Genetics

Genome-Wide Association Mapping for Seedling and Adult Plant Resistance to Stripe Rust in Synthetic Hexaploid Wheat

Use of genetic diversity from related wild and domesticated species has made a significant contribution to improving wheat productivity. Synthetic hexaploid wheats (SHWs) exhibit natural genetic variation for resistance and/or tolerance to biotic and abiotic stresses. Stripe rust caused by (Puccinia striiformis f. sp. tritici; Pst), is an important disease of wheat worldwide. To characterise loci conferring resistance to stripe rust in SHWs, we conducted a genome-wide association study (GWAS) with a panel of 181 SHWs using the wheat 9K SNP iSelect array.

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Genetic diversity of rice (Oryza sativa) germplasm from six countries using simple sequence repeats markers.

This experiment was carried out to determine the genotypic variation among rice (Oryza sativa) accessions using simple sequence repeats (SSR) markers. In the present study, a total of 12 SSR markers were used across 87 rice accessions from six countries. NTSYS-pc and PowerMarker software were used for data analysis. Six primers out of these 12 primers showed DNA amplification and polymorphism among the 87 rice accessions.

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Genetic analysis of tolerance to rice tungro bacilliform virus in rice (Oryza sativa L.) through agroinoculation.

Balimau Putih [an Indonesian cultivar tolerant to rice tungro bacilliform virus (RTBV)] was crossed with IR64 (RTBV, susceptible variety) to produce the three filial generations F1, F2 and F3. Agroinoculation was used to introduce RTBV into the test plants. RTBV tolerance was based on the RTBV level in plants by analysis of coat protein using enzyme-linked immunosorbent assay. The level of RTBV in cv. Balimau Putih was significantly lower than that of IR64 and the susceptible control, Taichung Native 1.

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Determination of genetic structure of germplasm collections: are traditional hierarchical clustering methods appropriate for molecular marker data?

Despite the availability of newer approaches, traditional hierarchical clustering remains very popular in genetic diversity studies in plants. However, little is known about its suitability for molecular marker data. We studied the performance of traditional hierarchical clustering techniques using real and simulated molecular marker data. Our study also compared the performance of traditional hierarchical clustering with model-based clustering (STRUCTURE).

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