Special Issue "Centenarians-A Model to Study the Molecular Basis of Lifespan and Healthspan 2.0"

: The global population is experiencing an increase in ageing and life expectancy [...]

Phenotype forecasting with SNPs data through gene-based Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p

Phenotype forecasting with SNPs data through gene-based Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p

Genomic organization and single-nucleotide polymorphism map of desmuslin, a novel intermediate filament protein on chromosome 15q26.3

BACKGROUND: Desmuslin is an α-dystrobrevin-interacting protein expressed primarily in heart and skeletal muscle. The desmuslin protein interacts with and is closely related to desmin, a protein encoded by a locus mutated in some forms of hereditary distal myopathy. As a muscle-specific intermediate filament protein, desmuslin is also a candidate for myopathies of unknown etiology. RESULTS: The desmuslin gene was localized to chromosome 15q26.3 by electronic screening of the human DNA sequence database. Primer pairs were designed to amplify the 5 exons of the desmuslin gene in 11 overlapping DNA segments. The desmuslin gene was screened for mutations in 71 patients with various forms of myopathy for which there was no known cause. In this analysis, 10 common and 2 rare amino acid altering single-nucleotide polymorphisms were identified, all of which were seen in a control population of individuals thus making these unlikely causes of the phenotype. Interestingly, one of the single-nucleotide polymorphisms found in a patient resulted in a premature stop codon in the first exon. The nonsense mutation was also detected in the patient's unaffected father and one unaffected control; it was detected in 0.44% (2/454) of unrelated chromosomes and is therefore predicted to have a homozygous frequency of 0.002%. CONCLUSION: No causative mutations were found in the desmuslin gene. However, the single-nucleotide polymorphisms mapped in this study represent a well-mapped group that can be used for disequilibrium studies of this region of chromosome 15q26.3

The genetics of exceptional longevity identifies new druggable targets forvascular protection and repair

Therapeutic angiogenesis is a relatively new medical strategy in the field of cardiovascular diseases. The underpinning concept is that angiogenic growth factors or proangiogenic cells could be exploited therapeutically in cardiovascular patients to enhance native revascularization responses to an ischemic insult, thereby accelerating tissue healing. The initial enthusiasm generated by preclinical studies has been tempered by the modest success of clinical trials assessing therapeutic angiogenesis. Similarly, proangiogenic cell therapy has so far not maintained the original promises. Intriguingly, the current trend is to consider regeneration as a prerogative of the youngest organism. Consequentially, the embryonic and foetal models are attracting much attention for clinical translation into corrective modalities in the adulthood. Scientists seem to undervalue the lesson from Mother Nature, e.g. all humans are born young but very few achieve the goal of an exceptional healthy longevity. Either natural experimentation is driven by a supreme intelligence or stochastic phenomena, one has to accept the evidence that healthy longevity is the fruit of an evolutionary process lasting million years. It is therefore extremely likely that results of this natural experimentation are more reliable and translatable than the intensive, but very short human investigation on mechanisms governing repair and regeneration. With this preamble in mind, here we propose to shift the focus from the very beginning to the very end of human life and thus capture the secret of prolonged health span to improve well-being in the adulthood

Serum BPIFB4 levels classify health status in long-living individuals

People that reach extreme ages (Long-Living Individuals, LLIs) are object of intense investigation for increase/decrease of genetic variant frequencies, genetic methylation levels, protein abundance in serum and tissues. The aim of these studies is the discovery of the mechanisms behind LLIs extreme longevity and the identification of markers of well-being. We have recently associated a BPIFB4 haplotype (LAV) with exceptional longevity under a homozygous genetic model, and identified that CD34(+) of LLIs subjects express higher BPIFB4 transcript as compared to CD34(+) of control population. It would be of interest to correlate serum BPIFB4 protein levels with exceptional longevity and health status of LLIs