# A First-Stage Approximation to Identify New Imprinted Genes through Sequence Analysis of Its Coding Regions

Authors: Elias Daura-Oller; Maria Cabré; Miguel A. Montero; José L. Paternáin; Antoni Romeu
Journal: Comparative and Functional Genomics
 (2009)

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## Abstract

In the present study, a positive training set of 30 known human imprinted gene coding regions are compared with a set of 72 randomly sampled human nonimprinted gene coding regions (negative training set) to identify genomic features common to human imprinted genes. The most important feature of the present work is its ability to use multivariate analysis to look at variation, at coding region DNA level, among imprinted and non-imprinted genes. There is a force affecting genomic parameters that appears through the use of the appropriate multivariate methods (principle components analysis (PCA) and quadratic discriminant analysis (QDA) to analyse quantitative genomic data. We show that variables, such as CG content, [bp]% CpG islands, [bp]% Large Tandem Repeats, and [bp]% Simple Repeats, are able to distinguish coding regions of human imprinted genes.


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## Body

## 1. Introduction

Genomic imprinting is an epigenetic
modification of dispersed regions of the genome depending on their exposure to
the maternal or paternal germline. This results in differential expression of
only one of the two alleles depending on the parent of origin. Allele-specific
CpG methylation, histone acetylation, asynchronous DNA replication, and
chromatin condensation are all associated with imprinted loci [1].Recently, the question of whether imprinted
genes have sequence characteristics that distinguish them from non-imprinted
genes is drawing the attention of several research groups. Such structural
differences may elucidate the mechanisms leading to allele-specific expression
of imprinted genes [2]. Greally [3] found that the main sequence
characteristic of human imprinted genes is a lower incidence of short interspersed
nuclear elements. For tandem repeats and CpG islands, there is accumulating
evidence correlating these elements and genomic imprinting. Accordingly, some
authors [4–7] suggested using these sequence features as a search tool for
imprinted genes.Identifying imprinted genes experimentally
is challenging because the monoallelic expression of an imprinted gene may
occur only in one of possibly several isoforms, only in particular tissues, or
only at particular stages of development. Many autosomal genes are imprinted
only in specific tissues or cell types, including GRB10 [8], Igf2/H19 [9],
UBE3A [10], ATP10A (formerly ATP10C) [11], and KCNQ1 [12].Consequently, in the absence of any method
for prioritising genes, an average of 100 genes must be examined before a new
imprinted gene can be identified. Indeed, experimental identification of human
imprinted genes to date has been slow. To date, only ~60 human imprinted genes
have been identified.For this reason, the application of sequence
analysis approaches to genome-wide screening of human genes, which can be
ranked to identify those with a sequence composition suggestive of imprinting, is
very useful.To date, imprinted genes are predicted using
a wide range of genomic features and sophisticated strategies and methodologies
[13–16], but no simple sequence patterns and models are known to accurately
distinguish imprinted genes from non-imprinted ones. But even so, a simple
approach would be potentially valuable for directing laboratory work in a first
stage.We are concerned with identifying possible candidate
imprinted genes to allow their imprinting status to be determined
experimentally. For this reason, human gene coding region features are
considered further with a view to developing an approximation to a first-stage
screening and classifying genes into imprinted and non-imprinted candidate
groups. This study uses statistical approaches for a first discrimination
between imprinted and non-imprinted genes based on the currently available coding
region sequences.


