Learning the properties of adaptive regions with functional data analysis

English version

Autoři: Mehreen R. Mughal ^aff001; Hillary Koch ^aff002; Jinguo Huang ^aff001; Francesca Chiaromonte ^aff002; Michael DeGiorgio ^aff003
Působiště autorů: Bioinformatics and Genomics at the Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, United States of America ^aff001; Department of Statistics, Pennsylvania State University, University Park, Pennsylvania, United States of America ^aff002; Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, United States of America ^aff003
Vyšlo v časopise: Learning the properties of adaptive regions with functional data analysis. PLoS Genet 16(8): e32767. doi:10.1371/journal.pgen.1008896
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008896

Souhrn

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.

Klíčová slova:

Forecasting – Genomics – Genomics statistics – Haplotypes – Human genomics – Introgression – Simulation and modeling – Statistical distributions

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