# Regression Modeling and Meta-Analysis of Diagnostic Accuracy of  SNP-Based Pathogenicity Detection Tools forUGT1A1 Gene Mutation

Authors: Fakher Rahim; Hamid Galehdari; Javad Mohammadi-asl; Najmaldin Saki
Journal: Genetics Research International
 (2013)

---

## Abstract

Aims. This review summarized all available evidence on the accuracy of SNP-based pathogenicity detection tools and introduced regression model based on functional scores, mutation score, and genomic variation degree. Materials and Methods. A comprehensive search was performed to find all mutations related to Crigler-Najjar syndrome. The pathogenicity prediction was done using SNP-based pathogenicity detection tools including SIFT, PHD-SNP, PolyPhen2, fathmm, Provean, and Mutpred. Overall, 59 different SNPs related to missense mutations in the UGT1A1 gene, were reviewed.  Results. Comparing the diagnostic OR, our model showed high detection potential (diagnostic OR: 16.71, 95% CI: 3.38–82.69). The highest MCC and ACC  belonged to our suggested model (46.8% and 73.3%), followed by SIFT (34.19% and 62.71%). The AUC analysis showed a significance overall performance of our suggested model compared to the selected SNP-based pathogenicity detection tool (P=0.046). Conclusion. Our suggested model is comparable to the well-established SNP-based pathogenicity detection tools that can appropriately reflect the role of a disease-associated SNP in both local and global structures. Although the accuracy of our suggested model is not relatively high, the functional impact of the pathogenic mutations is highlighted at the protein level, which improves the understanding of the molecular basis of mutation pathogenesis.


---

## Body

## 1. Introduction

Crigler-Najjar syndrome (CNS) (MIM nos. 218800, 606785) type I and type II are inherited as autosomal recessive conditions that is resulted from mutations in theUGT1A1gene (UGT1A1; MIM nos. 191740) [1–4]. Type I is characterized by almost complete absence of UGT1A1 enzyme activity, and these patients are refractory to phenobarbital treatment, while type II is a less severe form of deficiency [5, 6]. Patients with CNS are at permanent risk of developing severe neurologic complications such as hearing problems, mental retardation, and choreoathetosis due to severe unconjugated hyperbilirubinemia [7].  It is well known that UGT1A1 is expressed specifically in the liver and that it is difficult to perform an expression analysis directly on the patients by invasive liver biopsy but to state that the mutation causes inactivation of the enzyme you could perform an in vitro functional study by cloning the mutated cDNA of UGT1A1 in an expression vector. The constructs could be transfected in hepatic cell lines as HepG2 or HUH7. The expression analysis on these cells overexpressing the mutated forms of UGT1A1 will allow you to finally demonstrate the inactivation of the enzyme [8]. The UGT1A1gene comprises five consecutive exons located on chromosome 2q37 by which complete or partial inactivation of any exon causes CNS. Single variations in deoxyribonucleic acid (DNA) base pairs responsible for protein, called coding which is single nucleotides polymorphism (SNP), leads to changes in amino acids that ultimately affect the protein structure and function. Different such types of SNPs include, missense mutations, nonsense, silent mutations, and splice-site mutations. The majority of missense mutations leads to considerable variation in the protein structure and function, causing the disease symptoms. Data about nonsynonymous SNPs exists in public repositories such as SWISSPROT [9], dbSNP  [10], and HGVBASE  [11].Genetic methods including the detection of genes linked to the disease phenotypes and the identification of aberrant functions of these genes have, in recent years, provided worthy understanding into the biological foundations of genetic mutation [12]. The present review summarized all available evidence on the accuracy of SNP-based pathogenicity detection tools and introducing regression model based on different scores including functional scores, mutation score, and genomic variation degree and compared the results to the published clinical result.


