Clinical utility of sequence-based genotype compared with that derivable from genotyping arrays.

Abstract

Objective We investigated the common-disease relevant information obtained from sequencing compared with that reported from genotyping arrays. Materials and methods Using 187 publicly available individual human genomes, we constructed genomic disease risk summaries based on 55 common diseases with reported gene-disease associations in the research literature using two different risk models, one based on the product of likelihood ratios and the other on the allelic variant with the maximum associated disease risk. We also constructed risk profiles based on the single nucleotide polymorphisms (SNPs) of these individuals that could be measured or imputed from two common genotyping array platforms. Results We show that the model risk predictions derived from sequencing differ substantially from those obtained from the SNPs measured on commercially available genotyping arrays for several different non-monogenic diseases, although high density genotyping arrays give identical results for many diseases. Conclusions Our approach may be used to compare the ability of different platforms to probe known genetic risks disease by disease.

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Clinical utility of sequence-based genotype compared
with that derivable from genotyping arrays
Alexander A Morgan,1,2Rong Chen,2Atul Janardhan Butte2,3
ABSTRACT
Objective We investigated the common-disease relevant
information obtained from sequencing compared with
that reported from genotyping arrays.
Materials and methods Using 187 publicly available
individual human genomes, we constructed genomic
disease risk summaries based on 55 common diseases
with reported geneedisease associations in the research
literature using two different risk models, one based on
the product of likelihood ratios and the other on the
allelic variant with the maximum associated disease risk.
We also constructed risk profiles based on the single
nucleotide polymorphisms (SNPs) of these individuals
that could be measured or imputed from two common
genotyping array platforms.
Results We show that the model risk predictions
derived from sequencing differ substantially from those
obtained from the SNPs measured on commercially
available genotyping arrays for several different
non-monogenic diseases, although high density
genotyping arrays give identical results for many
diseases.
Conclusions Our approach may be used to compare the
ability of different platforms to probe known genetic risks
disease by disease.
OBJECTIVE
Access to a patient’s genome offers amazing
possibilities in clinical medicine. Researchers are
racing to develop the biomedical knowledge and
tools capable of turning the mass of individual
sequence data into clinically actionable findings.
Recent work has contrasted different direct-to-
consumer large-scale genotyping services using
single nucleotide polymorphism (SNP) arrays,1and
many individuals are receiving health related
information through this route. At the same time,
whole genome sequencing has been used to directly
implicate particular genetic variants in disease,2 3
while others have reported on methods for clinical
use of a full human genome sequence.4 5As the
price of large-scale sequencing continues to drop, it
is worthwhile to determine if the findings of whole
genome sequencing substantially differ from those
available through commercially available geno-
typing arrays and to what extent the results
depend on the diseases being investigated.
Large-scale sequencing provides more genetic
information than even the highest density arrays,
and the cost difference between technologies is easy
to calculate. However, determining the relative
benefits for any individual of a genetic-based test
for disease prognosis is substantially more difficult.
For any particular disease of interest, disease
prediction relies on the interplay of many factors
including disease prevalence, the predictive power
of disease associated variants, the frequency of the
disease associated genetic variations (eg, allele
frequency), the history of environmental exposures
that may alter disease risk, the existence or absence
of treatments for the disease, and many other
factors. We cannot address all of these issues;
however, we can provide some core insight into the
differences in information provided by separate
technologies. We can compare risk models derived
from genotyping arrays with those obtained from
more complete sequencing. If genotyping arrays
and sequencing provide substantially different risk
predictions, then sequencing has an information
advantage over genotyping arrays at the level of
individual diseases. If the reported risks are not
substantiallydifferent,
may not currently provide much useful clinical
information over genotyping arrays. This is a rela-
tively trivial exercise for monogenic (Mendelian)
diseases: either a particular technology measures
the variant with accuracy or it does not. However,
complexdiseases,with
associated with disease risk, are the topic of this
paper.
Because many different SNPs at different loca-
tions in the genome have been associated with
separate diseases and each is associated with
different variations in disease risk, it is important to
consider how to combine the results of genotyping
at multiple disease associated loci for each disease/
condition. We use two different models for inte-
grating multiple disease associated genetic variants.
