PMID- 22850062
OWN - NLM
STAT- MEDLINE
DCOM- 20130110
LR  - 20191210
IS  - 1544-6115 (Electronic)
IS  - 1544-6115 (Linking)
VI  - 11
IP  - 4
DP  - 2012 Jul 12
TI  - Correction for founder effects in host-viral association studies via principal 
      components.
LID - /j/sagmb.2012.11.issue-4/1544-6115.1827/1544-6115.1827.xml [pii]
LID - 10.1515/1544-6115.1827 [doi]
AB  - Viruses such as HIV and Hepatitis C (HCV) replicate rapidly and with high 
      transcription error rates, which may facilitate their escape from immune 
      detection through the encoding of mutations at key positions within human 
      leukocyte antigen (HLA)-specific peptides, thus impeding T-cell recognition. 
      Large-scale population-based host-viral association studies are conducted as 
      hypothesis-generating analyses which aim to determine the positions within the 
      viral sequence at which host HLA immune pressure may have led to these viral 
      escape mutations. When transmission of the virus to the host is HLA-associated, 
      however, standard tests of association can be confounded by the viral relatedness 
      of contemporarily circulating viral sequences, as viral sequences descended from 
      a common ancestor may share inherited patterns of polymorphisms, termed 'founder 
      effects'. Recognizing the correspondence between this problem and the confounding 
      of case-control genome-wide association studies by population stratification, we 
      adapt methods taken from that field to the analysis of host-viral associations. 
      In particular, we consider methods based on principal components analysis within 
      a logistic regression framework motivated by alternative formulations in the 
      Frisch-Waugh-Lovell Theorem. We demonstrate via simulation their utility in 
      detecting true host-viral associations whilst minimizing confounding by 
      associations generated by founder effects. The proposed methods incorporate 
      relatively robust, standard statistical procedures which can be easily 
      implemented using widely available software, and provide alternatives to the more 
      complex computer intensive methods often implemented in this area.
FAU - Reeves, Karyn L
AU  - Reeves KL
AD  - Murdoch University.
FAU - McKinnon, Elizabeth J
AU  - McKinnon EJ
FAU - James, Ian R
AU  - James IR
LA  - eng
PT  - Evaluation Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20120712
PL  - Germany
TA  - Stat Appl Genet Mol Biol
JT  - Statistical applications in genetics and molecular biology
JID - 101176023
RN  - 0 (HLA Antigens)
SB  - IM
MH  - Alleles
MH  - Calibration
MH  - Case-Control Studies
MH  - Cohort Studies
MH  - Computer Simulation
MH  - *Founder Effect
MH  - Genetic Association Studies/standards/statistics & numerical data
MH  - Genetic Predisposition to Disease/epidemiology/genetics
MH  - HLA Antigens/genetics
MH  - Host-Pathogen Interactions/*genetics
MH  - Humans
MH  - Logistic Models
MH  - Polymorphism, Genetic
MH  - *Principal Component Analysis/methods/standards
MH  - Research Design
MH  - Virus Diseases/epidemiology/genetics/*transmission
EDAT- 2012/08/02 06:00
MHDA- 2013/01/11 06:00
CRDT- 2012/08/02 06:00
PHST- 2012/08/02 06:00 [entrez]
PHST- 2012/08/02 06:00 [pubmed]
PHST- 2013/01/11 06:00 [medline]
AID - /j/sagmb.2012.11.issue-4/1544-6115.1827/1544-6115.1827.xml [pii]
AID - 10.1515/1544-6115.1827 [doi]
PST - epublish
SO  - Stat Appl Genet Mol Biol. 2012 Jul 
      12;11(4):/j/sagmb.2012.11.issue-4/1544-6115.1827/1544-6115.1827.xml. doi: 
      10.1515/1544-6115.1827.