From Summary Statistics to Gene Trees: Methods for Inferring Positive Selection. Issue 4 (April 2020)

• Record Type: Journal Article
• Authors: Hejase, Hussein A.; Dukler, Noah; Siepel, Adam
• Is Part Of: Trends in genetics. Volume 36:Issue 4(2020)
• Page Start: 243
• Page End: 258
• Publication Date: 2020-04
• Subjects: ancestral recombination graph -- simulation -- machine learning; Genetics -- Periodicals; 576.5
• DOI: 10.1016/j.tig.2019.12.008
• Languages: English
• ISSNs: 0168-9525

Abstract

Abstract : Methods to detect signals of natural selection from genomic data have traditionally emphasized the use of simple summary statistics. Here, we review a new generation of methods that consider combinations of conventional summary statistics and/or richer features derived from inferred gene trees and ancestral recombination graphs (ARGs). We also review recent advances in methods for population genetic simulation and ARG reconstruction. Finally, we describe opportunities for future work on a variety of related topics, including the genetics of speciation, estimation of selection coefficients, and inference of selection on polygenic traits. Together, these emerging methods offer promising new directions in the study of natural selection. Highlights: Gene trees and ARGs represent powerful and rich data structures for the detection of signatures of natural selection from DNA sequences. Methodological advances in inferring genome-wide genealogies provide an alternative and complementary way to infer natural selection by making use of the full data set rather than traditional summary statistics. In this review, we discuss the biological importance of studying selection and advances in selection simulators. Furthermore, we review traditional summary statistics and methods that aggregate multiple statistics, including approximate Bayesian computation (ABC) and supervised machine learning methods. We also discuss future directions in inferring sequences of gene trees and scalable ARGs and their use in studying selection.