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Retinoblastoma 1 (RB1) is the first discovered tumor suppressor gene and recognized as the simple model system whose encoded defective protein can cause a pediatric cancer retinoblastoma. It functions as a negative regulator of the cell cycle through the interactions with members of the E2F transcription factors family. The protein of the RB1 gene (pRB) is engaged in various cell cycle processes including apoptosis, cell cycle arrest and chromatin remodeling. Recent studies on Retinoblastoma also exhibited multiple sets of point mutation in the associated protein due to its large polymorphic information in the local database. In this study, we identified the list of disease associated non-synonymous single nucleotide polymorphisms (nsSNPs) in RB1 by incorporating different computational algorithms, web servers, modeling of the mutants and finally superimposing it. Out of 826 nsSNPs, W516G and W563G were predicted to be highly deleterious variants in the conserved regions and found to have an impact on protein structure and protein-protein interaction. Moreover, our study concludes the effect of W516G variant was more detrimental in destabilizing protein's nature as compared to W563G variant. We also found defective binding of pRB having W516G mutation with E2F2 protein. Findings of this study will aid in shortening of the expensive experimental cost of identifying disease associated SNPs in retinoblastoma for which specialized personalized treatment or therapy can be formulated.Communicated by Ramaswamy H. Sarma.

AMH, KISS1R and GDF9 genes play a vital role in human and animal reproduction and might be used as the genetic markers for the reproduction traits selection. The aim of this study was to screen the...

The majority of genetic variations in the human genome that lead to variety of different diseases are caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Neurofibromatosis type 2 (NF2) is a deadly disease caused by nsSNPs in the NF2 gene that encodes for a protein called merlin. This study used various

Chronic wasting disease (CWD) is caused by prions, infectious proteinaceous particles, PrPCWD. We sequenced the PRNP gene of 2,899 white-tailed deer (WTD) from Illinois and southern Wisconsin, find...

Leptin is a versatile hormone involved in many biological functions, including controlling body weight, energy homeostasis, reproduction, and immune function. Though exhaustive studies were performed on the bovine LEP gene, no efforts have been made to comprehensively and systematically analyze single nucleotide polymorphisms (SNPs) in its coding sequence. The present study was conducted to identify the most deleterious nonsynonymous SNPs (nsSNPs) of the bovine LEP gene. SNPs retrieved from the dbSNP database were investigated using various computational tools, including SIFT, PolyPhen-2, PANTHER, PROVEAN, SNAP2, I-Mutant2, mCSM, SDM, DUET, Cobalt, SPPIDER, ConSurf, and MutPred. A total of 28 nsSNPs were considered for the present study. Only 4 nsSNPs, namely, R66M, D186G, C191S, and C191G were found to be deleterious by all the used common nsSNP prediction tools and affected leptin protein structure, function, and biological stability. These variants were located in very highly conserved positions, and thus mutations in these amino acid positions have deleterious evolutionary consequences. The findings of the present study proved that R66M, D186G, C191S, and C191G nsSNPs have the most deleterious consequences on both the structure and the function of bovine leptin, with a special emphasis on the remarkable effects of the last two nsSNPs on the breakage of the disulfide linkage which may lead to a variety of deleterious consequences of this disturbed three-dimensional structure on bovine life and performance. This study provides the first comprehensive computation of the damaging effects of nsSNPs on leptin in bovines.

The Toll-interacting protein (TOLLIP), first detected by hybrid screening using Interleukin-1 receptor accessory protein in 2000, is ubiquitous and its TLR signaling cascade gets negatively regulated by TOLLIP in particular by impeding the TLR4 and TLR2 pathways. Toll-interacting protein facilitates TLR and TGF-β type 1 receptor intracellular localization and lysosomal degradation and exerts its anti-apoptosis and pro-autophagy effects through interaction with a target of Myb1 membrane trafficking protein 1 (TOM1) in autoimmunity. It also protects intestinal epithelial cells from apoptosis induced by (TNF-α) (IFN-γ) signaling and acts as a cargo adaptor linking (ATG8) autophagy gene 8 and microtubule-associated protein 1 light chain 3. Ubiquitin-modified cell debris along with coated autophagosomes removes harmful protein aggregates and maintains cellular homeostasis. It is therefore structurally and functionally important to detect TOLLIP polymorphisms to indicate the possible malfunctions and therapeutics. We have identified the gap of available data on nsSNPs in the TOLLIP gene in previous studies. Hence, We have used a wide range of bioinformatic techniques in this study to identify the most destructive nsSNPs in the TOLLIP gene. The in-silico tools such as PROVEAN, SIFT, SNP&GO, PhD SNP, and PolyPhen2 have been used followed by I Mutant, MutPred, and ConSurf. The 3-D mapping was carried out with I-TASSER and Phyre2. Though, STRING and GeneMANIA proposed the gene to gene interaction of TOLLIP. Our study identified G19D (rs866744102), G32R (rs1308704061), D71N (rs777772934), and E72G (rs1202660177) as the four most lethal non-synonymous SNPs in TOLLIP genes, which may play an essential part in defects of TOLLIP Protein and probably cause a different type of diseases. This is the first study of its kind, and it could pave the way in the field of medical drugs in the future. The influence of these nsSNPs in the normal functioning of TOLLIP can also be evaluated in animal models to identify the underlying biochemical pathway involved in diseases.

