Unconstrained evolution in short introns? – An analysis of genome‐wide polymorphism and divergence data from <i><scp>D</scp>rosophila</i>

Clemente, Florian; Vogl, Claus

doi:10.1111/j.1420-9101.2012.02580.x

Cited by 50 publications

(104 citation statements)

References 72 publications

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“…To do this, we computed a synonymous SFS that would be expected under neutrality given this extreme estimate of selection on synonymous sites (see SI Appendix, Text S3, for details). We further assumed that the SFS from short introns has a neutral shape (19,20). We only see a negligible effect on the DFE inference using this predicted neutral SFS compared with Fig.…”

Section: Resultsmentioning

confidence: 84%

“…The third model, the back-mutation model, predicts that there is a category of weakly advantageous mutations that restore fitness after deleterious mutations become fixed (24) 19 ). Importantly, the back-mutation model predicts that the average effect size of mutations (i.e., the absolute value of the selection coefficient) will be the same in both species, and that differences in the DFE are solely attributable to long-term differences in population size.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Determining the factors driving selective effects of new nonsynonymous mutations

Huber

Kim

Marsden

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

137

157

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The distribution of fitness effects (DFE) of new mutations plays a fundamental role in evolutionary genetics. However, the extent to which the DFE differs across species has yet to be systematically investigated. Furthermore, the biological mechanisms determining the DFE in natural populations remain unclear. Here, we show that theoretical models emphasizing different biological factors at determining the DFE, such as protein stability, back-mutations, species complexity, and mutational robustness make distinct predictions about how the DFE will differ between species. Analyzing amino acid-changing variants from natural populations in a comparative population genomic framework, we find that humans have a higher proportion of strongly deleterious mutations than Drosophila melanogaster. Furthermore, when comparing the DFE across yeast, Drosophila, mice, and humans, the average selection coefficient becomes more deleterious with increasing species complexity. Last, pleiotropic genes have a DFE that is less variable than that of nonpleiotropic genes. Comparing four categories of theoretical models, only Fisher's geometrical model (FGM) is consistent with our findings. FGM assumes that multiple phenotypes are under stabilizing selection, with the number of phenotypes defining the complexity of the organism. Our results suggest that long-term population size and cost of complexity drive the evolution of the DFE, with many implications for evolutionary and medical genomics. T he distribution of fitness effects (DFE) represents the distribution of selection coefficients, s, of random mutations in the genome. Here, s quantifies the relative change in fitness due to the mutation. The DFE plays a fundamental role in evolutionary genetics because it quantifies the amount of deleterious, neutral, and adaptive mutations entering a population (1). Despite the importance and considerable study of the DFE (1-3), the extent to which the DFE in terms of s differs across species has yet to be systematically quantified. Furthermore, the biological factors determining the DFE in different species remain elusive. Several theoretical models propose different mechanisms for the evolution of the DFE (Fig. 1) (4-8). Although each of these models has a reasonable theoretical basis as well as some support from experimental evolution studies or microbial studies, which model best explains differences in the DFE between species has not yet been determined. Nor have these models been tested with genetic variation data from natural populations in higher organisms. Because experimental evolution studies in laboratory organisms often use a homogeneous environment and genetically homogeneous organisms, they may better satisfy some of the assumptions of these theoretical models. However, natural populations may provide different qualitative results due to increased resolution to measure weakly deleterious mutations and the unnatural selection pressure in the laboratory (2, 9). Importantly, five theoretical models for the evolution of the DFE predic...

