Standing genetic variation as the predominant source for adaptation of a songbird

Lai, Yu Ting; Yeung, Carol K.L.; Omland, Kevin E.; Pang, Er Li; Yi, Hao; Liao, Ben-Yang; Cao, Hui Fen; Zhang, Bo Wen; Yeh, Chia Fen; Hung, Chih Ming; Hung, Hsin Yi; Yang, Ming; Liang, Wei; Hsu, Yu‐Kuei; Yao, Cheng Te; Dong, Lü; Lin, Kui; Li, Shou Hsien

doi:10.1073/pnas.1813597116

Cited by 140 publications

(133 citation statements)

References 67 publications

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“…To analyze continuous traits and phenotypes in many evolutionary applications, one must first disentangle adaptation and heredity [29–32]. The standard approach for this disentanglement is to explicitly account for the hierarchy of descent by adding genetic covariance or kinship across species to the likelihood either via phylogenetic regression [33] or linear mixed models (e.g.…”

Section: Discussionmentioning

confidence: 99%

A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes

Wang

Sudijono

Kirveslahti

et al. 2019

Preprint

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22The recent curation of large-scale databases with 3D surface scans of shapes has motivated the devel-23 opment of tools that better detect global-patterns in morphological variation. Studies which focus on 24 identifying differences between shapes have been limited to simple pairwise comparisons and rely on 25 pre-specified landmarks (that are often known). We present SINATRA: the first statistical pipeline for 26 analyzing collections of shapes without requiring any correspondences. Our novel algorithm takes in two 27 classes of shapes and highlights the physical features that best describe the variation between them. We 28 use a rigorous simulation framework to assess our approach. Lastly, as a case study, we use SINATRA to 29 analyze mandibular molars from four different suborders of primates and demonstrate its ability recover 30 known morphometric variation across phylogenies. 31 Introduction 32 Sub-image analysis is an important open problem in both medical imaging studies and geometric mor-33 phometric applications. The problem asks which physical features of shapes are most important for 34 differentiating between two classes of 3D images or shapes such as computed tomography (CT) scans of 35 bones or magnetic resonance images (MRI) of different tissues. More generally, the sub-image analysis 36problem can be framed as a regression-based task: given a collection of shapes, find the properties that 37 explain the greatest variation in some response variable (continuous or binary). One example is identify-38 ing the structures of glioblastoma tumors that best indicate signs of potential relapse and other clinical 39 outcomes [1]. From a statistical perspective, the sub-image selection problem is directly related to the 40 variable selection problem -given high-dimensional covariates and a univariate outcome, we want to 41 infer which variables are most relevant in explaining or predicting variation in the observed response. 42 2Framing sub-image analysis as a regression presents several challenges. The first challenge centers 43 around representing a 3D object as a (square integrable) covariate or feature vector. The transformation 44 should lose a minimum amount of geometric information and apply to a wide range of shape and imaging 45 datasets. In this paper, we use a tool from integral geometry and differential topology called the Eu-46 ler characteristic (EC) transform [1][2][3][4], which maps shapes into vectors without requiring pre-specified 47 landmark points or pairwise correspondences. This property is central to our innovations. 48After finding a vector representation of the shape, our second challenge is quantifying which topological 49 features are most relevant in explaining variation in a continuous outcome or binary class label. We 50 address this classic take on variable selection by using a Bayesian regression model and an information 51 theoretic metric to measure the relevance of each topological feature. Our Bayesian method allows us 52 to perform variable selection for nonlinear func...

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Section: Discussionmentioning

confidence: 99%

A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes

Wang

Sudijono

Kirveslahti

et al. 2019

Preprint

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“…1). Moreover, we found that allele reuse (repeated selection of the same haplotype, shared either via gene flow or from standing genetic variation) frequently underlies parallel adaptation between closely related lineages [29][30][31][32] , while parallelism from de-novo mutations dominates between distantly related taxa 13 . This suggests that the degree of allele reuse may be the primary mechanism behind the hypothesized divergence-dependency of parallel genome evolution, possibly reflecting either genetic (weak hybridization barriers, widespread ancestral polymorphism between closely related lineages 33 ) or ecological reasons (lower niche differentiation and geographical proximity 34,35 ).…”

Section: Introductionmentioning

confidence: 93%

Genomic basis of parallel adaptation varies with divergence inArabidopsisand its relatives

Bohutínská

Vlček

Yair

et al. 2020

Preprint

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Understanding the predictability of evolutionary change is of major importance in biology.Parallel adaptation provides unique insights through replicated natural experiments, yet mechanisms governing the ample variation in genomic parallelism remain unknown. Here, we investigate them using multi-scale genomic analyses of parallel evolution across populations, species and genera. By resequencing genomes of seven independent alpine lineages from two Arabidopsis species, we found that the degree of gene reuse decreases with increasing divergence between lineages. This relationship is well predicted by decreasing frequency of allele reuse, suggesting that availability of preexisting genetic variation is the prime mechanism. A meta-analysis demonstrated that the relationship further continues within the Brassicaceae family. Thus, we found empirical support for a longstanding hypothesis that the genetic basis of adaptive evolution is more predictable in closely related lineages while it becomes more contingent over larger evolutionary distances.

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“…This is important for evolution, because these processes can bias novel phenotypic variation toward adaptive variants (e.g., Lai et al, 2019;Rieseberg et al, 2003;Seehausen, 2004). Here, the mechanism of transmission can influence the frequency of phenotypic variants in the next generation.…”

Section: Biases Arising From Social Learningmentioning

confidence: 99%

Animal learning as a source of developmental bias

Laland

Toyokawa

Oudman

2019

Evolution and Development

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As a form of adaptive plasticity that allows organisms to shift their phenotype toward the optimum, learning is inherently a source of developmental bias. Learning may be of particular significance to the evolutionary biology community because it allows animals to generate adaptively biased novel behavior tuned to the environment and, through social learning, to propagate behavioral traits to other individuals, also in an adaptively biased manner. We describe several types of developmental bias manifest in learning, including an adaptive bias, historical bias, origination bias, and transmission bias, stressing that these can influence evolutionary dynamics through generating nonrandom phenotypic variation and/or nonrandom environmental states. Theoretical models and empirical data have established that learning can impose direction on adaptive evolution, affect evolutionary rates (both speeding up and slowing down responses to selection under different conditions) and outcomes, influence the probability of populations reaching global optimum, and affect evolvability. Learning is characterized by highly specific, path-dependent interactions with the (social and physical) environment, often resulting in new phenotypic outcomes. Consequently, learning regularly introduces novelty into phenotype space. These considerations imply that learning may commonly generate plasticity first evolution. K E Y W O R D Sdevelopmental bias, evolvability, learning, plasticity, plasticity first

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Standing genetic variation as the predominant source for adaptation of a songbird

Cited by 140 publications

References 67 publications

A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes

A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes

Genomic basis of parallel adaptation varies with divergence inArabidopsisand its relatives

Animal learning as a source of developmental bias

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