PMID- 25938221
OWN - NLM
STAT- MEDLINE
DCOM- 20160224
LR  - 20181113
IS  - 1544-6115 (Electronic)
IS  - 2194-6302 (Print)
IS  - 1544-6115 (Linking)
VI  - 14
IP  - 3
DP  - 2015 Jun
TI  - Modeling gene-covariate interactions in sparse regression with group structure 
      for genome-wide association studies.
PG  - 265-77
LID - 10.1515/sagmb-2014-0073 [doi]
AB  - In genome-wide association studies (GWAS), it is of interest to identify genetic 
      variants associated with phenotypes. For a given phenotype, the associated 
      genetic variants are usually a sparse subset of all possible variants. 
      Traditional Lasso-type estimation methods can therefore be used to detect 
      important genes. But the relationship between genotypes at one variant and a 
      phenotype may be influenced by other variables, such as sex and life style. Hence 
      it is important to be able to incorporate gene-covariate interactions into the 
      sparse regression model. In addition, because there is biological knowledge on 
      the manner in which genes work together in structured groups, it is desirable to 
      incorporate this information as well. In this paper, we present a novel sparse 
      regression methodology for gene-covariate models in association studies that not 
      only allows such interactions but also considers biological group structure. 
      Simulation results show that our method substantially outperforms another method, 
      in which interaction is considered, but group structure is ignored. Application 
      to data on total plasma immunoglobulin E (IgE) concentrations in the Framingham 
      Heart Study (FHS), using sex and smoking status as covariates, yields several 
      potentially interesting gene-covariate interactions.
FAU - Li, Yun
AU  - Li Y
FAU - O'Connor, George T
AU  - O'Connor GT
FAU - Dupuis, Josee
AU  - Dupuis J
FAU - Kolaczyk, Eric
AU  - Kolaczyk E
LA  - eng
GR  - R01 DK078616/DK/NIDDK NIH HHS/United States
GR  - ES020827/ES/NIEHS NIH HHS/United States
GR  - R21 ES020827/ES/NIEHS NIH HHS/United States
GR  - P01 AI050516/AI/NIAID NIH HHS/United States
GR  - N01 HC025195/HC/NHLBI NIH HHS/United States
GR  - P01AI050516/AI/NIAID NIH HHS/United States
GR  - U01 DK078616/DK/NIDDK NIH HHS/United States
GR  - DK078616/DK/NIDDK NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - Germany
TA  - Stat Appl Genet Mol Biol
JT  - Statistical applications in genetics and molecular biology
JID - 101176023
SB  - IM
MH  - Algorithms
MH  - *Epistasis, Genetic
MH  - Gene-Environment Interaction
MH  - *Genetic Variation
MH  - *Genome-Wide Association Study
MH  - Humans
MH  - *Models, Genetic
MH  - *Models, Statistical
PMC - PMC4510949
MID - NIHMS706996
EDAT- 2015/05/06 06:00
MHDA- 2016/02/26 06:00
PMCR- 2016/06/01
CRDT- 2015/05/05 06:00
PHST- 2015/05/05 06:00 [entrez]
PHST- 2015/05/06 06:00 [pubmed]
PHST- 2016/02/26 06:00 [medline]
PHST- 2016/06/01 00:00 [pmc-release]
AID - /j/sagmb.ahead-of-print/sagmb-2014-0073/sagmb-2014-0073.xml [pii]
AID - 10.1515/sagmb-2014-0073 [doi]
PST - ppublish
SO  - Stat Appl Genet Mol Biol. 2015 Jun;14(3):265-77. doi: 10.1515/sagmb-2014-0073.