scholarly journals Signatures of recent positive selection in enhancers across 41 human tissues

2019 ◽  
Author(s):  
Jiyun M. Moon ◽  
John A. Capra ◽  
Patrick Abbot ◽  
Antonis Rokas
Keyword(s):  
Positive Selection ◽  
Evolutionary History ◽  
Multiple Time ◽  
Human Tissues ◽  
Genome Sequences ◽  
Evolutionary Time ◽  
1000 Genomes ◽  
Recent Positive Selection ◽  
Evolutionary Changes ◽  
Functional Context

AbstractEvolutionary changes in enhancers are widely associated with variation in human traits and diseases. However, studies comprehensively quantifying levels of selection on enhancers at multiple evolutionary time points during recent human evolution and how enhancer evolution varies across human tissues are lacking. To address these questions, we integrated a dataset of 41,561 transcribed enhancers active in 41 different human tissues (FANTOM Consortium) with whole genome sequences of 1,668 individuals from the African, Asian, and European populations (1000 Genomes Project). Our analyses based on four different metrics (Tajima’s D, FST, H12, nSL) showed that ~5.90% of enhancers considered showed evidence of recent positive selection and that genes associated with enhancers under positive selection are enriched for diverse immune-related functions. The distributions of these metrics for brain and testis enhancers were often statistically significantly different compared to those of other tissues; the same was true for brain and testis enhancers that are tissue-specific compared to those that are tissue-broad and for testis enhancers associated with tissue-enriched and non-tissue-enriched genes. These differences varied considerably across metrics and tissues and were generally due to changes in distributions’ shapes rather than shifts in their values. These results suggest that many human enhancers experienced recent positive selection throughout multiple time periods in human evolutionary history, that this selection occurred in a tissue-dependent and immune-related functional context, and that much like the evolution of their coding counterparts, the evolution of brain and testis enhancers has been markedly different from that of enhancers in other tissues.

scholarly journals Signatures of Recent Positive Selection in Enhancers Across 41 Human Tissues

2019 ◽  
Vol 9 (8) ◽  
pp. 2761-2774 ◽  
Author(s):  
Jiyun M. Moon ◽  
John A. Capra ◽  
Patrick Abbot ◽  
Antonis Rokas
Keyword(s):  
Positive Selection ◽  
Human Tissues ◽  
Recent Positive Selection

A Phenotype–Genotype Codon Model for Detecting Adaptive Evolution

2019 ◽  
Vol 69 (4) ◽  
pp. 722-738 ◽  
Author(s):  
Christopher T Jones ◽  
Noor Youssef ◽  
Edward Susko ◽  
Joseph P Bielawski
Keyword(s):  
Positive Selection ◽  
Adaptive Evolution ◽  
Evolutionary History ◽  
Life History Trait ◽  
Molecular Adaptation ◽  
Simulation Studies ◽  
Site Model ◽  
Branch Site ◽  
A Site ◽  
Post Hoc

Abstract A central objective in biology is to link adaptive evolution in a gene to structural and/or functional phenotypic novelties. Yet most analytic methods make inferences mainly from either phenotypic data or genetic data alone. A small number of models have been developed to infer correlations between the rate of molecular evolution and changes in a discrete or continuous life history trait. But such correlations are not necessarily evidence of adaptation. Here, we present a novel approach called the phenotype–genotype branch-site model (PG-BSM) designed to detect evidence of adaptive codon evolution associated with discrete-state phenotype evolution. An episode of adaptation is inferred under standard codon substitution models when there is evidence of positive selection in the form of an elevation in the nonsynonymous-to-synonymous rate ratio $\omega$ to a value $\omega > 1$. As it is becoming increasingly clear that $\omega > 1$ can occur without adaptation, the PG-BSM was formulated to infer an instance of adaptive evolution without appealing to evidence of positive selection. The null model makes use of a covarion-like component to account for general heterotachy (i.e., random changes in the evolutionary rate at a site over time). The alternative model employs samples of the phenotypic evolutionary history to test for phenomenological patterns of heterotachy consistent with specific mechanisms of molecular adaptation. These include 1) a persistent increase/decrease in $\omega$ at a site following a change in phenotype (the pattern) consistent with an increase/decrease in the functional importance of the site (the mechanism); and 2) a transient increase in $\omega$ at a site along a branch over which the phenotype changed (the pattern) consistent with a change in the site’s optimal amino acid (the mechanism). Rejection of the null is followed by post hoc analyses to identify sites with strongest evidence for adaptation in association with changes in the phenotype as well as the most likely evolutionary history of the phenotype. Simulation studies based on a novel method for generating mechanistically realistic signatures of molecular adaptation show that the PG-BSM has good statistical properties. Analyses of real alignments show that site patterns identified post hoc are consistent with the specific mechanisms of adaptation included in the alternate model. Further simulation studies show that the covarion-like component of the PG-BSM plays a crucial role in mitigating recently discovered statistical pathologies associated with confounding by accounting for heterotachy-by-any-cause. [Adaptive evolution; branch-site model; confounding; mutation-selection; phenotype–genotype.]

