Abstract
Large-scale genetic studies are often composed of related participants, and utilizing familial relationships can be cumbersome and computationally challenging. We present an approach to efficiently handle sequencing data from complex pedigrees that incorporates information from rare variants as well as common variants. Our method employs a 2-step procedure that sequentially regresses out correlation from familial relatedness and then uses the resulting phenotypic residuals in a penalized regression framework to test for associations with variants within genetic units. The operating characteristics of this approach are detailed using simulation data based on a large, multigenerational cohort.
| Original language | English (US) |
|---|---|
| Article number | S25 |
| Journal | BMC Proceedings |
| Volume | 8 |
| DOIs | |
| State | Published - Jun 17 2014 |
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ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
Cite this
A 2-step penalized regression method for family-based next-generation sequencing association studies. / Ding, Xiuhua; Su, Shaoyong; Nandakumar, Kannabiran; Wang, Xiaoling; Fardo, David W.
In: BMC Proceedings, Vol. 8, S25, 17.06.2014.Research output: Contribution to journal › Article
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TY - JOUR
T1 - A 2-step penalized regression method for family-based next-generation sequencing association studies
AU - Ding, Xiuhua
AU - Su, Shaoyong
AU - Nandakumar, Kannabiran
AU - Wang, Xiaoling
AU - Fardo, David W.
PY - 2014/6/17
Y1 - 2014/6/17
N2 - Large-scale genetic studies are often composed of related participants, and utilizing familial relationships can be cumbersome and computationally challenging. We present an approach to efficiently handle sequencing data from complex pedigrees that incorporates information from rare variants as well as common variants. Our method employs a 2-step procedure that sequentially regresses out correlation from familial relatedness and then uses the resulting phenotypic residuals in a penalized regression framework to test for associations with variants within genetic units. The operating characteristics of this approach are detailed using simulation data based on a large, multigenerational cohort.
AB - Large-scale genetic studies are often composed of related participants, and utilizing familial relationships can be cumbersome and computationally challenging. We present an approach to efficiently handle sequencing data from complex pedigrees that incorporates information from rare variants as well as common variants. Our method employs a 2-step procedure that sequentially regresses out correlation from familial relatedness and then uses the resulting phenotypic residuals in a penalized regression framework to test for associations with variants within genetic units. The operating characteristics of this approach are detailed using simulation data based on a large, multigenerational cohort.
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UR - http://www.scopus.com/inward/citedby.url?scp=85018192816&partnerID=8YFLogxK
U2 - 10.1186/1753-6561-8-S1-S25
DO - 10.1186/1753-6561-8-S1-S25
M3 - Article
AN - SCOPUS:85018192816
VL - 8
JO - BMC Proceedings
JF - BMC Proceedings
SN - 1753-6561
M1 - S25
ER -