Realistic Scenarios of Phenotypic Variation and Errors in High-Throughput Phenotyping Experiments Minimally Impact the Results of Quantitative Trait Locus Mapping Analysis

Abstract

High-throughput phenotyping technologies increase the efficiency of breeding programs, but with larger datasets, 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 QTLs. 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 ( Erysiphe necator) severity, which previously resulted in discovery of large ( R 2 = 85%), intermediate ( R 2 = 45%), and small ( R 2 = 9%) effect QTLs. 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 QTLs. Unreplicated simulations produced the largest logarithm of the odds score range (5.55 to 8.27) and mean logarithm of the odds score deviation (−1.72 to −3.22; Cohen's D = 1.48 to 2.12). The large-effect-size QTL ( REN12) was always detected. The intermediate-effect-size QTL ( REN13) was detected except when three of the eight replicates were analyzed individually. Even for the small-effect-size locus ( NYVPLG9), error scenarios rarely (2 of 9,000 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, the focus should be on examining how each experimental replicate contributes to the results of the QTL mapping analysis.

Plant phenotyping relevance

高スループット画像表現型測定で生じる誤差・反復数・不整合がQTL解析結果に与える影響を系統的に評価しており、表現型取得データの技術的妥当性検証が中心である。

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