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Estimating heritability using family and unrelated individuals data

Overview of attention for article published in BMC Proceedings, November 2011
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Title
Estimating heritability using family and unrelated individuals data
Published in
BMC Proceedings, November 2011
DOI 10.1186/1753-6561-5-s9-s34
Pubmed ID
Authors

Priya B Shetty, Huaizhen Qin, Junghyun Namkung, Robert C Elston, Xiaofeng Zhu

Abstract

For the family data from Genetic Analysis Workshop 17, we obtained heritability estimates of quantitative traits Q1 and Q4 using the ASSOC program in the S.A.G.E. software package. ASSOC is a family-based method that estimates heritability through the estimation of variance components. The covariate-adjusted mean heritability was 0.650 for Q1 and 0.745 for Q4. For the unrelated individuals data, we estimated the heritability of Q1 as the proportion of total variance that can be accounted for by all single-nucleotide polymorphisms under an additive model. We examined a novel ordinary least-squares method, a naïve restricted maximum-likelihood method, and a calibrated restricted maximum-likelihood method. We applied the different methods to all 200 replicates for Q1. We observed that the ordinary least-squares method yielded many estimates outside the interval [0, 1]. The restricted maximum-likelihood estimates were more stable than the ordinary least-squares estimates. The naïve restricted maximum-likelihood method yielded an average estimate of 0.462 ± 0.1, and the calibrated restricted maximum-likelihood method yielded an average of 0.535 ± 0.121. Our results demonstrate discrepancies in heritability estimates using the family data and the unrelated individuals data.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 5%
United States 1 5%
Unknown 19 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 24%
Researcher 4 19%
Student > Master 3 14%
Lecturer 1 5%
Professor 1 5%
Other 3 14%
Unknown 4 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 43%
Medicine and Dentistry 3 14%
Computer Science 2 10%
Psychology 1 5%
Unspecified 1 5%
Other 0 0%
Unknown 5 24%
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 09 December 2011.
All research outputs
#20,152,153
of 22,659,164 outputs
Outputs from BMC Proceedings
#318
of 374 outputs
Outputs of similar age
#218,507
of 240,147 outputs
Outputs of similar age from BMC Proceedings
#32
of 44 outputs
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