Modeling activation of inflammatory response system: a molecular-genetic neural network analysis.

Abstract

Significant alterations of T-cell function, along with activation of the inflammatory response system, appear to be linked not only to treatment-resistant schizophrenia, but also to functional psychoses and mood disorders. Because there is a relatively high comorbidity between rheumatoid arthritis (RA), schizophrenia and major depression, the question arises whether there is a common, genetically modulated inflammatory process involved in these disorders. On the basis of three family studies from the U.S. and Europe which were ascertained through an index case suffering from RA (599 nuclear families, 1868 subjects), we aimed to predict the inter-individual variation of autoantibody IgM levels, as an unspecific indicator of inflammatory processes, through molecular-genetic factors. In a three-stage strategy, we first used nonparametric linkage (NPL) analysis to construct an initial configuration of genomic loci showing a sufficiently high NPL score in all three populations. This initial configuration was then modified by iteratively adding or removing genomic loci such that genotype-phenotype correlations were improved. Finally, neural network analysis (NNA) was applied to derive classifiers that predicted the phenotype from the multidimensional genotype. Our analysis led to an activation model that predicted individual IgM levels from the subjects' multidimensional genotypes very reliably. This allowed us to use the activation model for an analysis of the DNA of an existing sample of 1003 psychiatric patients in order to test, in a first approach, whether a deviant, genetically modulated inflammatory process is involved in the pathogenesis of major psychiatric disorders.

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BMC Proceedings
Open Access
Proceedings
Modeling activation of inflammatory response system: a
molecular-genetic neural network analysis
Hans H Stassen*1, Armin Szegedi†2 and Christian Scharfetter†1
Address: 1Psychiatric University Hospital, P.O. Box 1931, CH-8032 Zurich, Switzerland and 2Global Clinical Development, Organon
International, 56 Livingston Avenue, Roseland, New Jersey 07068, USA
Email: Hans H Stassen* - k454910@bli.unizh.ch; Armin Szegedi - armin.szegedi@organon.com;
Christian Scharfetter - christian.scharfetter@bluewin.ch
* Corresponding author †Equal contributors
Abstract
Significant alterations of T-cell function, along with activation of the inflammatory response system,
appear to be linked not only to treatment-resistant schizophrenia, but also to functional psychoses
and mood disorders. Because there is a relatively high comorbidity between rheumatoid arthritis
(RA), schizophrenia and major depression, the question arises whether there is a common,
genetically modulated inflammatory process involved in these disorders. On the basis of three
family studies from the U.S. and Europe which were ascertained through an index case suffering
from RA (599 nuclear families, 1868 subjects), we aimed to predict the inter-individual variation of
autoantibody IgM levels, as an unspecific indicator of inflammatory processes, through molecular-
genetic factors. In a three-stage strategy, we first used nonparametric linkage (NPL) analysis to
construct an initial configuration of genomic loci showing a sufficiently high NPL score in all three
populations. This initial configuration was then modified by iteratively adding or removing genomic
loci such that genotype-phenotype correlations were improved. Finally, neural network analysis
(NNA) was applied to derive classifiers that predicted the phenotype from the multidimensional
genotype. Our analysis led to an activation model that predicted individual IgM levels from the
subjects' multidimensional genotypes very reliably. This allowed us to use the activation model for
an analysis of the DNA of an existing sample of 1003 psychiatric patients in order to test, in a first
approach, whether a deviant, genetically modulated inflammatory process is involved in the
pathogenesis of major psychiatric disorders.
