Dataset Information

Discovery of SNPs and InDels in papaya genotypes and its potential for marker assisted selection of fruit quality traits.

ABSTRACT: Papaya is a tropical and climacteric fruit that is recognized for its nutritional benefits and medicinal applications. Its fruits ripen quickly and show a drastic fruit softening, leading to great post-harvest losses. To overcome this scenario, breeding programs of papaya must invest in exploring the available genetic variation to continue developing superior cultivars with improved fruit quality traits. The objective of this study was to perform a whole-genome genotyping (WGG) of papaya, predict the effects of the identified variants, and develop a list of ripening-related genes (RRGs) with linked variants. The Formosa elite lines of papaya Sekati and JS-12 were submitted to WGG with an Illumina Miseq platform. The effects of variants were predicted using the snpEff program. A total of 28,451 SNPs having Ts/Tv (Transition/Transversion) ratio of 2.45 and 1,982 small insertions/deletions (InDels) were identified. Most variant effects were predicted in non-coding regions, with only 2,104 and 138 effects placed in exons and splice site regions, respectively. A total of 106 RRGs were found to be associated with 460 variants, which may be converted into PCR markers to facilitate genetic mapping and diversity studies and to apply marker-assisted selection (MAS) for specific traits in papaya breeding programs.

SUBMITTER: Bohry D

PROVIDER: S-EPMC7801719 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Discovery of SNPs and InDels in papaya genotypes and its potential for marker assisted selection of fruit quality traits.

Bohry Dieimes D Ramos Helaine Christine Cancela HCC Dos Santos Pedro Henrique Dias PHD Boechat Marcela Santana Bastos MSB Arêdes Fernanda Abreu Santana FAS Pirovani Adriana Azevedo Vimercati AAV Pereira Messias Gonzaga MG

Scientific reports 20210111 1

Papaya is a tropical and climacteric fruit that is recognized for its nutritional benefits and medicinal applications. Its fruits ripen quickly and show a drastic fruit softening, leading to great post-harvest losses. To overcome this scenario, breeding programs of papaya must invest in exploring the available genetic variation to continue developing superior cultivars with improved fruit quality traits. The objective of this study was to perform a whole-genome genotyping (WGG) of papaya, predic ...[more]

PMID: 33431939

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Project description:BACKGROUND:The availability of high-density (HD) marker panels, genome wide variants and sequence data creates an unprecedented opportunity to dissect the genetic basis of complex traits, enhance genomic selection (GS) and identify causal variants of disease. The disproportional increase in the number of parameters in the genetic association model compared to the number of phenotypes has led to further deterioration in statistical power and an increase in co-linearity and false positive rates. At best, HD panels do not significantly improve GS accuracy and, at worst, reduce accuracy. This is true for both regression and variance component approaches. To remedy this situation, some form of single nucleotide polymorphisms (SNP) filtering or external information is needed. Current methods for prioritizing SNP markers (i.e. BayesB, BayesCπ) are sensitive to the increased co-linearity in HD panels which could limit their performance. RESULTS:In this study, the usefulness of FST, a measure of allele frequency variation among populations, as an external source of information in GS was evaluated. A simulation was carried out for a trait with heritability of 0.4. Data was divided into three subpopulations based on phenotype distribution (bottom 5%, middle 90%, top 5%). Marker data were simulated to mimic a 770 K and 1.5 million SNP marker panel. A ten-chromosome genome with 200 K and 400 K SNPs was simulated. Several scenarios with varying distributions for the quantitative trait loci (QTL) effects were simulated. Using all 200 K markers and no filtering, the accuracy of genomic prediction was 0.77. When marker effects were simulated from a gamma distribution, SNPs pre-selected based on the 99.5, 99.0 and 97.5% quantile of the FST score distribution resulted in an accuracy of 0.725, 0.797, and 0.853, respectively. Similar results were observed under other simulation scenarios. Clearly, the accuracy obtained using all SNPs can be easily achieved using only 0.5 to 1% of all markers. CONCLUSIONS:These results indicate that SNP filtering using already available external information could increase the accuracy of GS. This is especially important as next-generation sequencing technology becomes more affordable and accessible to human, animal and plant applications.

Dataset Information

Discovery of SNPs and InDels in papaya genotypes and its potential for marker assisted selection of fruit quality traits.

Publications

Discovery of SNPs and InDels in papaya genotypes and its potential for marker assisted selection of fruit quality traits.

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