Bayesian meta-analysis of genetic association studies with different sets of markers.
Robust assessment of genetic effects on quantitative traits or complex-disease risk requires synthesis of evidence from multiple studies. Frequently, studies have genotyped partially overlapping sets of SNPs within a gene or region of interest, hampering attempts to combine all the available data. By using the example of C-reactive protein (CRP) as a quantitative trait, we show how linkage disequilibrium in and around its gene facilitates use of Bayesian hierarchical models to integrate informative data from all available genetic association studies of this trait, irrespective of the SNP typed. A variable selection scheme, followed by contextualization of SNPs exhibiting independent associations within the haplotype structure of the gene, enhanced our ability to infer likely causal variants in this region with population-scale data. This strategy, based on data from a literature based systematic review and substantial new genotyping, facilitated the most comprehensive evaluation to date of the role of variants governing CRP levels, providing important information on the minimal subset of SNPs necessary for comprehensive evaluation of the likely causal relevance of elevated CRP levels for coronary-heart-disease risk by Mendelian randomization. The same method could be applied to evidence synthesis of other quantitative traits, whenever the typed SNPs vary among studies, and to assist fine mapping of causal variants.
Verzilli
Claudio
C
Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK.
Shah
Tina
T
Casas
Juan P
JP
Chapman
Juliet
J
Sandhu
Manjinder
M
Debenham
Sally L
SL
Boekholdt
Matthijs S
MS
Khaw
Kay Tee
KT
Wareham
Nicholas J
NJ
Judson
Richard
R
Benjamin
Emelia J
EJ
Kathiresan
Sekar
S
Larson
Martin G
MG
Rong
Jian
J
Sofat
Reecha
R
Humphries
Steve E
SE
Smeeth
Liam
L
Cavalleri
Gianpiero
G
Whittaker
John C
JC
Hingorani
Aroon D
AD
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D015415:Biomarkers / Q000032:analysis; D002097:C-Reactive Protein / Q000032:analysis* / Q000235:genetics*; D002648:Child; D003198:Computer Simulation; D003327:Coronary Disease / Q000235:genetics*; D005260:Female; D020022:Genetic Predisposition to Disease; D006239:Haplotypes; D006801:Humans; D015810:Linkage Disequilibrium; D008297:Male; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable; D012306:Risk; D012984:Software
D015415:Biomarkers; D002097:C-Reactive Protein