## 2. Materials and Methods

### 2.1. SNP Data Sources and Collection

An inclusive search was done to find all CNS-related mutations. The major data repositories, including HGMD, dbSNP, SNPdbe, and Ensembl, were reviewed. All CNS-related mutations were extracted and double checked for duplicated queries and then tabulated (Table1).Table 1
Prediction results of SNP-based pathogenicity detection tools compared with the published results.
0
SNP-ID
Variant
SIFT
PhD-SNP
PolyPhen-2
fathmm
Provean
MutPred
References
(1)
rs74720349
V3G
1
0
0
0
0
1
0
(2)
rs201984525
L11P
0
1
0
0
0
1
0
(3)
rs111033541
L15R
1
1
1
0
1
1
1
(4)
rs72551339
H39D
1
1
1
1
1
1
1
(5)
rs140365717
E56A
1
0
1
1
1
1
0
(6)
rs4148323
G71R
0
1
1
1
0
0
1
(7)
rs72551340
F83I
0
1
0
1
1
0
1
(8)
rs144217005
V109A
0
1
0
1
0
0
0
(9)
rs140867457
I116K
0
0
0
1
1
0
0
(10)
rs200734586
K118N
0
0
0
1
0
1
0
(11)
rs72551341
L175Q
1
0
1
1
1
1
1
(12)
rs72551342
C177R
0
0
1
1
1
1
1
(13)
rs201093245
Y192C
1
0
1
1
1
1
0
(14)
rs72551343
R209W
1
0
1
1
1
1
1
(15)
rs144398951
I215V
0
0
0
1
0
0
0
(16)
rs144721642
V225M
0
0
0
1
0
0
0
(17)
rs35003977
V225G
0
0
0
1
1
1
1
(18)
rs35350960
P229Q
0
1
1
1
0
1
1
(19)
rs147640261
T232N
0
0
0
1
0
1
0
(20)
rs57307513
S250P
0
1
0
1
0
1
0
(21)
rs141950052
P267R
1
1
1
1
1
1
0
(22)
rs143072292
V273F
1
1
0
1
1
1
0
(23)
rs72551345
G276R
1
1
1
1
1
1
1
(24)
rs72551347
I294T
1
0
1
1
1
1
1
(25)
rs62625011
G308E
1
1
1
1
1
1
1
(26)
rs114000345
K317E
0
1
0
0
0
0
0
(27)
rs200903749
I322V
1
0
1
1
0
1
1
(28)
rs17851756
I322T
1
1
1
1
1
1
0
(29)
rs202035422
I329T
1
0
1
1
1
1
1
(30)
rs72551348
Q331R
1
1
1
1
1
1
1
(31)
rs139607673
R336W
1
1
1
1
1
1
1
(32)
rs144978321
S343L
0
1
1
1
1
1
0
(33)
rs149750520
N344K
1
0
1
1
1
0
0
(34)
rs201372184
A346V
1
1
1
1
0
1
1
(35)
rs72551351
Q357R
1
1
1
1
1
1
1
(36)
rs34946978
P364L
0
0
1
1
1
0
0
(37)
rs55750087
R367G
1
0
1
1
1
1
1
(38)
rs72551352
A368T
1
0
1
1
1
1
1
(39)
rs72551353
S375F
1
0
1
1
1
1
1
(40)
rs72551354
S381R
1
1
0
1
0
1
1
(41)
rs143573365
V386I
0
0
1
1
0
0
0
(42)
rs28934877
N400H
1
1
1
1
1
1
1
(43)
rs72551355
A401P
0
1
1
1
1
1
1
(44)
rs140613392
R403H
0
0
1
1
1
1
0
(45)
rs36076514
V411L
0
0
0
1
0
1
0
(46)
rs72551356
K428E
0
0
1
1
1
1
1
(47)
rs202172337
M441T
0
0
0
1
0
0
0
(48)
rs143033456
R442C
1
1
1
1
1
0
0
(49)
rs201427749
R450C
1
0
1
1
1
0
0
(50)
rs200370335
R450H
1
0
1
1
1
1
0
(51)
rs114982090
P451L
1
1
1
1
1
1
0
(52)
rs115410088
F460L
1
0
1
1
1
1
0
(53)
rs72551358
E463A
1
1
1
1
1
1
0
(54)
rs115944950
E463D
0
1
1
1
0
1
0
(55)
rs72551359
L474M
1
1
1
1
0
0
0
(56)
rs150687296
R475H
1
0
1
1
1
1
0
(57)
rs34993780
S488C
0
1
1
1
1
1
0
(58)
rs72551360
V499M
1
0
1
1
0
0
1
(59)
rs199723856
A511P
0
1
1
1
0
1
0
Disease: 1; neutral: 0; references: OMIM, PMID, SNPdbe, HGMD, and Swissvar results.