## 2. Materials
and Methods

A positive
training set of 30 human genes (Table1) that showed
imprinting effects were selected for analysis from the Catalogue of Imprinted
Genes (http://igc.otago.ac.nz/home.html).
A negative training set of 72
randomly selected control genes and a test set of 31 predicted imprinted genes
were compiled from the recent literature [16] and were collected from the NCBI
nucleotide database (http://www.ncbi.nlm.nih.gov/). See supplementary data for more details about these genes used in this
study.Table 1
List of imprinted genes classified by
expression.
NameBandExpressionTP731p36MLRRTM12p12PNAP1L54q22PPRIM26p12MPLAGL16q24PHYMAI6q24PPEG107q21PPON17q21PCALCR7q21MPPP1R9A7q21MMEST7q32PCOPG27q32PCPA47q32MKLF147q32MKCNK98q24MINPP5F_V210q26PKCNQ111p15MIGF2AS11p15PSMPD111p15MIGF211p15PZNF21511p15MH1911p15MSLC22A1811p15MPHLDA211p15MNDN15q11PMKRN315q11PMAGEL215q11PUBE3A15q12MTCEB3C18q21MNNAT20q11PThe sequence characteristics of the coding
regions of each gene were examined in the analysis. These regions are the
portions of a gene or an mRNA which actually code for a protein.For CpG dinucleotide analysis, we used the
NEWCPGREPORT program (http://mobyle.pasteur.fr/cgibin/portal.py?form=newcpgreport), and the total number of CpG islands was counted. For the repeat element analysis, the Repeat
Masker program (http://www.repeatmasker.org/cgi-bin/WEBRepeatMasker) was used, and for tandem repeat analysis, the ETANDEM program (http://mobyle.pasteur.fr/cgi-bin/ MobylePortal/portal.py?form=etandem) was used. All classes of repeat
elements output from Repeat Masker were collected. We used ETANDEM to obtain
numbers of tandem repeat elements ranging from 5 bp to 100 bp. The Wilbur and
Lipman pairwise sequence alignment method, implemented in the MegaAlign program
of the DNAstar Sequence Analysis software (Lasergene v8.0;
http://www.dnastar.com) used to align sequences of Large Tandem Repeats
identified in imprinted genes.Principal
component analysis (PCA) and quadratic discriminant analysis (QDA) models of the[bp]% sequence
characteristics data were performed using the Minitab software [17].PCA analysis is a multivariate statistical
technique. The central idea of PCA is to reduce the dimensionality of a data
set that presents a large number of interrelated variables, while retaining as
much as possible the variation present in the data set. PCA can search the data
for qualitative and quantitative distinctions in situations where the number of
data available is too large.The purpose of the Quadratic Discriminant Analysis
is to predict membership of a group from a set of predictor variables (the
sequence characteristics). The discriminant is the quadratic combination of the
predictor variables that best predicts group membership, allowing each gene to
be classified into either imprinted or control groups on the basis of its
sequence characteristics.The performance of the classification was
assessed using internal and external validation methods according to our
software capabilities.With the QDA model, we used an internal
validation method calledcross-validation [18]. This method uses the training set to check the model. Here, the
training set is divided in several segments. One segment is reserved to corroborate
the results, and the rest of them are used to build the model.This process is repeated as many times as
segments you have, and every time one of these segments is out of the
calibration, and the other ones are used to build the model. Finally, all the
segments are used to both build and validate the model.With the PCA model, we used the external
validationtest set method. The
number of elements of this set must be large (at least 25% of the training set
size), and it must be independent of the training set, but also this test set
must represent the training set. The imprinted status of the test set is known, so it is possible to assess the PCA model using different elements that the ones
used to build the model.


