assumption is the absence of gene flow from other populations. Here, we use forward-time population genetic simulations of isolation-with-migration scenarios to explore how gene flow affects the accuracy of LD-based estimators of recombination rate. We find that moderate levels of gene flow can result in either the overestimation or underestimation of recombination rates by up to 20-50% depending on the timing of divergence. We also find that these biases can affect the detection of interpopulation differences in recombination rate, causing both false positive and false negatives depending on the scenario. We discuss future possibilities for mitigating these biases and recommend that investigators exercise caution and confirm that their study populations meet assumptions before deploying these methods.

Ancient introgression between distantly related white oaks (Quercus sect Quercus) shows evidence of climate-associated asymmetric gene exchange

2021 ◽  
Author(s):  
Scott T O’Donnell ◽  
Sorel T Fitz-Gibbon ◽  
Victoria L Sork
Keyword(s):  
Gene Flow ◽  
Genotyping By Sequencing ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Scrub Oak ◽  
Population Genetic Inference ◽  
Genetic Inference ◽  
California Floristic Province ◽  
Python Package

Abstract Ancient introgression can be an important source of genetic variation that shapes the evolution and diversification of many taxa. Here, we estimate the timing, direction and extent of gene flow between two distantly related oak species in the same section (Quercus sect. Quercus). We estimated these demographic events using genotyping by sequencing data (GBS), which generated 25,702 single nucleotide polymorphisms (SNPs) for 24 individuals of California scrub oak (Quercus berberidifolia) and 23 individuals of Engelmann oak (Q. engelmannii). We tested several scenarios involving gene flow between these species using the diffusion approximation-based population genetic inference framework and model-testing approach of the Python package DaDi. We found that the most likely demographic scenario includes a bottleneck in Q. engelmannii that coincides with asymmetric gene flow from Q. berberidifolia into Q. engelmannii. Given that the timing of this gene flow coincides with the advent of a Mediterranean-type climate in the California Floristic Province, we propose that changing precipitation patterns and seasonality may have favored the introgression of climate-associated genes from the endemic into the non-endemic California oak.

scholarly journals Ecological and Evolutionary Processes Shaping Viral Genetic Diversity

Viruses ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 220 ◽  
Author(s):  
Cas Retel ◽  
Hanna Märkle ◽  
Lutz Becks ◽  
Philine Feulner
Keyword(s):  
Joint Effect ◽  
Genomic Variation ◽  
Genomic Diversity ◽  
Model Systems ◽  
Evolutionary Processes ◽  
Biological Communities ◽  
Generation Times ◽  
Inference Methods ◽  
Genetic Inference

The contemporary genomic diversity of viruses is a result of the continuous and dynamic interaction of past ecological and evolutionary processes. Thus, genome sequences of viruses can be a valuable source of information about these processes. In this review, we first describe the relevant processes shaping viral genomic variation, with a focus on the role of host–virus coevolution and its potential to give rise to eco-evolutionary feedback loops. We further give a brief overview of available methodology designed to extract information about these processes from genomic data. Short generation times and small genomes make viruses ideal model systems to study the joint effect of complex coevolutionary and eco-evolutionary interactions on genetic evolution. This complexity, together with the diverse array of lifetime and reproductive strategies in viruses ask for extensions of existing inference methods, for example by integrating multiple information sources. Such integration can broaden the applicability of genetic inference methods and thus further improve our understanding of the role viruses play in biological communities.

scholarly journals The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference

2018 ◽  
Author(s):  
Lex Flagel ◽  
Yaniv Brandvain ◽  
Daniel R. Schrider
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Population Genetic ◽  
Sequence Data ◽  
Input Sequence ◽  
Evolutionary Model ◽  
Sequence Alignments ◽  
Likelihood Approach ◽  
Population Genetic Inference ◽  
Genetic Inference

ABSTRACTPopulation-scale genomic datasets have given researchers incredible amounts of information from which to infer evolutionary histories. Concomitant with this flood of data, theoretical and methodological advances have sought to extract information from genomic sequences to infer demographic events such as population size changes and gene flow among closely related populations/species, construct recombination maps, and uncover loci underlying recent adaptation. To date most methods make use of only one or a few summaries of the input sequences and therefore ignore potentially useful information encoded in the data. The most sophisticated of these approaches involve likelihood calculations, which require theoretical advances for each new problem, and often focus on a single aspect of the data (e.g. only allele frequency information) in the interest of mathematical and computational tractability. Directly interrogating the entirety of the input sequence data in a likelihood-free manner would thus offer a fruitful alternative. Here we accomplish this by representing DNA sequence alignments as images and using a class of deep learning methods called convolutional neural networks (CNNs) to make population genetic inferences from these images. We apply CNNs to a number of evolutionary questions and find that they frequently match or exceed the accuracy of current methods. Importantly, we show that CNNs perform accurate evolutionary model selection and parameter estimation, even on problems that have not received detailed theoretical treatments. Thus, when applied to population genetic alignments, CNN are capable of outperforming expert-derived statistical methods, and offer a new path forward in cases where no likelihood approach exists.

scholarly journals Population Genetic Inference From Resequencing Data

Genetics ◽  
2008 ◽  
Vol 181 (1) ◽  
pp. 187-197 ◽  
Author(s):  
Rong Jiang ◽  
Simon Tavaré ◽  
Paul Marjoram
Keyword(s):  
Population Genetic ◽  
Population Genetic Inference ◽  
Genetic Inference
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