Gene-disease Association Studies Research Articles

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer’s disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer’s disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

NSPA: characterizing the disease association of multiple genetic interactions at single-subject resolution.

The interaction between genetic variables is one of the major barriers to characterizing the genetic architecture of complex traits. To consider epistasis, network science approaches are increasingly being used in research to elucidate the genetic architecture of complex diseases. Network science approaches associate genetic variables' disease susceptibility to their topological importance in the network. However, this network only represents genetic interactions and does not describe how these interactions attribute to disease association at the subject-scale. We propose the Network-based Subject Portrait Approach (NSPA) and an accompanying feature transformation method to determine the collective risk impact of multiple genetic interactions for each subject. The feature transformation method converts genetic variants of subjects into new values that capture how genetic variables interact with others to attribute to a subject's disease association. We apply this approach to synthetic and genetic datasets and learn that (1) the disease association can be captured using multiple disjoint sets of genetic interactions and (2) the feature transformation method based on NSPA improves predictive performance comparing with using the original genetic variables. Our findings confirm the role of genetic interaction in complex disease and provide a novel approach for gene-disease association studies to identify genetic architecture in the context of epistasis. The codes of NSPA are now available in: https://github.com/MIB-Lab/Network-based-Subject-Portrait-Approach. ting.hu@queensu.ca. Supplementary data are available at Bioinformatics Advances online.

Screening of high-risk deleterious missense variations in the CYP1B1 gene implicated in the pathogenesis of primary congenital glaucoma: A comprehensive in silico approach.

Primary congenital glaucoma (PCG) is the most common subtype of glaucoma caused by defects in the cytochrome P450 1B1 (CYP1B1) gene. It is developing among infants in more than 80% of cases who exhibit impairments in the anterior chamber angle and the trabecular meshwork. Thus, a comprehensive in silico approach was performed to evaluate the effect of high-risk deleterious missense variations in the CYP1B1 gene. All the information for CYP1B1 missense variants was retrieved from the dbSNP database. Seven different tools, namely: SIFT, PolyPhen-2, PROVEAN, SNAP2, PANTHER, PhD-SNP, and Predict-SNP, were used for functional annotation, and two packages, which were I-Mutant 2.0 and MUpro, were used to predict the effect of the variants on protein stability. A phylogenetic conservation analysis using deleterious variants was performed by the ConSurf server. The 3D structures of the wild-type and mutants were generated using the I-TASSER tool, and a 50 ns molecular dynamic simulation (MDS) was executed using the GROMACS webserver to determine the stability of mutants compared to the native protein. Co-expression, protein-protein interaction (PPI), gene ontology (GO), and pathway analyses were additionally performed for the CYP1B1 in-depth study. All the retrieved data from the dbSNP database was subjected to functional, structural, and phylogenetic analysis. From the conducted analyses, a total of 19 high-risk variants (P52L, G61E, G90R, P118L, E173K, D291G, Y349D, G365W, G365R, R368H, R368C, D374N, N423Y, D430E, P442A, R444Q, F445L, R469W, and C470Y) were screened out that were considered to be deleterious to the CYP1B1 gene. The phylogenetic analysis revealed that the majority of the variants occurred in highly conserved regions. The MD simulation analysis exhibited that all mutants' average root mean square deviation (RMSD) values were higher compared to the wild-type protein, which could potentially cause CYP1B1 protein dysfunction, leading to the severity of the disease. Moreover, it has been discovered that CYP1A1, VCAN, HSD17B1, HSD17B2, and AKR1C3 are highly co-expressed and interact with CYP1B1. Besides, the CYP1B1 protein is primarily involved in the metabolism of xenobiotics, chemical carcinogenesis, the retinal metabolic process, and steroid hormone biosynthesis pathways, demonstrating its multifaceted and important roles. This is the first comprehensive study that adds essential information to the ongoing efforts to understand the crucial role of genetic signatures in the development of PCG and will be useful for more targeted gene-disease association studies.

The Human Pangenome Project: a global resource to map genomic diversity.

