Main conclusion Karyotyping using high-density genome-wide SNP markers identified various chromosomal aberrations in oil palm ( Elaeis guineensis Jacq.) with supporting evidence from the 2C DNA content measurements (determined using… Click to show full abstract
Main conclusion Karyotyping using high-density genome-wide SNP markers identified various chromosomal aberrations in oil palm ( Elaeis guineensis Jacq.) with supporting evidence from the 2C DNA content measurements (determined using FCM) and chromosome counts. Abstract Oil palm produces a quarter of the world’s total vegetable oil. In line with its global importance, an initiative to sequence the oil palm genome was carried out successfully, producing huge amounts of sequence information, allowing SNP discovery. High-capacity SNP genotyping platforms have been widely used for marker–trait association studies in oil palm. Besides genotyping, a SNP array is also an attractive tool for understanding aberrations in chromosome inheritance. Exploiting this, the present study utilized chromosome-wide SNP allelic distributions to determine the ploidy composition of over 1,000 oil palms from a commercial F 1 family, including 197 derived from twin-embryo seeds. Our method consisted of an inspection of the allelic intensity ratio using SNP markers. For palms with a shifted or abnormal distribution ratio, the SNP allelic frequencies were plotted along the pseudo-chromosomes. This method proved to be efficient in identifying whole genome duplication (triploids) and aneuploidy. We also detected several loss of heterozygosity regions which may indicate small chromosomal deletions and/or inheritance of identical by descent regions from both parents. The SNP analysis was validated by flow cytometry and chromosome counts. The triploids were all derived from twin-embryo seeds. This is the first report on the efficiency and reliability of SNP array data for karyotyping oil palm chromosomes, as an alternative to the conventional cytogenetic technique. Information on the ploidy composition and chromosomal structural variation can help to better understand the genetic makeup of samples and lead to a more robust interpretation of the genomic data in marker–trait association analyses.
               
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