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Quantification of Inbreeding Due to Distant Ancestors and Its Detection Using Dense Single Nucleotide Polymorphism Data

Overview of attention for article published in Genetics, September 2011
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Title
Quantification of Inbreeding Due to Distant Ancestors and Its Detection Using Dense Single Nucleotide Polymorphism Data
Published in
Genetics, September 2011
DOI 10.1534/genetics.111.130922
Pubmed ID
Authors

Matthew C. Keller, Peter M. Visscher, Michael E. Goddard

Abstract

Inbreeding depression, which refers to reduced fitness among offspring of related parents, has traditionally been studied using pedigrees. In practice, pedigree information is difficult to obtain, potentially unreliable, and rarely assessed for inbreeding arising from common ancestors who lived more than a few generations ago. Recently, there has been excitement about using SNP data to estimate inbreeding (F) arising from distant common ancestors in apparently "outbred" populations. Statistical power to detect inbreeding depression using SNP data depends on the actual variation in inbreeding in a population, the accuracy of detecting that with marker data, the effect size, and the sample size. No one has yet investigated what variation in F is expected in SNP data as a function of population size, and it is unclear which estimate of F is optimal for detecting inbreeding depression. In the present study, we use theory, simulated genetic data, and real genetic data to find the optimal estimate of F, to quantify the likely variation in F in populations of various sizes, and to estimate the power to detect inbreeding depression. We find that F estimated from runs of homozygosity (Froh), which reflects shared ancestry of genetic haplotypes, retains variation in even large populations (e.g., SD=0.5% when Ne=10,000) and is likely to be the most powerful method of detecting inbreeding effects from among several alternative estimates of F. However, large samples (e.g., 12,000-65,000) will be required to detect inbreeding depression for likely effect sizes, and so studies using Froh to date have probably been underpowered.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 2 <1%
Australia 1 <1%
Israel 1 <1%
Switzerland 1 <1%
Mexico 1 <1%
Finland 1 <1%
Spain 1 <1%
Argentina 1 <1%
Unknown 309 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 66 21%
Student > Master 51 16%
Researcher 49 15%
Student > Bachelor 33 10%
Student > Doctoral Student 22 7%
Other 47 15%
Unknown 50 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 164 52%
Biochemistry, Genetics and Molecular Biology 48 15%
Medicine and Dentistry 10 3%
Veterinary Science and Veterinary Medicine 9 3%
Environmental Science 6 2%
Other 16 5%
Unknown 65 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 August 2023.
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#20,656,161
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Outputs from Genetics
#6,728
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#112,871
of 136,084 outputs
Outputs of similar age from Genetics
#31
of 39 outputs
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