Purpose. Differentiation of the Mycobacterium tuberculosis complex (MTBc) from non-tuberculous mycobacteria (NTM) is important for tuberculosis diagnosis and is a prerequisite for reliable phenotypic drug-resistance testing. We evaluated the performance of the rapid MPT64 antigen identification test for the detection of Mycobacterium africanum lineage 5 (MAF L5). Methodology. Smear-positive tuberculosis patients' sputa were included prospectively. Culture was performed on Lowenstein-Jensen medium and, when positive, the MPT64 test and the classical para-nitro benzoic acid susceptibility and heat-labile catalase (PNB/catalase) identification tests were performed. The MPT64 test was repeated 14 days after an initially negative first testing. Direct spoligotyping was performed for MTBc lineage determination. Results. In total, 333 isolates were tested for all of the methods. Three hundred and twenty-two (96.7 %) were pure MTBc, by agreement between spoligotyping and PNB/catalase, and 11 were NTM or a mixture of MTBc/NTM. The MPT64 test conducted on day zero of culture-positivity correctly identified most of the pure MTBc isolates (93.2 %, 300/322), but it failed to detect 24% of the L5 isolates (18/75) versus 2% (4/202) of the L4 ones [OR=15.6 (5.3-45.8), P

MotivationProteins are the building-blocks of life. However with the deluge of data on protein sequences and their association with diseases, it is imperative to use computational tools to aid experimental analysis. Determination of the impact of disease-causing SNPs on protein’s structure is crucial in discovering how a disease affects the functions on a fundamental level and thereafter, determining potential drug targets.ResultsSNPs associated with the genetic disease permanent neonatal diabetus mellitus (PNDM) were studied to determine their impact on human insulin. Out of 16 missense variants, eight were predicted to be deleterious. 6 of the SNPs resulted in high structural differences (RMSD > 0.9, H bonds > 35). Stability changes were also determined.

Introduction: Noonan syndrome is a genetic disorder (autosomal dominant) characterized by short stature, congenital heart disease, bleeding problems, developmental delays, and skeletal malformation. It is mainly caused by a single nucleotide alteration in four genes PTPN11, SOS1, RAF1, and KRAS . In this study, we computationally analyzed the SOS1 gene to identify the pathogenic non-synonymous single nucleotide polymorphisms (nsSNPs), which is known to cause Noonan syndrome. Hypothesis: We hypothesize that in-silico analysis of human SOS1 mutations in Noonan syndrome would be a promising predictor to study the post-translational modifications. Methods and Results: The variant information of SOS1 was collected from the dbSNP database and the literature review on Noonan syndrome. They were further analyzed by in-silico tools such as I-Mutant, iPTREE-STAB, and MutPred for their structural and functional properties. We found that 11 nsSNPs are more pathogenic for Noonan syndrome. The 3D comparative protein of 11 nsSNPs with its wild-type SOS1 was modeled by using I-Tasser and validated via ERRAT and RAMPAGE. The protein-protein interactions of SOS1, GATA4, TNNT2, and ACTN2 were analyzed using STRING, which showed that HRAS was intermediate between SOS1 and ACTN2 (Fig. 1) . Conclusion: This is the first in-silico study of the SOS1 variant with Noonan syndrome. We proposed that this 11 nsSNPs are the most pathogenic variant of SOS1 , which helps to screen the Noonan patient. Furthermore, our results are promising to study the gain/loss of post-translational modification (PTM) by mutation in cardiac genes and helps to explore the novel molecular pathways.$graphic_{DB5B0E7D-4DA6-4569-A16F-E05B2C9C4D2F}$$

