Relative effects of mutability and selection on single nucleotide polymorphisms in transcribed regions of the human genome

<p>Abstract</p> <p>Motivation</p> <p>Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation in humans. However, the factors that affect SNP density are poorly understood. The goal of this study was to estimate the relative effects of mutability and selection on SNP density in transcribed regions of human genes. It is important for prediction of the regions that harbor functional polymorphisms.</p> <p>Results</p> <p>We used frequency-validated SNPs resulting from single-nucleotide substitutions. SNPs were subdivided into five functional categories: (i) 5' untranslated region (UTR) SNPs, (ii) 3' UTR SNPs, (iii) synonymous SNPs, (iv) SNPs producing conservative missense mutations, and (v) SNPs producing radical missense mutations. Each of these categories was further subdivided into nine mutational categories on the basis of the single-nucleotide substitution type. Thus, 45 functional/mutational categories were analyzed. The relative mutation rate in each mutational category was estimated on the basis of published data. The proportion of segregating sites (PSSs) for each functional/mutational category was estimated by dividing the observed number of SNPs by the number of potential sites in the genome for a given functional/mutational category. By analyzing each functional group separately, we found significant positive correlations between PSSs and relative mutation rates (Spearman's correlation coefficient, at least r = 0.96, df = 9, <it>P </it>< 0.001). We adjusted the PSSs for the mutation rate and found that the functional category had a significant effect on SNP density (F = 5.9, df = 4, <it>P </it>= 0.001), suggesting that selection affects SNP density in transcribed regions of the genome. We used analyses of variance and covariance to estimate the relative effects of selection (functional category) and mutability (relative mutation rate) on the PSSs and found that approximately 87% of variation in PSS was due to variation in the mutation rate and approximately 13% was due to selection, suggesting that the probability that a site located in a transcribed region of a gene is polymorphic mostly depends on the mutability of the site.</p

Antenatal Screening for Down Syndrome Using Serum Placental Growth Factor with the Combined, Quadruple, Serum Integrated and Integrated Tests

PMCID: PMC3463523This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Building a Statistical Model for Predicting Cancer Genes

More than 400 cancer genes have been identified in the human genome. The list is not yet complete. Statistical models predicting cancer genes may help with identification of novel cancer gene candidates. We used known prostate cancer (PCa) genes (identified through KnowledgeNet) as a training set to build a binary logistic regression model identifying PCa genes. Internal and external validation of the model was conducted using a validation set (also from KnowledgeNet), permutations, and external data on genes with recurrent prostate tumor mutations. We evaluated a set of 33 gene characteristics as predictors. Sixteen of the original 33 predictors were significant in the model. We found that a typical PCa gene is a prostate-specific transcription factor, kinase, or phosphatase with high interindividual variance of the expression level in adjacent normal prostate tissue and differential expression between normal prostate tissue and primary tumor. PCa genes are likely to have an antiapoptotic effect and to play a role in cell proliferation, angiogenesis, and cell adhesion. Their proteins are likely to be ubiquitinated or sumoylated but not acetylated. A number of novel PCa candidates have been proposed. Functional annotations of novel candidates identified antiapoptosis, regulation of cell proliferation, positive regulation of kinase activity, positive regulation of transferase activity, angiogenesis, positive regulation of cell division, and cell adhesion as top functions. We provide the list of the top 200 predicted PCa genes, which can be used as candidates for experimental validation. The model may be modified to predict genes for other cancer sites

How to Get the Most from Microarray Data: Advice from Reverse Genomics

Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data–derived predictor of known cancer associated genes. We found that the traditional approach of identifying cancer genes—identifying differentially expressed genes—is not very efficient. The analysis of interindividual variation of gene expression in tumor samples identifies cancer-associated genes more effectively. The results were consistent across 4 major types of cancer: breast, colorectal, lung, and prostate. We used recently reported cancer-associated genes (2011–2012) for validation and found that novel cancer-associated genes can be best identified by elevated variance of the gene expression in tumor samples

Prediction of the Gene Expression in Normal Lung Tissue by the Gene Expression in Blood

Background: Comparative analysis of gene expression in human tissues is important for understanding the molecular mechanisms underlying tissue-specific control of gene expression. It can also open an avenue for using gene expression in blood (which is the most easily accessible human tissue) to predict gene expression in other (less accessible) tissues, which would facilitate the development of novel gene expression based models for assessing disease risk and progression. Until recently, direct comparative analysis across different tissues was not possible due to the scarcity of paired tissue samples from the same individuals. Methods: In this study we used paired whole blood/lung gene expression data from the Genotype-Tissue Expression (GTEx) project. We built a generalized linear regression model for each gene using gene expression in lung as the outcome and gene expression in blood, age and gender as predictors. Results: For ~18 % of the genes, gene expression in blood was a significant predictor of gene expression in lung. We found that the number of single nucleotide polymorphisms (SNPs) influencing expression of a given gene in either blood or lung, also known as the number of quantitative trait loci (eQTLs), was positively associated with efficacy of blood-based prediction of that gene’s expression in lung. This association was strongest for shared eQTLs: those influencing gene expression in both blood and lung. Conclusions: In conclusion, for a considerable number of human genes, their expression levels in lung can be predicted using observable gene expression in blood. An abundance of shared eQTLs may explain the strong blood/lung correlations in the gene expression

Genetic Susceptibility Loci of Idiopathic Interstitial Pneumonia do not Represent Risk for Systemic Sclerosis: a Case Control Study in Caucasian Patients