from Genetic Analysis Workshop 15
St. Pete Beach, Florida, USA. 11–15 November 2006
Published: 18 December 2007
BMC Proceedings 2007, 1(Suppl 1):S61
<supplement> <title> <p>Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci</p> </title> <editor>Heather J Cordell, Mariza de Andrade, Marie-Claude Babron, Christopher W Bartlett, Joseph Beyene, Heike Bickeböller, Robert Culverhouse, Adrienne Cupples, E Warwick Daw, Josée Dupuis, Catherine T Falk, Saurabh Ghosh, Katrina A Goddard, Ellen L Goode, Elizabeth R Hauser, Lisa J Martin, Maria Martinez, Kari E North, Nancy L Saccone, Silke Schmidt, William Tapper, Duncan Thomas, David Tritchler, Veronica J Vieland, Ellen M Wijsman, Marsha A Wilcox, John S Witte, Qiong Yang, Andreas Ziegler, Laura Almasy and Jean W MacCluer</editor> <note>Proceedings</note> <url>http://www.biomedcentral.com/content/pdf/1753-6561-1-S1-info.pdf</url> </supplement>
This article is available from: http://www.biomedcentral.com/1753-6561/1/S1/S61
© 2007 Stassen et al; licensee BioMed Central Ltd.
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Background
While the ultimate goal of molecular-genetic research is
the detection of causality, clinicians are also interested in
reliable classification and prediction through objective
laboratory methods. Classification and prediction do not
require full understanding of causality but can, nonethe-
less, contribute to significantly improved treatments. This
is particularly true for rheumatoid arthritis (RA), where
autoantibody formation develops years before the first
symptoms of RA occur. From the psychiatric point of
view, it is most intriguing that active immune processes
may be involved in the pathogenesis of major psychiatric
disorders, as suggested by evidence from recent studies.
Specifically, significant alterations of T-cell function,
along with activation of the inflammatory response sys-
tem, appear to be linked to treatment-resistant schizo-
phrenia [1]. Similar processes have also been reported for
mood disorders in general [2]. The abnormalities of cen-
tral nervous system (CNS) metabolism observed with
functional psychoses and depression might, therefore,
arise because genetically modulated inflammatory reac-
tions damage the microvascular system of the brain, with
the nature of the infectious agent being less important
than the patients' genetically influenced inflammatory
response [3]. Rheumatoid factor IgM is in use as a diag-
nostic test for RA, but possesses a low specificity [4]. In
particular, because IgM autoantibody formation develops
years before the first symptoms of RA occur [5], IgM levels
may well be related to the patients' genetically predis-
posed inflammatory response system, and may even be
related to autoimmune diseases in general. The pathogen-
esis of these diseases, however, is insufficiently under-
stood, also because the question of autoantibody
appearance prior to inflammation – indicating an anti-
body-driven inflammatory response – has not yet been
answered on the basis of empirical data.
Methods
Neural network analysis (NNA)
NNA provides powerful tools for modeling pre-specified
responses to complex, multidimensional input stimuli. It
is the specific advantage of NNA that no causal relation-
ship between stimuli and response is required. NNA con-
nects the "neurons" of input and output layers via one or
more "hidden" layers. All outputs are computed using sig-
moid thresholding of the scalar product of the corre-
sponding weight and input vectors. All outputs at stage "s"
are connected to each input of stage "s + 1". The most pop-
ular learning strategy, the back-propagation algorithm,
looks for the minimum of the error function in the weight
space (goodness of fit) using the method of gradient
descent. The basic algorithm is:
(i) output:
yi observed (i = 1,
2,...,Ni)
(j) hidden layer: (j = 1, 2,...,Nj)
(k) input: sk = xk xk observed (k = 1, 2,...,Nk)
improvements:
Δw
ij
=α ε
(ν = 1, 2,..,p)
where xk denotes observed stimuli, yi denotes observed
responses, σ denotes the activation function of sigmoid-
type: R → (0, 1), α denotes the learning rate, and p is the
number of probes (genotyped subjects). This algorithm
can easily be adapted to genetic models.
k-Fold cross-validation
Results derived through the standard NNA approach,
which uses 80% of samples for training and the remaining
20% for testing, tend to be over optimistic, in particular if
genotype errors and missing data are present. Therefore,
in the k-fold cross-validation, the data are split into k
roughly equal parts, and k - 1 partitions are used for train-
ing, while one partition is used for testing. The process is
repeated until each partition has served as a testing set, so
that k estimates of prediction error are generated. The
choice of k is crucial in this approach, because the result-
ing prediction error is approximately unbiased for the
"true" error only for sufficiently large k (k ≈ 10 is a typical
value in practice).
