Association screening of common and rare genetic variants by penalized regression
Bioinformatics, ISSN: 1367-4803, Vol: 26, Issue: 19, Page: 2375-2382
2010
- 114Citations
- 126Captures
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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.
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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
- Citations114
- Citation Indexes114
- 114
- CrossRef104
- Captures126
- Readers126
- 109
- 17
Article Description
Motivation: This article extends our recent research on penalized estimation methods in genome-wide association studies to the realm of rare variants. Results: The new strategy is tested on both simulated and real data. Our findings on breast cancer data replicate previous results and shed light on variant effects within genes. © The Author 2010. Published by Oxford University Press. All rights reserved.
Bibliographic Details
Oxford University Press (OUP)
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