A reproducible ddRAD-seq protocol reveals novel genomic association signatures for fruit-related traits in peach.

IF 4.4 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Najla Ksouri, Gerardo Sánchez, Carolina Font I Forcada, Bruno Contreras-Moreira, Yolanda Gogorcena
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引用次数: 0

Abstract

Improving peach cultivars with superior traits is a primary objective of breeding initiatives. In this study, we aimed to elucidate the genetic basis of key agronomic and fruit-related traits using a reproducible ddRAD-seq protocol applied to a discovery panel of 90 peach accessions. Our customized workflow (available at https://github.com/najlaksouri/GWAS-Workflow ) integrates three variant callers and tests up to seven models to perform a robust genome-wide association analysis (GWAS). This approach produced 13,045 high-confidence SNPs and identified Blink as the most suitable model, effectively balancing false positive and negative associations. A total of 16 significant associations signals were unveiled for six highly heritable traits (H2 > 0.5), including harvest date, fruit weight, flesh firmness, contents of flavonoids, anthocyanins and sorbitol. By assessing the allelic effect of significant markers on phenotypic attributes, nine SNP alleles were deemed favorable. Notably, a promising marker (SNC_034014.1_7012470) demonstrated simultaneous association with harvest date and fruit firmness, displaying a positive allelic effect on both traits. We anticipate that this marker can serve as a good predictor of firmer varieties. Candidate causal genes were shortlisted when fulfilling the following criteria: (i) position within the linkage disequilibrium block, (ii) functional annotation and (iii) expression pattern. A comprehensive bibliographic review of previously reported QTLs mapping nearby the associated markers allowed us to benchmark the accuracy of our approach. Despite the moderate germplasm size, ddRAD-seq allowed us to produce an accurate representation of the peach genome, resulting in SNP markers suitable for empirical association studies. Together with candidate genes, they lay the foundation for further genetic dissection of peach key traits.

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一个可重复的ddRAD-seq协议揭示了桃子果实相关性状的新基因组关联特征。
培育具有优良性状的桃品种是桃育种工作的首要目标。在这项研究中,我们旨在利用可重复的ddRAD-seq协议,阐明关键农艺性状和果实相关性状的遗传基础,并应用于90个桃子材料的发现面板。我们定制的工作流程(可在https://github.com/najlaksouri/GWAS-Workflow上获得)集成了三个变体调用器,并测试了多达七个模型,以执行强大的全基因组关联分析(GWAS)。该方法产生了13045个高置信度snp,并确定Blink是最合适的模型,有效地平衡了假阳性和阴性关联。在收获日期、果实重量、果肉硬度、黄酮类化合物、花青素和山梨醇含量等6个高遗传性状(H2 > 0.5)中,共发现16个显著相关信号。通过评估显着标记对表型属性的等位基因效应,9个SNP等位基因被认为是有利的。值得注意的是,一个有希望的标记(SNC_034014.1_7012470)与收获日期和果实硬度同时相关,对这两个性状都显示出正的等位基因效应。我们预计该标记可以作为较结实品种的良好预测指标。候选因果基因在满足以下标准时入围:(i)在连锁不平衡区域内的位置,(ii)功能注释和(iii)表达模式。对先前报道的qtl在相关标记附近的映射进行全面的文献回顾,使我们能够对我们的方法的准确性进行基准测试。尽管种质大小适中,但ddRAD-seq使我们能够产生桃子基因组的准确表示,从而产生适合经验关联研究的SNP标记。与候选基因一起,为进一步解剖桃树关键性状奠定了基础。
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来源期刊
Plant Methods
Plant Methods 生物-植物科学
CiteScore
9.20
自引率
3.90%
发文量
121
审稿时长
2 months
期刊介绍: Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences. There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics. Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.
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