We have previously suggested using likelihood
ratios and modeling each measured diseaseevariant
association as an independent test for the disease
using the product of the likelihood ratios to combine
results, described in greater detail in previous
publications.4 5This has some advantage over other
approaches which model the independence of risk
contribution, particularly when ORs are used to
approximate risk ratios,6and when large numbers
of variants are combined, extremely large, nonsen-
sical values can be reported. However, this is
not the only model of genetic risk of disease.
Instead of looking at each genetically independent
variant measured as an independent test for disease,
we can assume that a single variant establishes
most of the genetic risk in each individual, but that
the particular genetic locus driving risk varies from
individual to individual. This model uses the
maximum likelihood ratio for the variants measured.
This is analogous to the idea of a physical chain
being only as strong as its weakest link; in our
model the genetic risk is approximated by the risk
then full sequencing
manydifferent loci
1Department of Biochemistry,
Stanford Genome Technology
Center, Stanford University,
Stanford, California, USA
2Biomedical Informatics
Graduate Training Program,
Division of Systems Medicine,
Department of Pediatrics,
Stanford University, Stanford,
California, USA
3Lucile Packard Children’s
Hospital, Palo Alto, California,
USA
Correspondence to
Dr Alexander A Morgan,
Department of Biochemistry,
Stanford Genome Technology
Center, 855 South California
Avenue, Palo Alto, CA 94304,
USA; alexmo@stanford.edu
Received 30 November 2011
Accepted 19 April 2012
This paper is freely available
online under the BMJ Journals
unlocked scheme, see http://
jamia.bmj.com/site/about/
unlocked.xhtml
J Am Med Inform Assoc 2012;19:e21ee27. doi:10.1136/amiajnl-2011-000737e21
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conferred by the single allele with the largest effect size, that is
the most damaging allele. The variant with the single largest
likelihood ratio is assumed to confer all the risk, and any genetic
contribution from any other variants is ignored. This is biased
toward the larger effect sizes and more deleterious alleles by
definition. Of course, neither of these models should be taken as
a perfect predictor of genetic risk; however, they provide parsi-
monious models. They make no assumptions about epistatic
interactions that have not yet been validated. Importantly, they
do not incorporate any other clinical covariate that may affect
the likelihood of a particular disease diagnosis, such as envi-
ronmental or demographic features, although one advantage of
these approaches, which are both fundamentally Bayesian in
formulation, is that additional diagnostic features, particularly
when they are independent of the genetic association with
disease, may be easily integrated into a more complex version of
the model.7 8
Only a handful of individuals have been fully sequenced in
great depth. However, the 1000 Genomes Project9provides
greater sequence data with better informed imputation across
a number of individuals of different ethnic backgrounds using
a variety of sequencing technologies at different research
centers. We can use the preliminary release of these data as
a source of sequences to generate clinical risk reports based on
our two models, the product of likelihood ratios and maximum
likelihood ratio. At the same time, we can also use the subset of
variants reported from common genotyping arrays, either
through direct measurement or imputed from the SNPs
measured in arrays, and compare them with clinical reports
from the fuller sequencing data. For our analysis, we focus
only on two commercially available genotyping platforms. It is
certainly possible to design a custom array around specific
variants of interest, including rare variants of clinical interest,
or to laboriously employ targeted use of PCR to examine all
the relevant variants desired, a strategy followed by many of
the companies carrying out personal genomic testing.10This
has the advantage of providing a clinical profile based on
variants vetted through expert curation (eg, Hsu et al),11but
has the disadvantage of some loss of flexibility if there are
differences in opinion concerning which variants to include in
the analysis.
We have found that even if a large amount of data can be
imputed from a very high-density genotyping array, the clinical
risk assessment using both the product of the likelihood ratios
model and the maximum likelihood ratio differs substantially
between the results obtained using two common genotyping
arrays (Illumina Omni and Affymetrix 500k) and the fuller
sequence data from 1000 Genomes for a number of diseases. This
suggests that clinical interpretation of the results of genotyping
using an ‘off-the-shelf’ array is likely to lack important infor-
mation relevant to a patient’s health.
METHODS
As explained in Ashley et al4and their associated supplementary
materials, we have compiled an extensive database of published
genetic associations with disease from over 2800 research
publications. We filter out any reported association that is not
significant: in a candidate gene study the p value must be <0.05
and in a genome-wide study, the p value must be <0.00001.
Reported associations can also be filtered out for a variety of
quality control reasons, including the fact that many do not
report the actual risk associated allele, or do not report enough
information to calculate a likelihood ratio.
