B RIEFINGS IN BIOINF ORMATICS . VOL 16. NO 1. 16 ^23
Advance Access published on 13 January 2014
doi:10.1093/bib/bbt091
The genomic and functional
characteristics of disease genes
Andrew Collins
Submitted: 10th October 2013; Received (in revised form) : 2nd December 2013
Abstract
Increasing evidence indicates that genes containing disease causal variation have distinct functional and genomic
properties. The importance of understanding these properties is highlighted by efforts to filter lists of variants
from next-generation sequencing studies, where the number of potentially deleterious variants, which are in fact unrelated to disease, may be large. Available evidence indicates that the majority of disease genes are ‘non-essential’
and their products occupy functionally peripheral positions in protein networks. They tend to be intermediate between genes that have core biological functions, particularly low mutation rates and low haplotype diversity, and
genes for which high haplotype diversity and high mutation rates are advantageous (such as those involved in sensory perception and some immune system functions). Evidence presented here supports these conclusions through
analysis of integrated data sets incorporating the latest mutational profiles, linkage disequilibrium structure and
other genomic properties of individual genes. The analysis highlights the contrasting functions of genes predicted
as least and most likely to contain disease variation and provides a basis for filtering gene variant lists to exclude
the least plausible disease candidates.
Keywords: disease genes; next-generation sequencing; eliminating false-positive variants; mutational heterogeneity; linkage
disequilibrium
INTRODUCTION
Accumulating evidence indicates genes containing
disease causal variation ‘disease genes’ have distinct
genomic and functional properties. The development of an understanding of these characteristics is
important, as researchers are faced with voluminous
next-generation sequencing (NGS) data, which may
identify hundreds or thousands of potentially deleterious variants per sample. Many of these variants
are implausible disease candidates, and a number of
analyses have considered strategies to identify false
positives and filter gene and variant lists to generate
a much smaller, and better supported, set of candidates. Fuentes Fajardo et al. [1] suggested excluding
variants when located in genomic regions known to
be highly polymorphic or reflect sequence read
alignment errors or the minor-allele status of the
reference genome used for alignment. Prominent
in their list of highly polymorphic genes unlikely
to contain pathogenic variation are genes from olfactory and taste receptor families. Lawrence et al. [2]
considered the difficulty of identifying true cancer
driver mutations in tumour-normal paired samples
and devised strategies to account for the wide variation in mutation frequencies across genes.
Specifically, they developed a tool, MutSigCV,
which models mutational heterogeneity and provides a robust filter to reduce the number of false
positives in candidate variant lists. Their initial analyses in lung squamous cell carcinoma samples revealed many signals from genes unlikely to be
involved in cancer (e.g. 25% were from olfactory
receptor genes). By accounting for mutational heterogeneity in individual genes, they eliminated many
Corresponding author. Andrew Collins, Genetic Epidemiology and Genomic Informatics Group, Human Genetics, Faculty of
Medicine, University of Southampton, Duthie Building (808), Southampton General Hospital, Tremona Road, Southampton
SO16 6YD, UK. Tel.: 02380796939; Fax: 02380794264; E-mail: [email protected]
Andrew Collins is the head of the Genetic Epidemiology and Bioinformatics Research Group at the University of Southampton and
is involved in next-generation sequencing studies of a number of diseases.
ß The Author 2014. Published by Oxford University Press. For Permissions, please email: [email protected]
Genomic and functional characteristics
false positives and determined a better supported set
of candidates. Based on extensive NGS and other
data, they characterized genomic features of 17 667
genes including mutation frequency, expression
levels and DNA replication timing.
Petrovski et al. [3] considered how well individual
genes tolerate functional genetic variation. Using data
from 6503 exome sequences, they assessed the extent
to which individual genes have relatively more, or
less, functional genetic variation than expected,
given the total amount of neutral variation in the
gene. They presented the Residual Variation
Intolerance Score (RVIS) for 16 956 genes: when
the score is 0, the gene has an average number of common functional variants, given its total mutational
burden; when the score is <0, the gene has less
common functional variation than might be expected; and when the score is >0, the gene has
more variation. Negative scores are therefore suggestive of purifying selection and positive scores balanced
or positive selection, or both. They found highly significant evidence that genes containing mutations
underlying Online Mendelian Inheritance in Man
(OMIM)-classified Mendelian diseases have lower
RVIS values than non-disease genes.