Background: Systemic sclerosis (SSc)-related interstitial lung disease (ILD) has phenotypic similarities to lung involvement in idiopathic interstitial pneumonia (IIP). We aimed to assess whether genetic susceptibility loci recently identified in the large IIP genome-wide association studies (GWASs) were also risk loci for SSc overall or severity of ILD in SSc. Methods: A total of 2571 SSc patients and 4500 healthy controls were investigated from the US discovery GWAS and additional US replication cohorts. Thirteen IIP-related selected single nucleotide polymorphisms (SNPs) were genotyped and analyzed for their association with SSc. Results: We found an association of SSc with the SNP rs6793295 in the LRRC34 gene (OR = 1.14, CI 95 % 1.03 to 1.25, p value = 0.009) and rs11191865 in the OBFC1 gene (OR = 1.09, CI 95 % 1.00 to 1.19, p value = 0.043) in the discovery cohort. Additionally, rs7934606 in MUC2 (OR = 1.24, CI 95 % 1.01 to 1.52, p value = 0.037) was associated with SSc-ILD defined by imaging. However, these associations failed to replicate in the validation cohort. Furthermore, SNPs rs2076295 in DSP ( β = -2.29, CI 95 % -3.85 to -0.74, p value = 0.004) rs17690703 in SPPL2C ( β = 2.04, CI 95 % 0.21 to 3.88, p value = 0.029) and rs1981997 in MAPT ( β = 2.26, CI 95 % 0.35 to 4.17, p value = 0.02) were associated with percent predicted forced vital capacity (FVC%) even after adjusting for the anti-topoisomerase (ATA)-positive subset. However, these associations also did not replicate in the validation cohort. Conclusions: Our results add new evidence that SSc and SSc-related ILD are genetically distinct from IIP, although they share phenotypic similarities

Influence of Yb:YAG laser beam parameters on Haynes 188 weld fusion zone microstructure and mechanical properties

The weldability of 1.2 mm thick Haynes 188 alloy sheets by a disk Yb:YAG laser welding was examined. Butt joints were made, and the influence of parameters such as power, size, and shape of the spot, welding speed, and gas flow has been investigated. Based on an iconographic correlation approach, optimum process parameters were determined. Depending on the distribution of the power density (circular or annular), acceptable welds were obtained. Powers greater than 1700 W, welding speeds higher than 3.8 m mm1, and spot sizes between 160 and 320 lm were needed in the circular (small fiber) configuration. By comparison, the annular (large fiber) configuration required a power as high as 2500 W, and a welding speed less than 3.8 m min�1. The mechanical properties of the welds depended on their shape and microstructure, which in turn depended on the welding conditions. The content of carbides, the proportion of areas consisting of cellular and dendritic substructures, and the size of these substructures were used to explain the welded joint mechanical properties

Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria

<p>Abstract</p> <p>Background</p> <p>Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome.</p> <p>Methods</p> <p>Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data.</p> <p>Results</p> <p>Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET.</p> <p>Conclusions</p> <p>The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.</p

Determinants of Salivary Cotinine among Smokeless Tobacco Users : A Cross-Sectional Survey in Bangladesh

INTRODUCTION: More than 80% of all smokeless tobacco (ST) products in the world are consumed in South Asia; yet little is known about their consumption behaviour, addictiveness, and toxic properties. This paper, for the first time, describes associations between salivary cotinine concentrations among ST users in Bangladesh and their socio-demographic characteristics and tobacco use behaviours. METHODS: In a survey of ST users in Dhaka, Bangladesh, we purposively recruited 200 adults who were non-smokers but consumed ST on a regular basis. In-person interviews were conducted to obtain information about socio-demographic and ST use behaviours, and saliva samples were collected to measure cotinine concentration. Simple and multiple linear regression analyses were conducted to test associations between the log transformed salivary cotinine concentration and other study variables. RESULTS: The geometric mean of cotinine concentration among ST users was 380ng/ml (GSD:2). Total duration of daily ST use in months had a statistically significant association with cotinine concentration. Other ST use characteristics including type and quantity of ST use, swallowing of tobacco juice, urges and strength of urges and attempts to cut down on tobacco use were not found to be associated with cotinine concentration in a multivariable model. CONCLUSION: This is the first report from Bangladesh studying cotinine concentration among ST users and it points towards high levels of addiction. This warrants effective tobacco control policies to help ST cessation and prevention

Longer telomere length in peripheral white blood cells is associated with risk of lung cancer and the rs2736100 (CLPTM1L-TERT) polymorphism in a prospective cohort study among women in China.

A recent genome-wide association study of lung cancer among never-smoking females in Asia demonstrated that the rs2736100 polymorphism in the TERT-CLPTM1L locus on chromosome 5p15.33 was strongly and significantly associated with risk of adenocarcinoma of the lung. The telomerase gene TERT is a reverse transcriptase that is critical for telomere replication and stabilization by controlling telomere length. We previously found that longer telomere length measured in peripheral white blood cell DNA was associated with increased risk of lung cancer in a prospective cohort study of smoking males in Finland. To follow up on this finding, we carried out a nested case-control study of 215 female lung cancer cases and 215 female controls, 94% of whom were never-smokers, in the prospective Shanghai Women's Health Study cohort. There was a dose-response relationship between tertiles of telomere length and risk of lung cancer (odds ratio (OR), 95% confidence interval [CI]: 1.0, 1.4 [0.8-2.5], and 2.2 [1.2-4.0], respectively; P trend = 0.003). Further, the association was unchanged by the length of time from blood collection to case diagnosis. In addition, the rs2736100 G allele, which we previously have shown to be associated with risk of lung cancer in this cohort, was significantly associated with longer telomere length in these same study subjects (P trend = 0.030). Our findings suggest that individuals with longer telomere length in peripheral white blood cells may have an increased risk of lung cancer, but require replication in additional prospective cohorts and populations