To maximize the independence assumptions needed in our
approach, we merge results reported from multiple SNPs for
the same condition that are in strong linkage disequilibrium
with one another in the same haploblock, as defined by the
CEU population in HapMap, and then take the most signifi-
cant. This could introduce some bias toward larger effect sizes,
but it is necessary to combine studies that use slightly
different genotyping technology and are indirectly measuring
the same variation, as we do not want to double count closely
linked SNPs. Here we operate under the assumption that the
most significant of a set of linked SNPs is more closely asso-
ciated with some underlying, perhaps directly causal, variant,
common in many studies which report the most strongly
associated variant when a group is linked with a disease in
a genome-wide association study. For this study we have
focused on genetic associations for 55 important diseases, as
many of the conditions in our database are either not at the
right level of specificity (eg, genetic risk associations for
cancers in general), not particularly clinically relevant (eg, eye
color), or related to medication response, something which
can be dealt with more comprehensively by experts in
pharmacogenomics.
The 187 genomes were taken from the 1000 Genomes Project
pilot 1 and pilot 2 studies,9downloaded on July 1, 2010. These
genomes represent a variety of ethnic groups and sequencing
technologies, and include related family members, all factors
that can influence the results of any comparative analysis. At the
same time, although these are extensive genetic sequences, they
have not been analyzed to the depth reported for other indi-
viduals.12 13However, this is a unique resource on sequencing
data for individuals, and although genetic association studies are
biased toward particular populations (eg, CEU), we will use this
as the standard of comparison.
The 187 sequences we have collected are derived from
a variety of high throughput sequencing technologies and
centers.9Although we do not have access to basic genotyping
array studies performed on these individuals, we can infer the
results from a theoretical array from the more complete
sequence information. We can examine all the SNPs measured by
the array platform of interest and use the sequence calls from
the human sequence as the reported genotypes from a theoret-
ical genotyping array. Also, we therefore automatically exclude
any experimental differences where the genotyping array does
not match the sequence data. We have chosen to compare the
set of variants measured and potentially imputable by the very
commonly used Affymetrix GeneChip Human Mapping 500K
Array Set (Affymetrix 500k), which measures approximately
500000 SNPs, and the very high coverage Human Omni1-Quad
Beadchip (Illumina Omni), which measures over one million
SNPs.
Genotyping arrays are frequently not used only for the vari-
ants that they directly measure, as often variations are in strong
linkage disequilibrium with one another and one can be accu-
rately imputed from measuring another. To increase the coverage
of our theoretical genotyping arrays, we also use the reported
sequence genotype for any variant which can be imputed from
those measured on the array platform with >75% accuracy
using the state-of-art MACH imputation software with CEU
phasing data in the HapMap as the reference.14This is not ideal
for non-Caucasian individuals, but our analysis is not meant to
provide perfect predictions, just illustrative results.
To model potential interactions between disease associated
alleles, we view each associated variant (per haploblocks, as
above) as providing an independent genetic test for the
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associated disease. For each variant we compute a likelihood
ratio:
Likelihood Ratio¼
Probability of genotype in diseased person
Probability of genotype in nondiseased person
PðGjDÞ
PðGj !DÞ
¼ LR ¼
(equation 1)
This enables the creation of a flexible likelihood ratio for any
conceivable genotype combination of homozygote or heterozy-
gote alleles, in contrast with many risk models which compare
only two sets of genotypes, grouping heterozygotes with one of
the homozygote genotypes. The genetic variants are free from
genetic direct linkage, as we look only at SNPs in distinct and
independent haploblocks and then assume that they are inde-
pendent tests for disease. We can then model the overall likeli-
hood ratio of disease simply as the product of likelihood ratios
from each individual disease associated variant to provide the
product model for likelihood ratios, depicted graphically in
previous work as a nomogram.7Genetic measurements at vari-
ants that are not associated with increased or decreased likeli-
hood of disease are assumed to be uninformative tests, with
a likelihood ratio of unity, and are therefore excluded.
In our alternative maximum likelihood ratio model, we
assume that the significant contribution to disease risk arises
from only the variant with the largest effect size. A likelihood
ratio is computed as described above in equation 1 for each
genotype at each relevant locus for an individual and the single
largest association with disease is used, the maximum likelihood
ratio. This is roughly proportional, although not exactly equiv-
alent, to taking the maximum OR or RR.
