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Estimating Selection Pressures from Limited Comparative Data
Author(s) -
Joshua B. Plotkin,
Jonathan Dushoff,
Michael M. Desai,
Hunter B. Fraser
Publication year - 2006
Publication title -
molecular biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.637
H-Index - 218
eISSN - 1537-1719
pISSN - 0737-4038
DOI - 10.1093/molbev/msl021
Subject(s) - biology , selection (genetic algorithm) , volatility (finance) , regression , computational biology , genome , population , evolutionary biology , econometrics , genetics , computer science , statistics , machine learning , mathematics , gene , demography , sociology
We recently introduced a novel method for estimating selection pressures on proteins, termed "volatility," which requires only a single genome sequence. Some criticisms that have been levied against this approach are valid, but many others are based on misconceptions of volatility, or they apply equally to comparative methods of estimating selection. Here, we introduce a simple regression technique for estimating selection pressures on all proteins in a genome, on the basis of limited comparative data. The regression technique does not depend on an underlying population-genetic mechanism. This new approach to estimating selection across a genome should be more powerful and more widely applicable than volatility itself.

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