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56 results for “variational inference”

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dryad28/100

Data from: Inference of genetic architecture from chromosome partitioning analyses is sensitive to genome variation, sample size, heritability and effect size distribution

Genomewide association studies have contributed immensely to our understanding of the genetic basis of complex traits. One major conclusion arising from these studies is that most traits are controlled by many loci of small effect, confirming the infinitesimal model of quantitative genetics. A popular approach to test for polygenic architecture involves so‐called "chromosome partitioning" where phenotypic variance explained by each chromosome is regressed on the size of the chromosome. First developed for humans, this has now been repeatedly used in other species, but there has been no evaluation of the suitability of this method in species that can differ in their genome characteristics such as number and size of chromosomes. Nor has the influence of sample size, heritability of the trait, effect size distribution of loci controlling the trait or the physical distribution of the causal loci in the genome been examined. Using simulated data, we show that these characteristics have major influence on the inferences of the genetic architecture of traits we can infer using chromosome partitioning analyses. In particular, small variation in chromosome size, small sample size, low heritability, a skewed effect size distribution and clustering of loci can lead to a loss of power and consequently altered inference from chromosome partitioning analyses. Future studies employing this approach need to consider and derive an appropriate null model for their study system, taking these parameters into consideration. Our simulation results can provide some guidelines on these matters, but further studies examining a broader parameter space are needed.

opencc-zeroDec 2017View details →

dryad28/100

Data from: Inferring the demographic history of Drosophila subobscura from nucleotide variation at regions not affected by chromosomal inversions

Drosophila subobscura presents a rich and complex chromosomal inversion polymorphism. It can thus be considered a model system i) to study the mechanisms originating inversions and how inversions affect the levels and patterns of variation in the inverted regions, and ii) to study adaptation at both the single-gene and chromosomal inversion levels. It is therefore important to infer its demographic history since previous information indicated that its nucleotide variation is not at mutation-drift equilibrium. For that purpose, we sequenced 16 non-coding regions distributed across those parts of the J chromosome not affected by inversions in the studied population and possibly either by other selective events. The pattern of variation detected in these 16 regions is similar to that previously reported within different chromosomal arrangements, suggesting that the latter results would, thus, mainly reflect recent demographic events rather than the partial selective sweep imposed by the origin and frequency increase of inversions. Among the simple demographic models considered in our ABC analysis of variation at the 16 regions, the model best supported by the data implies a population size expansion soon after the penultimate glacial period. This model constitutes a better null model and it is therefore an important resource for subsequent studies aiming among others to uncover selective events across the species genome. Our results also highlight the importance of introducing the possibility of multiple hits in the coalescent simulations with an outgroup.

opencc-zeroDec 2014View details →

zenodo28/100

Fig. 1 in Genetic variation in the spotted seal (Phoca largha Pallas, 1811) from the Rimsky-Korsakov Archipelago (Peter the Great Bay, western sea of Japan) as inferred from mitochondrial DNA control region sequences

Fig. 1. Map of the Phoca largha sampling site.

opennotspecifiedNov 2022View details →

dryad28/100

Data from: A Poissonian model of indel rate variation for phylogenetic tree inference

While indel rate variation has been observed and analyzed in detail, it is not taken into account by current indel-aware phylogenetic reconstruction methods. In this work, we introduce a continuous time stochastic process, the geometric Poisson indel process, that generalizes the Poisson indel process by allowing insertion and deletion rates to vary across sites. We design an efficient algorithm for computing the probability of a given multiple sequence alignment based on our new indel model. We describe a method to construct phylogeny estimates from a fixed alignment using neighbor joining. Using simulation studies, we show that ignoring indel rate variation may have a detrimental effect on the accuracy of the inferred phylogenies, and that our proposed method can sidestep this issue by inferring latent indel rate categories. We also show that our phylogenetic inference method may be more stable to taxa subsampling than methods that either ignore indels or indel rate variation.

opencc-zeroDec 2016View details →

dryad28/100