Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data
المؤلفون المشاركون
Xu, Haiming
Jiang, Beibei
Cao, Yujie
Zhang, Yingxin
Zhan, Xiaodeng
Shen, Xihong
Cheng, Shihua
Lou, Xiangyang
Cao, Liyong
المصدر
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-7، 7ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-08-04
دولة النشر
مصر
عدد الصفحات
7
التخصصات الرئيسية
الملخص EN
With development of sequencing technology, dense single nucleotide polymorphisms (SNPs) have been available, enabling uncovering genetic architecture of complex traits by genome-wide association study (GWAS).
However, the current GWAS strategy usually ignores epistatic and gene-environment interactions due to absence of appropriate methodology and heavy computational burden.
This study proposed a new GWAS strategy by combining the graphics processing unit- (GPU-) based generalized multifactor dimensionality reduction (GMDR) algorithm with mixed linear model approach.
The reliability and efficiency of the analytical methods were verified through Monte Carlo simulations, suggesting that a population size of nearly 150 recombinant inbred lines (RILs) had a reasonable resolution for the scenarios considered.
Further, a GWAS was conducted with the above two-step strategy to investigate the additive, epistatic, and gene-environment associations between 701,867 SNPs and three important quality traits, gelatinization temperature, amylose content, and gel consistency, in a RIL population with 138 individuals derived from super-hybrid rice Xieyou9308 in two environments.
Four significant SNPs were identified with additive, epistatic, and gene-environment interaction effects.
Our study showed that the mixed linear model approach combining with the GPU-based GMDR algorithm is a feasible strategy for implementing GWAS to uncover genetic architecture of crop complex traits.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Xu, Haiming& Jiang, Beibei& Cao, Yujie& Zhang, Yingxin& Zhan, Xiaodeng& Shen, Xihong…[et al.]. 2015. Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data. BioMed Research International،Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1054317
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Xu, Haiming…[et al.]. Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data. BioMed Research International No. 2015 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1054317
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Xu, Haiming& Jiang, Beibei& Cao, Yujie& Zhang, Yingxin& Zhan, Xiaodeng& Shen, Xihong…[et al.]. Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1054317
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1054317
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر