The Metabochip, a Custom Genotyping Array for Genetic
Studies of Metabolic, Cardiovascular, and
Benjamin F. Voight1,2., Hyun Min Kang3., Jun Ding4, Cameron D. Palmer1,5, Carlo Sidore3,6,7,
Peter S. Chines8, Noe ¨l P. Burtt1, Christian Fuchsberger3, Yanming Li3, Jeanette Erdmann9,
Timothy M. Frayling10, Iris M. Heid11,12, Anne U. Jackson3, Toby Johnson13, Tuomas O. Kilpela ¨inen14,
Cecilia M. Lindgren15, Andrew P. Morris15, Inga Prokopenko15,16, Joshua C. Randall15, Richa Saxena1,17,18,
Nicole Soranzo19, Elizabeth K. Speliotes1,20, Tanya M. Teslovich3, Eleanor Wheeler19, Jared Maguire1,
Melissa Parkin1, Simon Potter19, N. William Rayner15,16,19, Neil Robertson15,16, Kathleen Stirrups19,
Wendy Winckler1, Serena Sanna6, Antonella Mulas6, Ramaiah Nagaraja4, Francesco Cucca6,7,
Ine ˆs Barroso19,21, Panos Deloukas19, Ruth J. F. Loos14, Sekar Kathiresan1,17,22,23, Patricia B. Munroe13,
Christopher Newton-Cheh1,17,22,23, Arne Pfeufer24,25,26, Nilesh J. Samani27,28, Heribert Schunkert9,
Joel N. Hirschhorn1,5,29, David Altshuler1,17,23,29,30,31*, Mark I. McCarthy15,16,32*, Gonc ¸alo R. Abecasis3*,
1Medical Population Genetics, The Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America,
2Department of Pharmacology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America, 3Department of
Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America, 4Laboratory of Genetics, National Institute on Aging,
National Institutes of Health, Baltimore, Maryland, United States of America, 5Divisions of Endocrinology and Genetics and Program in Genomics, Children’s Hospital,
Boston, Massachusetts, United States of America, 6Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Italy, 7Dipartimento di
Scienze Biomediche, Universita ` di Sassari, Sassari, Italy, 8Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of
America, 9Universita ¨t zu Lu ¨beck, Medizinische Klinik II, and Nordic Center of Cardiovascular Research, Lu ¨beck, Germany, 10Genetics of Complex Traits, Peninsula College
of Medicine and Dentistry, University of Exeter, Exeter, United Kingdom, 11Department of Epidemiology and Preventive Medicine, University Hospital Regensburg,
Regensburg, Germany, 12Helmholtz Zentrum Mu ¨nchen—German Research Center for Environmental Health, Institute of Epidemiology, Neuherberg, Germany,
13Clinical Pharmacology and Barts and the London Genome Centre, William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary University
of London, London, United Kingdom, 14MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, United Kingdom, 15Wellcome Trust
Centre for Human Genetics, University of Oxford, Oxford, United Kingdom, 16Oxford Centre for Diabetes, Endocrinology, and Metabolism, Churchill Hospital, University of
Oxford, Oxford, United Kingdom, 17Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America,
18Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, 19Wellcome Trust
Sanger Institute, Hinxton, Cambridge, United Kingdom, 20Department of Internal Medicine, Division of Gastroenterology and Center for Computational Medicine and
Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America, 21University of Cambridge Metabolic Research Laboratories, Institute of Metabolic
Science, Addenbrooke’s Hospital, Cambridge, United Kingdom, 22Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Boston,
Massachusetts, United States of America, 23Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America, 24Institute of Human
Genetics, Klinikum Rechts der Isar Technische Universita ¨t Mu ¨nchen, Munich, Germany, 25Institute of Human Genetics, Helmholtz Zentrum Mu ¨nchen, Deutsches
Forschungszentrum fu ¨r Gesundheit und Umwelt, Neuherberg, Germany, 26EURAC Center of Biomedicine, Bolzano, Italy, 27Department of Cardiovascular Sciences,
Glenfield Hospital, University of Leicester, Leicester, United Kingdom, 28Leicester NIHR Biomedical Research Unit in Coronary Artery Disease, Glenfield Hospital, Leicester,
United Kingdom, 29Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America, 30Department of Molecular Biology, Harvard
Medical School, Boston, Massachusetts, United States of America, 31Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America,
32Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and
myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood
pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence.
To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we
designed the ‘‘Metabochip,’’ a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the
Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency
spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its
performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to
designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits.
The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale
genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and
PLoS Genetics | www.plosgenetics.org1 August 2012 | Volume 8 | Issue 8 | e1002793
Citation: Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, et al. (2012) The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic,
Cardiovascular, and Anthropometric Traits. PLoS Genet 8(8): e1002793. doi:10.1371/journal.pgen.1002793
Editor: Greg Gibson, Georgia Institute of Technology, United States of America
Received December 6, 2011; Accepted May 13, 2012; Published August 2, 2012
Copyright: ? 2012 Voight et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Support from the National Institutes of Health (HG000376, HG005214, HG005581, DK062370, NO1-AG-1-2109), the Wellcome Trust (098051), the British
Heart Foundation, and the Leicester NIHR Biomedical Research Unit in Cardiovascular Disease is gratefully acknowledged. The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org (MB); email@example.com (DA); firstname.lastname@example.org (MIM); email@example.com (GRA)
. These authors contributed equally to this work.
Recent data emerging from theoretical models [1,2] and
empirical observation through genome-wide association studies
(GWAS) (for example [3,4]) demonstrate that hundreds of genetic
loci contribute to complex traits in humans. These data prompt
two questions: (1) can additional genetic loci be identified by
follow-up of the most significantly associated variants after initial
GWAS meta-analysis? and (2) can further investigation via genetic
fine-mapping refine association signals at established genetic loci?
Systematically addressing these two questions should help improve
understanding of the genetic architecture of complex traits and
their shared genetic determinants, and suggest hypotheses and
disease mechanisms that can be tested in functional experiments or
model systems .
Addressing these two questions requires genotyping thousands of
individuals at many genetic markers. For most currently available
genotyping technologies, this kind of characterization is cost-
prohibitive. To address this need in the context of type 2 diabetes,
coronary artery disease and myocardial infarction, and quantitative
traits related to these diseases, we designed the Metabochip, a
custom genotyping array that provides accurate and cost-effective
genotyping of nearly 200,000 single nucleotide polymorphisms
(SNPs) chosen based on GWAS meta-analyses of 23 traits (Table 1).
Metabochip SNPs were selected from the catalogs developed by the
International HapMap  and 1000 Genomes  Projects,
allowing inclusion of SNPs across a wide range of the allele
frequency spectrum. These included 63,450 SNPs to follow-up the
top ,5,000 or ,1,000 (see Methods) independent association
signals for each of the 23 traits, 122,241 SNPs to fine-map 257 loci
which showed genome-wide significant evidence for association
with one or more of the 23 traits, and 16,992 SNPs chosen for a
variety of other reasons (see Methods and Table 2). In designing the
array, we sought to maximize assay success rates as well as the
number of variants that could be assayed; Illumina custom arrays
include a fixed number of ‘‘beads’’ and some sites can be assayed
with a single bead while others require two .
Here, we describe Metabochip array design, and evaluate
performance of the array in common genetic analysis steps,
including quality control steps such as genomic control calculations,
identification of related individuals, and fine-mapping of known
disease susceptibility loci. Our results provide practical guidance to
investigators and show that for fine-mapping loci the Metabochip
provides much greater resolution than prior GWAS arrays.
Core Features of the Metabochip: Traits and SNPs
The Metabochip was designed by representatives of the Body
Fat Percentage , CARDIoGRAM (coronary artery disease and
myocardial infarction) , DIAGRAM (type 2 diabetes) ,
GIANT (anthropometric traits) [3,12,13], Global Lipids Genetics
(lipids) , HaemGen (hematological measures) , ICBP (blood
pressure) , MAGIC (glucose and insulin) [16–18], and QT-
IGC (QT interval) [19,20] GWAS meta-analysis consortia. The
array is comprised of SNPs selected across two tiers of traits
(Table 1). Tier 1 is comprised of eleven traits deemed to be of
primary interest: type 2 diabetes (T2D), fasting glucose, coronary
artery disease and myocardial infarction (CAD/MI), low density
lipoprotein (LDL) cholesterol, high density lipoprotein (HDL)
cholesterol, triglycerides, body mass index (BMI), systolic and
diastolic blood pressure, QT interval, and waist-to-hip ratio
adjusted for BMI (WHR). Tier 2 is comprised of twelve traits of
secondary interest: fasting insulin, 2-hour glucose, glycated
hemoglobin (HbA1c), T2D age of diagnosis, early onset T2D
(diagnosis age,45 years), waist circumference adjusted for BMI,
height, body fat percentage, total cholesterol, platelet count, mean
platelet volume, and white blood cell count.
We included three design classes of SNPs on the Metabochip
1. Replication SNPs: ,5,000 (Tier 1) or ,1,000 (Tier 2) SNPs
were selected to follow-up the top independent association
signals from the largest available GWAS meta-analysis for each
of the 23 traits (Supplementary Table S1).
2. Fine-mapping SNPs: SNPs were selected from the catalogs of the
International HapMap Project  and the August 2009 release
of the 1000 Genomes Project  to fine-map 257 loci associated
at genome-wide significance (P,561028) in preliminary anal-
yses of one or more of the 23 traits (See Figure 1, Supplementary
Table S2 and S3, and Supplementary Text for details).
3. Other SNPs: These were comprised of independent SNPs for
which genome-wide significant associations had been reported
for any trait, SNP tags for copy number polymorphisms
(CNPs), the MHC region, and the mitochondrial genome,
fingerprint SNPs from GWA array products, a set of
chromosome X and Y markers for sex verification, and
‘‘wild-card’’ SNPs based on consortium-specific hypotheses and
interests (for example, based on a known pathway or early
deep-sequencing studies). A detailed description of how SNPs
were selected in each of these categories can be found in the
Supplementary Text [21–25].
In total, 217,695 SNPs were chosen for the array (Table 2).
