-
[show abstract]
[hide abstract]
ABSTRACT: Hirschsprung disease (HSCR, aganglionic megacolon) is a complex genetic disorder of the enteric nervous system (ENS) characterized by the absence of enteric neurons along a variable length of the intestine. While rare variants (RVs) in the coding sequence (CDS) of several genes involved in ENS development lead to disease, the association of common variants (CVs) with HSCR has only been reported for RET (the major HSCR gene) and NRG1. Importantly, RVs in the CDS of these two genes are also associated with the disorder. To assess independent and joint effects between the different types of RET and NRG1 variants identified in HSCR patients, we used 254 Chinese sporadic HSCR patients and 143 ethnically matched controls for whom the RET and/or NRG1 variants genotypes (rare and common) were available. Four genetic risk factors were defined and interaction effects were modeled using conditional logistic regression analyses and pair-wise Kendall correlations. Our analysis revealed a joint effect of RET CVs with RET RVs, NRG1 CVs or NRG1 RVs. To assess whether the genetic interaction translated into functional interaction, mouse neural crest cells (NCCs; enteric neuron precursors) isolated from embryonic guts were treated with NRG1 (ErbB2 ligand) or/and GDNF (Ret ligand) and monitored during the subsequent neural differentiation process. Nrg1 inhibited the Gdnf-induced neuronal differentiation and Gdnf negatively regulated Nrg1-signaling by down-regulating the expression of its receptor, ErbB2. This preliminary data suggest that the balance neurogenesis/gliogenesis is critical for ENS development.
Human Genetics 02/2013; · 5.07 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ∼22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases.
PLoS Genetics 01/2013; 9(1):e1003143. · 8.69 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The extended Simes' test (known as GATES) and scaled chi-square test were proposed to combine a set of dependent genome-wide association signals at multiple single-nucleotide polymorphisms (SNPs) for assessing the overall significance of association at the gene or pathway levels. The two tests use different strategies to combine association p values and can outperform each other when the number of and linkage disequilibrium between SNPs vary. In this paper, we introduce a hybrid set-based test (HYST) combining the two tests for genome-wide association studies (GWASs). We describe how HYST can be used to evaluate statistical significance for association at the protein-protein interaction (PPI) level in order to increase power for detecting disease-susceptibility genes of moderate effect size. Computer simulations demonstrated that HYST had a reasonable type 1 error rate and was generally more powerful than its parents and other alternative tests to detect a PPI pair where both genes are associated with the disease of interest. We applied the method to three complex disease GWAS data sets in the public domain; the method detected a number of highly connected significant PPI pairs involving multiple confirmed disease-susceptibility genes not found in the SNP- and gene-based association analyses. These results indicate that HYST can be effectively used to examine a collection of predefined SNP sets based on prior biological knowledge for revealing additional disease-predisposing genes of modest effects in GWASs.
The American Journal of Human Genetics 09/2012; 91(3):478-88. · 10.60 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Hallucinations are common in normal individuals and patients with schizophrenia and other psychotic disorders. Traditionally psycho-social approaches have emphasized the importance of environmental factors that contribute to variation of hallucinations. Using the Launay-Slade Hallucination Scale-Revised (LSHS-R), we investigated genetic and environmental influences on hallucinations in 598 pairs of healthy South Korean adolescent twins. Parameter estimates in the best-fitting model indicated that additive genetic and individual specific environmental factors for the LSHS-R were 33% (95% CI: 23-42%) and 67% (95% CI: 60-77%), respectively. There was no evidence for sex-specific genes for hallucinations. The magnitudes of genetic and environmental influences on hallucinations were similar in males and females. These results have implications in future molecular genetic studies that search for genes for hallucinations.
Psychiatry Research 05/2012; · 2.52 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Current psychiatric nosology, strongly influenced by Kraepelin’s dichotomy [1,2], classifies schizophrenia and bipolar disorder
as separate diagnostic categories. However, growing evidence indicates that the two disorders may be more closely related
than was thought in the past. Bipolar disorder and schizophrenia display considerable overlap in epidemiologic features; no
risk factor is known to be specific to either. Furthermore, family studies reveal familial co-aggregation of the two disorders,
and twin studies suggest a significant overlap in the genes contributing to schizophrenia, schizoaffective disorder, and mania.
Finally, despite the difficulties in the identification of convincing genetic loci for psychiatric disorders, there are at
least four genomic regions in which linkage has been shown for both schizophrenia and bipolar disorder. Thus, recent evidence
increasingly supports a dimensional approach in the understanding of the functional psychoses, and this is expected to have
implications for etiologic research and future clinical treatment.
