Cluster Of Single Nucleotide Polymorphisms Research Articles

Big Data Driven Oriented Graph Theory Aided tagSNPs Selection for Genetic Precision Therapy

Recently, the world-wide human genome-related projects have been vigorously launched and implemented. Gene-sequencing techniques play a critical role in disease diagnosis, prediction, and population stratification relying on efficiently mining genetic features in the gene pool. Exploring the association between the sites of the genetic mutation and the disease-based population classification becomes a hot topic, which beneficially supports disease diagnosis and treatment on the molecular level. However, there are numerous variable sites even on a single chromosome in the human gene pool, and hence, the traditional classifiers are not able to dig out all single nucleotide polymorphism (SNP) sites without clearly excavating the characteristic SNP sites, termed tagSNPs, in SNP clusters. By applying big data mining techniques, in this paper, we, first of all, propose a principal component analysis-based algorithm for reducing the gene data dimension in order to cluster SNP sites in the low-dimensional space. Moreover, an oriented graph theory-based tagSNPs selection algorithm is designed. Finally, relying on the real-world 1000 Genomes Project dataset, we can achieve fewer tagSNPs than the traditional methods by invoking the complete process of our designed SNP classifier.

Expression of mycolic acid in response to stress and association with differential clinical manifestations of tuberculosis.

Extrapulmonary tuberculosis (EPTB), accounting for 10%-20% of all cases of tuberculosis (TB), is known to be determined by host immunity. However, the contribution of bacterial factors to the development of EPTB has not been studied extensively. Mycolic acids are predominant lipids constituting the cell wall of Mycobacterium tuberculosis, and keto-mycolic acid is involved in the synthesis of foamy macrophages that facilitate persistence of mycobacteria. Hence, the present study was performed to gain an insight into variable expression of mycolic acids in clinical isolates of M. tuberculosis under stress. Pansusceptible clinical isolates of M. tuberculosis from patients with lymph node TB (LNTB) (n = 10) and pulmonary TB (PTB) (n = 10) were subjected to sodium dodecyl sulfate (SDS) stress, and the expression of mycolic acid and its biosynthetic genes was compared. Any bias arising due to the genotype of the clinical isolates was ruled out by performing single-nucleotide polymorphism cluster grouping (SCG), wherein no significant difference was observed between the SCG of LNTB or PTB isolates. The expression of α-mycolic acid during the exposure to SDS was high in 7/10 (70%) LNTB and 6/10 (60%) PTB isolates. Methoxy mycolic acid showed an increased expression in 7/10 (70%) LNTB isolates and 4/10 (40%) PTB isolates. Increased expression of keto-mycolic acid on exposure with SDS was observed in 8/10 (80%) M. tuberculosis LNTB and 3/10 (30%) PTB isolates. Similarly, the mycolic acid synthesis gene, fas, was upregulated more in LNTB isolates than PTB isolates in vitro and ex vivo. SCG 3a was the most common SCG observed in 40% (8/20) of the isolates, followed by SCG 3b in 30% (6/20) of the isolates. There was no significant difference between the SCG of LNTB or PTB isolates. The higher expression of keto-mycolic acid in LNTB as against PTB isolates may indicate better survival in LNTB isolates in the presence of stress.

Comparison of the Whole Cell Proteome and Secretome of Epidemic Bordetella pertussis Strains From the 2008-2012 Australian Epidemic Under Sulfate-Modulating Conditions.

