Gene flow biases population genetic inference of recombination rate

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.

Modifying the Chi-square and the CMH test for population genetic inference: Adapting to overdispersion

The Annals of Applied Statistics ◽

10.1214/19-aoas1301 ◽

2020 ◽

Vol 14 (1) ◽

pp. 202-220 ◽

Cited By ~ 6

Author(s):

Kerstin Spitzer ◽

Marta Pelizzola ◽

Andreas Futschik

Keyword(s):

Population Genetic ◽

Chi Square ◽

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

Journal of Heredity ◽

10.1093/jhered/esab053 ◽

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 ◽

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.

Variation in Recombination Rate Is Shaped by Domestication and Environmental Conditions in Barley

Molecular Biology and Evolution ◽

10.1093/molbev/msz141 ◽

2019 ◽

Vol 36 (9) ◽

pp. 2029-2039 ◽

Cited By ~ 4

Author(s):

Steven Dreissig ◽

Martin Mascher ◽

Stefan Heckmann

Keyword(s):

Environmental Conditions ◽

Recombination Rate ◽

Wild Barley ◽

Natural Populations ◽

Recombination Rates ◽

Distal Chromosome ◽

Positive Linear Correlation ◽

Short And Long Term ◽

Underlying Mechanisms ◽

Chromosome Regions

Abstract Meiotic recombination generates genetic diversity upon which selection can act. Recombination rates are highly variable between species, populations, individuals, sexes, chromosomes, and chromosomal regions. The underlying mechanisms are controlled at the genetic and epigenetic level and show plasticity toward the environment. Environmental plasticity may be divided into short- and long-term responses. We estimated recombination rates in natural populations of wild barley and domesticated landraces using a population genetics approach. We analyzed recombination landscapes in wild barley and domesticated landraces at high resolution. In wild barley, high recombination rates are found in more interstitial chromosome regions in contrast to distal chromosome regions in domesticated barley. Among subpopulations of wild barley, natural variation in effective recombination rate is correlated with temperature, isothermality, and solar radiation in a nonlinear manner. A positive linear correlation was found between effective recombination rate and annual precipitation. We discuss our findings with respect to how the environment might shape effective recombination rates in natural populations. Higher recombination rates in wild barley populations subjected to specific environmental conditions could be a means to maintain fitness in a strictly inbreeding species.

Stopping-Time Resampling and Population Genetic Inference under Coalescent Models

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1770 ◽

2012 ◽

Vol 11 (1) ◽

pp. 1-20 ◽

Cited By ~ 2

Author(s):

Paul A. Jenkins

Keyword(s):

Population Genetic ◽

Stopping Time ◽

Assessing the Accuracy and Power of Population Genetic Inference from Low-Pass Next-Generation Sequencing Data

Frontiers in Genetics ◽

10.3389/fgene.2012.00066 ◽

2012 ◽

Vol 3 ◽

Cited By ~ 29

Author(s):

Jacob E. Crawford ◽

Brian P. Lazzaro

Keyword(s):

Next Generation Sequencing ◽

Population Genetic ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Low Pass ◽

Genetic Inference ◽

Generation Sequencing

The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference

10.1101/336073 ◽

2018 ◽

Cited By ~ 3

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 ◽

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.

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

10.1101/2020.12.24.424361 ◽

2020 ◽

Author(s):

Yun Deng ◽

Yun S. Song ◽

Rasmus Nielsen

Keyword(s):

Accurate Approximation ◽

Ancestral Recombination Graph ◽

Recombination Rates ◽

Inference Problems ◽

Topology Changes ◽

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.

Gene buddies: Linked balanced polymorphisms reinforce each other even in the absence of epistasis

10.1101/220897 ◽

2017 ◽

Author(s):

Jacob A Tennessen

Keyword(s):

Recombination Rate ◽

Evolutionary Dynamics ◽

Balancing Selection ◽

Natural Populations ◽

Adaptive Variation ◽

Recombination Rates ◽

Future Studies ◽

Close Physical Proximity ◽

Selection For ◽

Independent Effects

The fates of genetic polymorphisms maintained by balancing selection depend on evolutionary dynamics at linked sites. While coevolution across linked, epigenetically-interacting loci has been extensively explored, such supergenes may be relatively rare. However, genes harboring adaptive variation can occur in close physical proximity while generating independent effects on fitness. Here, I present a model in which two linked loci without epistasis are both under balancing selection for unrelated reasons. Using forward-time simulations, I show that recombination rate strongly influences the retention of adaptive polymorphism, especially for intermediate selection coefficients. A locus is more likely to retain adaptive variation if it is closely linked to another locus under balancing selection, even if the two loci have no interaction. Thus, two linked polymorphisms can both be retained indefinitely even when they would both be lost to drift if unlinked. Such clusters of mutually reinforcing genes may underlie phenotypic variation in natural populations. Future studies that measure selection coefficients and recombination rates among closely linked genes will be fruitful for characterizing the extent of this phenomenon.