scholarly journals Long-term natural selection affects patterns of neutral divergence on the X chromosome more than the autosomes.

2015 ◽  
Author(s):  
Melissa Ann Wilson Sayres ◽  
Pooja Narang
Keyword(s):  
Natural Selection ◽  
X Chromosome ◽  
Great Apes ◽  
Regulatory Elements ◽  
Mutation Bias ◽  
Term Selection ◽  
Coding Regions ◽  
Male Mutation Bias ◽  
Genomic Regions

Natural selection reduces neutral population genetic diversity near coding regions of the genome because recombination has not had time to unlink selected alleles from nearby neutral regions. For ten sub-species of great apes, including human, we show that long-term selection affects estimates of divergence on the X differently from the autosomes. Divergence increases with increasing distance from genes on both the X chromosome and autosomes, but increases faster on the X chromosome than autosomes, resulting in increasing ratios of X/A divergence in putatively neutral regions. Similarly, divergence is reduced more on the X chromosome in neutral regions near conserved regulatory elements than on the autosomes. Consequently estimates of male mutation bias, which rely on comparing neutral divergence between the X and autosomes, are twice as high in neutral regions near genes versus far from genes. Our results suggest filters for putatively neutral genomic regions differ between the X and autosomes.

scholarly journals Quantifying adaptive evolution and the effects of natural selection across the Norway spruce genome

2020 ◽  
Author(s):  
Xi Wang ◽  
Pär K Ingvarsson
Keyword(s):  
Natural Selection ◽  
Norway Spruce ◽  
Balancing Selection ◽  
Enrichment Analysis ◽  
Coding Regions ◽  
Regulatory Changes ◽  
Whole Genomes ◽  
Adaptive Rate ◽  
Genomic Regions

AbstractDetecting natural selection is one of the major goals of evolutionary genomics. Here, we sequence whole genomes of 34 Picea abies individuals and quantify the amount of selection across the genome. Using an estimate of the distribution of fitness effects, we show that negative selection is very limited in coding regions, while positive selection is rare in coding regions but very strong in non-coding regions, suggesting the great importance of regulatory changes in evolution of Norway spruce. Additionally, we found a positive correlation between adaptive rate with recombination rate and a negative correlation between adaptive rate and gene density, suggesting a widespread influence from Hill-Robertson interference to efficiency of protein adaptation in P. abies. Finally, the distinct population statistics between genomic regions under either positive or balancing selection with that under neutral regions indicated impact from selection to genomic architecture of Norway spruce. Further gene ontology enrichment analysis for genes located in regions identified as undergoing either positive or long-term balancing selection also highlighted specific molecular functions and biological processes in that appear to be targets of selection in Norway spruce.

scholarly journals Long-term evolution is surprisingly predictable in lattice proteins

2013 ◽  
Vol 10 (82) ◽  
pp. 20130026 ◽  
Author(s):  
Michael E. Palmer ◽  
Arnav Moudgil ◽  
Marcus W. Feldman
Keyword(s):  
Natural Selection ◽  
Basins Of Attraction ◽  
Long Term Outcomes ◽  
Short Term ◽  
Term Selection ◽  
Term Evolution ◽  
Evolutionary Success ◽  
Lattice Proteins

It has long been debated whether natural selection acts primarily upon individual organisms, or whether it also commonly acts upon higher-level entities such as lineages. Two arguments against the effectiveness of long-term selection on lineages have been (i) that long-term evolutionary outcomes will not be sufficiently predictable to support a meaningful long-term fitness and (ii) that short-term selection on organisms will almost always overpower long-term selection. Here, we use a computational model of protein folding and binding called ‘lattice proteins’. We quantify the long-term evolutionary success of lineages with two metrics called the k -fitness and k -survivability. We show that long-term outcomes are surprisingly predictable in this model: only a small fraction of the possible outcomes are ever realized in multiple replicates. Furthermore, the long-term fitness of a lineage depends only partly on its short-term fitness; other factors are also important, including the ‘evolvability’ of a lineage—its capacity to produce adaptive variation. In a system with a distinct short-term and long-term fitness, evolution need not be ‘short-sighted’: lineages may be selected for their long-term properties, sometimes in opposition to short-term selection. Similar evolutionary basins of attraction have been observed in vivo , suggesting that natural biological lineages will also have a predictive long-term fitness.

scholarly journals How adaptive plasticity evolves when selected against

2018 ◽  
Author(s):  
Alfredo Rago ◽  
Kostas Kouvaris ◽  
Tobias Uller ◽  
Richard Watson
Keyword(s):  
Natural Selection ◽  
Environmental Change ◽  
Coarse Grained ◽  
Adaptive Plasticity ◽  
Short Term ◽  
Local Optima ◽  
Term Selection ◽  
Selection For ◽  
The Individual

