Efficient SNP Discovery by Combining Microarray and Lab-on-a-Chip Data for Animal Breeding and Selection.

Microarrays Pub Date : 2015-11-16 DOI:10.3390/microarrays4040570

Chao-Wei Huang, Yu-Tsung Lin, Shih-Torng Ding, Ling-Ling Lo, Pei-Hwa Wang, En-Chung Lin, Fang-Wei Liu, Yen-Wen Lu

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引用次数: 16

Abstract

The genetic markers associated with economic traits have been widely explored for animal breeding. Among these markers, single-nucleotide polymorphism (SNPs) are gradually becoming a prevalent and effective evaluation tool. Since SNPs only focus on the genetic sequences of interest, it thereby reduces the evaluation time and cost. Compared to traditional approaches, SNP genotyping techniques incorporate informative genetic background, improve the breeding prediction accuracy and acquiesce breeding quality on the farm. This article therefore reviews the typical procedures of animal breeding using SNPs and the current status of related techniques. The associated SNP information and genotyping techniques, including microarray and Lab-on-a-Chip based platforms, along with their potential are highlighted. Examples in pig and poultry with different SNP loci linked to high economic trait values are given. The recommendations for utilizing SNP genotyping in nimal breeding are summarized.

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结合微阵列和芯片上的实验室数据进行动物育种和选择的高效SNP发现。

与经济性状相关的遗传标记在动物育种中得到了广泛的探索。在这些标记中，单核苷酸多态性(snp)正逐渐成为一种普遍而有效的评价工具。由于snp只关注感兴趣的基因序列，因此减少了评估时间和成本。与传统方法相比，SNP基因分型技术结合了丰富的遗传背景，提高了育种预测的准确性和默许了农场的育种质量。因此，本文综述了利用snp进行动物育种的典型程序和相关技术的现状。强调了相关的SNP信息和基因分型技术，包括微阵列和基于芯片的实验室平台，以及它们的潜力。给出了猪和家禽中与高经济性状值相关的不同SNP位点的例子。综述了SNP基因分型在动物育种中的应用建议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Microarrays

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0.00%

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审稿时长

11 weeks

期刊介绍： High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: Microarrays, DNA Sequencing, RNA Sequencing, Protein Identification and Quantification, Cell-based Approaches, Omics Technologies, Imaging, Bioinformatics, Computational Biology/Chemistry, Statistics, Integrative Omics, Drug Discovery and Development, Microfluidics, Lab-on-a-chip, Data Mining, Databases, Multiplex Assays.