Defining window-boundaries for genomic analyses using smoothing spline techniques.
BACKGROUND
High-density genomic data is often analyzed by combining information over windows of adjacent markers. Interpretation of data grouped in windows versus at individual locations may increase statistical power, simplify computation, reduce sampling noise, and reduce the total number of tests performed. However, use of adjacent marker information can result in over- or under-smoothing, undesirable window boundary specifications, or highly correlated test statistics. We introduce a method for defining windows based on statistically guided breakpoints in the data, as a foundation for the analysis of multiple adjacent data points. This method involves first fitting a cubic smoothing spline to the data and then identifying the inflection points of the fitted spline, which serve as the boundaries of adjacent windows. This technique does not require prior knowledge of linkage disequilibrium, and therefore can be applied to data collected from individual or pooled sequencing experiments. Moreover, in contrast to existing methods, an arbitrary choice of window size is not necessary, since these are determined empirically and allowed to vary along the genome.


RESULTS
Simulations applying this method were performed to identify selection signatures from pooled sequencing FST data, for which allele frequencies were estimated from a pool of individuals. The relative ratio of true to false positives was twice that generated by existing techniques. A comparison of the approach to a previous study that involved pooled sequencing FST data from maize suggested that outlying windows were more clearly separated from their neighbors than when using a standard sliding window approach.


CONCLUSIONS
We have developed a novel technique to identify window boundaries for subsequent analysis protocols. When applied to selection studies based on F ST data, this method provides a high discovery rate and minimizes false positives. The method is implemented in the R package GenWin, which is publicly available from CRAN.
Department of Plant Sciences, University of California, Davis, 95616, USA. beissinger@ucdavis.edu.
Timothy M
TM
Beissinger
Department of Animal Sciences, University of Wisconsin, Madison, 53706, USA. grosa@wisc.edu.|Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, 53792, USA. grosa@wisc.edu.
Guilherme J M
GJ
Rosa
Department of Agronomy, University of Wisconsin, Madison, 53706, USA. smkaeppl@wisc.edu.|Department of Energy Great Lakes Bioenergy Research Center, University of Wisconsin, Madison, 53706, USA. smkaeppl@wisc.edu.
Shawn M
SM
Kaeppler
Department of Animal Sciences, University of Wisconsin, Madison, 53706, USA. gianola@ansci.wisc.edu.|Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, 53792, USA. gianola@ansci.wisc.edu.|Department of Dairy Science, University of Wisconsin, Madison, 53706, USA. gianola@ansci.wisc.edu.
Daniel
D
Gianola
Department of Agronomy, University of Wisconsin, Madison, 53706, USA. ndeleongatti@wisc.edu.|Department of Energy Great Lakes Bioenergy Research Center, University of Wisconsin, Madison, 53706, USA. ndeleongatti@wisc.edu.
Natalia
N
de Leon
D003627:Data Interpretation, Statistical; D005787:Gene Frequency; D023281:Genomics / Q000379:methods*; D003313:Zea mays / Q000235:genetics