scholarly journals Evidence for Very Recent Positive Selection in Mongolians

2017 ◽  
Vol 34 (8) ◽  
pp. 1936-1946 ◽  
Author(s):  
Kazuhiro Nakayama ◽  
Jun Ohashi ◽  
Kazuhisa Watanabe ◽  
Lkagvasuren Munkhtulga ◽  
Sadahiko Iwamoto
Keyword(s):  
Positive Selection ◽  
Recent Positive Selection

scholarly journals New Insights into the Evolutionary and Genomic Landscape of Molluscum Contagiosum Virus (MCV) based on Nine MCV1 and Six MCV2 Complete Genome Sequences

Viruses ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 586 ◽  
Author(s):  
Tomaž Zorec ◽  
Denis Kutnjak ◽  
Lea Hošnjak ◽  
Blanka Kušar ◽  
Katarina Trčko ◽  
...  
Keyword(s):  
Evolutionary History ◽  
Large Scale ◽  
Nk Cell ◽  
Molluscum Contagiosum ◽  
Molluscum Contagiosum Virus ◽  
Genome Sequences ◽  
Viral Interference ◽  
Genomic Landscape ◽  
Genetic Landscape ◽  
Common Skin

Molluscum contagiosum virus (MCV) is the sole member of the Molluscipoxvirus genus and the causative agent of molluscum contagiosum (MC), a common skin disease. Although it is an important and frequent human pathogen, its genetic landscape and evolutionary history remain largely unknown. In this study, ten novel complete MCV genome sequences of the two most common MCV genotypes were determined (five MCV1 and five MCV2 sequences) and analyzed together with all MCV complete genomes previously deposited in freely accessible sequence repositories (four MCV1 and a single MCV2). In comparison to MCV1, a higher degree of nucleotide sequence conservation was observed among MCV2 genomes. Large-scale recombination events were identified in two newly assembled MCV1 genomes and one MCV2 genome. One recombination event was located in a newly identified recombinant region of the viral genome, and all previously described recombinant regions were re-identified in at least one novel MCV genome. MCV genes comprising the identified recombinant segments have been previously associated with viral interference with host T-cell and NK-cell immune responses. In conclusion, the two most common MCV genotypes emerged along divergent evolutionary pathways from a common ancestor, and the differences in the heterogeneity of MCV1 and MCV2 populations may be attributed to the strictness of the constraints imposed by the host immune response.

scholarly journals High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability

2018 ◽  
Author(s):  
Pier Francesco Palamara ◽  
Jonathan Terhorst ◽  
Yun S. Song ◽  
Alkes L. Price
Keyword(s):  
Positive Selection ◽  
Snp Array ◽  
Complex Trait ◽  
Joint Analysis ◽  
Background Selection ◽  
Sequencing Data ◽  
Data Set ◽  
Coalescence Time ◽  
Recent Positive Selection ◽  
Coalescence Times

