From Summary Statistics to Gene Trees: Methods for Inferring Positive Selection

Hejase, H. A., Dukler, N., Siepel, A. (January 2020) From Summary Statistics to Gene Trees: Methods for Inferring Positive Selection. Trends Genet. ISSN 0168-9525 (Print)0168-9525

URL: https://www.ncbi.nlm.nih.gov/pubmed/31954511
DOI: 10.1016/j.tig.2019.12.008

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.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > genetics & nucleic acid processing > genomes > comparative genomics
bioinformatics > computational biology > algorithms > machine learning
CSHL Authors:
Communities: CSHL labs > Siepel lab
Depositing User: Adrian Gomez
Date: 15 January 2020
Date Deposited: 22 Jan 2020 19:33
Last Modified: 22 Jan 2020 19:33
Related URLs:
URI: https://repository.cshl.edu/id/eprint/38922

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