1,336 research outputs found
Inference of Ancestral Recombination Graphs through Topological Data Analysis
The recent explosion of genomic data has underscored the need for
interpretable and comprehensive analyses that can capture complex phylogenetic
relationships within and across species. Recombination, reassortment and
horizontal gene transfer constitute examples of pervasive biological phenomena
that cannot be captured by tree-like representations. Starting from hundreds of
genomes, we are interested in the reconstruction of potential evolutionary
histories leading to the observed data. Ancestral recombination graphs
represent potential histories that explicitly accommodate recombination and
mutation events across orthologous genomes. However, they are computationally
costly to reconstruct, usually being infeasible for more than few tens of
genomes. Recently, Topological Data Analysis (TDA) methods have been proposed
as robust and scalable methods that can capture the genetic scale and frequency
of recombination. We build upon previous TDA developments for detecting and
quantifying recombination, and present a novel framework that can be applied to
hundreds of genomes and can be interpreted in terms of minimal histories of
mutation and recombination events, quantifying the scales and identifying the
genomic locations of recombinations. We implement this framework in a software
package, called TARGet, and apply it to several examples, including small
migration between different populations, human recombination, and horizontal
evolution in finches inhabiting the Gal\'apagos Islands.Comment: 33 pages, 12 figures. The accompanying software, instructions and
example files used in the manuscript can be obtained from
https://github.com/RabadanLab/TARGe
A Unifying Model of Genome Evolution Under Parsimony
We present a data structure called a history graph that offers a practical
basis for the analysis of genome evolution. It conceptually simplifies the
study of parsimonious evolutionary histories by representing both substitutions
and double cut and join (DCJ) rearrangements in the presence of duplications.
The problem of constructing parsimonious history graphs thus subsumes related
maximum parsimony problems in the fields of phylogenetic reconstruction and
genome rearrangement. We show that tractable functions can be used to define
upper and lower bounds on the minimum number of substitutions and DCJ
rearrangements needed to explain any history graph. These bounds become tight
for a special type of unambiguous history graph called an ancestral variation
graph (AVG), which constrains in its combinatorial structure the number of
operations required. We finally demonstrate that for a given history graph ,
a finite set of AVGs describe all parsimonious interpretations of , and this
set can be explored with a few sampling moves.Comment: 52 pages, 24 figure
A general and efficient representation of ancestral recombination graphs
As a result of recombination, adjacent nucleotides can have different paths of genetic inheritance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). Classical formalisms have focused on mapping coalescence and recombination events to the nodes in an ARG. However, this approach is out of step with some modern developments, which do not represent genetic inheritance in terms of these events or explicitly infer them. We present a simple formalism that defines an ARG in terms of specific genomes and their intervals of genetic inheritance, and show how it generalizes these classical treatments and encompasses the outputs of recent methods. We discuss nuances arising from this more general structure, and argue that it forms an appropriate basis for a software standard in this rapidly growing field.</p
Genome-wide inference of ancestral recombination graphs
The complex correlation structure of a collection of orthologous DNA
sequences is uniquely captured by the "ancestral recombination graph" (ARG), a
complete record of coalescence and recombination events in the history of the
sample. However, existing methods for ARG inference are computationally
intensive, highly approximate, or limited to small numbers of sequences, and,
as a consequence, explicit ARG inference is rarely used in applied population
genomics. Here, we introduce a new algorithm for ARG inference that is
efficient enough to apply to dozens of complete mammalian genomes. The key idea
of our approach is to sample an ARG of n chromosomes conditional on an ARG of
n-1 chromosomes, an operation we call "threading." Using techniques based on
hidden Markov models, we can perform this threading operation exactly, up to
the assumptions of the sequentially Markov coalescent and a discretization of
time. An extension allows for threading of subtrees instead of individual
sequences. Repeated application of these threading operations results in highly
efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these
methods in a computer program called ARGweaver. Experiments with simulated data
indicate that ARGweaver converges rapidly to the true posterior distribution
and is effective in recovering various features of the ARG for dozens of
sequences generated under realistic parameters for human populations. In
applications of ARGweaver to 54 human genome sequences from Complete Genomics,
we find clear signatures of natural selection, including regions of unusually
ancient ancestry associated with balancing selection and reductions in allele
age in sites under directional selection. Preliminary results also indicate
that our methods can be used to gain insight into complex features of human
population structure, even with a noninformative prior distribution.Comment: 88 pages, 7 main figures, 22 supplementary figures. This version
contains a substantially expanded genomic data analysi
Conflation of short identity-by-descent segments bias their inferred length distribution
Identity-by-descent (IBD) is a fundamental concept in genetics with many
applications. In a common definition, two haplotypes are said to contain an IBD
segment if they share a segment that is inherited from a recent shared common
ancestor without intervening recombination. Long IBD segments (> 1cM) can be
efficiently detected by a number of algorithms using high-density SNP array
data from a population sample. However, these approaches detect IBD based on
contiguous segments of identity-by-state, and such segments may exist due to
the conflation of smaller, nearby IBD segments. We quantified this effect using
coalescent simulations, finding that nearly 40% of inferred segments 1-2cM long
are results of conflations of two or more shorter segments, under demographic
scenarios typical for modern humans. This biases the inferred IBD segment
length distribution, and so can affect downstream inferences. We observed this
conflation effect universally across different IBD detection programs and human
demographic histories, and found inference of segments longer than 2cM to be
much more reliable (less than 5% conflation rate). As an example of how this
can negatively affect downstream analyses, we present and analyze a novel
estimator of the de novo mutation rate using IBD segments, and demonstrate that
the biased length distribution of the IBD segments due to conflation can lead
to inflated estimates if the conflation is not modeled. Understanding the
conflation effect in detail will make its correction in future methods more
tractable
A minimal descriptor of an ancestral recombinations graph
<p>Abstract</p> <p>Background</p> <p>Ancestral Recombinations Graph (ARG) is a phylogenetic structure that encodes both duplication events, such as mutations, as well as genetic exchange events, such as recombinations: this captures the (genetic) dynamics of a population evolving over generations.</p> <p>Results</p> <p>In this paper, we identify structure-preserving and samples-preserving core of an ARG <it>G</it> and call it the minimal descriptor ARG of <it>G</it>. Its structure-preserving characteristic ensures that all the branch lengths of the marginal trees of the minimal descriptor ARG are identical to that of <it>G</it> and the samples-preserving property asserts that the patterns of genetic variation in the samples of the minimal descriptor ARG are exactly the same as that of <it>G</it>. We also prove that even an unbounded <it>G</it> has a finite minimal descriptor, that continues to preserve certain (graph-theoretic) properties of <it>G</it> and for an appropriate class of ARGs, our estimate (Eqn 8) as well as empirical observation is that the expected reduction in the number of vertices is exponential.</p> <p>Conclusions</p> <p>Based on the definition of this lossless and bounded structure, we derive local properties of the vertices of a minimal descriptor ARG, which lend itself very naturally to the design of efficient sampling algorithms. We further show that a class of minimal descriptors, that of binary ARGs, models the standard coalescent exactly (Thm 6).</p
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