بررسی ساختارهای جوامع و خرده جوامع دامی به روش خوشه‌بندی شبکه‌ای بدون نظارت با استفاده از نشانگرهای ژنتیکی متراکم

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری ژنتیک و اصلاح نژاد، گروه مهندسی علوم دامی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج

2 استاد، گروه مهندسی علوم دامی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج

3 استادیار، گروه مهندسی علوم دامی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج

چکیده

رشد روزافزون اطلاعات حاصل از تعیین ژنوتیپ نمونه‌ها به‌ویژه با استفاده از توالی‌یابی چندشکلی‌های تک‌نوکلئوتیدی (SNP) سبب تحول در تجزیه‌وتحلیل دقیق ساختار جوامع در گونه‌های مختلف شده است. تاکنون از روش‌های مختلفی برای بررسی ساختار جمعیتی با استفاده از نشانگرهای موجود در کل ژنوم استفاده شده است که هرکدام نقاط ضعف و قوتی دارند. در بررسی حاضر از خوشه‌بندی شبکه‌ای بدون نظارت یا SPC که روشی مبتنی بر داده‌کاوی است، برای بررسی ساختار جوامع شبیه‌سازی‌شده و کشف خرده‌جوامع موجود استفاده شد. هدف از به‌کاربردن این روش، دستیابی به ساختار جمعیتی بدون هیچ‌گونه آگاهی از اطلاعات شجره‌ای افراد بود. در شبیه‌سازی انجام‌گرفته بدین منظور پس از ویرایش داده‌ها، 29209 نشانگر اتوزومی از 159 دام، تجزیه‌وتحلیل شدند. نتایج نشان داد که حیوانات براساس شباهت‌ها و تفاوت‌ها به‌خوبی در جوامع مربوطه قرارگرفتند و خرده‌جوامع موجود نیز درون هر جمعیت نمایان شدند. مزیت اصلی این روش، کارایی محاسباتی بالا و نیازنبودن به فرض‌های پیشین در آن است؛ بنابراین به محقق این امکان را می‌دهد که ساختار جوامع متشکل از هزاران حیوان را بدون داشتن هرگونه اطلاعاتی از شجره و نژاد، تجزیه‌وتحلیل کند.

کلیدواژه‌ها

عنوان مقاله [English]

Unsupervised clustering analysis of population and subpopulation structure using dense SNP markers

نویسندگان [English]

  • Javad Rahmaninia 1
  • Seyed Reza Miraei-Ashtiani 2
  • Hossein Moradi Shahrbabak 3
1 Ph. D. Student, Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2 Professor, Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
3 Assistant Professor, Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
چکیده [English]

High through put sequencing of single nucleotide polymorphisms (SNP) has revolutionized the fine scale analysis of the population structure in different species. Various methods have been proposed and used for the study of population structure using whole-genome marker data that each has advantages and disadvantages with respect to their characteristics. Super Paramagnetic Clustering (SPC) which is based on data mining was used in this study in order to investigate the population and sub-population structures in simulated populations. The purpose of applying this method was to achieve population structure without using any information from ancestral population. After editing the data, 29209 autosomal markers from 159 animals were analyzed. The results showed that animals are placed properly in their respective population and sub-populations based on their similarities and dissimilarities. The main advantages of this method are the computational efficiency and not requiring any prior assumptions. Therefore, it might be used to analyze the data from thousands of animals without any pedigree and ancestry information to reveal their population structure.

کلیدواژه‌ها [English]

  • Data Mining
  • Population structure
  • single nucleotide polymorphism (SNP)
  • super paramagnetic clustering (SPC)

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سابقه مقاله

  • تاریخ دریافت: 09 اسفند 1393
  • تاریخ بازنگری: 01 شهریور 1394
  • تاریخ پذیرش: 07 شهریور 1394

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