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. 2010 Feb;184(2):517-27.
doi: 10.1534/genetics.109.111161. Epub 2009 Nov 30.

The power of the methods for detecting interlocus gene conversion

Sayaka P Mansai  1 , Hideki Innan
Affiliations

The power of the methods for detecting interlocus gene conversion

Sayaka P Mansai et al. Genetics. 2010 Feb.

Abstract

Interlocus gene conversion can homogenize DNA sequences of duplicated regions with high homology. Such nonvertical events sometimes cause a misleading evolutionary interpretation of data when the effect of gene conversion is ignored. To avoid this problem, it is crucial to test the data for the presence of gene conversion. Here, we performed extensive simulations to compare four major methods to detect gene conversion. One might expect that the power increases with increase of the gene conversion rate. However, we found this is true for only two methods. For the other two, limited power is expected when gene conversion is too frequent. We suggest using multiple methods to minimize the chance of missing the footprint of gene conversion.

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Figures

F<sc>igure</sc> 1.—
Figure 1.—
Summary of the simulations in the two-species two-locus model. (A) Illustration of the model. (B–E) The power of the four approaches. The average gene conversion tract length (1/q) is assumed to be 100 bp. See Figure S1 for the results with 1/q = 1000 bp.
F<sc>igure</sc> 2.—
Figure 2.—
Performance of GENECONV. Simulation was performed by following the history illustrated in Figure 1A. The simulation is different from the others in that the gene conversion tract length is fixed to either t = 100 or 1000 bp. (A) The effect of gene conversion initiation rate (g) on the number of detected tracts. (B) The effect of g on the lengths of detected tracts. (C–D) Illustrations of the outputs of GENECONV in five representative replications when g = 10−4 (C) and when g = 10−2 (D). For each replication of the simulations, the locations of the nucleotide differences between XA and XB are shown by vertical lines in the top part. Vertical lines in the bottom part are other variable sites in the alignment of the four sequences. Regions with significant signatures of gene conversion (P < 0.05) are presented by thick horizontal lines.
F<sc>igure</sc> 3.—
Figure 3.—
Power of GENECONV under different settings. (A) The effect of the “gscale” setting on the number of detected tracts. (B) The effect on the length of detected tracts.
F<sc>igure</sc> 4.—
Figure 4.—
The numbers of shared (A) and other variable sites (B) in polymorphism data from a pair of duplicated genes in a single species. The sample size is assumed to be n = 10. The average numbers per replication in the simulation under the two-species and two-locus model are shown.
F<sc>igure</sc> 5.—
Figure 5.—
Summary of the simulations in the one-species four-locus model. (A) Illustration of the model. (B–E) The power of the four approaches. The average gene conversion tract length (1/q) is assumed to be 100 bp. See Figure S2 for the results with 1/q = 1000 bp.
F<sc>igure</sc> 6.—
Figure 6.—
Summary of the simulations in the four-species two-locus model. (A) Illustration of the model. (B–E) The power of the four approaches. The average gene conversion tract length (1/q) is assumed to be 100 bp. See Figure S3 for the results with 1/q = 1000 bp.
F<sc>igure</sc> 7.—
Figure 7.—
The effects of entire branch length, orthologous divergence, and the length of the analyzed region on the power of the four approaches. Simulations were performed under the two-species two-locus model. The star represents the parameter used in Figure 1.
F<sc>igure</sc> 8.—
Figure 8.—
Relationship between the power of methods (ii) and (iii) and the heterogeneity in the proportions of compatible and incompatible sites.
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