The human reference genome is the most widely used resource in human genetics and is due for a major update. Its current structure is a linear composite of merged haplotypes from more than 20 people, with a single individual comprising most of the sequence. It contains biases and errors within a framework that does not represent global human genomic variation. A high-quality reference with global representation of common variants, including single-nucleotide variants, structural variants and functional elements, is needed. The Human Pangenome Reference Consortium aims to create a more sophisticated and complete human reference genome with a graph-based, telomere-to-telomere representation of global genomic diversity. Here we leverage innovations in technology, study design and global partnerships with the goalof constructing the highest-possible quality human pangenome reference. Our goal is toimprove data representation and streamline analyses to enable routine assembly of complete diploid genomes. With attention to ethical frameworks, the human pangenome reference will contain a more accurate and diverse representation of global genomic variation, improve gene-disease association studies across populations, expand the scope of genomics research to the most repetitive and polymorphic regions of the genome, and serve as the ultimate genetic resource for future biomedical research and precision medicine.

Efficient privacy-preserving whole-genome variant queries.

MotivationDiagnosis and treatment decisions on genomic data have become widespread as the cost of genome sequencing decreases gradually. In this context, disease–gene association studies are of great importance. However, genomic data are very sensitive when compared to other data types and contains information about individuals and their relatives. Many studies have shown that this information can be obtained from the query-response pairs on genomic databases. In this work, we propose a method that uses secure multi-party computation to query genomic databases in a privacy-protected manner. The proposed solution privately outsources genomic data from arbitrarily many sources to the two non-colluding proxies and allows genomic databases to be safely stored in semi-honest cloud environments. It provides data privacy, query privacy and output privacy by using XOR-based sharing and unlike previous solutions, it allows queries to run efficiently on hundreds of thousands of genomic data.ResultsWe measure the performance of our solution with parameters similar to real-world applications. It is possible to query a genomic database with 3 000 000 variants with five genomic query predicates under 400 ms. Querying 1 048 576 genomes, each containing 1 000 000 variants, for the presence of five different query variants can be achieved approximately in 6 min with a small amount of dedicated hardware and connectivity. These execution times are in the right range to enable real-world applications in medical research and healthcare. Unlike previous studies, it is possible to query multiple databases with response times fast enough for practical application. To the best of our knowledge, this is the first solution that provides this performance for querying large-scale genomic data.Availability and implementation https://gitlab.com/DIFUTURE/privacy-preserving-variant-queries.Supplementary information Supplementary data are available at Bioinformatics online.

STrengthening the Reporting Of Pharmacogenetic Studies: Development of the STROPS guideline.

BackgroundLarge sample sizes are often required to detect statistically significant associations between pharmacogenetic markers and treatment response. Meta-analysis may be performed to synthesize data from several studies, increasing sample size and, consequently, power to detect significant genetic effects. However, performing robust synthesis of data from pharmacogenetic studies is often challenging because of poor reporting of key data in study reports. There is currently no guideline for the reporting of pharmacogenetic studies that has been developed using a widely accepted robust methodology. The objective of this project was to develop the STrengthening the Reporting Of Pharmacogenetic Studies (STROPS) guideline.Methods and findingsWe established a preliminary checklist of reporting items to be considered for inclusion in the guideline. We invited representatives of key stakeholder groups to participate in a 2-round Delphi survey. A total of 52 individuals participated in both rounds of the survey, scoring items with regards to their importance for inclusion in the STROPS guideline. We then held a consensus meeting, at which 8 individuals considered the results of the Delphi survey and voted on whether each item ought to be included in the final guideline. The STROPS guideline consists of 54 items and is accompanied by an explanation and elaboration document. The guideline contains items that are particularly important in the field of pharmacogenetics, such as the drug regimen of interest and whether adherence to treatment was accounted for in the conducted analyses. The guideline also requires that outcomes be clearly defined and justified, because in pharmacogenetic studies, there may be a greater number of possible outcomes than in other types of study (for example, disease–gene association studies). A limitation of this project is that our consensus meeting involved a small number of individuals, the majority of whom are based in the United Kingdom.ConclusionsOur aim is for the STROPS guideline to improve the transparency of reporting of pharmacogenetic studies and also to facilitate the conduct of high-quality systematic reviews and meta-analyses. We encourage authors to adhere to the STROPS guideline when publishing pharmacogenetic studies.