### 2.2. Inclusion Criteria

OnlyUGT1A1-gene-related missense mutations were included.


### 2.3. Exclusion Criteria

Other types of mutation such as synonymous or nonsense were excluded.


### 2.4. Data Extraction

The pathogenicity prediction was done using SNP-based detection tools including SIFT [13], PHD-SNP [14], PolyPhen2 [15], fathmm [16], Provean [17], and Mutpred [18]. Then a regression model was designed using functional scores, mutation score, and genomic variation degree. For each SNP-based pathogenicity detection tool and our regression model, we extracted a 2×2 table including positive prediction of the disease (True Positive, TP), negative prediction as neutral (true negative, TN), positive prediction in nondisease (false positive, FP), and negative prediction in disease (false negative, FN). In order to assess the phenotypic characterization and clinical features of the disease of interest, we searched databases, including  SWISSPROT [9], dbSNP [10], Ensembl [19], OMIM [20], DECIPHER [21], and HGVBASE [11]. Furthermore, we compared the results of SNP-based pathogenicity detection tools with the results of phenotypic description tools. Then we calculated the diagnostic odds ratio (diagnostic OR), which is a single indicator of test performance and varies between 0 and infinity [22].


### 2.5. Statistical Analysis

All the analyses were done by SPSS 16.0. A regression model was designed using three categories, including functional score [23], structural score (GV, genomic variation score) [24], and conservation score [25]. Each SNP-based pathogenicity detection tool was compared by the reference values using logistic regression. The sensitivity (Sn), specificity (Sp), accuracy (ACC), diagnostic OR, and Matthew’s correlation coefficient (MCC) were calculated using the following formula: (1)Sensitivity(Sn)=TPTP+FN,Specificity(Sp)=TNTN+FP,Accuracy=TP+TNTP+FP+TN+FN,DiagnosticOR=Sn/1-Sn1-Sp/Sp,MCC=(TP×TN)-(FP×FN)(TP+FP)(TP+FN)(TN+FN)(TN+FP).The metadisk was used to calculate individual and pooled diagnostic OR, sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio [26]. We also compared the AUC (area under curve), which is a popular index of the overall performance of a test, using the summary receiver operating characteristic (SROC) curve [27].