## 3. Results
and Discussion

Recently, Ke et al. [14] found
significant statistical differences between some sequence descriptors of human imprinted
and control gene coding regions. These significant variables in their
regression model were the Simple and Large Tandem Repeats, GC content, CpG
islands, and short interspersed nuclear elements.Taking into account this fact, we considered
these descriptors (variables) as the most relevant ones for our study. So, the[bp]% genomic sequence characteristics of GC
content, CpG islands, simple repeats (SR), large tandem repeats (LTR) and SINEs
of all imprinted and non-imprinted coding region sequences were calculated.Before applying the pattern recognition
methods, each calculated descriptor was autoscaled. In the autoscaling method,
each variable is scaled to a mean of zero and a standard deviation of unity.
This method is very important because each variable is weighted equally, and
this provides a measure of the ability of a descriptor to discriminate classes
of compounds [19]. With this method, we can compare all descriptors at the same
level.Firstly, we started applying the PCA
technique. After several PCA analyses, the best separation was obtained by
using the following descriptors: GC content,[bp]% CpG islands, [bp]% Simple
Repeats and [bp]% Large Tandem
Repeats. This suggests that in this case, the other variables are not
significant for the classification of the coding regions studied.The PCA results show that the first
component (PC1) is responsible for 49.6% of the variance of the data.
Considering the first (PC1) and second (PC2) components, the accumulated variance
increases to 72%. Figure1 shows that both PC1 and PC2 are in fact responsible
for the discrimination between imprinted (two groups: I1 and I2) and non-imprinted
(two groups: NO_I1 and NO_I2) genes. PC1 and PC2 can be represented by the
following equations, that in fact form the PCA pattern recognition model: (1)PC1=0.535[GCcontent]+0.511[[bp]%CpGislands]+0.521[[bp]%LTR]+0.426[[bp]%SR],PC2=−0.425[GCcontent]−0.467[[bp]%CpGislands]+0.313[[bp]%LTR]+0.71[[bp]%SR]. From Figure 2 and (1), we
can see that the imprinted group I1 has large
values for GC content and [bp]% CpG islands and a major content of [bp]% LTR
compared with the I2 group. The imprinted
group I2 has small values for GC content and [bp]% CpG islands and a major content
of [bp]% SR.Figure 1
The separation of the training set into four groups: I1, I2, NO_I1 and NO_I2.
Notice that both PCs are responsible for the separation.Figure 2
Plot of the loading values of the selected variables used in the training
set.On the
other hand, we can see that the major part of non-imprinted genes, the NO_I2
group, has small values for[bp]% SR and [bp]% LTR, and the NO_I1 group has large
values for the same both descriptors. It is clear that there are four coding
region groups, and each one is located in practically one specific quadrant of
the XY axes.Genomic sequence characteristics of a total of 22544 bp from
the coding sequences of 12 (I1 group) imprinted genes were compared to those of
66959 bp of coding sequences of 18 (I2 group) imprinted genes (Table2) in
order to carry out a deep study of the most relevant imprinted descriptors. The
average number of CpG islands was higher in I1 group (1.8) than in I2 group
(0.4). The frequency of G  +  C was also higher in I1 genes (62%) than in I2 ones
(45%). Moreover, the average number of the ratio [bp]%LTR/[bp]%coding sequence coefficient is higher in the I1 group (I1) than in
I2 (0.03). Note that these results are in good agreement with the loadings of
the PCA model.Table 2
The number of large tandem repeats (LTR), CpG islands, and GC content in
coding sequences of imprinted genes.
I1 groupLenghtCG contentNumber CpG islandsNumber LTRSize countConsensusTP73223464.630——LRRTM1221758.42124_7ctgccgaaccacaccttccaggacKLF14138366.82118_9cggcgcgcccgccgcctcKCNK9130360.120——KCNQ1326263.41130_4cgcggccgccgccccgggccccgcgcccccIGF2AS20566410——SMPD1247359.8116_9cgctggIGF2135663.73114_18tccccccctctctcSLC22A1815496510——PHLDA293761.7119_14ccgcgccctNDN189752.32157_4cccaggcccacaacgccccgggcgccccgaaggcggttccgccggccgcggccccggTCEB3C187764.720——I2 groupLenghtCG contentNumber CpG islandsNumber LTRSize countConsensusNAP1L5191242.90112_7ggaggaggaggaPRIM2235340.700——PLAGL1435446.91125_3atcttacaaaaaaaaaaaaaaaaaaHYMAI500542.11113_7tatatatatataaPEG10662844.72242_3 12_4agaagctctcagaggagaacaacaaccttcgagagcaggtgg/ccgccgcctccaPON1239541.300——CALCR347040.400——PPP1R9A970539.9015_8ttttcMEST250745.11242_4 23_3ggcggctgcggctgccgcgcccggtgctgcccagcgctgcgg/caaaaaaaaaaaaaaaaaaaaaaCOPG2336543.100——CPA4280748.910——INPP5F_V2495543.510——ZNF215365840.41284_3 84_3tattcgacatcaaaaaattcatactgaagcgaaggcctataaatgcaataaatgtgggaaagccttcagccgaagtgcagacct
/aaaactgcatactggagataagtcctgaaaatgtaaaaaatgtaggaaaaccttcaaccggagttcagaacttatttaacatcaH19261555.9028_10 20_4ggggggga/ctttttcttcttcctcctttMKRN33107480129_5ttaaaaattatatatataagaatataaaaMAGEL2229453.70236_7 21_3cgggccctgagtgtctgggagggcccaagcacctcc/ggcctcctcaaaagagcgcagUBE3A449136.70110_7aaaacaaaaaNNAT133856.500——We found
an obvious functional difference between I1 and I2 groups in terms of
expression pattern. We observed maternal expression for 67% of the I1 imprinted
genes and paternal expression for 61% of the I2 imprinted genes.Moreover,
other important observation is that all the Large Tandem Repeats of the I1
group genes are inside CpG islands while this fact is not observed in the I2
group. These results agree with those of Meguro et al. [11]: the CpG islands of imprinted genes contain some
special DNA elements that distinguish them from CpG islands of biallelically
expressed genes.To
identify sequence fingerprints and similarities among Large Tandem Repeats in
the two imprinted groups, we used the Wilbur and Lipman pairwise sequence
alignment method (see supplementary data for details). The I1 sequences group is
