SNPs and other features as they predispose to complex disease: Genome-wide predictive analysis of a quantitative phenotype for hypertension
PLoS ONE, ISSN: 1932-6203, Vol: 6, Issue: 11, Page: e27891
2011
- 5Citations
- 28Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations5
- Citation Indexes5
- CrossRef2
- Captures28
- Readers28
- 28
Article Description
Though recently they have fallen into some disrepute, genome-wide association studies (GWAS) have been formulated and applied to understanding essential hypertension. The principal goal here is to use data gathered in a GWAS to gauge the extent to which SNPs and their interactions with other features can be combined to predict mean arterial blood pressure (MAP) in 3138 pre-menopausal and naturally post-menopausal white women. More precisely, we quantify the extent to which data as described permit prediction of MAP beyond what is possible from traditional risk factors such as blood cholesterol levels and glucose levels. Of course, these traditional risk factors are genetic, though typically not explicitly so. In all, there were 44 such risk factors/clinical variables measured and 377,790 single nucleotide polymorphisms (SNPs) genotyped. Data for women we studied are from first visit measurements taken as part of the Atherosclerotic Risk in Communities (ARIC) study. We begin by assessing non-SNP features in their abilities to predict MAP, employing a novel regression technique with two stages, first the discovery of main effects and next discovery of their interactions. The long list of SNPs genotyped is reduced to a manageable list for combining with non-SNP features in prediction. We adapted Efron's local false discovery rate to produce this reduced list. Selected non-SNP and SNP features and their interactions are used to predict MAP using adaptive linear regression. We quantify quality of prediction by an estimated coefficient of determination (R). We compare the accuracy of prediction with and without information from SNPs. © 2011 Won et al.
Bibliographic Details
10.1371/journal.pone.0027891; 10.1371/journal.pone.0027891.t003; 10.1371/journal.pone.0027891.t002; 10.1371/journal.pone.0027891.t001
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=82355181996&origin=inward; http://dx.doi.org/10.1371/journal.pone.0027891; http://www.ncbi.nlm.nih.gov/pubmed/22140480; https://dx.plos.org/10.1371/journal.pone.0027891.t003; http://dx.doi.org/10.1371/journal.pone.0027891.t003; https://dx.plos.org/10.1371/journal.pone.0027891.t002; http://dx.doi.org/10.1371/journal.pone.0027891.t002; https://dx.plos.org/10.1371/journal.pone.0027891; https://dx.plos.org/10.1371/journal.pone.0027891.t001; http://dx.doi.org/10.1371/journal.pone.0027891.t001; https://dx.doi.org/10.1371/journal.pone.0027891.t003; https://journals.plos.org/plosone/article/figure?id=10.1371/journal.pone.0027891.t003; https://dx.doi.org/10.1371/journal.pone.0027891.t001; https://journals.plos.org/plosone/article/figure?id=10.1371/journal.pone.0027891.t001; https://dx.doi.org/10.1371/journal.pone.0027891; https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0027891; https://dx.doi.org/10.1371/journal.pone.0027891.t002; https://journals.plos.org/plosone/article/figure?id=10.1371/journal.pone.0027891.t002; http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0027891; https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0027891&type=printable; http://dx.plos.org/10.1371/journal.pone.0027891.t001; http://www.plosone.org/article/metrics/info:doi/10.1371/journal.pone.0027891; http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0027891&type=printable; http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0027891; http://dx.plos.org/10.1371/journal.pone.0027891; http://dx.plos.org/10.1371/journal.pone.0027891.t002; http://dx.plos.org/10.1371/journal.pone.0027891.t003
Public Library of Science (PLoS)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know