Localizing and classifying adaptive targets with trend filtered regression

Mughal, Mehreen R.; DeGiorgio, Michael

doi:10.1101/320523

Cited by 18 publications

(69 citation statements)

References 90 publications

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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, as Trendsetter also models the spatial autocorrelation of summary statistics, and has already been extensively compared to other leading methods (Mughal and DeGiorgio, 2019).…”

Section: Resultsmentioning

confidence: 99%

“…We initially compared SURFDAWave to Trendsetter, using the same set of summary statistics calculated from the same simulated datasets. Note that the set of summary statistics and number of windows used here is slightly different from what was originally employed by Trendsetter in Mughal and DeGiorgio (2019). However, we chose to focus on how the modeling of the summary statistics, rather than the number or choice of summary statistics, would affect differences in classification rates between SURFDAwave and Trendsetter.…”

Section: Classification Of Selective Sweepsmentioning

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 (Mughal and DeGiorgio, 2019). Another common confounding factor is background selection, in which deleterious mutations cause a loss of diversity which might be confused for selection signatures (Charlesworth, 2013).…”

Section: Classification With Confounding Factorsmentioning

confidence: 99%

“…Another common confounding factor is background selection, in which deleterious mutations cause a loss of diversity which might be confused for selection signatures (Charlesworth, 2013). 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 Schrider and Kern (2017) and Mughal and DeGiorgio (2019) (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 ( Figure 6).…”

Section: Classification With Confounding Factorsmentioning

confidence: 99%

“…Other methods forgo explicitly measuring diversity and transform SNP data directly to images to learn population-genetic parameters such as recombination rates (Chan et al, 2018;Flagel et al, 2019) and to identify selected regions (Flagel et al, 2019). The complementary approach of Mughal and DeGiorgio (2019) 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

Mughal

Koch

Huang

et al. 2019

Preprint

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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: Classification Of Selective Sweepsmentioning

confidence: 99%

Section: Classification With Confounding Factorsmentioning

confidence: 99%

Section: Classification With Confounding Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning the properties of adaptive regions with functional data analysis

Mughal

Koch

Huang

et al. 2019

Preprint

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A selection pressure landscape for 870 human polygenic traits

et al. 2021

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Characterizing the natural selection of complex traits is essential for understanding human evolution and biological or pathological mechanisms. To fulfill this requirement, we leveraged Genome-wide summary statistics for 870 polygenic traits and quantified the selection pressure of different forms and time scales on them in European ancestry. We found that 88% of traits underwent polygenic adaptation in the past 2000 years. At the present time and Neolithic period, selection pressure showed profound alteration. Traits related to pigmentation, impedance, and nutrition intake exhibited strong selection signals across different time scales. Our result provided an overview of selection pressure on various human polygenic traits, which served as a foundation for further populational and medical genetic studies.

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A Likelihood Approach for Uncovering Selective Sweep Signatures from Haplotype Data

Harris

DeGiorgio

2020

Molecular Biology and Evolution

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Selective sweeps are frequent and varied signatures in the genomes of natural populations, and detecting them is consequently important in understanding mechanisms of adaptation by natural selection. Following a selective sweep, haplotypic diversity surrounding the site under selection decreases, and this deviation from the background pattern of variation can be applied to identify sweeps. Multiple methods exist to locate selective sweeps in the genome from haplotype data, but none leverage the power of a model-based approach to make their inference. Here, we propose a likelihood ratio test statistic T to probe whole genome polymorphism datasets for selective sweep signatures. Our framework uses a simple but powerful model of haplotype frequency spectrum distortion to find sweeps and additionally make an inference on the number of presently sweeping haplotypes in a population. We found that the T statistic is suitable for detecting both hard and soft sweeps across a variety of demographic models, selection strengths, and ages of the beneficial allele. Accordingly, we applied the T statistic to variant calls from European and sub-Saharan African human populations, yielding primarily literature-supported candidates, including LCT, RSPH3, and ZNF211 in CEU, SYT1, RGS18, and NNT in YRI, and HLA genes in both populations. We also searched for sweep signatures in Drosophila melanogaster, finding expected candidates at Ace, Uhg1, and Pimet. Finally, we provide open-source software to compute the T statistic and the inferred number of presently sweeping haplotypes from whole-genome data.

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Localizing and classifying adaptive targets with trend filtered regression

Cited by 18 publications

References 90 publications

Learning the properties of adaptive regions with functional data analysis

Learning the properties of adaptive regions with functional data analysis

A selection pressure landscape for 870 human polygenic traits

A Likelihood Approach for Uncovering Selective Sweep Signatures from Haplotype Data

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