Powerful association test combining rare variant and gene expression using family data from Genetic Analysis Workshop 19

Yen Yi Ho, Weihua Guan, Michael O'Connell, Saonli Basu

Research output: Contribution to journalArticlepeer-review

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Background: Genetic association studies aim to test for disease or trait association with genetic variants, either throughout the human genome or in regions of interest. However, for most diseases and traits, the combined effects of associated genetic variants explain only a small proportion of the genetic variation. This "missing heritability" may be a result of the small effects of common variants considered in the genetic association studies. Rare variants may also play an important role in understanding the missing heritability of complex traits. Method: We propose a novel weight-adjustment approach to combine gene expression into rare variant analysis. Results from previous simulation studies suggested that incorporating gene expression information can lead to substantial gain in statistical power. Results: Using the family data set provided through the Genetic Analysis Workshop 19, we identified susceptible genes associated with blood pressure regulation. Conclusions: These findings provide valuable information for further functional studies for blood pressure control and mechanism.

Original languageEnglish (US)
Article number26
JournalBMC Proceedings
StatePublished - 2016

Bibliographical note

Funding Information:
Y.-Y. Ho was partially supported by National Institutes of Health (NIH) grant P30 CA77598, P50CA101955, UL1TR000114, 1U19CA157345-01A1 and U54-MD008620. S. Basu is partially supported by NIH grant R01-DA033958.

Funding Information:
This article has been published as part of BMC Proceedings Volume 10 Supplement 7, 2016: Genetic Analysis Workshop 19: Sequence, Blood Pressure and Expression Data. Summary articles. The full contents of the supplement are available online at http://bmcproc.biomedcentral.com/ articles/supplements/volume-10-supplement-7. Publication of the proceedings of Genetic Analysis Workshop 19 was supported by National Institutes of Health grant R01 GM031575.

Publisher Copyright:
© 2016 The Author(s).


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