RESULTS
To compare genotyping array based results with the sequence
derived likelihood ratios, we compute the differences in the
natural logarithm of likelihood ratio for each of the 55 diseases,
for each of the 187 individuals. The results of these comparisons
are shown in figures 1 and 2. The boxplots show the difference
between the risk profiles derived from the genotyping arrays and
the risk profiles derived from the genome sequence for each
disease for each of the 187 individuals. Wider boxes correspond
to greater variation in the difference, and the median difference
is indicated by a vertical bar. To allow comparison on amount of
genetic information available for each disease, the number of
SNPs contributing to the risk profile is also shown.
As is apparent from the lists of numbers in the figures, the
number of SNPs used in each model is not always a simple
whole number; instead, an average number of usable SNPs
across individuals is reported. This fractional number can occur
for several reasons including a lack of accurate reporting for all
possible genotypes from the genetic association studies used, or
a missing or ambiguous sequence call at a key SNP. In such cases,
it is taken as an uninformative or inclusive test for disease
association, and a likelihood ratio of 1.0 is assumed for that
particular variantedisease association.
The root mean square (RMS) difference in the log likelihood
ratios within a disease averaged across the 55 diseases is shown
in table 1. This gives a quantitative, overall summary of vari-
ability between platforms and shows the relative contribution of
imputation. These 55 diseases were chosen to represent a wide
range of conditions, but the 187 individuals are not entirely
representative of any particular population.
Some potential biases are observable in the figures. In the
model that is derived from the maximum (reported) likelihood
ratio, there is a strong skew toward a result suggesting increased
risk compared with the product of likelihood ratios model which
combines risk and protective alleles, attenuating the derived
likelihood ratio. In other words, the median tends to be higher in
the max model compared with the product model. However, as
can be seen in the plots on the right of each figure, this is not
universally the case, and it may seem counter-intuitive that the
model using the maximum likelihood ratio can show consistently
lower reported risk relative to the product of likelihood ratios for
a disease. This is because a genotyping array may not measure
any of the SNPs associated with that disease and thus all indi-
viduals will have a likelihood ratio of 1.0, or no information.
However, most or all of the individuals sequenced may actually
have the protective allele, and thus when full sequence infor-
mation is available, the risk derived from the maximum likelihood
ratio may actually be less. This may be anticipated as an artifact
of our patient population, as we know that the subjects who
have been sequenced are biased toward older individuals, free
from disease, and perhaps selectively biased toward possessors of
alleles that have protected them from disease into middle age. In
general, meta-analyses, such as our synthesis of published
association studies, are subject to issues in reporting bias toward
positive findings over negative ones and toward over-inflated
effect size estimates.15 16
Although the absolute differences in the clinical profile are
important, a key parameter is the relative variability in disease
likelihood between that reported based on the genotyping arrays
and that derived from the more extensive sequence data. In
tables 2 and 3 we show these relative differences (equation 2) for
each of the 55 diseases, for the SNPs derived from the geno-
typing arrays, as explained for figures 1 and 2. For each disease,
we show the RMS of the difference in log likelihood ratio
between what is derived from the genotyping array SNPs over
the RMS of the log likelihood ratio derived from the variants in
the sequence data (equation 1). For each disease, d, and each
patient, p, in the set of patients P, we compare the RMS
difference in the log likelihood ratio derived from sequence data,
LLRseq, and that derived from the variants measured or imput-
able from the genotyping array, LLRgen.
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
A value of >1.0 in tables 2 and 3 indicates that the variability
in the clinical profile for that disease as reported from the SNPs
on the genotyping array relative to that from sequencing is
greater than the variability between individuals. We can see that
for many diseases, the variability between the results provided
by sequencing and genotyping SNPs is a large fraction of the
variability between individuals, and depending on the model and
genotyping array, may actually exceed the variability between
individuals.
When using a richer model that allows many individual
variants to contribute to adjustments in the risk, in this case the
product model, the medically relevant results from the geno-
typing arrays differ substantially from those derived from
sequencing across many diseases, even with the very high
coverage of the Illumina Omni platform and a liberal allowance
for imputation. For diseases like Alzheimer’s and type II diabetes
Sum
p
?
LLRseqp;d?LLRgenp;d
?
?
r
Sum
p
LLRseqp;d
?
r
(equation 2)
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with many associated variants not covered by the genotyping
array, the overall likelihood ratios can vary dramatically by as
much as a factor of 20 (shown as a difference in logs of three in
figures 1 and 2).
