Missense mutations in cancer suppressor gene TP53 are colocalized with exonic splicing enhancers (ESEs)

Mutation databases can be viewed as footprints of functional organization of a gene and thus can be used to infer its functional organization. We studied the association of exonic splicing enhancers (ESEs) with missense mutations in the tumor suppressor gene TP53 using the International Agency for Research on Cancer (IARC) mutation database. The goals of the study were: (i) to verify the hypothesis that deleterious missense mutations are colocalized with ESEs; (ii) to identify potentially functional ESE sites in the open reading frame (ORF) of the TP53. If some sequence functions as a splicing enhancer, then nucleotide substitutions in the site will disturb splicing, abrogate p53 function, and cause an increased susceptibility to cancer. Therefore, among cancers showing p53 mutations, more missense mutations are expected within functional ESE sites as compared to non-functional ESE motifs. Using several statistical tests, we found that missense mutations in TP53 are strongly colocalized with ESEs, and that only a small fraction of ESE sites contributes to the association. There are usually one or two ESEs per exon showing a statistically significant association with missense mutations--so-called significant ESE sites. In many respects significant ESE sites are different from those that do not show association with missense mutations. We found that positions of significant ESE sites are codon-dependent--significant ESEs preferentially start from the first position of a codon, whereas non-significant ESEs show no position dependence. Significant ESEs showed a more limited set of sequences compared to non-significant ESEs. These findings suggest that there is a limited number of missense mutations that influence ESE sites and our analysis provides further insight into the types of sites that harbor exonic enhancer elements.     

Allelic Spectra of Risk SNPs Are Different for Environment/Lifestyle Dependent versus Independent Diseases

Genome-wide association studies (GWAS) have generated sufficient data to assess the role of selection in shaping allelic diversity of disease-associated SNPs. Negative selection against disease risk variants is expected to reduce their frequencies making them overrepresented in the group of minor (<50%) alleles. Indeed, we found that the overall proportion of risk alleles was higher among alleles with frequency <50% (minor alleles) compared to that in the group of major alleles. We hypothesized that negative selection may have different effects on environment (or lifestyle)-dependent versus environment (or lifestyle)-independent diseases. We used an environment/lifestyle index (ELI) to assess influence of environmental/lifestyle factors on disease etiology. ELI was defined as the number of publications mentioning “environment” or “lifestyle” AND disease per 1,000 disease-mentioning publications. We found that the frequency distributions of the risk alleles for the diseases with strong environmental/lifestyle components follow the distribution expected under a selectively neutral model, while frequency distributions of the risk alleles for the diseases with weak environmental/lifestyle influences is shifted to the lower values indicating effects of negative selection. We hypothesized that previously selectively neutral variants become risk alleles when environment changes. The hypothesis of ancestrally neutral, currently disadvantageous risk-associated alleles predicts that the distribution of risk alleles for the environment/lifestyle dependent diseases will follow a neutral model since natural selection has not had enough time to influence allele frequencies. The results of our analysis suggest that prediction of SNP functionality based on the level of evolutionary conservation may not be useful for SNPs associated with environment/lifestyle dependent diseases.     

Associations between Dietary Intake of Choline and Betaine and Lung Cancer Risk

Evidence from human and animal research indicates that choline metabolic pathways may be activated during a variety of diseases, including cancer. We report results of a case-control study of 2821 lung cancer cases and 2923 controls that assessed associations of choline and betaine dietary intakes with lung cancer. Using multivariable logistic regression analyses, we report a significant association between higher betaine intake and lower lung cancer risk that varied by smoking status. Specifically, no significant association was observed between betaine intake and lung cancer among never-smokers. However, higher betaine intake was significantly associated with reduced lung cancer risk among smokers, and the protective effect was more evident among current than former smokers: for former and current smokers, the ORs (95% CI) of lung cancer for individuals with highest as compared to lowest quartiles of intake were 0.70(0.55–0.88) and 0.51(0.39–0.66) respectively. Significant linear trend of higher betaine intake and lower lung cancer risk was observed among both former (p_trend = 0.002) and current (p_trend<0.0001) smokers. A similar protective effect was also observed with choline intake both in overall analysis as well as among current smokers, with p-values for chi-square tests being 0.001 and 0.004 respectively, but the effect was less evident, as no linear trend was observed. Our results suggest that choline and betaine intake, especially higher betaine intake, may be protective against lung cancer through mitigating the adverse effect of smoking.     