Sulfate is an important modulator for virulence factor expression in Bordetella pertussis, the causative organism for whooping cough. During infection, sulfate is released when respiratory epithelial cells are damaged which can affect gene expression. The current predominant strains in Australia are found in single nucleotide polymorphism (SNP) cluster I (ptxP3/prn2). It has been reported that ptxP3 strains have higher mRNA expression of virulence genes than ptxP1 strains under intermediate sulfate-modulating conditions (5 mM MgSO4). Our previous proteomic study compared L1423 (cluster I, ptxP3) and L1191 (cluster II, ptxP1) in Thalen–IJssel (THIJS) media without sulfate modulation and identified an upregulation of transport proteins and a downregulation of immunogenic proteins. To determine whether proteomic differences exist between cluster I and cluster II strains in intermediate modulating conditions, this study compared the whole cell proteome and secretome between L1423 and L1191 grown in THIJS media with 5 mM MgSO4 using iTRAQ and high-resolution multiple reaction monitoring (MRM-hr). Two proteins (BP0200 and BP1175) in the whole cell were upregulated in L1423 [fold change (FC) >1.2, false discovery rate (FDR) <0.05]. In the secretome, four proteins from the type III secretion system (T3SS) effectors were downregulated (FC < 0.8, FDR < 0.05) while six proteins, including two adhesins, pertactin (Prn) and tracheal colonization factor A (TcfA), were upregulated which were consistent with our previous proteomic study. The upregulation of Prn and TcfA in SNP cluster I may result in improved adhesion while the downregulation of the T3SS and other immunogenic proteins may reduce immune recognition, which may contribute to the increased fitness of cluster I B. pertussis strains.

ShRangeSim: Simulation of Single Nucleotide Polymorphism Clusters in Next-Generation Sequencing Data

Genomic variations are in the focus of research to uncover mechanisms of host-pathogen interactions and diseases such as cancer. Nowadays, next-generation sequencing (NGS) data are analyzed through dedicated pipelines to detect them. Surrogate NGS data in conjunction with genomic variations help to evaluate pipelines and validate their outcomes, fostering selection of proper tools for a given scientific question. I describe how existing approaches for simulating NGS data in conjunction with genomic variations fail to model local enrichments of single nucleotide polymorphisms (SNPs), so called SNP clusters. Two distributions for count data are applied to publicly available collections of genomic variations. The results suggest modeling of SNP cluster sizes by overdispersion-aware distributions.

SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification.

A major use of genetic data is parentage verification and identification as inaccurate pedigrees negatively affect genetic gain. Since 2012 the international standard for single nucleotide polymorphism (SNP) verification in Bos taurus cattle has been the ISAG SNP panels. While these ISAG panels provide an increased level of parentage accuracy over microsatellite markers (MS), they can validate the wrong parent at ≤1% misconcordance rate levels, indicating that more SNP are needed if a more accurate pedigree is required. With rapidly increasing numbers of cattle being genotyped in Ireland that represent 61 B. taurus breeds from a wide range of farm types: beef/dairy, AI/pedigree/commercial, purebred/crossbred, and large to small herd size the Irish Cattle Breeding Federation (ICBF) analyzed different SNP densities to determine that at a minimum ≥500 SNP are needed to consistently predict only one set of parents at a ≤1% misconcordance rate. For parentage validation and prediction ICBF uses 800 SNP (ICBF800) selected based on SNP clustering quality, ISAG200 inclusion, call rate (CR), and minor allele frequency (MAF) in the Irish cattle population. Large datasets require sample and SNP quality control (QC). Most publications only deal with SNP QC via CR, MAF, parent-progeny conflicts, and Hardy-Weinberg deviation, but not sample QC. We report here parentage, SNP QC, and a genomic sample QC pipelines to deal with the unique challenges of >1 million genotypes from a national herd such as SNP genotype errors from mis-tagging of animals, lab errors, farm errors, and multiple other issues that can arise. We divide the pipeline into two parts: a Genotype QC and an Animal QC pipeline. The Genotype QC identifies samples with low call rate, missing or mixed genotype classes (no BB genotype or ABTG alleles present), and low genotype frequencies. The Animal QC handles situations where the genotype might not belong to the listed individual by identifying: >1 non-matching genotypes per animal, SNP duplicates, sex and breed prediction mismatches, parentage and progeny validation results, and other situations. The Animal QC pipeline make use of ICBF800 SNP set where appropriate to identify errors in a computationally efficient yet still highly accurate method.