20,970 SNPs (9.6%) failed during the assay manufacturing process,
resulting in 196,725 SNPs available for genotyping. A summary
file annotating each Metabochip SNP with ascertainment criteria,
SNP assay, a list of unintended duplicate SNPs (Supplementary
Table S4), and reference strand orientation for alleles is provided
Metabochip Array Design
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Data Generation and Quality Control (QC)
We evaluated the utility of the Metabochip and accuracy of its
genotype calls in three sample sets: (1) 15,896 northern European
individuals from the FUSION, METSIM, HUNT, Tromsø, and
Diagen studies [26–30] together with 67 HapMap samples
genotyped at least two times each and called using Illumina
GenomeStudio software by re-clustering these data; (2) 6,614
Sardinian individuals organized in 1,243 extended families from
the SardiNIA study [31,32] called by GenomeStudio software
using default cluster data; and (3) 9,715 Nordic individuals from
the Malmø Preventive Project, the Scania Diabetes Registry, and
the Botnia Study [33–35] genotyped using a modified version of
the BIRDSEED genotype calling algorithm .
We applied standard SNP- and sample-based QC filters based
on call rate, Hardy-Weinberg equilibrium deviations, duplicate
genotype inconsistencies, and failures of Mendelian inheritance; in
the Nordic sample, we also carried out checks based on plate-
specific characteristics. These filters resulted in final data sets of
163,222 polymorphic SNPs genotyped in 67 HapMap samples,
142,812 polymorphic SNPs genotyped in 6,164 Sardinians, and
Table 1. Summary of Metabochip SNPs by trait: Fine-mapping and replication.
Consortium Trait Name
# Loci Size (Mb)# SNPs
DIAGRAM Type 2 Diabetes346.56 16,7175,057
CARDIoGRAMMI and CAD30 9.60 19,5586,485
Lipids HDL Cholesterol 234.62 12,150 5,024
LDL Cholesterol 214.069,9815,060
Triglyceride20 4.689,784 5,057
GIANTBody Mass Index 247.48 18,2115,055
Waist-to-Hip Ratio*15 2.25 5,4645,056
MAGIC Fasting Glucose195.0513,644 5,058
ICBP Diastolic Blood Pressure20 8.3413,239 5,060
Systolic Blood Pressure21 6.01 10,6415,059
QT-IGC QT Interval18 4.08 10,9105,041
DIAGRAMT2D Age of Diagnosis0 0.000 1,039
T2D Early Onset0 0.000 1,040
HaemGen Mean Platelet Volume0 0.000 657
Platelet Count0 0.000 577
White Blood Cell0 0.000 598
Lipids Total Cholesterol0 0.000 941
Body Fat Body Fat Percentage0 0.000 1,035
GIANT Height0 0.000 1,050
Waist Circumference*2 0.50 1,374 1,048
MAGIC2-Hour Glucose3 0.611,2491,038
Glycated Hemoglobin5 0.46 2,1811,045
Fasting Insulin2 0.67 1,309 1,046
TOTAL With Redundancy 25764.97 146,45368,126
Unique Regions/SNPs257 45.52 122,24163,450
SNP counts are numbers of SNPs successfully manufactured on the Metabochip array.
*Waist-to-hip ratio and waist circumference were adjusted for body mass index.
Recent genetic studies have identified hundreds of regions
of the human genome that contribute to risk for type 2
diabetes, coronary artery disease and myocardial infarction,
and to related quantitative traits such as body mass index,
glucose and insulin levels, blood lipid levels, and blood
pressure. These results motivate two central questions: (1)
can further genetic investigation identify additional associ-
ated regions?; and (2) can more detailed genetic investiga-
tion help us identify the causal variants (or variants more
strongly correlated with the causal variants) in the regions
identified so far? Addressing these questions requires
assaying many genetic variants in DNA samples from
thousands of individuals, which is expensive and time-
consuming when done a few SNPs at a time. To facilitate
these investigations, we designed the ‘‘Metabochip,’’ a
custom genotyping array that assays variation in nearly
200,000 sites in the human genome. Here we describe the
Metabochip, evaluate its performance in assaying human
genetic variation, and describe solutions to methodological
challenges commonly encountered in its analysis.
Metabochip Array Design
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179,165 polymorphic SNPs genotyped in 8,473 Nordic individ-
Statistical Analysis Using Metabochip: Genomic Control,
PCA, and Kinship Estimation
Since Metabochip SNPs were selected to be associated with our
23 traits of interest, performing genomic control correction 
requires some care. To select a set of (near)-independent SNPs that
are not associated with an analysis trait of interest, we focused on
SNPs selected to replicate signals unrelated to the trait of interest
(for example, QT interval SNPs for a T2D association analysis),
also removing SNPs within 250 kb of SNPs previously associated
with the trait of interest, and then LD-pruning the remaining
SNPs so that no SNP pair is in strong LD (r2..3).
To estimate kinship coefficients or to correct for population
stratification using principal components analysis (PCA) or
multidimensional scaling (MDS) covariates, we require SNPs that
are not too rare and are not in strong pairwise LD. We found that
taking SNPs with MAF..05 and LD-pruning them so that no
SNP pair has r2..3 works well for PCA and MDS (data not
shown). The same subset of SNPs can be used for pairwise IBD
estimation using the maximum-likelihood method of Milligan 
implemented in PLINK  or the variance-components method
of Balding and Nichols  implemented in EMMAX .