AbstractAdaptive plasticity allows organisms to cope with environmental change, thereby increasing the population’s long-term fitness. However, individual selection can only compare the fitness of individuals within each generation: if the environment changes more slowly than the generation time (i.e., a coarse-grained environment) a population will not experience selection for plasticity even if it is adaptive in the long-term. How does adaptive plasticity then evolve? One explanation is that, if competing alleles conferring different degrees of plasticity persist across multiple environments, natural selection between lineages carrying those alleles could select for adaptive plasticity (lineage selection).We show that adaptive plasticity can evolve even in the absence of such lineage selection. Instead, we propose that adaptive plasticity in coarse-grained environments evolves as a by-product of inefficient short-term natural selection. In our simulations, populations that can efficiently respond to selective pressures follow short-term, local, optima and have lower long-term fitness. Conversely, populations that accumulate limited genetic change within each environment evolve long-term adaptive plasticity even when plasticity incurs short-term costs. These results remain qualitatively similar regardless of whether we decrease the efficiency of natural selection by increasing the rate of environmental change or decreasing mutation rate, demonstrating that both factors act via the same mechanism. We demonstrate how this mechanism can be understood through the concept of learning rate.Our work shows how plastic responses that are costly in the short term, yet adaptive in the long term, can evolve as a by-product of inefficient short-term selection, without selection for plasticity at either the individual or lineage level.

scholarly journals Computational prediction of CRISPR-impaired non-coding regulatory regions

2020 ◽  
Author(s):  
Nina Baumgarten ◽  
Florian Schmidt ◽  
Martin Wegner ◽  
Marie Hebel ◽  
Manuel Kaulich ◽  
...  
Keyword(s):  
Computational Prediction ◽  
Regulatory Elements ◽  
Cell Type ◽  
Coding Regions ◽  
Gene Coding ◽  
Genome Wide ◽  
A Genome ◽  
Crispr Screen ◽  
Genomic Regions ◽  
Made In

AbstractGenome-wide CRISPR screens are becoming more widespread and allow the simultaneous interrogation of thousands of genomic regions. Although recent progress has been made in the analysis of CRISPR screens, it is still an open problem how to interpret CRISPR mutations in non-coding regions of the genome. Most of the tools concentrate on the interpretation of mutations introduced in gene coding regions. We introduce a computational pipeline that uses epigenomic information about regulatory elements for the interpretation of CRISPR mutations in non-coding regions. We illustrate our approach on the analysis of a genome-wide CRISPR screen in hTERT-RPE-1 cells and reveal novel regulatory elements that mediate chemoresistance against doxorubicin in these cells. We infer links to established and to novel chemoresistance genes. Our approach is general and can be applied on any cell type and with different CRISPR enzymes.

scholarly journals Scoring of pathogenic non-coding variants in Mendelian diseases through supervised learning on ancient, recent and ongoing purifying selection signals in human

2018 ◽  
Author(s):  
Barthélémy Caron ◽  
Yufei Luo ◽  
Antonio Rausell
Keyword(s):  
Supervised Learning ◽  
State Of The Art ◽  
Purifying Selection ◽  
Regulatory Elements ◽  
Coding Regions ◽  
Mendelian Diseases ◽  
Art Methods ◽  
Causal Variants ◽  
Genomic Regions ◽  
Coding Variants

AbstractThe study of rare Mendelian diseases through exome sequencing typically yields incomplete diagnostic rates, ~8-70% depending on the disease type. Whole genome sequencing of the unresolved cases allows addressing the hypothesis that causal variants could lay in non-coding regions with damaging regulatory consequences. The large amount of rare and singleton variants found in each individual genome requires computational filtering and scoring strategies to gain power in downstream statistical genetics tests. However, state-of-the-art methods estimating the functional relevance of non-coding genomic regions have been mostly characterized on sets of variants largely composed of trait-associated polymorphisms and associated to common diseases, yet with modest accuracy and strong positional biases. In this work we first curated a collection of n=737 high-confidence pathogenic non-coding single-nucleotide variants in proximal cis-regulatory genomic regions associated to monogenic Mendelian diseases. We then systematically evaluated the ability to predict causal variants of a comprehensive set of natural selection features extracted at three genomic levels: the affected position, the flanking region and the associated gene. In addition to inter-species conservation, a comprehensive set of recent and ongoing purifying selection signals in human was explored, allowing to capture potential constraints associated to recently acquired regulatory elements in the human lineage. A supervised learning approach using gradient tree boosting on such features reached a high predictive performance characterized by an area under the ROC curve = 0.84 and an area under the Precision-Recall curve = 0.47. The figures represent a relative improvement of >10% and >34% respectively upon the performance of current state-of-the-art methods for prioritizing non-coding variants. Performance was consistent under multiple configurations of the sets of variants used for learning and for independent testing. The supervised learning design allowed the assessment of newly seen non-coding variants overcoming gene and positional bias. The scores produced by the approach allow a more consistent weighting and aggregation of candidate pathogenic variants from diverse non-coding regions within and across genes in the context of statistical tests for rare variant association analysis.

