Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model
Clayton E. Cressler, Marguerite A. Butler, Aaron A. King
(Submitted on 25 Jun 2015)
Phylogenetic comparative analysis is an approach to inferring evolutionary process from a combination of phylogenetic and phenotypic data. The last few years have seen increasingly sophisticated models employed in the evaluation of more and more detailed evolutionary hypotheses, including adaptive hypotheses with multiple selective optima and hypotheses with rate variation within and across lineages. The statistical performance of these sophisticated models has received relatively little systematic attention, however. We conducted an extensive simulation study to quantify the statistical properties of a class of models toward the simpler end of the spectrum that model phenotypic evolution using Ornstein-Uhlenbeck processes. We focused on identifying where, how, and why these methods break down so that users can apply them with greater understanding of their strengths and weaknesses. Our analysis identifies three key determinants of performance: a discriminability ratio, a signal-to-noise ratio, and the number of taxa sampled. Interestingly, we find that model-selection power can be high even in regions that were previously thought to be difficult, such as when tree size is small. On the other hand, we find that model parameters are in many circumstances difficult to estimate accurately, indicating a relative paucity of information in the data relative to these parameters. Nevertheless, we note that accurate model selection is often possible when parameters are only weakly identified. Our results have implications for more sophisticated methods inasmuch as the latter are generalizations of the case we study.
SSCM: A method to analyze and predict the pathogenicity of sequence variants
Sharad Vikram, Matthew D Rasmussen, Eric A Evans, Imran S Haque
The advent of cost-effective DNA sequencing has provided clinics with high-resolution information about patients’ genetic variants, which has resulted in the need for efficient interpretation of this genomic data. Traditionally, variant interpretation has been dominated by many manual, time-consuming processes due to the disparate forms of relevant information in clinical databases and literature. Computational techniques promise to automate much of this, and while they currently play only a supporting role, their continued improvement for variant interpretation is necessary to tackle the problem of scaling genetic sequencing to ever larger populations. Here, we present SSCM-Pathogenic, a genome-wide, allele-specific score for predicting variant pathogenicity. The score, generated by a semi-supervised clustering algorithm, shows predictive power on clinically relevant mutations, while also displaying predictive ability in noncoding regions of the genome.
Adaptive evolution is substantially impeded by Hill-Robertson interference in Drosophila
David Castellano, Marta Coronado, Jose Campos, Antonio Barbadilla, Adam Eyre-Walker
It is known that rates of mutation and recombination vary across the genome in many species. Here we investigate whether these factors affect the rate at which genes undergo adaptive evolution both individually and in combination and quantify the degree to which Hill-Robertson interference (HRi) impedes the rate of adaptive evolution. To do this we compiled a dataset of 6,141 autosomal protein coding genes from Drosophila, for which we have polymorphism data from D. melanogaster and divergence out to D. yakuba. We estimated the rate of adaptive evolution using a derivative of the McDonald-Kreitman test that controls for the slightly deleterious mutations. We find that the rate of adaptive amino acid substitution is positively correlated to both the rates of recombination and mutation. We also find that these correlations are robust to controlling for each other, synonymous codon bias and gene functions related to immune response and testes. We estimate that HRi reduces the rate of adaptive evolution by ~27%. We also show that this fraction depends on a gene’s mutation rate; genes with low mutation rates lose ~11% of their adaptive substitutions while genes with high mutation rates lose ~43%. In conclusion, we show that the mutation rate and the rate of recombination, are important modifiers of the rate of adaptive evolution in Drosophila.
