背景:Taro有着悠久的消费历史,仍然是孤儿和尼日利亚农民。农民驱动的人工选择的作用对于使地方品种适应特定的生态条件是不可忽视的。对全基因组关联进行了有限的研究,尚未对基因组-环境关联进行研究以适应芋的物种。因此,这项研究的目的是检测与环境变量和表型性状相关的基因座,以及对育种者的正向输入。该研究使用了从尼日利亚东南部(SE)收集的92个地理参考的芋地方品种。
结果:结果表明,SE尼日利亚芋具有未开发的表型和低掺量的遗传变异性。冗余分析表明,共线比单个气候变量更能解释SNP变化。总的来说,结果表明,对于所有六个性状,没有一种方法能够比其他方法更好地捕获种群混杂效应。然而,基于整体模型性能,与其他模型相比,闪烁似乎提供了轻微的优势,并被选择用于所有后续的基因组-环境关联(GEA)和全基因组关联研究(GWAS)模型的评估。基因组扫描和GEA确定了局部适应基因座和共位基因。共有9个与环境变量相关的SNP标记。一些SNP标记(如S_101024366)与先前报道的气候适应基因共位,如收敛性,二氨基庚二酸脱羧酶和MYB转录因子。全基因组关联还鉴定出45、40和34个与所研究性状相关的显著SNP标记,第1年和第2年数据集,分别。在这些中,五个SNP标记(S1_18891752S3_100795476,S1_100584471S1_100896936和S2_10058799)在两个不同的数据集中是一致的。
结论:这项研究的发现提高了我们对尼日利亚芋适应性和表型性状的遗传控制的理解。然而,该研究建议通过在全国范围内收集更多的地方品种来进一步研究当地适应性基因座和GWAS的识别,跨越不同的农业生态。
BACKGROUND: Taro has a long history of being consumed and remains orphan and on the hand Nigeria farmers. The role of farmer-driven artificial selection is not negligible to fit landraces to a particular ecological condition. Limited study has been conducted on genome-wide association and no study has been conducted on genome-environment association for clinal adaptation for taro. Therefore, the objective of this study was to detect loci that are associated with environmental variables and phenotype traits and forward input to breeders. The study used 92 geographical referred taro landraces collected from Southeast (SE) Nigeria.
RESULTS: The result indicates that SE Nigerian taro has untapped phenotype and genetic variability with low admixture. Redundancy analysis indicated that collinear explained SNP variation more than single climatic variable. Overall, the results indicated that no single method exclusively was able to capture population confounding effects better than the others for all six traits. Nevertheless, based on overall model performance, Blink seemed to provide slight advantage over other models and was selected for all subsequent assessment of genome-environment association (GEA) and genome-wide association study (GWAS) models. Genome scan and GEA identified local adapted loci and co-located genes. A total of nine SNP markers associated with environmental variables. Some of the SNP markers (such as S_101024366) co-located with genes which previously reported for climatic adaptation such as astringency, diaminopimelate decarboxylase and MYB transcription factor. Genome-wide association also identified 45, 40 and 34 significant SNP markers associated with studied traits in combined, year 1 and year 2 data sets, respectively. Out of these, five SNP markers (S1_18891752 S3_100795476, S1_100584471 S1_100896936 and S2_10058799) were consistent in two different data sets.
CONCLUSIONS: The findings from this study improve our understanding of the genetic control of adaptive and phenotypic traits in Nigerian taro. However, the study suggests further study on identification of local adaptive loci and GWAS through collection of more landraces throughout the country, and across different agro-ecologies.