Full Length Research Article
RETRACTED ARTICLE: Association Mapping Study of Various Desirable Traits of Rice
Sumaira Aslam Chohan2, Muhammad Ashfaq1, Farah Khan*2
Adv. life sci., vol. 10, no. 2, pp. 249-258, June 2023
*- Corresponding Author: Farah Khan (drfarah_khann@yahoo.com)
Authors' Affiliations
2. Department of Botany, Lahore College for Women University, Lahore – Pakistan
[Date Received: 26/12/2022; Date Revised: 17/04/2023; Date Published: 30/06/2023]
Abstract
Introduction
Methods
Results
Discussion
References
Abstract
Background: This study was performed to evaluate the diversity of various morphological characters and their relationship with yield in rice. The goal of this work was to find quantitative trait loci (QTL) for yield, yield components, and other agronomic variables in 100 different rice germplasm samples, as well as to assess the genetic structure and degree of linkage disequilibrium in the rice germplasm diversity panel. To establish Linkage Disequilibrium (LD) between markers and causative mutations, marker density is essential. Linkage disequilibrium (LD) patterns of various SNP markers on all chromosomes. If markers are sufficiently dense to have good coverage of LD, the LD decay with distance can be compared to the marker density.
Methods: Different traits were measured and recorded under Randomized Complete Block Design (RCBD) experiment. DNA extraction and PCR analysis was done to measure the genotypic characteristics of rice. Genotypic and phenotypic variability was measured by using ANOVA and GWAS.
Results: For pair-wise markers, linkage disequilibrium is calculated as R square and plotted versus the distance between the markers. In this study, the overall phenotypic variability among the examined traits was represented by R2 and ranged from 11.47% to 25.44%. The genetic architecture of these traits may be implied by the recently identified genomic regions (loci). An influential replacement for bi-parental gene maps, genome-wide association studies (GWAS) use data from genome-wide markers in large amounts of easily obtained germplasm.
Conclusion: The linkage disequilibrium, which is the non-random link between an allele at two or more loci, is used in this mapping method to infer the innate relationships between phenotypic variations and marker polymorphisms. Genome Wide Association Study (GWAS) of genotypes provides the information about for the selection of genotypes and determination of new marker trait association.
Keywords: Oryza sativa L; Rice; DNA; Association mapping; Traits
Retraction Note
24 Sept 2025: The Editor-in-Chief has retracted this article due to the below mentioned scientific deficiencies revealed by an internal audit.
1. The "Methods" and "Results" sections describe an association mapping study using SSR (microsatellite) markers. However, the "Discussion" section describes the results of a completely different analysis using SNP (single nucleotide polymorphism) markers from a "10k SNP array". The number of associated markers and the phenotypic variance values are completely different for the same traits between the "Results" (SSR data) and "Discussion" (SNP data) sections. The document states that "216 polymorphic SSR markers were employed" for the study. However, the discussion later claims a result was obtained "With the use of 204 polymorphic SSR markers".
2. The text states there are "thirteen SSR markers" for this trait. It then describes their locations as "two markers were from chromosomes 4, 6 and 8 and the remaining were from chromosomes 1, 2, 3, 5, 9, 11 and 9". Listing chromosome 9 twice appears to be an error, and the count is confusing (3 chromosomes x 2 markers + 7 chromosomes x 1 marker = 13 markers, but chromosome 9 is repeated).
3. The distribution of 14 markers is described as "four on chromosome 5, three on each of chromosomes 1, 2, and 10, two on each of chromosomes 1, 2, and 9, and one on chromosome 4". Chromosomes 1 and 2 are listed twice with different marker counts (three and two), making the description contradictory and impossible to parse.
4. The abstract states the overall phenotypic variability (R2) ranged from 11.47% to 25.44%. However, the results section lists multiple marker-trait associations with phenotypic variance values outside this range, such as 28.57% for plant height, 29.78% for spikelets per panicle, 26.85% for primary branches, 26.97% for secondary branches, 28.68% for seed thickness, and 29.88% for 1000-seed weight.
5. The methods state, "Markers were considered significant if the adjusted P value was greater than 0.05". This is scientifically incorrect. A significance threshold is conventionally set as a p-value less than a certain value (e.g., P<0.05).
6. The "Discussion" section is based on a Genome-Wide Association Study (GWAS) using a "10k SNP array" and "GAPIT in the R software". These materials and methods are never mentioned in the "Methods" section, which only describes SSR marker analysis using TASSEL and STRUCTURE software.
7. The method for multiple testing correction is described as "50000 mutations per mutation", which is nonsensical. This is likely a typographical error for "50,000 permutations".
8. The PCR program is described as consisting of "6 cycles". The subsequent description details multiple steps, including an initial denaturation, a final extension, and "36 repeated cycles" of amplification. This is a single multi-step program, not "6 cycles".
The authors have not responded to correspondence regarding this retraction.