Gene-disease association study of tumor necrosis factor-α G-308A gene polymorphism with risk of major depressive disorder: A systematic review and meta-analysis.

IntroductionThe single nucleotide polymorphism (SNP) of the tumor necrosis factor‐α (TNF‐α) G‐308A gene is the most studied regarding susceptibility to major depressive disorder (MDD). However, results have been controversial perhaps due to the heterogeneous genetic backgrounds influenced by race as well as subtypes of depression.Methods and materialsA systematic MEDLINE search was performed to retrieve all published studies that identified the connection between the TNF‐α G‐308A gene polymorphism and the risk of MDD.ResultsThere was no statistical difference between the allele frequencies or genotypes of TNF‐α G‐308A gene and the depressive patients or healthy subjects in the five models tested. Further, subgroup analyses showed that the TNF‐α G‐308A gene polymorphism also did not confer susceptibility to poststroke, late‐life, maternal, or major depression. Publication bias analysis showed p values were more than .05, suggesting that the 9 articles included in the current analysis had no publication bias.ConclusionNeither the allele frequencies nor genotypes of TNF‐α G‐308A gene could be served as an independent risk factor of depression.

An analytic framework for exploring sampling and observation process biases in genome and phenome-wide association studies using electronic health records.

Large-scale association analyses based on observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, challenges due to nonprobability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. The extent of the bias introduced by ignoring these factors is not well-characterized. In this paper, we develop an analytic framework for characterizing the bias expected in disease-gene association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis approach, this framework can be used to obtain plausible values for parameters of interest given summary results from standard analysis. We develop an online tool for performing this sensitivity analysis. Simulations demonstrate promising properties of the proposed method. We apply our approach to study bias in disease-gene association studies using electronic health record data from the Michigan Genomics Initiative, a longitudinal biorepository effort within The University Michigan health system.

Inter-individual genomic heterogeneity within European population isolates.

A number of studies carried out since the early ‘70s has investigated the effects of isolation on genetic variation within and among human populations in diverse geographical contexts. However, no extensive analysis has been carried out on the heterogeneity among genomes within isolated populations. This issue is worth exploring since events of recent admixture and/or subdivision could potentially disrupt the genetic homogeneity which is to be expected when isolation is prolonged and constant over time. Here, we analyze literature data relative to 87,815 autosomal single-nucleotide polymorphisms, which were obtained from a total of 28 European populations. Our results challenge the traditional paradigm of population isolates as structured as genetically (and genomically) uniform entities. In fact, focusing on the distribution of variance of intra-population diversity measures across individuals, we show that the inter-individual heterogeneity of isolated populations is at least comparable to the open ones. More in particular, three small and highly inbred isolates (Sappada, Sauris and Timau in Northeastern Italy) were found to be characterized by levels of inter-individual heterogeneity largely exceeding that of all other populations, possibly due to relatively recent events of genetic introgression. Finally, we propose a way to monitor the effects of inter-individual heterogeneity in disease-gene association studies.

Computational analysis of functional SNPs in Alzheimer's disease-associated endocytosis genes.

