ArticleJournal of experimental botany2019
To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?
Article in Journal of experimental botany, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The genomic basis of local adaptation to photoperiod across altitude in a self-fertilizing monkeyflower.bioRxiv : the preprint server for biology · 2026Article
- A bittersweet symphony: genetic insights into cider apple fruit quality.G3 (Bethesda, Md.) · 2026Article
- Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS.Scientific reports · 2023Article
- Preparation and Curation of Phenotypic Datasets.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Robust regression based genome-wide multi-trait QTL analysis.Molecular genetics and genomics : MGG · 2021Article
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Authors and funding
7 authors.
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Abstract
Based on case studies, we discuss the extent to which genome-wide association studies (GWAS) are affected by outlier plants, i.e. those deviating from the expected distribution on a multi-criteria basis. Using a raw dataset consisting of daily measurements of leaf area, biomass, and plant height for thousands of plants, we tested three different cleaning methods for their effects on genetic analyses. No-cleaning resulted in the highest number of dubious quantitative trait loci, especially at loci with highly unbalanced allelic frequencies. A trade-off was identified between the risk of false-positives (with no-cleaning and/or a low threshold for minor allele frequency) and the risk of missing interesting rare alleles. Cleaning can lower the risk of the latter by making it possible to choose a higher threshold in GWAS.
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