Genetic vector spaces
Once a function is defined that quantifies the genetic dis-
tance between any two subjects with n-dimensional geno-
type patterns at n loci, the Housholder-Torgerson formula
gives a routine method for computing directly from the
inter-individual genetic distances djk a matrix (bjk) of scalar
products between points with origin at the centroid of all
of the points. The matrix is then factored by any of the
usual factoring procedures to obtain the projections of the
points onto r orthogonal axes of a vector space. In this
s w s
ij ji
j
=
⎡
⎢
⎢
⎣
⎤
⎥
⎥
⎦
∑
σ
s w s
jk kj
k
=
⎡
⎢
⎢
⎣
⎤
⎥
⎥
⎦
∑
σ
sssys
ijiiiii
⋅⋅⋅−=−ε
νννν
()
1
Δwssswss
jki
i
N
=∑
kiiijjj
i
=⋅⋅⋅−⋅⋅−αεν
1
11
()(),
bdddd
jk
n
jk
j
n
n
jk
k
n
n
jk jk
k
n
j
n
=+−−
⎛
⎜
⎜
⎝
⎞
⎟
⎟
⎠
∑∑∑∑
1
2
1
2
1
2
1
22
2

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metric vector space, individuals are characterized as dis-
tinct "points" in such a way that individuals with similar
genotype patterns form compact clouds, while genetically
dissimilar individuals are located in more distant regions.
Accordingly, one expects the groupings associated with
different IgM classes to be well separated in a "genetic"
vector space constructed from those genomic loci that
influence IgM levels.
Learning to recognize: three-stage adaptive strategy
Although the detection of causal genotype-phenotype
relationships (in the strict sense) was not the primary goal
of our analysis, we have taken special precautions to
ensure that a biologically meaningful solution was estab-
lished. Specifically, we used a three-stage strategy: 1) non-
parametric linkage (NPL)
independently ascertained family samples was applied for
initial signal detection; 2) the initial configuration was
then modified by iteratively adding or removing genomic
loci to increase genotype-phenotype correlations; 3) sub-
sequent NNA was used to weight genomic loci and their
interactions optimally. Nonetheless, all of these steps do
not necessarily establish biological meaningfulness, but
merely identify genomic regions likely to harbor func-
tional DNA polymorphisms that are causally related to
the trait of interest. Accordingly, our approach to estab-
lishing biological significance also involves a large-scale
SNP analysis using 5728 selected SNPs. Because there are
complex patterns of linkage disequilibrium and haplo-
type block structure across the whole genome with strong
nonlinearities, special techniques are necessary to narrow
in on candidate regions successfully [6]. Results derived
from this SNP analysis are not presented.
analysis across three
Our sample comprised 599 nuclear families (NARAC
screen 1: 256; NARAC screen 2: 255; France: 88) with
1868 genotyped subjects (718 + 717 + 433) who were
genotyped for either 396 (NARAC) or 1083 microsatel-
lites (France). An integrated genetic map was constructed
on the basis of deCODE and NCBI-36 data, so that the
three populations could be compared through NPL anal-
yses. On the phenotype level, the quantitative clinical
measure rheumatoid factor IgM was available for the
NARAC screens, whereas the French data included only a
dichotomous affected/unaffected measure. For the NPL
analyses, which were carried out independently for the
three populations under investigation, we relied on the
dichotomous measures under the assumption of a suffi-
ciently close association between RA and the measured
antibody IgM, while the optimization procedures (NNA,
genetic vector space method) evaluated the quantitative
measures of NARAC screens 1 and 2 for which we defined
three subject classes using IgM levels: 1) normal: 0 ≤ IgM
< 13.5, 2) low: 13.5 ≤ IgM < 50, and 3) elevated: 50 ≤ IgM.
Due to incomplete data, only 926 subjects could be
included.