Similar earlier analyses considered the ratio of
non-synonymous to synonymous substitutions
(Dn/Ds) as a metric for understanding disease gene
properties [4]. The ratio represents the proportion of
amino acid changes in a gene that reach fixation and
are therefore not strongly deleterious. Conflicting
results were obtained when using this metric to
understand the impact of selection on disease variation in other studies [5, 6]. A particular difficulty
arises because the set of non-disease genes used in all
comparisons is likely to contain a proportion of genes
with essential functions that are under intense purifying selection, which do not therefore contribute
to disease. This point is highlighted by Gibson et al.
[7], who considered patterns of linkage disequilibrium (LD) for individual genes. Specifically, they
established that the known disease genes are underrepresented among genes with the strongest LD:
these are genes with the lowest haplotype diversity
suggesting they are least tolerant to mutation. They
found that many genes in this class are involved in
essential biological functions such as phosphorylation, cell division, cellular transport and metabolic
processes. In contrast, disease genes were found to be
over-represented in the class of genes that have average levels of LD. The class of genes with the weakest
17
LD was found to be enriched for genes with functions for which high haplotype diversity is advantageous (e.g. sensory perception and immune response
genes).
Human disease genes have been classified into essential and non-essential genes based on comparative
mouse data [8–10]. The classification considers essential genes as those that are embryonically lethal when
activated. Although the validity of comparisons with
the mouse genome has been questioned, these analyses concluded that the majority (60–75%) of disease
genes are non-essential. It has also been shown that
disease genes are less likely to operate as highly
connected protein nodes or ‘hubs’ [11] in gene interaction networks. The interaction network
properties of disease genes have been extensively
studied [8, 9, 12]. Goh et al. [8] found the expression
patterns of disease genes indicate that their products
are localized in the ‘functional periphery’ of protein
networks. Dancik et al. [13] described the proteins
encoded by disease genes as having ‘intermediate’
connectivity in biological networks. Cai et al. [14]
demonstrated that Mendelian and complex disease
genes have distinct protein–protein interaction properties. Specifically, disease genes tend to be highly
connected to other genes, but these genes are often
not well connected among themselves. Their analysis
aligned with other work [10] that recognized that
Mendelian disease genes tend to be evolutionarily
old and this duration has provided time for gene
products to develop significant protein–protein connections. They conclude that disease genes occupy
topologically important positions as ‘brokers’ in these
networks connecting proteins, which do not otherwise interact with each other. Their placement in
vulnerable positions in protein interaction networks
means their disruption contributes to disease
phenotypes.
A wealth of published evidence supports the suggestion that disease genes have distinct genomic and
functional properties and characterizing these properties is potentially useful in filtering NGS variant
lists to establish true disease candidates. Integration
and analysis of published data sets that quantify some
of the genomic properties of individual genes is presented here. The integrated data set forms a basis for
further development of multivariate models for ranking variant lists. Gene Ontology enrichment analyses
that consider the functional characteristics of genes
predicted as least and most likely to contain disease
variation are also presented.
18
Collins
MATERIALS AND METHODS
To investigate the genomic and functional properties
of disease genes, four data sets were integrated before
analysis:
(i) Lawrence et al. [2] (Supplementary Table S5) list
properties of 17 667 genes including average expression level across 91 cell lines in the Cancer
Cell Line Encyclopedia (CCLE, http://www.
broadinstitute.org/ccle/home); DNA replication
time [15] on a scale of 100 (early) to 1500 (late);
average non-coding mutation frequency (mutations per base pair) from a panel of 126 wholegenome sequenced cancer samples; and the local
GC content of the gene (on a 100-kb scale),
local gene density and a HiC-derived metric
[16]. The latter considers the 3D architecture
of genomes and, specifically, identifies genomic
regions (compartments) that contain loosely
packed chromatin (which is ‘open’, accessible
and active) versus densely packed regions (with
‘closed’, inaccessible and inactive chromatin).
The HiC data are encoded such that negative
values correspond to genes from the closed chromatin compartment ‘B’ and positive to genes
from the open compartment ‘A’.