Imputation Preparation and Evaluation
We carried out genotype imputation in the Sardinian data. We
imputed variants observed in a reference set of 280 Europeans
from the August 2010 1000 Genomes Project data into: (a) 6,164
individuals genotyped on the Metabochip , (b) 1,097
individuals genotyped on the Affymetrix 6.0 array, and (c) 1,412
individuals genotyped on the Affymetrix 500 K array . We
evaluated mean estimated r2within fine-mapping regions using
minimac (; www.genome.sph.umich.edu/wiki/minimac), and
empirically compared the imputation quality using the published
Sanger sequencing data in five fine mapping loci . In addition,
we evaluated mean estimated r2across different continental
populations by leaving one individual out from the 1000 Genomes
reference panel and imputing them using markers present in each
platform across the fine mapping regions and a 1 Mb window
flanking each region. We also compared association power
obtained by imputation into GWAS and Metabochip samples in
Metabochip fine-mapping regions by comparing LDL cholesterol
association evidence in 2,342 of these individuals genotyped using
both the Metabochip and one of the Affymetrix arrays.
Evaluation of Array Design and Genotype Quality
Of 217,695 SNPs chosen for the Metabochip across all design
categories, 196,725 (90.4%) were successfully manufactured on the
array (Table 2). The 48,846 previously manufactured SNPs had
higher success rate (95.4%) than the 168,849 new SNP assays
(88.7%). Illumina design score was predictive of the quality of
manufactured SNP assays. For example, 25% of SNPs with design
score,0.6 failed to produce genotype calls due to poor clustering
of the intensity data, compared to 3.1% of SNPs with design score
between 0.6 and 1.0 (Supplementary Figure S1).
We evaluated genotype calling accuracy for 67 HapMap
samples genotyped multiple times using three different calling
strategies: (a) Illumina GenomeStudio with reclustering the
intensity data using .15,000 samples; (b) Illumina GenomeStudio
based on default clusters provided by Illumina; and (c) GenoSNP
, which calls genotypes based on a within-sample-between-
markers analysis of intensity data rather than a between-sample-
The large majority of Metabochip SNPs yielded high quality
genotypes. For the 67 HapMap samples called using GenomeS-
tudio with reclustering, only 8,344 (4.2%) of the 196,725 SNP
assays had genotype call rates ,95%, while another 25,958 SNPs
(13.2%) were monomorphic. Using GenomeStudio and default
clusters, these numbers were 12,131 (6.2%) and 25,311 (12.9%),
while using GenoSNP, they were 18,107 (9.2%) and 25,532
Using GenomeStudio with reclustering, genotype concordance
between Metabochip genotypes for duplicate pairs was 99.998%
overall and 99.990% for heterozygotes. Comparing Metabochip
genotypes to HapMap 3 genotypes for the 59,935 SNPs in
common, genotype concordance was 99.93% overall and 99.84%
for heterozygotes, similar to the 99.87% Mendelian consistency
rate reported in the HapMap3 data . We observed similar
concordance rates for these sample sets using the Illumina caller
with default clusters (99.93% overall, 99.84% for heterozygotes),
or using GenoSNP  (99.85% overall, 99.81% for heterozy-
Table 2. Summary of Metabochip SNPs by SNP category.
SNP CategoryChosen for ArrayPassed Manufacture
Among 67 HapMap samples
. .95% Called MAF. .0 MAF, ,.05
Replication 66,130 63,450 (95.9%)61,386 (96.7%) 60,585 (98.7%)6,121 (10.1%)
Fine-Mapping139,877 122,241 (87.4%) 116,779 (95.5%)92,731 (79.4%)37,552 (40.5%)
Prior Trait Association2,210 2,116 (95.7%) 2,043 (96.5%)2,039 (99.8%) 235 (11.5%)
CNP tags6,8886,626 (96.2%) 6,250 (94.3%)6,160 (98.6%) 941 (15.3%)
MHC 3,203 2,909 (90.8%)2,550 (87.7%)2,537 (99.5%) 185 (7.3%)
Mitochondrial 144135 (93.8%)102 (75.6%) 66 (64.7%) 28 (42.4%)
Chromosome X/Y112 107 (95.5%)106 (99.1%) 104 (98.1%) 0 (0%)
Fingerprint4643 (93.5%)40 (93.0%) 40 (100%)0 (0%)
Wildcard5,323 5,056 (95.0%) 4,847 (95.9%) 4,108 (84.8%)493 (12.0%)
TOTAL (without redundancy) 217,695196,725 (90.4%) 188,395 (95.8%)163,107 (86.6%)44,967 (27.6%)
Numbers in parenthesis represents the proportion of the SNPs in the previous column. A SNP may fall into multiple categories.
Metabochip Array Design
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Genotype concordance for less common variants was slightly
lower than for common variants. For example, among the
singleton SNPs in the 67 HapMap samples, 98.9% of heterozy-
gous genotypes were concordant with HapMap3 for the two
GenomeStudio call sets and 97.8% for the GenoSNP set.
