Open AccessThe Trouble with Sliding Windows and the Selective Pressure in BRCA1
Author(s) -
Karl Schmid,
Ziheng Yang
Publication year - 2008
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0003746
Subject(s) - nonsynonymous substitution , sliding window protocol , negative selection , genetics , biology , synonymous substitution , selection (genetic algorithm) , gene , sequence (biology) , natural selection , population , coding region , variation (astronomy) , computational biology , statistics , evolutionary biology , mathematics , computer science , genome , artificial intelligence , window (computing) , physics , codon usage bias , demography , sociology , operating system , astrophysics
Sliding-window analysis has widely been used to uncover synonymous (silent, d S ) and nonsynonymous (replacement, d N ) rate variation along the protein sequence and to detect regions of a protein under selective constraint (indicated by d N < d S ) or positive selection (indicated by d N > d S ). The approach compares two or more protein-coding genes and plots estimates dˆ S and dˆ N from each sliding window along the sequence. Here we demonstrate that the approach produces artifactual trends of synonymous and nonsynonymous rate variation, with greater variation in dˆ S than in dˆ N . Such trends are generated even if the true d S and d N are constant along the whole protein and different codons are evolving independently. Many published tests of negative and positive selection using sliding windows that we have examined appear to be invalid because they fail to correct for multiple testing. Instead, likelihood ratio tests provide a more rigorous framework for detecting signals of natural selection affecting protein evolution. We demonstrate that a previous finding that a particular region of the BRCA1 gene experienced a synonymous rate reduction driven by purifying selection is likely an artifact of the sliding window analysis. We evaluate various sliding-window analyses in molecular evolution, population genetics, and comparative genomics, and argue that the approach is not generally valid if it is not known a priori that a trend exists and if no correction for multiple testing is applied.