quite consistent; all sequences are rich in GC content, and the similarity
index of the aligned fragments ranges from 60 to 100%. In contrast, the
sequences of the I2 group are longer, more heterogenous in terms of nucleotide
composition; in some of them, the presence of a polyA motif could be empathised. The I2 sequence repeats show a much more wide range of similarity index. In
addition, because of some significant differences in nucleotide composition between
members of I2 sequences, some I2 sequence pairs could not to be aligned. From
this analysis, we can conclude that these two Large Tandem Repeats: GC-motifs
(in I1 group) and AT-motifs (in I2 group) are highly conserved sequence patterns
across their respective coding regions.Then, we
built a new model using another statistical technique: the quadratic discriminant
analysis (QDA). QDA is also closely related to
principal component analysis (PCA) in that both look for combinations of
variables which best explain the data. QDA explicitly attempts to model the
difference between the classes of data (supervised pattern recognition). PCA, on
the other hand, does not take into account any difference in class
(nonsupervised pattern recognition).Table3 shows
the results of the QDA classification model. The total percentage of correct
classification was 93%, and the proportions for each group are 100% (I2), 92%
(I1), 90% (NO_I1) and 92% (NO_I2).Table 3
Classification obtained with the QDA analysis.
GroupI2I1NO_I1NO_I2count18122151Summary of classificationTrue groupPut into groupI2I1NO_I1NO_I2I218023I101101NO_I100190NO_I201047TotalN18122151N correct18111947Proportion1,000,920,900,92N  =  102; N correct  =  95; proportion correct  =  0,93; proportion correct with cross-validation  =  0.833.After the employing
of QDA and PCA methods, we proceeded to the validation of their respective
classification models.Thecross-validation approach was used to
validate the QDA model. The total percentage of correct classification was 83%
(Table 3). Therefore, this result confirms the existence of four groups between
the coding regions characteristics.On the
other hand, thetest set approach was
used to validate the PCA model. We decided to apply the PCA model to a series of
new predicted imprinted genes whose imprinting status was predicted by other
methodologies [16] but it is still not experimentally proved. In this way, apart from the
construction of a representative test set, we could compare our PCA results
with the ones of Luedi et al. [16].To form a randomly test set, we did a
full-text mining search with all Luedi’s predicted
gene names across the publication data of theNutrigenomics Database (http://133.11.220.243/nutdb.html). After
that, we formed a test group of 31 supposed imprinted genes related to
nutrigenomics in humans (Table 4). It is important to emphasise that these
possible imprinted genes are related
with dietary factors known to influence DNA methylation as alcohol, folate,
zinc, and cadmium. We thought that this fact may be interesting for future
nutrigenomic work.Table 4
List of 31 genes from the test group.
GeneExpressionLenghtChromosomeGFI1P27841EFNA4M12761HSPA6M26641SHC1M17521CYP1B1P51282SIX3P19262OTX1M21762BCL2L11P34222HOXD9M20892PER2M62192PPARGP18833POLR2HM8213PITX2P21224TLL1P66544NDUFS4P6685ITGB8M87877CDK6M116117PTPRN2M47677GADD45GP10789AKR1C2P166310GATA3P307010NRGNP129511KLRF1P124212KLRC3P104212POU4F1M501513F10M156013JAG2M507714SFRS2M292317GATA6M349418ELA2M93819ZNF42M262019We
calculated the genomic sequence characteristics of the 31 coding regions, and
then we checked if our PCA pattern recognition model could classify them as
imprinted genes, too.Figure3
shows the results of the PCA calculations for the first (PC1) and second (PC2)
principal components. Before carrying out the prediction calculations, the
descriptors were also autoscaled as previously. We found that 27 of the 31
genes were classified in the two correct imprinted quadrants (84%) by the PCA
model. The GFI1, HSPA6, HOXD9, PITX2, PTPRN2, GADD45G, GATA3, NRGN, F10, JAG2,
GATA6, ELA2, and ZNF42 genes are classified in the I1 imprinted group. The I2
imprinted groups are formed by EFNA4, BCL2L11, PER2, PPARG, POLR2H, TLL1,
NDUFSA4, ITGB8, CDK6, AKR1C2, KLRF1, KLRC3, POU4F1, and SFRS2 genes.Figure 3
Scores for the predicted imprinted
genes.Therefore,
taking together these results and the ones of Luedi et al., we can suggest these 27 genes as
good candidates for an experimental imprinting determination.


## 4. Conclusions

The most important feature of the present
work is its ability to use multivariate analysis to look at variation, at
coding region DNA level, among imprinted and non-imprinted genes. There is a
force affecting genomic parameters that appears through
the use of the appropriate multivariate methods (principle components analysis
(PCA) and quadratic discriminant
analysis (QDA) to analyse quantitative genomic data. We show that
variables, such as, CG content,[bp]% CpG
islands, [bp]% Large Tandem
Repeats, and [bp]% Simple
Repeats are able to distinguish human coding region imprinted genes.We know that a conclusive assessment of
prediction methods for imprinted genes is problematic due to the small number
of affected genes, their clustering in small genomic regions, and the difficulty
of experimental validation.However, we think that the application of
this PCA sequence analysis approach to genome-wide screening of human genes,
which can be ranked to identify those with a sequence composition suggestive of
imprinting, is potentially valuable for a first-stage approximation directing
follow-up laboratory work.Clearly an approach like this can be further
refined and the resolution improved as more imprinted genes are identified and
confirmed and the genome sequencing is completed.



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*Source: 549387-2009-04-08.xml*