DISCUSSION
There are some limitations to our work. We focus only on the
disease associations published in the literature from findings that
have been reported as statistically significant, from studies
primarily relying on relatively common genetic variants.
Undoubtedly, many genetic variants that contribute to disease
risk remain to be discovered, and these will help explain the
heritability of many common diseases.17 18However, our results
demonstrate that whole genome sequencing shows markedly
different results when only the currently known geneedisease
associations are studied, and before consideration of the even
Venous thrombosis
Ulcerative colitis
Type 2 diabetes
Type 1 diabetes
Tourette syndrome
Thyroid cancer
Systemic lupus erythematosus
Stroke
Schizophrenia
Rheumatoid arthritis
Psoriasis
Parkinson's disease
Osteoarthritis
Obesity
Multiple sclerosis
Multiple myeloma
Migraine
Hypertrophic cardiomyopathy
Hypertension
Hodgkin lymphoma
Graves' disease
Glioblastoma
Gastric cancer
Esophageal cancer
Epilepsy
Eczema
Depression
Crohn's disease
Colorectal cancer
Chronic obstructive pulmonary disease
Chronic lymphocytic leukemia
Cerebral malaria
Celiac disease
Cataract
Cardiovascular disease
Cardiac hypertrophy
Bronchopulmonary dysplasia
Bladder cancer
Bipolar disorder
Biliary tract cancer
Autoimmune thyroid disease
Atrial fibrillation
Asthma
Anorexia
Ankylosing spondylitis
Amyotrophic lateral sclerosis
Alzheimer's disease
Allergic rhinitis
Alcohol dependence
Age related macular degeneration
Adolescent idiopathic scoliosis
Adenocarcinoma
Acute myeloid leukemia
Acute lymphoblastic leukemia
Abdominal aortic aneurysm
−6
−4
−2
02
4
6
Product of LR’s
Log likelihood ratio difference
2.0
11.6
71.2
22.9
1.0
4.3
21.3
9.3
35.3
32.4
13.3
15.6
7.6
25.2
15.6
8.6
2.3
0.7
18.9
1.0
12.4
2.6
11.1
4.0
2.0
1.0
10.1
28.2
22.8
2.3
4.3
1.6
0.7
1.0
5.0
0.6
1.0
22.8
18.4
1.0
1.0
1.0
11.6
1.7
3.8
1.9
53.8
1.0
11.4
9.0
2.9
1.3
1.0
2.0
3.1
0.0
1.9
13.5
5.6
0.0
1.0
2.0
1.6
2.0
9.9
3.6
1.7
0.9
3.8
4.3
0.6
0.0
0.0
2.0
0.0
4.3
0.0
0.6
0.7
0.0
0.0
3.4
15.0
1.2
0.4
2.0
0.0
0.0
0.0
0.0
0.0
0.7
3.5
5.5
0.0
1.0
1.0
1.3
0.0
0.0
0.0
15.7
0.0
0.0
0.1
0.0
0.3
1.0
2.0
2.0
Sequence
Derived
Array
Derived
−1.0 −0.5
0.00.5
1.0
1.5
2.0
Max of LR’s
Log likelihood ratio difference
Figure 1
platform. Differences in natural logarithm of likelihood ratios derived for 55 diseases for the single nucleotide polymorphisms (SNPs) measured on the
Affymetrix 500k platform, compared with the likelihood ratios derived from the SNPs in the genome sequence for the 187 genomes examined. For each
disease, the log likelihood ratio reported by the array based SNPs is subtracted from the sequence derived value for each individual. The boxplot shows
the median as a dark bar, with the box width showing the center quartiles, and the whiskers showing the outer quartiles; extreme outliers are
excluded. The results using the product of likelihood ratios for all associated variants is depicted in the plot on the left, while the results from the
maximum likelihood ratio are shown in the plot on the right; note that the horizontal scales are different. To the left of each disease bar are two
numbers. The lighter colored, left number indicates the number of SNPs used in the likelihood ratio derived from the full sequence, while the darker
number to the right indicates the number of SNPs used in the likelihood ratios derived from the SNPs directly measured by the Affymetrix 500k
platform.