Current Psychiatry Reports 04/2012; 3(4):332-337. · 2.71 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Exome sequencing strategy is promising for finding novel mutations of human monogenic disorders. However, pinpointing the casual mutation in a small number of samples is still a big challenge. Here, we propose a three-level filtration and prioritization framework to identify the casual mutation(s) in exome sequencing studies. This efficient and comprehensive framework successfully narrowed down whole exome variants to very small numbers of candidate variants in the proof-of-concept examples. The proposed framework, implemented in a user-friendly software package, named KGGSeq (http://statgenpro.psychiatry.hku.hk/kggseq), will play a very useful role in exome sequencing-based discovery of human Mendelian disease genes.
Nucleic Acids Research 01/2012; 40(7):e53. · 8.03 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Current genome-wide association studies (GWAS) use commercial genotyping microarrays that can assay over a million single nucleotide polymorphisms (SNPs). The number of SNPs is further boosted by advanced statistical genotype-imputation algorithms and large SNP databases for reference human populations. The testing of a huge number of SNPs needs to be taken into account in the interpretation of statistical significance in such genome-wide studies, but this is complicated by the non-independence of SNPs because of linkage disequilibrium (LD). Several previous groups have proposed the use of the effective number of independent markers (M(e)) for the adjustment of multiple testing, but current methods of calculation for M(e) are limited in accuracy or computational speed. Here, we report a more robust and fast method to calculate M(e). Applying this efficient method [implemented in a free software tool named Genetic type 1 error calculator (GEC)], we systematically examined the M(e), and the corresponding p-value thresholds required to control the genome-wide type 1 error rate at 0.05, for 13 Illumina or Affymetrix genotyping arrays, as well as for HapMap Project and 1000 Genomes Project datasets which are widely used in genotype imputation as reference panels. Our results suggested the use of a p-value threshold of ~10(-7) as the criterion for genome-wide significance for early commercial genotyping arrays, but slightly more stringent p-value thresholds ~5 × 10(-8) for current or merged commercial genotyping arrays, ~10(-8) for all common SNPs in the 1000 Genomes Project dataset and ~5 × 10(-8) for the common SNPs only within genes.
Human Genetics 12/2011; 131(5):747-56. · 5.07 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: To identify heritable genetic factors altering susceptibility to refractive error in the general population.
This was a genetic association study of refractive error investigating genetic polymorphisms in regions previously reported through linkage. Two study panels were drawn from the British 1958 Birth Cohort, composed of 2211 persons 44 years of age at the time of visit. Two main outcomes were considered: refractive error as a continuous outcome (spherical equivalent) and myopia as a diagnosis (defined as spherical equivalent equal to or worse than-1.00 diopter). Genotyping was initially performed in 1188 subjects from the outer tertiles of the population distribution, using customized arrays of single nucleotide polymorphisms (SNPs) saturating regions of previously reported highly significant linkage. In a second stage, SNPs most significantly associated were validated in 1023 more persons. Findings were investigated further through human fetal expression studies.
Polymorphisms within the SERPINI2 gene were associated with refractive error in two different European subgroups from the 1958 British Birth Cohort (meta-analysis P = 7.4E-05 for rs9810473). Association was also significant for myopia (best association: OR = 0.80; 95% CI, 0.69-0.93; P = 0.003 for rs10936538). Expression profiling of SERPINI2 revealed that the gene is expressed in the retina and in other eye and CNS tissues.
The novel association of SERPINI2 with refractive error and myopia is suggestive of a possible link between physiological pathways controlling eye growth and development and those controlling glucose metabolism. The findings indicate that SERPINI2 is a promising candidate for further investigations of the genetic susceptibility to myopia.
Investigative ophthalmology & visual science 11/2011; 53(1):440-7. · 3.43 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Serum osteoprotegerin (OPG) level is a key biomarker for numerous traits of clinical importance like diabetes, coronary artery disease, blood pressure, lipid profile, and cancers, but its genetic basis remains poorly understood. We estimated the heritability (h(2)) of serum OPG level in 1442 southern Chinese subjects from 306 families. The h(2) for unadjusted OPG was 0.62 for females and 0.17 for males; and for age-adjusted OPG, 0.75 for females and 0.37 for males. Adjustment for lifestyle factors including calcium and phytoestrogen intake, exercise, smoking, and alcohol consumption exerted only a modest effect on the h(2). In conclusion, we confirmed that circulating OPG is a heritable trait and there is a significant difference in heritability between sexes.