Association of single nucleotide polymorphism in CD28(C/T-I3 + 17) and CD40 (C/T-1) genes with the Graves disease

To find out a correlation between the single nucleotide polymorphisms in cluster of differentiation 28 and cluster of differentiation 40 genes with Graves' disease, if any. This case-control study was conducted at the Multan Institute of Nuclear Medicine and Radiotherapy, Multan, Pakistan, and comprised blood samples of Graves' disease patients and controls. Various risk factors were also correlated either with the genotype at each single-nucleotide polymorphism or with various combinations of genotypes studied during present investigation. Of the 160 samples, there were 80(50%) each from patients and controls. Risk factor analysis revealed that gender (p=0.008), marital status (p<0.001), education (p<0.001), smoking (p<0.001), tri-iodothyronine (P <0.001), thyroxin (p<0.001) and thyroid-stimulating hormone (p<0.000) levels in blood were associated with Graves' disease. Both single-nucleotide polymorphisms in both genes were not associated with Graves' disease, either individually or in any combined form.

Human leukocyte antigen variants and risk of hepatocellular carcinoma modified by hepatitis C virus genotypes: A genome-wide association study.

We conducted a genome-wide association study to discover genetic variants associated with hepatitis C virus (HCV)-related hepatocellular carcinoma (HCC). We genotyped 502 HCC cases and 749 non-HCC controls using the Axiom-CHB genome-wide array. After identifying single-nucleotide polymorphism clusters located in the human leukocyte antigen (HLA) region which were potentially associated with HCC, HLA-DQB1 genotyping was performed to analyze 994 anti-HCV seropositives collected in the period 1991-2013 in a community-based cohort for evaluating long-term predictability of HLA variants for identifying the risk of HCC. Cox proportional hazards models were used to estimate the hazard ratios and 95% confidence intervals of HLA genotypes for determining the aforementioned HCC risk. Eight single-nucleotide polymorphisms in the proximity of HLA-DQB1 were associated with HCC (P < 8.7 × 10-8 ) in the genome-wide association study. Long-term follow-up showed a significant association with HLA-DQB1*03:01 and DQB1*06:02 (P < 0.05). The adjusted hazard ratios associated with HCC were 0.45 (0.30-0.68) and 2.11 (1.34-3.34) for DQB1*03:01 and DQB1*06:02, respectively. After stratification by HCV genotypes, DQB1*03:01 showed protective effects only in patients with HCV genotype 1, whereas DQB1*06:02 conferred risk of HCC only in patients with HCV non-1 genotypes. HLA imputation analyses revealed that HLA-DRB1*15:01, which is in linkage disequilibrium with DQB1*06:02, also increased the risk of HCC (odds ratio, 1.96; 95% confidence interval, 1.31-2.93). Haplotype analysis supported that DQB1*03:01 and DQB1*06:02 are primarily protective and susceptible variants, respectively. Conclusion: HLA-DQB1 was independently associated with HCC; HCV genotypes modified the effects of HLA-DQB1 on the risk of HCC. (Hepatology 2018;67:651-661).

Genomic risk prediction of aromatase inhibitor-related arthralgia in patients with breast cancer using a novel machine-learning algorithm.