Heterozygous genotype concordances for singleton SNPs between
duplicate pairs were 99.76%, 99.70%, and 99.83% for the three
Frequency Spectrum and Coverage
We evaluated the allele frequency spectrum for Metabochip
SNPs in the 67 HapMap samples (Figure 2). Mean MAF of
Metabochip SNPs was .152 overall, .109 among fine-mapping
SNPs, and .224 among replication SNPs. Among these three SNP
sets, 38%, 53%, and 12% of SNPs had MAF,.05, and 14%, 21%,
and 2% were monomorphic.
Within the 257 fine-mapping regions (45.52 Mb), 109,855 SNPs
were catalogued by the 1000 Genomes Project  pilot studies
and 240,805 SNPs are in the current Phase 1 release (as of
November 2011). Of these, 122,241 fine-mapping SNPs were
genotyped on the Metabochip (Supplementary Table S2). In the
1000 Genomes European samples, Metabochip SNPs tag 82.0%
and 54.5% of all Pilot and Phase 1 1000 Genomes variants in
these regions at r2$.8, compared to 61.3% and 40.3% coverage
using HapMap 3 SNPs (Figure 3). Among SNPs with MAF,.05,
Metabochip SNPs tag 61.9% and 33.8% at r2$.8, compared to
24.3% and 17.0% using HapMap 3. Using genotype imputation,
we can impute 82% of 1000 Genomes Phase 1 European SNPs
with MAF.0.5% with an estimated r2$0.8.
Genotype Imputation within the Metabochip Fine-
We next investigated accuracy of genotype imputation into the
257 Metabochip fine-mapping regions using the 280 Europeans
from 1000 Genomes Project  as reference set and the 6,164
individuals in the Sardinian Metabochip sample as target. Figure 3
displays estimated r2values in the Metabochip fine-mapping
regions as a function of MAF. Also displayed are estimated r2
values for SNPs in these regions using the 280 European 1000
Genomes project samples as reference set and 1,412 Sardinians
genotyped on the Affymetrix 500 K and 1,097 Sardinians
genotyped on the Affymetrix 6.0 chips as targets. Imputation
accuracy into the Sardinian Metabochip sample is greater in all
allele frequency ranges than for the samples genotyped using the
GWAS arrays. For example, among SNPs with .02#MAF,.05,
mean estimated r2for the Affymetrix 500 K, Affymetrix 6.0, and
Metabochip samples were .47, .62, and .84, respectively (Figure 4).
The improved imputation accuracy for Metabochip compared to
GWAS array is primarily due to increased marker density of the
Metabochip in these regions.
Figure 1. Example of signal fine mapping (SFM) and locus fine mapping (LFM) regions. A SFM region seeks to map the initial association
signal. SFM regions were designed using linkage disequilibrium (LD) r2estimates from the 1000 Genomes Project and HapMap CEU data. Initial
boundaries were determined by identifying all SNPs satisfying r2$.5 with the index SNP, and then expanded to the nearest flanking recombination
hotspot, but stopped if there was no hotspot nearby. LFM regions (blue) were similarly designed but expanded to capture functional units of interest
such as nearby coding genes. The figure plots LD r2for SNPs (red dots) within the region and recombination rate (blue lines) as a function of position
on the chromosome. Gene positions and structures are displayed in the lower panel. MI=myocardial Infarction; CAD=cardiovascular disease;
HDL=high-density lipoprotein; LDL=low-density lipoprotein; T2D=type 2 diabetes.
Metabochip Array Design
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Imputation quality in the Metabochip fine-mapping regions
using Metabochip is also improved for non-European individuals
compared to imputation using GWAS platforms. Using a leave-
one-sample-out approach, we evaluated the average r2from the
1000 Genomes reference panel into Affymetrix 500 k, Affymetrix
6.0, and Metabochip.For
.02,MAF,.05, mean estimated r2across European individuals
for the chips were .78, .83, and .93, respectively. For individuals
with African ancestry, corresponding values were .78, .85, and .94,
and for individuals of Asian ancestry, they were .67, .72, and .89
(Supplementary Figure S2). The fact that imputation of rare
variants in African ancestry populations is more accurate than in
European populations is probably explained by noting that – in
the short regions evaluated here – there will be only a limited
number of common variant haplotypes in Europeans and, in some
cases, these will not effectively tag specific rare variants. In African
populations, with a larger variety of rare haplotypes, it is more
likely (relative to Europeans) that at least one haplotype will
capture rare variants of interest.
In addition, we empirically evaluated the quality of experimen-
tally determined and imputed SNPs within the five fine mapping
regions by comparing individual genotypes with those obtained by
Sanger sequencing. For 126 SNPs evaluated, the average r2in
analyses based on the Affymetrix 500 k and 6.0 arrays was .46 and
.55, respectively. Analyses based on Metabochip showed average
r2=.79. Focusing on 48 SNPs that were imputed in all three
analyses, the average r2
was .31 (Affymetrix 500 K), .41
(Affymetrix 6.0), and .57 (Metabochip) (Supplementary Figure S3).
