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Assumptions and Properties of Limiting Pathway Models for Analysis of Epistasis in Complex Traits

Overview of attention for article published in PLOS ONE, July 2013
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Title
Assumptions and Properties of Limiting Pathway Models for Analysis of Epistasis in Complex Traits
Published in
PLOS ONE, July 2013
DOI 10.1371/journal.pone.0068913
Pubmed ID
Authors

Sven Stringer, Eske M. Derks, René S. Kahn, William G. Hill, Naomi R. Wray

Abstract

For most complex traits, results from genome-wide association studies show that the proportion of the phenotypic variance attributable to the additive effects of individual SNPs, that is, the heritability explained by the SNPs, is substantially less than the estimate of heritability obtained by standard methods using correlations between relatives. This difference has been called the "missing heritability". One explanation is that heritability estimates from family (including twin) studies are biased upwards. Zuk et al. revisited overestimation of narrow sense heritability from twin studies as a result of confounding with non-additive genetic variance. They propose a limiting pathway (LP) model that generates significant epistatic variation and its simple parametrization provides a convenient way to explore implications of epistasis. They conclude that over-estimation of narrow sense heritability from family data ('phantom heritability') may explain an important proportion of missing heritability. We show that for highly heritable quantitative traits large phantom heritability estimates from twin studies are possible only if a large contribution of common environment is assumed. The LP model is underpinned by strong assumptions that are unlikely to hold, including that all contributing pathways have the same mean and variance and are uncorrelated. Here, we relax the assumptions that underlie the LP model to be more biologically plausible. Together with theoretical, empirical, and pragmatic arguments we conclude that in outbred populations the contribution of additive genetic variance is likely to be much more important than the contribution of non-additive variance.

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Mendeley readers

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The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Unknown 44 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 39%
Researcher 9 20%
Professor 5 11%
Student > Master 4 9%
Other 2 4%
Other 2 4%
Unknown 6 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 39%
Biochemistry, Genetics and Molecular Biology 10 22%
Medicine and Dentistry 3 7%
Psychology 3 7%
Mathematics 2 4%
Other 2 4%
Unknown 8 17%
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 19 December 2013.
All research outputs
#17,190,674
of 25,252,667 outputs
Outputs from PLOS ONE
#154,616
of 219,060 outputs
Outputs of similar age
#129,020
of 205,320 outputs
Outputs of similar age from PLOS ONE
#3,139
of 4,894 outputs
Altmetric has tracked 25,252,667 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
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We're also able to compare this research output to 4,894 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.