(ii) Gibson etal. [7] present LD maps of 10 414 genes
based on genome-wide LD maps built from
exome sequences. Maps are in LD units
(LDUs; [17]), which are analogous to centimorgans in linkage maps, where one LDU is
the physical (kilobase) distance over which LD
declines to ‘background’ levels. Genes were allocated a category of 1–5, with 1 representing
‘strong’ LD, where LDU/kb ¼ 0.0; 2, where
0 < LDU/kb 0.01; 3, where 0.01 < LDU/
kb 0.02; 4, where 0.02 < LDU/kb 0.05 and
‘weak’ LD, where ldu/kb > 0.05. Gene lengths
in kilobases and total number of (exonic) singlenucleotide polymorphisms (SNPs) identified in
these genes from the exome samples were also
derived from this data set.
(iii) Petrovski et al. [3] computed RVIS for 16 956
genes made available in their data set S2. The
RVIS provides a measure of the departure from
the average number of common functional mutations found in genes with a similar mutational
burden.
(iv) The Galaxy server at http://main.g2.bx.psu.
edu/library describes 9133 unique variants
described by OMIM as associated with some
human diseases of which at least 7296 (80%)
are associated with Mendelian disease [18],
with the remainder associated with complex diseases. Gibson et al. [7] found these variants are
located within 1086 of 10 414, 10.4% of genes
with LDU maps in their analysis. For this analysis, the genes containing disease variation were
classified as ‘disease genes’, and all remaining
genes were classified as ‘non-disease’.
The integration of data sets 1–4 produces complete data for 9131 genes including 1033 disease
genes (11.3%) with comprehensively described genomic properties (totals given in Supplementary Table
S1). The list of 1033 genes containing known disease
variation is given in Supplementary Table S2.
Logistic regression with the dependent variable as
non-disease gene (0) and disease gene (1) was undertaken. The analysis considered univariate models
(Table 1) and a multivariate model in which variables
were selected stepwise (Table 2). To investigate
the stability of the multivariate model, two nonoverlapping subsets of the data, containing 4565
and 4566 genes (data sets S1 and S2), were produced.
Results from the final multivariate model evaluated
on each subset are given in Supplementary Table S3.
Using scores predicted under the multivariate model
(Table 3), Gene Ontology enrichment analysis was
undertaken for the 100 genes with lowest predicted
scores (Table 4) and the 100 genes with the highest
predicted scores (Table 5) using DAVID against a
‘whole genome’ background (http://david.abcc.
ncifcrf.gov/). This analysis establishes function enrichment among subsets of genes (clustered into
‘gene groups’) predicted to be least and most likely
to contain disease variation under this model. To test
for consistency, corresponding analyses were carried
out for the two subsets of the data using predicted
scores computed from a multivariate model established for each data set using the same predictor variables identified in Table 2.
RESULTS
Univariate analyses (Table 1) show that the average
expression level of genes in the CCLE is not significantly related to disease gene classification in these
data (P ¼ 0.548). Mutation rates are lower in genes
highly expressed in the germline [2, 19], and a relationship with disease gene status might have been
expected given the significant relationship with
Genomic and functional characteristics
19
Table 1: Univariate logistic regression analyses for disease versus non-disease genes
Variable
Odds ratio
Standard
error
z-score
95% Confidence
interval
Pseudo
R-squared
P-value
Number of SNPs in gene
Size of gene (kilobase)
Strength of LD (from 1 ¼strong, to 5 ¼ weak)
Somatic mutation frequency (log10 mutations/Mb)
DNA replication time (early ¼100 ^ late ¼1500)
Mean GC content of reads covering gene
HiC compartment (negative ¼ closed, gene poor,
positive ¼ open, gene rich)
Average expression level in the CCLE
Local gene density/Mb
RVIS
1.0101
1.0009
1.0819
0.4969
0.9998
3.9330
8.6198
0.0017
0.0003
0.0286
0.1210
0.0001
2.2962
11.8822
5.99
2.99
2.98
2.87
1.40
2.35
1.56
1.0068 ^1.0134
1.0003^1.0016
1.0273^1.1394
0.3082^ 0.8010
0.9995^1.0000
1.2525^12.3504
0.5783^128.4846
0.0056
0.0013
0.0014
0.0013
0.0003
0.0008
0.0004
<0.001
0.003
0.003
0.004
0.160
0.019
0.118
1
0.9934
0.8443
0.0000
0.0030
0.0262
0.60
2.20
5.45
0.9999^1.0000
0.9876 ^ 0.9993
0.7945^ 0.8973
0.0001
0.0008
0.0048
0.548
0.028
<0.001
Note: Significant P-values shown in bold.