Approaches to estimating inbreeding coefficients in clinical isolates of Plasmodium falciparum from genomic sequence data
John D O’Brien, Lucas Amenga-Etego, Ruiqi Li
A recent genomic characterization of more than $200$ Plasmodium falciparum samples isolated from the bloodstreams of clinical patients across three continents further supports the presence of significant strain mixture within infections. Consistent with previous studies, these data suggest that the degree of genetic strain admixture within infections varies significantly both within and across populations. The life cycle of the parasite implies that the mixture of multiple genotypes within an infected individual controls the outcrossing rate across populations, making methods for measuring this process in situ central to understanding the genetic epidemiology of the disease. Peculiar features of the P. falciparum genome mean that standard methods for assessing structure within a population — inbreeding coefficients and related $F$-statistics — cannot be used directly. Here we review an initial effort to estimate the degree of mixture within clinical isolates of P. falciparum using these statistics, and provide several generalizations using both frequentist and Bayesian approaches. Using the Bayesian approach, based on the Balding-Nichols model, we provide estimates of inbreeding coefficients for 168 samples from northern Ghana and find significant admixture in more than 70% of samples, and characterize the model fit using posterior predictive checks. We also compare this approach to a recently introduced mixture model and find that for a significant minority of samples the F-statistic-based approach provides a significantly better explanation for the data. We show how to extend this model to a multi-level testing framework that can integrate other data types and use it to demonstrate that transmission intensity significantly associates with degree of structure of within-sample mixture in northern Ghana.
A novel test for detecting gene-gene interactions in trio studies
Brunilda Balliu, Noah Zaitlen
Epistasis plays a significant role in the genetic architecture of many complex phenotypes in model organisms. To date, there have been very few interactions replicated in human studies due in part to the multiple hypothesis burden implicit in genome-wide tests of epistasis. Therefore, it is of paramount importance to develop the most powerful tests possible for detecting interactions. In this work we develop a new gene-gene interaction test for use in trio studies called the trio correlation (TC) test. The TC test computes the expected joint distribution of marker pairs in offspring conditional on parental genotypes. This distribution is then incorporated into a standard one degree of freedom correlation test of interaction. We show via extensive simulations that our test substantially outperforms existing tests of interaction in trio studies. The gain in power under standard models of phenotype is large, with previous tests requiring more than twice the number of trios to obtain the power of our test. We also demonstrate a bias in a previous trio interaction test and identify its origin. We conclude that the TC test shows improved power to identify interactions in existing, as well as emerging, trio association studies. The method is publicly available at http://www.github.com/BrunildaBalliu/TrioEpi.
On enhancing variation detection through pan-genome indexing
Daniel Valenzuela, Niko Välimäki, Esa Pitkänen, Veli Mäkinen
Detection of genomic variants is commonly conducted by aligning a set of reads sequenced from an individual to the reference genome of the species and analyzing the resulting read pileup. Typically, this process finds a subset of variants already reported in databases and additional novel variants characteristic to the sequenced individual. Most of the effort in the literature has been put to the alignment problem on a single reference sequence, although our gathered knowledge on species such as human is pan-genomic: We know most of the common variation in addition to the reference sequence. There have been some efforts to exploit pan-genome indexing, where the most widely adopted approach is to build an index structure on a set of reference sequences containing observed variation combinations. The enhancement in alignment accuracy when using pan-genome indexing has been demonstrated in experiments, but so far the above multiple references pan-genome indexing approach has not been tested on its final goal, that is, in enhancing variation detection. This is the focus of this article: We study a generic approach to add variation detection support on top of the multiple references pan-genomic indexing approach. Namely, we study the read pileup on a multiple alignment of reference genomes, and propose a heaviest path algorithm to extract a new recombined reference sequence. This recombined reference sequence can then be utilized in any standard read alignment and variation detection workflow. We demonstrate that the approach enhances variation detection on realistic data sets.
The structure of the genotype-phenotype map strongly constrains the evolution of non-coding RNA
Kamaludin Dingle, Steffen Schaper, Ard A. Louis
(Submitted on 17 Jun 2015)
The prevalence of neutral mutations implies that biological systems typically have many more genotypes than phenotypes. But can the way that genotypes are distributed over phenotypes determine evolutionary outcomes? Answering such questions is difficult because the number of genotypes can be hyper-astronomically large. By solving the genotype-phentoype (GP) map for RNA secondary structure for systems up to length L=126 nucleotides (where the set of all possible RNA strands would weigh more than the mass of the visible universe) we show that the GP map strongly constrains the evolution of non-coding RNA (ncRNA). Remarkably, simple random sampling over genotypes accurately predicts the distribution of properties such as the mutational robustness or the number of stems per secondary structure found in naturally occurring ncRNA. Since we ignore natural selection, this close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces. The mapping is extremely biased: the majority of genotypes map to an exponentially small portion of the morphospace of all biophysically possible structures. Such strong constraints provide a non-adaptive explanation for the convergent evolution of structures such as the hammerhead ribozyme. ncRNA presents a particularly clear example of bias in the arrival of variation strongly shaping evolutionary outcomes.