BackgroundFrom genome wide association studies on Alzheimer’s disease (AD), it has been shown that many single nucleotide polymorphisms (SNPs) of genes of different pathways affect the disease risk. One of the pathways is endocytosis, and variants in these genes may affect their functions in amyloid precursor protein (APP) trafficking, amyloid-beta (Aβ) production as well as its clearance in the brain. This study uses computational methods to predict the effect of novel SNPs, including untranslated region (UTR) variants, splice site variants, synonymous SNPs (sSNPs) and non-synonymous SNPs (nsSNPs) in three endocytosis genes associated with AD, namely PICALM, SYNJ1 and SH3KBP1.Materials and MethodsAll the variants’ information was retrieved from the Ensembl genome database, and then different variation prediction analyses were performed. UTRScan was used to predict UTR variants while MaxEntScan was used to predict splice site variants. Meta-analysis by PredictSNP2 was used to predict sSNPs. Parallel prediction analyses by five different software packages including SIFT, PolyPhen-2, Mutation Assessor, I-Mutant2.0 and SNPs&GO were used to predict the effects of nsSNPs. The level of evolutionary conservation of deleterious nsSNPs was further analyzed using ConSurf server. Mutant protein structures of deleterious nsSNPs were modelled and refined using SPARKS-X and ModRefiner for structural comparison.ResultsA total of 56 deleterious variants were identified in this study, including 12 UTR variants, 18 splice site variants, eight sSNPs and 18 nsSNPs. Among these 56 deleterious variants, seven variants were also identified in the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Mount Sinai Brain Bank (MSBB) studies.DiscussionThe 56 deleterious variants were predicted to affect the regulation of gene expression, or have functional impacts on these three endocytosis genes and their gene products. The deleterious variants in these genes are expected to affect their cellular function in endocytosis and may be implicated in the pathogenesis of AD as well. The biological consequences of these deleterious variants and their potential impacts on the disease risks could be further validated experimentally and may be useful for gene-disease association study.

A spectral analysis and network science approach to identify influential diseases based on disease-gene associations

We model a disease-disease network as an undirected graph of diseases (vertices) wherein two vertices are connected with an edge if the corresponding diseases have one or more common associated genes (the number of common genes is the weight of the edge). In such a weighted graph, a disease with a larger number of common genes with several other diseases is more likely to incur a higher eigenvector centrality (EVC). Our hypothesis is that a person with a higher EVC disease is more likely to acquire other related diseases compared to a person with a lower EVC disease. We tested our hypothesis on the disease-disease network constructed from the results of the disease-gene association studies reported in the NIH GWAS catalogue and OMIM database. The disease EVC values exhibited a Pareto distribution (80-20 rule): around 18 % of the diseases have larger and significantly different EVC values and the remaining 82% of the diseases had lower and similar EVC values. This implies that around 18% of the diseases in humans are more likely to have a larger likelihood of leading to other diseases.

A spectral analysis and network science approach to identify influential diseases based on disease-gene associations

We model a disease-disease network as an undirected graph of diseases (vertices) wherein two vertices are connected with an edge if the corresponding diseases have one or more common associated genes (the number of common genes is the weight of the edge). In such a weighted graph, a disease with a larger number of common genes with several other diseases is more likely to incur a higher eigenvector centrality (EVC). Our hypothesis is that a person with a higher EVC disease is more likely to acquire other related diseases compared to a person with a lower EVC disease. We tested our hypothesis on the disease-disease network constructed from the results of the disease-gene association studies reported in the NIH GWAS catalogue and OMIM database. The disease EVC values exhibited a Pareto distribution (80-20 rule): around 18 % of the diseases have larger and significantly different EVC values and the remaining 82% of the diseases had lower and similar EVC values. This implies that around 18% of the diseases in humans are more likely to have a larger likelihood of leading to other diseases.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

CYP1A1 MspI Polymorphism and Cervical Carcinoma Risk in the Multi-Ethnic Population of Malaysia: a Case-Control Study.

Tobacco smoking is considered a risk factor for cervical cancer development due to the presence of tobacco based carcinogenic metabolites in cervical cells of female smokers. In this study, we investigated the role of the T3801C (MspI) polymorphism of CYP1A1, a gene encoding an enzyme necessary for the initiation of tobacco based carcinogen metabolism, on cervical cancer risk. The T to C substitution may alter CYP1A1 activities, potentially elevating cervical cancer risk. Since results of gene-disease association studies vary according to the study population, the multi-ethnic population of Malaysia provides an excellent representative cohort for identifying and comparing the cervical cancer risk among the 3 major ethnics in Southeast Asia in relation to CYP1A1 MspI polymorphism. A total of 195 Thin Prep Pap smear samples from HPV negative and cancer free females were randomly selected as controls while 106 formalin fixed paraffin embedded samples from females with invasive cervical cancer were randomly selected for the cases group. The polymorphisms were identified using restriction fragment length polymorphism (RFLP) PCR. We found no significant associations between CYP1A1 MspI polymorphism and cervical cancer in the general Malaysian female population. However, upon ethnic stratification, the variant C/C genotype was significantly associated with a 4.66-fold increase in cervical cancer risk in Malay females (95% CI= 1.21-17.9; p=0.03). No significant association was observed in the Chinese and Indian females. Additionally, there were no significant associations in the dominant model and allele frequency model analysis in both the general and ethnically stratified female population of Malaysia. Our findings suggest that the C/C genotype of CYP1A1 MspI polymorphism is associated with the development of cervical carcinoma in the Malay females of Malaysia.

