Assessing the significance of quantitative trait loci in replicable mapping populations

Abstract

Replicable populations, such as panels of recombinant inbred or doubled haploid lines, are convenient resources for the mapping of QTL. To increase mapping power, replications are often collected within each RI line and a common way to analyze such data is to include in the QTL model only a single measurement from each line that represents the average among the replicates (a line means model). An obvious, but seldom explored, alternative, is to include every replicate in the model (a full data model). Here, we use simulations to compare these two approaches. Further, we propose an extension of the standard permutation procedure that is required to correctly control the type I error in mapping populations with nested structure.

Figure 1.—

Critical thresholds for QTL detection with different models and permutation procedures. Thresholds derived by simulation are labeled as “true.” Results are shown for a panel size of 100 lines and a marker spacing of 10 cM. (A) Line means model. True, open bars; standard, shaded bars. (B) Full data model. True, open bars; standard, shaded bars; nested, solid bars.

Figure 1.—

Critical thresholds for QTL detection with different models and permutation procedures. Thresholds derived by simulation are labeled as “true.” Results are shown for a panel size of 100 lines and a marker spacing of 10 cM. (A) Line means model. True, open bars; standard, shaded bars. (B) Full data model. True, open bars; standard, shaded bars; nested, solid bars.

Figure 2.—

Effect of polygenic background variation and balance of design on QTL mapping. Results are shown for a panel size of 100 lines and a marker spacing of 10 cM. Line means, open bars; standard, shaded bars; nested, solid bars. (A) False positives. No QTL is present. (B) False positives. Five QTL are present. (C) True positives. Five QTL are present.

Figure 2.—

Effect of polygenic background variation and balance of design on QTL mapping. Results are shown for a panel size of 100 lines and a marker spacing of 10 cM. Line means, open bars; standard, shaded bars; nested, solid bars. (A) False positives. No QTL is present. (B) False positives. Five QTL are present. (C) True positives. Five QTL are present.

Figure 2.—

Effect of polygenic background variation and balance of design on QTL mapping. Results are shown for a panel size of 100 lines and a marker spacing of 10 cM. Line means, open bars; standard, shaded bars; nested, solid bars. (A) False positives. No QTL is present. (B) False positives. Five QTL are present. (C) True positives. Five QTL are present.

Figure 3.—

Effect of line number and marker spacing on QTL mapping. Results are shown for simulations with five QTL in an unbalanced design. Line means, open bars; standard, shaded bars; nested, solid bars. (A) False positives. (B) True positives.

Figure 3.—

Effect of line number and marker spacing on QTL mapping. Results are shown for simulations with five QTL in an unbalanced design. Line means, open bars; standard, shaded bars; nested, solid bars. (A) False positives. (B) True positives.