AbstractInterest in reconstructing demographic histories has motivated the development of methods to estimate locus-specific pairwise coalescence times from whole-genome sequence data. We developed a new method, ASMC, that can estimate coalescence times using only SNP array data, and is 2-4 orders of magnitude faster than previous methods when sequencing data are available. We were thus able to apply ASMC to 113,851 phased British samples from the UK Biobank, aiming to detect recent positive selection by identifying loci with unusually high density of very recent coalescence times. We detected 12 genome-wide significant signals, including 6 loci with previous evidence of positive selection and 6 novel loci, consistent with coalescent simulations showing that our approach is well-powered to detect recent positive selection. We also applied ASMC to sequencing data from 498 Dutch individuals (Genome of the Netherlands data set) to detect background selection at deeper time scales. We observed highly significant correlations between average coalescence time inferred by ASMC and other measures of background selection. We investigated whether this signal translated into an enrichment in disease and complex trait heritability by analyzing summary association statistics from 20 independent diseases and complex traits (average N=86k) using stratified LD score regression. Our background selection annotation based on average coalescence time was strongly enriched for heritability (p = 7×10−153) in a joint analysis conditioned on a broad set of functional annotations (including other background selection annotations), meta-analyzed across traits; SNPs in the top 20% of our annotation were 3.8x enriched for heritability compared to the bottom 20%. These results underscore the widespread effects of background selection on disease and complex trait heritability.

scholarly journals Evolution and lineage dynamics of a transmissible cancer in Tasmanian devils

PLoS Biology ◽  
2020 ◽  
Vol 18 (11) ◽  
pp. e3000926 ◽  
Author(s):  
Young Mi Kwon ◽  
Kevin Gori ◽  
Naomi Park ◽  
Nicole Potts ◽  
Kate Swift ◽  
...  
Keyword(s):  
Positive Selection ◽  
Cancer Cells ◽  
Cell Lines ◽  
Copy Number ◽  
Convergent Evolution ◽  
Evolutionary History ◽  
Genome Instability ◽  
Copy Number Variants ◽  
Tasmanian Devil ◽  
Transmissible Cancer

Devil facial tumour 1 (DFT1) is a transmissible cancer clone endangering the Tasmanian devil. The expansion of DFT1 across Tasmania has been documented, but little is known of its evolutionary history. We analysed genomes of 648 DFT1 tumours collected throughout the disease range between 2003 and 2018. DFT1 diverged early into five clades, three spreading widely and two failing to persist. One clade has replaced others at several sites, and rates of DFT1 coinfection are high. DFT1 gradually accumulates copy number variants (CNVs), and its telomere lengths are short but constant. Recurrent CNVs reveal genes under positive selection, sites of genome instability, and repeated loss of a small derived chromosome. Cultured DFT1 cell lines have increased CNV frequency and undergo highly reproducible convergent evolution. Overall, DFT1 is a remarkably stable lineage whose genome illustrates how cancer cells adapt to diverse environments and persist in a parasitic niche.

scholarly journals Extinction in complex communities as driven by adaptive dynamics

2021 ◽  
Author(s):  
Vu Nguyen ◽  
Dervis Vural
Keyword(s):  
Time Scales ◽  
Computer Simulations ◽  
Analytical Solutions ◽  
Adaptive Dynamics ◽  
Mathematical Framework ◽  
Evolutionary Time ◽  
Community Interactions ◽  
Fitness Functions ◽  
Evolutionary Changes

In a complex community, species continuously adapt to each other. On rare occasions, the adaptation of a species can lead to the extinction of others, and even its own. ``Adaptive dynamics'' is the standard mathematical framework to describe evolutionary changes in community interactions, and in particular, predict adaptation driven extinction. Unfortunately, most authors implement the equations of adaptive dynamics through computer simulations, that require assuming a large number of questionable parameters and fitness functions. In this study we present analytical solutions to adaptive dynamics equations, thereby clarifying how outcomes depend on any computational input. We develop general formulas that predict equilibrium abundances over evolutionary time scales. Additionally, we predict which species will go extinct next, and when this will happen.

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