Major depressive disorder (MDD) is a serious mental disease with negative affects on almost all aspects of the patient. Currently, antidepressants are the major ways to ease or treat MDD. However, the existing antidepressants have limited efficacy in treating MDD, with a large fraction of patients either responding inadequately to antidepressants in the initial therapy, or responding differently to the drugs during the treatment. Pharmacogenetics studies have found that SNPs in some genes are associated with the efficacy of antidepressants. In order to obtain a better understanding on the relationship between the SNPs and antidepressant efficacy, we compiled a list of 233 SNPs significantly associated with the efficacy of antidepressants in MDD. Of the thirteen nonsynonymous SNPs (nsSNPs) in the list, three (rs1065852, rs3810651 and rs117986340) may influence the structures and function of the corresponding proteins. Besides, the influence of rs1065852 on the structure and structural flexibility of CYP2D6 was further investigated via molecular dynamics simulations. The results indicated that the flexibility of the F-G loop for mutant was reduced compared to the native CYP2D6. As a portion of the substrate access channel, this may decrease the approximability of the substrates to the channel. Hence, the lower flexibility of the substrate access channel may lead to the low enzyme activity of mutant, which could help to design the new antidepressants. Our results will help us to understand the impact of genetic variation on efficacy of antidepressants and provide useful information for developing more effective antidepressants.

No nonsynonymous single-nucleotide polymorphism (SNP) in PER1 has been reported with respect to diurnal preference. We aimed to determine whether a functional polymorphism in PER1 is involv...

The Mx gene encodes an antiviral protein and is induced by type 1 interferons (IFNs). In this study, bovine Mx1B gene was analysed using six online tools to evaluate the effect of single nucleotide polymorphisms (SNPs) that may cause some deleterious effect in the animal. Out of 115 nsSNPs evaluated, 23nsSNPs were classified as highly damaging. The results of computational analysis of the Mx1 gene further widens the scope of research on mutations that can be prioritized in future population and laboratory studies.

Interleukin 33 (IL-33) is the latest member of the IL-1 cytokine family, which plays both pro - and anti-inflammatory functions. Numerous Single-nucleotide polymorphisms (SNPs) in the IL-33 gene have been recognized to be associated with a vast variety of inflammatory disorders. SNPs associated studies have become a crucial approach in uncovering the genetic background of human diseases. However, distinguishing the functional SNPs in a disease-related gene from a pool of both functional and neutral SNPs is a major challenge and needs multiple experiments of hundreds or thousands of SNPs in candidate genes. This study aimed to identify the possible deleterious SNPs in the IL-33 gene using bioinformatics predictive tools. The nonsynonymous SNPs (nsSNPs) were analyzed by SIFT, PolyPhen, PROVEAN, SNP&GO, MutPred, SNAP, PhD SNP, and I-Mutant tools. The Non-coding SNPs (ncSNPs) were also analyzed by SNPinfo and RegulomeDB tools. In conclusion, our in-silico analysis predicted 5 nsSNPs and 22 ncSNPs as potential candidates in the IL-33 gene for future genetic association studies.

JAK1 gene plays a critical role in signalling. Malfunction of JAK1 is linked to numerous human diseases ranging from chronic inflammation to cancers and autoimmune diseases. Genetic variations in JAK1 exhibit deleterious effects on gene function leading to the deregulation of signalling pathways. A comprehensive list of nsSNPs potentially affecting the structure and function of JAK1 gene is not available. We report 3 deleterious nsSNPs (F78L, Q644R and S646F) in the coding region of JAK1 with predicted structure and function linking to diseases. However, further studies are needed to validate this preliminary observation.

For the past 30 years, chemoprophylaxis with macrocyclic lactone (ML) anthelmintics has been the primary strategy for canine heartworm (Dirofilaria immitis) control in both the USA and Australia. ML-resistant D. immitis isolates have been confirmed to exist in the USA and studies have shown that 42 single nucleotide polymorphisms (SNPs) are associated with phenotypic ML-resistance. Currently, ML-resistance has not been reported in any Australian clinical cases of canine heartworm. The aim of the study is to determine whether the 42 SNPs associated with resistance to MLs in the isolates from the USA are present in adult heartworms from a clinical case in Australia. Five adult D. immitis obtained from a dog at post-mortem (Sydney, Australia) were sequenced using the Illumina sequencing technology. The genomic analyses revealed 6 out of the 42 SNPs associated with ML-resistance to be present in our samples, 3 out of the 6 SNPs identified were nonsynonymous SNPs but not in candidate genes for ML-resistance. ML-susceptibility profile was mixed using the 42-SNP and 10-SNP models, but the 5-SNP, 3-SNP and 2-SNP models demonstrated ML susceptibility for all five individuals. In this study, the first whole-genome reference of D. immitis from Australia establishes a new baseline for comparative studies and will be valuable for tracking ML-resistance emergence.