Difference in likelihood ratios (LRs) derived from the genome sequence and genotyping array reportable variants from the Affymetrix 500k
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greater advantage provided by examining unique variations that
may be associated with other deleterious changes, including the
introduction of early stop codons or amino acid substitutions
predictive of deleterious effects.19In this work, we considered
two different, plausible, models of combining disease-associated
variants, and they give similar results, but many other models
are possible. Our work further highlights the need for more
comprehensive, multivariate models of disease risk, including
possible epistatic interactions,20and these more complex models
will be developed as the research community continues to
investigate the contributions of genetic variations in disease.
The clinical use of genomic data is in its infancy. However,
our results suggest that even with the currently limited knowl-
edge of geneedisease associations, genome sequencing provides
−1.0
Difference log likelihood ratio
−0.5
0.00.51.0
1.5
2.0
Venous thrombosis
Ulcerative colitis
Type 2 diabetes
Type 1 diabetes
Tourette syndrome
Thyroid cancer
Systemic lupus erythematosus
Stroke
Schizophrenia
Rheumatoid arthritis
Psoriasis
Parkinson's disease
Osteoarthritis
Obesity
Multiple sclerosis
Multiple myeloma
Migraine
Hypertrophic cardiomyopathy
Hypertension
Hodgkin lymphoma
Graves' disease
Glioblastoma
Gastric cancer
Esophageal cancer
Epilepsy
Eczema
Depression
Crohn's disease
Colorectal cancer
Chronic obstructive pulmonary disease
Chronic lymphocytic leukemia
Cerebral malaria
Celiac disease
Cataract
Cardiovascular disease
Cardiac hypertrophy
Bronchopulmonary dysplasia
Bladder cancer
Bipolar disorder
Biliary tract cancer
Autoimmune thyroid disease
Atrial fibrillation
Asthma
Anorexia
Ankylosing spondylitis
Amyotrophic lateral sclerosis
Alzheimer's disease
Allergic rhinitis
Alcohol dependence
Age related macular degeneration
Adolescent idiopathic scoliosis
Adenocarcinoma
Acute myeloid leukemia
Acute lymphoblastic leukemia
Abdominal aortic aneurysm
−6
−4−2
0
2
4
Product of LR’s
Difference log likelihood ratio
2.0
11.6
71.2
22.9
1.0
4.3
21.3
9.3
35.3
32.4
13.3
15.6
7.6
25.2
15.6
8.6
2.3
0.7
18.9
1.0
12.4
2.6
11.1
4.0
2.0
1.0
10.1
28.2
22.8
2.3
4.3
1.6
0.7
1.0
5.0
0.6
1.0
22.8
18.4
1.0
1.0
1.0
11.6
1.7
3.8
1.9
53.8
1.0
11.4
9.0
2.9
1.3
1.0
2.0
3.1
1.0
9.3
43.8
18.1
1.0
2.0
15.7
5.7
22.2
22.7
7.7
11.9
5.1
11.3
12.2
3.2
1.3
0.7
14.5
0.0
10.9
2.6
9.1
2.6
1.0
0.5
7.7
20.0
14.0
1.4
3.6
0.0
0.7
1.0
4.0
0.0
0.7
11.9
8.6
0.0
1.0
0.0
7.5
1.7
3.0
0.9
34.3
0.0
9.4
8.0
1.0
0.0
0.0
1.0
2.8
Max of LR’s
Sequence
Derived
Array
Derived
Figure 2
platform. Differences in log likelihood ratios derived for 55 diseases from the SNPs measured on the Illumina Omni platform, compared with the
likelihood ratios derived from the SNPs in the full genome sequence for the 187 genomes examined. The results are presented as in figure 1.
Difference in likelihood ratios (LRs) derived from the genome sequence and genotyping array reportable variants from the Illumina Omni
Table 1
(LRs) between the those derived from the full sequences and those
derivable from genotyping arrays, averaged across diseases
Root mean squared (RMS) difference in log likelihood ratios
Simulated
platform SNP callingRisk model
Averaged RMS log
likelihood difference
Affymetrix 500k MeasuredProduct of LRs
Maximum of LRs
Product of LRs
Maximum of LRs
Product of LRs
Maximum of LRs
Product of LRs
Maximum of LRs
0.895
0.384
0.514
0.204
0.519
0.203
0.292
0.102
Measured and
imputable
Illumina OmniMeasured
Measured and
imputable
SNP, single nucleotide polymorphism.
J Am Med Inform Assoc 2012;19:e21ee27. doi:10.1136/amiajnl-2011-000737e25
Research and applications