Realistic Scenarios of Phenotypic Variation and Errors in High-Throughput Phenotyping Experiments Minimally Impact the Results of QTL Mapping Analysis.

High-throughput phenotyping technologies increase the efficiency of breeding programs, but with larger data sets, errors can accumulate. Plant breeders often conduct quantitative trait locus (QTL) mapping, where large sample size and accurate quantitative response estimates are important for detecting small effect QTL. This study examined how phenotype error, inconsistency, and replication changed QTL magnitude and location. Three real sets of phenotype data were used from microscopy robot analysis of grapevine powdery mildew () severity, which previously resulted in discovery of large (R = 85%), intermediate (R = 45%), and small (R = 9%) effect QTL. Custom R scripts were written to induce several realistic sources of error, inconsistency, and varied replication. The results were remarkably robust to these changes. Swapping or shifting 2% of samples or changing disease severity by 50% on one replicate had negligible impact on QTL. Unreplicated simulations produced the largest LOD score range (5.55 to 8.27) and mean LOD score deviation (-1.72 to -3.22; Cohen's D = 1.48 to 2.12). The large effect size QTL () was always detected. The intermediate effect size QTL () was detected except when three of the eight replicates were analyzed individually. Even for the small effect size locus (), error scenarios rarely (2 of 9000 cases) eliminated significant QTL detection, versus no replication (9 of 10). Thus, the benefits of data volume associated with high-throughput phenotyping technologies outweigh the cost of the increased errors tested here. Instead, focus should be spent on examining how each experimental replicate contributes to the result of the QTL mapping analysis.