Table 2: Multivariate logistic regression model for disease versus non-disease genes
Variable
Odds ratio
Standard
error
z-score
95% Confidence
interval
P-valuea
Number of SNPs in gene
Strength of LD (from 1 ¼strong, to 5 ¼ weak)
Somatic non-coding mutation frequency (log10 mutations/Mb)
Mean GC content of reads covering the gene
Local gene density (1Mb)
RVIS
1.0106
1.0732
0.4672
6.8580
0.9886
0.8717
0.0018
0.0294
0.1208
5.0631
0.0037
0.0255
5.92
2.58
2.94
2.61
3.09
4.70
1.0071^1.0141
1.0171^1.1324
0.2815^ 0.7754
1.6135^29.1490
0.9814 ^ 0.9958
0.8231^ 0.9231
<0.001
0.010
0.003
0.009
0.002
<0.001
Note: aPseudo R-squared for the model ¼ 0.0152. Significant P-values shown in bold.
Table 3: Means for variables by predicted score from the multivariate model
Predicted
score from
multivariate
model
Number
of genes
Proportion
of disease
genes
Number
of SNPs
in gene
LD category
(1 ¼strong LD,
5 ¼ weak LD)
Log10
mutations/Mb
( 1 000 000)
Mean
GC content
(frequency)
Local gene
density/Mb
RVIS
<0.09
0.09^< 0.10
0.10<0.11
0.11<0.12
0.12<0.13
0.13
1818
1394
1574
1390
1021
1934
0.073
0.093
0.103
0.108
0.140
0.163
12.28
12.23
13.73
15.12
17.36
30.55
2.18
2.30
2.47
2.80
2.99
3.32
3.97
3.16
2.88
2.75
2.62
2.65
0.42
0.44
0.44
0.45
0.46
0.47
20.00
15.04
13.54
13.22
12.93
11.56
0.86
0.22
0.03
0.14
0.33
0.85
mutation frequency (P ¼ 0.004). Although these data
are based on expression in cancer cell lines, the authors note that matched normal tissue shows similar
expression patterns. The replication time of a DNA
region during the cell cycle is associated with high
mutation rates for late replicating regions, perhaps
due to depletion of the pool of free nucleotides
[20]. The data are based on replication timing from
HeLa cells but with similar results for blood cell lines
[15, 21]. There is no evidence for an association between disease gene classification and replication
timing in these data (P ¼ 0.160). The data for the
HiC chromatin compartment [16], which quantifies
chromosome regions with open or closed chromatin,
20
Collins
Table 4: Enriched GO terms (P < 0.01, biological process) for gene cluster 1 (enrichment score 39.6) from
the 100 genes with the lowest predicted score under
the multivariate model
GO term
Gene
count
Fold
enrichment
Sensory perception of smell
Sensory perception of chemical stimulus
Sensory perception
Cognition
Neurological system process
G-protein coupled receptor
protein signalling pathway
Cell surface receptor linked
signal transduction
55
55
55
55
56
55
29
26
15
14
10
11
55
6.7
Table 5: Enriched GO terms (P < 0.01, biological process) for gene cluster 1 (enrichment score 5.6) and
gene cluster 2 (enrichment score 5.4) from the 100
genes with the highest predicted score under the multivariate model
GO term
Gene
count
Fold
enrichment
GG1: Cell adhesion
GG1: Biological adhesion
GG2: Extracellular matrix organization
GG2: Extracellular structure organization
GG2: Epidermis development
GG2: Ectoderm development
6
6
3
3
3
3
12
12
43
28
25
23
are also not related to the disease status in these data
(P ¼ 0.118).
Disease genes are significantly longer overall [odds
ratio (OR) ¼ 1.0009, P ¼ 0.003] and contain more
SNPs (OR ¼ 1.0101, P < 0.001) compared with
non-disease genes (Table 1). Other authors have
found disease genes to be longer than reference
genes [10], with an average of 747 amino acids
versus 478 for reference genes.
Gibson et al. [7] found significantly more disease
genes in the gene category with intermediate
strengths of LD and significantly fewer among
genes with the strongest LD. Association with the
less strong LD categories is reflected here
(OR ¼ 1.082, P ¼ 0.003). The LD profile of disease
genes appears intermediate between genes with the
strongest LD, which have ‘essential’ biological functions (including phosphorylation, cell division and
metabolic processes), and the weakest, which are
associated with sensory perception and immune
response, roles for which high haplotype diversity
is advantageous.
