Quantitative Trait Locus (QTL) Mapping Using Different Testers and Independent Population Samples in Maize Reveals Low Power of QTL Detection and Large Bias in Estimates of QTL Effects

TLDR

Marker‑assisted selection relies on powerful QTL detection and unbiased effect estimation. The study genotyped 89 markers in two independent F2 samples (N = 344 and 107), evaluated testcross progenies of the corresponding F3 lines with two testers across four environments, mapped QTL for grain yield and other traits, and estimated QTL effects both from the calibration data and from an independent validation sample. The analysis identified 107 QTL in the larger sample and 39 in the smaller, with only 20 shared; QTL effects were consistent across testers but were markedly inflated when estimated from the calibration data, as validation estimates of variance explained were substantially lower, indicating that independent samples are needed to avoid over‑optimistic assessments of MAS efficiency.

Abstract

Abstract The efficiency of marker-assisted selection (MAS) depends on the power of quantitative trait locus (QTL) detection and unbiased estimation of QTL effects. Two independent samples (N = 344 and 107) of F2 plants were genotyped for 89 RFLP markers. For each sample, testcross (TC) progenies of the corresponding F3 lines with two testers were evaluated in four environments. QTL for grain yield and other agronomically important traits were mapped in both samples. QTL effects were estimated from the same data as used for detection and mapping of QTL (calibration) and, based on QTL positions from calibration, from the second, independent sample (validation). For all traits and both testers we detected a total of 107 QTL with N = 344, and 39 QTL with N = 107, of which only 20 were in common. Consistency of QTL effects across testers was in agreement with corresponding genotypic correlations between the two TC series. Most QTL displayed no significant QTL × environment nor epistatic interactions. Estimates of the proportion of the phenotypic and genetic variance explained by QTL were considerably reduced when derived from the independent validation sample as opposed to estimates from the calibration sample. We conclude that, unless QTL effects are estimated from an independent sample, they can be inflated, resulting in an overly optimistic assessment of the efficiency of MAS.