High-Resolution Association Analysis within Metabochip
To compare the power and resolution for association testing in
the Metabochip fine-mapping regions to that of standard GWAS
arrays, we revisited the LDL cholesterol association analysis from
the SardiNIA study  in 2,342 individuals genotyped for both
Metabochip and an Affymetrix (6.0 or 500 k) GWAS chip. Here,
we focus on five of the six most strongly associated loci from
Willer et al. , in and around PCSK9, LDLR, APOE/APOC1/
APOC2, SORT1, and APOB (Figure 5A–J), all of which were
designated for locus fine mapping by the Global Lipids Genetics
In the SORT1 and APOB regions, the peak association signals for
the two data sets are similar (Figure 5A–D). For PCSK9, LDLR,
and APOE/APOC1/APOC2, Metabochip based analysis resulted in
considerably stronger association signals. For PCSK9 and APOE/
APOC1/APOC2, the most strongly associated variants were low-
frequency SNPs (MAF=1.1% for PCSK9, MAF=3.4% for APOE)
that were directly genotyped on the Metabochip but not on the
Affymetrix chips (Figure 5E–J). Although the signals from
common variants are similar, the peak SNPs were not imputed
accurately in the Affymetrix data (estimated r2=.04 and .08,
respectively). Within the LDLR region, there are 165 SNPs in the
1000 Genomes European panel. None of these SNPs are on the
Affymetrix chips and only eight could be imputed at estimated
r2$.3 using the Affymetrix data; the locus is also hard to impute
using HapMap 2 as a reference, with the peak association signals
corresponding to r2of ,.40. In contrast, 36 of the 165 SNPs were
directly genotyped in Metabochip, and 122 were imputed at
estimated r2$.3. As a result, imputation into the Metabochip data
resulted in a substantial association signal (p=7.361026), while
for the Affymetrix data, p..02 at all markers (Figure 5I–J). These
results demonstrate that dense genotyping may substantially
improve imputation accuracy, increasing association power even
for common variants.
Performing Standard Statistical Analyses Using
Metabochip Genotype Data
We carried out kinship estimation between pairs of individuals
and calculated genotype-based principal components for inclusion
as covariates in genetic association analysis using all Metabochip
SNPs that passed QC, and then using the pruned subset of SNPs
described in the Methods section. When using all QC-passing
Figure 2. Allele frequency spectrum for Metabochip SNPs by design category. Blue dots, red squares, and green triangles display fractions
of replication, fine-mapping, and all other SNPs (see Table 2) in each of the tabulated minor allele-frequency bins. CNP=copy number polymorphism.
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SNPs, estimates of pairwise kinship coefficients in the Sardinia
sample had inflated variance (Supplementary Table S5), and
kinship coefficient estimates for the Nordic sample calculated using
PLINK suggested (incorrectly) that essentially all pairs of
individuals were related (Supplementary Figure S4). For each
analysis, using the pruned set of SNPs gave sensible results,
Figure 3. Coverage of 257 Metabochip fine-mapping regions. Fraction of 1000 Genomes Project SNPs in strong linkage disequilibrium (r2$.8)
with HapMap 3 (green squares) or Metabochip (blue dots) SNPs as a function of minor allele frequencies: (A) 1000 Genomes Pilot 1 SNPs, (B) 1000
Genomes Phase 1 SNPs (May 2011 release).
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reducing variance in estimated kinship coefficients in the Sardinia
sample and removing the artifactual estimates of close relatedness
in the Nordic sample.
Because many Metabochip SNPs were included specifically due
to prior evidence for association of T2D, CAD/MI and related
traits, controlling for potential population stratification in
Metabochip analysis requires some care. Not surprisingly, carrying
out T2D association analysis in the Nordic sample on all SNPs
passing QC without inclusion of genotype-based principal
components resulted in a large genomic control inflation factor
(lGC=1.44). Including all SNPs that passed QC to estimate
principal components (PCs), and then including those PCs as
covariates in the association analysis gave reduced but still
substantial inflation (lGC=1.13). When we instead estimated test
statistic inflation based only on the 3,772 LD-pruned QT interval
replication SNPs (not expected to associate with T2D) we obtained
a genomic control inflation factor near unity (lGC=1.01).
Assessing Overlap among SNPs across Traits
We were interested whether the replication SNP sets submitted
by the GWAS consortia for the different traits showed more or less
overlap than expected by chance. To address this question, we
counted the number of SNPs in common across pairs of traits, and
used simulation to test whether the observed overlaps were
different than expected under the null hypothesis of genetic
independence of pairs of traits (Supplementary Table S6). Not
surprisingly, we observed substantial SNP set overlaps (and greater
than expected assuming independence) for multiple pairs of
correlated traits, notably SBP and DBP (38% proportion of
maximum possible overlap), HDL and TG (17%), and TC and
LDL (87%). We also observed substantial genetic overlap (4%)
between LDL and SBP, which are nearly uncorrelated traits.
Overall, we observed an excess of nominally significant SNP set
overlaps, consistent with (but in no way proof of) the hypothesis a
shared genetic etiology between these cardiometabolic traits.
