RESUMO
We developed a variant-annotation method that combines sequence-based machine-learning classification with a context-dependent algorithm for selecting splice variants. Our approach is distinctive in that it compares the splice potential of a sequence bearing a variant with the splice potential of the reference sequence. After training, classification accurately identified 168 of 180 (93.3%) canonical splice sites of five genes. The combined method, CryptSplice, identified and correctly predicted the effect of 18 of 21 (86%) known splice-altering variants in CFTR, a well-studied gene whose loss-of-function variants cause cystic fibrosis (CF). Among 1,423 unannotated CFTR disease-associated variants, the method identified 32 potential exonic cryptic splice variants, two of which were experimentally evaluated and confirmed. After complete CFTR sequencing, the method found three cryptic intronic splice variants (one known and two experimentally verified) that completed the molecular diagnosis of CF in 6 of 14 individuals. CryptSplice interrogation of sequence data from six individuals with X-linked dyskeratosis congenita caused by an unknown disease-causing variant in DKC1 identified two splice-altering variants that were experimentally verified. To assess the extent to which disease-associated variants might activate cryptic splicing, we selected 458 pathogenic variants and 348 variants of uncertain significance (VUSs) classified as high confidence from ClinVar. Splice-site activation was predicted for 129 (28%) of the pathogenic variants and 75 (22%) of the VUSs. Our findings suggest that cryptic splice-site activation is more common than previously thought and should be routinely considered for all variants within the transcribed regions of genes.
Assuntos
Proteínas de Ciclo Celular/genética , Biologia Computacional , Regulador de Condutância Transmembrana em Fibrose Cística/genética , Variação Genética , Proteínas Nucleares/genética , Sítios de Splice de RNA , Algoritmos , Proteínas de Ciclo Celular/metabolismo , Fibrose Cística/genética , Regulador de Condutância Transmembrana em Fibrose Cística/metabolismo , Disceratose Congênita/genética , Éxons , Regulação da Expressão Gênica , Loci Gênicos , Genômica , Células HEK293 , Humanos , Íntrons , Mutação de Sentido Incorreto , Proteínas Nucleares/metabolismo , Splicing de RNA , Análise de Sequência de DNA , Máquina de Vetores de SuporteRESUMO
As scholars have rushed to either prove or refute cultural group selection (CGS), the debate lacks sufficient consideration of CGS's potential moderators. We argue that pressures for CGS are particularly strong when groups face ecological and human-made threat. Field, experimental, computational, and genetic evidence are presented to substantiate this claim.
Assuntos
Ecologia , Face , HumanosRESUMO
As punishment can be essential to cooperation and norm maintenance but costly to the punisher, many evolutionary game-theoretic studies have explored how direct punishment can evolve in populations. Compared to direct punishment, in which an agent acts to punish another for an interaction in which both parties were involved, the evolution of third-party punishment (3PP) is even more puzzling, because the punishing agent itself was not involved in the original interaction. Despite significant empirical studies of 3PP, little is known about the conditions under which it can evolve. We find that punishment reputation is not, by itself, sufficient for the evolution of 3PP. Drawing on research streams in sociology and psychology, we implement a structured population model and show that high strength-of-ties and low mobility are critical for the evolution of responsible 3PP. Only in such settings of high social-structural constraint are punishers able to induce self-interested agents toward cooperation, making responsible 3PP ultimately beneficial to individuals as well as the collective. Our results illuminate the conditions under which 3PP is evolutionarily adaptive in populations. Responsible 3PP can evolve and induce cooperation in cases where other mechanisms alone fail to do so.