Univariate analyses indicate that disease genes are
associated with relatively low non-coding region
mutation frequencies (OR ¼ 0.497, P ¼ 0.004),
high GC content (OR ¼ 3.93, P ¼ 0.019) and low
local gene density (OR ¼ 0.99, P ¼ 0.028). Low
mutation rates align with evidence from the RVIS
metric, which is significantly negative for disease
genes (OR ¼ 0.84, P < 0.001) consistent with other
findings [3] and reflecting the impact of purifying
selection on disease genes.
Multivariate analyses (Table 2) show similar relationships for the retained model terms (number of
SNPs in gene, strength of LD, mutation frequency,
GC content, local gene density and RVIS metric).
Scores from this model (Table 3), divided into six
categories, show that genes in the most disease geneenriched category have 2.5 as many SNPs as the
least enriched, two-third the mutation rate and just
over half the local gene density of the most disease
gene poor class. The strongly negative RVIS, higher
GC content and reduced LD trends for disease genes
are also shown here. Tests of the multivariate model
on the data subsets (Supplementary Table S3) show
that the components of the model have consistent
effect directions, although there is loss of significance,
reflecting reduced power, particularly for data set S2.
Gene functional enrichment analysis against the
‘whole genome’ background by DAVID for the
100 genes with the lowest predicted score show
high enrichment, compared with a whole genome
background, of functions related to sensory perception (perception of smell, chemical stimuli, cognition, etc.; Table 4). Genes with these functions are
prominent as a source of false positives in NGS gene
lists [1, 2]. Tests on the data subsets (S1, S2) using the
models in Supplementary Table S3 established that
the 100 genes with the lowest predicted score in
each set show identical gene group enrichment as
in Table 4, with enrichment scores of 28.4 and
18.8, respectively, for the most enriched gene group.
The 100 highest scoring genes from the multivariate model show enrichment of functions related to
cell adhesion, epidermis development and similar
functions (Table 5), and this cluster includes the disease genes COL5A1 (mutations cause Ehlers–Danlos
syndrome), COL6A3 (mutations cause Bethlem myopathy and Ullrich congenital muscular dystrophy)
and COL7A1 (which causes epidermolysis bullosa
dystrophica and related diseases). Reduced
Genomic and functional characteristics
enrichment scores for these gene groups, compared
with the 100 lowest scoring genes under the model
reflects, in part, fewer genes in each cluster, perhaps
reflecting the diversity of pathways containing disease variation. Tests on the 100 highest scoring genes
from each of the S1 and S2 data subsets show the
gene group with the highest enrichment score for S1
(score ¼ 2.44) comprises functions related to ion
transport and includes the RYR1 gene, which is
involved in central core disease of muscle and other
conditions. For the S2 sample, the gene group with
the highest enrichment score (score ¼ 4.99) includes
regulatory functions of the cytoskeleton and organelles and includes the SYNE1 gene, which is
involved in spinocerebellar ataxia.
DISCUSSION
The data support evidence that genes containing disease variants have distinct genomic and functional
characteristics, and this favours the further development of models that predict disease candidates to
facilitate filtering of NGS variant lists. The multivariate model (Table 2) demonstrates that genes known
to contain disease variants have low RVIS
(P < 0.001) and low overall mutation rates
(P < 0.003) consistent with the impact of purifying
selection. However, they also tend to be GC-rich
(P < 0.009), which is a feature associated with high
mutability. Analyses of non-coding DNAs have
shown CpG islands (adjacent C and G nucleotides)
have 10-fold greater mutation rate than other sites
[22]. Therefore, genes containing disease variants
may be relatively susceptible to mutation, but subject
to elevated purifying selection. Evidence from other
studies demonstrates that disease genes have functionally peripheral roles in gene networks [8],
rather than ‘hub’ functions encoded by genes subject
to more intense selection through in utero lethality.
Evidence for moderately elevated selection is supported by the finding [7] that disease genes have
levels of LD intermediate between essential genes
that have low haplotype diversity and genes involved
in sensory perception and some immune system
functions that have weak LD/high haplotype diversity. Gradually declining intensity in selective pressure might underlie this transition from strong to
weak LD reflecting the different functional roles of
the genes involved.
