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Porras, C.

Publications and source records attributed to Porras, C..

2 recordsLinked to original sources

Study design and the sampling of deleterious rare variants in biobank-scale datasets

One key component of study design in population genetics is the "geographic breadth" of a sample (i.e., how broad a region across which individuals are sampled). How the geographic breadth of a sample impacts observations of rare, deleterious variants is unclear, even though such variants are of particular interest for biomedical and evolutionary applications. Here, in order to gain insight into the effects of sample design on ascertained genetic variants, we formulate a stochastic model of dispersal, genetic drift, selection, mutation, and geographically concentrated sampling. We use this model to understand the effects of the geographic breadth of sampling effort on the discovery of negatively selected variants. We find that samples which are more geographically broad will discover a greater number variants as compared geographically narrow samples (an effect we label "discovery"); though the variants will be detected at lower average frequency than in narrow samples (e.g. as singletons, an effect we label "dilution"). Importantly, these effects are amplified for larger sample sizes and moderated by the magnitude of fitness effects. We validate these results using both population genetic simulations and empirical analyses in the UK Biobank. Our results are particularly important in two contexts: the association of large-effect rare variants with particular phenotypes and the inference of negative selection from allele frequency data. Overall, our findings emphasize the importance of considering geographic breadth when designing and carrying out genetic studies, especially at biobank scale. SignificanceAs genetic studies grow, researchers are increasingly seeking to identify rare genetic variants with large impacts on traits. In this paper, we combine theoretical methods and data analysis to show how differences in sampling with respect to geographic location can influence the number and frequency of genetic variants that are found. Our results suggest that geographically broad samples will include more distinct genetic variants, though each variant will be found at a lower frequency, as compared to geographically narrow samples. Our results can help researchers to consider the implications of study design on expected results when constructing new genetic samples.

genetics↗

Molecular features of premenopausal breast cancers in Latin American women: pilot results from the PRECAMA study

BackgroundIn Latin America (LA), there is a high incidence rate of breast cancer (BC) in premenopausal women, and the genomic features of these BC remain unknown. Here, we aim to characterize the molecular features of BC in young LA women within the framework of the PRECAMA study, a multicenter population-based case-control study on breast cancer in premenopausal women.\n\nMethodsPathological tumor tissues were collected from incident cases from four LA countries. Immunohistochemistry (IHC) was performed centrally for ER, PR, HER2, Ki67, EGFR, CK5/6 and p53 protein markers. Targeted deep sequencing was done on genomic DNA extracted from formalin-fixed paraffin-embedded (FFPE) tumour tissues and their paired blood samples to screen for somatic mutations in eight genes frequently mutated in BC. A subset of samples was analyzed by exome sequencing to identify somatic mutational signatures.\n\nResultsThe majority of cases were positive for ER or PR (168/233; 72%) and there were 21% triple negative (TN) cases, mainly of basal type. Most tumors were positive for Ki67 (189/233; 81%). In 126 sequenced cases, TP53 and PIK3CA were the most frequently mutated genes (32.5% and 21.4%, respectively), followed by AKT1 (9.5%). TP53 mutations were more frequent in HER2-enriched and TN IHC subtypes, while PIK3CA/AKT1 mutations were more frequent in ER positive tumors, as expected. Interestingly, a higher proportion of G:C>T:A mutations was observed in TP53 gene in PRECAMA cases compared to TCGA and METABRIC breast cancer series (27% vs 14%). Exome-wide mutational patterns in 10 TN cases revealed alterations in signaling transduction pathways and major contributions of mutational signatures caused by altered DNA repair pathways.\n\nConclusionsThis pilot results on PRECAMA tumors gives a preview of the molecular features of premenopausal BC in LA. Although, the overall mutation burden was as expected from data in other populations, mutational patterns observed in TP53 and exome-wide suggested possible differences in mutagenic processes giving rise to these tumors compared to other populations. Further omics analyses of a larger number of cases in the near future will allow investigating relationships between these molecular features and risk factors.

cancer biology↗