Localizing and Classifying Adaptive Targets with Trend Filtered Regression

Mughal, Mehreen R.; DeGiorgio, Michael

doi:10.1093/molbev/msy205

Cited by 37 publications

(58 citation statements)

References 85 publications

(159 reference statements)

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“…Here we briefly present its performance in terms of both classification of selective sweeps and in estimating parameters responsible for shaping sweeps. We compare classification performance of SURFDAWave to Trendsetter [9], as Trendsetter also models the spatial autocorrelation of summary statistics, and we also provide a comprehensive comparison to two other leading sweep classifiers-evolBoosting [19] and diploS/HIC [6]. See Materials and methods for details on these comparisons, as well as important considerations regarding the alteration ).…”

Section: Resultsmentioning

confidence: 99%

“…This expectation is also motivated by results in ref. [9] comparing the classification accuracy of Trendsetter when using constant and linear trend-filtering functions, which respectively model curves with similar characteristics to the Haar and Dabechies' least-asymmetric wavelets employed by SURFDA-Wave. We find that the type of wavelets used as basis functions does not dramatically influence the overall classification rates (S3 Fig).…”

Section: Plos Geneticsmentioning

confidence: 99%

“…We do, however, observe a slight decrease in performance in the classification of selective sweeps when sweep simulations are missing data, with an increase in the percentage of sweep simulations missing data being classified as neutral. This robustness to missing data can be attributed to the types of summary statistics applied and the manner in which they are calculated using SNP-delimited windows [9]. Another common confounding factor is background selection, in which deleterious mutations cause a loss of diversity which might be confused for selection signatures [26].…”

Section: Plos Geneticsmentioning

confidence: 99%

“…For this reason we test SURFDAWave's performance on background selection, which we simulate based on the distribution of effect sizes and spatial distribution of coding elements in the human genome, as in refs. [27] and [9] (see Materials and methods for details). We find that 93.4% and 94.2% of background selection scenarios under the CEU and YRI demographic histories, respectively, are classified as neutral (Fig 6).…”

Section: Plos Geneticsmentioning

confidence: 99%

“…The complementary approach shown in ref. [9] explicitly models the spatial autocorrelation of summary statistics to capture the underlying wave patterns produced by selective sweeps.…”

Section: Introductionmentioning

confidence: 99%

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Learning the properties of adaptive regions with functional data analysis

et al. 2020

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Identifying regions of positive selection in genomic data remains a challenge in population genetics. Most current approaches rely on comparing values of summary statistics calculated in windows. We present an approach termed SURFDAWave, which translates measures of genetic diversity calculated in genomic windows to functional data. By transforming our discrete data points to be outputs of continuous functions defined over genomic space, we are able to learn the features of these functions that signify selection. This enables us to confidently identify complex modes of natural selection, including adaptive introgression. We are also able to predict important selection parameters that are responsible for shaping the inferred selection events. By applying our model to human population-genomic data, we recapitulate previously identified regions of selective sweeps, such as OCA2 in Europeans, and predict that its beneficial mutation reached a frequency of 0.02 before it swept 1,802 generations ago, a time when humans were relatively new to Europe. In addition, we identify BNC2 in Europeans as a target of adaptive introgression, and predict that it harbors a beneficial mutation that arose in an archaic human population that split from modern humans within the hypothesized modern human-Neanderthal divergence range.

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Section: Resultsmentioning

confidence: 99%

Section: Plos Geneticsmentioning

confidence: 99%

Section: Plos Geneticsmentioning

confidence: 99%

Section: Plos Geneticsmentioning

confidence: 99%

“…The complementary approach shown in ref. [9] explicitly models the spatial autocorrelation of summary statistics to capture the underlying wave patterns produced by selective sweeps.…”

Section: Introductionmentioning

confidence: 99%

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Learning the properties of adaptive regions with functional data analysis

et al. 2020

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A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps

Song,

Chu,

et al. 2024

Advanced Science

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The identification and classification of selective sweeps are of great significance for improving the understanding of biological evolution and exploring opportunities for precision medicine and genetic improvement. Here, a domain adaptation sweep detection and classification (DASDC) method is presented to balance the alignment of two domains and the classification performance through a domain‐adversarial neural network and its adversarial learning modules. DASDC effectively addresses the issue of mismatch between training data and real genomic data in deep learning models, leading to a significant improvement in its generalization capability, prediction robustness, and accuracy. The DASDC method demonstrates improved identification performance compared to existing methods and excels in classification performance, particularly in scenarios where there is a mismatch between application data and training data. The successful implementation of DASDC in real data of three distinct species highlights its potential as a useful tool for identifying crucial functional genes and investigating adaptive evolutionary mechanisms, particularly with the increasing availability of genomic data.

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The population genomics of adaptive loss of function

et al. 2021

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Discoveries of adaptive gene knockouts and widespread losses of complete genes have in recent years led to a major rethink of the early view that loss-of-function alleles are almost always deleterious. Today, surveys of population genomic diversity are revealing extensive loss-of-function and gene content variation, yet the adaptive significance of much of this variation remains unknown. Here we examine the evolutionary dynamics of adaptive loss of function through the lens of population genomics and consider the challenges and opportunities of studying adaptive loss-of-function alleles using population genetics models. We discuss how the theoretically expected existence of allelic heterogeneity, defined as multiple functionally analogous mutations at the same locus, has proven consistent with empirical evidence and why this impedes both the detection of selection and causal relationships with phenotypes. We then review technical progress towards new functionally explicit population genomic tools and genotype-phenotype methods to overcome these limitations. More broadly, we discuss how the challenges of studying adaptive loss of function highlight the value of classifying genomic variation in a way consistent with the functional concept of an allele from classical population genetics.

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Localizing and Classifying Adaptive Targets with Trend Filtered Regression

Cited by 37 publications

References 85 publications

Learning the properties of adaptive regions with functional data analysis

Learning the properties of adaptive regions with functional data analysis

A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps

The population genomics of adaptive loss of function

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