Many breast cancer (BC) patients treated with aromatase inhibitors (AIs) develop aromatase inhibitor‐related arthralgia (AIA). Candidate gene studies to identify AIA risk are limited in scope. We evaluated the potential of a novel analytic algorithm (NAA) to predict AIA using germline single nucleotide polymorphisms (SNP) data obtained before treatment initiation. Systematic chart review of 700 AI‐treated patients with stage I‐III BC identified asymptomatic patients (n = 39) and those with clinically significant AIA resulting in AI termination or therapy switch (n = 123). Germline DNA was obtained and SNP genotyping performed using the Affymetrix UK BioBank Axiom Array to yield 695,277 SNPs. SNP clusters that most closely defined AIA risk were discovered using an NAA that sequentially combined statistical filtering and a machine‐learning algorithm. NCBI PhenGenI and Ensemble databases defined gene attribution of the most discriminating SNPs. Phenotype, pathway, and ontologic analyses assessed functional and mechanistic validity. Demographics were similar in cases and controls. A cluster of 70 SNPs, correlating to 57 genes, was identified. This SNP group predicted AIA occurrence with a maximum accuracy of 75.93%. Strong associations with arthralgia, breast cancer, and estrogen phenotypes were seen in 19/57 genes (33%) and were functionally consistent. Using a NAA, we identified a 70 SNP cluster that predicted AIA risk with fair accuracy. Phenotype, functional, and pathway analysis of attributed genes was consistent with clinical phenotypes. This study is the first to link a specific SNP/gene cluster to AIA risk independent of candidate gene bias.

Population differentiation in allele frequencies of obesity-associated SNPs

BackgroundObesity is emerging as a global health problem, with more than one-third of the world’s adult population being overweight or obese. In this study, we investigated worldwide population differentiation in allele frequencies of obesity-associated SNPs (single nucleotide polymorphisms).ResultsWe collected a total of 225 obesity-associated SNPs from a public database. Their population-level allele frequencies were derived based on the genotype data from 1000 Genomes Project (phase 3). We used hypergeometric model to assess whether the effect allele at a given SNP is significantly enriched or depleted in each of the 26 populations surveyed in the 1000 Genomes Project with respect to the overall pooled population. Our results indicate that 195 out of 225 SNPs (86.7%) possess effect alleles significantly enriched or depleted in at least one of the 26 populations. Populations within the same continental group exhibit similar allele enrichment/depletion patterns whereas inter-continental populations show distinct patterns. Among the 225 SNPs, 15 SNPs cluster in the first intron region of the FTO gene, which is a major gene associated with body-mass index (BMI) and fat mass. African populations exhibit much smaller blocks of LD (linkage disequilibrium) among these15 SNPs while European and Asian populations have larger blocks. To estimate the cumulative effect of all variants associated with obesity, we developed the personal composite genetic risk score for obesity. Our results indicate that the East Asian populations have the lowest averages of the composite risk scores, whereas three European populations have the highest averages. In addition, the population-level average of composite genetic risk scores is significantly correlated (R2 = 0.35, P = 0.0060) with obesity prevalence.ConclusionsWe have detected substantial population differentiation in allele frequencies of obesity-associated SNPs. The results will help elucidate the genetic basis which may contribute to population disparities in obesity prevalence.

Admixture mapping in a hybrid zone reveals loci associated with avian feather coloration.

Identifying the genetic bases for colour patterns has provided important insights into the control and expression of pigmentation and how these characteristics influence fitness. However, much more is known about the genetic bases for traits based on melanin pigments than for traits based on another major class of pigments, carotenoids. Here, we use natural admixture in a hybrid zone between Audubon's and myrtle warblers (Setophaga coronata auduboni/S. c. coronata) to identify genomic regions associated with both types of pigmentation. Warblers are known for rapid speciation and dramatic differences in plumage. For each of five plumage coloration traits, we found highly significant associations with multiple single-nucleotide polymorphisms (SNPs) across the genome and these were clustered in discrete regions. Regions near significantly associated SNPs were enriched for genes associated with keratin filaments, fibrils that make up feathers. A carotenoid-based trait that differs between the taxa-throat colour-had more than a dozen genomic regions of association. One cluster of SNPs for this trait overlaps the Scavenger Receptor Class F Member 2 (SCARF2) gene. Other scavenger receptors are presumed to be expressed at target tissues and involved in the selective movement of carotenoids into the target cells, making SCARF2 a plausible new candidate for carotenoid processing. In addition, two melanin-based plumage traits-colours of the eye line and eye spot-show very strong associations with a single genomic region mapping to chromosome 20 in the zebra finch. These findings indicate that only a subset of the genomic regions differentiated between these two warblers are associated with the plumage differences between them and demonstrate the utility of reduced-representation genomic scans in hybrid zones.