A gene-disease association study of IL18 in thyroid cancer: genotype and haplotype analyses.

Thyroid cancer is the most common malignancy of the endocrine system, and genetic factors have been shown to be associated with its risk. Interleukin-18 (IL-18) is a pleiotropic pro-inflammatory cytokine that induces IFN-γ production and is involved in T helper type 1 development. To determine the role of IL-18 gene in thyroid cancer susceptibility, we conducted a case-control study, and genotyped five single nucleotide polymorphisms (SNPs) in IL-18 gene (-656 G/T (rs1946519), -607 C/A (rs1946518), and -137 G/C (rs187238) in the promoter region and +113 T/G (rs360718) and +127 C/T (rs360717) in 5'-untranslated region) in 105 patients with thyroid cancer and 148 healthy controls from Iranian population. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and allele-specific primer-PCR were used for genotyping. The association of different genotypes with thyroid cancer, tumor type, and the tumor stage was analyzed. Comparing all of the patient population with the controls, TT genotype at position -656 G/T was observed to be associated with a significantly increased risk of thyroid cancer [31/105 (30.1 %) vs 19/148 (13.1 %), p = 0.002, OR 2.90, CI 1.40-5.70]. No association with thyroid cancer was found at other positions (-607 C/A, -137 G/C, +113 T/G, and +127 C/T). Excluding the patients with medullary carcinoma, and including only the ones with thyroid cancer derived from the follicular epithelium, nearly the same results were observed regarding the genotypes at position -656 G/T. Furthermore, significantly decreased risk of thyroid cancer derived from the follicular epithelium was observed upon inheritance of the homozygote genotype (CC) at position +127 C/T (40/94 (42.5 %) versus 84/148 (56.8 %) in patients and controls, respectively (OR 0.56, 95 % CI for OR 0.32-0.98, p = 0.04). Haplotype analysis indicated that among 32 possible haplotypes, TAGTT haplotype frequency was significantly higher in patients than in controls [12/188 (6.4 %) vs 2/292 (0.7 %), p = 0.0008] and this difference resisted Bonferroni correction (n = 19) and significant level set at 0.003. Nearly the same results were observed after excluding the patients with medullary carcinoma. No association was found between the SNPs and the stage of tumor. Our results suggest the increased susceptibility to thyroid cancer in subjects with TT genotype at position -656 G/T of the promoter of IL-18 gene, as well as TAGTT haplotype emerged from five studied SNPs in IL-18 gene. The data also suggest that the inheritance of +127 CC genotype may protect individuals from thyroid cancer derived from follicular epithelium.

DBDiaSNP: An Open-Source Knowledgebase of Genetic Polymorphisms and Resistance Genes Related to Diarrheal Pathogens.