Next-generation sequencing technology makes directly testing rare variants possible. However, existing statistical methods to detect common variants may not be optimal for testing rare variants because of allelic heterogeneity as well as the extreme rarity of individual variants. Recently, several statistical methods to detect associations of rare variants were developed, including population-based and family-based methods. Compared with population-based methods, family-based methods have more power and can prevent bias induced by population substructure. Both population-based and family-based methods for rare variant association studies are essentially testing the effect of a weighted combination of variants or its function. How to model the weights is critical for the testing power because the number of observations for any given rare variant is small and the multiple-test correction is more stringent for rare variants. We propose 4 weighting schemes for the family-based rare variants test (FBAT-v) to test for the effects of both rare and common variants across the genome. Applying FBAT-v with the proposed weighting schemes on the Genetic Analysis Workshop 19 family data indicates that the power of FBAT-v can be comparatively enhanced in most circumstances.

As a novel biomarker from the STEAP family, STEAP2 encodes six transmembrane epithelial antigens to prostate cancer. The overexpression of STEAP2 is predicted as the second most common cancer in the world that is responsible for male cancer-related deaths. Nonsynonymous SNPs are important group of SNPs which lead to alternations in encoded polypeptides. Changes in the amino acid sequence of gene products can lead to abnormal tissue function. The present study firstly sorted out those SNPs which exist in the coding region of STEAP2 and evaluated their impact through computational tools. Secondly, the three-dimensional structure of STEAP2 was formed through I-TASSER and validated by different software. Genomic data has been retrieved from the 1000 Genome project and Ensembl and subsequently analysed using computational tools. Out of 177 non-synonymous single nucleotide polymorphisms (nsSNPs) within the coding region, 42 mis-sense SNPs have been predicted as deleterious by all analyses. Our research shows a welldesigned computational methodology to inspect the prostate cancer associated nsSNPs. It can be concluded that these nsSNPs can play their role in the up-regulation of STEAP2 which further leads to progression of prostate cancer. It can benefit scientists in the handling of cancerassociated diseases related to STEAP2 through developing novel drug therapies.

Single-nucleotide polymorphisms (SNPs) associated studies are particularly useful to identify disease-related genetic variations. However, large amount of SNPs are a major challenge for the analysis of human genetic variations and potential pathogenic mutations detection. Computational prediction tools have a great potential to sort out the possible functional SNPs. In the present study, for the first time, deleterious single nucleotide polymorphisms were investigated in human interleukin 27 (IL-27) gene by using different bioinformatics predictive tools. To this aim only conserved and validated SNPs with minor allele frequency (MAF) > 0.01 (a total of 114 SNPs) were subjected to further analysis. The nonsynonymous SNPs (nsSNPs) were analyzed by SIFT, PolyPhen, PROVEAN, SNP&GO, MutPred, INPS, and I-Mutant tools. The Non-coding SNPs (ncSNPs) were also analyzed by SNPinfo and RegulomeDB tools. In our in silico analysis, we predicted 2 nsSNPs and 20 ncSNPs as potential candidates in the IL-27 gene, which could be a priority for future case-control studies.

Small heat shock proteins (sHSP) are molecular chaperons which play a key role in protein homeostasis under stress conditions. Point mutation of aspartic acid (D) substitution for asparagine (N) at...

Neurexin1 (NRXN1) gene is playing an important role in synaptic formation, plasticity and maturity. Studies have reported non-synonymous SNPs in NRXN1 in patient with mental disorders. The current ...