A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs.

Linkage disequilibrium (LD) block construction is required for research in population genetics and genetic epidemiology, including specification of sets of single nucleotide polymorphisms (SNPs) for analysis of multi-SNP based association and identification of haplotype blocks in high density sequencing data. Existing methods based on a narrow sense definition do not allow intermediate regions of low LD between strongly associated SNP pairs and tend to split high density SNP data into small blocks having high between-block correlation. We present Big-LD, a block partition method based on interval graph modeling of LD bins which are clusters of strong pairwise LD SNPs, not necessarily physically consecutive. Big-LD uses an agglomerative approach that starts by identifying small communities of SNPs, i.e. the SNPs in each LD bin region, and proceeds by merging these communities. We determine the number of blocks using a method to find maximum-weight independent set. Big-LD produces larger LD blocks compared to existing methods such as MATILDE, Haploview, MIG ++, or S-MIG ++ and the LD blocks better agree with recombination hotspot locations determined by sperm-typing experiments. The observed average runtime of Big-LD for 13 288 240 non-monomorphic SNPs from 1000 Genomes Project autosome data (286 East Asians) is about 5.83 h, which is a significant improvement over the existing methods. Source code and documentation are available for download at http://github.com/sunnyeesl/BigLD. yyoo@snu.ac.kr. Supplementary data are available at Bioinformatics online.

Genomic risk prediction of aromatase inhibitor-related arthralgias (AIA) in breast cancer (BC) patients using a novel analytical algorithm (NAA).

10102 Background: Many BC patients treated with aromatase inhibitors (AIs) develop AIA; 20% have symptoms severe enough to effect treatment compliance. Results of candidate gene studies to identify AIA risk are limited in scope. In this case-controlled study, we evaluated the potential of a NAA to predict AIA using germline single nucleotide polymorphism (SNP) data obtained prior to treatment initiation. Methods: Systematic chart review of 700 AI-treated patients with stage I-III BC between 2003-2012 identified asymptomatic patients (n = 39) and those with clinically significant AIA resulting in AI termination or therapy switch (n = 123). Germline DNA was obtained from peripheral blood cells and SNP genotyping performed using the Affymetrix UK BioBank Axiom Array to yield 695,277 SNPs. The identity of the cluster of SNPs that most closely defined AIA risk was discovered using an NAA that sequentially combined statistical filtering and a machine learning algorithm. NCBI PhenGenI and Ensemble databases were used to define gene attribution of the 200 most discriminating SNPs. Phenotype, pathway, and ontologic analyses assessed functional and mechanistic validity. Results: Cases and controls were similar in demographic characteristics. A cluster of 70 SNPs, correlated to 57 genes (accounting for linkage disequilibrium), was identified. This SNP group predicted AIA occurrence with a maximum accuracy of 75.93%. Strong associations with arthralgia, breast cancer, and estrogen phenotypes were seen in 19/57 genes (33%) and were functionally and ontologically consistent. Conclusions: Using a NAA, we identified a 70 SNP cluster that predicted AIA risk with fair accuracy. Phenotype, functional, and pathway analysis of attributed genes was consistent with clinical phenotypes. This study is the first to link a specific SNP/gene cluster to AIA risk independent of candidate gene bias. An ongoing prospective companion study will be used to validate and to expand upon results.

