Molecular Characterisation of Upland and Lowland Rice from Sarawak, Malaysian Borneo
A total of 39 Simple Sequence Repeat (SSR) markers distributed across 12 chromosomes were screened to assess genetic polymorphism among rice accessions. From the 39 tested SSR primer pairs, eight markers (RM1, RM489, RM552, RM444, RM257, RM481, RM166 and RM164) exhibited clear polymorphic banding...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
UNIMAS Publisher
2025
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/51173/1/Yeo-etal_2025_Rice-ISSR.pdf http://ir.unimas.my/id/eprint/51173/ https://publisher.unimas.my/ojs/index.php/BJRST/article/view/8507 https:doi.org/10.33736/bjrst.8507.2025 |
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| Summary: | A total of 39 Simple Sequence Repeat (SSR) markers distributed across 12 chromosomes were screened to assess
genetic polymorphism among rice accessions. From the 39 tested SSR primer pairs, eight markers (RM1, RM489,
RM552, RM444, RM257, RM481, RM166 and RM164) exhibited clear polymorphic banding patterns and strong
amplification, while the remaining were excluded due to monomorphism or unclear bands. Among the selected
primers, RM257 recorded the highest polymorphic information content (PIC) value (0.9316), while RM552 had
the lowest (0.4029), with an average PIC of 0.6891. The observed number of effective alleles (Ne) was 1.543 in
the lowland population and 1.566 in the upland population. Nei’s gene diversity (h) was 0.329 for lowland and
0.318 for upland populations, suggesting a low level of genetic divergence. Similarly, Shannon’s Information Index
(I) values were 0.475 and 0.490 for lowland and upland populations, respectively. Analysis of Molecular Variance
(AMOVA) revealed that 20% of genetic variation existed among populations, while 80% occurred within
populations. Clustering analysis using a UPGMA dendrogram based on SSR genotypes grouped the 44 rice
accessions into two major clusters, each further divided into sub-clusters. However, no clear grouping was
observed based on morphological traits or geographical origin, likely due to the higher sensitivity of molecular
markers in detecting underlying genetic differences that are not always reflected in visible traits. Unlike
conventional morphological classification which relies on observable characteristics such as grain shape, plant
height or habitat, genome-based clustering captures deeper genetic relationships that offer a more accurate picture
of population structure. Simultaneously, the Maturase-K barcoding gene marker grouped the accessions into a
single large cluster, which also included 94 accessions from diverse origins and three accessions of Oryza
rufipogon, indicating genome-level similarities. These findings underscore the utility of molecular markers in
revealing genetic diversity and population structure. The insights gained from this study can support the
development of targeted rice breeding programs essential for advancing sustainable agriculture. |
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