A gene frequency model for QTL mapping using Bayesian inference

Table 2 Precision

QTL Var %	marker spacing (cM)	sample size	BGF1 (cM)	BGF2 (cM)	LSR1 (cM)	LSR2 (cM)
2	0.1	200	0.18^a	0.17^b	0.23^c	0.21^d
2	0.05	200	0.19^a	0.19^b	0.23^c	0.23^c
2	0.02	200	0.21^a	0.21^b	0.25^c	0.23^d
2	0.1	500	0.15^a	0.14^a	0.19^b	0.18^b
2	0.05	500	0.15^a	0.15^b	0.17^c	0.17^c
2	0.02	500	0.16^a	0.16^b	0.18^c	0.18^c
5	0.1	200	0.15^a	0.14^b	0.19^c	0.18^c
5	0.05	200	0.16^ab	0.15^b	0.17^ab	0.17^ac
5	0.02	200	0.17^a	0.16^b	0.18^c	0.17^c
5	0.1	500	0.14^a	0.14^bc	0.16^a	0.15^ac
5	0.05	500	0.12^a	0.11^a	0.12^bd	0.12^cd
5	0.02	500	0.11^a	0.10^b	0.12^cd	0.12^ad

Precision to map a QTL using the gene frequency model (BGF) and the least squares regression model (LSR) with one marker (BGF1, LSR1) or two flanking markers (BGF2, LSR2) for different variances explained by the QTL (% of phenotypic variance), marker spacing, and sample size. Mean absolute error of estimates of QTL location was used as the statistic to quantify precision of QTL mapping. Paired t-tests were done to test whether the pairwise differences between the BGF1, BGF2, LSR1 and LSR2 are significant or not for all twelve different scenarios. The results are based on 1500 simulating data sets. ^{a, b, c, d}Within a row, means without a common superscript differ (P < 0.05).

ISSN: 1297-9686