Diarrhea is a highly common infection among children, responsible for significant morbidity and mortality rate worldwide. After pneumonia, diarrhea remains the second leading cause of neonatal deaths. Numerous viral, bacterial, and parasitic enteric pathogens are associated with diarrhea. With increasing antibiotic resistance among enteric pathogens, there is an urgent need for global surveillance of the mutations and resistance genes primarily responsible for resistance to antibiotic treatment. Single Nucleotide Polymorphisms are important in this regard as they have a vast potential to be utilized as molecular diagnostics for gene-disease or pharmacogenomics association studies linking genotype to phenotype. DBDiaSNP is a comprehensive repository of mutations and resistance genes among various diarrheal pathogens and hosts to advance breakthroughs that will find applications from development of sequence-based diagnostic tools to drug discovery. It contains information about 946 mutations and 326 resistance genes compiled from literature and various web resources. As of March 2015, it houses various pathogen genes and the mutations responsible for antibiotic resistance. The pathogens include, for example, DEC (Diarrheagenic E.coli), Salmonella spp., Campylobacter spp., Shigella spp., Clostridium difficile, Aeromonas spp., Helicobacter pylori, Entamoeba histolytica, Vibrio cholera, and viruses. It also includes mutations from hosts (e.g., humans, pigs, others) that render them either susceptible or resistant to a certain type of diarrhea. DBDiaSNP is therefore intended as an integrated open access database for researchers and clinicians working on diarrheal diseases. Additionally, we note that the DBDiaSNP is one of the first antibiotic resistance databases for the diarrheal pathogens covering mutations and resistance genes that have clinical relevance from a broad range of pathogens and hosts. For future translational research involving integrative biology and global health, the database offers veritable potentials, particularly for developing countries and worldwide monitoring and personalized effective treatment of pathogens associated with diarrhea. The database is accessible on the public domain at http://www.juit.ac.in/attachments/dbdiasnp/ .

Quantitative assessment of mutations in hepatitis B virus genome with liver cirrhosis and hepatocellular carcinoma development

// Lei Yu 1, 2, * , Bao-fang Zhang 3, * , Ming-liang Cheng 2 , Xue-ke Zhao 2 , Quan Zhang 2 , Ya-xin Hu 2 , Hua-juan Liu 2 , Mao Mu 2 , Bi Wang 2, 4 , Guo-zhen Yang 2 , Li-li Zhu 2 , Shuai Zhang 5 , Yu-mei Yao 2 , Yi-ju Cheng 2 , Wang-sheng Li 6 1 The First Affiliated Hospital of Jinan University, Guangdong 510632, Guangzhou, China 2 The Affiliated Hospital, Guizhou Medical University, Guiyang 550004, Guizhou, China 3 The First Affiliated Hospital, Soochow University, Suzhou 215006, Jiangsu, China 4 Department of Eugenics and Genetics, Guiyang Maternal and Child Health-Care Hospital, Guiyang 550003, Guizhou, China 5 Department of Interventional Radiology, Cancer Hospital of Guizhou Medical University, Guiyang 550004, Guizhou, China 6 Department of Clinical Laboratory, Liupanshui People’s Hospital, Liupanshui 553401, Guizhou, China * These authors contributed equally to this work and should be considered as co-first authors Correspondence to: Ming-liang Cheng, e-mail: gmcmingliang_cheng@163.com Guo-zhen Yang, e-mail: gmcguozhen_yang@163.com Keywords: hepatitis B virus, hepatocellular carcinoma, liver cirrhosis, mutation, risk Received: February 29, 2016 Accepted: April 28, 2016 Published: May 17, 2016 ABSTRACT The long-term outcomes of liver cirrhosis (LC) and hepatocellular carcinoma (HCC) in patients with chronic hepatitis B virus (HBV) infection are associated with specific HBV genotypes and mutations in the virus genome. However, a number of gene-disease association studies have yielded inconsistent results in the field. To investigate this inconsistency, we conducted a meta-analysis from 118 studies involving a total of 9,418 HCC cases, 2,697 LC cases, and 18,785 HBV-infected participants for 11 mutations of HBV to evaluate the epidemiological evidence of the relationship. Overall, 10 mutants (Pre-S mutation, A1762T/G1764A double mutations, G1896A, G1899A, T1753V, C1653T, G1766A, A1762T, G1764A, T1768A) were significantly associated with increased HCC risk with odds ratio (OR) range from 1.80 to 4.27, while no associations were found for C1858T. We found a significant dose–risk relationship between the number of mutations in HBV genome and HCC, in which high risks for HCC were associated with mutation numbers more than 5 (OR = 18.45, 95% CI: 7.86–43.29). By pooling 15 prospective studies, A1762T/G1764A, Pre-S, T1753V, and C1653T mutation was identified as good predictor of HCC risk, showing ORs from 1.73 to 4.54. In addition, significantly elevated LC risks were associated with 6 mutants (A1762T/G1764A double mutations, G1896A, G1899A, T1753V, C1653T, Pre-S mutation), with OR range from 1.76 to 4.10. Our results suggested that HBV mutations alone or in combination may be of clinical significance for predicting hepatocarcinogenesis.