Annals of Human Genetics 09/2011; 75(5):584-8. · 2.57 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Recently, an increasing number of susceptibility variants have been identified for complex diseases. At the same time, the concern of “missing heritability” has also emerged. There is however no unified way to assess the heritability explained by individual genetic variants for binary outcomes. A systemic and quantitative assessment of the degree of “missing heritability” for complex diseases is lacking. In this study, we measure the variance in liability explained by individual variants, which can be directly interpreted as the locus-specific heritability. The method is extended to deal with haplotypes, multi-allelic markers, multi-locus genotypes, and markers in linkage disequilibrium. Methods to estimate the standard error and confidence interval are proposed. To assess our current level of understanding of the genetic basis of complex diseases, we conducted a survey of 10 diseases, evaluating the total variance explained by the known variants. The diseases under evaluation included Alzheimer's disease, bipolar disorder, breast cancer, coronary artery disease, Crohn's disease, prostate cancer, schizophrenia, systemic lupus erythematosus (SLE), type 1 diabetes and type 2 diabetes. The median total variance explained across the 10 diseases was 9.81%, while the median variance explained per associated SNP was around 0.25%. Our results suggest that a substantial proportion of heritability remains unexplained for the diseases under study. Programs to implement the methodologies described in this paper are available at http://sites.google.com/site/honcheongso/software/varexp. Genet. Epidemiol. 2011. © 2011 Wiley-Liss, Inc. 35:310-317, 2011
Genetic Epidemiology 06/2011; 35(5):310 - 317. · 3.44 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.
Behavior Genetics 05/2011; 41(5):776-9. · 2.52 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Genome-wide association studies (GWAS) have become increasingly popular recently and contributed to the discovery of many susceptibility variants. However, a large proportion of the heritability still remained unexplained. This observation raises queries regarding the ability of GWAS to uncover the genetic basis of complex diseases. In this study, we propose a simple and fast statistical framework to estimate the total heritability explained by all true susceptibility variants in a GWAS. It is expected that many true risk variants will not be detected in a GWAS due to limited power. The proposed framework aims at recovering the "hidden" heritability. Importantly, only the summary z-statistics are required as input and no raw genotype data are needed. The strategy is to recover the true effect sizes from the observed z-statistics. The methodology does not rely on any distributional assumptions of the effect sizes of variants. Both binary and quantitative traits can be handled and covariates may be included. Population-based or family-based designs are allowed as long as the summary statistics are available. Simulations were conducted and showed satisfactory performance of the proposed approach. Application to real data (Crohn's disease, HDL, LDL, and triglycerides) reveals that at least around 10-20% of variance in liability or phenotype can be explained by GWAS panels. This translates to around 10-40% of the total heritability for the studied traits.
Genetic Epidemiology 05/2011; 35(6):447-56. · 3.44 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Risk prediction based on genomic profiles has raised a lot of attention recently. However, family history is usually ignored in genetic risk prediction. In this study we proposed a statistical framework for risk prediction given an individual's genotype profile and family history. Genotype information about the relatives can also be incorporated. We allow risk prediction given the current age and follow-up period and consider competing risks of mortality. The framework allows easy extension to any family size and structure. In addition, the predicted risk at any percentile and the risk distribution graphs can be computed analytically. We applied the method to risk prediction for breast and prostate cancers by using known susceptibility loci from genome-wide association studies. For breast cancer, in the population the 10-year risk at age 50 ranged from 1.1% at the 5th percentile to 4.7% at the 95th percentile. If we consider the average 10-year risk at age 50 (2.39%) as the threshold for screening, the screening age ranged from 62 at the 20th percentile to 38 at the 95th percentile (and some never reach the threshold). For women with one affected first-degree relative, the 10-year risks ranged from 2.6% (at the 5th percentile) to 8.1% (at the 95th percentile). For prostate cancer, the corresponding 10-year risks at age 60 varied from 1.8% to 14.9% in the population and from 4.2% to 23.2% in those with an affected first-degree relative. We suggest that for some diseases genetic testing that incorporates family history can stratify people into diverse risk categories and might be useful in targeted prevention and screening.