## 2.1. SNP Data Sources and Collection

An inclusive search was done to find all CNS-related mutations. The major data repositories, including HGMD, dbSNP, SNPdbe, and Ensembl, were reviewed. All CNS-related mutations were extracted and double checked for duplicated queries and then tabulated (Table1).Table 1
Prediction results of SNP-based pathogenicity detection tools compared with the published results.
0
SNP-ID
Variant
SIFT
PhD-SNP
PolyPhen-2
fathmm
Provean
MutPred
References
(1)
rs74720349
V3G
1
0
0
0
0
1
0
(2)
rs201984525
L11P
0
1
0
0
0
1
0
(3)
rs111033541
L15R
1
1
1
0
1
1
1
(4)
rs72551339
H39D
1
1
1
1
1
1
1
(5)
rs140365717
E56A
1
0
1
1
1
1
0
(6)
rs4148323
G71R
0
1
1
1
0
0
1
(7)
rs72551340
F83I
0
1
0
1
1
0
1
(8)
rs144217005
V109A
0
1
0
1
0
0
0
(9)
rs140867457
I116K
0
0
0
1
1
0
0
(10)
rs200734586
K118N
0
0
0
1
0
1
0
(11)
rs72551341
L175Q
1
0
1
1
1
1
1
(12)
rs72551342
C177R
0
0
1
1
1
1
1
(13)
rs201093245
Y192C
1
0
1
1
1
1
0
(14)
rs72551343
R209W
1
0
1
1
1
1
1
(15)
rs144398951
I215V
0
0
0
1
0
0
0
(16)
rs144721642
V225M
0
0
0
1
0
0
0
(17)
rs35003977
V225G
0
0
0
1
1
1
1
(18)
rs35350960
P229Q
0
1
1
1
0
1
1
(19)
rs147640261
T232N
0
0
0
1
0
1
0
(20)
rs57307513
S250P
0
1
0
1
0
1
0
(21)
rs141950052
P267R
1
1
1
1
1
1
0
(22)
rs143072292
V273F
1
1
0
1
1
1
0
(23)
rs72551345
G276R
1
1
1
1
1
1
1
(24)
rs72551347
I294T
1
0
1
1
1
1
1
(25)
rs62625011
G308E
1
1
1
1
1
1
1
(26)
rs114000345
K317E
0
1
0
0
0
0
0
(27)
rs200903749
I322V
1
0
1
1
0
1
1
(28)
rs17851756
I322T
1
1
1
1
1
1
0
(29)
rs202035422
I329T
1
0
1
1
1
1
1
(30)
rs72551348
Q331R
1
1
1
1
1
1
1
(31)
rs139607673
R336W
1
1
1
1
1
1
1
(32)
rs144978321
S343L
0
1
1
1
1
1
0
(33)
rs149750520
N344K
1
0
1
1
1
0
0
(34)
rs201372184
A346V
1
1
1
1
0
1
1
(35)
rs72551351
Q357R
1
1
1
1
1
1
1
(36)
rs34946978
P364L
0
0
1
1
1
0
0
(37)
rs55750087
R367G
1
0
1
1
1
1
1
(38)
rs72551352
A368T
1
0
1
1
1
1
1
(39)
rs72551353
S375F
1
0
1
1
1
1
1
(40)
rs72551354
S381R
1
1
0
1
0
1
1
(41)
rs143573365
V386I
0
0
1
1
0
0
0
(42)
rs28934877
N400H
1
1
1
1
1
1
1
(43)
rs72551355
A401P
0
1
1
1
1
1
1
(44)
rs140613392
R403H
0
0
1
1
1
1
0
(45)
rs36076514
V411L
0
0
0
1
0
1
0
(46)
rs72551356
K428E
0
0
1
1
1
1
1
(47)
rs202172337
M441T
0
0
0
1
0
0
0
(48)
rs143033456
R442C
1
1
1
1
1
0
0
(49)
rs201427749
R450C
1
0
1
1
1
0
0
(50)
rs200370335
R450H
1
0
1
1
1
1
0
(51)
rs114982090
P451L
1
1
1
1
1
1
0
(52)
rs115410088
F460L
1
0
1
1
1
1
0
(53)
rs72551358
E463A
1
1
1
1
1
1
0
(54)
rs115944950
E463D
0
1
1
1
0
1
0
(55)
rs72551359
L474M
1
1
1
1
0
0
0
(56)
rs150687296
R475H
1
0
1
1
1
1
0
(57)
rs34993780
S488C
0
1
1
1
1
1
0
(58)
rs72551360
V499M
1
0
1
1
0
0
1
(59)
rs199723856
A511P
0
1
1
1
0
1
0
Disease: 1; neutral: 0; references: OMIM, PMID, SNPdbe, HGMD, and Swissvar results.


## 2.2. Inclusion Criteria

OnlyUGT1A1-gene-related missense mutations were included.


## 2.3. Exclusion Criteria

Other types of mutation such as synonymous or nonsense were excluded.