SNP characteristics predict replication success in association studies

Successful independent replication is the most direct approach for distinguishing real genotype–disease associations from false discoveries in genome-wide association studies (GWAS). Selecting SNPs for replication has been primarily based on P values from the discovery stage, although additional characteristics of SNPs may be used to improve replication success. We used disease-associated SNPs from more than 2,000 published GWASs to identify predictors of SNP reproducibility. SNP reproducibility was defined as a proportion of successful replications among all replication attempts. The study reporting association for the first time was considered to be discovery and all consequent studies targeting the same phenotype replications. We found that ?Log(P), where P is a P value from the discovery study, is the strongest predictor of the SNP reproducibility. Other significant predictors include type of the SNP (e.g., missense vs intronic SNPs) and minor allele frequency. Features of the genes linked to the disease-associated SNP also predict SNP reproducibility. Based on empirically defined rules, we developed a reproducibility score (RS) to predict SNP reproducibility independently of ?Log(P). We used data from two lung cancer GWAS studies as well as recently reported disease-associated SNPs to validate RS. Minus Log(P) outperforms RS when the very top SNPs are selected, while RS works better with relaxed selection criteria. In conclusion, we propose an empirical model to predict SNP reproducibility, which can be used to select SNPs for validation and prioritization.     

Genetic structure, subdivision, and population differentiation in Stankewiczii pine Pinus stankewiczii (Sukacz.) Fomin from Mountain Crimea

In order to analyze the genetic structure, subdivision and differentiation within and between two small isolated populations of the Crimea relict endemic, Pinus stankewiczii (Sukacz.) Fomin, electrophoretic analysis of the isozyme variation at nine enzymatic systems was carried out using 183 oldest trees. It was demonstrated that in populations of P. stankewiczii, 80% of the genes were in polymorphic state. Each tree was heterozygous at 19.1% loci, and at 21.6% loci in artificial 50-year-old plantation. The genetic structure of two populations was less differentiated (DN = 0.006), compared to their individual localities (DN = 0.008-0.009). Within-population subdivision of the diffusely dispersed populations was higher (FST-GST = 1.8-2.0%) than that of the populations themselves (0.8%).     

Imprinting detection by extending a regression-based QTL analysis method

We present an extension of a regression-based quantitative-trait linkage analysis method to incorporate parent-of-origin effects. We separately regressed total, paternal, and maternal IBD sharing on traits’ squared sums and differences. We also developed a test for imprinting that indicates whether there is any difference between the paternal and maternal regression coefficients. Since this method treats the identity-by-descent information as the dependent variable that is conditioned on the trait, it can be readily applied to data from complex ascertainment processes. We performed a simulation study to examine the performance of the method. We found that when using empirical critical values, the method shows identical or higher power compared to existing methods for evaluation of parent-of-origin effect in linkage analysis of quantitative traits. Missing parental genotypes increase the type I error rate of the linkage test and decrease the power of the imprinting test. When the major gene has a low heritability, the power of the method decreases considerably, but the statistical tests still perform well. We also applied a permutation algorithm, which ensures the appropriate type I error rate for the test for imprinting. The method was applied to a data from a study of 6 body size related measures and 23 loci on chromosome 7 for 255 nuclear families. Multipoint identities-by-descent (IBD) were obtained using a modification of the SIMWALK 2 program. A parent-of-origin effect consistent with maternal imprinting was suggested at 99.67–111.26 Mb for body mass index, bioelectrical impedance analysis, waist circumference, and leptin concentration. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. An erratum to this article can be found at