With a variety of accessible Single Nucleotide Polymorphisms (SNPs) data on human p53 gene, this investigation is intended to deal with detrimental SNPs in p53 gene by executing diverse valid computational tools, including Filter, SIFT, PredictSNP, Fathmm, UTRScan, ConSurf, Phyre, Tm-Adjust, I-Mutant, Task Seek after practical and basic appraisal, dissolvable openness, atomic progression, and analysing the energy minimization. Of 581 p53 SNPs, 420 SNPs are found to be missense or non-synonymous and 435 SNPs are in the 3 prime UTR and 112 SNPs are of every 5 prime UTR from which 16 non synonymous SNPs (nsSNPs) as non-tolerable while PredictSNP package predicted 14 (taking consideration SNP colored green by two or more than 2 analyses is neutral). By concentrating on six bioinformatics tools of various dimensions a combined output is generated where 14 nsSNPs are prone to exert a deleterious effect. By using diverse SNP analysing tools we have found 5 missense SNPs in the 3 crucial amino acids position in the DNA binding domain. The underlying discoveries are fortified by I-Mutant and Project HOPE. The ExPASy-PROSITE tools characterized whether the mutations located in the functional part of the protein or not. This study provides a decisive outcome concluding the accessible SNPs information by recognizing the five harming nsSNPs: rs28934573 (S241F), rs11540652 (R248Q), rs121913342 (R248W), rs121913343 (R273C) and rs28934576 (R273H). The findings of this investigation recognize the detrimental nsSNPs which enhance the danger of various kinds of oncogenesis in patients of different populations’ in genome-wide studies (GWS).

Summary To map resistance genes for Fusarium wilt (FW) and sterility mosaic disease (SMD) in pigeonpea, sequencing‐based bulked segregant analysis (Seq‐BSA) was used. Resistant (R) and susceptible (S) bulks from the extreme recombinant inbred lines of ICPL 20096 × ICPL 332 were sequenced. Subsequently, SNP index was calculated between R‐ and S‐bulks with the help of draft genome sequence and reference‐guided assembly of ICPL 20096 (resistant parent). Seq‐BSA has provided seven candidate SNPs for FW and SMD resistance in pigeonpea. In parallel, four additional genotypes were re‐sequenced and their combined analysis with R‐ and S‐bulks has provided a total of 8362 nonsynonymous (ns) SNPs. Of 8362 nsSNPs, 60 were found within the 2‐Mb flanking regions of seven candidate SNPs identified through Seq‐BSA. Haplotype analysis narrowed down to eight nsSNPs in seven genes. These eight nsSNPs were further validated by re‐sequencing 11 genotypes that are resistant and susceptible to FW and SMD. This analysis revealed association of four candidate nsSNPs in four genes with FW resistance and four candidate nsSNPs in three genes with SMD resistance. Further, In silico protein analysis and expression profiling identified two most promising candidate genes namely C.cajan_01839 for SMD resistance and C.cajan_03203 for FW resistance. Identified candidate genomic regions/SNPs will be useful for genomics‐assisted breeding in pigeonpea.

This chapter focuses on functional and genetic association studies of single nucleotide polymorphisms (SNPs) on human CB2 cannabinoid receptor (CNR2). A review of original papers, published in English and indexed in PubMed up to Jun., 2014, is discussed in this chapter. Nonsynonymous SNPs in CNR2 have been reported to cause changes of the responses of the CB2 to various cannabinoid ligands. In addition, nonsynonymous SNPs, synonymous SNPs, as well as SNPs in the noncoding region of CNR2 have been shown to be associated with a variety of diseases, including autoimmune diseases, liver diseases, osteoporosis, and disorders of central nervous system.

Aspirin (ASA) is a commonly used nonsteroidal anti-inflammatory drug (NSAID), which exerts its therapeutic effects through inhibition of cyclooxygenase (COX) isoform 2 (COX-2), while the inhibition of COX-1 by ASA leads to apparent side effects. In the present study, the relationship between COX-1 non-synonymous single nucleotide polymorphisms (nsSNPs) and aspirin related side effects was investigated. The functional impacts of 37 nsSNPs on aspirin inhibition potency of COX-1 with COX-1/aspirin molecular docking were computationally analyzed, and each SNP was scored based on DOCK Amber score. The data predicted that 22 nsSNPs could reduce COX-1 inhibition, while 15 nsSNPs showed increasing inhibition level in comparison to the regular COX-1 protein. In order to perform a comparing state, the Amber scores for two Arg119 mutants (R119A and R119Q) were also calculated. Moreover, among nsSNP variants, rs117122585 represented the closest Amber score to R119A mutant. A separate docking computation validated the score and represented a new binding position for ASA that acetyl group was located within the distance of 3.86 A from Ser529 OH group. This could predict an associated loss of activity of ASA through this nsSNP variant. Our data represent a computational sub-population pattern for aspirin COX-1 related side effects, and provide basis for further research on COX-1/ASA interaction.