scholarly journals How to find genomic regions relevant for gene regulation

2021 ◽  
Vol 33 (2) ◽  
pp. 157-165
Author(s):  
Xuanzong Guo ◽  
Uwe Ohler ◽  
Ferah Yildirim
Keyword(s):  
Functional Characterization ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
High Throughput Analysis ◽  
Protein Coding ◽  
Coding Regions ◽  
The Past ◽  
Genomic Regions ◽  
Regulatory Functions

Abstract Genetic variants associated with human diseases are often located outside the protein coding regions of the genome. Identification and functional characterization of the regulatory elements in the non-coding genome is therefore of crucial importance for understanding the consequences of genetic variation and the mechanisms of disease. The past decade has seen rapid progress in high-throughput analysis and mapping of chromatin accessibility, looping, structure, and occupancy by transcription factors, as well as epigenetic modifications, all of which contribute to the proper execution of regulatory functions in the non-coding genome. Here, we review the current technologies for the definition and functional validation of non-coding regulatory regions in the genome.

scholarly journals Comparative Genomics Reveals Evolution of a Beak Morphology Locus in a High-Altitude Songbird

2020 ◽  
Vol 37 (10) ◽  
pp. 2983-2988
Author(s):  
Yalin Cheng ◽  
Matthew J Miller ◽  
Dezhi Zhang ◽  
Gang Song ◽  
Chenxi Jia ◽  
...  
Keyword(s):  
Tibet Plateau ◽  
Altitudinal Distribution ◽  
Term Selection ◽  
Cavity Nesting ◽  
Genome Wide ◽  
Nesting Habits ◽  
Beak Length ◽  
Genomic Regions ◽  
Qinghai Tibet Plateau

Abstract The Ground Tit (Pseudopodoces humilis) has lived on the Qinghai-Tibet Plateau for ∼5.7 My and has the highest altitudinal distribution among all parids. This species has evolved an elongated beak in response to long-term selection imposed by ground-foraging and cavity-nesting habits, yet the genetic basis for beak elongation remains unknown. Here, we perform genome-wide analyses across 14 parid species and identify 25 highly divergent genomic regions that are significantly associated with beak length, finding seven candidate genes involved in bone morphogenesis and remolding. Neutrality tests indicate that a model allowing for a selective sweep in the highly conserved COL27A1 gene best explains variation in beak length. We also identify two nonsynonymous fixed mutations in the collagen domain that are predicted to be functionally deleterious yet may have facilitated beak elongation. Our study provides evidence of adaptive alleles in COL27A1 with major effects on beak elongation of Ps. humilis.

scholarly journals EpiRegio: analysis and retrieval of regulatory elements linked to genes

2020 ◽  
Vol 48 (W1) ◽  
pp. W193-W199 ◽  
Author(s):  
Nina Baumgarten ◽  
Dennis Hecker ◽  
Sivarajan Karunanithi ◽  
Florian Schmidt ◽  
Markus List ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Association Studies ◽  
Web Server ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Genome Wide Association Studies ◽  
Coding Regions ◽  
Genomic Regions

Abstract A current challenge in genomics is to interpret non-coding regions and their role in transcriptional regulation of possibly distant target genes. Genome-wide association studies show that a large part of genomic variants are found in those non-coding regions, but their mechanisms of gene regulation are often unknown. An additional challenge is to reliably identify the target genes of the regulatory regions, which is an essential step in understanding their impact on gene expression. Here we present the EpiRegio web server, a resource of regulatory elements (REMs). REMs are genomic regions that exhibit variations in their chromatin accessibility profile associated with changes in expression of their target genes. EpiRegio incorporates both epigenomic and gene expression data for various human primary cell types and tissues, providing an integrated view of REMs in the genome. Our web server allows the analysis of genes and their associated REMs, including the REM’s activity and its estimated cell type-specific contribution to its target gene’s expression. Further, it is possible to explore genomic regions for their regulatory potential, investigate overlapping REMs and by that the dissection of regions of large epigenomic complexity. EpiRegio allows programmatic access through a REST API and is freely available at https://epiregio.de/.

scholarly journals Male Mutation Bias and Possible Long-Term Effects of Human Activities

2010 ◽  
Vol 24 (5) ◽  
pp. 1190-1197 ◽  
Author(s):  
SAMUEL COTTON ◽  
CLAUS WEDEKIND
Keyword(s):  
Human Activities ◽  
Long Term Effects ◽  
Mutation Bias ◽  
Male Mutation Bias

scholarly journals Variable Autosomal and X Divergence Near and Far from Genes Affects Estimates of Male Mutation Bias in Great Apes

2016 ◽  
Vol 8 (11) ◽  
pp. 3393-3405 ◽  
Author(s):  
Pooja Narang ◽  
Melissa A. Wilson Sayres
Keyword(s):  
Great Apes ◽  
Mutation Bias ◽  
Male Mutation Bias
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