Genotypic Characterisation of Mycobacterium tuberculosis Isolates from Tuberculous Meningitis Patients at a Tertiary Neurocare Centre in Southern India

Aims: Specific genotypes of Mycobacterium tuberculosis (MTB) have been reported to cause outbreaks of pulmonary tuberculosis (TB) in geographical areas that are endemic to TB. However, since there is little epidemiological evidence on the association of particular genotypes that cause tuberculous meningitis (TBM), we sought to investigate the association of specific MTB strains with infection of the central nervous system (CNS). Materials and Methods: We carried out a genetic characterisation of 89 MTB isolates from TBM patients at a Southern Indian tertiary neurocare centre and compared the genotypes with strains of pulmonary TB isolated from Indian immigrants in New York City. We applied the standard methods of genotyping of MTB, namely, IS6110-based restriction fragment length polymorphism and spoligotyping for strain identification, along with principal genetic grouping and single-nucleotide polymorphism cluster analysis. Results: The analysis revealed a high-level of diversity amongst the strain population. The genotypes of the isolates from TBM patients paralleled the pulmonary TB strain population recovered from the Indian immigrants in NYC. Conclusions: We conclude that there is no apparent association between genotypes of MTB and propensity to infect CNS tissue.

Sonic hedgehog regulation of Foxf2 promotes cranial neural crest mesenchyme proliferation and is disrupted in cleft lip morphogenesis.

Cleft lip is one of the most common human birth defects, yet our understanding of the mechanisms that regulate lip morphogenesis is limited. Here, we show in mice that sonic hedgehog (Shh)-induced proliferation of cranial neural crest cell (cNCC) mesenchyme is required for upper lip closure. Gene expression profiling revealed a subset of Forkhead box (Fox) genes that are regulated by Shh signaling during lip morphogenesis. During cleft pathogenesis, reduced proliferation in the medial nasal process mesenchyme paralleled the domain of reduced Foxf2 and Gli1 expression. SHH ligand induction of Foxf2 expression was dependent upon Shh pathway effectors in cNCCs, while a functional GLI-binding site was identified downstream of Foxf2 Consistent with the cellular mechanism demonstrated for cleft lip pathogenesis, we found that either SHH ligand addition or FOXF2 overexpression is sufficient to induce cNCC proliferation. Finally, analysis of a large multi-ethnic human population with cleft lip identified clusters of single-nucleotide polymorphisms in FOXF2 These data suggest that direct targeting of Foxf2 by Shh signaling drives cNCC mesenchyme proliferation during upper lip morphogenesis, and that disruption of this sequence results in cleft lip.

Genome-wide association scans for idiopathic osteonecrosis of the femoral head in a Korean population.

Osteonecrosis of the femoral head (ONFH) is a multifactorial disease and is associated with genetic predisposition, and exposure to certain risk factors. In particular, idiopathic ONFH in twins and the clustering of cases in families have indicated that genetic factors are involved. However, the majority of cases of ONFH are sporadic and various studies have demonstrated that differences in the study design and/or the ethnic groups analyzed leads to different results. The present study performed one of the first genome-wide association studies to identify genetic loci that may increase the risk of idiopathic ONFH. In total, 217 patients with idiopathic ONFH and 217 control samples, without ONFH, were genotyped using Axiom™ chips. Following quality control, 509,886 single-nucleotide polymorphisms (SNPs) were included in the association analysis to identify genetic variants that may influence susceptibility to idiopathic ONFH. The lowest P-value identified by the current study was for an association with rs220324 (P=3.57×10-7), an SNP that is located near to the uromodulin-like 1 gene region on chromosome 21q22.3, although none of the SNPs reached the traditional genome-wide significance level of 5×10–8. However, the DnaJ heat shock protein family (Hsp40) member C6 (DNAJC6) locus, a region between 65.37 and 65.67 Mb located on chromosome 1p31.3, harbored a cluster of SNPs that were associated with idiopathic ONFH at a significance level of P<1×10–5. Four variants, rs10493374, rs12032616, rs17127529 and rs6679032, with marginal associations were located in and around the DNAJC6 locus and were in strong linkage disequilibrium with each other. In conclusion, the current study did not identify any SNPs that were associated with idiopathic ONFH at a genome-wide significance level, however, the results suggest that future studies should investigate the effects of SNPs in the DNAJC6 gene on the idiopathic ONFH risk.