## 2.4. Data Extraction

The pathogenicity prediction was done using SNP-based detection tools including SIFT [13], PHD-SNP [14], PolyPhen2 [15], fathmm [16], Provean [17], and Mutpred [18]. Then a regression model was designed using functional scores, mutation score, and genomic variation degree. For each SNP-based pathogenicity detection tool and our regression model, we extracted a 2×2 table including positive prediction of the disease (True Positive, TP), negative prediction as neutral (true negative, TN), positive prediction in nondisease (false positive, FP), and negative prediction in disease (false negative, FN). In order to assess the phenotypic characterization and clinical features of the disease of interest, we searched databases, including  SWISSPROT [9], dbSNP [10], Ensembl [19], OMIM [20], DECIPHER [21], and HGVBASE [11]. Furthermore, we compared the results of SNP-based pathogenicity detection tools with the results of phenotypic description tools. Then we calculated the diagnostic odds ratio (diagnostic OR), which is a single indicator of test performance and varies between 0 and infinity [22].


## 2.5. Statistical Analysis

All the analyses were done by SPSS 16.0. A regression model was designed using three categories, including functional score [23], structural score (GV, genomic variation score) [24], and conservation score [25]. Each SNP-based pathogenicity detection tool was compared by the reference values using logistic regression. The sensitivity (Sn), specificity (Sp), accuracy (ACC), diagnostic OR, and Matthew’s correlation coefficient (MCC) were calculated using the following formula: (1)Sensitivity(Sn)=TPTP+FN,Specificity(Sp)=TNTN+FP,Accuracy=TP+TNTP+FP+TN+FN,DiagnosticOR=Sn/1-Sn1-Sp/Sp,MCC=(TP×TN)-(FP×FN)(TP+FP)(TP+FN)(TN+FN)(TN+FP).The metadisk was used to calculate individual and pooled diagnostic OR, sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio [26]. We also compared the AUC (area under curve), which is a popular index of the overall performance of a test, using the summary receiver operating characteristic (SROC) curve [27].


## 3. Results

Overall, 59 different SNPs related to missense mutations in theUGT1A1 gene were reviewed using the designed protocol (Figure 1). Our regression model was as y = 3.39 + (−0.24 × functional score) + (−0.14 × GV score) + (−2.44 × conservation score). Comparing the diagnostic OR, our model showed high detection potential (diagnostic OR: 16.71, 95%  CI: 3.38–82.69) (Figure 2). The highest MCC and ACC was belonged to our suggested model (46.8% and 73.3%), followed by SIFT (34.19% and 62.71%) (Table 2). The SROC curves reflected an acceptable and fairly good overall diagnostic performance for our suggested model compared to the SNP-based pathogenicity detection tools (Figure 3). The AUC analysis showed a significance overall performance of our suggested model compared to the selected SNP-based pathogenicity detection tool (Table 3).Table 2
Calculated Matthew’s correlation coefficient (MCC) and accuracy (ACC) of the selected SNP-based pathogenicity detection tools and suggested model.
Detection tools
TP
FP
FN
TN
MCC
ACC
SIFT
19
7
15
18
34.19%
62.71%
PHD-SNP
1
27
1
34
3.39%
55.56%
PolyPhen2
23
3
20
13
29.89%
61.02%
fathmm
1
27
1
34
3.39%
55.56%
Provean
20
6
18
15
29.99%
59.32%
MutPred
23
3
20
13
29.89%
61.02%
Model
26
2
14
18
46.80%
73.33%
TP: true positive; TN: true negative; FP: false positive; FN: false negative; MCC: Matthew’s correlation coefficient; ACC: accuracy.Table 3
Area under curve for all the selected SNP-based pathogenicity detection tools.
Area under the curve
Tools
Area
Std. error
P value
95% Confidence Interval
Model
.639
.071
.046
.499
.778
SIFT
.527
.075
.716
.380
.675
PolyPhen2
.516
.076
.829
.367
.666
PHD-SNP
.571
.074
.345
.426
.716
Provean
.587
.074
.249
.442
.732
Fathmm
.560
.075
.427
.413
.707
Mutpred
.580
.075
.288
.433
.727
*Significant, P<.05.Figure 1
Flowchart of searching for SNPs.The individual and pooled diagnostic OR, sensitivity, specificity, negative likelihood ratio, positive likelihood ratio.
(a)
(b)
(c)
(d)
(e)Figure 3
The summary receiver operating characteristic (SROC) curve of the selected SNP-based pathogenicity detection tools.


