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Set‐based tests for genetic association in longitudinal studies

Overview of attention for article published in Biometrics, April 2015
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  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

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5 tweeters

Citations

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8 Dimensions

Readers on

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12 Mendeley
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Title
Set‐based tests for genetic association in longitudinal studies
Published in
Biometrics, April 2015
DOI 10.1111/biom.12310
Pubmed ID
Authors

He, Zihuai, Zhang, Min, Lee, Seunggeun, Smith, Jennifer A, Guo, Xiuqing, Palmas, Walter, Kardia, Sharon L R, Roux, Ana V Diez, Mukherjee, Bhramar, Smith, Jennifer A., Kardia, Sharon L. R., Roux, Ana V. Diez

Abstract

Genetic association studies with longitudinal markers of chronic diseases (e.g., blood pressure, body mass index) provide a valuable opportunity to explore how genetic variants affect traits over time by utilizing the full trajectory of longitudinal outcomes. Since these traits are likely influenced by the joint effect of multiple variants in a gene, a joint analysis of these variants considering linkage disequilibrium (LD) may help to explain additional phenotypic variation. In this article, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set of genetic variants. Generalized score type tests are developed, which we show are robust to misspecification of within-subject correlation, a feature that is desirable for longitudinal analysis. In addition, a joint test incorporating gene-time interaction is further proposed. Computational advancement is made for scalable implementation of the proposed methods in large-scale genome-wide association studies (GWAS). The proposed methods are evaluated through extensive simulation studies and illustrated using data from the Multi-Ethnic Study of Atherosclerosis (MESA). Our simulation results indicate substantial gain in power using LGRF when compared with two commonly used existing alternatives: (i) single marker tests using longitudinal outcome and (ii) existing gene-based tests using the average value of repeated measurements as the outcome.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 8%
United States 1 8%
Unknown 10 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 33%
Unspecified 3 25%
Student > Ph. D. Student 2 17%
Student > Postgraduate 1 8%
Professor > Associate Professor 1 8%
Other 1 8%
Readers by discipline Count As %
Unspecified 4 33%
Biochemistry, Genetics and Molecular Biology 2 17%
Medicine and Dentistry 2 17%
Agricultural and Biological Sciences 2 17%
Business, Management and Accounting 1 8%
Other 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 02 May 2016.
All research outputs
#5,272,204
of 10,616,067 outputs
Outputs from Biometrics
#649
of 1,150 outputs
Outputs of similar age
#84,377
of 213,013 outputs
Outputs of similar age from Biometrics
#3
of 20 outputs
Altmetric has tracked 10,616,067 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,150 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 213,013 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.