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Increased Accuracy of Genomic Prediction Using Preselected SNPs from GWAS with Imputed Whole-Genome Sequence Data in Pigs

التفاصيل البيبلوغرافية
العنوان: Increased Accuracy of Genomic Prediction Using Preselected SNPs from GWAS with Imputed Whole-Genome Sequence Data in Pigs
المؤلفون: Yiyi Liu, Yuling Zhang, Fuchen Zhou, Zekai Yao, Yuexin Zhan, Zhenfei Fan, Xianglun Meng, Zebin Zhang, Langqing Liu, Jie Yang, Zhenfang Wu, Gengyuan Cai, Enqin Zheng
المصدر: Animals, Vol 13, Iss 24, p 3871 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Veterinary medicine
LCC:Zoology
مصطلحات موضوعية: pigs, GS, accuracy, imputed WGS data, genome-wide association study, SNP preselection, Veterinary medicine, SF600-1100, Zoology, QL1-991
الوصف: Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-2615
Relation: https://www.mdpi.com/2076-2615/13/24/3871; https://doaj.org/toc/2076-2615
DOI: 10.3390/ani13243871
URL الوصول: https://doaj.org/article/c017d72be92f43b382689b666212062e
رقم الأكسشن: edsdoj.017d72be92f43b382689b666212062e
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20762615
DOI:10.3390/ani13243871