## 4. Discussion

Since the late 1990s, the initiation of research using genetic testing or molecular medicine, development of diagnostics accuracy tests, and molecular assays that measure levels of genes or specific mutations are used to provide a specific therapy for an individual’s diseases. We suggested a regression model based on different scores including functional scores, conservation score, and genomic variation degree and compared the results to the published clinical result as reference. We observed the effect of a set of disease-causing missense mutations, determined from the general population. The susceptibility of Mendelian inherited disease is most frequently associated with SNPs; hence, the mechanisms by which this occurs are still poorly known. From a biological point of view, the mutated residues are important for the proper functioning of a suitable protein structure [28].Genetic variation in phenotype of the diseases is often difficult to detect because of the complex genetic nature of these species. Using functional characteristics of the genetic mutation will provide a powerful tool to uncovering genetic traits in more complex species  and provide novel insights into the molecular mechanisms of the diseases [29]. More importantly, the associations between genetic variations of SNPs of candidate genes that are selected to represent the phenotype are variable and an important feature from the disease study point of view [30].Sensitivity was not reduced, while higher sensitivity was observed in our suggested model followed by PolyPhen2, Mutpred, and SIFT. We compared our suggested model to several well-established SNP-based pathogenicity detection tools, by which the satisfactory performance of our model and SIFT indicates the importance of a mutation position in the context of the entire protein. It is therefore reasonable to believe that analyzing the results of some SNP-based pathogenicity detection tools such as, our proposed model, SIFT and PolyPhen2 is both feasible and promising but not very excellent.Saunders and Baker [31] and Bao and Cui [32] claimed that in case of unavailability of the conservation score, structural characteristics are valuable predictors. In this study we support using the sequence conservation score which is a good predictor and showed that an acceptable level of accuracy is achieved using the conservation score. Dobson et al., used machine learning methods to measure the sequence conservation score and showed that it is the most powerful single predictor and reported a high level of accuracy using the conservation score alone [33]. They also reported higher accuracy in structural characteristics in combination with the conservation score. We also showed that structural characteristics in combination with the conservation score improves prediction accuracy and can reduce the error rate of the conservation score alone.Ng and Henikoff used sequence and/or structure to predict the effect of a missense mutation on protein function in a mathematical model and claimed that their suggested model is a good SNP-based pathogenicity detection tools [13]. Capriotti et al. [14] developed a mathematical method that started from the protein sequence information, which can predict whether a new phenotype derived from a nsSNP can be related to a genetic disease in humans. They reported more than 74% accuracy in predicting whether a single point mutation can be disease related or not. Stitziel et al. [15] introduced a tool based on the hidden Markov models (HMM) for analyzing sequence homology of SNPs and reported 68% accuracy in predicting whether a single point mutation can be disease related or not. Shihab et al. [16], described a functional analysis Through Hidden markov models software and server and reported 71% accuracy in the predicton, which was less than SIFT (74%) but equal to PolyPhen2 (71%). Choi et al. [17] developed a new algorithm, which provides a generalized approach to predict the functional effects of protein sequence variations including single or multiple amino acid substitutions and in-frame insertions and deletions. They reported 84.8% accuracy compared to SIFT (84.5%) and PolyPhen2 (84.7%) in whether predicting that mutation can be disease related or not. In the present study we observed the highest accuracy with our suggested model as 73.33% compared with SIFT (62.71%) followed by PolyPhen2 and Mutpred (61.02%, in both).


## 5. Conclusions

Our suggested model is comparable to the well-established SNP-based pathogenicity detection tools and can appropriately reflect the role of a disease-associated SNP in both local and global structures. A major drawback of the weighted SNP-based pathogenicity detection tools is the inherited restriction that falls within conserved protein domains. Hence, unlike other sequence-based prediction tools, which are too slow for practical use in large-scale sequencing projects, the weighted tools are computationally inexpensive and fast. Although the accuracy of our suggested model is not relatively high, the functional impact of the pathogenic mutations at the protein level is highlighted, which improves the understanding of the molecular basis of mutation pathogenesis.



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