Biology subjects

Boca, S. M.

Publications and source records attributed to Boca, S. M..

3 recordsLinked to original sources

bioRxiv2019

On the heterozygosity of an admixed population

A population is termed admixed if its members possess recent ancestry from two or more separate sources. As a result of the fusion of source populations with different genetic variants, admixed populations can exhibit high levels of genetic variation, reflecting contributions of their multiple ancestral groups. For a model of an admixed population derived from K source groups, we obtain a relationship between its level of genetic variation, as measured by heterozygosity, and its proportions of admixture from the various source populations. We show that the heterozygosity of the admixed population is at least as great as that of the least heterozygous source population, and that it potentially exceeds the heterozygosities of all of the source populations. The admixture proportions that maximize the heterozygosity possible for an admixed population formed from a specified set of source populations are also obtained under specific conditions. We examine the special case of K = 2 source populations in detail, characterizing the maximal admixture in terms of the heterozygosities of the two source populations and the value of FST between them. In this case, the heterozygosity of the admixed population exceeds the maximal heterozygosity of the source groups if the divergence between them, measured by FST, is large enough, namely above a certain bound that is a function of the heterozygosities of the source groups. We present applications to simulated data as well as to data from human admixture scenarios, providing results useful for interpreting the properties of genetic variability in admixed populations.

genetics↗

bioRxiv2019

An evidence-based network approach to recommending targeted cancer therapies

In this work, we introduce CDGnet, an evidence-based network approach for recommending targeted cancer therapies, available as a user-friendly informatics tool. Our approach can be used to expand the range of options of targeted therapies for cancer patients who undergo molecular profiling. It considers biological pathway information specifically by looking at downstream targets of oncogenes and is personalized for individual patients via the user-inputted molecular alterations and cancer type. CDGnet integrates disparate sources of knowledge and provides results in a number of easily-accessible and usable forms, while separating targeted cancer therapies into categories in an evidence-based manner.

bioinformatics↗

bioRxiv2018

High-resolution mass spectrometry-based non-targeted metabolomic discovery of disease and glucocorticoid biomarkers in an animal model of muscular dystrophy

Urine is increasingly being considered as a source of biomarker development in Duchenne Muscular Dystrophy (DMD), a severe, life-limiting disorder that affects approximately 1 in 4500 boys. In this study, we used the mdx mice--a murine model of DMD--to discover biomarkers of disease, as well as pharmacodynamic biomarkers responsive to prednisolone, commonly used to treat DMD. Longitudinal urine samples were analyzed from male age-matched mdx and wild-type mice randomized to prednisolone or vehicle. We used high-resolution mass spectrometry to discover metabolic biomarkers of both disease and glucocorticoid treatment. A large number of metabolites (869 out of 6,334) were found to be significantly different between mdx and wild-type mice at baseline (Bonferroni-adjusted p-value < 0.05), thus being associated with disease status. These included a peak with m/z=357 and creatine, which were also discovered in a previous human study looking at serum. Novel observations included biliverdin and hypusine. These four peaks were also significantly higher in mdx mice compared to wild-type, as well as significantly associated with time after the baseline. Creatine and biliverdin were also associated with treatment after the baseline, but the association with creatine may have been driven by an imbalance at baseline. In conclusion, our study reports a number of biomarkers, both known and novel, which may be related to either the mechanisms of muscle injury in DMD and/or prednisolone treatment.

bioinformatics↗