When a scientific article is found to be either fraudulent or erroneous, one course of action available to both the authors and the publisher is to retract said article. Unfortunately, not all retraction notices properly inform the reader of the problems with a retracted article. This study developed a novel rubric for rating and standardizing the quality of retraction notices, and used it to assess the retraction notices of 171 retracted articles from 15 journals. Results suggest the rubric to be a robust, if preliminary, tool. Analysis of the retraction notices suggest that their quality has not improved over the last 50 years, that it varies both between and within journals, and that it is dependent on the field of science, the author of the retraction notice, and the reason for retraction. These results indicate a lack of uniformity in the retraction policies of individual journals and throughout the scientific literature. The rubric presented in this study could be adopted by journals to help standardize the writing of retraction notices.
The composition of the microorganisms in the gut is a contributor to overall health, prompting the development of strategies to alter the microbiome composition. Studies have investigated the role of the diet on the microbiome, as it is a major modifiable risk factor contributing to health; however, little is known about the causal effects of consumption of specific foods on the gut microbiota.
Summary
Apple (Malus × domestica) has commercial and nutritional value, but breeding constraints of tree crops limit varietal improvement. Marker‐assisted selection minimises these drawbacks, but breeders lack applications for targeting fruit phytochemicals. To understand genotype–phytochemical associations in apples, we have developed a high‐throughput integration strategy for genomic and multiplatform metabolomics data.
Here, 124 apple genotypes, including members of three pedigree‐connected breeding families alongside diverse cultivars and wild selections, were genotyped and phenotyped. Metabolite genome‐wide association studies (mGWAS) were conducted with c. 10 000 single nucleotide polymorphisms and phenotypic data acquired via LC–MS and 1H NMR untargeted metabolomics. Putative metabolite quantitative trait loci (mQTL) were then validated via pedigree‐based analyses (PBA).
Using our developed method, 519, 726 and 177 putative mQTL were detected in LC–MS positive and negative ionisation modes, and NMR, respectively. mQTL were indicated on each chromosome, with hotspots on linkage groups 16 and 17. A chlorogenic acid mQTL was discovered on chromosome 17 via mGWAS and validated with a two‐step PBA, enabling discovery of novel candidate gene–metabolite relationships.
Complementary data from three metabolomics approaches and dual genomics analyses increased confidence in validity of compound annotation and mQTL detection. Our platform demonstrates the utility of multiomic integration to advance data‐driven, phytochemical‐based plant breeding.
Research Conducted: Apple (Malus x domestica) has commercial and nutritional value, but breeding constraints of tree crops limit varietal improvement. Marker-assisted selection minimizes these drawbacks, but breeders lack applications for targeting fruit phytochemicals. To understand genotype-phytochemical associations in apples, we have developed a high-throughput integration strategy for genomic and multi-platform metabolomics data.
Methods: 124 apple genotypes, including members of three pedigree-connected breeding families alongside diverse cultivars and wild selections, were genotyped and phenotyped. Metabolite genome-wide association studies (mGWAS) were conducted with ~10,000 single nucleotide polymorphisms and phenotypic data acquired via LC-MS and 1H NMR untargeted metabolomics. Putative metabolite quantitative trait loci (mQTL) were then validated via pedigree-based analyses (PBA).
Key Results: Using our developed method, 519, 726, and 177 putative mQTL were detected in LC-MS positive and negative ionization modes and NMR, respectively. mQTL were indicated on each chromosome, with hotspots on linkage groups 16 and 17. A chlorogenic acid mQTL was discovered on chromosome 17 via mGWAS and validated with a two-step PBA, enabling discovery of novel candidate gene-metabolite relationships.
Main Conclusion: Complementary data from three metabolomics approaches and dual genomics analyses increased confidence in validity of compound annotation and mQTL detection. Our platform demonstrates the utility of multi-omics integration to advance data-driven, phytochemical-based plant breeding.
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