Results
NPL analyses carried out separately for the three popula-
tions under investigation revealed several candidate
regions that showed significant NPL scores in all three
samples (Figure 1). Twenty markers from these candidate
regions then served as starting configuration for an itera-
tive optimization of genotype-phenotype correlations: 1)
using a set-theoretical similarity measure [7] we com-
puted the 926(926 - 1)/2 genetic similarities sjk between
any two subjects j, k; 2) from these sjk we constructed a
genetic vector space; 3) linear discriminant analysis of the
three IgM classes then yielded an estimate of the underly-
ing genotype-phenotype correlation; and 4) genotype-
phenotype correlations were improved by iteratively add-
ing or removing genomic loci and repeating steps 1 to 3.
Results suggested a configuration of 16 polymorphisms
on chromosomes 1, 2, 5, 6, 11, and 22 which allowed a
fairly powerful classification of subjects by means of lin-
ear discriminant analysis, offering an overall performance
of 73.8% across NARAC screens 1 and 2. The overlap
between the subgroups, as seen in Figure 2, speaks against
direct clinical application of these classifiers; therefore,
the configuration's predictive power needed boosting
through NNA.
Subsequent NNA (subjects with >12.5% missing data
removed; 926 probes: 576 normal, 237 low, 113 elevated;
5.8% missing data; three layers; 32 input nodes; 40 nodes
on hidden layer; 0.05 convergence criterion; 40,000 itera-
tions; 0.2 learning rate), under the constraint of reproduc-
ibility across NARAC screens 1 and 2, yielded weights that
enabled re-classification of subjects through genotype-
based classifiers at a sensitivity and specificity of >90%
(Table 1), thus indicating that the chosen polymorphisms
possess an informativeness high enough to enable predic-
tion of correct IgM class for almost all probes under inves-
tigation. Given missing data rates of up to 12.5% per
subject and an estimated genotype error rate of 5%, it is
not really surprising that k-fold cross-validation (k = 10)
revealed reduced overall performances in the range of
78.2% (± 3.9) which, nonetheless, mean slight improve-
ment compared to 73.8% derived through discriminant
analysis.
Conclusion
This study has demonstrated the feasibility of deriving suf-
ficiently sensitive and specific genotype-based classifiers
through NNA. However, NNA does not necessarily estab-
lish a causal relationship between stimuli (input) and
responses (output). Epiphenomena that are only indi-
rectly related, or may even be physiologically unrelated, to
the inflammatory response system are likely to explain the

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NPL analysis
Figure 1
NPL analysis. NPL analyses carried out separately for the three populations: NARAC screen1 (green), NARAC screen2
(red), and French samples (blue) yielded several candidate regions which showed significant NPL scores across the three sam-
ples under investigation.
0.0 31.5 63.1
94.6 126.1 157.7 189.2
-1.0
1.8
3.5
5.3
7.0
D6S344 D6S1713 F13A1 D6S263 D6S470 D6S2434 D6S443 D6S259 D6S1605 D6S1584 D6S1959 D6S422 D6S1686 D6S1691 D6S299 D6S2439 D6S276 D6S258 D6S265 HLA D6S1615 TNFa D6S273 D6S1629 D6S1568 D6S291 D6S389 D6S1610 D6S2427 D6S1562 D6S1672 D6S1017 D6S282 D6S1650 D6S452 D6S2410 D6S1662 D6S294 D6S402 D6S1053 D6S455 D6S1596 D6S1031 D6S286 D6S1627 D6S1613 D6S1570 D6S1043 D6S275 D6S1056 D6S300 D6S468 GATA164H01 D6S1021 D6S1664 D6S278 D6S474 D6S261 D6S1608 D6S1639 D6S1715 D6S1620 D6S1040 D6S262 D6S472 D6S292 D6S1009 D6S1569 D6S308 GATA184A08 D6S1553 D6S1687 D6S440 D6S2436 D6S1556 D6S442 D6S1633 D6S437
D6S1581 D6S1599 D6S1277 D6S264 D6S297 D6S1027 D6S281 D6S1693 D6S446
[NPL score]
[cM]
Projection of 926 subjects onto the plane defined by the two largest eigenvectors of a genetic vector space spanned by 16 pol- ymorphisms
Figure 2
Projection of 926 subjects onto the plane defined by the two largest eigenvectors of a genetic vector space
spanned by 16 polymorphisms. The projections revealed differences on the genotype level between IgM-related groups:
circles, normal subjects (0 ≤ IgM < 13.5; n = 576; 100% correctly classified by subsequent NNA); triangles, subjects with low
IgM levels (13.5 ≤ IgM < 50; n = 237; 77.6% correctly classified by subsequent NNA); squares, subjects with elevated IgM levels
(n = 113; 98.2% correctly classified by subsequent NNA).