Genome-wide association and replication study of anti-tuberculosis drugs-induced liver toxicity.

BackgroundDrug-induced liver injury (DILI) is a well-recognized adverse event of anti tuberculosis drugs (ATD) possibly associated with genetic variations. The objective of this study was to perform genome-wide association study (GWAS) to identify genetic variants associated with the risk for ATD induced liver toxicity in Ethiopian patients.ResultTreatment-naïve newly diagnosed tuberculosis patients (n = 646) were enrolled prospectively and treated with rifampicin based short course anti-tuberculosis therapy. Whole genome genotyping was done using Illumina Omni Express Exome Bead Chip genotyping array with 951,117 single nucleotide polymorphisms (SNPs) on 48 DILI cases and 354 ATD tolerants. Replication study was carried out for 50 SNPs with the lowest P-values (top SNPs) using an independent cohort consisting of 27 DILI cases and 217 ATD tolerants. In the combined analysis, the top SNP identified was rs10946737 (P = 4.4 × 10−6, OR = 3.4, 95 % confidence interval = 2.2–5.3) in the intron of FAM65B in chromosome 6. In addition, we identified a cluster of SNPs with suggestive genome-wide significance in the intron of ATP/GTP binding protein-like 4 (AGBL4).ConclusionWe identified genetic variants that are potentially associated with ATD induced liver toxicity. Further studies with larger sample sizes are essential to confirm the findings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3078-3) contains supplementary material, which is available to authorized users.

Genome-wide association study of antisocial personality disorder.

The pathophysiology of antisocial personality disorder (ASPD) remains unclear. Although the most consistent biological finding is reduced grey matter volume in the frontal cortex, about 50% of the total liability to developing ASPD has been attributed to genetic factors. The contributing genes remain largely unknown. Therefore, we sought to study the genetic background of ASPD. We conducted a genome-wide association study (GWAS) and a replication analysis of Finnish criminal offenders fulfilling DSM-IV criteria for ASPD (N=370, N=5850 for controls, GWAS; N=173, N=3766 for controls and replication sample). The GWAS resulted in suggestive associations of two clusters of single-nucleotide polymorphisms at 6p21.2 and at 6p21.32 at the human leukocyte antigen (HLA) region. Imputation of HLA alleles revealed an independent association with DRB1*01:01 (odds ratio (OR)=2.19 (1.53–3.14), P=1.9 × 10-5). Two polymorphisms at 6p21.2 LINC00951–LRFN2 gene region were replicated in a separate data set, and rs4714329 reached genome-wide significance (OR=1.59 (1.37–1.85), P=1.6 × 10−9) in the meta-analysis. The risk allele also associated with antisocial features in the general population conditioned for severe problems in childhood family (β=0.68, P=0.012). Functional analysis in brain tissue in open access GTEx and Braineac databases revealed eQTL associations of rs4714329 with LINC00951 and LRFN2 in cerebellum. In humans, LINC00951 and LRFN2 are both expressed in the brain, especially in the frontal cortex, which is intriguing considering the role of the frontal cortex in behavior and the neuroanatomical findings of reduced gray matter volume in ASPD. To our knowledge, this is the first study showing genome-wide significant and replicable findings on genetic variants associated with any personality disorder.