We designed the Metabochip, a custom genotyping array for
replication of the top association signals from the largest available
GWAS meta-analysis for 23 T2D and CAD/MI related traits and
15 of these traits (Table 1). The Metabochip also includes a set of
SNPs representing genome-wide significant associations across a
range of human traits; SNPs that tag known copy number
polymorphisms, the MHC, and mitochondrial variants; X and Y
chromosome SNPs for sex verification, fingerprint SNPs for sample
tracking, and ‘‘wildcard’’ SNPs selected by the participating GWAS
consortia (Table 2). The array has already been genotyped on DNA
samples from hundreds of thousands of individuals and preliminary
analyses across the contributing GWAS consortia have identified
hundreds of new genome-wide association signals (manuscripts
being prepared by each of the consortia).
In designing the Metabochip, 90.4% of chosen SNPs were
successfully designed and manufactured onto the array, and of
these, ,82% passed QC filters in our three example studies,
resulting in very complete coverage of variation in our 257
fine-mapping regions. Of course, as time passes and catalogs of
SNPs expand, potential shortcomings in coverage should
become apparent. Currently, coverage of 1000 Genomes Pilot
Study European SNPs in the fine-mapping regions is 82.0% at
a tagging threshold of r2$.8. Coverage of Phase 1 European
SNPs in these regions is 54.5%, and the number increases to
73.7% for SNPs at MAF.0.5%. Using genotype imputation,
we can impute 82% of 1000 Genomes Phase 1 European SNPs
with MAF.0.5% with estimated r2$0.8. The resulting data
are of high quality, with 99.99% duplicate consistency in
heterozygotes and 99.77% Mendelian consistency in hetero-
zygotes in our studies. Further, Metabochip fine-mapping
regions provide an excellent target for genotype imputation
from relevant reference sets, and in our experience can provide
more complete coverage than provided by standard HapMap-
Figure 4. Imputation accuracy (estimated r2) in fine mapping regions. Imputation accuracy for differing numbers of Sardinian individuals as
measured by estimated r2value across the 257 Metabochip fine mapping regions for Metabochip (red squares), Affymetrix 6.0 GWAS SNPs (green
triangles), and Affymetrix 500 k GWAS SNPs (blue circles) as a function of minor allele frequency bin.
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Figure 5. Regional association plots for LDL cholesterol association in the SardiNIA study. Association plots for a study of 2,432 Sardinian
individuals for five Metabochip fine-mapping regions using 1000 Genomes data as reference set and Affymetrix genotypes (left panels : A,C,E,G,H) or
Metabochip genotypes (right panels : B,D,F,H,J) as target sets. The figures plot 2log10of the association p-value within the region and recombination
rate (blue lines) as a function of position on the chromosome. Blue, green, and red dots and triangles indicate genotyped and imputed SNPs with
minor allele frequencies less than 0.02, greater than or equal 0.02 and less than 0.05, and greater than or equal 0.05, respectively. Gene positions and
structures are displayed in the lower panel.
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based GWAS arrays (Figure 3) for both common and less
A key decision in the fine-mapping of any GWAS signal concerns
the size of the region where genetic variation will be examined
exhaustively. In designing the Metabochip, we focused on relatively
small regions surrounding each lead SNP – these included allvariants
in strong linkage disequilibrium (r2..5) and a small shoulder
extending .02 cM beyond that (typically, ,20 kb). This decision
was informed by the observation that, in cases where GWAS signals
and Mendelian disease loci overlap, they are typically very close
together (typically within ,10 kb of each other and nearly always
within ,100 kb; see  for a discussion of the issue), although there
are exceptions to this rule (see , for example).
Within each fine-mapping region, we selected variants identified
by the HapMap consortium and early analyses of the 1000
Genomes Consortium data. The 1000 Genomes Project and other
sequence based catalogs of genetic variation are now more
extensive that at the time of array design, but (as noted above) our
analyses show that the SNPs selected for inclusion in the
Metabochip form a useful reagent for genotyping imputation –
not only for the imputation of newly discovered SNPs in the fine-
mapping regions (see above) but also for the imputation of other
types of variants, such as indel polymorphisms, that have become
part of newer 1000 Genomes Project analyses (unpublished data).
Several other design choices for Metabochip were to some
degree arbitrary: which traits to include; balance in numbers of
SNPs for replication, fine mapping, and other purposes; and how
to prioritize among SNPs available for each purpose. Were we to
design a similar chip now, we would take advantage of the now
available more extensive and deeply annotated SNP catalogs. In
addition, we would likely include a set of randomly ascertained
SNPs to facilitate analysis that control for population structure and
other artifacts. Finally, with empirical evidence from this and other
projects on the relationship between SNP design score and
empirical probability of successful design, we would likely replace
design score by probability of successful design. This approach
would likely result in even higher call rates.
