Detection and quantification of inbreeding depression for complex traits from SNP data

Yengo, Loic, Zhu, Zhihong, Wray, Naomi R., Weir, Bruce S., Yang, Jian, Robinson, Matthew R. and Visscher, Peter M. (2017) Detection and quantification of inbreeding depression for complex traits from SNP data. PNAS: Proceedings of the National Academy of Sciences of the United States of America, 114 32: 8602-8607. doi:10.1073/pnas.1621096114


Author Yengo, Loic
Zhu, Zhihong
Wray, Naomi R.
Weir, Bruce S.
Yang, Jian
Robinson, Matthew R.
Visscher, Peter M.
Title Detection and quantification of inbreeding depression for complex traits from SNP data
Journal name PNAS: Proceedings of the National Academy of Sciences of the United States of America   Check publisher's open access policy
ISSN 1091-6490
0027-8424
Publication date 2017-08-01
Year available 2017
Sub-type Article (original research)
DOI 10.1073/pnas.1621096114
Open Access Status Not yet assessed
Volume 114
Issue 32
Start page 8602
End page 8607
Total pages 6
Place of publication Washington, DC United States
Publisher National Academy of Sciences
Language eng
Abstract Inbreeding depression (ID) is the reduction of fitness in offspring of related parents. This phenomenon can be quantified from SNP data through a number of measures of inbreeding. Our study addresses two key questions. How accurate are the different methods to estimate ID? And how and why should investigators choose among the multiple inbreeding measures to detect and quantify ID? Here, we compare the behaviors of ID estimates from three commonly used SNP-based measures of inbreeding and provide both theoretical and empirical arguments to answer these questions. Our work illustrates how to analyze SNP data efficiently to detect and quantify ID, across species and traits.
Keyword Inbreeding depression
Directional dominance
Quantitative genetics
Single-nucleotide polymorphism
Homozygosity
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID DP130102666
1078037
R01AG042568
12505
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
Institute for Molecular Bioscience - Publications
 
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