Disease genes are significantly longer (P ¼ 0.003,
Table 1) and contain more (exonic) SNPs
21
(P < 0.001; Table 2) than non-disease genes.
Several studies have found disease genes are long,
in particular, with longer protein-coding sequence
[6]. Gene length is related to the finding that disease
genes tend to be old [5, 6, 10], implying that
although interaction network and transcriptional
analyses suggest disease genes are not concentrated
in hubs, their ancient origin suggests roles in old
biological processes. Disease mutations have been
found to occur at conserved locations in proteins
[23, 24]. Thus, there is the puzzle that processes
with an ancient evolutionary origin are still vulnerable to disease mutations, given the length of time
for fitness-reducing mutations to have been eliminated by selection. The evidence here suggests that
disease genes may be exposed to moderate purifying
selection operating against a relatively mutable genomic background, which contributes to the persistence of mutations.
Untangling the dominant processes underlying
disease or non-disease status of a gene is challenging.
LD structure, for example, is particularly complex
reflecting the interaction of recombination, selection, population history and also mutation. The independently derived non-coding mutation rate data,
the mutation intolerance score data (RVIS) and
measures of LD contribute significantly to the
model (Table 2). Therefore, evidence from LD
structure unrelated to mutation is contributing to
the model. The component of LD structure that reflects selection may be dominant, but independently
derived, measures of selection at the individual gene
level would presumably enhance the resolution of
the model. Furthermore, because many disease
genes remain undiscovered (the ‘missing heritability’
[25]), the resolution of the model is reduced because
a proportion of genes classified as non-disease do in
fact contain disease variation. The data presented
here represent known disease variants of which at
least 80% are Mendelian [18]. The extent to which
the findings can be related to genes underlying more
complex phenotypes is uncertain. Cai et al. [14]
found a difference between complex disease genes
identified through genome-wide association study
(GWAS) methods and those found through other
methods. They noted that Mendelian and nonGWAS complex trait genes showed distinct and remarkably consistent protein network properties and
GWAS-identified complex trait genes deviated only
slightly from non-disease genes in their network
properties. They attribute the weakness of this
22
Collins
signal, in part, to the small sample size of GWAS
genes, but also because GWAS genes may underlie
etiologically different diseases and more polygenic
phenotypes. However, the observation that nonGWAS-derived complex trait genes show distinct
network properties might suggest that the functional
roles of many GWAS-derived (regulatory) genes are
not well understood and the gene(s) they are impacting may be uncertain. Models that identify and rank
disease gene candidates might therefore be valuable
in establishing the target genes impacted by variants
identified by GWAS.
The data presented here represent some of the genomic properties of genes (mutation rates, LD structure, gene size, GC content, etc.), and the available
evidence suggests many genes associated with disease
show significant differences from non-disease genes.
The predictive ability of a gene classifier is reduced by
unobserved disease genes in the non-disease category
and incomplete understanding of patterns of selection
operating on genes with essential functions. It seems
likely that the utility of a predictive model will be
enhanced through integration of data describing
roles of genes in protein networks and functional
roles in pathways. The utility of analyses, which integrate functional and genomic profiles of individual
genes, is likely to increase, as more genomes are
sequenced and gene functions are better understood.
FUNDING
This work was funded through a research grant provided by the Newlife Foundation for disabled
children.
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SUPPLEMENTARY DATA
Supplementary data are available online at http://
bib.oxfordjournals.org/
10.
11.
Key points
NGS identifies many variants that appear potentially deleterious
but are implausible disease candidates.
Many of these variants are found in genes with particularly high
mutation rates with biological functions for which high haplotype diversity is advantageous (e.g. sensory perception).
By integrating published data sets and contrasting genes containing known disease variation with all other genes, disease genes
are shown to have low mutation rates and are relatively intolerant to functional genetic variation. They also show average
levels of LD are relatively GC-rich and are longer than non-disease genes.
The findings consistent with other evidence that suggests disease genes are subject to moderate purifying selection, are relatively old in evolutionary terms and occupy functionally
peripheral roles in gene networks.
The integration of genomic and functional properties of disease
and non-disease genes establishes a classifier, which is useful for
ranking candidate disease genes in NGS data and for highlighting
those variants that are least likely to be causal.
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