-0.58 -0.42 -0.26 -0.10 0.06 0.22 0.38
-0.43
-0.23
-0.03
0.17
0.37
1st eigenvector
2nd eigenvector

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observed genotype-based, reproducible classification of
patient IgM levels. Even though our current activation
model with its overall performance of 78.2% does not yet
meet the clinical requirements of diagnostic tools, its per-
formance is high enough to justify an analysis of the DNA
of our existing sample of 1003 psychiatric patients so that
the hypothesis of whether deviant, genetically modulated
inflammatory processes are involved in the pathogenesis
of major psychiatric disorders can be tested in a first
approach.
Competing interests
The author(s) declare that they have no competing inter-
ests.
Acknowledgements
This article has been published as part of BMC Proceedings Volume 1 Sup-
plement 1, 2007: Genetic Analysis Workshop 15: Gene Expression Analysis
and Approaches to Detecting Multiple Functional Loci. The full contents of
the supplement are available online at http://www.biomedcentral.com/
1753-6561/1?issue=S1.
References
1. Eaton WW, Byrne M, Ewald H, Mors O, Chen CY, Agerbo E,
Mortensen PB: Association of schizophrenia and autoimmune
diseases: linkage of Danish national registers. Am J Psychiatry
2006, 163:521-528.
2. Muller N, Schwarz MJ: Neuroimmune-endocrine crosstalk in
schizophrenia and mood disorders. Expert Rev Neurother 2006,
6:1017-1038.
3. Hanson DR, Gottesman II: Theories of schizophrenia: a genetic-
inflammatory-vascular synthesis. BMC Med Genet 2005, 6:7.
4.Bas S, Perneger TV, Seitz M, Tiercy JM, Roux-Lombard P, Guerne PA:
Diagnostic tests for rheumatoid arthritis: comparison of
anti-cyclic citrullinated peptide antibodies, anti-keratin anti-
bodies and IgM rheumatoid factors. Rheumatology 2002,
41:809-814.
5.Nielen MM, van Schaardenburg D, Reesink HW, Twisk JW, van de
Stadt RJ, van der Horst-Bruinsma IE, de Koning MH, Habibuw MR,
Dijkmans BA: Simultaneous development of acute phase
response and autoantibodies in preclinical rheumatoid
arthritis. Ann Rheum Dis 2006, 65:535-537.
6.Saccone SF, Rice JP, Saccone NL: Power-based, phase-informed
selection of single nucleotide polymorphisms for disease
association screens. Genet Epidemiol 2006, 30:459-470.
7.Stassen HH, Bridler R, Hell D, Weisbrod M, Scharfetter C: Ethnic-
ity-independent genetic basis of functional psychoses. A gen-
otype-to-phenotype approach. Am J Med Genet 2004,
124:101-112.
Table 1: Genotype-based classification of subjects with respect to IgM levels
IgM level
N
PrevalenceNormalLowElevatedSensitivitySpecificity
Normal
Low
Elevated
576
237
113
62.2%
25.6%
12.2%
576a
1
0
00
52
111
0.988
0.776
0.982
0.997
0.987
0.936
184
2
Neural Network Analysis yielded weights that enabled re-classification of 926 subjects with respect to IgM levels through genotype-based classifiers
at a sensitivity and specificity of >90% (with the exception of 23.4% of low IgM level subjects who were classified as subjects with elevated IgM
levels).
aBold text indicates correctly re-classified subjects prior to k-fold cross-validation.