 | |  | |  | |  | |  | |  | |  |
| IJMEG Copyright © 2010-present. All rights reserved. Published by e-Century Publishing Corporation, Madison, WI 53711 |
Int J Mol Epidemiol Genet 2010;1(2):134-144.
Original Article
Using In silico LD clumping and meta-analysis of genomewide datasets as a
complementary tool to investigate and validate new candidate biomarkers in
Alzheimer’s disease
Christopher Medway, Hui Shi, James Bullock, Holly Black, Kristelle Brown, Baharak Vafadar-isfahani,
Balwir Matharoo-ball, Graham Ball, Robert Rees, Noor Kalsheker, Kevin Morgan
Department of Clinical Chemistry, Institute of Genetics, School of Molecular Medical Sciences. A Floor, West Block,
QMC, Nottingham. NG7 2UH; The John Van Geest Cancer Research Centre, School of Science and Technology,
Nottingham Trent University, Nottingham, UK.
Received November 25, 2009, accepted March 12, 2010, available online: March 18, 2010
Abstract: Despite the recent wealth of genome-wide association studies, insufficient power may explain why much of
the heritable contribution to common diseases remains hidden. As different SNP panels are genotyped by commercial
chips, increasing study power through meta-analysis is made problematic. To address these power issues we
suggest an approach which permits meta-analysis of candidate SNPs from multiple GWAS. By identifying correlated
SNPs from different platforms (r2=1), using PLINK’s ‘clumping’ method, we generated combined p-values (using
Fisher’s combined and random effects meta-analysis) for each clump. P-values were corrected for the number of
clumps (representing the number of independent tests). We also explored to what extent commercial platforms tag
HapMap SNPs within these candidate genes. To illustrate this approach, and to serve as ‘proof-of-principle’, we used
3 late-onset Alzheimer’s disease GWAS datasets to explore SNP-disease associations in 4 new candidate genes encoding
cerebro-spinal fluid biomarkers for Alzheimer’s disease; Fibrinogen γ-chain (FGG), SPARC-like1 (SPARCL1),
Contactin-1 (CNTN1) and Contactin-2 (CNTN2). Genes encoding current Alzheimer’s biomarkers; APP, (Aβ), MAPT
(Tau) and APOE were also included. This method identified two SNP ‘clumps’; one clump in APOE (rs4420638) and
one downstream of CNTN2 (which harboured rs7523477 and rs4951168) which were significant following random
effects meta-analysis (P < 0.05). The latter was linked to three conserved SNPs in the 3’-UTR of CNTN2. We cannot
rule out that this result is a false positive due to the large number of statistical tests applied; nevertheless this approach
is easily applied and might well have utility in future ‘–omics’ studies..(IJMEG911001).
Key words: PLINK, Clumping, Alzheimer’s disease, genome-wide association study (GWAS), CNTN1, CNTN2, SPARCL1,
FGG, APOE, meta-analysis
Full Text PDF
Address all correspondence to:
Kevin Morgan, PhD
Department of Clinical Chemistry
Institute of Genetics, School of Molecular Medical Sciences
A Floor, West Block, QMC, Nottingham. NG7 2UH
The John Van Geest Cancer Research Centre
School of Science and Technology
Nottingham Trent University,
Nottingham, UK.
E-mail: kevin.morgan@nottingham.
Original Article
Using In silico LD clumping and meta-analysis of genomewide datasets as a
complementary tool to investigate and validate new candidate biomarkers in
Alzheimer’s disease
Christopher Medway, Hui Shi, James Bullock, Holly Black, Kristelle Brown, Baharak Vafadar-isfahani,
Balwir Matharoo-ball, Graham Ball, Robert Rees, Noor Kalsheker, Kevin Morgan
Department of Clinical Chemistry, Institute of Genetics, School of Molecular Medical Sciences. A Floor, West Block,
QMC, Nottingham. NG7 2UH; The John Van Geest Cancer Research Centre, School of Science and Technology,
Nottingham Trent University, Nottingham, UK.
Received November 25, 2009, accepted March 12, 2010, available online: March 18, 2010
Abstract: Despite the recent wealth of genome-wide association studies, insufficient power may explain why much of
the heritable contribution to common diseases remains hidden. As different SNP panels are genotyped by commercial
chips, increasing study power through meta-analysis is made problematic. To address these power issues we
suggest an approach which permits meta-analysis of candidate SNPs from multiple GWAS. By identifying correlated
SNPs from different platforms (r2=1), using PLINK’s ‘clumping’ method, we generated combined p-values (using
Fisher’s combined and random effects meta-analysis) for each clump. P-values were corrected for the number of
clumps (representing the number of independent tests). We also explored to what extent commercial platforms tag
HapMap SNPs within these candidate genes. To illustrate this approach, and to serve as ‘proof-of-principle’, we used
3 late-onset Alzheimer’s disease GWAS datasets to explore SNP-disease associations in 4 new candidate genes encoding
cerebro-spinal fluid biomarkers for Alzheimer’s disease; Fibrinogen γ-chain (FGG), SPARC-like1 (SPARCL1),
Contactin-1 (CNTN1) and Contactin-2 (CNTN2). Genes encoding current Alzheimer’s biomarkers; APP, (Aβ), MAPT
(Tau) and APOE were also included. This method identified two SNP ‘clumps’; one clump in APOE (rs4420638) and
one downstream of CNTN2 (which harboured rs7523477 and rs4951168) which were significant following random
effects meta-analysis (P < 0.05). The latter was linked to three conserved SNPs in the 3’-UTR of CNTN2. We cannot
rule out that this result is a false positive due to the large number of statistical tests applied; nevertheless this approach
is easily applied and might well have utility in future ‘–omics’ studies..(IJMEG911001).
Key words: PLINK, Clumping, Alzheimer’s disease, genome-wide association study (GWAS), CNTN1, CNTN2, SPARCL1,
FGG, APOE, meta-analysis
Full Text PDF
Address all correspondence to:
Kevin Morgan, PhD
Department of Clinical Chemistry
Institute of Genetics, School of Molecular Medical Sciences
A Floor, West Block, QMC, Nottingham. NG7 2UH
The John Van Geest Cancer Research Centre
School of Science and Technology
Nottingham Trent University,
Nottingham, UK.
E-mail: kevin.morgan@nottingham.