Use of diplotypes - matched haplotype pairs from homologous chromosomes - in gene-disease association studies.

SummaryAlleles, genotypes and haplotypes (combinations of alleles) have been widely used in gene-disease association studies. More recently, association studies using diplotypes (haplotype pairs on homologous chromosomes) have become increasingly common. This article reviews the rationale of the four types of association analyses and discusses the situations in which diplotype-based analyses are more powerful than the other types of association analyses. Haplotype-based association analyses are more powerful than allele-based association analyses, and diplotype-based association analyses are more powerful than genotype-based analyses. In circumstances where there are no interaction effects between markers and where the criteria for Hardy-Weinberg Equilibrium (HWE) are met, the larger sample size and smaller degrees of freedom of allele-based and haplotype-based association analyses make them more powerful than genotype-based and diplotype-based association analyses, respectively. However, under certain circumstances diplotype-based analyses are more powerful than haplotype-based analysis.

Association between DAOA gene polymorphisms and the risk of schizophrenia, bipolar disorder and depressive disorder

Schizophrenia (SCZ), bipolar disorder (BD) and depressive disorder (DD) are common psychiatric disorders, which show common genetic vulnerability. Previous gene-disease association studies have reported correlations between d-amino acid oxidase activator (DAOA) gene polymorphisms and the three psychiatric disorders. However, the findings were contradictory. A meta-analysis was therefore conducted to provide more robust investigations into DAOA polymorphisms and the risk of SCZ, BD and DD. This meta-analysis recruited 46 published studies up to July 2013, including 17,515 cases and 25,189 controls. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate the association between three specific DAOA SNPs and SCZ, BD and DD. Publication bias was tested by Begg's test and funnel plot, and heterogeneity was assessed by the Cochran's chi-square-based Q statistic and the inconsistency index (I(2)). Moreover, the robustness of the findings was estimated by cumulative meta-analysis. DAOA genetic polymorphisms (M15, M18 and M23) were not found to confer a statistically significant increased risk of SCZ, BD or DD in the overall sample, or in Caucasians and Asians following subgroup analysis. The current study indicated that M15, M18 and M23 might not be the risk factor for SCZ, BD or DD. However, further studies are required to provide robust evidence to estimate the association between DAOA polymorphisms and psychiatric disorders.

Allele and genotype frequency of a genetic variant in ataxia telangiectasia mutated gene affecting glycemic response to metformin in South Indian population.

Allele and genotype frequency of a genetic variant in ATM gene affecting glycemic response to metformin in South Indian population.Context:The novel polymorphism in ATM gene (rs11212617), which is implicated to have association with metformin response, exhibits inter-ethnic variability in the allele and genotype frequency distribution.Aims and Design:The objective of the present study is to establish the allele and genotype frequency of rs11212617 single nucleotide polymorphism in ATM gene, in South Indian population and to find if this variant has any role in the etiology of type 2 diabetes mellitus.Materials and Methods:The study was performed in 2 cohorts of populations, 112 healthy volunteers and 118 type 2 diabetes mellitus patients. Genomic deoxyribonucleic acid (DNA) was extracted from peripheral blood leucocytes by phenol-chloroform method and genotyping was performed by real-time polymerase chain reaction using TaqMan assay.Results:In South Indian population, the frequency of major A allele was 0.65 and the minor C allele was 0.35. AA and CC are the homozygous genotypes with frequency of 0.39 and 0.09 respectively. The frequency of heterozygous genotype AC (0.52) was found to be higher than the homozygotes. There was no significant difference in the frequency distribution in the diabetic population, which implies that this variant does not have any causative role in the disease etiology. The frequency distributions were found to be significantly different from the distributions in other ethnic populations such as Caucasians, Chinese, Japanese and Africans. But there was no significant difference when compared with the Gujarati Indians of Houston.Conclusion:The frequency distribution of this novel variant in South Indian population forms a framework for further gene disease association studies to establish the association of this variant with metformin response. Our study could not find any association of this variant with respect to the disease etiology.