Assuntos
Adaptação Biológica/fisiologia , Evolução Biológica , Comportamento Cooperativo , Modelos Biológicos , Punição/psicologia , Meio Social , Animais , Simulação por Computador , Teoria dos Jogos , HumanosRESUMO
Nearly all major conflicts across the globe, both current and historical, are characterized by individuals defining themselves and others by group membership. This existence of group-biased behavior (in-group favoring and out-group hostile) has been well established empirically, and has been shown to be an inevitable outcome in many evolutionary studies. Thus it is puzzling that statistics show violence and out-group conflict declining dramatically over the past few centuries of human civilization. Using evolutionary game-theoretic models, we solve this puzzle by showing for the first time that out-group hostility is dramatically reduced by mobility. Technological and societal advances over the past centuries have greatly increased the degree to which humans change physical locations, and our results show that in highly mobile societies, one's choice of action is more likely to depend on what individual one is interacting with, rather than the group to which the individual belongs. Our empirical analysis of archival data verifies that contexts with high residential mobility indeed have less out-group hostility than those with low mobility. This work suggests that, in fact, group-biased behavior that discriminates against out-groups is not inevitable after all.
Assuntos
Civilização , Modelos Teóricos , Mobilidade Social , Problemas Sociais , HumanosRESUMO
Some researchers in other regions have recommended human papillomavirus (HPV) vaccination to reduce risk of ovarian cancer, but not in North America, where evidence has previously suggested no role for HPV in ovarian cancer. Here we use a large sample of ovarian cancer transcriptomes (RNA-Seq) from The Cancer Genome Atlas (TCGA) database to address whether HPV is involved with ovarian cancer in North America. We estimate that a known high-risk type of HPV (type 18) is present and active in 1.5% of cases of ovarian epithelial cancers in the US and Canada. Our detection methods were verified by negative and positive controls, and our sequence matches indicated high validity, leading to strong confidence in our conclusions. Our results indicate that previous reports of zero prevalence of HPV in North American cases of ovarian cancer should not be considered conclusive. This is important because currently used vaccines protect against the HPV-18 that is active in ovarian tumors and, therefore, may reduce risk in North America of cancers of the ovaries as well as of the cervix and several other organ sites.
Assuntos
Expressão Gênica , Genes Virais , Papillomavirus Humano 18/genética , Oncogenes/genética , Neoplasias Ovarianas/genética , Infecções por Papillomavirus/complicações , Infecções Tumorais por Vírus/complicações , Canadá/epidemiologia , Feminino , Glioblastoma/epidemiologia , Glioblastoma/etiologia , Papillomavirus Humano 6/genética , Humanos , Neoplasias Ovarianas/epidemiologia , Prevalência , RNA Viral , Transcrição Gênica , Estados Unidos/epidemiologia , Neoplasias do Colo do Útero/epidemiologia , Neoplasias do Colo do Útero/etiologiaRESUMO
Evolutionary graph theory studies the evolutionary dynamics of populations structured on graphs. A central problem is determining the probability that a small number of mutants overtake a population. Currently, Monte Carlo simulations are used for estimating such fixation probabilities on general directed graphs, since no good analytical methods exist. In this paper, we introduce a novel deterministic framework for computing fixation probabilities for strongly connected, directed, weighted evolutionary graphs under neutral drift. We show how this framework can also be used to calculate the expected number of mutants at a given time step (even if we relax the assumption that the graph is strongly connected), how it can extend to other related models (e.g. voter model), how our framework can provide non-trivial bounds for fixation probability in the case of an advantageous mutant, and how it can be used to find a non-trivial lower bound on the mean time to fixation. We provide various experimental results determining fixation probabilities and expected number of mutants on different graphs. Among these, we show that our method consistently outperforms Monte Carlo simulations in speed by several orders of magnitude. Finally we show how our approach can provide insight into synaptic competition in neurology.
Assuntos
Algoritmos , Evolução Molecular , Modelos Genéticos , Modelos Estatísticos , Análise Numérica Assistida por Computador , Simulação por Computador , HumanosRESUMO
Evolutionary graph theory (EGT), studies the ability of a mutant gene to overtake a finite structured population. In this review, we describe the original framework for EGT and the major work that has followed it. This review looks at the calculation of the "fixation probability" - the probability of a mutant taking over a population and focuses on game-theoretic applications. We look at varying topics such as alternate evolutionary dynamics, time to fixation, special topological cases, and game theoretic results. Throughout the review, we examine several interesting open problems that warrant further research.