ErbB3 is a significant oncogenic target that is involved in the development of numerous malignancies. In the present in silico study, we evaluated the structural and functional impact of single nucleotide polymorphisms (SNPs) on the ErbB3 gene. The nonsynonymous SNPs (nsSNPs) are known to be deleterious or disease-causing variations because they alter protein sequence, structure, and function. Out of a total 531 SNPs in ErbB3, we investigated 77 coding nsSNPs and observed that 20 of them could be expected to alter the protein's function based on the predictions of both sequence homology–based (SIFT) and structural homology–based (Polyphen) algorithms. Thereafter, we computed the stability of mutants in units of free energy using I-Mutant 3.0, MuStab, and iPTree-STAB programs and identified seven crucial point mutations (V89M, V105G, C290Y, I418N, R669C, I744T, and A1131T) in epidermal growth factor receptor 3 that are manifested as nsSNPs. Furthermore, FASTSNP determined 14 synonymous SNPs that may have a profound impact on splicing regulation. The computational study identified seven novel hotspots predicted to maintain the native structural conformation and functional activity of ErbB3 and may account for cancer if mutated.

Many non-synonymous SNPs (nsSNPs) are associated with diseases, and numerous machine learning methods have been applied to train classifiers for sorting disease-associated nsSNPs from neutral ones. The continuously accumulated nsSNP data allows us to further explore better prediction approaches. In this work, we partitioned the training data into 20 subsets according to either original or substituted amino acid type at the nsSNP site. Using support vector machine (SVM), training classification models on each subset resulted in an overall accuracy of 76.3% or 74.9% depending on the two different partition criteria, while training on the whole dataset obtained an accuracy of only 72.6%. Moreover, the dataset was also randomly divided into 20 subsets, but the corresponding accuracy was only 73.2%. Our results demonstrated that partitioning the whole training dataset into subsets properly, i.e., according to the residue type at the nsSNP site, will improve the performance of the trained classifiers significantly, which should be valuable in developing better tools for predicting the disease-association of nsSNPs.

Congenital heart disease (CHD) is a common congenital birth defect, affecting nearly 1% of all live births, and is the most common cause of infant death. NKX2.5 is an important transcription factor expressed during vertebrate heart development and involved in the regulation of septation during cardiac morphogenesis and in the maturation and maintenance of the atrioventricular node throughout life. There are many reports on association of single-nucleotide polymorphisms (SNPs) of NKX2.5 with CHD but none have been reported from Mysore, South India. With informed consent, 150 clinically diagnosed CHD patients and 70 unrelated healthy controls in Mysore, South India, were recruited. In the first phase the DNAs of 50 CHD patients and 20 controls were subjected to polymerase chain reaction amplification of coding regions of NKX2.5 and further sequenced for SNP genotyping. Additionally, mass array analysis of SNPs was performed for 100 CHD and 50 controls. Analysis revealed the occurrence of six SNPs in different types of CHDs. Two were synonymous SNPs, the most common c.239A>G (p.E21E) and newly identified c.896C>A (p.A240A), as well as three nonsynonymous SNPs, c.608A>G (p.E203G), c.646C>T (p.R216C), and c.852G>A (p.N226D). The sixth SNP 1212G>T in the 3'UTR was observed in 40% of the CHD cases. The SNPs c.646C>T and c.608A>G were shown to cause changes in their secondary structure. Ventricular septal defect was the more prominent CHD observed in our study population. The SNPs c.608A>G (p.E203G) and c.852G>A (p.N226D) were present only in CHD patients, indicating their association with CHDs.

Summary: The human UDP-glucuronosyltransferase, UGT1A9, catalyzes glucuronidation of various endobiotics and xenobiotics. In this study, we sequenced the promoter and exon 1 regions of the UGT1A9 gene in 93 Thai individuals and identified 7 genetic polymorphisms. The allele frequencies of all 3 novel single nucleotide polymorphisms (SNPs): 454A>G and 455A>C (N152A) and 760C>T (R254X) were 0.005. The other 4 known polymorphisms, − 688A>C, − 440 T>C, − 331C>T and − 118A(T)1 UGT1A9 * 1b ), were identified and found to have frequencies of 0.124, 0.978, 0.968 and 0.532, respectively.