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Section: Resultsmentioning

confidence: 84%

Section: Resultsmentioning

confidence: 99%

Determining the factors driving selective effects of new nonsynonymous mutations

Huber

Kim

Marsden

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

137

157

View full text Add to dashboard Cite

show abstract

“…We used these ancestral sequences to infer AT → GC and GC → AT directional substitution rates within lncRNA exons and introns (DuMont et al 2009). For comparison we also computed rates for small introns (<86 nt) as a proxy for neutrally evolving sequences (Clemente and Vogl 2012).…”

Section: Lncrna Composition and Constraintmentioning

confidence: 99%

Unexpected selection to retain high GC content and splicing enhancers within exons of multiexonic lncRNA loci

Haerty

Ponting

2015

RNA

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If sequencing was possible only for genomes, and not for RNAs or proteins, then functional protein-coding exons would be recognizable by their unusual patterns of nucleotide composition, specifically a high GC content across the body of exons, and an unusual nucleotide content near their edges. RNAs and proteins can, of course, be sequenced but the extent of functionality of intergenic long noncoding RNAs (lncRNAs) remains under question owing to their low nucleotide conservation. Inspired by the nucleotide composition patterns of protein-coding exons, we sought evidence for functionality across lncRNA loci from diverse species. We found that such patterns across multiexonic lncRNA loci mirror those of proteincoding genes, although to a lesser degree: Specifically, compared with introns, lncRNA exons are GC rich. Additionally we report evidence for the action of purifying selection to preserve exonic splicing enhancers within human multiexonic lncRNAs and nucleotide composition in fruit fly lncRNAs. Our findings provide evidence for selection for more efficient rates of transcription and splicing within lncRNA loci. Despite only a minor proportion of their RNA bases being constrained, multiexonic intergenic lncRNAs appear to require accurate splicing of their exons to transact their function.

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“…With the infinite sites model, infinitely many sites may be hit by mutation at a finite rate, such that each site is hit only once [19,21]. Furthermore, it is usually assumed that the ancestral and derived allelic states can be inferred with outgroup information, i.e., with information from closely-related species or populations (e.g., [12,13,[22][23][24]). Then, segregating mutations are assumed to only arise once from the ancestral background.…”

Section: Inference Based On the Assumptions Of Equilibrium And Rare Mmentioning

confidence: 99%

Computation of the Likelihood of Joint Site Frequency Spectra Using Orthogonal Polynomials

Vogl

Bergman

2016

Computation

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Abstract:In population genetics, information about evolutionary forces, e.g., mutation, selection and genetic drift, is often inferred from DNA sequence information. Generally, DNA consists of two long strands of nucleotides or sites that pair via the complementary bases cytosine and guanine (C and G), on the one hand, and adenine and thymine (A and T), on the other. With whole genome sequencing, most genomic information stored in the DNA has become available for multiple individuals of one or more populations, at least in humans and model species, such as fruit flies of the genus Drosophila. In a genome-wide sample of L sites for M (haploid) individuals, the state of each site may be made binary, by binning the complementary bases, e.g., C with G to C/G, and contrasting C/G to A/T, to obtain a "site frequency spectrum" (SFS). Two such samples of either a single population from different time-points or two related populations from a single time-point are called joint site frequency spectra (joint SFS). While mathematical models describing the interplay of mutation, drift and selection have been available for more than 80 years, calculation of exact likelihoods from joint SFS is difficult. Sufficient statistics for inference of, e.g., mutation or selection parameters that would make use of all the information in the genomic data are rarely available. Hence, often suites of crude summary statistics are combined in simulation-based computational approaches. In this article, we use a bi-allelic boundary-mutation and drift population genetic model to compute the transition probabilities of joint SFS using orthogonal polynomials. This allows inference of population genetic parameters, such as the mutation rate (scaled by the population size) and the time separating the two samples. We apply this inference method to a population dataset of neutrally-evolving short intronic sites from six DNA sequences of the fruit fly Drosophila melanogaster and the reference sequence of the related species Drosophila sechellia.

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Unconstrained evolution in short introns? – An analysis of genome‐wide polymorphism and divergence data from Drosophila

Cited by 50 publications

References 72 publications

Determining the factors driving selective effects of new nonsynonymous mutations

Determining the factors driving selective effects of new nonsynonymous mutations

Unexpected selection to retain high GC content and splicing enhancers within exons of multiexonic lncRNA loci

Computation of the Likelihood of Joint Site Frequency Spectra Using Orthogonal Polynomials

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