The American Journal of Human Genetics 05/2011; 88(5):548-65. · 10.60 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Recently, an increasing number of susceptibility variants have been identified for complex diseases. At the same time, the concern of "missing heritability" has also emerged. There is however no unified way to assess the heritability explained by individual genetic variants for binary outcomes. A systemic and quantitative assessment of the degree of "missing heritability" for complex diseases is lacking. In this study, we measure the variance in liability explained by individual variants, which can be directly interpreted as the locus-specific heritability. The method is extended to deal with haplotypes, multi-allelic markers, multi-locus genotypes, and markers in linkage disequilibrium. Methods to estimate the standard error and confidence interval are proposed. To assess our current level of understanding of the genetic basis of complex diseases, we conducted a survey of 10 diseases, evaluating the total variance explained by the known variants. The diseases under evaluation included Alzheimer's disease, bipolar disorder, breast cancer, coronary artery disease, Crohn's disease, prostate cancer, schizophrenia, systemic lupus erythematosus (SLE), type 1 diabetes and type 2 diabetes. The median total variance explained across the 10 diseases was 9.81%, while the median variance explained per associated SNP was around 0.25%. Our results suggest that a substantial proportion of heritability remains unexplained for the diseases under study. Programs to implement the methodologies described in this paper are available at http://sites.google.com/site/honcheongso/software/varexp.
Genetic Epidemiology 03/2011; 35(5):310-7. · 3.44 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Central obesity predisposes to various cardiometabolic diseases and is a key component of the metabolic syndrome (MetS). We have previously demonstrated that three obesity-susceptible single nucleotide polymorphisms (SNPs), rs10938397 (GNPDA2), rs8050136 (FTO) and rs17782313 (MC4R), were associated with obesity and waist circumference in cross-sectional studies in the Chinese population. In this study, we investigate whether these SNPs could also predict the persistence of central obesity and MetS in subjects from the Hong Kong Cardiovascular Risk Factors Prevalence Study (CRISPS) cohort.
We genotyped these SNPs in i) 354 subjects with and 994 subjects without central obesity at both baseline and a 12-year follow-up, ii) 2214 subjects (816 cases and 1398 controls) in an MetS cross-sectional case-control study and iii) 225 subjects with and 1221 subjects without MetS at both baseline and the 12-year follow-up.
Both FTO rs8050136 (P(age, sex-adjusted)=0.019; odds ratio (OR) (95% confidence intervals (CI)): 1.35 (1.05, 1.73)) and GNPDA2 rs10938397 (P(age, sex-adjusted)=3 × 10(-3); OR (95% CI): 1.34 (1.11, 1.63)) were significantly associated with persistent central obesity. GNPDA2 rs10938397 was also significantly associated with MetS (P(age, sex-adjusted)=0.011, OR (95% CI): 1.20 (1.04, 1.38)) in the case-control study. However, none of these SNPs showed an individual association with persistent MetS. In the combined genetic risk analyses for persistent central obesity and persistent MetS, the combined genetic risk score of the three SNPs showed an OR of 1.25 (95% CI: 1.10, 1.42; P(age, sex-adjusted)=4.92 × 10(-3)) and 1.19 (95% CI: 1.03, 1.38; P(age, sex-adjusted)=0.019) for each additional risk allele respectively.
This study demonstrated that FTO and GNPDA2 variants predicted persistent central obesity in the Chinese population, further supporting their importance as obesity-susceptible genes.
European Journal of Endocrinology 03/2011; 164(3):381-8. · 3.42 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The gene has been proposed as an attractive unit of analysis for association studies, but a simple yet valid, powerful, and sufficiently fast method of evaluating the statistical significance of all genes in large, genome-wide datasets has been lacking. Here we propose the use of an extended Simes test that integrates functional information and association evidence to combine the p values of the single nucleotide polymorphisms within a gene to obtain an overall p value for the association of the entire gene. Our computer simulations demonstrate that this test is more powerful than the SNP-based test, offers effective control of the type 1 error rate regardless of gene size and linkage-disequilibrium pattern among markers, and does not need permutation or simulation to evaluate empirical significance. Its statistical power in simulated data is at least comparable, and often superior, to that of several alternative gene-based tests. When applied to real genome-wide association study (GWAS) datasets on Crohn disease, the test detected more significant genes than SNP-based tests and alternative gene-based tests. The proposed test, implemented in an open-source package, has the potential to identify additional novel disease-susceptibility genes for complex diseases from large GWAS datasets.
The American Journal of Human Genetics 03/2011; 88(3):283-93. · 10.60 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The association of genetic variants with outcomes is usually assessed under an additive model, for example by the trend test. However, misspecification of the genetic model will lead to a reduction in power. More robust tests for association might therefore be preferred. A useful approach is to consider the maximum of the three test statistics under additive, dominant and recessive models (MAX3). The p-value however has to be adjusted to maintain the type I error rate. Previous studies and software on robust association tests have focused on binary traits without covariates. In this study we developed an analytic approach to robust association tests using MAX3, allowing for quantitative or binary traits as well as covariates. The p-values from our theoretical calculations match very well with those from a bootstrap resampling procedure. The methodology is implemented in the R package RobustSNP which is able to handle both small-scale studies and GWAS. The package and documentation are available at http://sites.google.com/site/honcheongso/software/robustsnp .