A snapshot of the predominant single nucleotide polymorphism cluster groups of Mycobacterium tuberculosis clinical isolates in Delhi, India

Several attempts have been made to associate phylogenetic differences among Mycobacterium tuberculosis strains to variations in the clinical outcome of the disease and to drug resistance. We genotyped 139 clinical isolates of M.tuberculosis obtained from patients of pulmonary tuberculosis in North Delhi region. The isolates were analyzed using nine Single nucleotide polymorphism (SNP) markers, spoligotyping and MIRU-VNTRs; and the results were correlated with their drug susceptibility profile. Results of SNP cluster group (SCG) analysis (available for 138 isolates) showed that the most predominant cluster was SCG 3a, observed in 58.7% (81/138) of the isolates with 44.4% (36/81) of these being drug susceptible, while 16% (13/81) were multidrug resistant (MDR). Of the ancestral cluster SCG 1 observed in 19.5% (27/138) of the isolates, 14.8% (4/27) were MDR while 44.4% (12/27) were drug susceptible. SCG 2 formed 5.79% (8/138) of the isolates and 50% (4/8) of these were multidrug resistant (MDR). Spoligotyping subdivided the strains into 45 shared types (n=125) and 14 orphan strains. The orphan strains were mostly associated with SCG 3a or SCG 1, reflecting the principal SCGs found in the Indian population. SCG 1 and SCG 2 genotypes were concordant with the East African Indian (EAI) and Beijing families respectively. Central Asian (CAS) clade and its sublineages were predominantly associated with SCG 3a. No consistent association was seen between the SCGs and Harlem, T or X clades. The 15 loci MIRU-VNTR typing revealed 123/136 isolates to be unclustered, while 13 isolates were present in 6 clusters of 2-3 isolates each. However, correlating the cluster analysis with patient details did not suggest any evidence of recent transmission. In conclusion, though our study revealed the preponderance of SCG 1 and 3a in the M.tuberculosis population circulating in the region, the diversity of strains highlights the changes occurring within lineages and reemphasizes the importance of cluster investigations in extended studies.

Better colonisation of newly emerged Bordetella pertussis in the co-infection mouse model study

Molecular epidemiological data indicates that the resurgence of pertussis (whooping cough) in populations with high vaccine coverage is associated with genomic adaptation of Bordetella pertussis, the causative agent of the disease, to vaccine selection pressure. We have previously shown that in the period after the introduction of acellular pertussis vaccine (ACV), the majority of circulating strains in Australia switched to single nucleotide polymorphism (SNP) cluster I (carrying ptxP3/prn2), replacing SNP cluster II (carrying ptxP1/prn3). In this study, we carried out an in vivo competition assay using a mouse model infected with SNP cluster I and II B. pertussis strains from Australia. We found that the SNP cluster I strain colonised better than the SNP cluster II strain, in both naïve and immunised mice, suggesting that SNP cluster I strains had better fitness regardless of immunisation status of the host, consistent with SNP cluster I strains replacing SNP cluster II. Nevertheless, we found that ACV enhanced clearance of both SNP cluster I and II strains from the mouse respiratory tract.

Genetic risk scores link body fat distribution with specific cardiometabolic profiles

Forty-nine known single nucleotide polymorphisms (SNPs) associating with body mass index (BMI)-adjusted waist-hip-ratio (WHR) (WHRadjBMI) were recently suggested to cluster into three groups with different associations to cardiometabolic traits. Genetic risk scores of the clusters on the risk of incident diabetes and associations with detailed cardiometabolic phenotypes were tested. In a prospective study of 6,121 Inter99 individuals, the risk of incident diabetes using Cox proportional hazards regression was evaluated. Using linear regession, the associations between genetic risk scores and anthropometry and blood samples at fasting and during an oral glucose tolerance test were tested. Analyses were adjusted for age, sex, and BMI. Cluster 1 associated with an increased risk of diabetes (HR = 1.05, P = 2.74 × 10(-) (4) ) and with a poor metabolic profile, including fasting serum triglyceride (β = 0.98% mmol/L, P = 3.33 × 10(-) (8) ) and Matsuda index (β = -0.74%, P = 1.29 × 10(-) (4) ). No similar associations for Clusters 2 and 3 were found. The three clusters showed different patterns of association with waist circumference, hip circumference, and height. Our results suggest that the 49 WHRadjBMI-associated SNPs affect metabolic health differently depending on the cluster of SNPs. The clusters further associate differently with anthropometric measures.