Accurate prediction of quantitative traits with failed SNP calls in canola and maize
Sven E. Weber; Harmeet Singh Chawla; Lennard Ehrig; Lee T. Hickey; Matthias Frisch; Rod J. Snowdon · 2023 · Frontiers in Plant Science
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed al
Abstract by Sven E. Weber; Harmeet Singh Chawla; Lennard Ehrig; Lee T. Hickey; Matthias Frisch; Rod J. Snowdon, Frontiers in Plant Science (2023) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3389/fpls.2023.1221750