Behavior Genetics 02/2011; 41(5):768-75. · 2.52 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: ABSTRACT:
Though rooted in genomic expression studies, pathway analysis for genome-wide association studies (GWAS) has gained increasing popularity, since it has the potential to discover hidden disease pathogenic mechanisms by combining statistical methods with biological knowledge. Generally, algorithms or programs proposed recently can be categorized by different types of input data, null hypothesis or counts of analysis stages. Due to complexity caused by SNP, gene and pathway relationships, re-sampling strategies like permutation are always utilized to derive an empirical distribution for test statistics for evaluating the significance of candidate pathways. However, evaluation of these algorithms on real GWAS datasets and real biological pathway databases needs to be addressed before we apply them widely with confidence.
Two algorithms which use summary statistics from GWAS as input were implemented in KGG, a novel and user-friendly software tool for GWAS pathway analysis. Comparisons of these two algorithms as well as the other five selected algorithms were conducted by analyzing the WTCCC Crohn's Disease dataset utilizing the MsigDB canonical pathways. As a result of using permutation to obtain empirical p-value, most of these methods could control Type I error rate well, although some are conservative. However, the methods varied greatly in terms of power and running time, with the PLINK truncated set-based test being the most powerful and KGG being the fastest.
Raw data-based algorithms, such as those implemented in PLINK, are preferable for GWAS pathway analysis as long as computational capacity is available. It may be worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset, since the methods differ greatly in their outputs and might provide complementary findings for the studied complex disease.
BMC Research Notes 01/2011; 4:386.
-
[show abstract]
[hide abstract]
ABSTRACT: Modern genetic association studies typically involve multiple single-nucleotide polymorphisms (SNPs) and/or multiple genes. With the development of high-throughput genotyping technologies and the reduction in genotyping cost, investigators can now assay up to a million SNPs for direct or indirect association with disease phenotypes. In addition, some studies involve multiple disease or related phenotypes and use multiple methods of statistical analysis. The combination of multiple genetic loci, multiple phenotypes, and multiple methods of evaluating associations between genotype and phenotype means that modern genetic studies often involve the testing of an enormous number of hypotheses. When multiple hypothesis tests are performed in a study, there is a risk of inflation of the type I error rate (i.e., the chance of falsely claiming an association when there is none). Several methods for multiple-testing correction are in popular use, and they all have strengths and weaknesses. Because no single method is universally adopted or always appropriate, it is important to understand the principles, strengths, and weaknesses of the methods so that they can be applied appropriately in practice. In this article, we review the three principle methods for multiple-testing correction and provide guidance for calculating statistical power.
Cold Spring Harbor Protocols 01/2011; 2011(1):pdb.top95. · 4.63 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: MPAs (minor physical anomalies) frequently occur in neurodevelopmental disorders because both face and brain are derived from neuroectoderm in the first trimester. Conventionally, MPAs are measured by evaluation of external appearance. Using MRI can help overcome inherent observer bias, facilitate multi-centre data acquisition, and explore how MPAs relate to brain dysmorphology in the same individual. Optical MPAs exhibit a tightly synchronized trajectory through fetal, postnatal and adult life. As head size enlarges with age, inter-orbital distance increases, and is mostly completed before age 3 years. We hypothesized that optical MPAs might afford a retrospective 'window' to early neurodevelopment; specifically, inter-orbital distance increase may represent a biomarker for early brain dysmaturation in autism.
We recruited 91 children aged 7-16; 36 with an autism spectrum disorder and 55 age- and gender-matched typically developing controls. All children had normal IQ. Inter-orbital distance was measured on T1-weighted MRI scans. This value was entered into a voxel-by-voxel linear regression analysis with grey matter segmented from a bimodal MRI data-set. Age and total brain tissue volume were entered as covariates.
Intra-class coefficient for measurement of the inter-orbital distance was 0.95. Inter-orbital distance was significantly increased in the autism group (p = 0.03, 2-tailed). The autism group showed a significant relationship between inter-orbital distance grey matter volume of bilateral amygdalae extending to the unci and inferior temporal poles.
Greater inter-orbital distance in the autism group compared with healthy controls is consistent with infant head size expansion in autism. Inter-orbital distance positively correlated with volume of medial temporal lobe structures, suggesting a link to "social brain" dysmorphology in the autism group. We suggest these data support the role of optical MPAs as a "fossil record" of early aberrant neurodevelopment, and potential biomarker for brain dysmaturation in autism.
PLoS ONE 01/2011; 6(6):e20246. · 4.09 Impact Factor