Selection against maternal microRNA target sites in maternal transcripts
In animals, before the zygotic genome is expressed, the egg already contains gene products deposited by the mother. These maternal products are crucial during the initial steps of development. In Drosophila melanogaster a large number of maternal products are found in the oocyte, some of which are indispensable. Many of these products are RNA molecules, such as gene transcripts and ribosomal RNAs. Recently, microRNAs ? small RNA gene regulators ? have been detected early during development and are important in these initial steps. The presence of some microRNAs in unfertilized eggs has been reported, but whether they have a functional impact in the egg or early embryo has not being explored. I have extracted and sequenced small RNAs from Drosophila unfertilized eggs. The unfertilized egg is rich in small RNAs and contains multiple microRNA products. Maternal microRNAs are often encoded within the intron of maternal genes, suggesting that many maternal microRNAs are the product of transcriptional hitch-hiking. Comparative genomics and population data suggest that maternal transcripts tend to avoid target sites for maternal microRNAs. A potential role of the maternal microRNA mir-9c in maternal-to-zygotic transition is also discussed. In conclusion, maternal microRNAs in Drosophila have a functional impact in maternal protein-coding transcripts.
Exact simulation of the Wright-Fisher diffusion
Paul A. Jenkins, Dario Spano
(Submitted on 23 Jun 2015)
The Wright-Fisher family of diffusion processes is a class of evolutionary models widely used in population genetics, with applications also in finance and Bayesian statistics. Simulation and inference from these diffusions is therefore of widespread interest. However, simulating a Wright-Fisher diffusion is difficult because there is no known closed-form formula for its transition function. In this article we demonstrate that it is in fact possible to simulate exactly from the scalar Wright-Fisher diffusion with general drift, extending ideas based on retrospective simulation. Our key idea is to exploit an eigenfunction expansion representation of the transition function. This approach also yields methods for exact simulation from several processes related to the Wright-Fisher diffusion: (i) its moment dual, the ancestral process of an infinite-leaf Kingman coalescent tree; (ii) its infinite-dimensional counterpart, the Fleming-Viot process; and (iii) its bridges. Finally, we illustrate our method with an application to an evolutionary model for mutation and diploid selection. We believe our new perspective on diffusion simulation holds promise for other models admitting a transition eigenfunction expansion.
Computational Performance and Statistical Accuracy of *BEAST and Comparisons with Other Methods
Huw A. Ogilvie, Joseph Heled, Dong Xie, Alexei J. Drummond
(Submitted on 22 Jun 2015)
Under the multispecies coalescent model of molecular evolution gene trees evolve within a species tree, and follow predicted distributions of topologies and coalescent times. In comparison, supermatrix concatenation methods assume that gene trees share a common history and equate gene coalescence with species divergence. The multispecies coalescent is supported by previous studies which found that its predicted distributions fit empirical data, and that concatenation is not a consistent estimator of the species tree. *BEAST, a fully Bayesian implementation of the multispecies coalescent, is popular but computationally intensive, so the advent of large phylogenomic data sets is both a computational challenge and an opportunity for better systematics. Using simulation studies, we characterise the scaling behaviour of *BEAST, and enable quantitative prediction of the impact increasing the number of loci has on both computational performance and statistical accuracy. Follow up simulations over a wide range of parameters show that the statistical performance of *BEAST relative to concatenation improves both as branch length is reduced and as the number of loci is increased. Finally, using simulations based on estimated parameters from two phylogenomic data sets, we compare the performance of a range of species tree and concatenation methods to show that using *BEAST with a small subset of loci can be preferable to using concatenation with thousands of loci. Our results provide insight into the practicalities of Bayesian species tree estimation, the number of genes required to obtain a given level of accuracy and the situations in which supermatrix or summary methods will be outperformed by the fully Bayesian multispecies coalescent.