GenomegaMap: Within-Species Genome-Wide dN/dS Estimation from over 10,000 Genomes
Daniel J. Wilson, Derrick W. Crook, Tim Peto, A Sarah Walker, Sarah Hoosdally, Ana Lúıza Gibertoni Cruz, Joshua A. Carter, Clara Grazian, Sarah G. Earle, Samaneh Kouchaki, Alexander S. Lachapelle, Yang Yang, David A. Clifton, Philip W. Fowler, Zamin Iqbal, Martin Hunt, Jeffrey D. Knaggs, E. Grace Smith, Priti Rathod, Lisa Jarrett, Daniela Matias, Daniela María Cirillo, Emanuele Borroni, Simone Battaglia, Arash Ghodousi, Andrea Spitaleri, Andrea Maurizio Cabibbe, Sabira Tahseen, Kayzad Nilgiriwala, Sanchi Shah, Camilla Rodrigues, Priti Kambli, Utkarsha Surve, Rukhsar Khot, Stefan Niemann, Thomas A. Kohl, Matthias Merker, Harald Hoffmann, Katharina Todt, Sara Plesnik, Nazir Ismail, Shaheed Vally Omar, Lavania Joseph, Guy Thwaites, Thuong Nguyen Thuy Thuong, Nhung Hoang Ngoc, Vijay Srinivasan, A Sarah Walker, David Moore, Jorge Coronel, Walter Solano, George F. Gao, Guangxue He, Yanlin Zhao, Chunfa Liu, Aijing Ma, Baoli Zhu, Ian F. Laurenson, Pauline Claxton, Anastasia Koch, Robert J. Wilkinson, Ajit Lalvani, James E. Posey, Jennifer L. Gardy, Jim Werngren, Nicholas I. Paton, Ruwen Jou, Mei-Hua Wu, Wan-Hsuan Lin, Lucilaine Ferrazoli, Rosângela Siqueira de Oliveira, Irena Arandjelović, Angkana Chaiprasert, Iñaki Comas, Calle Jaime Roig, Francis Drobniewski, Maha Farhat, Qian Gao, Rick Ong Twee Hee, Vitali Sintchenko, Philip Supply, Dick van Soolingen
Abstract
The dN/dS ratio provides evidence of adaptation or functional constraint in protein-coding genes by quantifying the relative excess or deficit of amino acid-replacing versus silent nucleotide variation. Inexpensive sequencing promises a better understanding of parameters, such as dN/dS, but analyzing very large data sets poses a major statistical challenge. Here, I introduce genomegaMap for estimating within-species genome-wide variation in dN/dS, and I apply it to 3,979 genes across 10,209 tuberculosis genomes to characterize the selection pressures shaping this global pathogen. GenomegaMap is a phylogeny-free method that addresses two major problems with existing approaches: 1) It is fast no matter how large the sample size and 2) it is robust to recombination, which causes phylogenetic methods to report artefactual signals of adaptation. GenomegaMap uses population genetics theory to approximate the distribution of allele frequencies under general, parent-dependent mutation models. Coalescent simulations show that substitution parameters are well estimated even when genomegaMap's simplifying assumption of independence among sites is violated. I demonstrate the ability of genomegaMap to detect genuine signatures of selection at antimicrobial resistance-conferring substitutions in Mycobacterium tuberculosis and describe a novel signature of selection in the cold-shock DEAD-box protein A gene deaD/csdA. The genomegaMap approach helps accelerate the exploitation of big data for gaining new insights into evolution within species.