Because Metabochip SNPs are highly enriched for trait-
associated SNPs and .60% are clustered in the ,1.5% of the
genome that comprises the fine-mapping regions, Metabochip
genotype data present some challenges to standard analyses such
as relationship estimation, principal components analysis, and
genomic control determination. However, as we demonstrated,
these challenges can be overcome by focusing on replication SNPs
expected to be unrelated to the trait of interest. An alternative
approach is to use SNPs that were not associated with the trait(s) of
interest in the corresponding GWAS (for example, p-value..50
for all such traits) and then to LD-prune the resulting set of SNPs
to identify a near-independent set. An alternative that is also
worthy of investigation in the analysis of case-control samples is
the application of principal component factor loadings derived
from a controls-only analysis to the combined sample of cases and
controls. When this last alternative is considered, it is important to
check that PCA axes derived from controls represent all relevant
ancestries present in cases. The design of the array, focused on
replication and fine-mapping and selecting SNPs from early
releases of the HapMap and 1000 Genomes Projects, resulted in a
highly non-random ascertainment of SNPs. Thus, we cannot
recommend use of Metabochip SNPs for population genetic
analyses that rely on unbiased, and/or comprehensive ascertain-
ment schemes for SNPs.
The need for follow-up genotyping is a frequent requirement of
GWAS and sequencing studies of complex human traits.
Approaching array design in a coordinated fashion across related
studies and traits can be particularly cost-effective, since per array
costs often drop dramatically with increasing numbers of
individuals to be genotyped, and (given sufficient numbers of
individuals) may increase only modestly with increasing numbers
of SNPs. For example, a custom chip designed to genotype the
,22,000 DIAGRAM-selected type 2 diabetes Metabochip SNPs
in the ,80,000 individuals genotyped on Metabochip by the
DIAGRAM consortium studies would have cost ,$55 compared
to the Metabochip cost of $39, delivering only 1/9 as many
genotypes at .40% greater cost. Furthermore, examining the
association between SNPs tentatively associated with one trait for
other related traits can also be informative, highlighting pleiotropy
across related traits and helping discover new association signals;
for example, two of the ten novel type 2 diabetes loci identified to
date by Metabochip analysis by the DIAGRAM consortium were
placed on Metabochip for other traits . In the case of the
Metabochip, which is less expensive than many smaller trait
specific arrays, this opportunity to collect more information and
investigate the effects of SNPs associated with other traits actually
comes with reduced costs (compared to trait specific arrays),
although with the need to organize across multiple consortia and
to share the number of SNPs that can be cost-effectively
genotyped. The ‘‘Immunochip’’  follows this same paradigm
and supports genotyping of ,200,000 SNPs identified on the basis
of GWAS meta-analyses for immunological disorders, while the
recently designed ‘‘exome chip’’ (Benjamin Neale, Gonc ¸alo
Abecasis, personal communication) supports genotyping of
,250,000 exonic SNPs identified via large-scale exome sequenc-
ing studies totaling .12,000 individuals. These and other similar
array products represent valuable tools in ongoing efforts to
understand the genetic architecture of complex human traits.
Distribution of Illumina design scores by Metabochip
three continental populations for (A) Europeans (B) Africans, and
(C) East Asians.
Imputation accuracy in fine mapping regions across
data and imputed genotypes. Empirical r2was evaluated between
Sanger sequencing data and imputed genotypes from Metabochip
or (A) Affymetrix 500 K SNPs and (B) Affymetrix 6.0 SNPs across
five loci in 256 Sardinians.
Empirical concordance between Sanger sequencing
identity-by-descent (IBD) sharing generated by PLINK for all
SNPs and for pruned SNPs.
Distribution of estimates of pairwise genome-wide
Summary of replication SNP submission.
Summary of fine-mapping regions.
Summary of SNPs within fine-mapping loci.
List of unintended duplicated SNPs.
Estimation of pairwise kinship coefficients.
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Tier 1 and Tier 2 replication traits submissions and significance of
observed overlap (lower).
Observed count of SNPs in common (upper) between
Technical details of SNP selection criteria.
We thank members of the Body Fat Percentage, CARDIoGRAM,
DIAGRAM, HaemGen, GIANT, Global Lipids, ICBP-GWAS, MAGIC,
and QT-IGC GWAS consortia for association results contributing to
replication SNPs selection and for nomination of fine-mapping regions and
their physical boundaries, the 1000 Genome Project for early access to pilot
SNP calls, and the FUSION, METSIM, HUNT, Tromsø, Diagen,
SardiNIA, Malmø Preventive Project, Scania Diabetes Registry, and
Botnia Study investigators for use of their Metabochip genotype data. We
thank Jennifer Stone, Kimberly Gietzen, Mike Eberle, Luana Galver,
Tristan Orpin, and their Illumina team for technical, logistical, and
informatics support in array design and development, and Damien
Croteau-Chonka for contributing annotations for duplicated SNPs
provided in the Supplementary Table S4.
Conceived and designed the experiments: BFV HMK JNH DA MIM
GRA MB. Analyzed the data: BFV HMK JD CDP CS PSC NPB CF YL
JE TMF IMH AUJ TJ TOK CML APM IP JCR RS NS EKS TMT EW
JM MP SP NWR NR KS WW SS AM. Contributed reagents/materials/
analysis tools: AM RN FC IB PD RJFL SK PBM CN-C AP NJS HS.
Wrote the paper: BFV HMK GRA MB. Critically revised the manuscript,
including comments and feedback: BFV HMK JE TMF TOK CML NS
NWR SS RN IB RJFL SK CN-C NJS JNH DA MIM GRA MB.
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