DiveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors

Summary: We present a new R package, diveRsity, for the calculation of various diversity statistics, including common diversity partitioning statistics (?, G) and population differentiation statistics (D, GST ', ? test for population heterogeneity), among others. The package calculates these estimators along with their respective bootstrapped confidence intervals for loci, sample population pairwise and global levels. Various plotting tools are also provided for a visual evaluation of estimated values, allowing users to critically assess the validity and significance of statistical tests from a biological perspective. diveRsity has a set of unique features, which facilitate the use of an informed framework for assessing the validity of the use of traditional F-statistics for the inference of demography, with reference to specific marker types, particularly focusing on highly polymorphic microsatellite loci. However, the package can be readily used for other co-dominant marker types (e.g. allozymes, SNPs). Detailed examples of usage and descriptions of package capabilities are provided. The examples demonstrate useful strategies for the exploration of data and interpretation of results generated by diveRsity. Additional online resources for the package are also described, including a GUI web app version intended for those with more limited experience using R for statistical analysis. © 2013 British Ecological Society.

Empirical assessment of non-invasive population genetics in bats:Comparison of DNA quality from faecal and tissue samples

Non-invasive population genetics has become a valuable tool in ecology and conservation biology, allowing genetic studies of wild populations without the need to catch, handle or even observe the study subjects directly. We address some of the concerns regarding the limitations of using non-invasive samples by comparing the quality of population genetic information gained through DNA extracted from faecal samples and biopsy samples of two elusive bat species, Myotis mystacinus and Myotis nattereri. We demonstrate that DNA extracted from faeces and tissue samples gives comparable results for frequency based population genetic analyses, despite the occurrence of genotyping errors when using faecal DNA. We conclude that non-invasive genetic sampling for population genetic analysis in bats is viable, and although more labour-intensive and expensive, it is an alternative to tissue sampling, which is particularly pertinent when specimens are rare, endangered or difficult to capture. © 2012 Museum and Institute of Zoology PAS.

Genetics of New-Onset Diabetes after Transplantation

New-onset diabetes after transplantation is a common complication that reduces recipient survival. Research in renal transplant recipients has suggested that pancreatic ß-cell dysfunction, as opposed to insulin resistance, may be the key pathologic process. In this study, clinical and genetic factors associated with new-onset diabetes after transplantation were identified in a white population. A joint analysis approach, with an initial genome-wide association study in a subset of cases followed by de novo genotyping in the complete case cohort, was implemented to identify single-nucleotide polymorphisms (SNPs) associated with the development of new-onset diabetes after transplantation. Clinical variables associated with the development of diabetes after renal transplantation included older recipient age, female sex, and percentage weight gain within 12 months of transplantation. The genome-wide association study identified 26 SNPs associated with new-onset diabetes after transplantation; this association was validated for eight SNPs (rs10484821, rs7533125, rs2861484, rs11580170, rs2020902, rs1836882, rs198372, and rs4394754) by de novo genotyping. These associations remained significant after multivariate adjustment for clinical variables. Seven of these SNPs are associated with genes implicated in ß-cell apoptosis. These results corroborate recent clinical evidence implicating ß-cell dysfunction in the pathophysiology of new-onset diabetes after transplantation and support the pursuit of therapeutic strategies to protect ß cells in the post-transplant period.

Health status and 6 years survival of 552 90+ Italian sib-ships recruited within the EU Project GEHA (GEnetics of Healthy Ageing).

In a scenario of increasing life expectancy worldwide, it is mandatory to identify the characteristics of a healthy aging phenotype, including survival predictors, and to disentangle those related to environment/lifestyle versus those related to familiarity/genetics. To this aim we comprehensively characterised a cohort of 1,160 Italian subjects of 90 years and over (90+, mean age 93 years; age range 90-106 years) followed for 6 years survival, belonging to 552 sib-ships (familiar longevity) recruited (2005-2008) within the EU-funded GEHA project in three Italian geographic areas (Northern, Central and Southern Italy) different for urban/rural and socio-economical characteristics. On the whole, the following factors emerged as significant predictors of survival after 90 years of age: absence of cognitive impairment and physical disability, high hand grip strength scores and body mass index (BMI) values, "excellent/good" self-reported health, high haemoglobin and total cholesterol levels and low creatinine levels. These parameters, excluding BMI values, were also significantly associated within sib-ships, suggesting a strong familial/genetic component. Geographical micro-heterogeneity of survival predictors emerged, such as functional and physical status being more important in Southern than in Central and Northern Italy. In conclusion, we identified modifiable survival predictors related to specific domains, whose role and importance vary according to the geographic area considered and which can help in interpreting the genetic results obtained by the GEHA project, whose major aim is the comprehensive evaluation of phenotypic and genetic data.

Investigation into the radiobiological consequences of pre-treatment verification imaging with megavoltage X-rays in radiotherapy

Objective: The aim of this study was to investigate the effect of pre-treatment verification imaging with megavoltage (MV) X-rays on cancer and normal cell survival in vitro and to compare the findings with theoretically modelled data. Since the dose received from pre-treatment imaging can be significant, incorporation of this dose at the planning stage of treatment has been suggested.

Methods: The impact of imaging dose incorporation on cell survival was investigated by clonogenic assay, irradiating DU-145 prostate cancer, H460 non-small cell lung cancer and AGO-1522b normal tissue fibroblast cells. Clinically relevant imaging-to-treatment times of 7.5 minutes and 15 minutes were chosen for this study. The theoretical magnitude of the loss of radiobiological efficacy due to sublethal damage repair was investigated using the Lea-Catcheside dose protraction factor model.

Results: For the cell lines investigated, the experimental data showed that imaging dose incorporation had no significant impact upon cell survival. These findings were in close agreement with the theoretical results.

Conclusions: For the conditions investigated, the results suggest that allowance for the imaging dose at the planning stage of treatment should not adversely affect treatment efficacy.

Advances in Knowledge: There is a paucity of data in the literature on imaging effects in radiotherapy. This paper presents a systematic study of imaging dose effects on cancer and normal cell survival, providing both theoretical and experimental evidence for clinically relevant imaging doses and imaging-to-treatment times. The data provide a firm foundation for further study into this highly relevant area of research.